reward_best:\\n reward_best = algo_num_nodes\\n s_best = s_oracle\\n u_e_best = u_e\\n\\n # ---- Step 3 - Calculating updates ----\\n for i in algo_obs_edges:\\n x_e = np.array([df_feats.loc[i].values])\\n m_inv -= (m_inv @ x_e.T @ x_e @ m_inv) / (\\n x_e @ m_inv @ x_e.T + sigma * sigma\\n )\\n b += x_e.T * int(i in algo_act_edges)\\n\\n return_dict = {\\n \\\"rewards\\\": rewards,\\n \\\"rewards_edges\\\": rewards_edges,\\n \\\"s_best\\\": s_best,\\n \\\"u_e_best\\\": u_e_best,\\n \\\"reward_best\\\": reward_best,\\n }\\n logger_tlu.debug(\\\"The algorithm has finished running.\\\")\\n logger_tlu.debug(f\\\"Returning: {return_dict}\\\")\\n return return_dict\",\n \"def TNG_net(self): \\n \\n import h5py as h5\\n filename = localpath+'input/yields/TNG/SNII.hdf5'\\n # Read H5 file\\n f = h5.File(filename, \\\"r\\\")\\n \\n # Define element indexing\\t\\t\\t\\n indexing = {}\\n indexing['H'] = 'Hydrogen'\\n indexing['He'] = 'Helium'\\n indexing['C'] = 'Carbon'\\n indexing['N']= 'Nitrogen'\\n indexing['O'] = 'Oxygen'\\n indexing['Ne'] = 'Neon'\\n indexing['Mg'] = 'Magnesium'\\n indexing['Si'] = 'Silicon'\\n indexing['S'] = 'Sulphur' # Not used by TNG simulation\\n indexing['Ca'] = 'Calcium' # Not used by TNG simulation\\n indexing['Fe'] = 'Iron'\\n \\n self.elements = list(indexing.keys())\\n \\n self.table = {}\\n \\n # Define masses / metallicities\\n self.metallicities = list(f['Metallicities'].value)\\n self.masses = f['Masses'].value\\n\\n \\n for z_index,z in enumerate(self.metallicities):\\n \\n yield_subtable = {}\\n \\n z_name = f['Yield_names'].value[z_index].decode('utf-8')\\n z_data = f['Yields/'+z_name+'/Yield']\\n \\n ejecta_mass = f['Yields/'+z_name+'/Ejected_mass'].value\\n \\n yield_subtable['Mass'] = self.masses\\n remnants = self.masses-ejecta_mass\\n yield_subtable['mass_in_remnants'] = np.divide(remnants,self.masses)\\n for el in list(indexing.keys()):\\n yield_subtable[el] = np.zeros(len(self.masses))\\n \\n summed_yields = np.zeros(len(self.masses))\\n \\n for m_index,mass in enumerate(self.masses):\\n for el_index,el in enumerate(self.elements):\\n el_yield_fraction = z_data[el_index][m_index]/mass #(mass-remnants[m_index]) # Find fraction of ejecta per element\\n yield_subtable[el][m_index] = el_yield_fraction\\t\\t\\t\\t\\t\\n summed_yields[m_index]+=el_yield_fraction # Compute total yield\\n \\n yield_subtable['unprocessed_mass_in_winds'] = 1.-summed_yields-yield_subtable['mass_in_remnants']\\n \\n # Restructure table\\n all_keys = ['Mass','mass_in_remnants','unprocessed_mass_in_winds']+self.elements\\n \\n list_of_arrays = [yield_subtable[key] for key in all_keys]\\n restructure_subtable = np.core.records.fromarrays(list_of_arrays,names=all_keys)\\n \\n self.table[z] = restructure_subtable\",\n \"def EC(f):\\n return dmp_ground_EC(f.rep, f.lev, f.dom)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6423988","0.6371142","0.63001156","0.62050164","0.6046099","0.5791749","0.5715767","0.55602425","0.5521478","0.5479102","0.54652435","0.5458608","0.54561746","0.54515","0.5428723","0.5406992","0.53999406","0.53977454","0.52835554","0.5282024","0.526819","0.52627015","0.525324","0.52465224","0.52282745","0.52223384","0.52139306","0.5208815","0.5199048","0.51972765","0.5187306","0.51800704","0.5162468","0.5151861","0.5145392","0.5141868","0.51332206","0.5130792","0.5123518","0.51209503","0.51199037","0.51151127","0.5113465","0.5109911","0.510649","0.5085126","0.50820094","0.5074213","0.5070344","0.50620407","0.5058786","0.5058563","0.5058321","0.5042551","0.5039829","0.5033683","0.5033063","0.50319207","0.5030837","0.50269693","0.5026725","0.502036","0.5018034","0.50171226","0.50117904","0.5008313","0.5007962","0.5006484","0.50047773","0.50039756","0.49953583","0.49896276","0.49878004","0.4985337","0.49801028","0.4979974","0.49799415","0.49782574","0.49780762","0.4977716","0.49686027","0.4964278","0.49599102","0.4954316","0.4952656","0.49467131","0.49454597","0.49444523","0.494209","0.49371326","0.493556","0.49352157","0.49352026","0.49340907","0.49335375","0.49312297","0.49297988","0.49265376","0.49260777","0.49216086"],"string":"[\n \"0.6423988\",\n \"0.6371142\",\n \"0.63001156\",\n \"0.62050164\",\n \"0.6046099\",\n \"0.5791749\",\n \"0.5715767\",\n \"0.55602425\",\n \"0.5521478\",\n \"0.5479102\",\n \"0.54652435\",\n \"0.5458608\",\n \"0.54561746\",\n \"0.54515\",\n \"0.5428723\",\n \"0.5406992\",\n \"0.53999406\",\n \"0.53977454\",\n \"0.52835554\",\n \"0.5282024\",\n \"0.526819\",\n \"0.52627015\",\n \"0.525324\",\n \"0.52465224\",\n \"0.52282745\",\n \"0.52223384\",\n \"0.52139306\",\n \"0.5208815\",\n \"0.5199048\",\n \"0.51972765\",\n \"0.5187306\",\n \"0.51800704\",\n \"0.5162468\",\n \"0.5151861\",\n \"0.5145392\",\n \"0.5141868\",\n \"0.51332206\",\n \"0.5130792\",\n \"0.5123518\",\n \"0.51209503\",\n \"0.51199037\",\n \"0.51151127\",\n \"0.5113465\",\n \"0.5109911\",\n \"0.510649\",\n \"0.5085126\",\n \"0.50820094\",\n \"0.5074213\",\n \"0.5070344\",\n \"0.50620407\",\n \"0.5058786\",\n \"0.5058563\",\n \"0.5058321\",\n \"0.5042551\",\n \"0.5039829\",\n \"0.5033683\",\n \"0.5033063\",\n \"0.50319207\",\n \"0.5030837\",\n \"0.50269693\",\n \"0.5026725\",\n \"0.502036\",\n \"0.5018034\",\n \"0.50171226\",\n \"0.50117904\",\n \"0.5008313\",\n \"0.5007962\",\n \"0.5006484\",\n \"0.50047773\",\n \"0.50039756\",\n \"0.49953583\",\n \"0.49896276\",\n \"0.49878004\",\n \"0.4985337\",\n \"0.49801028\",\n \"0.4979974\",\n \"0.49799415\",\n \"0.49782574\",\n \"0.49780762\",\n \"0.4977716\",\n \"0.49686027\",\n \"0.4964278\",\n \"0.49599102\",\n \"0.4954316\",\n \"0.4952656\",\n \"0.49467131\",\n \"0.49454597\",\n \"0.49444523\",\n \"0.494209\",\n \"0.49371326\",\n \"0.493556\",\n \"0.49352157\",\n \"0.49352026\",\n \"0.49340907\",\n \"0.49335375\",\n \"0.49312297\",\n \"0.49297988\",\n \"0.49265376\",\n \"0.49260777\",\n \"0.49216086\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65178627"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17441,"cells":{"query":{"kind":"string","value":"SubstanceEC graph of \"neofunctionalised\" EC numbers, derived from the lost core metabolism."},"document":{"kind":"string","value":"def lostMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n parentGraph = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(lostECs)\n parentGraph.name = 'Lost metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return parentGraph"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is False:\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\n \n else:\n unifiedMetabolism = self.unifiedMetabolism(majorityPercentageCoreMetabolism, colour = True)\n \n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = unifiedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph","def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n childGraph = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(addedECs)\n childGraph.name = 'Added metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return childGraph","def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:\n # get neofunctionalisations \n neofunctionalisedECs = NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs))\n \n # filter core metabolism EC graph\n coreMetabolism = self.coreMetabolism(majorityPercentageCoreMetabolism)\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))\n \n neofunctionalisedMetabolism = neofunctionalisedECs.filterGraph(coreMetabolism, minimumEcDifference = None, minimumOrganismsCount = minimumOrganismsCount)\n \n # colour core metabolism\n if colour is not False:\n \n if colour is True:\n colourToUse = Export.Colour.GREEN\n else:\n colourToUse = colour\n \n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\n neofunctionalisedMetabolism = coreMetabolism\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\n \n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism ' + ' '.join(self.ncbiNames)\n \n return neofunctionalisedMetabolism","def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\n conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)\n \n parentNeofunctionalised= self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = conservedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = conservedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\n childGraph = conservedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\n \n parentGraph.name = 'Conserved metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def divergedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\n divergedMetabolism = self.divergedMetabolism(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = divergedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = divergedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\n childGraph = divergedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\n \n parentGraph.name = 'Diverged metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def redundantECsForContributingNeofunctionalisation(self, \n majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, \n majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, \n eValue = defaultEValue, \n redundancyType: 'RedundancyType' = None,\n considerOnlyECs = None) -> Dict[Neofunctionalisation, Set[EcNumber]]:\n from FEV_KEGG.Robustness.Topology.Redundancy import Redundancy, RedundancyContribution, RedundancyType\n \n if redundancyType is None:\n redundancyType = RedundancyType.default\n \n #- calculate \"neofunctionalised\" ECs\n neofunctionalisedMetabolismSet = self.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs).getECs()\n neofunctionalisationsForFunctionChange = self.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs)\n \n #- calculate redundancy\n redundancy = Redundancy( self.coreMetabolism(majorityPercentageCoreMetabolism) )\n redundancyContribution = RedundancyContribution(redundancy, neofunctionalisedMetabolismSet)\n \n contributedECsForContributingNeofunctionalisedEC = redundancyContribution.getContributedKeysForSpecial(redundancyType)\n contributingNeofunctionalisedECs = set(contributedECsForContributingNeofunctionalisedEC.keys())\n \n #- REPEAT for each function change consisting of \"neofunctionalised\" ECs, which also contribute to redundancy\n contributingNeofunctionalisations = dict()\n \n for functionChange, neofunctionalisations in neofunctionalisationsForFunctionChange.items():\n #- report enzyme pairs of neofunctionalisations, which caused the EC to be considered \"neofunctionalised\", and are in return contributing to redundancy \n \n if functionChange.ecA in contributingNeofunctionalisedECs or functionChange.ecB in contributingNeofunctionalisedECs: # function change contributes to redundancy\n \n for neofunctionalisation in neofunctionalisations:\n currentSetOfContributedECs = contributingNeofunctionalisations.get(neofunctionalisation, None)\n \n if currentSetOfContributedECs is None:\n currentSetOfContributedECs = set()\n contributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs\n \n for ec in functionChange.ecPair:\n contributedECs = contributedECsForContributingNeofunctionalisedEC.get(ec, None)\n if contributedECs is not None:\n currentSetOfContributedECs.update(contributedECs)\n \n return contributingNeofunctionalisations","def __repr__(self):\n return \"EC(%s, %s)\" % (str(self.coefficient), repr(self.basefield))","def c_edges(self):\n self.compute_c_edges(self)\n return self._c_edges","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/26')","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/27')","def get_ecg_graph():\n titles = ['ecg1', 'ecg2', 'ecg3']\n colors = ['rgb(240,0,0)', 'rgb(0,240,0)', 'rgb(0,0,240)']\n update()\n signames_ecg = queries['signames_ecg']\n signals = queries['signals']\n latesthr = queries['latesthr']\n return html.Div(className='ecg', children=[\n html.Div(style={'display': 'flex', 'height': '40vh'},\n children=[dcc.Graph(\n id=titles[i] + signame,\n style={'width': '100%'},\n figure={\n 'data': [\n {'x': signals[signame]['time'],\n 'y': signals[signame][titles[i]],\n 'mode': 'line', 'name': signame, 'line': {'color':colors[i]}}\n ],\n 'layout': {\n 'font': {'color':'#fff'},\n 'title': '{}-{}'.format(signame, titles[i]),\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\n 'yaxis': {'title': 'voltage (mv)', 'color': '#fff', 'showgrid': 'False', 'range': np.linspace(-2.5, 2.5, 10)},\n 'paper_bgcolor':'#000', 'plot_bgcolor':'#000'\n }\n }\n ) for i in range(len(titles))]\n +\n [html.Div(\n style={'justify-content': 'center', 'display': 'flex',\n 'align-items': 'center', 'width': '10vh', 'font-size': '30pt', 'color': 'white'},\n children=['{}'.format(latesthr[signame][0])])\n ]\n ) for signame in signames_ecg])","def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\n return graph","def complex_network_mapping(graph):\n vect = []\n\n n = nx.number_of_nodes(graph)\n e = nx.number_of_edges(graph)\n print n, e\n\n# adj = nx.adjacency_matrix(graph).toarray()\n# adj_bin = np.where(adj > 0, 1., 0.)\n# adj_conn = 1 - adj\n adj_bin = nx.adjacency_matrix(graph).toarray()\n adj_bin = np.array(adj_bin, dtype=np.float)\n\n # Node Betweenness binary\n bt_bin = nx.betweenness_centrality(graph).values()\n avg_btb = np.mean(bt_bin)\n vect.append(avg_btb)\n\n # Edge betweenness\n ebt = np.array(nx.edge_betweenness_centrality(graph).values())\n vect.append(np.mean(ebt))\n\n # Eigen vector centrality binary\n evc_bin = eigenvector_centrality_und(adj_bin)\n avg_evcb = np.mean(evc_bin)\n vect.append(avg_evcb)\n\n # Flow coefficient\n _, flow_bin, _ = flow_coef_bd(adj_bin)\n avg_flow = np.mean(flow_bin)\n vect.append(avg_flow)\n\n # Kcoreness centrality\n kcor_bin, _ = kcoreness_centrality_bu(adj_bin)\n avg_kcor = np.mean(kcor_bin)\n vect.append(avg_kcor)\n\n # Degree assortivity\n dac = nx.degree_assortativity_coefficient(graph)\n vect.append(dac)\n\n # Page rank centrality\n# pgr_wei = pagerank_centrality(adj_bin, d=0.85)\n# avg_pgr = np.mean(pgr_wei)\n# vect.append(avg_pgr)\n\n # Rich club coefficient\n# rcc = nx.rich_club_coefficient(graph).values()\n# avg_rcc = np.mean(rcc)\n# vect.append(avg_rcc)\n\n # Transitivity\n tr = nx.transitivity(graph)\n vect.append(tr)\n\n # average clustering\n avg_clst = nx.average_clustering(graph)\n vect.append(avg_clst)\n\n glb_ef = efficiency_bin(adj_bin)\n vect.append(glb_ef)\n\n return vect","def NND_eta( eqCat, dConst, verbose = False, **kwargs):\n #-------------------------------set args and kwargs----------------------------------------------- \n rmax = 500 # in km\n tmax = 20 # in years\n M0 = 0\n if 'M0' in kwargs.keys() and kwargs['M0'] is not None:\n M0 = kwargs['M0']\n if 'rmax' in kwargs.keys() and kwargs['rmax'] is not None:\n rmax = kwargs['rmax']\n if 'tmax' in kwargs.keys() and kwargs['tmax'] is not None:\n tmax = kwargs['tmax']\n #-----------------------------add small uncertainty to X in case events are colocated-------------------------- \n if 'correct_co_located' in kwargs.keys() and kwargs['correct_co_located'] == True:\n vUncer = np.random.randn( eqCat.size())*1e-10\n eqCat.data['X'] += vUncer\n eqCat.data['Time'] += abs( vUncer)#time has to stay positive otherwise parent-offspring gets switched\n #------------------------------------------------------------------------------\n aNND = np.zeros( eqCat.size())\n vID_p = np.zeros( eqCat.size())\n vID_c = np.zeros( eqCat.size())\n deltaMag = (eqCat.data['Mag'] - M0)\n \n for jC in range( eqCat.size()):\n if verbose == True:\n print 'event %i of %i'%( jC+1, eqCat.size())\n # interevent times: take events that happend before t_i \n # child - parent > 0 \n tau = eqCat.data['Time'][jC] - eqCat.data['Time']\n sel_tau_par = tau > 0\n if sel_tau_par.sum() > 0:\n\n vcurr_ID = np.arange( eqCat.size(), dtype = int)[sel_tau_par]\n vR = np.sqrt( (eqCat.data['X'][jC] - eqCat.data['X'][vcurr_ID])**2 + (eqCat.data['Y'][jC] - eqCat.data['Y'][vcurr_ID])**2 )\n # haversine distance\n # = projUtils.haversine( eqCat.data['Lon'][jC], eqCat.data['Lat'][jC],eqCat.data['Lon'][curr_vID], eqCat.data['Lat'][curr_vID] ) \n sel_r_par = vR < rmax\n if sel_r_par.sum() > 0:\n vcurr_ID = vcurr_ID[sel_r_par]\n curr_Eta = tau[vcurr_ID]* (vR[sel_r_par]**dConst['D']) *( 10**(-dConst['b']*deltaMag[vcurr_ID]))\n sel_min = curr_Eta == curr_Eta.min()\n aNND[jC] = curr_Eta[sel_min][0]\n vID_p[jC] = eqCat.data['N'][vcurr_ID][sel_min][0]\n vID_c[jC] = eqCat.data['N'][jC]\n #print 'parent', eqCat.data['N'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['N'][jC]\n #print 'parent', eqCat.data['Time'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['Time'][jC]\n\n if sel_min.sum() > 1:\n print aNND[jC], curr_Eta[sel_min], eqCat.data['N'][vcurr_ID][sel_min]\n print eqCat.data['Lon'][vcurr_ID][sel_min], eqCat.data['Lat'][vcurr_ID][sel_min]\n print eqCat.data['X'][vcurr_ID][sel_min], eqCat.data['Y'][vcurr_ID][sel_min]\n sel2 = aNND > 0\n if np.logical_not(sel2).sum() > 0:\n print 'remove %i offspring without prior parents in catalog'%(np.logical_not(sel2).sum())\n #raise ValueError, error_str\n # remove events with aNND < 0; i.e. event at the beginning with no preceding parent\n return { 'aNND' : aNND[sel2], 'aEqID_p' : vID_p[sel2], 'aEqID_c' : vID_c[sel2]}","def colored_edges(genome):\n edges = []\n for chromo in genome:\n nodes = [0] + chromosome_to_cycle(chromo)\n nodes.append(nodes[1])\n for j in range(1, len(chromo) + 1):\n edges.append((nodes[2 * j], nodes[2 * j + 1]))\n\n return edges","def cell_edges(self):","def set_ec(self, etacalc):\n if not self.__thermodyn:\n C = np.zeros((6,6))\n \n LC = self.__structures.items()[0][1].LC\n if self.__mthd == 'Energy':\n if type(etacalc)==list:\n A2=[]\n for i in range(len(etacalc)):\n A2.append(self.__A2[i][etacalc[i]])\n else:\n if not etacalc in self.__A2[0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[0].keys()))\n A2 = [a2[etacalc] for a2 in self.__A2]\n \n \n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'CI' or \\\n LC == 'CII'):\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\n C[1,1] = C[0,0]\n C[2,2] = C[0,0]\n C[3,3] = A2[2]/6.\n C[4,4] = C[3,3]\n C[5,5] = C[3,3]\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\n C[0,2] = C[0,1]\n C[1,2] = C[0,1]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[2]\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RI'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[1,4] =-C[0,4] \n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[3,5] =-C[0,4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TI'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[3]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TII'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\n C[1,1] = C[0,0]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\n C[1,2] = C[0,2]\n C[1,5] =-C[0,5]\n C[2,2] = 2.*A2[4]\n C[3,3] = .5*A2[5]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[6]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'O'):\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\n C[1,1] = 2.*A2[4]\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\n C[2,2] = 2.*A2[5]\n C[3,3] = .5*A2[6]\n C[4,4] = .5*A2[7]\n C[5,5] = .5*A2[8]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'M'):\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\n C[1,1] = 2.*A2[8]\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\n C[2,2] = 2.*A2[9]\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\n C[3,3] = .5*A2[10]\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\n C[4,4] = .5*A2[11]\n C[5,5] = .5*A2[12]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'N'):\n C[0,0] = 2.*A2[0]\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\n C[1,1] = 2.*A2[1]\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\n C[2,2] = 2.*A2[2] \n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\n C[3,3] = .5*A2[3]\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\n C[4,4] = .5*A2[4]\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n elif self.__mthd == 'Stress':\n \n if (LC == 'CI' or \\\n LC == 'CII'):\n Matrix = np.mat([[1.0, 5.0, 0.0],\n [2.0, 4.0, 0.0],\n [3.0, 3.0, 0.0],\n [0.0, 0.0, 4.0],\n [0.0, 0.0, 5.0],\n [0.0, 0.0, 6.0]])\n \n if (LC == 'HI' or \\\n LC == 'HII'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\n [ 2, 1, 3, 0, 0],\n [ 0, 0, 3, 3, 0],\n [ 0, 0, 0, 0, 4],\n [ 0, 0, 0, 0, 5],\n [ 3,-3, 0, 0, 0],\n [ 3,-5,-1, 0, 0],\n [-5, 3,-1, 0, 0],\n [ 0, 0,-2,-1, 0],\n [ 0, 0, 0, 0, 6],\n [ 0, 0, 0, 0, 2],\n [-2, 2, 0, 0, 0]])\n \n if (LC == 'RI'):\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\n [ 2, 1, 3,-4, 0, 0],\n [ 0, 0, 3, 0, 3, 0],\n [ 0, 0, 0,-1, 0, 4],\n [ 0, 0, 0, 6, 0, 5],\n [ 3,-3, 0, 5, 0, 0],\n [ 3,-5,-1, 6, 0, 0],\n [-5, 3,-1,-6, 0, 0],\n [ 0, 0,-2, 0,-1, 0],\n [ 0, 0, 0, 8, 0, 6],\n [ 0, 0, 0,-4, 0, 2],\n [-2, 2, 0, 2, 0, 0]])\n \n if (LC == 'RII'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\n [ 2, 1, 3,-4,-5, 0, 0],\n [ 0, 0, 3, 0, 0, 3, 0],\n [ 0, 0, 0,-1,-6, 0, 4],\n [ 0, 0, 0, 6,-1, 0, 5],\n [ 3,-3, 0, 5,-4, 0, 0],\n [ 3,-5,-1, 6, 2, 0, 0],\n [-5, 3,-1,-6,-2, 0, 0],\n [ 0, 0,-2, 0, 0,-1, 0],\n [ 0, 0, 0, 8, 4, 0, 6],\n [ 0, 0, 0,-4, 8, 0, 2],\n [-2, 2, 0, 2,-6, 0, 0]])\n \n if (LC == 'TI'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\n [ 2, 1, 3, 0, 0, 0],\n [ 0, 0, 3, 3, 0, 0],\n [ 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0, 0, 0, 6],\n [ 3,-5,-1, 0, 0, 0],\n [-5, 3,-1, 0, 0, 0],\n [ 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 0, 0,-4]])\n \n if (LC == 'TII'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\n [ 2, 1, 3,-6, 0, 0, 0],\n [ 0, 0, 3, 0, 3, 0, 0],\n [ 0, 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0,-1, 0, 0, 6],\n [ 3,-5,-1,-4, 0, 0, 0],\n [-5, 3,-1, 4, 0, 0, 0],\n [ 0, 0,-2, 0,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 8, 0, 0,-4]])\n \n if (LC == 'O'):\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\n \n if (LC == 'M'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\n \n if (LC == 'N'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\n \n sigma = np.array(self.__sigma[etacalc])\n \n ci = np.linalg.lstsq(Matrix,sigma)\n \n #-- Cubic structures ------------------------------------------------------------------------------\n if (LC == 'CI' or \\\n LC == 'CII'):\n \n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[3,3]=ci[0][2]\n C[1,1]=C[0,0]\n C[2,2]=C[0,0]\n C[0,2]=C[0,1]\n C[1,2]=C[0,1]\n C[4,4]=C[3,3]\n C[5,5]=C[3,3]\n \n #-- Hexagonal Structures --------------------------------------------------------------------------\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[2,2]=ci[0][3]\n C[3,3]=ci[0][4]\n C[1,1]=C[0,0]\n C[1,2]=C[0,2]\n C[4,4]=C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral I Structures ---------------------------------------------------------------------\n if (LC == 'RI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral II Structures --------------------------------------------------------------------\n if (LC == 'RII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[0,4]= ci[0][4]\n C[2,2]= ci[0][5]\n C[3,3]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[1,4]=-C[0,4]\n C[3,5]=-C[0,4]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Tetragonal I Structures -----------------------------------------------------------------------\n if (LC == 'TI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[2,2]= ci[0][3]\n C[3,3]= ci[0][4]\n C[5,5]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[4,4]= C[3,3]\n \n #-- Tetragonal II Structures ----------------------------------------------------------------------\n if (LC == 'TII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,5]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[5,5]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,5]=-C[0,5]\n C[4,4]= C[3,3]\n \n #-- Orthorhombic Structures -----------------------------------------------------------------------\n if (LC == 'O'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[1,1]=ci[0][3]\n C[1,2]=ci[0][4]\n C[2,2]=ci[0][5]\n C[3,3]=ci[0][6]\n C[4,4]=ci[0][7]\n C[5,5]=ci[0][8]\n \n #-- Monoclinic Structures -------------------------------------------------------------------------\n if (LC == 'M'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,5]=ci[0][3]\n C[1,1]=ci[0][4]\n C[1,2]=ci[0][5]\n C[1,5]=ci[0][6]\n C[2,2]=ci[0][7]\n C[2,5]=ci[0][8]\n C[3,3]=ci[0][9]\n C[3,4]=ci[0][10]\n C[4,4]=ci[0][11]\n C[5,5]=ci[0][12]\n \n #-- Triclinic Structures --------------------------------------------------------------------------\n if (LC == 'N'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,3]=ci[0][3]\n C[0,4]=ci[0][4]\n C[0,5]=ci[0][5]\n C[1,1]=ci[0][6]\n C[1,2]=ci[0][7]\n C[1,3]=ci[0][8]\n C[1,4]=ci[0][9]\n C[1,5]=ci[0][10]\n C[2,2]=ci[0][11]\n C[2,3]=ci[0][12]\n C[2,4]=ci[0][13]\n C[2,5]=ci[0][14]\n C[3,3]=ci[0][15]\n C[3,4]=ci[0][16]\n C[3,5]=ci[0][17]\n C[4,4]=ci[0][18]\n C[4,5]=ci[0][19]\n C[5,5]=ci[0][20]\n #--------------------------------------------------------------------------------------------------\n \n \n \n for i in range(5):\n for j in range(i+1,6):\n C[j,i] = C[i,j] \n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if self.__cod in ['espresso']: C = -C/10.\n elif self.__cod in ['vasp','exciting','wien']: C = C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0\n elif self.__cod in ['emto']: C = C*self.__ToGPa/self.__V0\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\n self.S = np.linalg.inv(C)\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\n self.BH = 0.50*(self.BV+self.BR)\n self.GH = 0.50*(self.GV+self.GR)\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\n #--------------------------------------------------------------------------------------------------------------------------------\n self.__C = C\n \n else:\n Cs = []\n for t in map(str,self.__T):#for t in range(len(self.__T)):\n C = np.zeros((6,6))\n \n LC = self.__structures.items()[0][1].LC\n if self.__mthd == 'Energy':\n if type(etacalc)==list:\n A2=[]\n for i in range(len(etacalc)):\n A2.append(self.__A2[t][i][etacalc[i]])\n else:\n if not etacalc in self.__A2[t][0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[t][0].keys()))\n A2 = [a2[etacalc] for a2 in self.__A2[t]]\n \n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'CI' or \\\n LC == 'CII'):\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\n C[1,1] = C[0,0]\n C[2,2] = C[0,0]\n C[3,3] = A2[2]/6.\n C[4,4] = C[3,3]\n C[5,5] = C[3,3]\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\n C[0,2] = C[0,1]\n C[1,2] = C[0,1]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[2]\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RI'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[1,4] =-C[0,4] \n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[3,5] =-C[0,4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TI'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[3]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TII'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\n C[1,1] = C[0,0]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\n C[1,2] = C[0,2]\n C[1,5] =-C[0,5]\n C[2,2] = 2.*A2[4]\n C[3,3] = .5*A2[5]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[6]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'O'):\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\n C[1,1] = 2.*A2[4]\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\n C[2,2] = 2.*A2[5]\n C[3,3] = .5*A2[6]\n C[4,4] = .5*A2[7]\n C[5,5] = .5*A2[8]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'M'):\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\n C[1,1] = 2.*A2[8]\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\n C[2,2] = 2.*A2[9]\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\n C[3,3] = .5*A2[10]\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\n C[4,4] = .5*A2[11]\n C[5,5] = .5*A2[12]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'N'):\n C[0,0] = 2.*A2[0]\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\n C[1,1] = 2.*A2[1]\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\n C[2,2] = 2.*A2[2] \n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\n C[3,3] = .5*A2[3]\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\n C[4,4] = .5*A2[4]\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n elif self.__mthd == 'Stress':\n \n if (LC == 'CI' or \\\n LC == 'CII'):\n Matrix = np.mat([[1.0, 5.0, 0.0],\n [2.0, 4.0, 0.0],\n [3.0, 3.0, 0.0],\n [0.0, 0.0, 4.0],\n [0.0, 0.0, 5.0],\n [0.0, 0.0, 6.0]])\n \n if (LC == 'HI' or \\\n LC == 'HII'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\n [ 2, 1, 3, 0, 0],\n [ 0, 0, 3, 3, 0],\n [ 0, 0, 0, 0, 4],\n [ 0, 0, 0, 0, 5],\n [ 3,-3, 0, 0, 0],\n [ 3,-5,-1, 0, 0],\n [-5, 3,-1, 0, 0],\n [ 0, 0,-2,-1, 0],\n [ 0, 0, 0, 0, 6],\n [ 0, 0, 0, 0, 2],\n [-2, 2, 0, 0, 0]])\n \n if (LC == 'RI'):\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\n [ 2, 1, 3,-4, 0, 0],\n [ 0, 0, 3, 0, 3, 0],\n [ 0, 0, 0,-1, 0, 4],\n [ 0, 0, 0, 6, 0, 5],\n [ 3,-3, 0, 5, 0, 0],\n [ 3,-5,-1, 6, 0, 0],\n [-5, 3,-1,-6, 0, 0],\n [ 0, 0,-2, 0,-1, 0],\n [ 0, 0, 0, 8, 0, 6],\n [ 0, 0, 0,-4, 0, 2],\n [-2, 2, 0, 2, 0, 0]])\n \n if (LC == 'RII'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\n [ 2, 1, 3,-4,-5, 0, 0],\n [ 0, 0, 3, 0, 0, 3, 0],\n [ 0, 0, 0,-1,-6, 0, 4],\n [ 0, 0, 0, 6,-1, 0, 5],\n [ 3,-3, 0, 5,-4, 0, 0],\n [ 3,-5,-1, 6, 2, 0, 0],\n [-5, 3,-1,-6,-2, 0, 0],\n [ 0, 0,-2, 0, 0,-1, 0],\n [ 0, 0, 0, 8, 4, 0, 6],\n [ 0, 0, 0,-4, 8, 0, 2],\n [-2, 2, 0, 2,-6, 0, 0]])\n \n if (LC == 'TI'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\n [ 2, 1, 3, 0, 0, 0],\n [ 0, 0, 3, 3, 0, 0],\n [ 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0, 0, 0, 6],\n [ 3,-5,-1, 0, 0, 0],\n [-5, 3,-1, 0, 0, 0],\n [ 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 0, 0,-4]])\n \n if (LC == 'TII'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\n [ 2, 1, 3,-6, 0, 0, 0],\n [ 0, 0, 3, 0, 3, 0, 0],\n [ 0, 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0,-1, 0, 0, 6],\n [ 3,-5,-1,-4, 0, 0, 0],\n [-5, 3,-1, 4, 0, 0, 0],\n [ 0, 0,-2, 0,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 8, 0, 0,-4]])\n \n if (LC == 'O'):\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\n \n if (LC == 'M'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\n \n if (LC == 'N'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\n \n sigma = np.array(self.__sigma[etacalc])\n \n ci = np.linalg.lstsq(Matrix,sigma)\n \n #-- Cubic structures ------------------------------------------------------------------------------\n if (LC == 'CI' or \\\n LC == 'CII'):\n \n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[3,3]=ci[0][2]\n C[1,1]=C[0,0]\n C[2,2]=C[0,0]\n C[0,2]=C[0,1]\n C[1,2]=C[0,1]\n C[4,4]=C[3,3]\n C[5,5]=C[3,3]\n \n #-- Hexagonal Structures --------------------------------------------------------------------------\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[2,2]=ci[0][3]\n C[3,3]=ci[0][4]\n C[1,1]=C[0,0]\n C[1,2]=C[0,2]\n C[4,4]=C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral I Structures ---------------------------------------------------------------------\n if (LC == 'RI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral II Structures --------------------------------------------------------------------\n if (LC == 'RII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[0,4]= ci[0][4]\n C[2,2]= ci[0][5]\n C[3,3]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[1,4]=-C[0,4]\n C[3,5]=-C[0,4]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Tetragonal I Structures -----------------------------------------------------------------------\n if (LC == 'TI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[2,2]= ci[0][3]\n C[3,3]= ci[0][4]\n C[5,5]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[4,4]= C[3,3]\n \n #-- Tetragonal II Structures ----------------------------------------------------------------------\n if (LC == 'TII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,5]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[5,5]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,5]=-C[0,5]\n C[4,4]= C[3,3]\n \n #-- Orthorhombic Structures -----------------------------------------------------------------------\n if (LC == 'O'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[1,1]=ci[0][3]\n C[1,2]=ci[0][4]\n C[2,2]=ci[0][5]\n C[3,3]=ci[0][6]\n C[4,4]=ci[0][7]\n C[5,5]=ci[0][8]\n \n #-- Monoclinic Structures -------------------------------------------------------------------------\n if (LC == 'M'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,5]=ci[0][3]\n C[1,1]=ci[0][4]\n C[1,2]=ci[0][5]\n C[1,5]=ci[0][6]\n C[2,2]=ci[0][7]\n C[2,5]=ci[0][8]\n C[3,3]=ci[0][9]\n C[3,4]=ci[0][10]\n C[4,4]=ci[0][11]\n C[5,5]=ci[0][12]\n \n #-- Triclinic Structures --------------------------------------------------------------------------\n if (LC == 'N'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,3]=ci[0][3]\n C[0,4]=ci[0][4]\n C[0,5]=ci[0][5]\n C[1,1]=ci[0][6]\n C[1,2]=ci[0][7]\n C[1,3]=ci[0][8]\n C[1,4]=ci[0][9]\n C[1,5]=ci[0][10]\n C[2,2]=ci[0][11]\n C[2,3]=ci[0][12]\n C[2,4]=ci[0][13]\n C[2,5]=ci[0][14]\n C[3,3]=ci[0][15]\n C[3,4]=ci[0][16]\n C[3,5]=ci[0][17]\n C[4,4]=ci[0][18]\n C[4,5]=ci[0][19]\n C[5,5]=ci[0][20]\n #--------------------------------------------------------------------------------------------------\n \n \n \n for i in range(5):\n for j in range(i+1,6):\n C[j,i] = C[i,j] \n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if self.__cod == 'espresso': C = -C/10.\n elif self.__cod in ['vasp','emto','exciting','wien']: C=C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0#C = C/4.\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\n self.S = np.linalg.inv(C)\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\n self.BH = 0.50*(self.BV+self.BR)\n self.GH = 0.50*(self.GV+self.GR)\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\n #--------------------------------------------------------------------------------------------------------------------------------\n Cs.append(C)\n self.__C = Cs","def sage_graph(self):\n self.fe.load_cache()\n edges = []\n is_bipartite = self.variant.is_bipartite()\n for X in self.L:\n for Y in self.L:\n a = self.op_norm(X, Y)\n if not self.K.is_zero(a):\n for c in self.K.unit_group:\n d = a - c\n if X != Y or c < d or is_bipartite:\n edges.append(((X, c, False), (Y, d, is_bipartite)))\n if X == Y and not is_bipartite:\n break\n return sage.all.Graph(edges)","def final_components(self):\n DG = self.digraph()\n condensation = DG.strongly_connected_components_digraph()\n return [self.induced_sub_finite_state_machine([self.state(_) for _ in component])\n for component in condensation.vertices()\n if condensation.out_degree(component) == 0]","def compute_edge_logits(self):","def _nelec(self):\n pd = self.particle_distribution(self._gam * mec2)\n return pd.to(1/mec2_unit).value","def nE(self):\n return int(self.vnE.sum())","def edges(self):\n return self.dovetails + self.containments + self.internals","def collectiveMetabolism(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes, addEcDescriptions = False) -> SubstanceEcGraph:\n graph = self.group.collectiveEcGraph(noMultifunctional = excludeMultifunctionalEnzymes, addCount = True, keepOnHeap = True, addEcDescriptions = addEcDescriptions)\n graph.name = 'Collective metabolism ECs ' + ' '.join(self.ncbiNames)\n return graph","def all_subconstituents(self, compute=False):\n out = {}\n for i in range(self._.d+1):\n try:\n out[i] = self.subconstituent(i, compute=compute)\n except IndexError:\n pass\n return out","def _get_full_graph(self):","def connected_component(self):\n t1 = datetime.datetime.now()\n nodes = set(x.hex for x in self.agents)\n result = []\n while nodes:\n node = nodes.pop()\n # This set will contain the next group of nodes connected to each other.\n group = {node}\n # Build a queue with this node in it.\n queue = [node]\n # Iterate the queue.\n # When it's empty, we finished visiting a group of connected nodes.\n while queue:\n # Consume the next item from the queue.\n node = queue.pop(0)\n # Fetch the neighbors.\n neighbors = set(x for x in node.fon if x.is_occupied == 1)\n # Remove the neighbors we already visited.\n neighbors.difference_update(group)\n # Remove the remaining nodes from the global set.\n nodes.difference_update(neighbors)\n # Add them to the group of connected nodes.\n group.update(neighbors)\n # Add them to the queue, so we visit them in the next iterations.\n queue.extend(neighbors)\n\n # Add the group to the list of groups.\n result.append(len(group))\n td = datetime.datetime.now() - t1\n print(\"calculated {} connected components in {} seconds\".format(len(result),td.total_seconds()))\n return len(result), np.histogram(result, self.cluster_hist_breaks)[0]","def calEachCrossflowAllAxialNode():\n AxialNodeno = 14 # axial node number in CFD data\n Nodes = []\n base = 'Node'\n for i in range(0, AxialNodeno):\n Nodes.append(base+str(i))\n \n crossFlow = pd.read_csv('Data_crossflow.csv', index_col = 'Unnamed: 0')\n lateralFactors = []\n for node in Nodes:\n lateralFactors.append(crossFlow[node]/0.8)\n #need to judge the sign of lateral flow according to CTF rule!!\n gapsToFlip = [2,4,6,7,9,11,13,14,16,18,20,21] #gaps in y direction\n gapsToFlipIndex = [x - 1 for x in gapsToFlip]\n for factors in lateralFactors:\n for index in gapsToFlipIndex:\n factors[index] = -factors[index] \n #note: lateralFactors is a list of list\n \n #below calculate factors averaged over all subchannels\n crossFlowAveFactor = crossFlow.apply(abs).mean(axis = 0)/0.8\n lateralFactorsAvelist = []\n for i in range(0,14):\n base = []\n for j in range(0,24):\n base.append(crossFlowAveFactor[i])\n lateralFactorsAvelist.append(base)\n \n \n for i in range(0, 14):\n for j in range(0, 24):\n #note, in the original model there is only one sign for all source\n #terms in one sub-channel. therefore -- sign(crossFlow.iloc[j,2])\n lateralFactorsAvelist[i][j] = lateralFactorsAvelist[i][j] *sign(crossFlow.iloc[j,2]) \n for each in lateralFactorsAvelist:\n for index in gapsToFlipIndex:\n each[index] = -each[index] \n \n \n return lateralFactors, lateralFactorsAvelist","def discreteComplexDecomposeGraph(self,graph):\n s=[complex(*graph[i]) for i in range(len(graph))]\n N=len(s)\n M=self.coefficients_number\n d=0\n c=[]\n for k in range(-M//2,M//2):\n d+=sum([s[n]*cmath.exp(2j*cmath.pi*k*n/N) for n in range(N)])/N\n c.append(d)\n return c","def display_energy_levels_0d(diagram, num_atoms, atoms, h_poly):\n h = eval_hamiltonian(num_atoms, h_poly, (1, 1))\n\n e, v = eigensystem(h)\n\n left = 0\n bottom = 0\n right = max([len(row) for row in diagram.split('\\n')])\n top = len(diagram.split('\\n'))\n\n plot_rows = numpy.ceil(math.sqrt(num_atoms+1))\n plot_cols = plot_rows\n\n for i in range(num_atoms):\n matplotlib.pyplot.subplot(plot_rows, plot_cols, i+1, axisbg=\"#000000\")\n y = [atom[0] for atom in atoms]\n x = [atom[1] for atom in atoms]\n c = numpy.abs(v[i]*v[i])\n\n matplotlib.pyplot.title('E = %f' % numpy.real(e[i]), fontsize = 10)\n norm = matplotlib.colors.Normalize(vmin = min(c),\n vmax = max(0.0001, max(c)))\n #x = [0,0,1,1]\n #y = [0,1,0,1]\n #c = [1,2,3,4]\n matplotlib.pyplot.hexbin(x, y, C = c,\n gridsize = (right-left, top-bottom),\n extent = (left, right, bottom, top),\n cmap = matplotlib.pyplot.get_cmap(\"gray\"),\n norm = norm\n )\n\n matplotlib.pyplot.subplot(plot_rows, plot_cols, num_atoms+1)\n matplotlib.pyplot.scatter(num_atoms*[0], e, s = 0.1)","def eclogite_massive():\n\n rho = 3490.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\n\n return C, rho","def eci(self):\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\n eci = coefs.copy()\n eci = eci / self._subspace.function_total_multiplicities\n return eci","def calculate_E(self):\n \n E = 0\n for i in xrange(self.size):\n Ei = self.h[i]\n Ei += 0.5*sum((1 if self.spins[j] else -1)*self.J[i,j] for j in self.adjacency[i])\n if not self.spins[i]:\n Ei *= -1\n E += Ei\n \n return E","def extract_activities(ncfile):\n\n # Get current dimensions.\n (niterations, nstates) = ncfile.variables['energies'].shape\n\n # Extract energies.\n print \"Reading energies...\"\n energies = ncfile.variables['energies']\n u_kln_replica = zeros([nstates, nstates, niterations], float64)\n for n in range(niterations):\n u_kln_replica[:,:,n] = energies[n,:,:]\n print \"Done.\"\n\n # Deconvolute replicas\n print \"Deconvoluting replicas...\"\n u_kln = zeros([nstates, nstates, niterations], float64)\n for iteration in range(niterations):\n state_indices = ncfile.variables['states'][iteration,:]\n u_kln[state_indices,:,iteration] = energies[iteration,:,:]\n print \"Done.\"\n\n # Show all self-energies\n print 'all self-energies for all replicas'\n for iteration in range(niterations):\n for replica in range(nstates):\n state = int(ncfile.variables['states'][iteration,replica])\n print '%12.1f' % energies[iteration, replica, state],\n print ''\n\n # If no energies are 'nan', we're clean.\n if not any(isnan(energies[:,:,:])):\n return\n\n # There are some energies that are 'nan', so check if the first iteration has nans in their *own* energies:\n u_k = diag(energies[0,:,:])\n if any(isnan(u_k)):\n print \"First iteration has exploded replicas. Check to make sure structures are minimized before dynamics\"\n print \"Energies for all replicas after equilibration:\"\n print u_k\n sys.exit(1)\n\n # There are some energies that are 'nan' past the first iteration. Find the first instances for each replica and write PDB files.\n first_nan_k = zeros([nstates], int32)\n for iteration in range(niterations):\n for k in range(nstates):\n if isnan(energies[iteration,k,k]) and first_nan_k[k]==0:\n first_nan_k[k] = iteration\n if not all(first_nan_k == 0):\n print \"Some replicas exploded during the simulation.\"\n print \"Iterations where explosions were detected for each replica:\"\n print first_nan_k\n print \"Writing PDB files immediately before explosions were detected...\"\n for replica in range(nstates): \n if (first_nan_k[replica] > 0):\n state = ncfile.variables['states'][iteration,replica]\n iteration = first_nan_k[replica] - 1\n filename = 'replica-%d-before-explosion.pdb' % replica\n title = 'replica %d state %d iteration %d' % (replica, state, iteration)\n write_pdb(atoms, filename, iteration, replica, title, ncfile)\n filename = 'replica-%d-before-explosion.crd' % replica \n write_crd(filename, iteration, replica, title, ncfile)\n sys.exit(1)\n\n # There are some energies that are 'nan', but these are energies at foreign lambdas. We'll just have to be careful with MBAR.\n # Raise a warning.\n print \"WARNING: Some energies at foreign lambdas are 'nan'. This is recoverable.\"\n \n return","def components(self):\n comps = 0\n unexplored = self.nodes()\n while unexplored:\n comps += 1\n queue = {unexplored.pop()}\n while queue:\n new = queue.pop()\n unexplored.remove(new)\n for adjacent in new.parents() | new.children():\n if adjacent in unexplored:\n queue.add(adjacent)\n return comps","def n_cs(self):\n pass","def get_econs(self):\n eham = self.beads.vpath*self.nm.omegan2 + self.nm.kin + self.forces.pot\n eham += self.bias.pot # bias\n for e in self._elist:\n eham += e.get()\n\n return eham + self.eens","def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n childGraph = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)\n childGraph.name = 'Added metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return childGraph","def CS2e(Type=\"DFA\"):\n CC50, CC51, CC52, CC53 = state('CC50'), state('CC51'), state('CC52'), state('CC53')\n for i in sigma:\n CC52.transit[i] = CC52\n CC53.transit[i] = CC53\n for i in sigma_1:\n CC50.transit[i] = CC50\n for i in sigma_2:\n CC50.transit[i] = CC53\n CC51.transit[i] = CC51\n for i in sigma_cc:\n CC50.transit[i] = CC51\n CC51.transit[i] = CC51\n for i in sigma_ncc:\n CC51.transit[i] = CC50\n for i in sigma_B_A:\n CC51.transit[i] = CC50\n for i in sigma_e:\n CC51.transit[i] = CC53\n for i in sigma_A:\n CC51.transit[i] = CC52\n if Type == \"pDFA\":\n CC5 = pDFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\n else:\n CC5 = DFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\n if (SIZEOF):\n EM_size[\"CS2e\"] = asizeof.asizeof(CC5)\n return CC5","def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:\n # get neofunctionalisations \n neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)\n \n # filter core metabolism enzyme graph\n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism) \n neofunctionalisedMetabolism = neofunctionalisedEnzymes.filterGraph(enzymeGraph, minimumEcDifference = None)\n \n # colour core metabolism \n if colour is not False:\n \n if colour is True:\n colourToUse = Export.Colour.GREEN\n else:\n colourToUse = colour\n \n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\n neofunctionalisedMetabolism = enzymeGraph\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\n \n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism enzymes ' + ' '.join(self.ncbiNames)\n \n return neofunctionalisedMetabolism","def num_of_subgraphs(self):\n \n G = self.to_undirected_graph()\n \n count = G.num_subgraph()\n \n print('The number of disconnected components in the graph is ', count)","def neofunctionalisations(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None) -> Set[Neofunctionalisation]:\n # get neofunctionalisations \n return self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs).getNeofunctionalisations()","def Excentricity_graph(self,ccenter,cradius,icenter):\n \n ellip = 0.\n q = 1. - ellip\n pa = 0.\n \n stamp = self['STAMP'].copy()\n mask = self['MASK'].copy()\n sky = self['BACKGROUND']\n Img = stamp - sky\n Img[num.where(mask != 0)] = 0.\n \n id = self._getGraphId()\n root = 'Excentricity_%s' % (id,)\n pngname = root + '.png' ; epsname = root + '.eps'\n jpgname = root + '.jpg'\n doStamp(Img,pngname,format='PNG')\n Convert(pngname,jpgname)\n \n Painted = Paint(jpgname)\n Painted.load()\n \n Painted.DrawEllipse(ccenter,cradius,q,pa,color='red',linewidth=2)\n Painted.DrawCross(ccenter,length=20,color='red')\n Painted.DrawCross(icenter,length=20,color='green')\n \n text = 'E=%.2f o/o' % self['M_E']\n \n # Painted.Graffiti(text,commtextpos)\n \n Painted.save(jpgname) \n Painted.release()\n \n Convert(jpgname,epsname)\n os.system('rm %s %s' % (pngname,jpgname))\n \n self['figures']['Excentricity'] = epsname\n self['figcomms']['Excentricity'] = text","def connected_components(self) -> List[list]:\n for n in self.dw_graph.get_all_v().values():\n n.distance=0.0\n mega_list = []\n for n in self.dw_graph.get_all_v().values():\n if n.distance!=-10:\n mega_list.append(self.connected_component(n.node_id))\n return mega_list","def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised= self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = conservedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\n \n parentGraph.name = 'Conserved metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def plot_eigenvector_centrality(net, label, outpath):\n _, eigenvector_centrality_values = networkit_util.get_eigenvector_centrality(net, label, outpath)\n unique_value, unique_cnt = np.unique(eigenvector_centrality_values, return_counts=True)\n unique_cumcnt = np.cumsum(unique_cnt) / sum(unique_cnt)\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(unique_value, unique_cumcnt, 'g.')\n # ax.set_title('Cumulative distribution of eigenvector centrality of nodes')\n ax.set_xlabel('eigenvector centrality e')\n ax.set_ylabel('p(x <= e)')\n plt.savefig(outpath + label + \"-eigenvector-distribution.eps\")","def eclogite_foliated():\n\n rho = 3300.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\n\n return C, rho","def get_band_edges():\n # Vacuum level energy from LOCPOT.\n locpot = Locpot.from_file('LOCPOT')\n evac = max(locpot.get_average_along_axis(2))\n\n vasprun = Vasprun('vasprun.xml')\n bs = vasprun.get_band_structure()\n eigenvals = vasprun.eigenvalues\n efermi = vasprun.efermi - evac\n\n if bs.is_metal():\n edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,\n 'efermi': efermi}\n\n elif bs.is_spin_polarized:\n up_cbm = min(\n [min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])\n for i in range(len(eigenvals[Spin.up]))]) - evac\n up_vbm = max(\n [max([e[0] for e in eigenvals[Spin.up][i] if e[1]])\n for i in range(len(eigenvals[Spin.up]))]) - evac\n dn_cbm = min(\n [min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])\n for i in range(len(eigenvals[Spin.down]))]) - evac\n dn_vbm = max(\n [max([e[0] for e in eigenvals[Spin.down][i] if e[1]])\n for i in range(len(eigenvals[Spin.down]))]) - evac\n edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,\n 'dn_vbm': dn_vbm, 'efermi': efermi}\n\n else:\n cbm = bs.get_cbm()['energy'] - evac\n vbm = bs.get_vbm()['energy'] - evac\n edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,\n 'efermi': efermi}\n\n return edges","def generate_empirical_nodes(reduced_basis):\n \n # Get the index of the 0th empirical node T_0\n # from the maximum value of the 0th reduced basis.\n imax = np.argmax(np.abs(reduced_basis[0]))\n empirical_node_indices = [imax]\n \n # Iterate to find the other impirical nodes.\n for j in range(1, len(reduced_basis)):\n # The jth empirical node\n imax = generate_new_empirical_node(reduced_basis, empirical_node_indices)\n empirical_node_indices.append(imax)\n \n return empirical_node_indices","def edges(self):\n return map(Edge, self._top_exp.edges())","def epidote():\n\n rho = 3465.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 211.5; C[0,1] = 65.6; C[0,2] = 43.2; C[0,3] = 0.; C[0,4] = -6.5; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 239.; C[1,2] = 43.6; C[1,3] = 0.; C[1,4] = -10.4; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 202.1; C[2,3] = 0.; C[2,4] = -20.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.1; C[3,4] = 0.; C[3,5] = -2.3\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 43.4; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.5\n\n return C, rho","def ec_geometric_series(p0, T, n):\n if(n<=0):\n EC=np.zeros(T.shape)\n return EC\n else:\n R, D, L=rdl_decomposition(T)\n w=np.diagonal(D)\n L=np.transpose(L)\n\n D_sum=np.diag(geometric_series(w, n-1))\n T_sum=np.dot(np.dot(R, D_sum), np.conjugate(np.transpose(L)))\n p_sum=np.dot(p0, T_sum)\n EC=p_sum[:,np.newaxis]*T\n \"\"\"Truncate imginary part - which is zero, but we want real\n return values\"\"\"\n EC=EC.real\n return EC","def cyclic_subgroups(C):\n ps = C.points()\n Gs = []\n \n for i in ps:\n P = Elliptic_Point(i[0],i[1],C)\n Gs.append(cyclic_subgroup(P))\n\n return Gs","def _sigma_ee_nonrel(self,gam,eps):\n s0 = 4 * r0**2 * alpha / (15 * eps)\n x = 4 * eps / (gam**2 - 1)\n sigma_nonrel = s0 * self._F(x,gam)\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\n return sigma_nonrel / mec2_unit","def vnE(self):\n return np.array(\n [x for x in [self.nEx, self.nEy, self.nEz] if x is not None],\n dtype=int\n )","def get_edges(self):\n for i in self.gens:\n if self.active[i]:\n elist = set()\n H = (i,) # edge-stabilizing subgroup\n reps = set(self.word_generator(parabolic=H))\n reps = self.G.sort_words(reps)\n for word in reps:\n v1 = self.G.move(self.vtable, 0, word)\n v2 = self.G.move(self.vtable, 0, word + (i,))\n if v1 is not None and v2 is not None:\n if v1 > v2:\n v1, v2 = v2, v1\n if (v1, v2) not in elist:\n elist.add((v1, v2))\n\n self.edge_indices[i] = elist\n\n self.num_edges = sum(len(L) for L in self.edge_indices.values())","def graph(self):\n ...","def findcc(self):\n for vertex in self.graph.vertices:\n if vertex not in self.preOrderNumbers:\n self.dfs(vertex)\n return self.scComponents","def gen_graph(self):","def edges(self):\r\n return self.capacity.keys()","def _get_graph_based_ic_dictionary(self):\n\n\t\t# TODO find the literature reference or presentation where this equation is from instead of just the presentation.\n\n\t\t#ic_dict = {}\n\t\t#num_terms_in_ontology = len(self)\n\t\t#for term in self.terms():\n\t\t#\tdepth = self._depth_dict[term.id]\n\t\t#\tnum_descendants = len(list(term.subclasses(with_self=False)))\n\t\t#\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\n\t\t#\tic_dict[term.id] = ic_value\n\t\t#return(ic_dict)\n\n\n\t\t# Getting the information content of each term in the ontology based on graph structure.\n\t\tic_dict = {}\n\t\tnum_terms_in_ontology = len(self)\n\t\tfor term in self.terms():\n\t\t\tdepth = self._depth_dict[term.id]\n\t\t\tnum_descendants = len(list(term.subclasses(with_self=False)))\n\t\t\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\n\t\t\tic_dict[term.id] = ic_value\n\n\n\t\t# Converting to weights based on information content rather than raw value.\n\t\tic_dict_as_weights = {}\n\t\tic_values = ic_dict.values()\n\t\tmin_ic = min(ic_values)\n\t\tmax_ic = max(ic_values)\n\t\tnew_max = 1.00\n\t\tnew_min = 0.00\n\t\tfor k,v in ic_dict.items():\n\t\t\told_range = max_ic-min_ic\n\t\t\tnew_range = new_max-new_min\n\t\t\tnew_value = (((v - min_ic) * new_range) / old_range) + new_min\n\t\t\tic_dict_as_weights[k] = new_value\n\n\t\treturn(ic_dict, ic_dict_as_weights)","def life_expectancy_graph(country_name):\r\n dat = read_data(pre_file_name())\r\n MainDct = dat[0]\r\n # MetaDct = dat[1]\r\n f_life_expectancy = {}\r\n for idx in MainDct:\r\n if idx == country_name or MainDct[idx].country_code == country_name:\r\n for idx1 in MainDct[idx].values:\r\n if idx1[1] != 0:\r\n f_life_expectancy[idx1[0]] = idx1[1]\r\n return f_life_expectancy","def eccentricity(self):\n new_data = self._data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\n 'pl_masse', 'pl_orbeccen',\n 'pl_radj', 'pl_dens', 'st_teff',\n 'st_mass', 'st_rad']]\n new_data = new_data.dropna()\n\n features = new_data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\n 'pl_masse',\n 'pl_radj', 'pl_dens', 'st_teff',\n 'st_mass', 'st_rad']]\n labels = new_data['pl_orbeccen']\n\n features_train, features_test, labels_train, labels_test = \\\n train_test_split(features, labels, test_size=0.2)\n\n # Create an untrained model\n model = DecisionTreeRegressor()\n\n # Train it on the **training set**\n model.fit(features_train, labels_train)\n\n # Compute test accuracy\n test_predictions = model.predict(features_test)\n test_acc = mean_absolute_error(labels_test, test_predictions)\n test_acc_r2 = r2_score(labels_test, test_predictions)\n\n # Plot ML vs Actual\n fig, [ax1, ax2] = plt.subplots(2, figsize=(15, 12))\n\n sns.distplot(test_predictions, kde=False, ax=ax1)\n sns.distplot(labels_test, kde=False, ax=ax2)\n\n ax1.set_title('Distribution of Predicted Eccentricities of Orbits')\n ax1.set_xlabel('Eccentricity of Orbit')\n ax1.set_ylabel('Number of Planets')\n\n ax2.set_title('Distribution of Actual Eccentricities of Orbits')\n ax2.set_xlabel('Eccentricity of Orbit')\n ax2.set_ylabel('Number of Planets')\n\n plt.savefig('figures/ML_Eccentricity.png', bbox_inches='tight')\n\n return (test_acc, test_acc_r2)","def get_eulerian_graph_edges(bbox, source):\n osm_graph = OSMGraph(bbox, source)\n # input all nodes and get odd nodes, update node attributes\n odd_nodes = get_odd_nodes(osm_graph.nodes_dict)\n\n # initialize all_pairs_list\n all_pairs_list = []\n\n # if there are 6 or fewer odd nodes look for all possible options,\n # otherwise look for just three basic pairing options\n\n if len(odd_nodes) <= 10:\n print(\"ROBUST PAIRING FUNCTION\")\n all_pairs_list = get_list_of_all_pairs_lists(odd_nodes)\n\n else:\n print(\"CHEAP PAIRING FUNCTION\")\n all_pairs_list = get_list_of_all_pairs_lists_short(odd_nodes)\n\n for item in all_pairs_list:\n print(\"\\n\\nPair option:\", item)\n print(\"Pair option len:\", len(item))\n\n dict_pairings_lists_lengths = get_dict_pairings_lists_lengths(\n all_pairs_list, osm_graph\n )\n twice_traversals_edges = get_twice_traversals_edges(dict_pairings_lists_lengths)\n updated_graph_instance = update_twice_traversal_edges(\n twice_traversals_edges, osm_graph\n )\n return updated_graph_instance","def _get_gs_energies(self):\n energy = []\n for ground_state in self._ground_states:\n gs_energy = 0.0\n for key in ground_state[\"eci\"].keys():\n gs_energy += ground_state[\"eci\"][key] * ground_state[\"cf\"][key]\n energy.append(len(ground_state[\"atoms\"]) * gs_energy)\n return energy","def buildE(self, debug = False):\n E = np.zeros([len(self.sta), len(self.dist)])\n i = 0\n for ikey, state in self.sta.items():\n j = 0\n c = state.c\n for jkey, disturbance in self.dist.items():\n if debug:\n print(i, ' ', j)\n if isinstance(disturbance, InputT) and disturbance in \\\n self.rc.adj[state]:\n # input is temperature and connected to this state\n\n E[i, j] = self.rc.adj[state][disturbance]['H'] / c\n elif isinstance(disturbance, InputQ) and disturbance in \\\n self.rc.adj[\n state]: # input is heat flow and connected to state\n E[i, j] = self.rc.adj[state][disturbance]['gain'] / c\n j += 1\n i += 1\n\n return E, list(self.sta.keys()), list(self.dist.keys())","def Hasse_diagram_from_incidences(atom_to_coatoms, coatom_to_atoms,\n face_constructor=None,\n required_atoms=None,\n key = None,\n **kwds):\n from sage.graphs.all import DiGraph\n from sage.combinat.posets.posets import FinitePoset\n def default_face_constructor(atoms, coatoms, **kwds):\n return (atoms, coatoms)\n if face_constructor is None:\n face_constructor = default_face_constructor\n atom_to_coatoms = [frozenset(atc) for atc in atom_to_coatoms]\n A = frozenset(range(len(atom_to_coatoms))) # All atoms\n coatom_to_atoms = [frozenset(cta) for cta in coatom_to_atoms]\n C = frozenset(range(len(coatom_to_atoms))) # All coatoms\n # Comments with numbers correspond to steps in Section 2.5 of the article\n L = DiGraph(1) # 3: initialize L\n faces = dict()\n atoms = frozenset()\n coatoms = C\n faces[atoms, coatoms] = 0\n next_index = 1\n Q = [(atoms, coatoms)] # 4: initialize Q with the empty face\n while Q: # 5\n q_atoms, q_coatoms = Q.pop() # 6: remove some q from Q\n q = faces[q_atoms, q_coatoms]\n # 7: compute H = {closure(q+atom) : atom not in atoms of q}\n H = dict()\n candidates = set(A.difference(q_atoms))\n for atom in candidates:\n coatoms = q_coatoms.intersection(atom_to_coatoms[atom])\n atoms = A\n for coatom in coatoms:\n atoms = atoms.intersection(coatom_to_atoms[coatom])\n H[atom] = (atoms, coatoms)\n # 8: compute the set G of minimal sets in H\n minimals = set([])\n while candidates:\n candidate = candidates.pop()\n atoms = H[candidate][0]\n if atoms.isdisjoint(candidates) and atoms.isdisjoint(minimals):\n minimals.add(candidate)\n # Now G == {H[atom] : atom in minimals}\n for atom in minimals: # 9: for g in G:\n g_atoms, g_coatoms = H[atom]\n if not required_atoms is None:\n if g_atoms.isdisjoint(required_atoms):\n continue\n if (g_atoms, g_coatoms) in faces:\n g = faces[g_atoms, g_coatoms]\n else: # 11: if g was newly created\n g = next_index\n faces[g_atoms, g_coatoms] = g\n next_index += 1\n Q.append((g_atoms, g_coatoms)) # 12\n L.add_edge(q, g) # 14\n # End of algorithm, now construct a FinitePoset.\n # In principle, it is recommended to use Poset or in this case perhaps\n # even LatticePoset, but it seems to take several times more time\n # than the above computation, makes unnecessary copies, and crashes.\n # So for now we will mimic the relevant code from Poset.\n\n # Enumeration of graph vertices must be a linear extension of the poset\n new_order = L.topological_sort()\n # Make sure that coatoms are in the end in proper order\n tail = [faces[atoms, frozenset([coatom])]\n for coatom, atoms in enumerate(coatom_to_atoms)]\n tail.append(faces[A, frozenset()])\n new_order = [n for n in new_order if n not in tail] + tail\n # Make sure that atoms are in the beginning in proper order\n head = [0] # We know that the empty face has index 0\n head.extend(faces[frozenset([atom]), coatoms] \n for atom, coatoms in enumerate(atom_to_coatoms)\n if required_atoms is None or atom in required_atoms)\n new_order = head + [n for n in new_order if n not in head]\n # \"Invert\" this list to a dictionary\n labels = dict()\n for new, old in enumerate(new_order):\n labels[old] = new\n L.relabel(labels)\n # Construct the actual poset elements\n elements = [None] * next_index\n for face, index in faces.items():\n atoms, coatoms = face\n elements[labels[index]] = face_constructor(\n tuple(sorted(atoms)), tuple(sorted(coatoms)), **kwds)\n return FinitePoset(L, elements, key = key)","def get_incident_nodes(self):\n # return the set of incident edges\n return \\\n {\n self.first_incident_node,\n self.second_incident_node\n }","def lostMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n parentGraph = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(lostECs)\n parentGraph.name = 'Lost metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return parentGraph","def ncusps(self):\n n = self.level()\n return sum([arith.euler_phi(arith.gcd(d,n//d)) for d in n.divisors()])","def line_graph_forbidden_subgraphs():\n from sage.graphs.all import Graph\n from sage.graphs.generators.basic import ClawGraph\n graphs = [ClawGraph()]\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3],\n 4: [2],\n 5: [3]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3, 4],\n 1: [2, 3, 4],\n 3: [4],\n 2: [5]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3],\n 4: [2, 3]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3],\n 4: [2],\n 5: [3, 4]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3, 4],\n 1: [2, 3, 4],\n 3: [4],\n 5: [2, 0, 1]\n }))\n\n graphs.append(Graph({\n 5: [0, 1, 2, 3, 4],\n 0: [1, 4],\n 2: [1, 3],\n 3: [4]\n }))\n\n graphs.append(Graph({\n 1: [0, 2, 3, 4],\n 3: [0, 4],\n 2: [4, 5],\n 4: [5]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3, 4],\n 2: [3, 4],\n 3: [4]\n }))\n\n return graphs","def getSigma(E, opt=\"\"):\n # HG resolutions, from the energy unidoc.\n eRes = {\n 0 : {\"nat\": [1.260e-1, 1.790e-2, 2.370e-4], \"enr\": [1.500e-1, 1.750e-2, 2.820e-4], \"both\": [1.470e-1, 1.730e-2, 3.000e-4]},\n 1 : {\"nat\": [1.470e-1, 1.770e-2, 2.010e-4], \"enr\": [1.340e-1, 1.750e-2, 2.820e-4], \"both\": [1.360e-1, 1.740e-2, 2.800e-4]},\n 2 : {\"nat\": [1.410e-1, 1.800e-2, 1.680e-4], \"enr\": [1.420e-1, 1.720e-2, 2.860e-4], \"both\": [1.430e-1, 1.720e-2, 2.840e-4]},\n 3 : {\"nat\": [1.800e-1, 1.820e-2, 2.090e-4], \"enr\": [1.580e-1, 1.710e-2, 3.090e-4], \"both\": [1.620e-1, 1.720e-2, 2.970e-4]},\n 4 : {\"nat\": [2.140e-1, 1.540e-2, 3.970e-4], \"enr\": [2.170e-1, 1.490e-2, 3.190e-4], \"both\": [2.180e-1, 1.500e-2, 3.500e-4]},\n \"5A\" : {\"nat\": [2.248e-1, 1.894e-2, 2.794e-4], \"enr\": [2.660e-1, 2.215e-2, 2.868e-4], \"both\": [2.592e-1, 2.057e-2, 3.086e-4]},\n \"5B\" : {\"nat\": [1.650e-1, 1.760e-2, 2.828e-4], \"enr\": [1.815e-1, 1.705e-2, 3.153e-4], \"both\": [1.815e-1, 1.690e-2, 3.187e-4]},\n \"5C\" : {\"nat\": [1.565e-1, 1.810e-2, 2.201e-4], \"enr\": [1.361e-1, 1.740e-2, 2.829e-4], \"both\": [1.519e-1, 1.718e-2, 2.762e-4]}\n }\n\n if len(dsList)==1:\n p = eRes[dsList[0]][opt]\n return np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2)\n else:\n # weight the curve by exposure\n sig, expTot = 0, 0\n for ds in dsList:\n if opt==\"enr\": exp = dsExpo[ds][0]\n if opt==\"nat\": exp = dsExpo[ds][1]\n if opt==\"both\": exp = dsExpo[ds][0] + dsExpo[ds][1]\n p = eRes[ds][opt]\n sig += np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2) * exp\n expTot += exp\n sig /= expTot\n return sig","def get_eddy_instances(parents, root_nodes, gate_dist):\n\tsnodes = []\n\te_instances = dict()\n\tchild_score = dict()\n\tfor parent in parents:\n\t\tfor child in parent.children:\n\t\t\tif is_term(child):\n\t\t\t\tcontinue\n\t\t\tchild.score = parent.score + max(score(hyp, gate_dist) for hyp in child.tracks())\n\t\t\tchild_score[child] = (child.score, parent)\n\t\t\tif child.obj in e_instances:\n\t\t\t\te_instances[child.obj].append(child)\n\t\t\telse:\n\t\t\t\te_instances[child.obj] = [child]\n\tfor node in root_nodes:\n\t\tnode.score = max(score(hyp, gate_dist) for hyp in node.tracks())\n\t\tchild_score[node] = (node.score, None)\n\t\tif node.obj in e_instances:\n\t\t\te_instances[node.obj].append(node)\n\t\telse:\n\t\t\te_instances[node.obj] = [node]\n\treturn (e_instances, child_score)","def n_e(self) -> u.m**-3:\n return np.sum(self._n_elem * self.ionic_fractions * self.charge_numbers)","def discreteComplexComposeGraph(self,S,n):\n N=len(S)\n M=N//2\n g=[]\n d=0\n for k in range(M):\n d+=S[k+M]*cmath.exp(2j*cmath.pi*k*n/N)+S[-k+M]*cmath.exp(-2j*cmath.pi*k*n/N)\n g.append((d.real,d.imag))\n return g","def computemodes(self):\n evals, modes = np.linalg.eig(self.A)\n return evals, modes","def edges(self):\r\n return self.__generate_edges()","def non_pol_neighbours_graph():\n data = pd.read_csv(\"/Users/emg/GitHub/thesis/output/2019_01/1000_residuals_output_utf8.csv\", index_col=0)\n\n labelled = label_subs(data)\n labelled['resid_rank'] = labelled.resid.rank(pct=True)\n top = subset_df(labelled, 'resid', q=0.95)\n\n edges = top.copy()[['source','target','resid']]\n edges_rev = edges.copy()\n edges_rev.columns = ['target','source','resid']\n directed_edges = pd.concat([edges,edges_rev], sort=True)\n directed_edges['resid_rank'] = directed_edges['resid'].rank(pct=True)\n\n df = label_subs(directed_edges)\n\n pol_subs = load_pol_subs()\n pol_names = pol_subs.subreddit.str.replace('\\\\','')\n pol_subs.subreddit=pol_subs.subreddit.str.replace('\\\\','')\n\n pol_neighbours = df[df['source'].isin(pol_names)].sort_values('resid', ascending=False)\n\n top_pol_neigh = pol_neighbours.groupby('source').head(10).sort_values(['source','resid'], ascending=[True,False])\n \n x = top_pol_neigh[~top_pol_neigh.target.isin(pol_names)][['source','target']]\n\n col_dict = pol_subs.set_index('subreddit').col.to_dict()\n for sub in x.target.unique():\n col_dict[sub] = 'gray'\n\n G = nx.from_pandas_edgelist(x)\n nx.set_node_attributes(G, col_dict, 'col')\n\n f = plt.figure(1)\n ax = f.add_subplot(1,1,1)\n\n colors = dict(G.nodes(data='col')).values()\n\n pos = nx.spring_layout(G, k=0.2)\n nx.draw_networkx(G, pos=pos, with_labels=False, node_color=colors, alpha=0.3)\n #nx.draw_networkx_labels(G, pos=pos, with_labels=True)\n\n plt.axis('off')\n f.set_facecolor('w')\n \n f.tight_layout()\n plt.savefig(figures_path(f\"{date}/non_pol_neighbours_graph.png\"))\n plt.close()","def xsec_CEvNS(E_R, E_nu, N_p, N_n):\n \n A = N_p + N_n\n Z = N_p\n \n m_A = A*0.9315 #Mass of target nucleus (in GeV)\n q = np.sqrt(2.0*E_R*m_A) #Recoil momentum (in MeV)\n #Note: m_A in GeV, E_R in keV, E_nu in MeV\n \n #Calculate SM contribution\n Qv = (A-Z) - (1.0-4.0*SIN2THETAW)*Z #Coherence factor\n \n xsec_SM = (G_FERMI*G_FERMI/(4.0*np.pi))*Qv*Qv*m_A* \\\n (1.0-(q*q)/(4.0*E_nu*E_nu))\n \n #Calculate New-Physics correction from Z' coupling\n #Assume universal coupling to quarks (u and d)\n #QvNP = 3.0*A*gsq\n\n #Factor of 1e6 from (GeV/MeV)^2\n #G_V = 1 - 1e6*(SQRT2/G_FERMI)*(QvNP/Qv)*1.0/(q*q + m_med*m_med)\n \n #Convert from (GeV^-3) to (cm^2/keV)\n #and multiply by form factor\n return xsec_SM*1e-6*(1.98e-14)*(1.98e-14)*calcSIFormFactor(E_R, A)","def getEnergyEvolution(self):\n\n\t\tEBefore = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleBefore]\n\t\tEAfter = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleAfter]\n\n\t\treturn EBefore, EAfter","def coaccessible_components(self):\n DG = self.digraph().reverse()\n coaccessible_states = DG.breadth_first_search(\n [_.label() for _ in self.iter_final_states()])\n return self.induced_sub_finite_state_machine(\n [self.state(_) for _ in coaccessible_states])","def plot_mcc_tree():\n t = ete2.Tree(\"mcct.nex\")\n ts = ete2.treeview.TreeStyle()\n ts.show_scale = False\n ts.show_leaf_name = False\n ts.show_branch_support = False\n ts.scale = 500\n margin = 10\n ts.margin_top = margin\n ts.margin_bottom = margin\n ts.margin_left = margin\n ts.margin_right = margin\n\n germ_style = ete2.NodeStyle()\n germ_style[\"bgcolor\"] = \"LightSteelBlue\"\n proto_germ = t.get_common_ancestor(\"Danish\", \"Norwegian\",\"Icelandic\",\"Swedish\", \"Dutch\", \"German\", \"English\")\n proto_germ.set_style(germ_style)\n\n bs_style = ete2.NodeStyle()\n bs_style[\"bgcolor\"] = \"Moccasin\"\n proto_bs = t.get_common_ancestor(\"Bulgarian\", \"Czech\",\"Polish\",\"Russian\")\n proto_bs.set_style(bs_style)\n\n ital_style = ete2.NodeStyle()\n ital_style[\"bgcolor\"] = \"DarkSeaGreen\"\n proto_ital = t.get_common_ancestor(\"French\", \"Romanian\", \"Italian\", \"Portuguese\", \"Spanish\")\n proto_ital.set_style(ital_style)\n\n t.render(\"mcct.eps\", style_func, tree_style=ts, dpi=600, units=\"px\", w=2250)","def interacting_conductivity(self,\n sigma=0.04,\n freq_range=(0.0, 6.0, 1000),\n imag_dielectric=False,\n broadening='lorentz'):\n cell_area = np.abs(np.cross(self.a1, self.a2))*len(self.k_grid)\n broad_fnc = get_broadening_function(broadening, sigma)\n\n frequency_grid = np.linspace(*freq_range)\n output_grid = np.zeros(len(frequency_grid))\n\n energy_power = 1 + int(imag_dielectric)\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\n prefactor = prefactor/cell_area\n\n # Assuming x polarisation for now\n position_dipole_matrix = self.construct_position_dipole_matrix()\n\n n_shift = self.n_spins*self.n_orbs\n cutoff_band_min_maxes = self.exciton_obj.get_cutoff_band_min_maxes()\n # Construct vector of velocity matrix elements to dot with the\n # bse eigenvector.\n velocity_element_lists = []\n for s0 in range(self.n_spins):\n velocity_elements = []\n for idx_1, kpt in enumerate(self.k_grid):\n velocity_matrix = self.load_velocity_matrix(idx_1)\n bands = self.exciton_obj.get_number_conduction_valence_bands(\n idx_1, s0\n )\n v_num, c_num = bands\n v_min = 0\n if self.exciton_obj.energy_cutoff is not None:\n v_min = int(cutoff_band_min_maxes[s0][idx_1][0])\n c_num = int(cutoff_band_min_maxes[s0][idx_1][1]) + 1\n v_num = v_num - v_min\n j1 = n_shift*idx_1 + s0*self.n_orbs\n j2 = j1 + n_shift - (1 - s0)*self.n_orbs*(self.n_spins - 1)\n eigvecs = np.array(\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\n )\n if self.use_dipole_term:\n eigvals = np.array(\n self.file_storage['eigensystem']['eigenvalues'][idx_1]\n )\n else:\n eigvals = None\n for c, v in product(range(c_num), range(v_num)):\n cb_vector = eigvecs[:, v_min + v_num + c]\n vb_vector = eigvecs[:, v_min + v]\n\n matrix_elem = velocity_matrix_element(cb_vector,\n vb_vector,\n velocity_matrix)\n if self.use_dipole_term:\n cb_energy = eigvals[v_min + v_num + c]\n vb_energy = eigvals[v_min + v]\n position_dipole_term = get_position_dipole_element(\n vb_vector,\n cb_vector,\n vb_energy,\n cb_energy,\n position_dipole_matrix\n )\n matrix_elem = matrix_elem + position_dipole_term\n velocity_elements.append(matrix_elem)\n velocity_elements = np.array(velocity_elements, dtype=complex)\n velocity_element_lists.append(velocity_elements)\n\n bse_eigsys = self.exciton_obj.get_bse_eigensystem_direct(solve=True)\n for s0 in range(self.n_spins):\n for idx_1 in range(len(bse_eigsys[s0][0])):\n bse_vector = bse_eigsys[s0][1][:, idx_1]\n bse_value = bse_eigsys[s0][0][idx_1]\n energy_denominator = bse_value**energy_power\n interact_elem = np.dot(bse_vector, velocity_element_lists[s0])\n for idx_2, omega in enumerate(frequency_grid):\n smearing = broad_fnc(bse_value, omega)\n main_term = np.abs(interact_elem)**2/energy_denominator\n output = prefactor*main_term*smearing\n output_grid[idx_2] += output\n\n return frequency_grid, output_grid","def non_interacting_conductivity(self,\n sigma=0.04,\n freq_range=(0.0, 6.0, 1000),\n imag_dielectric=False,\n broadening='lorentz'):\n cell_area = np.abs(float(np.cross(self.a1, self.a2)))*len(self.k_grid)\n broad_fnc = get_broadening_function(broadening, sigma)\n\n frequency_grid = np.linspace(*freq_range)\n energy_increment = np.abs(frequency_grid[1] - frequency_grid[0])\n num_freqs, min_freq = len(frequency_grid), min(frequency_grid)\n output_grid = np.zeros(num_freqs)\n\n energy_power = 1 + int(imag_dielectric)\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\n prefactor = prefactor/cell_area\n\n # Assuming x polarisation for now\n position_dipole_matrix = self.construct_position_dipole_matrix()\n\n n_shift = self.n_spins*self.n_orbs\n for idx1, kpt in enumerate(self.k_grid):\n for s0 in range(self.n_spins):\n velocity_matrix = self.load_velocity_matrix(idx1)\n bands = self.exciton_obj.get_number_conduction_valence_bands(\n idx1, s0\n )\n v_num, c_num = bands\n eigvals = np.array(\n self.file_storage['eigensystem']['eigenvalues'][idx1]\n )\n\n j1 = n_shift*idx1 + (self.n_spins - 1)*s0*self.n_orbs\n j2 = j1 + self.n_orbs\n eigvecs = np.array(\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\n )\n for v, c in product(range(v_num), range(c_num)):\n cb_vector = eigvecs[:, v_num + c]\n vb_vector = eigvecs[:, v]\n cb_energy, vb_energy = eigvals[v_num + c], eigvals[v]\n energy_diff = (cb_energy - vb_energy)\n\n energy_diff_pow = energy_diff**energy_power\n matrix_elem = velocity_matrix_element(vb_vector,\n cb_vector,\n velocity_matrix)\n if self.use_dipole_term:\n position_dipole_term = get_position_dipole_element(\n vb_vector,\n cb_vector,\n vb_energy,\n cb_energy,\n position_dipole_matrix\n )\n matrix_elem = matrix_elem + position_dipole_term\n\n main_term = np.abs(matrix_elem)**2/energy_diff_pow\n closest_idx = (energy_diff - min_freq)//energy_increment\n idx_reach = self.reach_multiplier*(sigma//energy_increment)\n\n lower_idx = max([closest_idx - idx_reach, 0])\n upper_idx = min([closest_idx + idx_reach, num_freqs - 1])\n\n for idx2 in range(int(lower_idx), int(upper_idx)):\n smearing = broad_fnc(energy_diff, frequency_grid[idx2])\n output = prefactor*main_term*smearing\n output_grid[idx2] += output\n\n return frequency_grid, output_grid","def EC(f):\n return dmp_ground_EC(f.rep, f.lev, f.dom)","def coreMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:\n graph = self.group.collectiveEnzymeGraphByEcMajority(majorityPercentage = majorityPercentageCoreMetabolism, majorityTotal = None, noMultifunctional = excludeMultifunctionalEnzymes)\n graph.name = 'Core metabolism Enzymes ' + ' '.join(self.ncbiNames)\n return graph","def getNumNestedCVTerms(self):\n return _libsbml.CVTerm_getNumNestedCVTerms(self)","def edges(self):\n return [(a, b) for a in self._consequences_of\n for b in self._consequences_of[a]]","def euclid_ccl(Omega_c, sigma8=0.83):\n\n cosmo_fid = ccl.Cosmology(Omega_c=Omega_c, Omega_b=0.045, h=0.71, sigma8=sigma8, n_s=0.963)\n\n dNdzs = np.zeros((nbins, z.size))\n shears = []\n \n for i in range(nbins):\n # edges of 1 equal width redshift bins, between 0 and 2\n zmin, zmax = i*(2./nbins), (i+1)*(2./nbins)\n # generate dNdz per bin\n dNdzs[i,:] = ccl.dNdz_tomog(z=z, zmin=zmin, zmax=zmax, pz_func=pz, dNdz_func = dNdz_true)\n # calculate the shear per bin\n gal_shapes = ccl.WeakLensingTracer(cosmo_fid, dndz=(z, dNdzs[i,:]))\n shears.append(gal_shapes)\n \n print ('Shears calculated, calculating power spectra now...')\n # calculate nbin*(nbin+1)/2 = 1 spectra from the shears\n Cls = []\n for i in range(nbins):\n for j in range(0,i+1):\n Cls.append(ccl.angular_cl(cosmo_fid, shears[i], shears[j], ells))\n \n return np.array(Cls), dNdzs","def getEntrophy(df):\r\n dct = group(df)\r\n n = len(df)\r\n ent = [0]*len(dct)\r\n j = 0\r\n for col in dct.keys(): \r\n for colVal in dct[col].keys():\r\n det = 0 \r\n aux = np.zeros(len(dct[col][colVal].keys()))\r\n i = 0 \r\n for clas in dct[col][colVal].keys():\r\n det+= dct[col][colVal][clas]\r\n aux[i] = dct[col][colVal][clas]\r\n i+=1\r\n aux = 1/det*aux\r\n aux_copy = aux.copy()\r\n aux = np.log2(aux) \r\n s = sum(np.multiply(aux_copy,aux)) \r\n ent[j] += (-1*det/n*s)\r\n j+=1 \r\n return (ent,dct)","def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:\n # get neofunctionalisations\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100)) \n return NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)).getNeofunctionalisationsForFunctionChange(minimumOrganismsCount = minimumOrganismsCount)","def neopentane():\n coords = [\n [0.000000, 0.0, 0.0],\n [0.881905, 0.881905, 0.881905],\n [-0.881905, -0.881905, 0.881905],\n [0.881905, -0.881905, -0.881905],\n [-0.881905, 0.881905, -0.881905],\n [-1.524077, 0.276170, -1.524077],\n [1.524077, 1.524077, 0.276170],\n [1.524077, -0.276170, -1.524077],\n [1.524077, 0.276170, 1.524077],\n [-1.524077, -0.276170, 1.524077],\n [1.524077, -1.524077, -0.276170],\n [-0.276170, 1.524077, -1.524077],\n [0.276170, 1.524077, 1.524077],\n [0.276170, -1.524077, -1.524077],\n [-0.276170, -1.524077, 1.524077],\n [-1.524077, 1.524077, -0.276170],\n [-1.524077, -1.524077, 0.276170],\n ]\n coords = [[float(j) / Bohr for j in i] for i in coords]\n\n symbols = [\n \"C\",\n \"C\",\n \"C\",\n \"C\",\n \"C\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n ]\n\n atoms = []\n for i, _ in enumerate(coords):\n atoms.append(Atom(symbols[i], position=coords[i]))\n return Molecule(symbols=atoms)","def solve_csp(nodes, arcs, max_steps):\n\n nodes = list(nodes)\n print 'nodes:', nodes\n\n node_values_dict = dict(zip(nodes, '2'*len(set(nodes))))\n print 'initial random assignment', node_values_dict\n indexes = np.arange(len(nodes))\n\n graph = {}\n for arc in arcs:\n if not arc[0] in graph:\n graph[arc[0]] = []\n if not arc[1] in graph:\n graph[arc[1]] = []\n graph[arc[0]].append(arc[1])\n graph[arc[1]].append(arc[0])\n for i in indexes:\n if i in graph:\n continue\n else:\n graph[i] = []\n graph = dict(sorted(graph.items()))\n print 'graph:', graph\n\n domain = [i for i in np.arange(1, 10, 1)]\n print 'initial domain for each node:', domain\n\n superAdjacency ={}\n for i in np.arange(len(nodes)):\n superAdjacency[i] = []\n superAdjacency[i].append(nodes[i])\n superAdjacency[i].append(node_values_dict[nodes[i]])\n superAdjacency[i].append(graph[i])\n superAdjacency[i].append(domain)\n\n print 'superAdjacency', superAdjacency\n\n def getNodeType(superAdjacency, index):\n return list(superAdjacency[index])[0]\n\n def getCurrentAssignment(superAdjacency, index):\n return list(superAdjacency[index])[1]\n\n def getCurrentAssignmentForList(superAdjacency, indexList):\n return [int(list(superAdjacency[index])[1]) for index in indexList]\n\n def getSolution(superAdjacency):\n return [int(list(superAdjacency[index])[1]) for index in superAdjacency]\n\n def getNeighbours(superAdjacency, index):\n return list(superAdjacency[index])[2]\n\n def getDomain(superAdjacency, index):\n return list(superAdjacency[index])[3]\n\n def updateSuperAdjacency(superAdjacency, nodeType, newValue):\n updateList =[]\n for i in superAdjacency:\n if str(getNodeType(superAdjacency, i)) == nodeType:\n updateList.append(i)\n for i in updateList:\n superAdjacency[i][1] = newValue\n\n def isSolution():\n return graphConstraints(superAdjacency)\n\n def graphConstraints(superAdjacency):\n graphEval = []\n\n for index in superAdjacency:\n neighbours = getNeighbours(superAdjacency, index)\n nodeType = getNodeType(superAdjacency, index)\n\n if nodeType == 'T':\n graphEval.append(int(str(eval(str(\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]))\n elif nodeType == 'C':\n return 'NA'\n elif nodeType == 'S':\n graphEval.append(int(str(eval(str(\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]))\n elif nodeType == 'H':\n graphEval.append(int(str(np.sum(\n getCurrentAssignmentForList(superAdjacency, neighbours)))[0]))\n if nodeType == 'P':\n graphEval.append(int(str(np.sum(\n getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]))\n\n currentAssignment = [item[1] for item in superAdjacency.values()]\n difference = map(sub, currentAssignment, graphEval)\n\n if sum(difference) == 0:\n return True\n else:\n return difference\n\n def findConflictVariable(superAdjacency, lastUpdateNode):\n node_conflict_count = {}\n for node in node_values_dict:\n node_conflict_count[node] = 0\n for index in superAdjacency:\n neighbours = getNeighbours(superAdjacency, index)\n nodeType = getNodeType(superAdjacency, index)\n if nodeType == 'T':\n try:\n if getCurrentAssignment(superAdjacency, index) != \\\n int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n elif nodeType == 'S':\n try:\n if getCurrentAssignment(superAdjacency, index) != int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n elif nodeType == 'H':\n try:\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n if nodeType == 'P':\n try:\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n choices = [k for k, v in node_conflict_count.items() if v > 0]\n if len(choices) > 0:\n updateNode = random.choice(choices)\n\n if updateNode == lastUpdateNode:\n choices.pop(choices.index(updateNode))\n try:\n lastUpdateNode = random.choice(choices)\n return lastUpdateNode, lastUpdateNode\n except:\n return lastUpdateNode, lastUpdateNode\n else:\n lastUpdateNode = updateNode\n return updateNode, lastUpdateNode\n else:\n return 'NA', 'NA'\n\n\n\n def valueForConflictedVariable(superAdjacency, var):\n for index in superAdjacency:\n nodeType = getNodeType(superAdjacency, index)\n neighbours = getNeighbours(superAdjacency, index)\n if not neighbours:\n continue\n elif str(nodeType) == str(var):\n domain = getDomain(superAdjacency, index)\n\n choice = random.choice(domain)\n if nodeType == 'T':\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0])\n elif nodeType == 'S':\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0])\n elif nodeType == 'H':\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0])\n if nodeType == 'P':\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0])\n\n choice = int(choice)\n if choice % 2 == 0:\n return choice\n else:\n return choice\n\n def min_conflicts(nodes, arcs, max_steps):\n lastUpdateNode = ''\n for i in range(max_steps):\n if isSolution() == True:\n return\n var, lastUpdateNode = findConflictVariable(superAdjacency, lastUpdateNode)\n if var != 'NA':\n value = valueForConflictedVariable(superAdjacency, var)\n updateSuperAdjacency(superAdjacency, var, value)\n node_values_dict[var] = value\n else:\n pass\n\n return\n\n min_conflicts(nodes, arcs, max_steps)\n node_values = getSolution(superAdjacency)\n return node_values","def connect_cells(dfte,vari):\n # Create the variabel cell for mother, grand mother and grand grand mother\n if 'g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\n if 'g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_parent_cell')\n if 'g_g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\n #give unique index to all cells\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\n vac=[];sc=[];uid = []\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\n for c,idx in enumerate(dfte['cell'].unique()):\n dau = dfte.loc[dfte['cell']==idx]\n pc = dau['parent_cell'].iloc[0]\n mum = dfte.loc[dfte['cell']==pc]\n gpc = dau['g_parent_cell'].iloc[0]\n gmum = dfte.loc[dfte['cell']==gpc]\n ggpc = dau['g_g_parent_cell'].iloc[0]\n ggmum = dfte.loc[dfte['cell']==ggpc]\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\n gggmum = dfte.loc[dfte['cell']==gggpc]\n fte = lambda x: x[['{}'.format(vari),'uid']].values\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\n vac.append(tmp[:,0])\n uid.append(tmp[:,1])\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})","def _excitonic_reorg_energy(self, SS, AG, n):\n \n # SystemBathInteraction\n sbi = AG.get_SystemBathInteraction()\n # CorrelationFunctionMatrix\n cfm = sbi.CC\n \n rg = 0.0\n \n # electronic states corresponding to single excited states\n elst = numpy.where(AG.which_band == 1)[0]\n for el1 in elst:\n reorg = cfm.get_reorganization_energy(el1-1,el1-1)\n for kk in AG.vibindices[el1]:\n rg += ((SS[kk,n]**2)*(SS[kk,n]**2)*reorg)\n return rg","def remove_trivial_edges(self):\n if self.E > 0:\n valid = self.edges[:, 0] != self.edges[:, 1]\n self.edges = self.edges[valid]\n self.weights = self.weights[valid]\n self.E = np.sum(valid)\n return self.E","def graph_eigenvalues(N, eigenvalues,interval):\n (a,b) =interval\n a=abs(a-b)\n n=[]\n E_numeric=[]\n E_anlytic=[]\n E_anlytic_harm=[]\n \n for i in range(N-1):\n n.append(i)\n E_numeric.append(eigenvalues[0,i])\n E_anlytic.append((((math.pi)**2)*(i**2))/(a**2))\n E_anlytic_harm.append(((1/2)**0.5)*((i)+(1/2)))\n plt.plot(n,E_numeric,'b')\n plt.plot(n,E_anlytic,'g')\n plt.plot(n,E_anlytic_harm,'r')\n plt.xlabel('n', fontsize=20, color='black')\n plt.ylabel('E', fontsize=20, color='black')\n plt.show()\n return None","def edges(self):\n return self.generate_edges()","def oim_node2vec_simple(\n df,\n df_feats,\n num_inf=10,\n sigma=4,\n c=0.1,\n epsilon=0.4,\n num_repeats=15,\n num_nodes_tim=-1,\n oracle=tim,\n):\n logger_tlu.debug(\"Started Online Influence Maximization...\")\n logger_tlu.debug(\"Setting parameters\")\n num_feats = df_feats.shape[1]\n num_edges_t = df.shape[0]\n\n # \"True\" probabilities - effectively our test set\n true_weights = df[\"probab\"].copy()\n\n # b, M_inv - used by IMLinUCB\n b = np.zeros((num_feats, 1))\n m_inv = np.eye(num_feats, num_feats)\n\n # Returning these\n s_best = []\n reward_best = 0\n u_e_best = []\n rewards = []\n rewards_edges = []\n\n for iter_oim in tqdm(\n range(num_repeats),\n desc=f\"OIM iters {num_edges_t} edges\",\n leave=False,\n file=sys.stderr,\n ):\n # ---- Step 1 - Calculating the u_e ----\n theta = (m_inv @ b) / (sigma * sigma)\n # xMx = (df_feats.values @ m_inv @ df_feats.T.values).clip(min=0)\n\n u_e = []\n for i in range(num_edges_t):\n x_e = df_feats.loc[i].values\n xMx = x_e @ m_inv @ x_e.T # .clip(min=0)\n u_e.append(np.clip(x_e @ theta + c * np.sqrt(xMx), 0, 1))\n # u_e.append(expit(x_e @ theta + c * np.sqrt(xMx)))\n\n u_e = np.array(u_e)\n\n # ---- Step 2 - Evaluating the performance ----\n # Loss function\n df[\"probab\"] = u_e\n s_oracle = sorted(\n oracle(\n df[[\"source\", \"target\", \"probab\"]],\n num_nodes_tim,\n num_edges_t,\n num_inf,\n epsilon,\n )\n )\n\n # Observing edge-level feedback\n df[\"probab\"] = true_weights\n\n algo_act_nodes, algo_act_edges, algo_obs_edges = run_ic_eff(df, s_oracle)\n\n algo_num_nodes = len(algo_act_nodes)\n algo_num_edges = len(algo_act_edges)\n\n rewards.append(algo_num_nodes)\n rewards_edges.append(algo_num_edges)\n\n logger_tlu.debug(f\"Algo seeds: {s_oracle}\")\n logger_tlu.debug(f\"Algo reward: {algo_num_nodes}\")\n logger_tlu.debug(f\"Best algo reward: {reward_best}\")\n logger_tlu.debug(f\"Rewards: {rewards}\")\n logger_tlu.debug(f\"Edge rewards: {rewards_edges}\")\n logger_tlu.debug(f\"Algo weights {u_e[80:90]}\".replace(\"\\n\", \"\"))\n logger_tlu.debug(f\"Real weights {true_weights[80:90]}\".replace(\"\\n\", \"\"))\n\n if algo_num_nodes > reward_best:\n reward_best = algo_num_nodes\n s_best = s_oracle\n u_e_best = u_e\n\n # ---- Step 3 - Calculating updates ----\n for i in algo_obs_edges:\n x_e = np.array([df_feats.loc[i].values])\n m_inv -= (m_inv @ x_e.T @ x_e @ m_inv) / (\n x_e @ m_inv @ x_e.T + sigma * sigma\n )\n b += x_e.T * int(i in algo_act_edges)\n\n return_dict = {\n \"rewards\": rewards,\n \"rewards_edges\": rewards_edges,\n \"s_best\": s_best,\n \"u_e_best\": u_e_best,\n \"reward_best\": reward_best,\n }\n logger_tlu.debug(\"The algorithm has finished running.\")\n logger_tlu.debug(f\"Returning: {return_dict}\")\n return return_dict","def _generateEdges(self, separate_frames=False):\n\n # 3D edges\n Xe = []\n Ye = []\n Ze = []\n\n if not separate_frames:\n if self.showcuts:\n self.nxgraph.add_nodes_from(range(len(self.df)))\n for index, curr in self.df.iterrows():\n if curr['first']:\n self._symbol += ['circle']\n # skip root node\n if curr['number'] == 1:\n continue\n # found first LP solution of a new child node\n # parent is last LP of parent node\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\n else:\n # found an improving LP solution at the same node as before\n self._symbol += ['diamond']\n parent = self.df.iloc[index - 1]\n\n Xe += [float(parent['x']), curr['x'], None]\n Ye += [float(parent['y']), curr['y'], None]\n Ze += [float(parent['objval']), curr['objval'], None]\n self.nxgraph.add_edge(parent.name, curr.name)\n else:\n self.nxgraph.add_nodes_from(list(self.df['number']))\n for index, curr in self.df.iterrows():\n self._symbol += ['circle']\n if curr['number'] == 1:\n continue\n parent = self.df[self.df['number'] == curr['parent']]\n Xe += [float(parent['x']), curr['x'], None]\n Ye += [float(parent['y']), curr['y'], None]\n Ze += [float(parent['objval']), curr['objval'], None]\n self.nxgraph.add_edge(parent.iloc[0]['number'], curr['number'])\n\n else:\n max_age = self.df['age'].max()\n for i in range(1, max_age + 1):\n tmp = self.df[self.df['age'] == i]\n Xe_ = []\n Ye_ = []\n Ze_ = []\n for index, curr in tmp.iterrows():\n if curr['first']:\n self._symbol += ['circle']\n # skip root node\n if curr['number'] == 1:\n continue\n # found first LP solution of a new child node\n # parent is last LP of parent node\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\n else:\n # found an improving LP solution at the same node as before\n self._symbol += ['diamond']\n parent = self.df.iloc[index - 1]\n\n Xe_ += [float(parent['x']), curr['x'], None]\n Ye_ += [float(parent['y']), curr['y'], None]\n Ze_ += [float(parent['objval']), curr['objval'], None]\n Xe.append(Xe_)\n Ye.append(Ye_)\n Ze.append(Ze_)\n\n self.Xe = Xe\n self.Ye = Ye\n self.Ze = Ze"],"string":"[\n \"def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \\n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n \\n if colour is False:\\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\\n \\n else:\\n unifiedMetabolism = self.unifiedMetabolism(majorityPercentageCoreMetabolism, colour = True)\\n \\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = unifiedMetabolism\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\",\n \"def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n childGraph = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(addedECs)\\n childGraph.name = 'Added metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return childGraph\",\n \"def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:\\n # get neofunctionalisations \\n neofunctionalisedECs = NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs))\\n \\n # filter core metabolism EC graph\\n coreMetabolism = self.coreMetabolism(majorityPercentageCoreMetabolism)\\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))\\n \\n neofunctionalisedMetabolism = neofunctionalisedECs.filterGraph(coreMetabolism, minimumEcDifference = None, minimumOrganismsCount = minimumOrganismsCount)\\n \\n # colour core metabolism\\n if colour is not False:\\n \\n if colour is True:\\n colourToUse = Export.Colour.GREEN\\n else:\\n colourToUse = colour\\n \\n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\\n neofunctionalisedMetabolism = coreMetabolism\\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\\n \\n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism ' + ' '.join(self.ncbiNames)\\n \\n return neofunctionalisedMetabolism\",\n \"def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\\n conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)\\n \\n parentNeofunctionalised= self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = conservedMetabolism\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = conservedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\\n childGraph = conservedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\\n \\n parentGraph.name = 'Conserved metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def divergedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\\n divergedMetabolism = self.divergedMetabolism(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = divergedMetabolism\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = divergedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\\n childGraph = divergedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\\n \\n parentGraph.name = 'Diverged metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def redundantECsForContributingNeofunctionalisation(self, \\n majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, \\n majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, \\n eValue = defaultEValue, \\n redundancyType: 'RedundancyType' = None,\\n considerOnlyECs = None) -> Dict[Neofunctionalisation, Set[EcNumber]]:\\n from FEV_KEGG.Robustness.Topology.Redundancy import Redundancy, RedundancyContribution, RedundancyType\\n \\n if redundancyType is None:\\n redundancyType = RedundancyType.default\\n \\n #- calculate \\\"neofunctionalised\\\" ECs\\n neofunctionalisedMetabolismSet = self.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs).getECs()\\n neofunctionalisationsForFunctionChange = self.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs)\\n \\n #- calculate redundancy\\n redundancy = Redundancy( self.coreMetabolism(majorityPercentageCoreMetabolism) )\\n redundancyContribution = RedundancyContribution(redundancy, neofunctionalisedMetabolismSet)\\n \\n contributedECsForContributingNeofunctionalisedEC = redundancyContribution.getContributedKeysForSpecial(redundancyType)\\n contributingNeofunctionalisedECs = set(contributedECsForContributingNeofunctionalisedEC.keys())\\n \\n #- REPEAT for each function change consisting of \\\"neofunctionalised\\\" ECs, which also contribute to redundancy\\n contributingNeofunctionalisations = dict()\\n \\n for functionChange, neofunctionalisations in neofunctionalisationsForFunctionChange.items():\\n #- report enzyme pairs of neofunctionalisations, which caused the EC to be considered \\\"neofunctionalised\\\", and are in return contributing to redundancy \\n \\n if functionChange.ecA in contributingNeofunctionalisedECs or functionChange.ecB in contributingNeofunctionalisedECs: # function change contributes to redundancy\\n \\n for neofunctionalisation in neofunctionalisations:\\n currentSetOfContributedECs = contributingNeofunctionalisations.get(neofunctionalisation, None)\\n \\n if currentSetOfContributedECs is None:\\n currentSetOfContributedECs = set()\\n contributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs\\n \\n for ec in functionChange.ecPair:\\n contributedECs = contributedECsForContributingNeofunctionalisedEC.get(ec, None)\\n if contributedECs is not None:\\n currentSetOfContributedECs.update(contributedECs)\\n \\n return contributingNeofunctionalisations\",\n \"def __repr__(self):\\n return \\\"EC(%s, %s)\\\" % (str(self.coefficient), repr(self.basefield))\",\n \"def c_edges(self):\\n self.compute_c_edges(self)\\n return self._c_edges\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/26')\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/27')\",\n \"def get_ecg_graph():\\n titles = ['ecg1', 'ecg2', 'ecg3']\\n colors = ['rgb(240,0,0)', 'rgb(0,240,0)', 'rgb(0,0,240)']\\n update()\\n signames_ecg = queries['signames_ecg']\\n signals = queries['signals']\\n latesthr = queries['latesthr']\\n return html.Div(className='ecg', children=[\\n html.Div(style={'display': 'flex', 'height': '40vh'},\\n children=[dcc.Graph(\\n id=titles[i] + signame,\\n style={'width': '100%'},\\n figure={\\n 'data': [\\n {'x': signals[signame]['time'],\\n 'y': signals[signame][titles[i]],\\n 'mode': 'line', 'name': signame, 'line': {'color':colors[i]}}\\n ],\\n 'layout': {\\n 'font': {'color':'#fff'},\\n 'title': '{}-{}'.format(signame, titles[i]),\\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\\n 'yaxis': {'title': 'voltage (mv)', 'color': '#fff', 'showgrid': 'False', 'range': np.linspace(-2.5, 2.5, 10)},\\n 'paper_bgcolor':'#000', 'plot_bgcolor':'#000'\\n }\\n }\\n ) for i in range(len(titles))]\\n +\\n [html.Div(\\n style={'justify-content': 'center', 'display': 'flex',\\n 'align-items': 'center', 'width': '10vh', 'font-size': '30pt', 'color': 'white'},\\n children=['{}'.format(latesthr[signame][0])])\\n ]\\n ) for signame in signames_ecg])\",\n \"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def complex_network_mapping(graph):\\n vect = []\\n\\n n = nx.number_of_nodes(graph)\\n e = nx.number_of_edges(graph)\\n print n, e\\n\\n# adj = nx.adjacency_matrix(graph).toarray()\\n# adj_bin = np.where(adj > 0, 1., 0.)\\n# adj_conn = 1 - adj\\n adj_bin = nx.adjacency_matrix(graph).toarray()\\n adj_bin = np.array(adj_bin, dtype=np.float)\\n\\n # Node Betweenness binary\\n bt_bin = nx.betweenness_centrality(graph).values()\\n avg_btb = np.mean(bt_bin)\\n vect.append(avg_btb)\\n\\n # Edge betweenness\\n ebt = np.array(nx.edge_betweenness_centrality(graph).values())\\n vect.append(np.mean(ebt))\\n\\n # Eigen vector centrality binary\\n evc_bin = eigenvector_centrality_und(adj_bin)\\n avg_evcb = np.mean(evc_bin)\\n vect.append(avg_evcb)\\n\\n # Flow coefficient\\n _, flow_bin, _ = flow_coef_bd(adj_bin)\\n avg_flow = np.mean(flow_bin)\\n vect.append(avg_flow)\\n\\n # Kcoreness centrality\\n kcor_bin, _ = kcoreness_centrality_bu(adj_bin)\\n avg_kcor = np.mean(kcor_bin)\\n vect.append(avg_kcor)\\n\\n # Degree assortivity\\n dac = nx.degree_assortativity_coefficient(graph)\\n vect.append(dac)\\n\\n # Page rank centrality\\n# pgr_wei = pagerank_centrality(adj_bin, d=0.85)\\n# avg_pgr = np.mean(pgr_wei)\\n# vect.append(avg_pgr)\\n\\n # Rich club coefficient\\n# rcc = nx.rich_club_coefficient(graph).values()\\n# avg_rcc = np.mean(rcc)\\n# vect.append(avg_rcc)\\n\\n # Transitivity\\n tr = nx.transitivity(graph)\\n vect.append(tr)\\n\\n # average clustering\\n avg_clst = nx.average_clustering(graph)\\n vect.append(avg_clst)\\n\\n glb_ef = efficiency_bin(adj_bin)\\n vect.append(glb_ef)\\n\\n return vect\",\n \"def NND_eta( eqCat, dConst, verbose = False, **kwargs):\\n #-------------------------------set args and kwargs----------------------------------------------- \\n rmax = 500 # in km\\n tmax = 20 # in years\\n M0 = 0\\n if 'M0' in kwargs.keys() and kwargs['M0'] is not None:\\n M0 = kwargs['M0']\\n if 'rmax' in kwargs.keys() and kwargs['rmax'] is not None:\\n rmax = kwargs['rmax']\\n if 'tmax' in kwargs.keys() and kwargs['tmax'] is not None:\\n tmax = kwargs['tmax']\\n #-----------------------------add small uncertainty to X in case events are colocated-------------------------- \\n if 'correct_co_located' in kwargs.keys() and kwargs['correct_co_located'] == True:\\n vUncer = np.random.randn( eqCat.size())*1e-10\\n eqCat.data['X'] += vUncer\\n eqCat.data['Time'] += abs( vUncer)#time has to stay positive otherwise parent-offspring gets switched\\n #------------------------------------------------------------------------------\\n aNND = np.zeros( eqCat.size())\\n vID_p = np.zeros( eqCat.size())\\n vID_c = np.zeros( eqCat.size())\\n deltaMag = (eqCat.data['Mag'] - M0)\\n \\n for jC in range( eqCat.size()):\\n if verbose == True:\\n print 'event %i of %i'%( jC+1, eqCat.size())\\n # interevent times: take events that happend before t_i \\n # child - parent > 0 \\n tau = eqCat.data['Time'][jC] - eqCat.data['Time']\\n sel_tau_par = tau > 0\\n if sel_tau_par.sum() > 0:\\n\\n vcurr_ID = np.arange( eqCat.size(), dtype = int)[sel_tau_par]\\n vR = np.sqrt( (eqCat.data['X'][jC] - eqCat.data['X'][vcurr_ID])**2 + (eqCat.data['Y'][jC] - eqCat.data['Y'][vcurr_ID])**2 )\\n # haversine distance\\n # = projUtils.haversine( eqCat.data['Lon'][jC], eqCat.data['Lat'][jC],eqCat.data['Lon'][curr_vID], eqCat.data['Lat'][curr_vID] ) \\n sel_r_par = vR < rmax\\n if sel_r_par.sum() > 0:\\n vcurr_ID = vcurr_ID[sel_r_par]\\n curr_Eta = tau[vcurr_ID]* (vR[sel_r_par]**dConst['D']) *( 10**(-dConst['b']*deltaMag[vcurr_ID]))\\n sel_min = curr_Eta == curr_Eta.min()\\n aNND[jC] = curr_Eta[sel_min][0]\\n vID_p[jC] = eqCat.data['N'][vcurr_ID][sel_min][0]\\n vID_c[jC] = eqCat.data['N'][jC]\\n #print 'parent', eqCat.data['N'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['N'][jC]\\n #print 'parent', eqCat.data['Time'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['Time'][jC]\\n\\n if sel_min.sum() > 1:\\n print aNND[jC], curr_Eta[sel_min], eqCat.data['N'][vcurr_ID][sel_min]\\n print eqCat.data['Lon'][vcurr_ID][sel_min], eqCat.data['Lat'][vcurr_ID][sel_min]\\n print eqCat.data['X'][vcurr_ID][sel_min], eqCat.data['Y'][vcurr_ID][sel_min]\\n sel2 = aNND > 0\\n if np.logical_not(sel2).sum() > 0:\\n print 'remove %i offspring without prior parents in catalog'%(np.logical_not(sel2).sum())\\n #raise ValueError, error_str\\n # remove events with aNND < 0; i.e. event at the beginning with no preceding parent\\n return { 'aNND' : aNND[sel2], 'aEqID_p' : vID_p[sel2], 'aEqID_c' : vID_c[sel2]}\",\n \"def colored_edges(genome):\\n edges = []\\n for chromo in genome:\\n nodes = [0] + chromosome_to_cycle(chromo)\\n nodes.append(nodes[1])\\n for j in range(1, len(chromo) + 1):\\n edges.append((nodes[2 * j], nodes[2 * j + 1]))\\n\\n return edges\",\n \"def cell_edges(self):\",\n \"def set_ec(self, etacalc):\\n if not self.__thermodyn:\\n C = np.zeros((6,6))\\n \\n LC = self.__structures.items()[0][1].LC\\n if self.__mthd == 'Energy':\\n if type(etacalc)==list:\\n A2=[]\\n for i in range(len(etacalc)):\\n A2.append(self.__A2[i][etacalc[i]])\\n else:\\n if not etacalc in self.__A2[0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[0].keys()))\\n A2 = [a2[etacalc] for a2 in self.__A2]\\n \\n \\n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\\n C[1,1] = C[0,0]\\n C[2,2] = C[0,0]\\n C[3,3] = A2[2]/6.\\n C[4,4] = C[3,3]\\n C[5,5] = C[3,3]\\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\\n C[0,2] = C[0,1]\\n C[1,2] = C[0,1]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[2]\\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RI'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[1,4] =-C[0,4] \\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[3,5] =-C[0,4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TI'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[3]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TII'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\\n C[1,1] = C[0,0]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\\n C[1,2] = C[0,2]\\n C[1,5] =-C[0,5]\\n C[2,2] = 2.*A2[4]\\n C[3,3] = .5*A2[5]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[6]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'O'):\\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\\n C[1,1] = 2.*A2[4]\\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\\n C[2,2] = 2.*A2[5]\\n C[3,3] = .5*A2[6]\\n C[4,4] = .5*A2[7]\\n C[5,5] = .5*A2[8]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'M'):\\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\\n C[1,1] = 2.*A2[8]\\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\\n C[2,2] = 2.*A2[9]\\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\\n C[3,3] = .5*A2[10]\\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\\n C[4,4] = .5*A2[11]\\n C[5,5] = .5*A2[12]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'N'):\\n C[0,0] = 2.*A2[0]\\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \\n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\\n C[1,1] = 2.*A2[1]\\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\\n C[2,2] = 2.*A2[2] \\n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\\n C[3,3] = .5*A2[3]\\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\\n C[4,4] = .5*A2[4]\\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n elif self.__mthd == 'Stress':\\n \\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n Matrix = np.mat([[1.0, 5.0, 0.0],\\n [2.0, 4.0, 0.0],\\n [3.0, 3.0, 0.0],\\n [0.0, 0.0, 4.0],\\n [0.0, 0.0, 5.0],\\n [0.0, 0.0, 6.0]])\\n \\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\\n [ 2, 1, 3, 0, 0],\\n [ 0, 0, 3, 3, 0],\\n [ 0, 0, 0, 0, 4],\\n [ 0, 0, 0, 0, 5],\\n [ 3,-3, 0, 0, 0],\\n [ 3,-5,-1, 0, 0],\\n [-5, 3,-1, 0, 0],\\n [ 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 0, 6],\\n [ 0, 0, 0, 0, 2],\\n [-2, 2, 0, 0, 0]])\\n \\n if (LC == 'RI'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\\n [ 2, 1, 3,-4, 0, 0],\\n [ 0, 0, 3, 0, 3, 0],\\n [ 0, 0, 0,-1, 0, 4],\\n [ 0, 0, 0, 6, 0, 5],\\n [ 3,-3, 0, 5, 0, 0],\\n [ 3,-5,-1, 6, 0, 0],\\n [-5, 3,-1,-6, 0, 0],\\n [ 0, 0,-2, 0,-1, 0],\\n [ 0, 0, 0, 8, 0, 6],\\n [ 0, 0, 0,-4, 0, 2],\\n [-2, 2, 0, 2, 0, 0]])\\n \\n if (LC == 'RII'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\\n [ 2, 1, 3,-4,-5, 0, 0],\\n [ 0, 0, 3, 0, 0, 3, 0],\\n [ 0, 0, 0,-1,-6, 0, 4],\\n [ 0, 0, 0, 6,-1, 0, 5],\\n [ 3,-3, 0, 5,-4, 0, 0],\\n [ 3,-5,-1, 6, 2, 0, 0],\\n [-5, 3,-1,-6,-2, 0, 0],\\n [ 0, 0,-2, 0, 0,-1, 0],\\n [ 0, 0, 0, 8, 4, 0, 6],\\n [ 0, 0, 0,-4, 8, 0, 2],\\n [-2, 2, 0, 2,-6, 0, 0]])\\n \\n if (LC == 'TI'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\\n [ 2, 1, 3, 0, 0, 0],\\n [ 0, 0, 3, 3, 0, 0],\\n [ 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0, 0, 0, 6],\\n [ 3,-5,-1, 0, 0, 0],\\n [-5, 3,-1, 0, 0, 0],\\n [ 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 0, 0,-4]])\\n \\n if (LC == 'TII'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\\n [ 2, 1, 3,-6, 0, 0, 0],\\n [ 0, 0, 3, 0, 3, 0, 0],\\n [ 0, 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0,-1, 0, 0, 6],\\n [ 3,-5,-1,-4, 0, 0, 0],\\n [-5, 3,-1, 4, 0, 0, 0],\\n [ 0, 0,-2, 0,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 8, 0, 0,-4]])\\n \\n if (LC == 'O'):\\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\\n \\n if (LC == 'M'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\\n \\n if (LC == 'N'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\\n \\n sigma = np.array(self.__sigma[etacalc])\\n \\n ci = np.linalg.lstsq(Matrix,sigma)\\n \\n #-- Cubic structures ------------------------------------------------------------------------------\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n \\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[3,3]=ci[0][2]\\n C[1,1]=C[0,0]\\n C[2,2]=C[0,0]\\n C[0,2]=C[0,1]\\n C[1,2]=C[0,1]\\n C[4,4]=C[3,3]\\n C[5,5]=C[3,3]\\n \\n #-- Hexagonal Structures --------------------------------------------------------------------------\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[2,2]=ci[0][3]\\n C[3,3]=ci[0][4]\\n C[1,1]=C[0,0]\\n C[1,2]=C[0,2]\\n C[4,4]=C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral I Structures ---------------------------------------------------------------------\\n if (LC == 'RI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral II Structures --------------------------------------------------------------------\\n if (LC == 'RII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[0,4]= ci[0][4]\\n C[2,2]= ci[0][5]\\n C[3,3]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[1,4]=-C[0,4]\\n C[3,5]=-C[0,4]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Tetragonal I Structures -----------------------------------------------------------------------\\n if (LC == 'TI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[2,2]= ci[0][3]\\n C[3,3]= ci[0][4]\\n C[5,5]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[4,4]= C[3,3]\\n \\n #-- Tetragonal II Structures ----------------------------------------------------------------------\\n if (LC == 'TII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,5]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[5,5]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,5]=-C[0,5]\\n C[4,4]= C[3,3]\\n \\n #-- Orthorhombic Structures -----------------------------------------------------------------------\\n if (LC == 'O'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[1,1]=ci[0][3]\\n C[1,2]=ci[0][4]\\n C[2,2]=ci[0][5]\\n C[3,3]=ci[0][6]\\n C[4,4]=ci[0][7]\\n C[5,5]=ci[0][8]\\n \\n #-- Monoclinic Structures -------------------------------------------------------------------------\\n if (LC == 'M'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,5]=ci[0][3]\\n C[1,1]=ci[0][4]\\n C[1,2]=ci[0][5]\\n C[1,5]=ci[0][6]\\n C[2,2]=ci[0][7]\\n C[2,5]=ci[0][8]\\n C[3,3]=ci[0][9]\\n C[3,4]=ci[0][10]\\n C[4,4]=ci[0][11]\\n C[5,5]=ci[0][12]\\n \\n #-- Triclinic Structures --------------------------------------------------------------------------\\n if (LC == 'N'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,3]=ci[0][3]\\n C[0,4]=ci[0][4]\\n C[0,5]=ci[0][5]\\n C[1,1]=ci[0][6]\\n C[1,2]=ci[0][7]\\n C[1,3]=ci[0][8]\\n C[1,4]=ci[0][9]\\n C[1,5]=ci[0][10]\\n C[2,2]=ci[0][11]\\n C[2,3]=ci[0][12]\\n C[2,4]=ci[0][13]\\n C[2,5]=ci[0][14]\\n C[3,3]=ci[0][15]\\n C[3,4]=ci[0][16]\\n C[3,5]=ci[0][17]\\n C[4,4]=ci[0][18]\\n C[4,5]=ci[0][19]\\n C[5,5]=ci[0][20]\\n #--------------------------------------------------------------------------------------------------\\n \\n \\n \\n for i in range(5):\\n for j in range(i+1,6):\\n C[j,i] = C[i,j] \\n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if self.__cod in ['espresso']: C = -C/10.\\n elif self.__cod in ['vasp','exciting','wien']: C = C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0\\n elif self.__cod in ['emto']: C = C*self.__ToGPa/self.__V0\\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\\n self.S = np.linalg.inv(C)\\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\\n self.BH = 0.50*(self.BV+self.BR)\\n self.GH = 0.50*(self.GV+self.GR)\\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\\n #--------------------------------------------------------------------------------------------------------------------------------\\n self.__C = C\\n \\n else:\\n Cs = []\\n for t in map(str,self.__T):#for t in range(len(self.__T)):\\n C = np.zeros((6,6))\\n \\n LC = self.__structures.items()[0][1].LC\\n if self.__mthd == 'Energy':\\n if type(etacalc)==list:\\n A2=[]\\n for i in range(len(etacalc)):\\n A2.append(self.__A2[t][i][etacalc[i]])\\n else:\\n if not etacalc in self.__A2[t][0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[t][0].keys()))\\n A2 = [a2[etacalc] for a2 in self.__A2[t]]\\n \\n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\\n C[1,1] = C[0,0]\\n C[2,2] = C[0,0]\\n C[3,3] = A2[2]/6.\\n C[4,4] = C[3,3]\\n C[5,5] = C[3,3]\\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\\n C[0,2] = C[0,1]\\n C[1,2] = C[0,1]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[2]\\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RI'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[1,4] =-C[0,4] \\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[3,5] =-C[0,4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TI'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[3]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TII'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\\n C[1,1] = C[0,0]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\\n C[1,2] = C[0,2]\\n C[1,5] =-C[0,5]\\n C[2,2] = 2.*A2[4]\\n C[3,3] = .5*A2[5]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[6]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'O'):\\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\\n C[1,1] = 2.*A2[4]\\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\\n C[2,2] = 2.*A2[5]\\n C[3,3] = .5*A2[6]\\n C[4,4] = .5*A2[7]\\n C[5,5] = .5*A2[8]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'M'):\\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\\n C[1,1] = 2.*A2[8]\\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\\n C[2,2] = 2.*A2[9]\\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\\n C[3,3] = .5*A2[10]\\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\\n C[4,4] = .5*A2[11]\\n C[5,5] = .5*A2[12]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'N'):\\n C[0,0] = 2.*A2[0]\\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \\n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\\n C[1,1] = 2.*A2[1]\\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\\n C[2,2] = 2.*A2[2] \\n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\\n C[3,3] = .5*A2[3]\\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\\n C[4,4] = .5*A2[4]\\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n elif self.__mthd == 'Stress':\\n \\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n Matrix = np.mat([[1.0, 5.0, 0.0],\\n [2.0, 4.0, 0.0],\\n [3.0, 3.0, 0.0],\\n [0.0, 0.0, 4.0],\\n [0.0, 0.0, 5.0],\\n [0.0, 0.0, 6.0]])\\n \\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\\n [ 2, 1, 3, 0, 0],\\n [ 0, 0, 3, 3, 0],\\n [ 0, 0, 0, 0, 4],\\n [ 0, 0, 0, 0, 5],\\n [ 3,-3, 0, 0, 0],\\n [ 3,-5,-1, 0, 0],\\n [-5, 3,-1, 0, 0],\\n [ 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 0, 6],\\n [ 0, 0, 0, 0, 2],\\n [-2, 2, 0, 0, 0]])\\n \\n if (LC == 'RI'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\\n [ 2, 1, 3,-4, 0, 0],\\n [ 0, 0, 3, 0, 3, 0],\\n [ 0, 0, 0,-1, 0, 4],\\n [ 0, 0, 0, 6, 0, 5],\\n [ 3,-3, 0, 5, 0, 0],\\n [ 3,-5,-1, 6, 0, 0],\\n [-5, 3,-1,-6, 0, 0],\\n [ 0, 0,-2, 0,-1, 0],\\n [ 0, 0, 0, 8, 0, 6],\\n [ 0, 0, 0,-4, 0, 2],\\n [-2, 2, 0, 2, 0, 0]])\\n \\n if (LC == 'RII'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\\n [ 2, 1, 3,-4,-5, 0, 0],\\n [ 0, 0, 3, 0, 0, 3, 0],\\n [ 0, 0, 0,-1,-6, 0, 4],\\n [ 0, 0, 0, 6,-1, 0, 5],\\n [ 3,-3, 0, 5,-4, 0, 0],\\n [ 3,-5,-1, 6, 2, 0, 0],\\n [-5, 3,-1,-6,-2, 0, 0],\\n [ 0, 0,-2, 0, 0,-1, 0],\\n [ 0, 0, 0, 8, 4, 0, 6],\\n [ 0, 0, 0,-4, 8, 0, 2],\\n [-2, 2, 0, 2,-6, 0, 0]])\\n \\n if (LC == 'TI'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\\n [ 2, 1, 3, 0, 0, 0],\\n [ 0, 0, 3, 3, 0, 0],\\n [ 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0, 0, 0, 6],\\n [ 3,-5,-1, 0, 0, 0],\\n [-5, 3,-1, 0, 0, 0],\\n [ 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 0, 0,-4]])\\n \\n if (LC == 'TII'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\\n [ 2, 1, 3,-6, 0, 0, 0],\\n [ 0, 0, 3, 0, 3, 0, 0],\\n [ 0, 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0,-1, 0, 0, 6],\\n [ 3,-5,-1,-4, 0, 0, 0],\\n [-5, 3,-1, 4, 0, 0, 0],\\n [ 0, 0,-2, 0,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 8, 0, 0,-4]])\\n \\n if (LC == 'O'):\\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\\n \\n if (LC == 'M'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\\n \\n if (LC == 'N'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\\n \\n sigma = np.array(self.__sigma[etacalc])\\n \\n ci = np.linalg.lstsq(Matrix,sigma)\\n \\n #-- Cubic structures ------------------------------------------------------------------------------\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n \\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[3,3]=ci[0][2]\\n C[1,1]=C[0,0]\\n C[2,2]=C[0,0]\\n C[0,2]=C[0,1]\\n C[1,2]=C[0,1]\\n C[4,4]=C[3,3]\\n C[5,5]=C[3,3]\\n \\n #-- Hexagonal Structures --------------------------------------------------------------------------\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[2,2]=ci[0][3]\\n C[3,3]=ci[0][4]\\n C[1,1]=C[0,0]\\n C[1,2]=C[0,2]\\n C[4,4]=C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral I Structures ---------------------------------------------------------------------\\n if (LC == 'RI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral II Structures --------------------------------------------------------------------\\n if (LC == 'RII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[0,4]= ci[0][4]\\n C[2,2]= ci[0][5]\\n C[3,3]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[1,4]=-C[0,4]\\n C[3,5]=-C[0,4]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Tetragonal I Structures -----------------------------------------------------------------------\\n if (LC == 'TI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[2,2]= ci[0][3]\\n C[3,3]= ci[0][4]\\n C[5,5]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[4,4]= C[3,3]\\n \\n #-- Tetragonal II Structures ----------------------------------------------------------------------\\n if (LC == 'TII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,5]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[5,5]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,5]=-C[0,5]\\n C[4,4]= C[3,3]\\n \\n #-- Orthorhombic Structures -----------------------------------------------------------------------\\n if (LC == 'O'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[1,1]=ci[0][3]\\n C[1,2]=ci[0][4]\\n C[2,2]=ci[0][5]\\n C[3,3]=ci[0][6]\\n C[4,4]=ci[0][7]\\n C[5,5]=ci[0][8]\\n \\n #-- Monoclinic Structures -------------------------------------------------------------------------\\n if (LC == 'M'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,5]=ci[0][3]\\n C[1,1]=ci[0][4]\\n C[1,2]=ci[0][5]\\n C[1,5]=ci[0][6]\\n C[2,2]=ci[0][7]\\n C[2,5]=ci[0][8]\\n C[3,3]=ci[0][9]\\n C[3,4]=ci[0][10]\\n C[4,4]=ci[0][11]\\n C[5,5]=ci[0][12]\\n \\n #-- Triclinic Structures --------------------------------------------------------------------------\\n if (LC == 'N'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,3]=ci[0][3]\\n C[0,4]=ci[0][4]\\n C[0,5]=ci[0][5]\\n C[1,1]=ci[0][6]\\n C[1,2]=ci[0][7]\\n C[1,3]=ci[0][8]\\n C[1,4]=ci[0][9]\\n C[1,5]=ci[0][10]\\n C[2,2]=ci[0][11]\\n C[2,3]=ci[0][12]\\n C[2,4]=ci[0][13]\\n C[2,5]=ci[0][14]\\n C[3,3]=ci[0][15]\\n C[3,4]=ci[0][16]\\n C[3,5]=ci[0][17]\\n C[4,4]=ci[0][18]\\n C[4,5]=ci[0][19]\\n C[5,5]=ci[0][20]\\n #--------------------------------------------------------------------------------------------------\\n \\n \\n \\n for i in range(5):\\n for j in range(i+1,6):\\n C[j,i] = C[i,j] \\n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if self.__cod == 'espresso': C = -C/10.\\n elif self.__cod in ['vasp','emto','exciting','wien']: C=C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0#C = C/4.\\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\\n self.S = np.linalg.inv(C)\\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\\n self.BH = 0.50*(self.BV+self.BR)\\n self.GH = 0.50*(self.GV+self.GR)\\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\\n #--------------------------------------------------------------------------------------------------------------------------------\\n Cs.append(C)\\n self.__C = Cs\",\n \"def sage_graph(self):\\n self.fe.load_cache()\\n edges = []\\n is_bipartite = self.variant.is_bipartite()\\n for X in self.L:\\n for Y in self.L:\\n a = self.op_norm(X, Y)\\n if not self.K.is_zero(a):\\n for c in self.K.unit_group:\\n d = a - c\\n if X != Y or c < d or is_bipartite:\\n edges.append(((X, c, False), (Y, d, is_bipartite)))\\n if X == Y and not is_bipartite:\\n break\\n return sage.all.Graph(edges)\",\n \"def final_components(self):\\n DG = self.digraph()\\n condensation = DG.strongly_connected_components_digraph()\\n return [self.induced_sub_finite_state_machine([self.state(_) for _ in component])\\n for component in condensation.vertices()\\n if condensation.out_degree(component) == 0]\",\n \"def compute_edge_logits(self):\",\n \"def _nelec(self):\\n pd = self.particle_distribution(self._gam * mec2)\\n return pd.to(1/mec2_unit).value\",\n \"def nE(self):\\n return int(self.vnE.sum())\",\n \"def edges(self):\\n return self.dovetails + self.containments + self.internals\",\n \"def collectiveMetabolism(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes, addEcDescriptions = False) -> SubstanceEcGraph:\\n graph = self.group.collectiveEcGraph(noMultifunctional = excludeMultifunctionalEnzymes, addCount = True, keepOnHeap = True, addEcDescriptions = addEcDescriptions)\\n graph.name = 'Collective metabolism ECs ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def all_subconstituents(self, compute=False):\\n out = {}\\n for i in range(self._.d+1):\\n try:\\n out[i] = self.subconstituent(i, compute=compute)\\n except IndexError:\\n pass\\n return out\",\n \"def _get_full_graph(self):\",\n \"def connected_component(self):\\n t1 = datetime.datetime.now()\\n nodes = set(x.hex for x in self.agents)\\n result = []\\n while nodes:\\n node = nodes.pop()\\n # This set will contain the next group of nodes connected to each other.\\n group = {node}\\n # Build a queue with this node in it.\\n queue = [node]\\n # Iterate the queue.\\n # When it's empty, we finished visiting a group of connected nodes.\\n while queue:\\n # Consume the next item from the queue.\\n node = queue.pop(0)\\n # Fetch the neighbors.\\n neighbors = set(x for x in node.fon if x.is_occupied == 1)\\n # Remove the neighbors we already visited.\\n neighbors.difference_update(group)\\n # Remove the remaining nodes from the global set.\\n nodes.difference_update(neighbors)\\n # Add them to the group of connected nodes.\\n group.update(neighbors)\\n # Add them to the queue, so we visit them in the next iterations.\\n queue.extend(neighbors)\\n\\n # Add the group to the list of groups.\\n result.append(len(group))\\n td = datetime.datetime.now() - t1\\n print(\\\"calculated {} connected components in {} seconds\\\".format(len(result),td.total_seconds()))\\n return len(result), np.histogram(result, self.cluster_hist_breaks)[0]\",\n \"def calEachCrossflowAllAxialNode():\\n AxialNodeno = 14 # axial node number in CFD data\\n Nodes = []\\n base = 'Node'\\n for i in range(0, AxialNodeno):\\n Nodes.append(base+str(i))\\n \\n crossFlow = pd.read_csv('Data_crossflow.csv', index_col = 'Unnamed: 0')\\n lateralFactors = []\\n for node in Nodes:\\n lateralFactors.append(crossFlow[node]/0.8)\\n #need to judge the sign of lateral flow according to CTF rule!!\\n gapsToFlip = [2,4,6,7,9,11,13,14,16,18,20,21] #gaps in y direction\\n gapsToFlipIndex = [x - 1 for x in gapsToFlip]\\n for factors in lateralFactors:\\n for index in gapsToFlipIndex:\\n factors[index] = -factors[index] \\n #note: lateralFactors is a list of list\\n \\n #below calculate factors averaged over all subchannels\\n crossFlowAveFactor = crossFlow.apply(abs).mean(axis = 0)/0.8\\n lateralFactorsAvelist = []\\n for i in range(0,14):\\n base = []\\n for j in range(0,24):\\n base.append(crossFlowAveFactor[i])\\n lateralFactorsAvelist.append(base)\\n \\n \\n for i in range(0, 14):\\n for j in range(0, 24):\\n #note, in the original model there is only one sign for all source\\n #terms in one sub-channel. therefore -- sign(crossFlow.iloc[j,2])\\n lateralFactorsAvelist[i][j] = lateralFactorsAvelist[i][j] *sign(crossFlow.iloc[j,2]) \\n for each in lateralFactorsAvelist:\\n for index in gapsToFlipIndex:\\n each[index] = -each[index] \\n \\n \\n return lateralFactors, lateralFactorsAvelist\",\n \"def discreteComplexDecomposeGraph(self,graph):\\n s=[complex(*graph[i]) for i in range(len(graph))]\\n N=len(s)\\n M=self.coefficients_number\\n d=0\\n c=[]\\n for k in range(-M//2,M//2):\\n d+=sum([s[n]*cmath.exp(2j*cmath.pi*k*n/N) for n in range(N)])/N\\n c.append(d)\\n return c\",\n \"def display_energy_levels_0d(diagram, num_atoms, atoms, h_poly):\\n h = eval_hamiltonian(num_atoms, h_poly, (1, 1))\\n\\n e, v = eigensystem(h)\\n\\n left = 0\\n bottom = 0\\n right = max([len(row) for row in diagram.split('\\\\n')])\\n top = len(diagram.split('\\\\n'))\\n\\n plot_rows = numpy.ceil(math.sqrt(num_atoms+1))\\n plot_cols = plot_rows\\n\\n for i in range(num_atoms):\\n matplotlib.pyplot.subplot(plot_rows, plot_cols, i+1, axisbg=\\\"#000000\\\")\\n y = [atom[0] for atom in atoms]\\n x = [atom[1] for atom in atoms]\\n c = numpy.abs(v[i]*v[i])\\n\\n matplotlib.pyplot.title('E = %f' % numpy.real(e[i]), fontsize = 10)\\n norm = matplotlib.colors.Normalize(vmin = min(c),\\n vmax = max(0.0001, max(c)))\\n #x = [0,0,1,1]\\n #y = [0,1,0,1]\\n #c = [1,2,3,4]\\n matplotlib.pyplot.hexbin(x, y, C = c,\\n gridsize = (right-left, top-bottom),\\n extent = (left, right, bottom, top),\\n cmap = matplotlib.pyplot.get_cmap(\\\"gray\\\"),\\n norm = norm\\n )\\n\\n matplotlib.pyplot.subplot(plot_rows, plot_cols, num_atoms+1)\\n matplotlib.pyplot.scatter(num_atoms*[0], e, s = 0.1)\",\n \"def eclogite_massive():\\n\\n rho = 3490.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\\n\\n return C, rho\",\n \"def eci(self):\\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\\n eci = coefs.copy()\\n eci = eci / self._subspace.function_total_multiplicities\\n return eci\",\n \"def calculate_E(self):\\n \\n E = 0\\n for i in xrange(self.size):\\n Ei = self.h[i]\\n Ei += 0.5*sum((1 if self.spins[j] else -1)*self.J[i,j] for j in self.adjacency[i])\\n if not self.spins[i]:\\n Ei *= -1\\n E += Ei\\n \\n return E\",\n \"def extract_activities(ncfile):\\n\\n # Get current dimensions.\\n (niterations, nstates) = ncfile.variables['energies'].shape\\n\\n # Extract energies.\\n print \\\"Reading energies...\\\"\\n energies = ncfile.variables['energies']\\n u_kln_replica = zeros([nstates, nstates, niterations], float64)\\n for n in range(niterations):\\n u_kln_replica[:,:,n] = energies[n,:,:]\\n print \\\"Done.\\\"\\n\\n # Deconvolute replicas\\n print \\\"Deconvoluting replicas...\\\"\\n u_kln = zeros([nstates, nstates, niterations], float64)\\n for iteration in range(niterations):\\n state_indices = ncfile.variables['states'][iteration,:]\\n u_kln[state_indices,:,iteration] = energies[iteration,:,:]\\n print \\\"Done.\\\"\\n\\n # Show all self-energies\\n print 'all self-energies for all replicas'\\n for iteration in range(niterations):\\n for replica in range(nstates):\\n state = int(ncfile.variables['states'][iteration,replica])\\n print '%12.1f' % energies[iteration, replica, state],\\n print ''\\n\\n # If no energies are 'nan', we're clean.\\n if not any(isnan(energies[:,:,:])):\\n return\\n\\n # There are some energies that are 'nan', so check if the first iteration has nans in their *own* energies:\\n u_k = diag(energies[0,:,:])\\n if any(isnan(u_k)):\\n print \\\"First iteration has exploded replicas. Check to make sure structures are minimized before dynamics\\\"\\n print \\\"Energies for all replicas after equilibration:\\\"\\n print u_k\\n sys.exit(1)\\n\\n # There are some energies that are 'nan' past the first iteration. Find the first instances for each replica and write PDB files.\\n first_nan_k = zeros([nstates], int32)\\n for iteration in range(niterations):\\n for k in range(nstates):\\n if isnan(energies[iteration,k,k]) and first_nan_k[k]==0:\\n first_nan_k[k] = iteration\\n if not all(first_nan_k == 0):\\n print \\\"Some replicas exploded during the simulation.\\\"\\n print \\\"Iterations where explosions were detected for each replica:\\\"\\n print first_nan_k\\n print \\\"Writing PDB files immediately before explosions were detected...\\\"\\n for replica in range(nstates): \\n if (first_nan_k[replica] > 0):\\n state = ncfile.variables['states'][iteration,replica]\\n iteration = first_nan_k[replica] - 1\\n filename = 'replica-%d-before-explosion.pdb' % replica\\n title = 'replica %d state %d iteration %d' % (replica, state, iteration)\\n write_pdb(atoms, filename, iteration, replica, title, ncfile)\\n filename = 'replica-%d-before-explosion.crd' % replica \\n write_crd(filename, iteration, replica, title, ncfile)\\n sys.exit(1)\\n\\n # There are some energies that are 'nan', but these are energies at foreign lambdas. We'll just have to be careful with MBAR.\\n # Raise a warning.\\n print \\\"WARNING: Some energies at foreign lambdas are 'nan'. This is recoverable.\\\"\\n \\n return\",\n \"def components(self):\\n comps = 0\\n unexplored = self.nodes()\\n while unexplored:\\n comps += 1\\n queue = {unexplored.pop()}\\n while queue:\\n new = queue.pop()\\n unexplored.remove(new)\\n for adjacent in new.parents() | new.children():\\n if adjacent in unexplored:\\n queue.add(adjacent)\\n return comps\",\n \"def n_cs(self):\\n pass\",\n \"def get_econs(self):\\n eham = self.beads.vpath*self.nm.omegan2 + self.nm.kin + self.forces.pot\\n eham += self.bias.pot # bias\\n for e in self._elist:\\n eham += e.get()\\n\\n return eham + self.eens\",\n \"def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n childGraph = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)\\n childGraph.name = 'Added metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return childGraph\",\n \"def CS2e(Type=\\\"DFA\\\"):\\n CC50, CC51, CC52, CC53 = state('CC50'), state('CC51'), state('CC52'), state('CC53')\\n for i in sigma:\\n CC52.transit[i] = CC52\\n CC53.transit[i] = CC53\\n for i in sigma_1:\\n CC50.transit[i] = CC50\\n for i in sigma_2:\\n CC50.transit[i] = CC53\\n CC51.transit[i] = CC51\\n for i in sigma_cc:\\n CC50.transit[i] = CC51\\n CC51.transit[i] = CC51\\n for i in sigma_ncc:\\n CC51.transit[i] = CC50\\n for i in sigma_B_A:\\n CC51.transit[i] = CC50\\n for i in sigma_e:\\n CC51.transit[i] = CC53\\n for i in sigma_A:\\n CC51.transit[i] = CC52\\n if Type == \\\"pDFA\\\":\\n CC5 = pDFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\\n else:\\n CC5 = DFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\\n if (SIZEOF):\\n EM_size[\\\"CS2e\\\"] = asizeof.asizeof(CC5)\\n return CC5\",\n \"def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:\\n # get neofunctionalisations \\n neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)\\n \\n # filter core metabolism enzyme graph\\n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism) \\n neofunctionalisedMetabolism = neofunctionalisedEnzymes.filterGraph(enzymeGraph, minimumEcDifference = None)\\n \\n # colour core metabolism \\n if colour is not False:\\n \\n if colour is True:\\n colourToUse = Export.Colour.GREEN\\n else:\\n colourToUse = colour\\n \\n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\\n neofunctionalisedMetabolism = enzymeGraph\\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\\n \\n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism enzymes ' + ' '.join(self.ncbiNames)\\n \\n return neofunctionalisedMetabolism\",\n \"def num_of_subgraphs(self):\\n \\n G = self.to_undirected_graph()\\n \\n count = G.num_subgraph()\\n \\n print('The number of disconnected components in the graph is ', count)\",\n \"def neofunctionalisations(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None) -> Set[Neofunctionalisation]:\\n # get neofunctionalisations \\n return self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs).getNeofunctionalisations()\",\n \"def Excentricity_graph(self,ccenter,cradius,icenter):\\n \\n ellip = 0.\\n q = 1. - ellip\\n pa = 0.\\n \\n stamp = self['STAMP'].copy()\\n mask = self['MASK'].copy()\\n sky = self['BACKGROUND']\\n Img = stamp - sky\\n Img[num.where(mask != 0)] = 0.\\n \\n id = self._getGraphId()\\n root = 'Excentricity_%s' % (id,)\\n pngname = root + '.png' ; epsname = root + '.eps'\\n jpgname = root + '.jpg'\\n doStamp(Img,pngname,format='PNG')\\n Convert(pngname,jpgname)\\n \\n Painted = Paint(jpgname)\\n Painted.load()\\n \\n Painted.DrawEllipse(ccenter,cradius,q,pa,color='red',linewidth=2)\\n Painted.DrawCross(ccenter,length=20,color='red')\\n Painted.DrawCross(icenter,length=20,color='green')\\n \\n text = 'E=%.2f o/o' % self['M_E']\\n \\n # Painted.Graffiti(text,commtextpos)\\n \\n Painted.save(jpgname) \\n Painted.release()\\n \\n Convert(jpgname,epsname)\\n os.system('rm %s %s' % (pngname,jpgname))\\n \\n self['figures']['Excentricity'] = epsname\\n self['figcomms']['Excentricity'] = text\",\n \"def connected_components(self) -> List[list]:\\n for n in self.dw_graph.get_all_v().values():\\n n.distance=0.0\\n mega_list = []\\n for n in self.dw_graph.get_all_v().values():\\n if n.distance!=-10:\\n mega_list.append(self.connected_component(n.node_id))\\n return mega_list\",\n \"def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentNeofunctionalised= self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = conservedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\\n \\n parentGraph.name = 'Conserved metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def plot_eigenvector_centrality(net, label, outpath):\\n _, eigenvector_centrality_values = networkit_util.get_eigenvector_centrality(net, label, outpath)\\n unique_value, unique_cnt = np.unique(eigenvector_centrality_values, return_counts=True)\\n unique_cumcnt = np.cumsum(unique_cnt) / sum(unique_cnt)\\n fig = plt.figure()\\n ax = fig.add_subplot(1, 1, 1)\\n ax.plot(unique_value, unique_cumcnt, 'g.')\\n # ax.set_title('Cumulative distribution of eigenvector centrality of nodes')\\n ax.set_xlabel('eigenvector centrality e')\\n ax.set_ylabel('p(x <= e)')\\n plt.savefig(outpath + label + \\\"-eigenvector-distribution.eps\\\")\",\n \"def eclogite_foliated():\\n\\n rho = 3300.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\\n\\n return C, rho\",\n \"def get_band_edges():\\n # Vacuum level energy from LOCPOT.\\n locpot = Locpot.from_file('LOCPOT')\\n evac = max(locpot.get_average_along_axis(2))\\n\\n vasprun = Vasprun('vasprun.xml')\\n bs = vasprun.get_band_structure()\\n eigenvals = vasprun.eigenvalues\\n efermi = vasprun.efermi - evac\\n\\n if bs.is_metal():\\n edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,\\n 'efermi': efermi}\\n\\n elif bs.is_spin_polarized:\\n up_cbm = min(\\n [min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])\\n for i in range(len(eigenvals[Spin.up]))]) - evac\\n up_vbm = max(\\n [max([e[0] for e in eigenvals[Spin.up][i] if e[1]])\\n for i in range(len(eigenvals[Spin.up]))]) - evac\\n dn_cbm = min(\\n [min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])\\n for i in range(len(eigenvals[Spin.down]))]) - evac\\n dn_vbm = max(\\n [max([e[0] for e in eigenvals[Spin.down][i] if e[1]])\\n for i in range(len(eigenvals[Spin.down]))]) - evac\\n edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,\\n 'dn_vbm': dn_vbm, 'efermi': efermi}\\n\\n else:\\n cbm = bs.get_cbm()['energy'] - evac\\n vbm = bs.get_vbm()['energy'] - evac\\n edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,\\n 'efermi': efermi}\\n\\n return edges\",\n \"def generate_empirical_nodes(reduced_basis):\\n \\n # Get the index of the 0th empirical node T_0\\n # from the maximum value of the 0th reduced basis.\\n imax = np.argmax(np.abs(reduced_basis[0]))\\n empirical_node_indices = [imax]\\n \\n # Iterate to find the other impirical nodes.\\n for j in range(1, len(reduced_basis)):\\n # The jth empirical node\\n imax = generate_new_empirical_node(reduced_basis, empirical_node_indices)\\n empirical_node_indices.append(imax)\\n \\n return empirical_node_indices\",\n \"def edges(self):\\n return map(Edge, self._top_exp.edges())\",\n \"def epidote():\\n\\n rho = 3465.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 211.5; C[0,1] = 65.6; C[0,2] = 43.2; C[0,3] = 0.; C[0,4] = -6.5; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 239.; C[1,2] = 43.6; C[1,3] = 0.; C[1,4] = -10.4; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 202.1; C[2,3] = 0.; C[2,4] = -20.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.1; C[3,4] = 0.; C[3,5] = -2.3\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 43.4; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.5\\n\\n return C, rho\",\n \"def ec_geometric_series(p0, T, n):\\n if(n<=0):\\n EC=np.zeros(T.shape)\\n return EC\\n else:\\n R, D, L=rdl_decomposition(T)\\n w=np.diagonal(D)\\n L=np.transpose(L)\\n\\n D_sum=np.diag(geometric_series(w, n-1))\\n T_sum=np.dot(np.dot(R, D_sum), np.conjugate(np.transpose(L)))\\n p_sum=np.dot(p0, T_sum)\\n EC=p_sum[:,np.newaxis]*T\\n \\\"\\\"\\\"Truncate imginary part - which is zero, but we want real\\n return values\\\"\\\"\\\"\\n EC=EC.real\\n return EC\",\n \"def cyclic_subgroups(C):\\n ps = C.points()\\n Gs = []\\n \\n for i in ps:\\n P = Elliptic_Point(i[0],i[1],C)\\n Gs.append(cyclic_subgroup(P))\\n\\n return Gs\",\n \"def _sigma_ee_nonrel(self,gam,eps):\\n s0 = 4 * r0**2 * alpha / (15 * eps)\\n x = 4 * eps / (gam**2 - 1)\\n sigma_nonrel = s0 * self._F(x,gam)\\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\\n return sigma_nonrel / mec2_unit\",\n \"def vnE(self):\\n return np.array(\\n [x for x in [self.nEx, self.nEy, self.nEz] if x is not None],\\n dtype=int\\n )\",\n \"def get_edges(self):\\n for i in self.gens:\\n if self.active[i]:\\n elist = set()\\n H = (i,) # edge-stabilizing subgroup\\n reps = set(self.word_generator(parabolic=H))\\n reps = self.G.sort_words(reps)\\n for word in reps:\\n v1 = self.G.move(self.vtable, 0, word)\\n v2 = self.G.move(self.vtable, 0, word + (i,))\\n if v1 is not None and v2 is not None:\\n if v1 > v2:\\n v1, v2 = v2, v1\\n if (v1, v2) not in elist:\\n elist.add((v1, v2))\\n\\n self.edge_indices[i] = elist\\n\\n self.num_edges = sum(len(L) for L in self.edge_indices.values())\",\n \"def graph(self):\\n ...\",\n \"def findcc(self):\\n for vertex in self.graph.vertices:\\n if vertex not in self.preOrderNumbers:\\n self.dfs(vertex)\\n return self.scComponents\",\n \"def gen_graph(self):\",\n \"def edges(self):\\r\\n return self.capacity.keys()\",\n \"def _get_graph_based_ic_dictionary(self):\\n\\n\\t\\t# TODO find the literature reference or presentation where this equation is from instead of just the presentation.\\n\\n\\t\\t#ic_dict = {}\\n\\t\\t#num_terms_in_ontology = len(self)\\n\\t\\t#for term in self.terms():\\n\\t\\t#\\tdepth = self._depth_dict[term.id]\\n\\t\\t#\\tnum_descendants = len(list(term.subclasses(with_self=False)))\\n\\t\\t#\\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\\n\\t\\t#\\tic_dict[term.id] = ic_value\\n\\t\\t#return(ic_dict)\\n\\n\\n\\t\\t# Getting the information content of each term in the ontology based on graph structure.\\n\\t\\tic_dict = {}\\n\\t\\tnum_terms_in_ontology = len(self)\\n\\t\\tfor term in self.terms():\\n\\t\\t\\tdepth = self._depth_dict[term.id]\\n\\t\\t\\tnum_descendants = len(list(term.subclasses(with_self=False)))\\n\\t\\t\\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\\n\\t\\t\\tic_dict[term.id] = ic_value\\n\\n\\n\\t\\t# Converting to weights based on information content rather than raw value.\\n\\t\\tic_dict_as_weights = {}\\n\\t\\tic_values = ic_dict.values()\\n\\t\\tmin_ic = min(ic_values)\\n\\t\\tmax_ic = max(ic_values)\\n\\t\\tnew_max = 1.00\\n\\t\\tnew_min = 0.00\\n\\t\\tfor k,v in ic_dict.items():\\n\\t\\t\\told_range = max_ic-min_ic\\n\\t\\t\\tnew_range = new_max-new_min\\n\\t\\t\\tnew_value = (((v - min_ic) * new_range) / old_range) + new_min\\n\\t\\t\\tic_dict_as_weights[k] = new_value\\n\\n\\t\\treturn(ic_dict, ic_dict_as_weights)\",\n \"def life_expectancy_graph(country_name):\\r\\n dat = read_data(pre_file_name())\\r\\n MainDct = dat[0]\\r\\n # MetaDct = dat[1]\\r\\n f_life_expectancy = {}\\r\\n for idx in MainDct:\\r\\n if idx == country_name or MainDct[idx].country_code == country_name:\\r\\n for idx1 in MainDct[idx].values:\\r\\n if idx1[1] != 0:\\r\\n f_life_expectancy[idx1[0]] = idx1[1]\\r\\n return f_life_expectancy\",\n \"def eccentricity(self):\\n new_data = self._data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\\n 'pl_masse', 'pl_orbeccen',\\n 'pl_radj', 'pl_dens', 'st_teff',\\n 'st_mass', 'st_rad']]\\n new_data = new_data.dropna()\\n\\n features = new_data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\\n 'pl_masse',\\n 'pl_radj', 'pl_dens', 'st_teff',\\n 'st_mass', 'st_rad']]\\n labels = new_data['pl_orbeccen']\\n\\n features_train, features_test, labels_train, labels_test = \\\\\\n train_test_split(features, labels, test_size=0.2)\\n\\n # Create an untrained model\\n model = DecisionTreeRegressor()\\n\\n # Train it on the **training set**\\n model.fit(features_train, labels_train)\\n\\n # Compute test accuracy\\n test_predictions = model.predict(features_test)\\n test_acc = mean_absolute_error(labels_test, test_predictions)\\n test_acc_r2 = r2_score(labels_test, test_predictions)\\n\\n # Plot ML vs Actual\\n fig, [ax1, ax2] = plt.subplots(2, figsize=(15, 12))\\n\\n sns.distplot(test_predictions, kde=False, ax=ax1)\\n sns.distplot(labels_test, kde=False, ax=ax2)\\n\\n ax1.set_title('Distribution of Predicted Eccentricities of Orbits')\\n ax1.set_xlabel('Eccentricity of Orbit')\\n ax1.set_ylabel('Number of Planets')\\n\\n ax2.set_title('Distribution of Actual Eccentricities of Orbits')\\n ax2.set_xlabel('Eccentricity of Orbit')\\n ax2.set_ylabel('Number of Planets')\\n\\n plt.savefig('figures/ML_Eccentricity.png', bbox_inches='tight')\\n\\n return (test_acc, test_acc_r2)\",\n \"def get_eulerian_graph_edges(bbox, source):\\n osm_graph = OSMGraph(bbox, source)\\n # input all nodes and get odd nodes, update node attributes\\n odd_nodes = get_odd_nodes(osm_graph.nodes_dict)\\n\\n # initialize all_pairs_list\\n all_pairs_list = []\\n\\n # if there are 6 or fewer odd nodes look for all possible options,\\n # otherwise look for just three basic pairing options\\n\\n if len(odd_nodes) <= 10:\\n print(\\\"ROBUST PAIRING FUNCTION\\\")\\n all_pairs_list = get_list_of_all_pairs_lists(odd_nodes)\\n\\n else:\\n print(\\\"CHEAP PAIRING FUNCTION\\\")\\n all_pairs_list = get_list_of_all_pairs_lists_short(odd_nodes)\\n\\n for item in all_pairs_list:\\n print(\\\"\\\\n\\\\nPair option:\\\", item)\\n print(\\\"Pair option len:\\\", len(item))\\n\\n dict_pairings_lists_lengths = get_dict_pairings_lists_lengths(\\n all_pairs_list, osm_graph\\n )\\n twice_traversals_edges = get_twice_traversals_edges(dict_pairings_lists_lengths)\\n updated_graph_instance = update_twice_traversal_edges(\\n twice_traversals_edges, osm_graph\\n )\\n return updated_graph_instance\",\n \"def _get_gs_energies(self):\\n energy = []\\n for ground_state in self._ground_states:\\n gs_energy = 0.0\\n for key in ground_state[\\\"eci\\\"].keys():\\n gs_energy += ground_state[\\\"eci\\\"][key] * ground_state[\\\"cf\\\"][key]\\n energy.append(len(ground_state[\\\"atoms\\\"]) * gs_energy)\\n return energy\",\n \"def buildE(self, debug = False):\\n E = np.zeros([len(self.sta), len(self.dist)])\\n i = 0\\n for ikey, state in self.sta.items():\\n j = 0\\n c = state.c\\n for jkey, disturbance in self.dist.items():\\n if debug:\\n print(i, ' ', j)\\n if isinstance(disturbance, InputT) and disturbance in \\\\\\n self.rc.adj[state]:\\n # input is temperature and connected to this state\\n\\n E[i, j] = self.rc.adj[state][disturbance]['H'] / c\\n elif isinstance(disturbance, InputQ) and disturbance in \\\\\\n self.rc.adj[\\n state]: # input is heat flow and connected to state\\n E[i, j] = self.rc.adj[state][disturbance]['gain'] / c\\n j += 1\\n i += 1\\n\\n return E, list(self.sta.keys()), list(self.dist.keys())\",\n \"def Hasse_diagram_from_incidences(atom_to_coatoms, coatom_to_atoms,\\n face_constructor=None,\\n required_atoms=None,\\n key = None,\\n **kwds):\\n from sage.graphs.all import DiGraph\\n from sage.combinat.posets.posets import FinitePoset\\n def default_face_constructor(atoms, coatoms, **kwds):\\n return (atoms, coatoms)\\n if face_constructor is None:\\n face_constructor = default_face_constructor\\n atom_to_coatoms = [frozenset(atc) for atc in atom_to_coatoms]\\n A = frozenset(range(len(atom_to_coatoms))) # All atoms\\n coatom_to_atoms = [frozenset(cta) for cta in coatom_to_atoms]\\n C = frozenset(range(len(coatom_to_atoms))) # All coatoms\\n # Comments with numbers correspond to steps in Section 2.5 of the article\\n L = DiGraph(1) # 3: initialize L\\n faces = dict()\\n atoms = frozenset()\\n coatoms = C\\n faces[atoms, coatoms] = 0\\n next_index = 1\\n Q = [(atoms, coatoms)] # 4: initialize Q with the empty face\\n while Q: # 5\\n q_atoms, q_coatoms = Q.pop() # 6: remove some q from Q\\n q = faces[q_atoms, q_coatoms]\\n # 7: compute H = {closure(q+atom) : atom not in atoms of q}\\n H = dict()\\n candidates = set(A.difference(q_atoms))\\n for atom in candidates:\\n coatoms = q_coatoms.intersection(atom_to_coatoms[atom])\\n atoms = A\\n for coatom in coatoms:\\n atoms = atoms.intersection(coatom_to_atoms[coatom])\\n H[atom] = (atoms, coatoms)\\n # 8: compute the set G of minimal sets in H\\n minimals = set([])\\n while candidates:\\n candidate = candidates.pop()\\n atoms = H[candidate][0]\\n if atoms.isdisjoint(candidates) and atoms.isdisjoint(minimals):\\n minimals.add(candidate)\\n # Now G == {H[atom] : atom in minimals}\\n for atom in minimals: # 9: for g in G:\\n g_atoms, g_coatoms = H[atom]\\n if not required_atoms is None:\\n if g_atoms.isdisjoint(required_atoms):\\n continue\\n if (g_atoms, g_coatoms) in faces:\\n g = faces[g_atoms, g_coatoms]\\n else: # 11: if g was newly created\\n g = next_index\\n faces[g_atoms, g_coatoms] = g\\n next_index += 1\\n Q.append((g_atoms, g_coatoms)) # 12\\n L.add_edge(q, g) # 14\\n # End of algorithm, now construct a FinitePoset.\\n # In principle, it is recommended to use Poset or in this case perhaps\\n # even LatticePoset, but it seems to take several times more time\\n # than the above computation, makes unnecessary copies, and crashes.\\n # So for now we will mimic the relevant code from Poset.\\n\\n # Enumeration of graph vertices must be a linear extension of the poset\\n new_order = L.topological_sort()\\n # Make sure that coatoms are in the end in proper order\\n tail = [faces[atoms, frozenset([coatom])]\\n for coatom, atoms in enumerate(coatom_to_atoms)]\\n tail.append(faces[A, frozenset()])\\n new_order = [n for n in new_order if n not in tail] + tail\\n # Make sure that atoms are in the beginning in proper order\\n head = [0] # We know that the empty face has index 0\\n head.extend(faces[frozenset([atom]), coatoms] \\n for atom, coatoms in enumerate(atom_to_coatoms)\\n if required_atoms is None or atom in required_atoms)\\n new_order = head + [n for n in new_order if n not in head]\\n # \\\"Invert\\\" this list to a dictionary\\n labels = dict()\\n for new, old in enumerate(new_order):\\n labels[old] = new\\n L.relabel(labels)\\n # Construct the actual poset elements\\n elements = [None] * next_index\\n for face, index in faces.items():\\n atoms, coatoms = face\\n elements[labels[index]] = face_constructor(\\n tuple(sorted(atoms)), tuple(sorted(coatoms)), **kwds)\\n return FinitePoset(L, elements, key = key)\",\n \"def get_incident_nodes(self):\\n # return the set of incident edges\\n return \\\\\\n {\\n self.first_incident_node,\\n self.second_incident_node\\n }\",\n \"def lostMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n parentGraph = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(lostECs)\\n parentGraph.name = 'Lost metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return parentGraph\",\n \"def ncusps(self):\\n n = self.level()\\n return sum([arith.euler_phi(arith.gcd(d,n//d)) for d in n.divisors()])\",\n \"def line_graph_forbidden_subgraphs():\\n from sage.graphs.all import Graph\\n from sage.graphs.generators.basic import ClawGraph\\n graphs = [ClawGraph()]\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3],\\n 4: [2],\\n 5: [3]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3, 4],\\n 1: [2, 3, 4],\\n 3: [4],\\n 2: [5]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3],\\n 4: [2, 3]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3],\\n 4: [2],\\n 5: [3, 4]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3, 4],\\n 1: [2, 3, 4],\\n 3: [4],\\n 5: [2, 0, 1]\\n }))\\n\\n graphs.append(Graph({\\n 5: [0, 1, 2, 3, 4],\\n 0: [1, 4],\\n 2: [1, 3],\\n 3: [4]\\n }))\\n\\n graphs.append(Graph({\\n 1: [0, 2, 3, 4],\\n 3: [0, 4],\\n 2: [4, 5],\\n 4: [5]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3, 4],\\n 2: [3, 4],\\n 3: [4]\\n }))\\n\\n return graphs\",\n \"def getSigma(E, opt=\\\"\\\"):\\n # HG resolutions, from the energy unidoc.\\n eRes = {\\n 0 : {\\\"nat\\\": [1.260e-1, 1.790e-2, 2.370e-4], \\\"enr\\\": [1.500e-1, 1.750e-2, 2.820e-4], \\\"both\\\": [1.470e-1, 1.730e-2, 3.000e-4]},\\n 1 : {\\\"nat\\\": [1.470e-1, 1.770e-2, 2.010e-4], \\\"enr\\\": [1.340e-1, 1.750e-2, 2.820e-4], \\\"both\\\": [1.360e-1, 1.740e-2, 2.800e-4]},\\n 2 : {\\\"nat\\\": [1.410e-1, 1.800e-2, 1.680e-4], \\\"enr\\\": [1.420e-1, 1.720e-2, 2.860e-4], \\\"both\\\": [1.430e-1, 1.720e-2, 2.840e-4]},\\n 3 : {\\\"nat\\\": [1.800e-1, 1.820e-2, 2.090e-4], \\\"enr\\\": [1.580e-1, 1.710e-2, 3.090e-4], \\\"both\\\": [1.620e-1, 1.720e-2, 2.970e-4]},\\n 4 : {\\\"nat\\\": [2.140e-1, 1.540e-2, 3.970e-4], \\\"enr\\\": [2.170e-1, 1.490e-2, 3.190e-4], \\\"both\\\": [2.180e-1, 1.500e-2, 3.500e-4]},\\n \\\"5A\\\" : {\\\"nat\\\": [2.248e-1, 1.894e-2, 2.794e-4], \\\"enr\\\": [2.660e-1, 2.215e-2, 2.868e-4], \\\"both\\\": [2.592e-1, 2.057e-2, 3.086e-4]},\\n \\\"5B\\\" : {\\\"nat\\\": [1.650e-1, 1.760e-2, 2.828e-4], \\\"enr\\\": [1.815e-1, 1.705e-2, 3.153e-4], \\\"both\\\": [1.815e-1, 1.690e-2, 3.187e-4]},\\n \\\"5C\\\" : {\\\"nat\\\": [1.565e-1, 1.810e-2, 2.201e-4], \\\"enr\\\": [1.361e-1, 1.740e-2, 2.829e-4], \\\"both\\\": [1.519e-1, 1.718e-2, 2.762e-4]}\\n }\\n\\n if len(dsList)==1:\\n p = eRes[dsList[0]][opt]\\n return np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2)\\n else:\\n # weight the curve by exposure\\n sig, expTot = 0, 0\\n for ds in dsList:\\n if opt==\\\"enr\\\": exp = dsExpo[ds][0]\\n if opt==\\\"nat\\\": exp = dsExpo[ds][1]\\n if opt==\\\"both\\\": exp = dsExpo[ds][0] + dsExpo[ds][1]\\n p = eRes[ds][opt]\\n sig += np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2) * exp\\n expTot += exp\\n sig /= expTot\\n return sig\",\n \"def get_eddy_instances(parents, root_nodes, gate_dist):\\n\\tsnodes = []\\n\\te_instances = dict()\\n\\tchild_score = dict()\\n\\tfor parent in parents:\\n\\t\\tfor child in parent.children:\\n\\t\\t\\tif is_term(child):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tchild.score = parent.score + max(score(hyp, gate_dist) for hyp in child.tracks())\\n\\t\\t\\tchild_score[child] = (child.score, parent)\\n\\t\\t\\tif child.obj in e_instances:\\n\\t\\t\\t\\te_instances[child.obj].append(child)\\n\\t\\t\\telse:\\n\\t\\t\\t\\te_instances[child.obj] = [child]\\n\\tfor node in root_nodes:\\n\\t\\tnode.score = max(score(hyp, gate_dist) for hyp in node.tracks())\\n\\t\\tchild_score[node] = (node.score, None)\\n\\t\\tif node.obj in e_instances:\\n\\t\\t\\te_instances[node.obj].append(node)\\n\\t\\telse:\\n\\t\\t\\te_instances[node.obj] = [node]\\n\\treturn (e_instances, child_score)\",\n \"def n_e(self) -> u.m**-3:\\n return np.sum(self._n_elem * self.ionic_fractions * self.charge_numbers)\",\n \"def discreteComplexComposeGraph(self,S,n):\\n N=len(S)\\n M=N//2\\n g=[]\\n d=0\\n for k in range(M):\\n d+=S[k+M]*cmath.exp(2j*cmath.pi*k*n/N)+S[-k+M]*cmath.exp(-2j*cmath.pi*k*n/N)\\n g.append((d.real,d.imag))\\n return g\",\n \"def computemodes(self):\\n evals, modes = np.linalg.eig(self.A)\\n return evals, modes\",\n \"def edges(self):\\r\\n return self.__generate_edges()\",\n \"def non_pol_neighbours_graph():\\n data = pd.read_csv(\\\"/Users/emg/GitHub/thesis/output/2019_01/1000_residuals_output_utf8.csv\\\", index_col=0)\\n\\n labelled = label_subs(data)\\n labelled['resid_rank'] = labelled.resid.rank(pct=True)\\n top = subset_df(labelled, 'resid', q=0.95)\\n\\n edges = top.copy()[['source','target','resid']]\\n edges_rev = edges.copy()\\n edges_rev.columns = ['target','source','resid']\\n directed_edges = pd.concat([edges,edges_rev], sort=True)\\n directed_edges['resid_rank'] = directed_edges['resid'].rank(pct=True)\\n\\n df = label_subs(directed_edges)\\n\\n pol_subs = load_pol_subs()\\n pol_names = pol_subs.subreddit.str.replace('\\\\\\\\','')\\n pol_subs.subreddit=pol_subs.subreddit.str.replace('\\\\\\\\','')\\n\\n pol_neighbours = df[df['source'].isin(pol_names)].sort_values('resid', ascending=False)\\n\\n top_pol_neigh = pol_neighbours.groupby('source').head(10).sort_values(['source','resid'], ascending=[True,False])\\n \\n x = top_pol_neigh[~top_pol_neigh.target.isin(pol_names)][['source','target']]\\n\\n col_dict = pol_subs.set_index('subreddit').col.to_dict()\\n for sub in x.target.unique():\\n col_dict[sub] = 'gray'\\n\\n G = nx.from_pandas_edgelist(x)\\n nx.set_node_attributes(G, col_dict, 'col')\\n\\n f = plt.figure(1)\\n ax = f.add_subplot(1,1,1)\\n\\n colors = dict(G.nodes(data='col')).values()\\n\\n pos = nx.spring_layout(G, k=0.2)\\n nx.draw_networkx(G, pos=pos, with_labels=False, node_color=colors, alpha=0.3)\\n #nx.draw_networkx_labels(G, pos=pos, with_labels=True)\\n\\n plt.axis('off')\\n f.set_facecolor('w')\\n \\n f.tight_layout()\\n plt.savefig(figures_path(f\\\"{date}/non_pol_neighbours_graph.png\\\"))\\n plt.close()\",\n \"def xsec_CEvNS(E_R, E_nu, N_p, N_n):\\n \\n A = N_p + N_n\\n Z = N_p\\n \\n m_A = A*0.9315 #Mass of target nucleus (in GeV)\\n q = np.sqrt(2.0*E_R*m_A) #Recoil momentum (in MeV)\\n #Note: m_A in GeV, E_R in keV, E_nu in MeV\\n \\n #Calculate SM contribution\\n Qv = (A-Z) - (1.0-4.0*SIN2THETAW)*Z #Coherence factor\\n \\n xsec_SM = (G_FERMI*G_FERMI/(4.0*np.pi))*Qv*Qv*m_A* \\\\\\n (1.0-(q*q)/(4.0*E_nu*E_nu))\\n \\n #Calculate New-Physics correction from Z' coupling\\n #Assume universal coupling to quarks (u and d)\\n #QvNP = 3.0*A*gsq\\n\\n #Factor of 1e6 from (GeV/MeV)^2\\n #G_V = 1 - 1e6*(SQRT2/G_FERMI)*(QvNP/Qv)*1.0/(q*q + m_med*m_med)\\n \\n #Convert from (GeV^-3) to (cm^2/keV)\\n #and multiply by form factor\\n return xsec_SM*1e-6*(1.98e-14)*(1.98e-14)*calcSIFormFactor(E_R, A)\",\n \"def getEnergyEvolution(self):\\n\\n\\t\\tEBefore = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleBefore]\\n\\t\\tEAfter = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleAfter]\\n\\n\\t\\treturn EBefore, EAfter\",\n \"def coaccessible_components(self):\\n DG = self.digraph().reverse()\\n coaccessible_states = DG.breadth_first_search(\\n [_.label() for _ in self.iter_final_states()])\\n return self.induced_sub_finite_state_machine(\\n [self.state(_) for _ in coaccessible_states])\",\n \"def plot_mcc_tree():\\n t = ete2.Tree(\\\"mcct.nex\\\")\\n ts = ete2.treeview.TreeStyle()\\n ts.show_scale = False\\n ts.show_leaf_name = False\\n ts.show_branch_support = False\\n ts.scale = 500\\n margin = 10\\n ts.margin_top = margin\\n ts.margin_bottom = margin\\n ts.margin_left = margin\\n ts.margin_right = margin\\n\\n germ_style = ete2.NodeStyle()\\n germ_style[\\\"bgcolor\\\"] = \\\"LightSteelBlue\\\"\\n proto_germ = t.get_common_ancestor(\\\"Danish\\\", \\\"Norwegian\\\",\\\"Icelandic\\\",\\\"Swedish\\\", \\\"Dutch\\\", \\\"German\\\", \\\"English\\\")\\n proto_germ.set_style(germ_style)\\n\\n bs_style = ete2.NodeStyle()\\n bs_style[\\\"bgcolor\\\"] = \\\"Moccasin\\\"\\n proto_bs = t.get_common_ancestor(\\\"Bulgarian\\\", \\\"Czech\\\",\\\"Polish\\\",\\\"Russian\\\")\\n proto_bs.set_style(bs_style)\\n\\n ital_style = ete2.NodeStyle()\\n ital_style[\\\"bgcolor\\\"] = \\\"DarkSeaGreen\\\"\\n proto_ital = t.get_common_ancestor(\\\"French\\\", \\\"Romanian\\\", \\\"Italian\\\", \\\"Portuguese\\\", \\\"Spanish\\\")\\n proto_ital.set_style(ital_style)\\n\\n t.render(\\\"mcct.eps\\\", style_func, tree_style=ts, dpi=600, units=\\\"px\\\", w=2250)\",\n \"def interacting_conductivity(self,\\n sigma=0.04,\\n freq_range=(0.0, 6.0, 1000),\\n imag_dielectric=False,\\n broadening='lorentz'):\\n cell_area = np.abs(np.cross(self.a1, self.a2))*len(self.k_grid)\\n broad_fnc = get_broadening_function(broadening, sigma)\\n\\n frequency_grid = np.linspace(*freq_range)\\n output_grid = np.zeros(len(frequency_grid))\\n\\n energy_power = 1 + int(imag_dielectric)\\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\\n prefactor = prefactor/cell_area\\n\\n # Assuming x polarisation for now\\n position_dipole_matrix = self.construct_position_dipole_matrix()\\n\\n n_shift = self.n_spins*self.n_orbs\\n cutoff_band_min_maxes = self.exciton_obj.get_cutoff_band_min_maxes()\\n # Construct vector of velocity matrix elements to dot with the\\n # bse eigenvector.\\n velocity_element_lists = []\\n for s0 in range(self.n_spins):\\n velocity_elements = []\\n for idx_1, kpt in enumerate(self.k_grid):\\n velocity_matrix = self.load_velocity_matrix(idx_1)\\n bands = self.exciton_obj.get_number_conduction_valence_bands(\\n idx_1, s0\\n )\\n v_num, c_num = bands\\n v_min = 0\\n if self.exciton_obj.energy_cutoff is not None:\\n v_min = int(cutoff_band_min_maxes[s0][idx_1][0])\\n c_num = int(cutoff_band_min_maxes[s0][idx_1][1]) + 1\\n v_num = v_num - v_min\\n j1 = n_shift*idx_1 + s0*self.n_orbs\\n j2 = j1 + n_shift - (1 - s0)*self.n_orbs*(self.n_spins - 1)\\n eigvecs = np.array(\\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\\n )\\n if self.use_dipole_term:\\n eigvals = np.array(\\n self.file_storage['eigensystem']['eigenvalues'][idx_1]\\n )\\n else:\\n eigvals = None\\n for c, v in product(range(c_num), range(v_num)):\\n cb_vector = eigvecs[:, v_min + v_num + c]\\n vb_vector = eigvecs[:, v_min + v]\\n\\n matrix_elem = velocity_matrix_element(cb_vector,\\n vb_vector,\\n velocity_matrix)\\n if self.use_dipole_term:\\n cb_energy = eigvals[v_min + v_num + c]\\n vb_energy = eigvals[v_min + v]\\n position_dipole_term = get_position_dipole_element(\\n vb_vector,\\n cb_vector,\\n vb_energy,\\n cb_energy,\\n position_dipole_matrix\\n )\\n matrix_elem = matrix_elem + position_dipole_term\\n velocity_elements.append(matrix_elem)\\n velocity_elements = np.array(velocity_elements, dtype=complex)\\n velocity_element_lists.append(velocity_elements)\\n\\n bse_eigsys = self.exciton_obj.get_bse_eigensystem_direct(solve=True)\\n for s0 in range(self.n_spins):\\n for idx_1 in range(len(bse_eigsys[s0][0])):\\n bse_vector = bse_eigsys[s0][1][:, idx_1]\\n bse_value = bse_eigsys[s0][0][idx_1]\\n energy_denominator = bse_value**energy_power\\n interact_elem = np.dot(bse_vector, velocity_element_lists[s0])\\n for idx_2, omega in enumerate(frequency_grid):\\n smearing = broad_fnc(bse_value, omega)\\n main_term = np.abs(interact_elem)**2/energy_denominator\\n output = prefactor*main_term*smearing\\n output_grid[idx_2] += output\\n\\n return frequency_grid, output_grid\",\n \"def non_interacting_conductivity(self,\\n sigma=0.04,\\n freq_range=(0.0, 6.0, 1000),\\n imag_dielectric=False,\\n broadening='lorentz'):\\n cell_area = np.abs(float(np.cross(self.a1, self.a2)))*len(self.k_grid)\\n broad_fnc = get_broadening_function(broadening, sigma)\\n\\n frequency_grid = np.linspace(*freq_range)\\n energy_increment = np.abs(frequency_grid[1] - frequency_grid[0])\\n num_freqs, min_freq = len(frequency_grid), min(frequency_grid)\\n output_grid = np.zeros(num_freqs)\\n\\n energy_power = 1 + int(imag_dielectric)\\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\\n prefactor = prefactor/cell_area\\n\\n # Assuming x polarisation for now\\n position_dipole_matrix = self.construct_position_dipole_matrix()\\n\\n n_shift = self.n_spins*self.n_orbs\\n for idx1, kpt in enumerate(self.k_grid):\\n for s0 in range(self.n_spins):\\n velocity_matrix = self.load_velocity_matrix(idx1)\\n bands = self.exciton_obj.get_number_conduction_valence_bands(\\n idx1, s0\\n )\\n v_num, c_num = bands\\n eigvals = np.array(\\n self.file_storage['eigensystem']['eigenvalues'][idx1]\\n )\\n\\n j1 = n_shift*idx1 + (self.n_spins - 1)*s0*self.n_orbs\\n j2 = j1 + self.n_orbs\\n eigvecs = np.array(\\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\\n )\\n for v, c in product(range(v_num), range(c_num)):\\n cb_vector = eigvecs[:, v_num + c]\\n vb_vector = eigvecs[:, v]\\n cb_energy, vb_energy = eigvals[v_num + c], eigvals[v]\\n energy_diff = (cb_energy - vb_energy)\\n\\n energy_diff_pow = energy_diff**energy_power\\n matrix_elem = velocity_matrix_element(vb_vector,\\n cb_vector,\\n velocity_matrix)\\n if self.use_dipole_term:\\n position_dipole_term = get_position_dipole_element(\\n vb_vector,\\n cb_vector,\\n vb_energy,\\n cb_energy,\\n position_dipole_matrix\\n )\\n matrix_elem = matrix_elem + position_dipole_term\\n\\n main_term = np.abs(matrix_elem)**2/energy_diff_pow\\n closest_idx = (energy_diff - min_freq)//energy_increment\\n idx_reach = self.reach_multiplier*(sigma//energy_increment)\\n\\n lower_idx = max([closest_idx - idx_reach, 0])\\n upper_idx = min([closest_idx + idx_reach, num_freqs - 1])\\n\\n for idx2 in range(int(lower_idx), int(upper_idx)):\\n smearing = broad_fnc(energy_diff, frequency_grid[idx2])\\n output = prefactor*main_term*smearing\\n output_grid[idx2] += output\\n\\n return frequency_grid, output_grid\",\n \"def EC(f):\\n return dmp_ground_EC(f.rep, f.lev, f.dom)\",\n \"def coreMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:\\n graph = self.group.collectiveEnzymeGraphByEcMajority(majorityPercentage = majorityPercentageCoreMetabolism, majorityTotal = None, noMultifunctional = excludeMultifunctionalEnzymes)\\n graph.name = 'Core metabolism Enzymes ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def getNumNestedCVTerms(self):\\n return _libsbml.CVTerm_getNumNestedCVTerms(self)\",\n \"def edges(self):\\n return [(a, b) for a in self._consequences_of\\n for b in self._consequences_of[a]]\",\n \"def euclid_ccl(Omega_c, sigma8=0.83):\\n\\n cosmo_fid = ccl.Cosmology(Omega_c=Omega_c, Omega_b=0.045, h=0.71, sigma8=sigma8, n_s=0.963)\\n\\n dNdzs = np.zeros((nbins, z.size))\\n shears = []\\n \\n for i in range(nbins):\\n # edges of 1 equal width redshift bins, between 0 and 2\\n zmin, zmax = i*(2./nbins), (i+1)*(2./nbins)\\n # generate dNdz per bin\\n dNdzs[i,:] = ccl.dNdz_tomog(z=z, zmin=zmin, zmax=zmax, pz_func=pz, dNdz_func = dNdz_true)\\n # calculate the shear per bin\\n gal_shapes = ccl.WeakLensingTracer(cosmo_fid, dndz=(z, dNdzs[i,:]))\\n shears.append(gal_shapes)\\n \\n print ('Shears calculated, calculating power spectra now...')\\n # calculate nbin*(nbin+1)/2 = 1 spectra from the shears\\n Cls = []\\n for i in range(nbins):\\n for j in range(0,i+1):\\n Cls.append(ccl.angular_cl(cosmo_fid, shears[i], shears[j], ells))\\n \\n return np.array(Cls), dNdzs\",\n \"def getEntrophy(df):\\r\\n dct = group(df)\\r\\n n = len(df)\\r\\n ent = [0]*len(dct)\\r\\n j = 0\\r\\n for col in dct.keys(): \\r\\n for colVal in dct[col].keys():\\r\\n det = 0 \\r\\n aux = np.zeros(len(dct[col][colVal].keys()))\\r\\n i = 0 \\r\\n for clas in dct[col][colVal].keys():\\r\\n det+= dct[col][colVal][clas]\\r\\n aux[i] = dct[col][colVal][clas]\\r\\n i+=1\\r\\n aux = 1/det*aux\\r\\n aux_copy = aux.copy()\\r\\n aux = np.log2(aux) \\r\\n s = sum(np.multiply(aux_copy,aux)) \\r\\n ent[j] += (-1*det/n*s)\\r\\n j+=1 \\r\\n return (ent,dct)\",\n \"def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:\\n # get neofunctionalisations\\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100)) \\n return NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)).getNeofunctionalisationsForFunctionChange(minimumOrganismsCount = minimumOrganismsCount)\",\n \"def neopentane():\\n coords = [\\n [0.000000, 0.0, 0.0],\\n [0.881905, 0.881905, 0.881905],\\n [-0.881905, -0.881905, 0.881905],\\n [0.881905, -0.881905, -0.881905],\\n [-0.881905, 0.881905, -0.881905],\\n [-1.524077, 0.276170, -1.524077],\\n [1.524077, 1.524077, 0.276170],\\n [1.524077, -0.276170, -1.524077],\\n [1.524077, 0.276170, 1.524077],\\n [-1.524077, -0.276170, 1.524077],\\n [1.524077, -1.524077, -0.276170],\\n [-0.276170, 1.524077, -1.524077],\\n [0.276170, 1.524077, 1.524077],\\n [0.276170, -1.524077, -1.524077],\\n [-0.276170, -1.524077, 1.524077],\\n [-1.524077, 1.524077, -0.276170],\\n [-1.524077, -1.524077, 0.276170],\\n ]\\n coords = [[float(j) / Bohr for j in i] for i in coords]\\n\\n symbols = [\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n ]\\n\\n atoms = []\\n for i, _ in enumerate(coords):\\n atoms.append(Atom(symbols[i], position=coords[i]))\\n return Molecule(symbols=atoms)\",\n \"def solve_csp(nodes, arcs, max_steps):\\n\\n nodes = list(nodes)\\n print 'nodes:', nodes\\n\\n node_values_dict = dict(zip(nodes, '2'*len(set(nodes))))\\n print 'initial random assignment', node_values_dict\\n indexes = np.arange(len(nodes))\\n\\n graph = {}\\n for arc in arcs:\\n if not arc[0] in graph:\\n graph[arc[0]] = []\\n if not arc[1] in graph:\\n graph[arc[1]] = []\\n graph[arc[0]].append(arc[1])\\n graph[arc[1]].append(arc[0])\\n for i in indexes:\\n if i in graph:\\n continue\\n else:\\n graph[i] = []\\n graph = dict(sorted(graph.items()))\\n print 'graph:', graph\\n\\n domain = [i for i in np.arange(1, 10, 1)]\\n print 'initial domain for each node:', domain\\n\\n superAdjacency ={}\\n for i in np.arange(len(nodes)):\\n superAdjacency[i] = []\\n superAdjacency[i].append(nodes[i])\\n superAdjacency[i].append(node_values_dict[nodes[i]])\\n superAdjacency[i].append(graph[i])\\n superAdjacency[i].append(domain)\\n\\n print 'superAdjacency', superAdjacency\\n\\n def getNodeType(superAdjacency, index):\\n return list(superAdjacency[index])[0]\\n\\n def getCurrentAssignment(superAdjacency, index):\\n return list(superAdjacency[index])[1]\\n\\n def getCurrentAssignmentForList(superAdjacency, indexList):\\n return [int(list(superAdjacency[index])[1]) for index in indexList]\\n\\n def getSolution(superAdjacency):\\n return [int(list(superAdjacency[index])[1]) for index in superAdjacency]\\n\\n def getNeighbours(superAdjacency, index):\\n return list(superAdjacency[index])[2]\\n\\n def getDomain(superAdjacency, index):\\n return list(superAdjacency[index])[3]\\n\\n def updateSuperAdjacency(superAdjacency, nodeType, newValue):\\n updateList =[]\\n for i in superAdjacency:\\n if str(getNodeType(superAdjacency, i)) == nodeType:\\n updateList.append(i)\\n for i in updateList:\\n superAdjacency[i][1] = newValue\\n\\n def isSolution():\\n return graphConstraints(superAdjacency)\\n\\n def graphConstraints(superAdjacency):\\n graphEval = []\\n\\n for index in superAdjacency:\\n neighbours = getNeighbours(superAdjacency, index)\\n nodeType = getNodeType(superAdjacency, index)\\n\\n if nodeType == 'T':\\n graphEval.append(int(str(eval(str(\\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]))\\n elif nodeType == 'C':\\n return 'NA'\\n elif nodeType == 'S':\\n graphEval.append(int(str(eval(str(\\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]))\\n elif nodeType == 'H':\\n graphEval.append(int(str(np.sum(\\n getCurrentAssignmentForList(superAdjacency, neighbours)))[0]))\\n if nodeType == 'P':\\n graphEval.append(int(str(np.sum(\\n getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]))\\n\\n currentAssignment = [item[1] for item in superAdjacency.values()]\\n difference = map(sub, currentAssignment, graphEval)\\n\\n if sum(difference) == 0:\\n return True\\n else:\\n return difference\\n\\n def findConflictVariable(superAdjacency, lastUpdateNode):\\n node_conflict_count = {}\\n for node in node_values_dict:\\n node_conflict_count[node] = 0\\n for index in superAdjacency:\\n neighbours = getNeighbours(superAdjacency, index)\\n nodeType = getNodeType(superAdjacency, index)\\n if nodeType == 'T':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != \\\\\\n int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n elif nodeType == 'S':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n elif nodeType == 'H':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n if nodeType == 'P':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n choices = [k for k, v in node_conflict_count.items() if v > 0]\\n if len(choices) > 0:\\n updateNode = random.choice(choices)\\n\\n if updateNode == lastUpdateNode:\\n choices.pop(choices.index(updateNode))\\n try:\\n lastUpdateNode = random.choice(choices)\\n return lastUpdateNode, lastUpdateNode\\n except:\\n return lastUpdateNode, lastUpdateNode\\n else:\\n lastUpdateNode = updateNode\\n return updateNode, lastUpdateNode\\n else:\\n return 'NA', 'NA'\\n\\n\\n\\n def valueForConflictedVariable(superAdjacency, var):\\n for index in superAdjacency:\\n nodeType = getNodeType(superAdjacency, index)\\n neighbours = getNeighbours(superAdjacency, index)\\n if not neighbours:\\n continue\\n elif str(nodeType) == str(var):\\n domain = getDomain(superAdjacency, index)\\n\\n choice = random.choice(domain)\\n if nodeType == 'T':\\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0])\\n elif nodeType == 'S':\\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0])\\n elif nodeType == 'H':\\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0])\\n if nodeType == 'P':\\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0])\\n\\n choice = int(choice)\\n if choice % 2 == 0:\\n return choice\\n else:\\n return choice\\n\\n def min_conflicts(nodes, arcs, max_steps):\\n lastUpdateNode = ''\\n for i in range(max_steps):\\n if isSolution() == True:\\n return\\n var, lastUpdateNode = findConflictVariable(superAdjacency, lastUpdateNode)\\n if var != 'NA':\\n value = valueForConflictedVariable(superAdjacency, var)\\n updateSuperAdjacency(superAdjacency, var, value)\\n node_values_dict[var] = value\\n else:\\n pass\\n\\n return\\n\\n min_conflicts(nodes, arcs, max_steps)\\n node_values = getSolution(superAdjacency)\\n return node_values\",\n \"def connect_cells(dfte,vari):\\n # Create the variabel cell for mother, grand mother and grand grand mother\\n if 'g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\\n if 'g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_parent_cell')\\n if 'g_g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\\n #give unique index to all cells\\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\\n vac=[];sc=[];uid = []\\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\\n for c,idx in enumerate(dfte['cell'].unique()):\\n dau = dfte.loc[dfte['cell']==idx]\\n pc = dau['parent_cell'].iloc[0]\\n mum = dfte.loc[dfte['cell']==pc]\\n gpc = dau['g_parent_cell'].iloc[0]\\n gmum = dfte.loc[dfte['cell']==gpc]\\n ggpc = dau['g_g_parent_cell'].iloc[0]\\n ggmum = dfte.loc[dfte['cell']==ggpc]\\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\\n gggmum = dfte.loc[dfte['cell']==gggpc]\\n fte = lambda x: x[['{}'.format(vari),'uid']].values\\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\\n vac.append(tmp[:,0])\\n uid.append(tmp[:,1])\\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})\",\n \"def _excitonic_reorg_energy(self, SS, AG, n):\\n \\n # SystemBathInteraction\\n sbi = AG.get_SystemBathInteraction()\\n # CorrelationFunctionMatrix\\n cfm = sbi.CC\\n \\n rg = 0.0\\n \\n # electronic states corresponding to single excited states\\n elst = numpy.where(AG.which_band == 1)[0]\\n for el1 in elst:\\n reorg = cfm.get_reorganization_energy(el1-1,el1-1)\\n for kk in AG.vibindices[el1]:\\n rg += ((SS[kk,n]**2)*(SS[kk,n]**2)*reorg)\\n return rg\",\n \"def remove_trivial_edges(self):\\n if self.E > 0:\\n valid = self.edges[:, 0] != self.edges[:, 1]\\n self.edges = self.edges[valid]\\n self.weights = self.weights[valid]\\n self.E = np.sum(valid)\\n return self.E\",\n \"def graph_eigenvalues(N, eigenvalues,interval):\\n (a,b) =interval\\n a=abs(a-b)\\n n=[]\\n E_numeric=[]\\n E_anlytic=[]\\n E_anlytic_harm=[]\\n \\n for i in range(N-1):\\n n.append(i)\\n E_numeric.append(eigenvalues[0,i])\\n E_anlytic.append((((math.pi)**2)*(i**2))/(a**2))\\n E_anlytic_harm.append(((1/2)**0.5)*((i)+(1/2)))\\n plt.plot(n,E_numeric,'b')\\n plt.plot(n,E_anlytic,'g')\\n plt.plot(n,E_anlytic_harm,'r')\\n plt.xlabel('n', fontsize=20, color='black')\\n plt.ylabel('E', fontsize=20, color='black')\\n plt.show()\\n return None\",\n \"def edges(self):\\n return self.generate_edges()\",\n \"def oim_node2vec_simple(\\n df,\\n df_feats,\\n num_inf=10,\\n sigma=4,\\n c=0.1,\\n epsilon=0.4,\\n num_repeats=15,\\n num_nodes_tim=-1,\\n oracle=tim,\\n):\\n logger_tlu.debug(\\\"Started Online Influence Maximization...\\\")\\n logger_tlu.debug(\\\"Setting parameters\\\")\\n num_feats = df_feats.shape[1]\\n num_edges_t = df.shape[0]\\n\\n # \\\"True\\\" probabilities - effectively our test set\\n true_weights = df[\\\"probab\\\"].copy()\\n\\n # b, M_inv - used by IMLinUCB\\n b = np.zeros((num_feats, 1))\\n m_inv = np.eye(num_feats, num_feats)\\n\\n # Returning these\\n s_best = []\\n reward_best = 0\\n u_e_best = []\\n rewards = []\\n rewards_edges = []\\n\\n for iter_oim in tqdm(\\n range(num_repeats),\\n desc=f\\\"OIM iters {num_edges_t} edges\\\",\\n leave=False,\\n file=sys.stderr,\\n ):\\n # ---- Step 1 - Calculating the u_e ----\\n theta = (m_inv @ b) / (sigma * sigma)\\n # xMx = (df_feats.values @ m_inv @ df_feats.T.values).clip(min=0)\\n\\n u_e = []\\n for i in range(num_edges_t):\\n x_e = df_feats.loc[i].values\\n xMx = x_e @ m_inv @ x_e.T # .clip(min=0)\\n u_e.append(np.clip(x_e @ theta + c * np.sqrt(xMx), 0, 1))\\n # u_e.append(expit(x_e @ theta + c * np.sqrt(xMx)))\\n\\n u_e = np.array(u_e)\\n\\n # ---- Step 2 - Evaluating the performance ----\\n # Loss function\\n df[\\\"probab\\\"] = u_e\\n s_oracle = sorted(\\n oracle(\\n df[[\\\"source\\\", \\\"target\\\", \\\"probab\\\"]],\\n num_nodes_tim,\\n num_edges_t,\\n num_inf,\\n epsilon,\\n )\\n )\\n\\n # Observing edge-level feedback\\n df[\\\"probab\\\"] = true_weights\\n\\n algo_act_nodes, algo_act_edges, algo_obs_edges = run_ic_eff(df, s_oracle)\\n\\n algo_num_nodes = len(algo_act_nodes)\\n algo_num_edges = len(algo_act_edges)\\n\\n rewards.append(algo_num_nodes)\\n rewards_edges.append(algo_num_edges)\\n\\n logger_tlu.debug(f\\\"Algo seeds: {s_oracle}\\\")\\n logger_tlu.debug(f\\\"Algo reward: {algo_num_nodes}\\\")\\n logger_tlu.debug(f\\\"Best algo reward: {reward_best}\\\")\\n logger_tlu.debug(f\\\"Rewards: {rewards}\\\")\\n logger_tlu.debug(f\\\"Edge rewards: {rewards_edges}\\\")\\n logger_tlu.debug(f\\\"Algo weights {u_e[80:90]}\\\".replace(\\\"\\\\n\\\", \\\"\\\"))\\n logger_tlu.debug(f\\\"Real weights {true_weights[80:90]}\\\".replace(\\\"\\\\n\\\", \\\"\\\"))\\n\\n if algo_num_nodes > reward_best:\\n reward_best = algo_num_nodes\\n s_best = s_oracle\\n u_e_best = u_e\\n\\n # ---- Step 3 - Calculating updates ----\\n for i in algo_obs_edges:\\n x_e = np.array([df_feats.loc[i].values])\\n m_inv -= (m_inv @ x_e.T @ x_e @ m_inv) / (\\n x_e @ m_inv @ x_e.T + sigma * sigma\\n )\\n b += x_e.T * int(i in algo_act_edges)\\n\\n return_dict = {\\n \\\"rewards\\\": rewards,\\n \\\"rewards_edges\\\": rewards_edges,\\n \\\"s_best\\\": s_best,\\n \\\"u_e_best\\\": u_e_best,\\n \\\"reward_best\\\": reward_best,\\n }\\n logger_tlu.debug(\\\"The algorithm has finished running.\\\")\\n logger_tlu.debug(f\\\"Returning: {return_dict}\\\")\\n return return_dict\",\n \"def _generateEdges(self, separate_frames=False):\\n\\n # 3D edges\\n Xe = []\\n Ye = []\\n Ze = []\\n\\n if not separate_frames:\\n if self.showcuts:\\n self.nxgraph.add_nodes_from(range(len(self.df)))\\n for index, curr in self.df.iterrows():\\n if curr['first']:\\n self._symbol += ['circle']\\n # skip root node\\n if curr['number'] == 1:\\n continue\\n # found first LP solution of a new child node\\n # parent is last LP of parent node\\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\\n else:\\n # found an improving LP solution at the same node as before\\n self._symbol += ['diamond']\\n parent = self.df.iloc[index - 1]\\n\\n Xe += [float(parent['x']), curr['x'], None]\\n Ye += [float(parent['y']), curr['y'], None]\\n Ze += [float(parent['objval']), curr['objval'], None]\\n self.nxgraph.add_edge(parent.name, curr.name)\\n else:\\n self.nxgraph.add_nodes_from(list(self.df['number']))\\n for index, curr in self.df.iterrows():\\n self._symbol += ['circle']\\n if curr['number'] == 1:\\n continue\\n parent = self.df[self.df['number'] == curr['parent']]\\n Xe += [float(parent['x']), curr['x'], None]\\n Ye += [float(parent['y']), curr['y'], None]\\n Ze += [float(parent['objval']), curr['objval'], None]\\n self.nxgraph.add_edge(parent.iloc[0]['number'], curr['number'])\\n\\n else:\\n max_age = self.df['age'].max()\\n for i in range(1, max_age + 1):\\n tmp = self.df[self.df['age'] == i]\\n Xe_ = []\\n Ye_ = []\\n Ze_ = []\\n for index, curr in tmp.iterrows():\\n if curr['first']:\\n self._symbol += ['circle']\\n # skip root node\\n if curr['number'] == 1:\\n continue\\n # found first LP solution of a new child node\\n # parent is last LP of parent node\\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\\n else:\\n # found an improving LP solution at the same node as before\\n self._symbol += ['diamond']\\n parent = self.df.iloc[index - 1]\\n\\n Xe_ += [float(parent['x']), curr['x'], None]\\n Ye_ += [float(parent['y']), curr['y'], None]\\n Ze_ += [float(parent['objval']), curr['objval'], None]\\n Xe.append(Xe_)\\n Ye.append(Ye_)\\n Ze.append(Ze_)\\n\\n self.Xe = Xe\\n self.Ye = Ye\\n self.Ze = Ze\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62724626","0.6260132","0.6220738","0.62084836","0.61106306","0.5663413","0.5543432","0.5502784","0.5501765","0.5500046","0.54863024","0.54842806","0.54061544","0.5403373","0.5400027","0.53941774","0.5352519","0.5325761","0.53250736","0.53149056","0.53115666","0.5306996","0.526918","0.5265991","0.5254259","0.5243553","0.52290905","0.52282494","0.5224716","0.52055657","0.52005386","0.51941615","0.5193339","0.5188282","0.51864666","0.51854265","0.51760507","0.5174955","0.5171417","0.5168517","0.5159051","0.5158999","0.5153718","0.51382506","0.5137737","0.5136095","0.51352763","0.51339185","0.51217055","0.51175123","0.5113538","0.5102049","0.51015127","0.51007175","0.5093736","0.5092021","0.5091709","0.5077755","0.5074246","0.5073924","0.5066322","0.5065321","0.5065001","0.5064112","0.50583357","0.50556606","0.5043079","0.5041697","0.5040193","0.50386786","0.5038284","0.50368273","0.5031379","0.5027421","0.5027298","0.5023644","0.50201845","0.50157785","0.50120676","0.50112164","0.50008345","0.5000306","0.49989903","0.49970567","0.49967635","0.49959135","0.4984377","0.4980188","0.49798447","0.4979192","0.49770603","0.49748465","0.4965421","0.49608988","0.495963","0.4958558","0.4954571","0.49496815","0.4948998","0.4948781"],"string":"[\n \"0.62724626\",\n \"0.6260132\",\n \"0.6220738\",\n \"0.62084836\",\n \"0.61106306\",\n \"0.5663413\",\n \"0.5543432\",\n \"0.5502784\",\n \"0.5501765\",\n \"0.5500046\",\n \"0.54863024\",\n \"0.54842806\",\n \"0.54061544\",\n \"0.5403373\",\n \"0.5400027\",\n \"0.53941774\",\n \"0.5352519\",\n \"0.5325761\",\n \"0.53250736\",\n \"0.53149056\",\n \"0.53115666\",\n \"0.5306996\",\n \"0.526918\",\n \"0.5265991\",\n \"0.5254259\",\n \"0.5243553\",\n \"0.52290905\",\n \"0.52282494\",\n \"0.5224716\",\n \"0.52055657\",\n \"0.52005386\",\n \"0.51941615\",\n \"0.5193339\",\n \"0.5188282\",\n \"0.51864666\",\n \"0.51854265\",\n \"0.51760507\",\n \"0.5174955\",\n \"0.5171417\",\n \"0.5168517\",\n \"0.5159051\",\n \"0.5158999\",\n \"0.5153718\",\n \"0.51382506\",\n \"0.5137737\",\n \"0.5136095\",\n \"0.51352763\",\n \"0.51339185\",\n \"0.51217055\",\n \"0.51175123\",\n \"0.5113538\",\n \"0.5102049\",\n \"0.51015127\",\n \"0.51007175\",\n \"0.5093736\",\n \"0.5092021\",\n \"0.5091709\",\n \"0.5077755\",\n \"0.5074246\",\n \"0.5073924\",\n \"0.5066322\",\n \"0.5065321\",\n \"0.5065001\",\n \"0.5064112\",\n \"0.50583357\",\n \"0.50556606\",\n \"0.5043079\",\n \"0.5041697\",\n \"0.5040193\",\n \"0.50386786\",\n \"0.5038284\",\n \"0.50368273\",\n \"0.5031379\",\n \"0.5027421\",\n \"0.5027298\",\n \"0.5023644\",\n \"0.50201845\",\n \"0.50157785\",\n \"0.50120676\",\n \"0.50112164\",\n \"0.50008345\",\n \"0.5000306\",\n \"0.49989903\",\n \"0.49970567\",\n \"0.49967635\",\n \"0.49959135\",\n \"0.4984377\",\n \"0.4980188\",\n \"0.49798447\",\n \"0.4979192\",\n \"0.49770603\",\n \"0.49748465\",\n \"0.4965421\",\n \"0.49608988\",\n \"0.495963\",\n \"0.4958558\",\n \"0.4954571\",\n \"0.49496815\",\n \"0.4948998\",\n \"0.4948781\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61469126"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":17442,"cells":{"query":{"kind":"string","value":"Two SubstanceEC graphs of \"neofunctionalised\" EC numbers, derived from the diverged core metabolism."},"document":{"kind":"string","value":"def divergedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\n divergedMetabolism = self.divergedMetabolism(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = divergedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = divergedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\n childGraph = divergedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\n \n parentGraph.name = 'Diverged metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is False:\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\n \n else:\n unifiedMetabolism = self.unifiedMetabolism(majorityPercentageCoreMetabolism, colour = True)\n \n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = unifiedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph","def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\n conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)\n \n parentNeofunctionalised= self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = conservedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = conservedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\n childGraph = conservedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\n \n parentGraph.name = 'Conserved metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n childGraph = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(addedECs)\n childGraph.name = 'Added metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return childGraph","def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:\n # get neofunctionalisations \n neofunctionalisedECs = NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs))\n \n # filter core metabolism EC graph\n coreMetabolism = self.coreMetabolism(majorityPercentageCoreMetabolism)\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))\n \n neofunctionalisedMetabolism = neofunctionalisedECs.filterGraph(coreMetabolism, minimumEcDifference = None, minimumOrganismsCount = minimumOrganismsCount)\n \n # colour core metabolism\n if colour is not False:\n \n if colour is True:\n colourToUse = Export.Colour.GREEN\n else:\n colourToUse = colour\n \n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\n neofunctionalisedMetabolism = coreMetabolism\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\n \n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism ' + ' '.join(self.ncbiNames)\n \n return neofunctionalisedMetabolism","def lostMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n parentGraph = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(lostECs)\n parentGraph.name = 'Lost metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return parentGraph","def get_ecg_graph():\n titles = ['ecg1', 'ecg2', 'ecg3']\n colors = ['rgb(240,0,0)', 'rgb(0,240,0)', 'rgb(0,0,240)']\n update()\n signames_ecg = queries['signames_ecg']\n signals = queries['signals']\n latesthr = queries['latesthr']\n return html.Div(className='ecg', children=[\n html.Div(style={'display': 'flex', 'height': '40vh'},\n children=[dcc.Graph(\n id=titles[i] + signame,\n style={'width': '100%'},\n figure={\n 'data': [\n {'x': signals[signame]['time'],\n 'y': signals[signame][titles[i]],\n 'mode': 'line', 'name': signame, 'line': {'color':colors[i]}}\n ],\n 'layout': {\n 'font': {'color':'#fff'},\n 'title': '{}-{}'.format(signame, titles[i]),\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\n 'yaxis': {'title': 'voltage (mv)', 'color': '#fff', 'showgrid': 'False', 'range': np.linspace(-2.5, 2.5, 10)},\n 'paper_bgcolor':'#000', 'plot_bgcolor':'#000'\n }\n }\n ) for i in range(len(titles))]\n +\n [html.Div(\n style={'justify-content': 'center', 'display': 'flex',\n 'align-items': 'center', 'width': '10vh', 'font-size': '30pt', 'color': 'white'},\n children=['{}'.format(latesthr[signame][0])])\n ]\n ) for signame in signames_ecg])","def redundantECsForContributingNeofunctionalisation(self, \n majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, \n majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, \n eValue = defaultEValue, \n redundancyType: 'RedundancyType' = None,\n considerOnlyECs = None) -> Dict[Neofunctionalisation, Set[EcNumber]]:\n from FEV_KEGG.Robustness.Topology.Redundancy import Redundancy, RedundancyContribution, RedundancyType\n \n if redundancyType is None:\n redundancyType = RedundancyType.default\n \n #- calculate \"neofunctionalised\" ECs\n neofunctionalisedMetabolismSet = self.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs).getECs()\n neofunctionalisationsForFunctionChange = self.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs)\n \n #- calculate redundancy\n redundancy = Redundancy( self.coreMetabolism(majorityPercentageCoreMetabolism) )\n redundancyContribution = RedundancyContribution(redundancy, neofunctionalisedMetabolismSet)\n \n contributedECsForContributingNeofunctionalisedEC = redundancyContribution.getContributedKeysForSpecial(redundancyType)\n contributingNeofunctionalisedECs = set(contributedECsForContributingNeofunctionalisedEC.keys())\n \n #- REPEAT for each function change consisting of \"neofunctionalised\" ECs, which also contribute to redundancy\n contributingNeofunctionalisations = dict()\n \n for functionChange, neofunctionalisations in neofunctionalisationsForFunctionChange.items():\n #- report enzyme pairs of neofunctionalisations, which caused the EC to be considered \"neofunctionalised\", and are in return contributing to redundancy \n \n if functionChange.ecA in contributingNeofunctionalisedECs or functionChange.ecB in contributingNeofunctionalisedECs: # function change contributes to redundancy\n \n for neofunctionalisation in neofunctionalisations:\n currentSetOfContributedECs = contributingNeofunctionalisations.get(neofunctionalisation, None)\n \n if currentSetOfContributedECs is None:\n currentSetOfContributedECs = set()\n contributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs\n \n for ec in functionChange.ecPair:\n contributedECs = contributedECsForContributingNeofunctionalisedEC.get(ec, None)\n if contributedECs is not None:\n currentSetOfContributedECs.update(contributedECs)\n \n return contributingNeofunctionalisations","def uncertainty_ee(self,e1,e2):\n # reco\n unc = (self._eleRecoWeight[(e1.pt(),e1.eta())][1]/self._eleRecoWeight[(e1.pt(),e1.eta())][0] + \\\n self._eleRecoWeight[(e2.pt(),e2.eta())][1]/self._eleRecoWeight[(e2.pt(),e2.eta())][0])**2\n # id-isolation\n unc += (self._eleIdIsoWeight[(e1.pt(),e1.eta())][1]/self._eleIdIsoWeight[(e1.pt(),e1.eta())][0] + \\\n self._eleIdIsoWeight[(e2.pt(),e2.eta())][1]/self._eleIdIsoWeight[(e2.pt(),e2.eta())][0])**2\n # trigger (approximate)\n unc += (abs(self._ele8TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][1]+ \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][1]*self._ele8TrgWeight[(e2.pt(),e2.eta())][0]- \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][1]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]- \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][1])/ \\\n (self._ele8TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]+ \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele8TrgWeight[(e2.pt(),e2.eta())][0]- \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]))**2\n unc += ((self._ele8TrgWeight[(e1.pt(),e1.eta())][1]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]+ \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele8TrgWeight[(e2.pt(),e2.eta())][1])/ \\\n (self._ele8TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]+ \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele8TrgWeight[(e2.pt(),e2.eta())][0]- \\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]))**2\n #outcome\n return sqrt(unc)","def algo_Euclide_etendu(a,b):\n r1=a\n u1=1\n v1=0\n r2=b\n u2=0\n v2=1\n while r2!=0 : #Invariants de boucle : r1=u1*a+v1*b et r2=u2*a+v2*b\n q=r1//r2\n rs=r1 ; us=u1 ; vs=v1 #Variables de sauvegarde\n r1=r2 ; u1=u2 ; v1=v2\n r2 = rs - q*r2 # r2 <- Reste de la division euclidienne de r1 par r2\n u2 = us - q*u2\n v2 = vs - q*v2\n return(r1,u1,v1) #On prend le dernier reste non nul.","def intersectConics(E1, E2):\n\n P = np.array([])\n r1 = matrix_rank(E1)\n r2 = matrix_rank(E2)\n \n if(r1==3 and r2==3):\n P = completeIntersection(E1,E2) \n else:\n if (r2 < 3): #E2 is degenerate\n defE = E2\n fullE = E1\n else:\n defE = E1 #E1 is degenerate\n fullE = E2\n m, l = decomposeDegenerateConic(defE)\n P1 = intersectConicLine(fullE,m)\n P2 = intersectConicLine(fullE,l)\n P = np.array([P1, P2])\n points_x = []\n points_y = []\n for i in range(2):\n P1 = P[i]\n if(P1.size!=0):\n for j in range(P1.shape[0]):\n points_x.append(P1[j,0]/P1[j,2])\n points_y.append(P1[j,1]/P1[j,2])\n return points_x, points_y","def sub_graph_merging(self):","def betweenness_pol(G, ms):\n dict_eb = nx.edge_betweenness_centrality(G, k = int(0.75*len(G)))\n #n_pivots = min(1000, len(G))\n #dict_eb = nx.edge_betweenness_centrality(G, k=n_pivots)\n \n cut_edges = []\n rest_edges = []\n \n BCC_dist = []\n\n for e in G.edges():\n s, t = e\n\n if ms[s] != ms[t]:\n cut_edges += [e]\n else:\n rest_edges += [e]\n\n cut_ebc = [dict_eb[e] for e in cut_edges]\n rest_ebc = [dict_eb[e] for e in rest_edges]\n \n if len(cut_ebc) <= 1:\n print(\"Error in the gap!\")\n return -1\n \n kernel_for_cut = scipy.stats.gaussian_kde(cut_ebc, \"silverman\")\n kernel_for_rest = scipy.stats.gaussian_kde(rest_ebc, \"silverman\")\n\n BCC = []\n \n for _ in range(10):\n cut_dist = kernel_for_cut.resample(10000)[0]\n rest_dist = kernel_for_rest.resample(10000)[0]\n\n cut_dist = [max(0.00001, value) for value in cut_dist]\n rest_dist = [max(0.00001, value) for value in rest_dist]\n\n kl_divergence = scipy.stats.entropy(rest_dist, cut_dist)\n\n BCCval = 1-2.71828**(-kl_divergence)\n \n BCC.append(BCCval)\n \n return sum(BCC)/len(BCC)","def cell_edges2d_cartesian(self, axis2):","def plot_ecdf(self, variant_one, variant_two):\n if variant_one == variant_two:\n raise ValueError('variant_one and variant_two cannot be the same')\n if variant_one not in self.posteriors.keys() or \\\n variant_two not in self.posteriors.keys():\n raise ValueError(('Variants must only be a value in column '\n '{}'.format(self.bucket_col_name)))\n\n if variant_one in self.ecdf.keys() and \\\n variant_two in self.ecdf[variant_one].keys():\n self._plot_ecdf(numerator_name=variant_one,\n denominator_name=variant_two)\n plt.ylabel('Cumulative Lift: {0} vs {1}'\n .format(variant_two, variant_one))\n else:\n self._plot_ecdf(numerator_name=variant_two,\n denominator_name=variant_one)\n plt.ylabel('Cumulative Lift: {0} vs {1}'\n .format(variant_one, variant_two))","def __repr__(self):\n return \"EC(%s, %s)\" % (str(self.coefficient), repr(self.basefield))","def division_euclidienne(n1, n2):","def question_2():\r\n comparison_graph = er_algorithm(1000, random.uniform(0, 1))\r\n in_degree_dist = utility_graph.in_degree_distribution(comparison_graph)\r\n normalized_dist = utility_graph.normalize_distribution(in_degree_dist)\r\n\r\n utility_graph.plot_log_log_scatter(normalized_dist,\r\n 'ER Algorithm In-degree Distribution',\r\n 'in-degree log-base-10',\r\n 'normalized distribution log-base-10')","def CS2e(Type=\"DFA\"):\n CC50, CC51, CC52, CC53 = state('CC50'), state('CC51'), state('CC52'), state('CC53')\n for i in sigma:\n CC52.transit[i] = CC52\n CC53.transit[i] = CC53\n for i in sigma_1:\n CC50.transit[i] = CC50\n for i in sigma_2:\n CC50.transit[i] = CC53\n CC51.transit[i] = CC51\n for i in sigma_cc:\n CC50.transit[i] = CC51\n CC51.transit[i] = CC51\n for i in sigma_ncc:\n CC51.transit[i] = CC50\n for i in sigma_B_A:\n CC51.transit[i] = CC50\n for i in sigma_e:\n CC51.transit[i] = CC53\n for i in sigma_A:\n CC51.transit[i] = CC52\n if Type == \"pDFA\":\n CC5 = pDFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\n else:\n CC5 = DFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\n if (SIZEOF):\n EM_size[\"CS2e\"] = asizeof.asizeof(CC5)\n return CC5","def test_efficiency_disconnected_nodes(self):\n assert_equal(nx.efficiency(self.G1, 1, 2), 0)","def _nelec(self):\n pd = self.particle_distribution(self._gam * mec2)\n return pd.to(1/mec2_unit).value","def eccentricity(self):\n new_data = self._data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\n 'pl_masse', 'pl_orbeccen',\n 'pl_radj', 'pl_dens', 'st_teff',\n 'st_mass', 'st_rad']]\n new_data = new_data.dropna()\n\n features = new_data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\n 'pl_masse',\n 'pl_radj', 'pl_dens', 'st_teff',\n 'st_mass', 'st_rad']]\n labels = new_data['pl_orbeccen']\n\n features_train, features_test, labels_train, labels_test = \\\n train_test_split(features, labels, test_size=0.2)\n\n # Create an untrained model\n model = DecisionTreeRegressor()\n\n # Train it on the **training set**\n model.fit(features_train, labels_train)\n\n # Compute test accuracy\n test_predictions = model.predict(features_test)\n test_acc = mean_absolute_error(labels_test, test_predictions)\n test_acc_r2 = r2_score(labels_test, test_predictions)\n\n # Plot ML vs Actual\n fig, [ax1, ax2] = plt.subplots(2, figsize=(15, 12))\n\n sns.distplot(test_predictions, kde=False, ax=ax1)\n sns.distplot(labels_test, kde=False, ax=ax2)\n\n ax1.set_title('Distribution of Predicted Eccentricities of Orbits')\n ax1.set_xlabel('Eccentricity of Orbit')\n ax1.set_ylabel('Number of Planets')\n\n ax2.set_title('Distribution of Actual Eccentricities of Orbits')\n ax2.set_xlabel('Eccentricity of Orbit')\n ax2.set_ylabel('Number of Planets')\n\n plt.savefig('figures/ML_Eccentricity.png', bbox_inches='tight')\n\n return (test_acc, test_acc_r2)","def ordinal_difference(evs, evs2):\n \n sigma = calc_sigma(evs)\n \n # print sigma\n # ordinal difference\n ordDiff = zeros(vecLen-1, dtype='d')\n for j in range(vecLen-1):\n ordDiff[j] = absolute(evs[j] - evs2[j]) / sigma[j]\n del j\n return ordDiff","def set_ec(self, etacalc):\n if not self.__thermodyn:\n C = np.zeros((6,6))\n \n LC = self.__structures.items()[0][1].LC\n if self.__mthd == 'Energy':\n if type(etacalc)==list:\n A2=[]\n for i in range(len(etacalc)):\n A2.append(self.__A2[i][etacalc[i]])\n else:\n if not etacalc in self.__A2[0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[0].keys()))\n A2 = [a2[etacalc] for a2 in self.__A2]\n \n \n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'CI' or \\\n LC == 'CII'):\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\n C[1,1] = C[0,0]\n C[2,2] = C[0,0]\n C[3,3] = A2[2]/6.\n C[4,4] = C[3,3]\n C[5,5] = C[3,3]\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\n C[0,2] = C[0,1]\n C[1,2] = C[0,1]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[2]\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RI'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[1,4] =-C[0,4] \n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[3,5] =-C[0,4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TI'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[3]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TII'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\n C[1,1] = C[0,0]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\n C[1,2] = C[0,2]\n C[1,5] =-C[0,5]\n C[2,2] = 2.*A2[4]\n C[3,3] = .5*A2[5]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[6]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'O'):\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\n C[1,1] = 2.*A2[4]\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\n C[2,2] = 2.*A2[5]\n C[3,3] = .5*A2[6]\n C[4,4] = .5*A2[7]\n C[5,5] = .5*A2[8]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'M'):\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\n C[1,1] = 2.*A2[8]\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\n C[2,2] = 2.*A2[9]\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\n C[3,3] = .5*A2[10]\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\n C[4,4] = .5*A2[11]\n C[5,5] = .5*A2[12]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'N'):\n C[0,0] = 2.*A2[0]\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\n C[1,1] = 2.*A2[1]\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\n C[2,2] = 2.*A2[2] \n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\n C[3,3] = .5*A2[3]\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\n C[4,4] = .5*A2[4]\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n elif self.__mthd == 'Stress':\n \n if (LC == 'CI' or \\\n LC == 'CII'):\n Matrix = np.mat([[1.0, 5.0, 0.0],\n [2.0, 4.0, 0.0],\n [3.0, 3.0, 0.0],\n [0.0, 0.0, 4.0],\n [0.0, 0.0, 5.0],\n [0.0, 0.0, 6.0]])\n \n if (LC == 'HI' or \\\n LC == 'HII'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\n [ 2, 1, 3, 0, 0],\n [ 0, 0, 3, 3, 0],\n [ 0, 0, 0, 0, 4],\n [ 0, 0, 0, 0, 5],\n [ 3,-3, 0, 0, 0],\n [ 3,-5,-1, 0, 0],\n [-5, 3,-1, 0, 0],\n [ 0, 0,-2,-1, 0],\n [ 0, 0, 0, 0, 6],\n [ 0, 0, 0, 0, 2],\n [-2, 2, 0, 0, 0]])\n \n if (LC == 'RI'):\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\n [ 2, 1, 3,-4, 0, 0],\n [ 0, 0, 3, 0, 3, 0],\n [ 0, 0, 0,-1, 0, 4],\n [ 0, 0, 0, 6, 0, 5],\n [ 3,-3, 0, 5, 0, 0],\n [ 3,-5,-1, 6, 0, 0],\n [-5, 3,-1,-6, 0, 0],\n [ 0, 0,-2, 0,-1, 0],\n [ 0, 0, 0, 8, 0, 6],\n [ 0, 0, 0,-4, 0, 2],\n [-2, 2, 0, 2, 0, 0]])\n \n if (LC == 'RII'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\n [ 2, 1, 3,-4,-5, 0, 0],\n [ 0, 0, 3, 0, 0, 3, 0],\n [ 0, 0, 0,-1,-6, 0, 4],\n [ 0, 0, 0, 6,-1, 0, 5],\n [ 3,-3, 0, 5,-4, 0, 0],\n [ 3,-5,-1, 6, 2, 0, 0],\n [-5, 3,-1,-6,-2, 0, 0],\n [ 0, 0,-2, 0, 0,-1, 0],\n [ 0, 0, 0, 8, 4, 0, 6],\n [ 0, 0, 0,-4, 8, 0, 2],\n [-2, 2, 0, 2,-6, 0, 0]])\n \n if (LC == 'TI'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\n [ 2, 1, 3, 0, 0, 0],\n [ 0, 0, 3, 3, 0, 0],\n [ 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0, 0, 0, 6],\n [ 3,-5,-1, 0, 0, 0],\n [-5, 3,-1, 0, 0, 0],\n [ 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 0, 0,-4]])\n \n if (LC == 'TII'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\n [ 2, 1, 3,-6, 0, 0, 0],\n [ 0, 0, 3, 0, 3, 0, 0],\n [ 0, 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0,-1, 0, 0, 6],\n [ 3,-5,-1,-4, 0, 0, 0],\n [-5, 3,-1, 4, 0, 0, 0],\n [ 0, 0,-2, 0,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 8, 0, 0,-4]])\n \n if (LC == 'O'):\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\n \n if (LC == 'M'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\n \n if (LC == 'N'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\n \n sigma = np.array(self.__sigma[etacalc])\n \n ci = np.linalg.lstsq(Matrix,sigma)\n \n #-- Cubic structures ------------------------------------------------------------------------------\n if (LC == 'CI' or \\\n LC == 'CII'):\n \n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[3,3]=ci[0][2]\n C[1,1]=C[0,0]\n C[2,2]=C[0,0]\n C[0,2]=C[0,1]\n C[1,2]=C[0,1]\n C[4,4]=C[3,3]\n C[5,5]=C[3,3]\n \n #-- Hexagonal Structures --------------------------------------------------------------------------\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[2,2]=ci[0][3]\n C[3,3]=ci[0][4]\n C[1,1]=C[0,0]\n C[1,2]=C[0,2]\n C[4,4]=C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral I Structures ---------------------------------------------------------------------\n if (LC == 'RI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral II Structures --------------------------------------------------------------------\n if (LC == 'RII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[0,4]= ci[0][4]\n C[2,2]= ci[0][5]\n C[3,3]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[1,4]=-C[0,4]\n C[3,5]=-C[0,4]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Tetragonal I Structures -----------------------------------------------------------------------\n if (LC == 'TI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[2,2]= ci[0][3]\n C[3,3]= ci[0][4]\n C[5,5]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[4,4]= C[3,3]\n \n #-- Tetragonal II Structures ----------------------------------------------------------------------\n if (LC == 'TII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,5]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[5,5]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,5]=-C[0,5]\n C[4,4]= C[3,3]\n \n #-- Orthorhombic Structures -----------------------------------------------------------------------\n if (LC == 'O'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[1,1]=ci[0][3]\n C[1,2]=ci[0][4]\n C[2,2]=ci[0][5]\n C[3,3]=ci[0][6]\n C[4,4]=ci[0][7]\n C[5,5]=ci[0][8]\n \n #-- Monoclinic Structures -------------------------------------------------------------------------\n if (LC == 'M'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,5]=ci[0][3]\n C[1,1]=ci[0][4]\n C[1,2]=ci[0][5]\n C[1,5]=ci[0][6]\n C[2,2]=ci[0][7]\n C[2,5]=ci[0][8]\n C[3,3]=ci[0][9]\n C[3,4]=ci[0][10]\n C[4,4]=ci[0][11]\n C[5,5]=ci[0][12]\n \n #-- Triclinic Structures --------------------------------------------------------------------------\n if (LC == 'N'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,3]=ci[0][3]\n C[0,4]=ci[0][4]\n C[0,5]=ci[0][5]\n C[1,1]=ci[0][6]\n C[1,2]=ci[0][7]\n C[1,3]=ci[0][8]\n C[1,4]=ci[0][9]\n C[1,5]=ci[0][10]\n C[2,2]=ci[0][11]\n C[2,3]=ci[0][12]\n C[2,4]=ci[0][13]\n C[2,5]=ci[0][14]\n C[3,3]=ci[0][15]\n C[3,4]=ci[0][16]\n C[3,5]=ci[0][17]\n C[4,4]=ci[0][18]\n C[4,5]=ci[0][19]\n C[5,5]=ci[0][20]\n #--------------------------------------------------------------------------------------------------\n \n \n \n for i in range(5):\n for j in range(i+1,6):\n C[j,i] = C[i,j] \n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if self.__cod in ['espresso']: C = -C/10.\n elif self.__cod in ['vasp','exciting','wien']: C = C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0\n elif self.__cod in ['emto']: C = C*self.__ToGPa/self.__V0\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\n self.S = np.linalg.inv(C)\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\n self.BH = 0.50*(self.BV+self.BR)\n self.GH = 0.50*(self.GV+self.GR)\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\n #--------------------------------------------------------------------------------------------------------------------------------\n self.__C = C\n \n else:\n Cs = []\n for t in map(str,self.__T):#for t in range(len(self.__T)):\n C = np.zeros((6,6))\n \n LC = self.__structures.items()[0][1].LC\n if self.__mthd == 'Energy':\n if type(etacalc)==list:\n A2=[]\n for i in range(len(etacalc)):\n A2.append(self.__A2[t][i][etacalc[i]])\n else:\n if not etacalc in self.__A2[t][0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[t][0].keys()))\n A2 = [a2[etacalc] for a2 in self.__A2[t]]\n \n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'CI' or \\\n LC == 'CII'):\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\n C[1,1] = C[0,0]\n C[2,2] = C[0,0]\n C[3,3] = A2[2]/6.\n C[4,4] = C[3,3]\n C[5,5] = C[3,3]\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\n C[0,2] = C[0,1]\n C[1,2] = C[0,1]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[2]\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RI'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[1,4] =-C[0,4] \n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[3,5] =-C[0,4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TI'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[3]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TII'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\n C[1,1] = C[0,0]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\n C[1,2] = C[0,2]\n C[1,5] =-C[0,5]\n C[2,2] = 2.*A2[4]\n C[3,3] = .5*A2[5]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[6]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'O'):\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\n C[1,1] = 2.*A2[4]\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\n C[2,2] = 2.*A2[5]\n C[3,3] = .5*A2[6]\n C[4,4] = .5*A2[7]\n C[5,5] = .5*A2[8]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'M'):\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\n C[1,1] = 2.*A2[8]\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\n C[2,2] = 2.*A2[9]\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\n C[3,3] = .5*A2[10]\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\n C[4,4] = .5*A2[11]\n C[5,5] = .5*A2[12]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'N'):\n C[0,0] = 2.*A2[0]\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\n C[1,1] = 2.*A2[1]\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\n C[2,2] = 2.*A2[2] \n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\n C[3,3] = .5*A2[3]\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\n C[4,4] = .5*A2[4]\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n elif self.__mthd == 'Stress':\n \n if (LC == 'CI' or \\\n LC == 'CII'):\n Matrix = np.mat([[1.0, 5.0, 0.0],\n [2.0, 4.0, 0.0],\n [3.0, 3.0, 0.0],\n [0.0, 0.0, 4.0],\n [0.0, 0.0, 5.0],\n [0.0, 0.0, 6.0]])\n \n if (LC == 'HI' or \\\n LC == 'HII'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\n [ 2, 1, 3, 0, 0],\n [ 0, 0, 3, 3, 0],\n [ 0, 0, 0, 0, 4],\n [ 0, 0, 0, 0, 5],\n [ 3,-3, 0, 0, 0],\n [ 3,-5,-1, 0, 0],\n [-5, 3,-1, 0, 0],\n [ 0, 0,-2,-1, 0],\n [ 0, 0, 0, 0, 6],\n [ 0, 0, 0, 0, 2],\n [-2, 2, 0, 0, 0]])\n \n if (LC == 'RI'):\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\n [ 2, 1, 3,-4, 0, 0],\n [ 0, 0, 3, 0, 3, 0],\n [ 0, 0, 0,-1, 0, 4],\n [ 0, 0, 0, 6, 0, 5],\n [ 3,-3, 0, 5, 0, 0],\n [ 3,-5,-1, 6, 0, 0],\n [-5, 3,-1,-6, 0, 0],\n [ 0, 0,-2, 0,-1, 0],\n [ 0, 0, 0, 8, 0, 6],\n [ 0, 0, 0,-4, 0, 2],\n [-2, 2, 0, 2, 0, 0]])\n \n if (LC == 'RII'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\n [ 2, 1, 3,-4,-5, 0, 0],\n [ 0, 0, 3, 0, 0, 3, 0],\n [ 0, 0, 0,-1,-6, 0, 4],\n [ 0, 0, 0, 6,-1, 0, 5],\n [ 3,-3, 0, 5,-4, 0, 0],\n [ 3,-5,-1, 6, 2, 0, 0],\n [-5, 3,-1,-6,-2, 0, 0],\n [ 0, 0,-2, 0, 0,-1, 0],\n [ 0, 0, 0, 8, 4, 0, 6],\n [ 0, 0, 0,-4, 8, 0, 2],\n [-2, 2, 0, 2,-6, 0, 0]])\n \n if (LC == 'TI'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\n [ 2, 1, 3, 0, 0, 0],\n [ 0, 0, 3, 3, 0, 0],\n [ 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0, 0, 0, 6],\n [ 3,-5,-1, 0, 0, 0],\n [-5, 3,-1, 0, 0, 0],\n [ 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 0, 0,-4]])\n \n if (LC == 'TII'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\n [ 2, 1, 3,-6, 0, 0, 0],\n [ 0, 0, 3, 0, 3, 0, 0],\n [ 0, 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0,-1, 0, 0, 6],\n [ 3,-5,-1,-4, 0, 0, 0],\n [-5, 3,-1, 4, 0, 0, 0],\n [ 0, 0,-2, 0,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 8, 0, 0,-4]])\n \n if (LC == 'O'):\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\n \n if (LC == 'M'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\n \n if (LC == 'N'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\n \n sigma = np.array(self.__sigma[etacalc])\n \n ci = np.linalg.lstsq(Matrix,sigma)\n \n #-- Cubic structures ------------------------------------------------------------------------------\n if (LC == 'CI' or \\\n LC == 'CII'):\n \n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[3,3]=ci[0][2]\n C[1,1]=C[0,0]\n C[2,2]=C[0,0]\n C[0,2]=C[0,1]\n C[1,2]=C[0,1]\n C[4,4]=C[3,3]\n C[5,5]=C[3,3]\n \n #-- Hexagonal Structures --------------------------------------------------------------------------\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[2,2]=ci[0][3]\n C[3,3]=ci[0][4]\n C[1,1]=C[0,0]\n C[1,2]=C[0,2]\n C[4,4]=C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral I Structures ---------------------------------------------------------------------\n if (LC == 'RI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral II Structures --------------------------------------------------------------------\n if (LC == 'RII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[0,4]= ci[0][4]\n C[2,2]= ci[0][5]\n C[3,3]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[1,4]=-C[0,4]\n C[3,5]=-C[0,4]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Tetragonal I Structures -----------------------------------------------------------------------\n if (LC == 'TI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[2,2]= ci[0][3]\n C[3,3]= ci[0][4]\n C[5,5]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[4,4]= C[3,3]\n \n #-- Tetragonal II Structures ----------------------------------------------------------------------\n if (LC == 'TII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,5]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[5,5]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,5]=-C[0,5]\n C[4,4]= C[3,3]\n \n #-- Orthorhombic Structures -----------------------------------------------------------------------\n if (LC == 'O'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[1,1]=ci[0][3]\n C[1,2]=ci[0][4]\n C[2,2]=ci[0][5]\n C[3,3]=ci[0][6]\n C[4,4]=ci[0][7]\n C[5,5]=ci[0][8]\n \n #-- Monoclinic Structures -------------------------------------------------------------------------\n if (LC == 'M'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,5]=ci[0][3]\n C[1,1]=ci[0][4]\n C[1,2]=ci[0][5]\n C[1,5]=ci[0][6]\n C[2,2]=ci[0][7]\n C[2,5]=ci[0][8]\n C[3,3]=ci[0][9]\n C[3,4]=ci[0][10]\n C[4,4]=ci[0][11]\n C[5,5]=ci[0][12]\n \n #-- Triclinic Structures --------------------------------------------------------------------------\n if (LC == 'N'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,3]=ci[0][3]\n C[0,4]=ci[0][4]\n C[0,5]=ci[0][5]\n C[1,1]=ci[0][6]\n C[1,2]=ci[0][7]\n C[1,3]=ci[0][8]\n C[1,4]=ci[0][9]\n C[1,5]=ci[0][10]\n C[2,2]=ci[0][11]\n C[2,3]=ci[0][12]\n C[2,4]=ci[0][13]\n C[2,5]=ci[0][14]\n C[3,3]=ci[0][15]\n C[3,4]=ci[0][16]\n C[3,5]=ci[0][17]\n C[4,4]=ci[0][18]\n C[4,5]=ci[0][19]\n C[5,5]=ci[0][20]\n #--------------------------------------------------------------------------------------------------\n \n \n \n for i in range(5):\n for j in range(i+1,6):\n C[j,i] = C[i,j] \n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if self.__cod == 'espresso': C = -C/10.\n elif self.__cod in ['vasp','emto','exciting','wien']: C=C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0#C = C/4.\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\n self.S = np.linalg.inv(C)\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\n self.BH = 0.50*(self.BV+self.BR)\n self.GH = 0.50*(self.GV+self.GR)\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\n #--------------------------------------------------------------------------------------------------------------------------------\n Cs.append(C)\n self.__C = Cs","def NND_eta( eqCat, dConst, verbose = False, **kwargs):\n #-------------------------------set args and kwargs----------------------------------------------- \n rmax = 500 # in km\n tmax = 20 # in years\n M0 = 0\n if 'M0' in kwargs.keys() and kwargs['M0'] is not None:\n M0 = kwargs['M0']\n if 'rmax' in kwargs.keys() and kwargs['rmax'] is not None:\n rmax = kwargs['rmax']\n if 'tmax' in kwargs.keys() and kwargs['tmax'] is not None:\n tmax = kwargs['tmax']\n #-----------------------------add small uncertainty to X in case events are colocated-------------------------- \n if 'correct_co_located' in kwargs.keys() and kwargs['correct_co_located'] == True:\n vUncer = np.random.randn( eqCat.size())*1e-10\n eqCat.data['X'] += vUncer\n eqCat.data['Time'] += abs( vUncer)#time has to stay positive otherwise parent-offspring gets switched\n #------------------------------------------------------------------------------\n aNND = np.zeros( eqCat.size())\n vID_p = np.zeros( eqCat.size())\n vID_c = np.zeros( eqCat.size())\n deltaMag = (eqCat.data['Mag'] - M0)\n \n for jC in range( eqCat.size()):\n if verbose == True:\n print 'event %i of %i'%( jC+1, eqCat.size())\n # interevent times: take events that happend before t_i \n # child - parent > 0 \n tau = eqCat.data['Time'][jC] - eqCat.data['Time']\n sel_tau_par = tau > 0\n if sel_tau_par.sum() > 0:\n\n vcurr_ID = np.arange( eqCat.size(), dtype = int)[sel_tau_par]\n vR = np.sqrt( (eqCat.data['X'][jC] - eqCat.data['X'][vcurr_ID])**2 + (eqCat.data['Y'][jC] - eqCat.data['Y'][vcurr_ID])**2 )\n # haversine distance\n # = projUtils.haversine( eqCat.data['Lon'][jC], eqCat.data['Lat'][jC],eqCat.data['Lon'][curr_vID], eqCat.data['Lat'][curr_vID] ) \n sel_r_par = vR < rmax\n if sel_r_par.sum() > 0:\n vcurr_ID = vcurr_ID[sel_r_par]\n curr_Eta = tau[vcurr_ID]* (vR[sel_r_par]**dConst['D']) *( 10**(-dConst['b']*deltaMag[vcurr_ID]))\n sel_min = curr_Eta == curr_Eta.min()\n aNND[jC] = curr_Eta[sel_min][0]\n vID_p[jC] = eqCat.data['N'][vcurr_ID][sel_min][0]\n vID_c[jC] = eqCat.data['N'][jC]\n #print 'parent', eqCat.data['N'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['N'][jC]\n #print 'parent', eqCat.data['Time'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['Time'][jC]\n\n if sel_min.sum() > 1:\n print aNND[jC], curr_Eta[sel_min], eqCat.data['N'][vcurr_ID][sel_min]\n print eqCat.data['Lon'][vcurr_ID][sel_min], eqCat.data['Lat'][vcurr_ID][sel_min]\n print eqCat.data['X'][vcurr_ID][sel_min], eqCat.data['Y'][vcurr_ID][sel_min]\n sel2 = aNND > 0\n if np.logical_not(sel2).sum() > 0:\n print 'remove %i offspring without prior parents in catalog'%(np.logical_not(sel2).sum())\n #raise ValueError, error_str\n # remove events with aNND < 0; i.e. event at the beginning with no preceding parent\n return { 'aNND' : aNND[sel2], 'aEqID_p' : vID_p[sel2], 'aEqID_c' : vID_c[sel2]}","def connect_cells(dfte,vari):\n # Create the variabel cell for mother, grand mother and grand grand mother\n if 'g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\n if 'g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_parent_cell')\n if 'g_g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\n #give unique index to all cells\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\n vac=[];sc=[];uid = []\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\n for c,idx in enumerate(dfte['cell'].unique()):\n dau = dfte.loc[dfte['cell']==idx]\n pc = dau['parent_cell'].iloc[0]\n mum = dfte.loc[dfte['cell']==pc]\n gpc = dau['g_parent_cell'].iloc[0]\n gmum = dfte.loc[dfte['cell']==gpc]\n ggpc = dau['g_g_parent_cell'].iloc[0]\n ggmum = dfte.loc[dfte['cell']==ggpc]\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\n gggmum = dfte.loc[dfte['cell']==gggpc]\n fte = lambda x: x[['{}'.format(vari),'uid']].values\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\n vac.append(tmp[:,0])\n uid.append(tmp[:,1])\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})","def hr_egn(A, B, R, x0):\n # A - Adjacency matrix, np.ndarray (N,N)\n # B - A 2D or 3D matrix with all payoff matrices, np.ndarray (S,S,N)\n # R - Relationship or preference matrix, np.ndarray (N,N)\n # x0 - Initial state of our system, np.ndarray (N,S), must be double\n\n # Number of players\n N = A[:, 0].size\n # Number of strategies\n S = x0[0, :].size\n # Degree and degree of preferences\n d = np.zeros([N, 2])\n d[:, 0] = np.dot(A, np.ones(N))\n\n for v in range(N):\n d[v, 1] = np.dot(np.ceil(np.abs(R[v, :])), A[v, :])\n\n # Player v neighborhood\n k = np.zeros([N, S], dtype='double')\n for v in range(N):\n for u in range(N):\n k[v, :] = np.add(k[v, :], np.multiply(A[v, u], x0[u, :]))\n # Weights the neighborhood\n k[v, :] = np.multiply(np.divide(1, d[v, 0]), k[v, :])\n\n # This variable is the increments that x0 receives, the derivative\n x = np.zeros([N, S], dtype='double')\n # This is the unit vector with 1 in some entry\n es = np.zeros(S, dtype='int')\n\n # Phi and gamma\n p = 0\n g = 0\n\n # Auxiliary variables for better clarity\n aux1 = 0\n aux2 = 0\n\n # Here is the derivative calculation\n # We first test if all payoffs are the same so we do less comparisons\n if B.ndim == 2:\n for v in range(N):\n for s in range(S):\n # Set es value\n es[s] = 1\n for u in range(N):\n if v == u:\n # Same payoff personal equation\n # First we will do the dot products\n # e_s*B*k_v\n aux1 = np.dot(es, np.dot(B, k[v, :]))\n # x_v*B*k_v\n aux2 = np.dot(x0[v, :], np.dot(B, k[v, :]))\n # Finally we subtract them to multiply by r_vv\n p = np.multiply(R[v, u], np.subtract(aux1, aux2))\n elif A[v, u] != 0:\n # Same payoff social equation\n # x_u*B*e_s\n aux1 = np.dot(x0[u, :], np.dot(B, es))\n # x_u*B*x_v\n aux2 = np.dot(x0[u, :], np.dot(B, x0[v, :]))\n # Subtract then multiply\n aux1 = np.subtract(aux1, aux2)\n aux2 = np.multiply(R[v, u], A[v, u])\n g = np.add(g, np.multiply(aux2, aux1))\n # Weights the social part\n if d[v, 1] != 0:\n g = np.multiply(np.divide(1, d[v, 1]), g)\n # Estimates the derivative\n x[v, s] = np.multiply(x0[v, s], np.add(p, g))\n # Prepare variables to next iteration\n p = 0\n g = 0\n es[s] = 0\n else:\n for v in range(N):\n for s in range(S):\n # Same thing as before, but now with individual payoffs\n es[s] = 1\n for u in range(N):\n if v == u:\n # Individual payoffs personal equation\n # e_s*B_v*k_v\n aux1 = np.dot(es, np.dot(B[:, :, v], k[v, :]))\n # x_u*B_v*k_v\n aux2 = np.dot(x0[v, :], np.dot(B[:, :, v], k[v, :]))\n p = np.multiply(R[v, u], np.subtract(aux1, aux2))\n elif A[v, u] != 0:\n # Individual payoffs social equation\n # x_u*B_u*e_s\n aux1 = np.dot(x0[u, :], np.dot(B[:, :, u], es))\n # x_u*B_u*x_v\n aux2 = np.dot(x0[u, :], np.dot(B[:, :, u], x0[v, :]))\n # Subtract then multiply\n aux1 = np.subtract(aux1, aux2)\n aux2 = np.multiply(R[v, u], A[v, u])\n g = np.add(g, np.multiply(aux2, aux1))\n # Weights the social part\n if d[v, 1] != 0:\n g = np.multiply(np.divide(1, d[v, 1]), g)\n # Estimates the derivative\n x[v, s] = np.multiply(x0[v, s], np.add(p, g))\n # Prepare variables to next iteration\n p = 0\n g = 0\n es[s] = 0\n return x","def tao_BC_and_LEM_lines(epics):\n bc1_e0=epics.caget('SIOC:SYS0:ML00:AO483')*1e6\n bc2_e0=epics.caget('SIOC:SYS0:ML00:AO489')*1e9\n l3_e0 =epics.caget('SIOC:SYS0:ML00:AO500')*1e9\n \n # Charge in LTU\n q_after_horn_cutting = epics.caget('SIOC:SYS0:ML00:CALC252')*1e-12 # pC -> C\n bc1_offset=epics.caget('BMLN:LI21:235:MOTR')*1e-3\n bc2_offset=epics.caget('BMLN:LI24:805:MOTR')*1e-3\n \n bc1_current=epics.caget('SIOC:SYS0:ML00:AO485')\n bc2_current=epics.caget('SIOC:SYS0:ML00:AO195')\n \n # Catch bad settings\n if bc1_current==0:\n print('Warning: BC1 current is zero!')\n bc1_sigma_z = 0\n else:\n # Assumes parabolic distribution\n bc1_sigma_z = q_after_horn_cutting*299792458 / sqrt(10) / bc1_current\n\n if bc2_current==0:\n print('Warning: BC1 current is zero!')\n bc2_sigma_z = 0\n else:\n # Assumes Gaussian distribution\n bc2_sigma_z = q_after_horn_cutting*299792458 / sqrt(12) / bc2_current \n \n lines = []\n lines.append('set dat BC1.energy[1]|meas = '+str(bc1_e0))\n lines.append('set dat BC2.energy[1]|meas = '+str(bc2_e0))\n lines.append('set dat L3.energy[2]|meas = '+str(l3_e0))\n lines.append('set dat BC1.offset[1]|meas = '+str(bc1_offset))\n lines.append('set dat BC2.offset[1]|meas = '+str(bc2_offset))\n \n lines.append(f'! Charge after horn cutting: {q_after_horn_cutting*1e12:10.4} pC')\n lines.append(f'! For BC1 current {bc1_current} A')\n lines.append('set dat BC1.beam[1]|meas = '+str( bc1_sigma_z))\n lines.append(f'! For BC2 current {bc2_current} A')\n lines.append('set dat BC2.beam[1]|meas = '+str( bc2_sigma_z)) \n\n return lines","def epidote():\n\n rho = 3465.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 211.5; C[0,1] = 65.6; C[0,2] = 43.2; C[0,3] = 0.; C[0,4] = -6.5; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 239.; C[1,2] = 43.6; C[1,3] = 0.; C[1,4] = -10.4; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 202.1; C[2,3] = 0.; C[2,4] = -20.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.1; C[3,4] = 0.; C[3,5] = -2.3\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 43.4; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.5\n\n return C, rho","def add_electronetivity_and_lone_pair(df):\n assert 'atom_0' in df.columns\n assert 'atom_1' in df.columns\n electro_df = get_electonegativity()\n electro_df.index.name = 'atom_0'\n df = df.join(electro_df, how='left', on='atom_0')\n df.rename({\n 'Electronegativity': 'Electronegativity_0'\n },\n axis=1,\n inplace=True)\n\n electro_df.index.name = 'atom_1'\n df = df.join(electro_df, how='left', on='atom_1')\n df.rename({\n 'Electronegativity': 'Electronegativity_1'\n },\n axis=1,\n inplace=True)\n df['Electronegativity_diff'] = df['Electronegativity_1'] - df[\n 'Electronegativity_0']\n\n # lone pair\n df['atom_0_lone_pair'] = df['atom_0'].map(get_lone_pair())\n df['atom_1_lone_pair'] = df['atom_1'].map(get_lone_pair())\n return df","def getEnergyEvolution(self):\n\n\t\tEBefore = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleBefore]\n\t\tEAfter = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleAfter]\n\n\t\treturn EBefore, EAfter","def get_core_bonds(core_xyz, inp):\n core_bonds = []\n\n if inp.core_en:\n dists = cdist(core_xyz, core_xyz)\n if inp.core_shape != \"shell\":\n logger.info(\"\\tBuilding elastic network based on first neighbors...\")\n close_dists = dists <= (2*inp.bead_radius+0.01)\n for i in range(len(dists)):\n ndx1 = i*1\n close_ndxs = np.where(close_dists[i])[0]\n if len(close_ndxs) == 1:\n dists_sorted = np.argsort(dists[i])\n close_ndxs = dists_sorted[[1,2,3,4,5,6]]\n for ndx2 in close_ndxs:\n if ndx2 != i and [ndx1, ndx2] not in core_bonds and [ndx2, ndx1] not in core_bonds:\n core_bonds.append([ndx1, ndx2])\n\n else:\n logger.info(\"\\tBuilding elastic network based on six nearest neighbours and one farthest neighbour...\")\n neighboring_bonds = []\n antipodal_bonds = []\n dists_sorted = np.argsort(dists, axis=1)\n for i in range(len(dists)):\n ndx1 = i*1\n close_ndxs = dists_sorted[i,[1,2,3,4,5,6]]\n for ndx2 in close_ndxs:\n if ndx2 != i and [ndx1, ndx2] not in core_bonds and [ndx2, ndx1] not in core_bonds:\n neighboring_bonds.append([ndx1, ndx2])\n antipodal_ndx = dists_sorted[i,-1]\n if antipodal_ndx != i and [ndx1, antipodal_ndx] not in core_bonds and [antipodal_ndx, ndx1] not in core_bonds:\n antipodal_bonds.append([ndx1, antipodal_ndx, \"antipodal\"])\n core_bonds = neighboring_bonds + antipodal_bonds\n\n return core_bonds","def cell_edges(self):","def noe_analysis():\n\n files = ['f34k_7p2_noe_heights.txt', 'dphs_7p4_noe_heights.txt']\n proteins = ['f34k','dphs']\n\n heights = [nmrfn.parse_noe(f) for f in files]\n heights = pd.concat(heights, axis=1, keys=proteins)\n\n results = [noe_calcs(heights[i]) for i in proteins]\n results = pd.concat(results, axis=1, keys=proteins)\n\n fig = plt.figure(figsize=(10,6))\n val = plt.subplot(211)\n dif = plt.subplot(212)\n \n seqmin = 6\n seqmax = 144\n noemin = 0.5\n noemax = 0.95\n\n dy = results.dphs.noe - results.f34k.noe\n dif_plot(val, dy.index, dy.ix[8:141], seqmin, seqmax)\n noe_plot(proteins, results, seqmin, seqmax, noemin, noemax, xax=False)\n\n s1 = plt.Rectangle((0, 0), 1, 1, fc=proteins['f34k'])\n s2 = plt.Rectangle((0, 0), 1, 1, fc=proteins['dphs'])\n val.legend([s1, s2], ['F34K', '∆+PHS'], loc=8)\n\n title = 'HN NOE: F34K pH 7.2 compared with ∆+PHS pH 7.4'\n savefile = 'f34k_7p2_noe_analysis.pdf'\n save_plot(title, savefile)","def interacting_conductivity(self,\n sigma=0.04,\n freq_range=(0.0, 6.0, 1000),\n imag_dielectric=False,\n broadening='lorentz'):\n cell_area = np.abs(np.cross(self.a1, self.a2))*len(self.k_grid)\n broad_fnc = get_broadening_function(broadening, sigma)\n\n frequency_grid = np.linspace(*freq_range)\n output_grid = np.zeros(len(frequency_grid))\n\n energy_power = 1 + int(imag_dielectric)\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\n prefactor = prefactor/cell_area\n\n # Assuming x polarisation for now\n position_dipole_matrix = self.construct_position_dipole_matrix()\n\n n_shift = self.n_spins*self.n_orbs\n cutoff_band_min_maxes = self.exciton_obj.get_cutoff_band_min_maxes()\n # Construct vector of velocity matrix elements to dot with the\n # bse eigenvector.\n velocity_element_lists = []\n for s0 in range(self.n_spins):\n velocity_elements = []\n for idx_1, kpt in enumerate(self.k_grid):\n velocity_matrix = self.load_velocity_matrix(idx_1)\n bands = self.exciton_obj.get_number_conduction_valence_bands(\n idx_1, s0\n )\n v_num, c_num = bands\n v_min = 0\n if self.exciton_obj.energy_cutoff is not None:\n v_min = int(cutoff_band_min_maxes[s0][idx_1][0])\n c_num = int(cutoff_band_min_maxes[s0][idx_1][1]) + 1\n v_num = v_num - v_min\n j1 = n_shift*idx_1 + s0*self.n_orbs\n j2 = j1 + n_shift - (1 - s0)*self.n_orbs*(self.n_spins - 1)\n eigvecs = np.array(\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\n )\n if self.use_dipole_term:\n eigvals = np.array(\n self.file_storage['eigensystem']['eigenvalues'][idx_1]\n )\n else:\n eigvals = None\n for c, v in product(range(c_num), range(v_num)):\n cb_vector = eigvecs[:, v_min + v_num + c]\n vb_vector = eigvecs[:, v_min + v]\n\n matrix_elem = velocity_matrix_element(cb_vector,\n vb_vector,\n velocity_matrix)\n if self.use_dipole_term:\n cb_energy = eigvals[v_min + v_num + c]\n vb_energy = eigvals[v_min + v]\n position_dipole_term = get_position_dipole_element(\n vb_vector,\n cb_vector,\n vb_energy,\n cb_energy,\n position_dipole_matrix\n )\n matrix_elem = matrix_elem + position_dipole_term\n velocity_elements.append(matrix_elem)\n velocity_elements = np.array(velocity_elements, dtype=complex)\n velocity_element_lists.append(velocity_elements)\n\n bse_eigsys = self.exciton_obj.get_bse_eigensystem_direct(solve=True)\n for s0 in range(self.n_spins):\n for idx_1 in range(len(bse_eigsys[s0][0])):\n bse_vector = bse_eigsys[s0][1][:, idx_1]\n bse_value = bse_eigsys[s0][0][idx_1]\n energy_denominator = bse_value**energy_power\n interact_elem = np.dot(bse_vector, velocity_element_lists[s0])\n for idx_2, omega in enumerate(frequency_grid):\n smearing = broad_fnc(bse_value, omega)\n main_term = np.abs(interact_elem)**2/energy_denominator\n output = prefactor*main_term*smearing\n output_grid[idx_2] += output\n\n return frequency_grid, output_grid","def plot_resiliences(nodes, network_vals, er_vals, upa_vals):\n node_vals = range(0, nodes)\n\n plt.plot(node_vals, network_vals, '-b', label='Network')\n plt.plot(node_vals, er_vals, '-r', label='ER')\n plt.plot(node_vals, upa_vals, '-g', label='UPA')\n\n plt.legend(loc='upper right')\n plt.ylabel('Size of Largest Connected Component')\n plt.xlabel('Number of Nodes Removed')\n plt.grid(True)\n plt.title('Comparison of Graph Resilience\\nMeasured by Largest Connected Component vs Nodes Removed by Target Attack\\n')\n plt.show()","def extended_euclidean(self):\n self.a = gmpy2.invert(self.e1, self.e2)\n self.b = (float(self.gcd(self.e1, self.e2)-(self.a*self.e1)))/float(self.e2)","def TwoE(A, B, C, D, Rab2, Rcd2, Rpq2):\n return 2.0 * (np.pi ** 2.5) / ((A + B) * (C + D) * np.sqrt(A + B + C + D)) * F0(\n (A + B) * (C + D) * Rpq2 / (A + B + C + D)) * np.exp(-A * B * Rab2 / (A + B) - C * D * Rcd2 / (C + D))","def get_nb_edit_operations(g1, g2, forward_map, backward_map):\n n_vi = 0\n n_vr = 0\n n_vs = 0\n n_ei = 0\n n_er = 0\n n_es = 0\n\n nodes1 = [n for n in g1.nodes()]\n for i, map_i in enumerate(forward_map):\n if map_i == np.inf:\n n_vr += 1\n elif g1.nodes[nodes1[i]]['atom_symbol'] != g2.nodes[map_i]['atom_symbol']:\n n_vs += 1\n for map_i in backward_map:\n if map_i == np.inf:\n n_vi += 1\n\n# idx_nodes1 = range(0, len(node1))\n\n edges1 = [e for e in g1.edges()]\n nb_edges2_cnted = 0\n for n1, n2 in edges1:\n idx1 = nodes1.index(n1)\n idx2 = nodes1.index(n2)\n # one of the nodes is removed, thus the edge is removed.\n if forward_map[idx1] == np.inf or forward_map[idx2] == np.inf:\n n_er += 1\n # corresponding edge is in g2. Edge label is not considered.\n elif (forward_map[idx1], forward_map[idx2]) in g2.edges() or \\\n (forward_map[idx2], forward_map[idx1]) in g2.edges():\n nb_edges2_cnted += 1\n # corresponding nodes are in g2, however the edge is removed.\n else:\n n_er += 1\n n_ei = nx.number_of_edges(g2) - nb_edges2_cnted\n\n return n_vi, n_vr, n_vs, n_ei, n_er, n_es","def non_interacting_conductivity(self,\n sigma=0.04,\n freq_range=(0.0, 6.0, 1000),\n imag_dielectric=False,\n broadening='lorentz'):\n cell_area = np.abs(float(np.cross(self.a1, self.a2)))*len(self.k_grid)\n broad_fnc = get_broadening_function(broadening, sigma)\n\n frequency_grid = np.linspace(*freq_range)\n energy_increment = np.abs(frequency_grid[1] - frequency_grid[0])\n num_freqs, min_freq = len(frequency_grid), min(frequency_grid)\n output_grid = np.zeros(num_freqs)\n\n energy_power = 1 + int(imag_dielectric)\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\n prefactor = prefactor/cell_area\n\n # Assuming x polarisation for now\n position_dipole_matrix = self.construct_position_dipole_matrix()\n\n n_shift = self.n_spins*self.n_orbs\n for idx1, kpt in enumerate(self.k_grid):\n for s0 in range(self.n_spins):\n velocity_matrix = self.load_velocity_matrix(idx1)\n bands = self.exciton_obj.get_number_conduction_valence_bands(\n idx1, s0\n )\n v_num, c_num = bands\n eigvals = np.array(\n self.file_storage['eigensystem']['eigenvalues'][idx1]\n )\n\n j1 = n_shift*idx1 + (self.n_spins - 1)*s0*self.n_orbs\n j2 = j1 + self.n_orbs\n eigvecs = np.array(\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\n )\n for v, c in product(range(v_num), range(c_num)):\n cb_vector = eigvecs[:, v_num + c]\n vb_vector = eigvecs[:, v]\n cb_energy, vb_energy = eigvals[v_num + c], eigvals[v]\n energy_diff = (cb_energy - vb_energy)\n\n energy_diff_pow = energy_diff**energy_power\n matrix_elem = velocity_matrix_element(vb_vector,\n cb_vector,\n velocity_matrix)\n if self.use_dipole_term:\n position_dipole_term = get_position_dipole_element(\n vb_vector,\n cb_vector,\n vb_energy,\n cb_energy,\n position_dipole_matrix\n )\n matrix_elem = matrix_elem + position_dipole_term\n\n main_term = np.abs(matrix_elem)**2/energy_diff_pow\n closest_idx = (energy_diff - min_freq)//energy_increment\n idx_reach = self.reach_multiplier*(sigma//energy_increment)\n\n lower_idx = max([closest_idx - idx_reach, 0])\n upper_idx = min([closest_idx + idx_reach, num_freqs - 1])\n\n for idx2 in range(int(lower_idx), int(upper_idx)):\n smearing = broad_fnc(energy_diff, frequency_grid[idx2])\n output = prefactor*main_term*smearing\n output_grid[idx2] += output\n\n return frequency_grid, output_grid","def eclogite_foliated():\n\n rho = 3300.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\n\n return C, rho","def elemental_descriptor(A1_ion, A2_ion, B_ion):\n ele_A1 = mg.Element(A1_ion)\n ele_A2 = mg.Element(A2_ion)\n ele_B = mg.Element(B_ion)\n ele_O = mg.Element('O') \n # A/B ion oxidation state \n common_oxidation_states_A1 = ele_A1.common_oxidation_states[0]\n common_oxidation_states_A2 = ele_A2.common_oxidation_states[0]\n common_oxidation_states_A = np.mean(common_oxidation_states_A1 + common_oxidation_states_A2)\n common_oxidation_states_B = ele_B.common_oxidation_states[0]\n # ionic radius property\n ionic_radius_A1 = float(str(ele_A1.average_ionic_radius)[:-4])\n ionic_radius_A2 = float(str(ele_A2.average_ionic_radius)[:-4])\n ionic_radius_A = (ionic_radius_A1+ ionic_radius_A2)/2\n ionic_radius_B = float(str(ele_B.average_ionic_radius)[:-4])\n ionic_radius_O = float(str(ele_O.average_ionic_radius)[:-4])\n # Tolerance factor \n TF = (ionic_radius_A + ionic_radius_O)/(np.sqrt(2)*(ionic_radius_B + ionic_radius_O))\n # Octahedral factor\n OF = ionic_radius_B/ionic_radius_O \n # ionic_radius ratios\n ionic_ration_AO = ionic_radius_A / ionic_radius_O\n ionic_ration_BO = ionic_radius_B / ionic_radius_O\n # averaged electronegativity for A and B atoms\n Pauling_electronegativity_A1 = ele_A1.X\n Pauling_electronegativity_A2 = ele_A2.X\n Pauling_electronegativity_A = (Pauling_electronegativity_A1 + Pauling_electronegativity_A2)/2\n Pauling_electronegativity_B = ele_B.X\n Pauling_electronegativity_O = ele_O.X\n # Difference in the electronegativity for A-O and B-O\n Diff_A_O = Pauling_electronegativity_A - Pauling_electronegativity_O\n Diff_B_O = Pauling_electronegativity_B - Pauling_electronegativity_O\n return [common_oxidation_states_A, common_oxidation_states_B, Pauling_electronegativity_A, Pauling_electronegativity_B, TF, OF, ionic_ration_AO, ionic_ration_BO, Diff_A_O, Diff_B_O]","def compute_edge_logits(self):","def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\n return graph","def test_superposition_cnot():\n\n program = dedent(\n \"\"\"\\\n register q0[0]\n register q1[1]\n H q0\n CNOT q0 q1\n \"\"\"\n )\n\n result = run(program, run_gate_array, return_distribution=True)\n assert isclose(result, [0.5, 0.0, 0.0, 0.5]).all()","def plot_dereddening():\n extinction_coefficients = {'2365-2764-1': np.array([0.2622, 0.844]), '4109-638-1': np.array([0.0524, 0.1576]),\n '2058-56-1': np.array([0.0751, 0.248]), '3642-2459-1': np.array([0.1907, 0.608]),\n '3999-1391-1': np.array([0.3911, 1.2480]), '2607-1448-1': np.array([0.0430, 0.1310])}\n cepheids = {'2365-2764-1': np.array([0.959, 2.09]), '4109-638-1': np.array([0.705, 2.385]), '2058-56-1':\n np.array([1.222, 1.333]), '3642-2459-1': np.array([1.088, 2.0518]), '3999-1391-1':\n np.array([1.360, 1.2567]), '2607-1448-1': np.array([1.484, 0.6963])}\n periods = {'2365-2764-1': 1.61, '4109-638-1': 15.31, '2058-56-1': 63.08, '3642-2459-1': 1.86, '3999-1391-1': 24.98,\n '2607-1448-1': 8.54}\n max_periods = max(periods.values())\n\n new_positions_bv_mv = [] # in M_V vs B-V space\n colors = []\n theoretical_position = []\n for obj in extinction_coefficients.keys():\n # new_positions_bv_mv.append(cepheids[obj]-extinction_coefficients[obj])\n new_positions_bv_mv.append(cepheids[obj])\n colors.append(periods[obj]/max_periods)\n theoretical_position.append(-2.78*np.log10(periods[obj])-1.35)\n\n for pos in range(len(new_positions_bv_mv)):\n plt.scatter(new_positions_bv_mv[pos][0], new_positions_bv_mv[pos][1], marker='^', facecolor='w', s=40)\n plt.scatter(new_positions_bv_mv[pos][0], theoretical_position[pos], marker='o', facecolor='r', s=50)\n return new_positions_bv_mv, colors","def plot_links_2d_intra(epo1: mne.Epochs, epo2: mne.Epochs,\n C1: np.ndarray, C2: np.ndarray,\n threshold: str='auto', steps: int=2):\n\n # extract sensor infos and transform loc to fit with headmodel\n loc1 = copy(np.array([ch['loc'][:3] for ch in epo1.info['chs']]))\n loc1 = transform_2d_intra(loc1, traX=-0.178, traY=0.012, traZ=0, rotZ=(-np.pi/2))\n\n loc2 = copy(np.array([ch['loc'][:3] for ch in epo2.info['chs']]))\n loc2 = transform_2d_intra(loc2, traX=0.178, traY=0.012, traZ=0, rotZ=(-np.pi/2))\n\n ctr1 = np.nanmean(loc1, 0)\n ctr2 = np.nanmean(loc2, 0)\n \n # Calculate vmin and vmax for colormap as min and max [C1, C2]\n Cmax1=np.nanmax(C1[:])\n Cmax2=np.nanmax(C2[:])\n Cmax=[]\n Cmax=[Cmax1, Cmax2]\n vmax=np.nanmax(Cmax)\n Cmin1=np.nanmin(C1[:])\n Cmin2=np.nanmin(C2[:])\n Cmin=[]\n Cmin=[Cmin1, Cmin2]\n vmin=np.min(Cmin)\n\n # Calculate automatic threshold\n if threshold == 'auto':\n threshold = np.max([np.median(C1, 0),np.median(C2,0)])+np.max([np.std(C1, 0),np.std(C2, 0)])\n else:\n threshold = threshold\n \n\n # Define colormap for both participant\n cmap_p = matplotlib.cm.get_cmap('Reds')\n norm_p = matplotlib.colors.Normalize(vmin=threshold, vmax=vmax)\n cmap_n = matplotlib.cm.get_cmap('Blues_r')\n norm_n = matplotlib.colors.Normalize(vmin=vmin, vmax=-threshold)\n\n # plot links\n for e1 in range(len(loc1)):\n x1 = loc1[e1, 0]\n y1 = loc1[e1, 1]\n for e2 in range(len(loc1)):\n x2 = loc1[e2, 0]\n y2 = loc1[e2, 1]\n if C1[e1, e2] >= threshold:\n color_p = cmap_p(norm_p(C1[e1, e2]))\n if steps <= 2:\n weight = 0.2 +1.6*((C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n plt.plot([loc1[e1, 0], loc1[e2, 0]],\n [loc1[e1, 1], loc1[e2, 1]],\n '-', color=color_p, linewidth=weight)\n else:\n alphas = np.linspace(0, 1, steps)\n weight = 0.2 +1.6*((C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n for idx in range(len(alphas)-1):\n a = alphas[idx]\n b = alphas[idx+1]\n xn = ((1-a)**3 * x1 +\n 3 * (1-a)**2 * a * (2 * x1 - ctr1[0]) +\n 3 * (1-a) * a**2 * (2 * x2 - ctr1[0]) +\n a**3 * x2)\n xnn = ((1-b)**3 * x1 +\n 3 * (1-b)**2 * b * (2 * x1 - ctr1[0]) +\n 3 * (1-b) * b**2 * (2 * x2 - ctr1[0]) +\n b**3 * x2)\n yn = ((1-a)**3 * y1 +\n 3 * (1-a)**2 * a * (2 * y1 - ctr1[1]) +\n 3 * (1-a) * a**2 * (2 * y2 - ctr1[1]) +\n a**3 * y2)\n ynn = ((1-b)**3 * y1 +\n 3 * (1-b)**2 * b * (2 * y1 - ctr1[1]) +\n 3 * (1-b) * b**2 * (2 * y2 - ctr1[1]) +\n b**3 * y2)\n plt.plot([xn, xnn], [yn, ynn],\n '-', color=color_p, linewidth=weight)\n if C1[e1, e2] <= -threshold:\n color_n = cmap_n(norm_n(C1[e1, e2]))\n if steps <= 2:\n weight = 0.2 +1.6*((-C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n plt.plot([loc1[e1, 0], loc1[e2, 0]],\n [loc1[e1, 1], loc1[e2, 1]],\n '-', color=color_n, linewidth=weight)\n else:\n alphas = np.linspace(0, 1, steps)\n weight = 0.2 +1.6*((-C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n for idx in range(len(alphas)-1):\n a = alphas[idx]\n b = alphas[idx+1]\n xn = ((1-a)**3 * x1 +\n 3 * (1-a)**2 * a * (2 * x1 - ctr1[0]) +\n 3 * (1-a) * a**2 * (2 * x2 - ctr1[0]) +\n a**3 * x2)\n xnn = ((1-b)**3 * x1 +\n 3 * (1-b)**2 * b * (2 * x1 - ctr1[0]) +\n 3 * (1-b) * b**2 * (2 * x2 - ctr1[0]) +\n b**3 * x2)\n yn = ((1-a)**3 * y1 +\n 3 * (1-a)**2 * a * (2 * y1 - ctr1[1]) +\n 3 * (1-a) * a**2 * (2 * y2 - ctr1[1]) +\n a**3 * y2)\n ynn = ((1-b)**3 * y1 +\n 3 * (1-b)**2 * b * (2 * y1 - ctr1[1]) +\n 3 * (1-b) * b**2 * (2 * y2 - ctr1[1]) +\n b**3 * y2)\n plt.plot([xn, xnn], [yn, ynn],\n '-', color=color_n, linewidth=weight)\n\n for e1 in range(len(loc2)):\n x1 = loc2[e1, 0]\n y1 = loc2[e1, 1]\n for e2 in range(len(loc2)):\n x2 = loc2[e2, 0]\n y2 = loc2[e2, 1]\n if C2[e1, e2] >= threshold:\n color_p = cmap_p(norm_p(C2[e1, e2]))\n if steps <= 2:\n weight = 0.2 +1.6*((C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n plt.plot([loc2[e1, 0], loc2[e2, 0]],\n [loc2[e1, 1], loc2[e2, 1]],\n '-', color=color_p, linewidth=weight)\n else:\n alphas = np.linspace(0, 1, steps)\n weight = 0.2 +1.6*((C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n for idx in range(len(alphas)-1):\n a = alphas[idx]\n b = alphas[idx+1]\n xn = ((1-a)**3 * x1 +\n 3 * (1-a)**2 * a * (2 * x1 - ctr2[0]) +\n 3 * (1-a) * a**2 * (2 * x2 - ctr2[0]) +\n a**3 * x2)\n xnn = ((1-b)**3 * x1 +\n 3 * (1-b)**2 * b * (2 * x1 - ctr2[0]) +\n 3 * (1-b) * b**2 * (2 * x2 - ctr2[0]) +\n b**3 * x2)\n yn = ((1-a)**3 * y1 +\n 3 * (1-a)**2 * a * (2 * y1 - ctr2[1]) +\n 3 * (1-a) * a**2 * (2 * y2 - ctr2[1]) +\n a**3 * y2)\n ynn = ((1-b)**3 * y1 +\n 3 * (1-b)**2 * b * (2 * y1 - ctr2[1]) +\n 3 * (1-b) * b**2 * (2 * y2 - ctr2[1]) +\n b**3 * y2)\n plt.plot([xn, xnn], [yn, ynn],\n '-', color=color_p, linewidth=weight)\n if C2[e1, e2] <= -threshold:\n color_n = cmap_n(norm_n(C2[e1, e2]))\n if steps <= 2:\n weight = 0.2 +1.6*((-C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n plt.plot([loc2[e1, 0], loc2[e2, 0]],\n [loc2[e1, 1], loc2[e2, 1]],\n '-', color=color_n, linewidth=weight)\n else:\n alphas = np.linspace(0, 1, steps)\n weight = 0.2 +1.6*((-C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\n for idx in range(len(alphas)-1):\n a = alphas[idx]\n b = alphas[idx+1]\n xn = ((1-a)**3 * x1 +\n 3 * (1-a)**2 * a * (2 * x1 - ctr2[0]) +\n 3 * (1-a) * a**2 * (2 * x2 - ctr2[0]) +\n a**3 * x2)\n xnn = ((1-b)**3 * x1 +\n 3 * (1-b)**2 * b * (2 * x1 - ctr2[0]) +\n 3 * (1-b) * b**2 * (2 * x2 - ctr2[0]) +\n b**3 * x2)\n yn = ((1-a)**3 * y1 +\n 3 * (1-a)**2 * a * (2 * y1 - ctr2[1]) +\n 3 * (1-a) * a**2 * (2 * y2 - ctr2[1]) +\n a**3 * y2)\n ynn = ((1-b)**3 * y1 +\n 3 * (1-b)**2 * b * (2 * y1 - ctr2[1]) +\n 3 * (1-b) * b**2 * (2 * y2 - ctr2[1]) +\n b**3 * y2)\n plt.plot([xn, xnn], [yn, ynn],\n '-', color=color_n, linewidth=weight)","def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised= self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = conservedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\n \n parentGraph.name = 'Conserved metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def get_eulerian_graph_edges(bbox, source):\n osm_graph = OSMGraph(bbox, source)\n # input all nodes and get odd nodes, update node attributes\n odd_nodes = get_odd_nodes(osm_graph.nodes_dict)\n\n # initialize all_pairs_list\n all_pairs_list = []\n\n # if there are 6 or fewer odd nodes look for all possible options,\n # otherwise look for just three basic pairing options\n\n if len(odd_nodes) <= 10:\n print(\"ROBUST PAIRING FUNCTION\")\n all_pairs_list = get_list_of_all_pairs_lists(odd_nodes)\n\n else:\n print(\"CHEAP PAIRING FUNCTION\")\n all_pairs_list = get_list_of_all_pairs_lists_short(odd_nodes)\n\n for item in all_pairs_list:\n print(\"\\n\\nPair option:\", item)\n print(\"Pair option len:\", len(item))\n\n dict_pairings_lists_lengths = get_dict_pairings_lists_lengths(\n all_pairs_list, osm_graph\n )\n twice_traversals_edges = get_twice_traversals_edges(dict_pairings_lists_lengths)\n updated_graph_instance = update_twice_traversal_edges(\n twice_traversals_edges, osm_graph\n )\n return updated_graph_instance","def setUpTwoElectricalSeries(self):\n TestElectricalSeriesIO.make_electrode_table(self)\n region1 = DynamicTableRegion(name='electrodes',\n data=[0, 2],\n description='the first and third electrodes',\n table=self.table)\n region2 = DynamicTableRegion(name='electrodes',\n data=[1, 3],\n description='the second and fourth electrodes',\n table=self.table)\n data1 = list(zip(range(10), range(10, 20)))\n data2 = list(zip(reversed(range(10)), reversed(range(10, 20))))\n timestamps = list(map(lambda x: x/10., range(10)))\n es1 = ElectricalSeries(name='test_eS1', data=data1, electrodes=region1, timestamps=timestamps)\n es2 = ElectricalSeries(name='test_eS2', data=data2, electrodes=region2, channel_conversion=[4., .4],\n timestamps=timestamps)\n return es1, es2","def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n childGraph = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)\n childGraph.name = 'Added metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return childGraph","def plot_Epair_Etagm(run):\n global fin\n global pstags\n global last_run\n last_run = run\n fin = ROOT.TXNetFile(xrootdir + f\"/PStagstudy2_{run}.root\")\n pstags = fin.Get(\"pstags\")\n get_from_ccdb(run)\n pstags.Process(\"pstags\", \"\", 5000000)\n h1 = ROOT.gROOT.FindObject(\"ttagm_pair\")\n h2 = ROOT.gROOT.FindObject(\"Etagm_Epair\")\n h3 = ROOT.gROOT.FindObject(\"Epair_Etagm\")\n return h3, h2, h1","def nsi_cross_edge_density(self, node_list1, node_list2):\n W_j = sum(self.node_weights[node_list2])\n return self.nsi_cross_mean_degree(node_list1, node_list2) / W_j","def Eee2(neutron_spectrum):\n neutron_spectrum.Fit('gaus')\n results_fit = neutron_spectrum.GetFunction('gaus')\n mean_energy = results_fit.GetParameter(1)\n spread_energy = results_fit.GetParError(1)\n return mean_energy, spread_energy","def etable_atom_pair_energies(res1, atom_index_1, res2, atom_index_2, sfxn):\n score_manager = rosetta.core.scoring.ScoringManager.get_instance()\n etable_ptr = score_manager.etable(sfxn.energy_method_options().etable_type())\n etable = etable_ptr.lock()\n etable_energy = rosetta.core.scoring.etable.AnalyticEtableEnergy(\n etable, sfxn.energy_method_options()\n )\n\n # Construct coulomb class for calculating fa_elec energies\n coulomb = rosetta.core.scoring.etable.coulomb.Coulomb(sfxn.energy_method_options())\n\n # Construct AtomPairEnergy container to hold computed energies.\n ape = rosetta.core.scoring.etable.AtomPairEnergy()\n\n # Set all energies in the AtomPairEnergy to zero prior to calculation.\n ape.attractive, ape.bead_bead_interaction, ape.repulsive, ape.solvation = [0.] * 4\n\n # Calculate the distance squared and set it in the AtomPairEnergy.\n ape.distance_squared = res1.xyz(atom_index_1).distance_squared(\n res2.xyz(atom_index_2)\n )\n\n # Evaluate energies from pre-calculated etable, using a weight of 1.0\n # in order to match the raw energies from eval_ci_2b.\n atom1 = res1.atom(atom_index_1)\n atom2 = res2.atom(atom_index_2)\n etable_energy.atom_pair_energy(atom1, atom2, 1.0, ape)\n\n # Calculate atom-atom scores.\n lj_atr = ape.attractive\n lj_rep = ape.repulsive\n solv = ape.solvation\n fa_elec = coulomb.eval_atom_atom_fa_elecE(\n res1.xyz(atom_index_1),\n res1.atomic_charge(atom_index_1),\n res2.xyz(atom_index_2),\n res2.atomic_charge(atom_index_2),\n )\n\n return lj_atr, lj_rep, solv, fa_elec","def test_2():\n h = iotbx.pdb.input(source_info=None, lines=test_pdb_2).construct_hierarchy()\n asc = h.atom_selection_cache()\n ncs_inp = iotbx.ncs.input(\n hierarchy=h,\n params=ncs_pars.ncs_search)\n ncs_groups = ncs_inp.get_ncs_restraints_group_list()\n assert len(ncs_groups) == 1\n # group 1\n assert ncs_groups[0].master_iselection.all_eq(\n asc.selection(string = \"chain A\").iselection())\n g1_c = ncs_groups[0].copies\n assert len(g1_c)==1\n assert g1_c[0].iselection.all_eq(\n asc.selection(string = \"chain B\").iselection())","def divergedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n \n if colour is True:\n lostGraph = GeneFunctionLoss.getGraph(parentCoreMetabolism, childCoreMetabolism)\n lostEdges = lostGraph.getEdges()\n \n addedGraph = GeneFunctionAddition.getGraph(parentCoreMetabolism, childCoreMetabolism)\n addedEdges = addedGraph.getEdges()\n \n graph = lostGraph.union(addedGraph, addCount = False, updateName = False) \n \n Export.addColourAttribute(graph, colour = Export.Colour.BLUE, nodes = False, edges = lostEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.RED, nodes = False, edges = addedEdges)\n \n else: \n graph = GeneFunctionDivergence.getGraph(parentCoreMetabolism, childCoreMetabolism)\n \n graph.name = 'Diverged metabolism ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph","def divergedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = divergedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\n childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\n \n parentGraph.name = 'Diverged metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def compute_cluster_similarities(emb_clusters1, emb_clusters2, compare, order, clmethod, plot):\n def compute_sim(e, e1, cls, cls1):\n sims = np.empty((20, 20))\n xticks, yticks = [], []\n for i, c in enumerate(cls):\n yticks.append(', '.join(c[1]) + (f' {round(c[3], 5)}' if order == 'avgfreq' else ''))\n for j, c1 in enumerate(cls1):\n if len(xticks) < 20:\n xticks.append(', '.join(c1[1]) + (f' {round(c1[3], 5)}' if order == 'avgfreq' else ''))\n sims[i, j] = jaccard_similarity_score(c[2], c1[2])\n jaccard_similarities[f'{e}-{e1}'] = sims\n\n if plot:\n if order == 'clustermap':\n similarity_clustermap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}')\n elif order == 'default' or order == 'avgfreq':\n similarity_heatmap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}', order)\n else:\n pass\n\n jaccard_similarities = {}\n if compare == 'cross':\n for ie, (e, cls) in enumerate(emb_clusters1.items()):\n for ie1, (e1, cls1) in enumerate(emb_clusters2.items()):\n if ie < ie1:\n compute_sim(e, e1, cls, cls1)\n elif compare == 'dot':\n for (e, cls), (e1, cls1) in zip(emb_clusters1.items(), emb_clusters2.items()):\n compute_sim(e, e1, cls, cls1)\n\n return jaccard_similarities","def show_dcr_results(dg):\n cycle = dg.fileDB['cycle'].values[0]\n df_dsp = pd.read_hdf(f'./temp_{cycle}.h5', 'opt_dcr')\n # print(df_dsp.describe()) \n\n # compare DCR and A/E distributions\n fig, (p0, p1) = plt.subplots(2, 1, figsize=(8, 8))\n \n elo, ehi, epb = 0, 25000, 100\n \n # aoe distribution\n # ylo, yhi, ypb = -1, 2, 0.1\n # ylo, yhi, ypb = -0.1, 0.3, 0.005\n ylo, yhi, ypb = 0.05, 0.08, 0.0005\n nbx = int((ehi-elo)/epb)\n nby = int((yhi-ylo)/ypb)\n h = p0.hist2d(df_dsp['trapEmax'], df_dsp['aoe'], bins=[nbx,nby],\n range=[[elo, ehi], [ylo, yhi]], cmap='jet',\n norm=LogNorm())\n # p0.set_xlabel('Energy (uncal)', ha='right', x=1)\n p0.set_ylabel('A/E', ha='right', y=1)\n\n # dcr distribution\n # ylo, yhi, ypb = -20, 20, 1 # dcr_raw\n # ylo, yhi, ypb = -5, 2.5, 0.1 # dcr = dcr_raw / trapEmax\n # ylo, yhi, ypb = -3, 2, 0.1\n ylo, yhi, ypb = 0.9, 1.08, 0.001\n ylo, yhi, ypb = 1.034, 1.0425, 0.00005 # best for 64.4 us pz\n # ylo, yhi, ypb = 1.05, 1.056, 0.00005 # best for 50 us pz\n # ylo, yhi, ypb = 1.016, 1.022, 0.00005 # best for 100 us pz\n nbx = int((ehi-elo)/epb)\n nby = int((yhi-ylo)/ypb)\n h = p1.hist2d(df_dsp['trapEmax'], df_dsp['dcr'], bins=[nbx,nby],\n range=[[elo, ehi], [ylo, yhi]], cmap='jet',\n norm=LogNorm())\n p1.set_xlabel('Energy (uncal)', ha='right', x=1)\n p1.set_ylabel('DCR', ha='right', y=1)\n \n # plt.show()\n plt.savefig(f'./plots/dcr_cyc{cycle}.png', dpi=300)\n plt.cla()","def odd_decomposition(G) -> Tuple[Set[int], Set[int]]:\n multi = isinstance(G, nx.MultiGraph)\n\n # create a MultiGraph copy of G\n G = nx.MultiGraph(G)\n\n # remove isolated nodes from the graph, since they are irrelevant and we are working on a copy\n G.remove_nodes_from(list(nx.isolates(G)))\n\n # if the base graph is already odd\n if is_odd(G):\n return (set(G.edges(keys=multi)), set())\n\n odd_subgraph = G.subgraph(odd_nodes(G))\n even_subgraph = G.subgraph(even_nodes(G))\n\n odd_components = nx.connected_components(odd_subgraph)\n even_components = nx.connected_components(even_subgraph)\n\n # use the same notation as in our source paper\n X = list(odd_components)\n Y, Z = partition(even_components, lambda x: len(x) % 2 == 0)\n\n lX, lY, lZ = len(X), len(Y), len(Z)\n linear_system = Matrix(lY + lZ, lX + 1, bin_field)\n\n # create a linear system over GF(2) as described in our source paper\n for i, Yi in enumerate(Y):\n for j, Xi in enumerate(X):\n if n_joining(G, Xi, Yi) % 2 == 1:\n linear_system.set(i, j, 1)\n else:\n linear_system.set(i, j, 0)\n linear_system.set(i, lX, 1)\n\n for i, Zi in enumerate(Z):\n for j, Xi in enumerate(X):\n if n_joining(G, Xi, Zi) % 2 == 1:\n linear_system.set(i + lY, j, 1)\n else:\n linear_system.set(i + lY, j, 0)\n linear_system.set(i + lY, lX, 0)\n\n # transform the system into RREF\n linear_system.reduced_row_echelon_form()\n red = set()\n\n # we only need one solution\n # every non pivot is set to 0 (meaning blue)\n # while every pivot matches the augmented value\n # only create the red set since this is the only one we need\n\n for i in range(lY + lZ):\n val = linear_system.get(i, lX)\n # don't cross the last column (augmented part)\n for j in range(lX):\n v = linear_system.get(i, j)\n if v == 1:\n if val == 1:\n red.add(j)\n break\n else:\n if val == 1:\n # if any of the lines has no pivot and 1 as the augmented value this system is not solvable\n raise NotDecomposableError(\"The graph is not decomposable due to unsolvable system\")\n else:\n # this is a zero row ane there are no pivots after this row\n break\n\n red_nodes = set.union(*[X[i] for i in red]) if red else set()\n # edges adjacent to red nodes\n red_edges = set(G.edges(nbunch=red_nodes, keys=True))\n # calculate the red degree for every even node\n degrees = dict()\n for i in even_subgraph.nodes():\n degrees[i] = 0\n\n for i, j, k in red_edges:\n if i in degrees:\n degrees[i] += 1\n if j in degrees:\n degrees[j] += 1\n\n # T set is the set of all nodes of even red degree\n T = {i for i, deg in degrees.items() if deg % 2 == 0}\n\n # compute the T-join and mark the nodes as red/blue\n red_join = T_join(even_subgraph, T)\n\n red_edges.update(red_join)\n blue_edges = set(G.edges(keys=True)).difference(red_edges)\n\n if not multi:\n red_edges = {(i, j) for i, j, k in red_edges}\n blue_edges = {(i, j) for i, j, k in blue_edges}\n\n return (red_edges, blue_edges)","def extended_euclides(self, a, b):\n x = last_y = 0\n y = last_x = 1\n while b != 0:\n quot = a // b\n a, b = b, a % b\n x, last_x = last_x - quot * x, x\n y, last_y = last_y - quot * y, y\n\n return [last_x, last_y]","def complex_network_mapping(graph):\n vect = []\n\n n = nx.number_of_nodes(graph)\n e = nx.number_of_edges(graph)\n print n, e\n\n# adj = nx.adjacency_matrix(graph).toarray()\n# adj_bin = np.where(adj > 0, 1., 0.)\n# adj_conn = 1 - adj\n adj_bin = nx.adjacency_matrix(graph).toarray()\n adj_bin = np.array(adj_bin, dtype=np.float)\n\n # Node Betweenness binary\n bt_bin = nx.betweenness_centrality(graph).values()\n avg_btb = np.mean(bt_bin)\n vect.append(avg_btb)\n\n # Edge betweenness\n ebt = np.array(nx.edge_betweenness_centrality(graph).values())\n vect.append(np.mean(ebt))\n\n # Eigen vector centrality binary\n evc_bin = eigenvector_centrality_und(adj_bin)\n avg_evcb = np.mean(evc_bin)\n vect.append(avg_evcb)\n\n # Flow coefficient\n _, flow_bin, _ = flow_coef_bd(adj_bin)\n avg_flow = np.mean(flow_bin)\n vect.append(avg_flow)\n\n # Kcoreness centrality\n kcor_bin, _ = kcoreness_centrality_bu(adj_bin)\n avg_kcor = np.mean(kcor_bin)\n vect.append(avg_kcor)\n\n # Degree assortivity\n dac = nx.degree_assortativity_coefficient(graph)\n vect.append(dac)\n\n # Page rank centrality\n# pgr_wei = pagerank_centrality(adj_bin, d=0.85)\n# avg_pgr = np.mean(pgr_wei)\n# vect.append(avg_pgr)\n\n # Rich club coefficient\n# rcc = nx.rich_club_coefficient(graph).values()\n# avg_rcc = np.mean(rcc)\n# vect.append(avg_rcc)\n\n # Transitivity\n tr = nx.transitivity(graph)\n vect.append(tr)\n\n # average clustering\n avg_clst = nx.average_clustering(graph)\n vect.append(avg_clst)\n\n glb_ef = efficiency_bin(adj_bin)\n vect.append(glb_ef)\n\n return vect","def buildE(self, debug = False):\n E = np.zeros([len(self.sta), len(self.dist)])\n i = 0\n for ikey, state in self.sta.items():\n j = 0\n c = state.c\n for jkey, disturbance in self.dist.items():\n if debug:\n print(i, ' ', j)\n if isinstance(disturbance, InputT) and disturbance in \\\n self.rc.adj[state]:\n # input is temperature and connected to this state\n\n E[i, j] = self.rc.adj[state][disturbance]['H'] / c\n elif isinstance(disturbance, InputQ) and disturbance in \\\n self.rc.adj[\n state]: # input is heat flow and connected to state\n E[i, j] = self.rc.adj[state][disturbance]['gain'] / c\n j += 1\n i += 1\n\n return E, list(self.sta.keys()), list(self.dist.keys())","def es(d1, d2, verbose=False):\n\n d1 = assure_numpy_array(d1)\n d2 = assure_numpy_array(d2)\n\n es, pvalue = stats.epps_singleton_2samp(d1, d2)\n\n return es, pvalue","def figure_2_combined_cross_sections(crange=CRANGE, koffrange=KOFFRANGE,\n scale_factor=alpha, label_style=0):\n def combined_error_c(ctilde, z, scale_factor=scale_factor):\n c = ctilde * c0\n koff = z * KP\n x = c * KON / koff\n num = 2 * KP * x + koff * KP * T * (1 + x) ** 3 + koff ** 2 * T * x ** 2 * (1 + x) ** 3\n den = koff ** 2 * KP * T ** 2 * x * (1 + x) ** 2\n val = num / den\n return scale_factor * val\n def combined_error_koff(ctilde, z, scale_factor=scale_factor):\n c = ctilde * c0\n koff = z * KP\n x = c * KON / koff\n num = 2 * KP * x + koff * KP * T * (1 + x) ** 3 + koff**2 * T * (1 + x) ** 3\n den = koff ** 2 * KP * T**2 * x * (1 + x) ** 2\n val = num / den\n return scale_factor * val\n\n def cross_section_combined_error_c():\n arr = [combined_error_c(cval, 1) for cval in crange]\n return dict({'xpts': crange, 'ypts':arr})\n def cross_section_combined_error_koff():\n arr = [combined_error_koff(c, 1) for c in crange]\n return dict({'xpts': koffrange, 'ypts':arr})\n\n figname = 'combined_error_cross_sections'\n curve1 = cross_section_combined_error_c()\n curve2 = cross_section_combined_error_koff()\n # plot\n plt.figure(figsize=(3, 3))\n ax1 = plt.gca()\n ax2 = ax1.twiny()\n\n if label_style == 0:\n ln1 = ax1.plot(curve1['xpts'], curve1['ypts'], color=cs['simple_fisher'], label=r'$c$', zorder=1)\n ln2 = ax2.plot(curve2['xpts'], curve2['ypts'], color=cs['heuristic'], label=r'$k_{\\mathrm{off}}$', zorder=1)\n #plt.title('Mode 2: MLE relative error comparison\\n' + r'($\\tilde{c}_0=10$, $\\alpha=1 \\times 10^4$, $k_{p}=10$)')\n\n elif label_style == 1:\n ln1 = ax1.plot(curve1['xpts'], curve1['ypts'], color=cs['simple_fisher'], label=r'$\\delta c^{2}/c^{2}$', zorder=1)\n ln2 = ax2.plot(curve2['xpts'], curve2['ypts'], color=cs['heuristic'],label=r'$\\delta k_{\\mathrm{off}}^{2}/k_{\\mathrm{off}}^{2}$', zorder=1)\n plt.title('Mode 2: MLE relative error comparison\\n' + r'($k_p=10$, $t=100$, $k_{\\mathrm{off}}=k_{\\mathrm{on}}=1$)')\n plt.ylabel(r'$\\langle\\delta (\\cdot)^{2}\\rangle$/$(\\cdot)^{2}$')\n\n # axis\n ax1.set_xlabel(r'$k_{\\mathrm{on}}c/k_{p}$')\n ax1.set_ylabel(r'$k_{p}t \\langle\\delta (\\cdot)^{2}\\rangle$/$(\\cdot)^{2}$')\n #ax2.set_xlabel(r'$k_{\\mathrm{off}}$')\n\n ax1.set_xscale('log')\n ax2.set_xscale('log')\n ax1.set_xlim([1E-2, 1E2])\n ax2.set_xlim([1E-2, 1E2])\n plt.ylim([0, 0.01*alpha])\n\n lns = ln1 + ln2\n labs = [l.get_label() for l in lns]\n ax1.legend(lns, labs)\n\n plt.tight_layout()\n # save figure\n plt.savefig(DIR_OUTPUT + os.sep + figname + '.pdf', transparent=True)\n plt.savefig(DIR_OUTPUT + os.sep + figname + '.eps')","def eclogite_massive():\n\n rho = 3490.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\n\n return C, rho","def test_pid_ccs2():\n d = trivariates['anddup']\n pid = PID_CCS(d, [[0], [1], [2]], [3])\n for atom in pid._lattice:\n if atom == ((0,), (1,), (2,)):\n assert pid[atom] == pytest.approx(0.10375937481971094)\n elif atom in [((0,), (2,)), ((1,),)]:\n assert pid[atom] == pytest.approx(0.20751874963942191)\n elif atom in [((2,), (0, 1)), ((0,), (1, 2)), ((0, 1), (0, 2)), ((0, 1), (1, 2)), ((0, 2), (1, 2)), ((0, 1, 2),)]:\n assert pid[atom] == pytest.approx(0.14624062518028902)\n elif atom in [((2,),), ((0,),), ((0, 1), (0, 2), (1, 2)), ((0, 2),)]:\n assert pid[atom] == pytest.approx(-0.14624062518028902)\n else:\n assert pid[atom] == pytest.approx(0.0)","def xsec_CEvNS(E_R, E_nu, N_p, N_n):\n \n A = N_p + N_n\n Z = N_p\n \n m_A = A*0.9315 #Mass of target nucleus (in GeV)\n q = np.sqrt(2.0*E_R*m_A) #Recoil momentum (in MeV)\n #Note: m_A in GeV, E_R in keV, E_nu in MeV\n \n #Calculate SM contribution\n Qv = (A-Z) - (1.0-4.0*SIN2THETAW)*Z #Coherence factor\n \n xsec_SM = (G_FERMI*G_FERMI/(4.0*np.pi))*Qv*Qv*m_A* \\\n (1.0-(q*q)/(4.0*E_nu*E_nu))\n \n #Calculate New-Physics correction from Z' coupling\n #Assume universal coupling to quarks (u and d)\n #QvNP = 3.0*A*gsq\n\n #Factor of 1e6 from (GeV/MeV)^2\n #G_V = 1 - 1e6*(SQRT2/G_FERMI)*(QvNP/Qv)*1.0/(q*q + m_med*m_med)\n \n #Convert from (GeV^-3) to (cm^2/keV)\n #and multiply by form factor\n return xsec_SM*1e-6*(1.98e-14)*(1.98e-14)*calcSIFormFactor(E_R, A)","def test_cherenkov_instability( show=False ):\n # Dictionary to record the final value of E\n slope_Erms = {}\n\n for scheme in [ 'standard', 'galilean', 'pseudo-galilean']:\n\n # Choose the correct parameters for the scheme\n if scheme == 'standard':\n v_comoving = 0.\n use_galilean = False\n else:\n v_comoving = 0.9999*c\n if scheme == 'galilean':\n use_galilean = True\n else:\n use_galilean = False\n\n # Initialize the simulation object\n sim = Simulation( Nz, zmax, Nr, rmax, Nm, dt,\n p_zmin, p_zmax, p_rmin, p_rmax, p_nz, p_nr, p_nt, n_e,\n zmin=zmin, initialize_ions=True,\n v_comoving=v_comoving, use_galilean=use_galilean,\n boundaries={'z':'periodic', 'r':'reflective'}, use_cuda=use_cuda )\n\n # Give a relativistic velocity to the particle, with some noise\n sim.ptcl[0].uz[:] = uz_m\n sim.ptcl[0].inv_gamma[:] = 1./np.sqrt( 1 + sim.ptcl[0].uz**2 )\n sim.ptcl[1].uz[:] = uz_m\n sim.ptcl[1].inv_gamma[:] = 1./np.sqrt( 1 + sim.ptcl[1].uz**2 )\n\n # Perform the simulation;\n # record the rms electric field every 50 timestep\n Er_rms = np.zeros(int(N_step/30)+1)\n t = np.zeros(int(N_step/30+1))\n Er_rms[0] = get_Er_rms(sim)\n t[0] += sim.time\n for i in range(int(N_step/30)):\n sim.step( 30, show_progress=False )\n print('Checkpoint %d' %i)\n Er_rms[i+1] = get_Er_rms(sim)\n t[i+1] += sim.time\n print('Calculated RMS')\n\n # Check/plot the results\n if show:\n import matplotlib.pyplot as plt\n # Add a plot\n plt.semilogy( t, Er_rms, '-', label=scheme )\n plt.ylabel('RMS(Er)')\n plt.xlabel('Time')\n else:\n # Registed the final value of the slope of the electric field\n slope_Erms[scheme] = np.log( Er_rms[-1] ) - np.log(Er_rms[-2] )\n\n if show:\n # Show the plot\n plt.legend(loc=0)\n plt.show()\n else:\n # Check that, in the standard case, the electric field is\n # growing much faster, due to the Cherenkov instability\n assert slope_Erms['standard'] > 3.5*slope_Erms['galilean']\n assert slope_Erms['standard'] > 3.5*slope_Erms['pseudo-galilean']","def etio_by_csa_dep(df):\n\n #TODO: need to scale subplots to same axes, spread subplots apart a little.\n #Note: plan is to replace this with some seaborn graphs that are easier.\n\n # fig, axs = plt.subplots(4,1)\n fig = plt.figure()\n spec = gridspec.GridSpec(ncols=1, nrows=4, figure=fig)\n\n f_ax1 = fig.add_subplot(spec[0, 0])\n f_ax2 = fig.add_subplot(spec[1, 0])\n f_ax3 = fig.add_subplot(spec[2, 0])\n f_ax4 = fig.add_subplot(spec[3, 0])\n\n OSA_pure = df.loc[df['BaseDx'] == \"Mainly OSA\"]\n OSA_predom = df.loc[df['BaseDx'] == \"Combined OSA/CSA\"]\n CSA_predom = df.loc[df['BaseDx'] == \"Predominantly CSA\"]\n CSA_pure = df.loc[df['BaseDx'] == \"Pure CSA\"]\n\n fig.suptitle(\"Contributing etiologies grouped by percentage central events\")\n\n etio_subplot(OSA_pure, f_ax1, \"Pure OSA\", graph_color='forestgreen')\n etio_subplot(OSA_predom, f_ax2, \"Predom OSA\", graph_color='aquamarine')\n etio_subplot(CSA_predom, f_ax3, \"Predom CSA\", graph_color='lightskyblue')\n etio_subplot(CSA_pure, f_ax4, \"Pure CSA\", graph_color='slategray')\n\n plt.show()","def plot_2d(self):\n fig = plt.figure(figsize=(10,8))\n \n d = int(len(self.a_scale.flat)**0.5)\n a_scale = self.a_scale.reshape(d,d)\n c_scale = self.c_scale.reshape(d,d)\n E_coh = self.E_coh.reshape(d,d)\n plt.pcolormesh(a_scale, c_scale, E_coh)\n plt.xlabel('xy linear deformation coefficient')\n plt.xlabel('z linear deformation coefficient')\n cbar = plt.colorbar()\n cbar.ax.set_ylabel('cohesive energy (eV/atom)',\n fontsize='x-large')\n plt.show()\n \n return fig","def get_eddy_instances(parents, root_nodes, gate_dist):\n\tsnodes = []\n\te_instances = dict()\n\tchild_score = dict()\n\tfor parent in parents:\n\t\tfor child in parent.children:\n\t\t\tif is_term(child):\n\t\t\t\tcontinue\n\t\t\tchild.score = parent.score + max(score(hyp, gate_dist) for hyp in child.tracks())\n\t\t\tchild_score[child] = (child.score, parent)\n\t\t\tif child.obj in e_instances:\n\t\t\t\te_instances[child.obj].append(child)\n\t\t\telse:\n\t\t\t\te_instances[child.obj] = [child]\n\tfor node in root_nodes:\n\t\tnode.score = max(score(hyp, gate_dist) for hyp in node.tracks())\n\t\tchild_score[node] = (node.score, None)\n\t\tif node.obj in e_instances:\n\t\t\te_instances[node.obj].append(node)\n\t\telse:\n\t\t\te_instances[node.obj] = [node]\n\treturn (e_instances, child_score)","def calculate_E(self):\n \n E = 0\n for i in xrange(self.size):\n Ei = self.h[i]\n Ei += 0.5*sum((1 if self.spins[j] else -1)*self.J[i,j] for j in self.adjacency[i])\n if not self.spins[i]:\n Ei *= -1\n E += Ei\n \n return E","def plot_gene_expression(data, tsne, genes):\n\n not_in_dataframe = set(genes).difference(data.columns)\n if not_in_dataframe:\n if len(not_in_dataframe) < len(genes):\n print('The following genes were either not observed in the experiment, '\n 'or the wrong gene symbol was used: {!r}'.format(not_in_dataframe))\n else:\n print('None of the listed genes were observed in the experiment, or the '\n 'wrong symbols were used.')\n return 0\n\n # remove genes missing from experiment\n genes = set(genes).difference(not_in_dataframe)\n\n out_dir = '/Users/herman/dev/Bioinf/master-scrna/nextflow_scripts/results'\n\n # Plot\n fig = FigureGrid(len(genes), 5)\n points2 = None\n\n for g, ax in zip(genes, fig):\n points2 = ax.scatter(tsne['x'], tsne['y'], c=(int(len(tsne['x'])/2)*[0] + int(len(tsne['x'])/2)*[1]), s=3,\n cmap=matplotlib.cm.Spectral_r)\n points = ax.scatter(tsne['x'], tsne['y'], c=data.loc[tsne.index, g], s=3,\n cmap=matplotlib.cm.Spectral_r)\n\n #plt.colorbar(z1_plot,cax=ax1)\n ax.set_axis_off()\n ax.set_title(g)\n\n t = plt.colorbar(points2, label='expression', ax=ax)\n plt.savefig(os.path.join(out_dir, 'test_expression.svg'))","def cue_integration_output(self, cue1, cue2):\r\n pi_left = cue1[0:8]\r\n\r\n pi_l = np.zeros((self.num_neuron, int(self.nt)))\r\n pi_l[:, int(self.ni):] = np.repeat(pi_left.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\r\n\r\n pi_right = cue1[8:]\r\n pi_r = np.zeros((self.num_neuron, int(self.nt)))\r\n pi_r[:, int(self.ni):] = np.repeat(pi_right.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\r\n\r\n v_left = cue2[0:8]\r\n v_l = np.zeros((self.num_neuron, int(self.nt)))\r\n v_l[:, int(self.ni):] = np.repeat(v_left.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\r\n v_right = cue2[8:]\r\n v_r = np.zeros((self.num_neuron, int(self.nt)))\r\n v_r[:, int(self.ni):] = np.repeat(v_right.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\r\n\r\n # generate the array for integration cells and the uniform inhibitory cell\r\n it_l = np.zeros((self.num_neuron, int(self.nt)))\r\n it_l[:, 0] = 0.1 * np.ones(self.num_neuron, )\r\n it_r = np.zeros((self.num_neuron, int(self.nt)))\r\n it_r[:, 0] = 0.1 * np.ones(self.num_neuron, )\r\n ul = np.zeros((1, int(self.nt)))\r\n ur = np.zeros((1, int(self.nt)))\r\n # iteration to the stable state\r\n for t in range(1, int(self.nt)):\r\n it_l[:, t] = it_l[:, t - 1] + (-it_l[:, t - 1] + np.max(\r\n [np.zeros((self.num_neuron,)),\r\n self.gammaE + np.dot(self.W_E_E, it_l[:, t - 1]) + self.W_E_I * ul[:, t - 1] + pi_l[:, t - 1]\r\n + v_l[:, t - 1]], axis=0)) * self.dt / self.tauE\r\n ul[:, t] = ul[:, t - 1] + (\r\n -ul[:, t - 1] + np.max([0, self.gammaI + self.W_I_E * np.sum(it_l[:, t - 1]) + self.W_I_I * ul[:, t - 1]],\r\n axis=0)) * self.dt / self.tauI\r\n\r\n it_r[:, t] = it_r[:, t - 1] + (-it_r[:, t - 1] + np.max(\r\n [np.zeros((self.num_neuron,)),\r\n self.gammaE + np.dot(self.W_E_E, it_r[:, t - 1]) + self.W_E_I * ur[:, t - 1] + pi_r[:, t - 1]\r\n + v_r[:, t - 1]], axis=0)) * self.dt / self.tauE\r\n ur[:, t] = ur[:, t - 1] + (\r\n -ur[:, t - 1] + np.max([0, self.gammaI + self.W_I_E * np.sum(it_r[:, t - 1]) + self.W_I_I * ur[:, t - 1]],\r\n axis=0)) * self.dt / self.tauI\r\n\r\n # get the final iteration as the output\r\n # update the neuron activation\r\n self.integration_neuron = np.hstack([it_l[:, -1], it_r[:, -1]])\r\n self.integration_neuron = noisy_sigmoid(self.integration_neuron, 5.0, 2.5, 0)\r\n self.inhibition_neuron = np.hstack([ul[-1], ur[-1]])\r\n\r\n return self.integration_neuron","def divergedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\n \n parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childGeneDuplicated = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is True:\n parentEdges = parentGeneDuplicated.getEdges()\n childEdges = childGeneDuplicated.getEdges()\n \n graph = divergedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentGeneDuplicated.getEnzymes())\n childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childGeneDuplicated.getEnzymes())\n \n parentGraph.name = 'Diverged metabolism gene-duplicated enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Diverged metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def punto2_1():\r\n\tprint(\"2.1R/\")\r\n\tn = 15\r\n\tp = 0.2\r\n\tseed = 12\r\n\tvis = [False for i in range(n)]\r\n\tG,graph=erdos_renyi_seed(n,p,seed)\r\n\tnx.draw(graph,with_labels = True)\r\n\tplt.show()\t\r\n\tm1 = tarjan(G)\r\n\tnumCC = 0\r\n\tfor u in range(n):\r\n\t\tif vis[u] == False:\r\n\t\t\tnumCC+=1\r\n\t\t\tdfsCC(G,vis,u)\r\n\r\n\tavNumCC = n/numCC\r\n\tnumAp,numB = m1[0],m1[1]\r\n\tavnDeg = deg(G)\r\n\tt = tripTrian(G)\r\n\tnumTrian,numTrip = t[0],t[1]\r\n\tprint(\"numero de componentes conectados: \" + str(numCC))\r\n\tprint(\"tamaño promedio de componentes conectados: \" + str(avNumCC))\r\n\tprint(\"numero de puntos de articulacion: \" + str(numAp))\r\n\tprint(\"numero de puentes: \" + str(numB))\r\n\tprint(\"Grado promedio por nodo: \" + str(avnDeg))\r\n\tprint(\"numero de triangulos: \" + str(numTrian))\r\n\tprint(\"numero de tripletas: \" + str(numTrip))\r\n\treturn","def testNonLinearity():\n vis = VISinformation()\n data = np.linspace(1, vis['fullwellcapacity'], 10000)\n nonlin = CCDnonLinearityModel(data.copy())\n\n txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())\n\n fig = plt.figure(frameon=False)\n\n left, width = 0.1, 0.8\n rect1 = [left, 0.3, width, 0.65]\n rect2 = [left, 0.1, width, 0.2]\n\n ax1 = fig.add_axes(rect1, title='VIS Non-linearity Model')\n ax2 = fig.add_axes(rect2) #left, bottom, width, height\n\n ax1.axhline(y=0, c='k', ls='--')\n ax1.plot(data, (nonlin/data - 1.)*100, 'r-', label='Model')\n\n ax2.axhline(y=0, c='k', ls='--')\n ax2.plot(data, (nonlin - data)/vis['gain'], 'g-')\n\n ax1.axvline(x=97, c='k', ls='--')\n ax2.axvline(x=97, c='k', ls='--')\n\n ax1.set_xticklabels([])\n ax2.set_xlabel('Real Charge [electrons]')\n ax1.set_ylabel('(Output / Real - 1)*100')\n ax2.set_ylabel('O - R [ADUs]')\n\n ax1.set_xlim(0, vis['fullwellcapacity'])\n ax2.set_xlim(0, vis['fullwellcapacity'])\n ax1.set_ylim(-.15, .2)\n\n ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)\n ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0)\n plt.savefig('NonlinearityModel.pdf')\n\n ax1.set_ylim(-.1, 8)\n ax2.set_ylim(0, 2)\n ax1.set_xlim(50, 800)\n ax2.set_xlim(50, 800)\n plt.savefig('NonlinearityModel2.pdf')\n\n plt.close()","def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:\n # get neofunctionalisations \n neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)\n \n # filter core metabolism enzyme graph\n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism) \n neofunctionalisedMetabolism = neofunctionalisedEnzymes.filterGraph(enzymeGraph, minimumEcDifference = None)\n \n # colour core metabolism \n if colour is not False:\n \n if colour is True:\n colourToUse = Export.Colour.GREEN\n else:\n colourToUse = colour\n \n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\n neofunctionalisedMetabolism = enzymeGraph\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\n \n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism enzymes ' + ' '.join(self.ncbiNames)\n \n return neofunctionalisedMetabolism","def RE2e(Type=\"DFA\"):\n CC10, CC11 = state('CC10'), state('CC11')\n for i in sigma:\n CC10.transit[i] = CC10\n CC11.transit[i] = CC11\n for i in sigma_AW:\n CC10.transit[i] = CC11\n for i in sigma_B:\n CC11.transit[i] = CC10\n if Type == \"pDFA\":\n CC1 = pDFA('CC1', sigma, [CC10, CC11], CC10, [CC10])\n else:\n CC1 = DFA('CC1', sigma, [CC10, CC11], CC10, [CC10])\n if (SIZEOF):\n EM_size[\"RE2e\"] = asizeof.asizeof(CC1)\n return CC1","def test_linear_buckling_iso_CCSS(plot_static=False, plot_lb=False):\n # number of nodes\n nx = 5 # along x\n ny = 5 # along y\n\n # getting integration points\n nint = 4\n points, weights = get_points_weights(nint=nint)\n\n # geometry\n a = 3 # along x\n b = 3 # along y\n\n # material properties\n E = 200e9\n nu = 0.3\n laminaprop = (E, E, nu)\n stack = [0]\n h = 0.001\n lam = read_stack(stack=stack, plyt=h, laminaprop=laminaprop)\n\n # creating mesh\n x = np.linspace(0, a, nx)\n y = np.linspace(0, b, ny)\n xmesh, ymesh = np.meshgrid(x, y)\n\n # node coordinates and position in the global matrix\n ncoords = np.vstack((xmesh.T.flatten(), ymesh.T.flatten())).T\n nids = 1 + np.arange(ncoords.shape[0])\n nid_pos = dict(zip(nids, np.arange(len(nids))))\n\n # identifying nodal connectivity for plate elements\n # similar than Nastran's CQUAD4\n #\n # ^ y\n # |\n #\n # 4 ________ 3\n # | |\n # | | --> x\n # | |\n # |_______|\n # 1 2\n\n\n nids_mesh = nids.reshape(nx, ny)\n n1s = nids_mesh[:-1, :-1].flatten()\n n2s = nids_mesh[1:, :-1].flatten()\n n3s = nids_mesh[1:, 1:].flatten()\n n4s = nids_mesh[:-1, 1:].flatten()\n\n num_elements = len(n1s)\n print('num_elements', num_elements)\n\n N = DOF*nx*ny\n Kr = np.zeros(KC0_SPARSE_SIZE*num_elements, dtype=INT)\n Kc = np.zeros(KC0_SPARSE_SIZE*num_elements, dtype=INT)\n Kv = np.zeros(KC0_SPARSE_SIZE*num_elements, dtype=DOUBLE)\n KGr = np.zeros(KG_SPARSE_SIZE*num_elements, dtype=INT)\n KGc = np.zeros(KG_SPARSE_SIZE*num_elements, dtype=INT)\n KGv = np.zeros(KG_SPARSE_SIZE*num_elements, dtype=DOUBLE)\n init_k_KC0 = 0\n init_k_KG = 0\n\n plates = []\n for n1, n2, n3, n4 in zip(n1s, n2s, n3s, n4s):\n plate = BFSPlate2D()\n plate.n1 = n1\n plate.n2 = n2\n plate.n3 = n3\n plate.n4 = n4\n plate.c1 = DOF*nid_pos[n1]\n plate.c2 = DOF*nid_pos[n2]\n plate.c3 = DOF*nid_pos[n3]\n plate.c4 = DOF*nid_pos[n4]\n plate.ABD = lam.ABD\n plate.lex = a/(nx - 1)\n plate.ley = b/(ny - 1)\n plate.init_k_KC0 = init_k_KC0\n plate.init_k_KG = init_k_KG\n update_KC0(plate, points, weights, Kr, Kc, Kv)\n init_k_KC0 += KC0_SPARSE_SIZE\n init_k_KG += KG_SPARSE_SIZE\n plates.append(plate)\n\n KC0 = coo_matrix((Kv, (Kr, Kc)), shape=(N, N)).tocsc()\n\n # applying boundary conditions\n\n # locating nodes\n bk = np.zeros(KC0.shape[0], dtype=bool) # constrained DOFs, can be used to prescribe displacements\n\n x = ncoords[:, 0]\n y = ncoords[:, 1]\n\n # applying boundary conditions\n # simply supported\n check = isclose(x, 0) | isclose(x, a) | isclose(y, 0) | isclose(y, b)\n bk[2::DOF] = check\n check = isclose(x, 0) | isclose(x, a)\n bk[3::DOF] = check\n # point supports\n check = isclose(x, a/2) & (isclose(y, 0) | isclose(y, b))\n bk[0::DOF] = check\n check = isclose(y, b/2) & (isclose(x, 0) | isclose(x, a))\n bk[1::DOF] = check\n\n # unconstrained nodes\n bu = ~bk # logical_not\n\n # defining external force vector\n fext = np.zeros(KC0.shape[0], dtype=float)\n\n # applying unitary load along u at x=a\n # nodes at vertices get 1/2 the force\n for plate in plates:\n pos1 = nid_pos[plate.n1]\n pos2 = nid_pos[plate.n2]\n pos3 = nid_pos[plate.n3]\n pos4 = nid_pos[plate.n4]\n if isclose(x[pos3], a):\n Nxx = -1\n xi = +1\n elif isclose(x[pos1], 0):\n Nxx = +1\n xi = -1\n else:\n continue\n lex = plate.lex\n ley = plate.ley\n indices = []\n c1 = DOF*pos1\n c2 = DOF*pos2\n c3 = DOF*pos3\n c4 = DOF*pos4\n cs = [c1, c2, c3, c4]\n for ci in cs:\n for i in range(DOF):\n indices.append(ci + i)\n fe = np.zeros(4*DOF, dtype=float)\n for j in range(nint):\n eta = points[j]\n plate.update_Nu(xi, eta)\n Nu = np.asarray(plate.Nu)\n fe += ley/2*weights[j]*Nu*Nxx\n fext[indices] += fe\n\n Kuu = KC0[bu, :][:, bu]\n fextu = fext[bu]\n\n # static solver\n uu = spsolve(Kuu, fextu)\n u = np.zeros(KC0.shape[0], dtype=float)\n u[bu] = uu\n\n if plot_static:\n import matplotlib\n matplotlib.use('TkAgg')\n import matplotlib.pyplot as plt\n plt.gca().set_aspect('equal')\n uplot = u[0::DOF].reshape(nx, ny).T\n vplot = u[1::DOF].reshape(nx, ny).T\n print('u extremes', uplot.min(), uplot.max())\n print('v extremes', vplot.min(), vplot.max())\n levels = np.linspace(uplot.min(), uplot.max(), 300)\n plt.contourf(xmesh, ymesh, uplot, levels=levels)\n plt.colorbar()\n plt.show()\n\n # eigenvalue solver\n\n # getting integration points\n for plate in plates:\n update_KG(u, plate, points, weights, KGr, KGc, KGv)\n KG = coo_matrix((KGv, (KGr, KGc)), shape=(N, N)).tocsc()\n KGuu = KG[bu, :][:, bu]\n\n # solving modified generalized eigenvalue problem\n # Original: (KC0 + lambda*KG)*v = 0\n # Modified: (-1/lambda)*KC0*v = KG*v #NOTE here we find (-1/lambda)\n num_eigenvalues = 5\n eigvals, eigvecsu = eigsh(A=KGuu, k=num_eigenvalues, which='SM', M=Kuu,\n tol=1e-6, sigma=1., mode='cayley')\n eigvals = -1./eigvals\n eigvecs = np.zeros((KC0.shape[0], num_eigenvalues), dtype=float)\n eigvecs[bu, :] = eigvecsu\n\n if plot_lb:\n import matplotlib\n matplotlib.use('TkAgg')\n import matplotlib.pyplot as plt\n plt.gca().set_aspect('equal')\n mode = 0\n wplot = eigvecs[2::DOF, mode].reshape(nx, ny).T\n levels = np.linspace(wplot.min(), wplot.max(), 300)\n plt.contourf(xmesh, ymesh, wplot, levels=levels)\n plt.colorbar()\n plt.show()\n\n kc = eigvals[0]/(E*np.pi**2*(h/b)**2/(12*(1 - nu**2))*h)\n assert isclose(kc, 6.6, rtol=0.05)","def get_econs(self):\n eham = self.beads.vpath*self.nm.omegan2 + self.nm.kin + self.forces.pot\n eham += self.bias.pot # bias\n for e in self._elist:\n eham += e.get()\n\n return eham + self.eens","def electoral_college_outcome(ec_rows, state_edges):\r\n ec_votes = {} # maps from state to number of electoral votes\r\n for row in ec_rows:\r\n ec_votes[row[\"State\"]] = float(row[\"Electors\"])\r\n\r\n outcome = {\"Dem\": 0, \"Rep\": 0}\r\n for state in state_edges:\r\n votes = ec_votes[state]\r\n if state_edges[state] > 0:\r\n outcome[\"Dem\"] += votes\r\n elif state_edges[state] < 0:\r\n outcome[\"Rep\"] += votes\r\n else:\r\n outcome[\"Dem\"] += votes/2.0\r\n outcome[\"Rep\"] += votes/2.0\r\n return outcome","def get_second_eigenvector(graph):\n ###TODO\n pass","def dual(self):\n def set_twins():\n for edge_idx in range(0, len(dual_dcel.edges), 2):\n dual_dcel.edges[edge_idx].twin = dual_dcel.edges[edge_idx + 1]\n dual_dcel.edges[edge_idx + 1].twin = dual_dcel.edges[edge_idx]\n\n def set_next_and_previous():\n for face in dual_dcel.faces:\n face_edges = [edge for edge in dual_dcel.edges if edge.incident_face == face]\n for edge in face_edges:\n if(not edge.get_destination().is_infinity()):\n edge.nxt = [e for e in face_edges if e.origin == edge.get_destination()][0]\n if(not edge.origin.is_infinity()):\n edge.prev = [e for e in face_edges if edge.origin == e.get_destination()][0]\n\n dual_dcel = DCEL()\n for edge in self.edges:\n incident_face = dual_dcel.add_face(Face(circumcentre=edge.twin.origin.as_points()))\n origin = dual_dcel.add_vertex(Vertex(coordinates=edge.incident_face.circumcentre))\n dual_edge = HalfEdge(\n origin=origin,\n incident_face=incident_face\n )\n incident_face.outer_component = dual_edge\n origin.incident_edge = dual_edge\n dual_dcel.edges.append(dual_edge)\n\n set_twins()\n set_next_and_previous()\n return dual_dcel","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/27')","def computemodes(self):\n evals, modes = np.linalg.eig(self.A)\n return evals, modes","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/26')","def ehermite(a, b):\n [c, d, g] = extgcd(a, b)\n if g:\n E = np.array([[c, d], [-b/g, a/g]])\n else:\n E = np.array([[1, 0], [0, 1]])\n\n return E\n #Leila: check this \"http://www.ece.northwestern.edu/local-apps/matlabhelp/techdoc/ref/gcd.html\"","def part1c_0():\n xs = exampleInput\n T = submission.computeEdgeMarginals(simpleCRF, xs)\n for t in T:\n grader.requireIsEqual( 1.0, sum(t.values()) )","def eci(self):\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\n eci = coefs.copy()\n eci = eci / self._subspace.function_total_multiplicities\n return eci","def get_core(evth, ncore=0):\n x, y = (evth[98 + ncore], evth[118 + ncore])\n return (x, y, sqrt((x * x) + (y * y)))","def display_energy_levels_0d(diagram, num_atoms, atoms, h_poly):\n h = eval_hamiltonian(num_atoms, h_poly, (1, 1))\n\n e, v = eigensystem(h)\n\n left = 0\n bottom = 0\n right = max([len(row) for row in diagram.split('\\n')])\n top = len(diagram.split('\\n'))\n\n plot_rows = numpy.ceil(math.sqrt(num_atoms+1))\n plot_cols = plot_rows\n\n for i in range(num_atoms):\n matplotlib.pyplot.subplot(plot_rows, plot_cols, i+1, axisbg=\"#000000\")\n y = [atom[0] for atom in atoms]\n x = [atom[1] for atom in atoms]\n c = numpy.abs(v[i]*v[i])\n\n matplotlib.pyplot.title('E = %f' % numpy.real(e[i]), fontsize = 10)\n norm = matplotlib.colors.Normalize(vmin = min(c),\n vmax = max(0.0001, max(c)))\n #x = [0,0,1,1]\n #y = [0,1,0,1]\n #c = [1,2,3,4]\n matplotlib.pyplot.hexbin(x, y, C = c,\n gridsize = (right-left, top-bottom),\n extent = (left, right, bottom, top),\n cmap = matplotlib.pyplot.get_cmap(\"gray\"),\n norm = norm\n )\n\n matplotlib.pyplot.subplot(plot_rows, plot_cols, num_atoms+1)\n matplotlib.pyplot.scatter(num_atoms*[0], e, s = 0.1)","def euclid_ccl(Omega_c, sigma8=0.83):\n\n cosmo_fid = ccl.Cosmology(Omega_c=Omega_c, Omega_b=0.045, h=0.71, sigma8=sigma8, n_s=0.963)\n\n dNdzs = np.zeros((nbins, z.size))\n shears = []\n \n for i in range(nbins):\n # edges of 1 equal width redshift bins, between 0 and 2\n zmin, zmax = i*(2./nbins), (i+1)*(2./nbins)\n # generate dNdz per bin\n dNdzs[i,:] = ccl.dNdz_tomog(z=z, zmin=zmin, zmax=zmax, pz_func=pz, dNdz_func = dNdz_true)\n # calculate the shear per bin\n gal_shapes = ccl.WeakLensingTracer(cosmo_fid, dndz=(z, dNdzs[i,:]))\n shears.append(gal_shapes)\n \n print ('Shears calculated, calculating power spectra now...')\n # calculate nbin*(nbin+1)/2 = 1 spectra from the shears\n Cls = []\n for i in range(nbins):\n for j in range(0,i+1):\n Cls.append(ccl.angular_cl(cosmo_fid, shears[i], shears[j], ells))\n \n return np.array(Cls), dNdzs","def geneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:\n \n \n \n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)\n \n geneDuplicationModel = SimpleGeneDuplication\n# geneDuplicationModel = SimpleGroupGeneDuplication(sameGroupOrganisms = self.group)\n \n # filter core metabolism enzyme graph \n geneDuplicatedEnzymes = geneDuplicationModel.filterEnzymes(enzymeGraph, eValue = defaultEValue, ignoreDuplicatesOutsideSet = True, preCalculatedEnzymes = None)\n \n # colour core metabolism\n if colour is not False:\n \n if colour is True:\n colourToUse = Export.Colour.GREEN\n else:\n colourToUse = colour\n \n geneDuplicatedEnzymesOnly = geneDuplicatedEnzymes\n geneDuplicatedEnzymes = enzymeGraph\n Export.addColourAttribute(geneDuplicatedEnzymes, colourToUse, nodes = False, edges = geneDuplicatedEnzymesOnly.getEdges())\n \n geneDuplicatedEnzymes.name = 'Gene-duplicated core metabolism enzymes ' + ' '.join(self.ncbiNames)\n \n return geneDuplicatedEnzymes","def main():\n Nrep = 8 # number of repetition of EM steps\n nm = 3 # number of mixed gaussians.\n ns = 300 # number of samples.\n \n mu, sg, lm, lm_ind, smp, L_true = generate_synthetic_data(nm, ns)\n plt.figure(1, figsize=(5,4))\n plt.clf()\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\n \n mue, sge, lme = generate_initial_state(nm, ns)\n axi = 0 # subplot number\n plt.figure(2, figsize=(12,9))\n plt.clf()\n for rep in range(Nrep):\n # E-step\n r, L_infer = e_step(smp, mue, sge, lme, nm, ns)\n axi += 1 \n ax = plt.subplot(Nrep/2, 6, axi)\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\n ax.set_title('E-step : %d' % (rep + 1))\n ax.set_yticklabels([])\n ax.set_xticklabels([])\n ax.set_ylim((-0.1, 0.3))\n\n # M-step\n mue, sge, lme = m_step(smp, r, nm, ns)\n axi += 1 \n ax = plt.subplot(Nrep/2, 6, axi)\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\n ax.set_title('M-step : %d' % (rep + 1))\n ax.set_yticklabels([])\n ax.set_xticklabels([])\n ax.set_ylim((-0.1, 0.3))\n\n # plot the ground truth for comparison\n axi += 1 \n ax = plt.subplot(Nrep/2, 6, axi)\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\n ax.set_title('grn_truth')\n ax.set_yticklabels([])\n ax.set_xticklabels([])\n ax.set_ylim((-0.1, 0.3))\n\n print('L_infer = %2.6f , L_true = %2.6f' % (L_infer, L_true))","def electoral_college_outcome(ec_rows, state_edges):\n ec_votes = {} # maps from state to number of electoral votes\n for row in ec_rows:\n ec_votes[row[\"State\"]] = float(row[\"Electors\"])\n\n outcome = {\"Dem\": 0, \"Rep\": 0}\n for state in state_edges:\n votes = ec_votes[state]\n if state_edges[state] > 0:\n outcome[\"Dem\"] += votes\n elif state_edges[state] < 0:\n outcome[\"Rep\"] += votes\n else:\n outcome[\"Dem\"] += votes/2.0\n outcome[\"Rep\"] += votes/2.0\n return outcome","def conservedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\n \n parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childGeneDuplicated = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is True:\n parentEdges = parentGeneDuplicated.getEdges()\n childEdges = childGeneDuplicated.getEdges()\n \n graph = conservedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Conserved metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentGeneDuplicated.getEnzymes())\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childGeneDuplicated.getEnzymes())\n \n parentGraph.name = 'Conserved metabolism gene-duplicated enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Conserved metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def find_5sigma_diff(pop1, pop2):\n\n grb1 = pop1.g_tot\n grb2 = pop2.g_tot\n\n first1 = True\n first2 = True\n\n #----------------------\n def show():\n print(\" {:8s} {:5s} 1: sigmx={:5.1f} +/-{:5.1f} * CL={:5d} * vis={:2d} * err={:5d}\"\n .format(g1[\"name\"], g1.loca, g1.sigmx, g1.esigmx, g1.d5s,g1.prpt,g1.err))\n print(\" {:8s} {:5s} 2: {:5.1f} +/-{:5.1f} {:5d} * {:2d} * {:5d}\"\n .format(\" \",\" \",g2.sigmx, g2.esigmx, g2.d5s,g2.prpt,g2.err))\n return\n #----------------------\n\n for (i1, g1), (i2, g2) in zip(grb1.iterrows(), grb2.iterrows()):\n\n if (g1.d5s >= pop1.eff_lvl and g2.d5s < pop2.eff_lvl) :\n if (first1):\n print(\"Error : detected in 1 not in 2\")\n first1 = False\n show()\n\n if (g1.d5s < pop1.eff_lvl and g2.d5s >= pop2.eff_lvl) :\n if (first2):\n print(\"Error : detected in 2 not in 1\")\n first2 = False\n show()\n print(\"All done!\")","def unifiedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph: \n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is False:\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\n \n else:\n unifiedMetabolismEnzymes = self.unifiedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = True)\n \n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = unifiedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph"],"string":"[\n \"def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \\n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n \\n if colour is False:\\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\\n \\n else:\\n unifiedMetabolism = self.unifiedMetabolism(majorityPercentageCoreMetabolism, colour = True)\\n \\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = unifiedMetabolism\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\",\n \"def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\\n conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)\\n \\n parentNeofunctionalised= self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = conservedMetabolism\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = conservedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\\n childGraph = conservedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\\n \\n parentGraph.name = 'Conserved metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n childGraph = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(addedECs)\\n childGraph.name = 'Added metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return childGraph\",\n \"def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:\\n # get neofunctionalisations \\n neofunctionalisedECs = NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs))\\n \\n # filter core metabolism EC graph\\n coreMetabolism = self.coreMetabolism(majorityPercentageCoreMetabolism)\\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))\\n \\n neofunctionalisedMetabolism = neofunctionalisedECs.filterGraph(coreMetabolism, minimumEcDifference = None, minimumOrganismsCount = minimumOrganismsCount)\\n \\n # colour core metabolism\\n if colour is not False:\\n \\n if colour is True:\\n colourToUse = Export.Colour.GREEN\\n else:\\n colourToUse = colour\\n \\n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\\n neofunctionalisedMetabolism = coreMetabolism\\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\\n \\n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism ' + ' '.join(self.ncbiNames)\\n \\n return neofunctionalisedMetabolism\",\n \"def lostMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n parentGraph = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(lostECs)\\n parentGraph.name = 'Lost metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return parentGraph\",\n \"def get_ecg_graph():\\n titles = ['ecg1', 'ecg2', 'ecg3']\\n colors = ['rgb(240,0,0)', 'rgb(0,240,0)', 'rgb(0,0,240)']\\n update()\\n signames_ecg = queries['signames_ecg']\\n signals = queries['signals']\\n latesthr = queries['latesthr']\\n return html.Div(className='ecg', children=[\\n html.Div(style={'display': 'flex', 'height': '40vh'},\\n children=[dcc.Graph(\\n id=titles[i] + signame,\\n style={'width': '100%'},\\n figure={\\n 'data': [\\n {'x': signals[signame]['time'],\\n 'y': signals[signame][titles[i]],\\n 'mode': 'line', 'name': signame, 'line': {'color':colors[i]}}\\n ],\\n 'layout': {\\n 'font': {'color':'#fff'},\\n 'title': '{}-{}'.format(signame, titles[i]),\\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\\n 'yaxis': {'title': 'voltage (mv)', 'color': '#fff', 'showgrid': 'False', 'range': np.linspace(-2.5, 2.5, 10)},\\n 'paper_bgcolor':'#000', 'plot_bgcolor':'#000'\\n }\\n }\\n ) for i in range(len(titles))]\\n +\\n [html.Div(\\n style={'justify-content': 'center', 'display': 'flex',\\n 'align-items': 'center', 'width': '10vh', 'font-size': '30pt', 'color': 'white'},\\n children=['{}'.format(latesthr[signame][0])])\\n ]\\n ) for signame in signames_ecg])\",\n \"def redundantECsForContributingNeofunctionalisation(self, \\n majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, \\n majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, \\n eValue = defaultEValue, \\n redundancyType: 'RedundancyType' = None,\\n considerOnlyECs = None) -> Dict[Neofunctionalisation, Set[EcNumber]]:\\n from FEV_KEGG.Robustness.Topology.Redundancy import Redundancy, RedundancyContribution, RedundancyType\\n \\n if redundancyType is None:\\n redundancyType = RedundancyType.default\\n \\n #- calculate \\\"neofunctionalised\\\" ECs\\n neofunctionalisedMetabolismSet = self.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs).getECs()\\n neofunctionalisationsForFunctionChange = self.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs)\\n \\n #- calculate redundancy\\n redundancy = Redundancy( self.coreMetabolism(majorityPercentageCoreMetabolism) )\\n redundancyContribution = RedundancyContribution(redundancy, neofunctionalisedMetabolismSet)\\n \\n contributedECsForContributingNeofunctionalisedEC = redundancyContribution.getContributedKeysForSpecial(redundancyType)\\n contributingNeofunctionalisedECs = set(contributedECsForContributingNeofunctionalisedEC.keys())\\n \\n #- REPEAT for each function change consisting of \\\"neofunctionalised\\\" ECs, which also contribute to redundancy\\n contributingNeofunctionalisations = dict()\\n \\n for functionChange, neofunctionalisations in neofunctionalisationsForFunctionChange.items():\\n #- report enzyme pairs of neofunctionalisations, which caused the EC to be considered \\\"neofunctionalised\\\", and are in return contributing to redundancy \\n \\n if functionChange.ecA in contributingNeofunctionalisedECs or functionChange.ecB in contributingNeofunctionalisedECs: # function change contributes to redundancy\\n \\n for neofunctionalisation in neofunctionalisations:\\n currentSetOfContributedECs = contributingNeofunctionalisations.get(neofunctionalisation, None)\\n \\n if currentSetOfContributedECs is None:\\n currentSetOfContributedECs = set()\\n contributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs\\n \\n for ec in functionChange.ecPair:\\n contributedECs = contributedECsForContributingNeofunctionalisedEC.get(ec, None)\\n if contributedECs is not None:\\n currentSetOfContributedECs.update(contributedECs)\\n \\n return contributingNeofunctionalisations\",\n \"def uncertainty_ee(self,e1,e2):\\n # reco\\n unc = (self._eleRecoWeight[(e1.pt(),e1.eta())][1]/self._eleRecoWeight[(e1.pt(),e1.eta())][0] + \\\\\\n self._eleRecoWeight[(e2.pt(),e2.eta())][1]/self._eleRecoWeight[(e2.pt(),e2.eta())][0])**2\\n # id-isolation\\n unc += (self._eleIdIsoWeight[(e1.pt(),e1.eta())][1]/self._eleIdIsoWeight[(e1.pt(),e1.eta())][0] + \\\\\\n self._eleIdIsoWeight[(e2.pt(),e2.eta())][1]/self._eleIdIsoWeight[(e2.pt(),e2.eta())][0])**2\\n # trigger (approximate)\\n unc += (abs(self._ele8TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][1]+ \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][1]*self._ele8TrgWeight[(e2.pt(),e2.eta())][0]- \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][1]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]- \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][1])/ \\\\\\n (self._ele8TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]+ \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele8TrgWeight[(e2.pt(),e2.eta())][0]- \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]))**2\\n unc += ((self._ele8TrgWeight[(e1.pt(),e1.eta())][1]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]+ \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele8TrgWeight[(e2.pt(),e2.eta())][1])/ \\\\\\n (self._ele8TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]+ \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele8TrgWeight[(e2.pt(),e2.eta())][0]- \\\\\\n self._ele17TrgWeight[(e1.pt(),e1.eta())][0]*self._ele17TrgWeight[(e2.pt(),e2.eta())][0]))**2\\n #outcome\\n return sqrt(unc)\",\n \"def algo_Euclide_etendu(a,b):\\n r1=a\\n u1=1\\n v1=0\\n r2=b\\n u2=0\\n v2=1\\n while r2!=0 : #Invariants de boucle : r1=u1*a+v1*b et r2=u2*a+v2*b\\n q=r1//r2\\n rs=r1 ; us=u1 ; vs=v1 #Variables de sauvegarde\\n r1=r2 ; u1=u2 ; v1=v2\\n r2 = rs - q*r2 # r2 <- Reste de la division euclidienne de r1 par r2\\n u2 = us - q*u2\\n v2 = vs - q*v2\\n return(r1,u1,v1) #On prend le dernier reste non nul.\",\n \"def intersectConics(E1, E2):\\n\\n P = np.array([])\\n r1 = matrix_rank(E1)\\n r2 = matrix_rank(E2)\\n \\n if(r1==3 and r2==3):\\n P = completeIntersection(E1,E2) \\n else:\\n if (r2 < 3): #E2 is degenerate\\n defE = E2\\n fullE = E1\\n else:\\n defE = E1 #E1 is degenerate\\n fullE = E2\\n m, l = decomposeDegenerateConic(defE)\\n P1 = intersectConicLine(fullE,m)\\n P2 = intersectConicLine(fullE,l)\\n P = np.array([P1, P2])\\n points_x = []\\n points_y = []\\n for i in range(2):\\n P1 = P[i]\\n if(P1.size!=0):\\n for j in range(P1.shape[0]):\\n points_x.append(P1[j,0]/P1[j,2])\\n points_y.append(P1[j,1]/P1[j,2])\\n return points_x, points_y\",\n \"def sub_graph_merging(self):\",\n \"def betweenness_pol(G, ms):\\n dict_eb = nx.edge_betweenness_centrality(G, k = int(0.75*len(G)))\\n #n_pivots = min(1000, len(G))\\n #dict_eb = nx.edge_betweenness_centrality(G, k=n_pivots)\\n \\n cut_edges = []\\n rest_edges = []\\n \\n BCC_dist = []\\n\\n for e in G.edges():\\n s, t = e\\n\\n if ms[s] != ms[t]:\\n cut_edges += [e]\\n else:\\n rest_edges += [e]\\n\\n cut_ebc = [dict_eb[e] for e in cut_edges]\\n rest_ebc = [dict_eb[e] for e in rest_edges]\\n \\n if len(cut_ebc) <= 1:\\n print(\\\"Error in the gap!\\\")\\n return -1\\n \\n kernel_for_cut = scipy.stats.gaussian_kde(cut_ebc, \\\"silverman\\\")\\n kernel_for_rest = scipy.stats.gaussian_kde(rest_ebc, \\\"silverman\\\")\\n\\n BCC = []\\n \\n for _ in range(10):\\n cut_dist = kernel_for_cut.resample(10000)[0]\\n rest_dist = kernel_for_rest.resample(10000)[0]\\n\\n cut_dist = [max(0.00001, value) for value in cut_dist]\\n rest_dist = [max(0.00001, value) for value in rest_dist]\\n\\n kl_divergence = scipy.stats.entropy(rest_dist, cut_dist)\\n\\n BCCval = 1-2.71828**(-kl_divergence)\\n \\n BCC.append(BCCval)\\n \\n return sum(BCC)/len(BCC)\",\n \"def cell_edges2d_cartesian(self, axis2):\",\n \"def plot_ecdf(self, variant_one, variant_two):\\n if variant_one == variant_two:\\n raise ValueError('variant_one and variant_two cannot be the same')\\n if variant_one not in self.posteriors.keys() or \\\\\\n variant_two not in self.posteriors.keys():\\n raise ValueError(('Variants must only be a value in column '\\n '{}'.format(self.bucket_col_name)))\\n\\n if variant_one in self.ecdf.keys() and \\\\\\n variant_two in self.ecdf[variant_one].keys():\\n self._plot_ecdf(numerator_name=variant_one,\\n denominator_name=variant_two)\\n plt.ylabel('Cumulative Lift: {0} vs {1}'\\n .format(variant_two, variant_one))\\n else:\\n self._plot_ecdf(numerator_name=variant_two,\\n denominator_name=variant_one)\\n plt.ylabel('Cumulative Lift: {0} vs {1}'\\n .format(variant_one, variant_two))\",\n \"def __repr__(self):\\n return \\\"EC(%s, %s)\\\" % (str(self.coefficient), repr(self.basefield))\",\n \"def division_euclidienne(n1, n2):\",\n \"def question_2():\\r\\n comparison_graph = er_algorithm(1000, random.uniform(0, 1))\\r\\n in_degree_dist = utility_graph.in_degree_distribution(comparison_graph)\\r\\n normalized_dist = utility_graph.normalize_distribution(in_degree_dist)\\r\\n\\r\\n utility_graph.plot_log_log_scatter(normalized_dist,\\r\\n 'ER Algorithm In-degree Distribution',\\r\\n 'in-degree log-base-10',\\r\\n 'normalized distribution log-base-10')\",\n \"def CS2e(Type=\\\"DFA\\\"):\\n CC50, CC51, CC52, CC53 = state('CC50'), state('CC51'), state('CC52'), state('CC53')\\n for i in sigma:\\n CC52.transit[i] = CC52\\n CC53.transit[i] = CC53\\n for i in sigma_1:\\n CC50.transit[i] = CC50\\n for i in sigma_2:\\n CC50.transit[i] = CC53\\n CC51.transit[i] = CC51\\n for i in sigma_cc:\\n CC50.transit[i] = CC51\\n CC51.transit[i] = CC51\\n for i in sigma_ncc:\\n CC51.transit[i] = CC50\\n for i in sigma_B_A:\\n CC51.transit[i] = CC50\\n for i in sigma_e:\\n CC51.transit[i] = CC53\\n for i in sigma_A:\\n CC51.transit[i] = CC52\\n if Type == \\\"pDFA\\\":\\n CC5 = pDFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\\n else:\\n CC5 = DFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\\n if (SIZEOF):\\n EM_size[\\\"CS2e\\\"] = asizeof.asizeof(CC5)\\n return CC5\",\n \"def test_efficiency_disconnected_nodes(self):\\n assert_equal(nx.efficiency(self.G1, 1, 2), 0)\",\n \"def _nelec(self):\\n pd = self.particle_distribution(self._gam * mec2)\\n return pd.to(1/mec2_unit).value\",\n \"def eccentricity(self):\\n new_data = self._data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\\n 'pl_masse', 'pl_orbeccen',\\n 'pl_radj', 'pl_dens', 'st_teff',\\n 'st_mass', 'st_rad']]\\n new_data = new_data.dropna()\\n\\n features = new_data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\\n 'pl_masse',\\n 'pl_radj', 'pl_dens', 'st_teff',\\n 'st_mass', 'st_rad']]\\n labels = new_data['pl_orbeccen']\\n\\n features_train, features_test, labels_train, labels_test = \\\\\\n train_test_split(features, labels, test_size=0.2)\\n\\n # Create an untrained model\\n model = DecisionTreeRegressor()\\n\\n # Train it on the **training set**\\n model.fit(features_train, labels_train)\\n\\n # Compute test accuracy\\n test_predictions = model.predict(features_test)\\n test_acc = mean_absolute_error(labels_test, test_predictions)\\n test_acc_r2 = r2_score(labels_test, test_predictions)\\n\\n # Plot ML vs Actual\\n fig, [ax1, ax2] = plt.subplots(2, figsize=(15, 12))\\n\\n sns.distplot(test_predictions, kde=False, ax=ax1)\\n sns.distplot(labels_test, kde=False, ax=ax2)\\n\\n ax1.set_title('Distribution of Predicted Eccentricities of Orbits')\\n ax1.set_xlabel('Eccentricity of Orbit')\\n ax1.set_ylabel('Number of Planets')\\n\\n ax2.set_title('Distribution of Actual Eccentricities of Orbits')\\n ax2.set_xlabel('Eccentricity of Orbit')\\n ax2.set_ylabel('Number of Planets')\\n\\n plt.savefig('figures/ML_Eccentricity.png', bbox_inches='tight')\\n\\n return (test_acc, test_acc_r2)\",\n \"def ordinal_difference(evs, evs2):\\n \\n sigma = calc_sigma(evs)\\n \\n # print sigma\\n # ordinal difference\\n ordDiff = zeros(vecLen-1, dtype='d')\\n for j in range(vecLen-1):\\n ordDiff[j] = absolute(evs[j] - evs2[j]) / sigma[j]\\n del j\\n return ordDiff\",\n \"def set_ec(self, etacalc):\\n if not self.__thermodyn:\\n C = np.zeros((6,6))\\n \\n LC = self.__structures.items()[0][1].LC\\n if self.__mthd == 'Energy':\\n if type(etacalc)==list:\\n A2=[]\\n for i in range(len(etacalc)):\\n A2.append(self.__A2[i][etacalc[i]])\\n else:\\n if not etacalc in self.__A2[0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[0].keys()))\\n A2 = [a2[etacalc] for a2 in self.__A2]\\n \\n \\n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\\n C[1,1] = C[0,0]\\n C[2,2] = C[0,0]\\n C[3,3] = A2[2]/6.\\n C[4,4] = C[3,3]\\n C[5,5] = C[3,3]\\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\\n C[0,2] = C[0,1]\\n C[1,2] = C[0,1]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[2]\\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RI'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[1,4] =-C[0,4] \\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[3,5] =-C[0,4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TI'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[3]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TII'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\\n C[1,1] = C[0,0]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\\n C[1,2] = C[0,2]\\n C[1,5] =-C[0,5]\\n C[2,2] = 2.*A2[4]\\n C[3,3] = .5*A2[5]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[6]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'O'):\\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\\n C[1,1] = 2.*A2[4]\\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\\n C[2,2] = 2.*A2[5]\\n C[3,3] = .5*A2[6]\\n C[4,4] = .5*A2[7]\\n C[5,5] = .5*A2[8]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'M'):\\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\\n C[1,1] = 2.*A2[8]\\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\\n C[2,2] = 2.*A2[9]\\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\\n C[3,3] = .5*A2[10]\\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\\n C[4,4] = .5*A2[11]\\n C[5,5] = .5*A2[12]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'N'):\\n C[0,0] = 2.*A2[0]\\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \\n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\\n C[1,1] = 2.*A2[1]\\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\\n C[2,2] = 2.*A2[2] \\n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\\n C[3,3] = .5*A2[3]\\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\\n C[4,4] = .5*A2[4]\\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n elif self.__mthd == 'Stress':\\n \\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n Matrix = np.mat([[1.0, 5.0, 0.0],\\n [2.0, 4.0, 0.0],\\n [3.0, 3.0, 0.0],\\n [0.0, 0.0, 4.0],\\n [0.0, 0.0, 5.0],\\n [0.0, 0.0, 6.0]])\\n \\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\\n [ 2, 1, 3, 0, 0],\\n [ 0, 0, 3, 3, 0],\\n [ 0, 0, 0, 0, 4],\\n [ 0, 0, 0, 0, 5],\\n [ 3,-3, 0, 0, 0],\\n [ 3,-5,-1, 0, 0],\\n [-5, 3,-1, 0, 0],\\n [ 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 0, 6],\\n [ 0, 0, 0, 0, 2],\\n [-2, 2, 0, 0, 0]])\\n \\n if (LC == 'RI'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\\n [ 2, 1, 3,-4, 0, 0],\\n [ 0, 0, 3, 0, 3, 0],\\n [ 0, 0, 0,-1, 0, 4],\\n [ 0, 0, 0, 6, 0, 5],\\n [ 3,-3, 0, 5, 0, 0],\\n [ 3,-5,-1, 6, 0, 0],\\n [-5, 3,-1,-6, 0, 0],\\n [ 0, 0,-2, 0,-1, 0],\\n [ 0, 0, 0, 8, 0, 6],\\n [ 0, 0, 0,-4, 0, 2],\\n [-2, 2, 0, 2, 0, 0]])\\n \\n if (LC == 'RII'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\\n [ 2, 1, 3,-4,-5, 0, 0],\\n [ 0, 0, 3, 0, 0, 3, 0],\\n [ 0, 0, 0,-1,-6, 0, 4],\\n [ 0, 0, 0, 6,-1, 0, 5],\\n [ 3,-3, 0, 5,-4, 0, 0],\\n [ 3,-5,-1, 6, 2, 0, 0],\\n [-5, 3,-1,-6,-2, 0, 0],\\n [ 0, 0,-2, 0, 0,-1, 0],\\n [ 0, 0, 0, 8, 4, 0, 6],\\n [ 0, 0, 0,-4, 8, 0, 2],\\n [-2, 2, 0, 2,-6, 0, 0]])\\n \\n if (LC == 'TI'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\\n [ 2, 1, 3, 0, 0, 0],\\n [ 0, 0, 3, 3, 0, 0],\\n [ 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0, 0, 0, 6],\\n [ 3,-5,-1, 0, 0, 0],\\n [-5, 3,-1, 0, 0, 0],\\n [ 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 0, 0,-4]])\\n \\n if (LC == 'TII'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\\n [ 2, 1, 3,-6, 0, 0, 0],\\n [ 0, 0, 3, 0, 3, 0, 0],\\n [ 0, 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0,-1, 0, 0, 6],\\n [ 3,-5,-1,-4, 0, 0, 0],\\n [-5, 3,-1, 4, 0, 0, 0],\\n [ 0, 0,-2, 0,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 8, 0, 0,-4]])\\n \\n if (LC == 'O'):\\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\\n \\n if (LC == 'M'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\\n \\n if (LC == 'N'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\\n \\n sigma = np.array(self.__sigma[etacalc])\\n \\n ci = np.linalg.lstsq(Matrix,sigma)\\n \\n #-- Cubic structures ------------------------------------------------------------------------------\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n \\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[3,3]=ci[0][2]\\n C[1,1]=C[0,0]\\n C[2,2]=C[0,0]\\n C[0,2]=C[0,1]\\n C[1,2]=C[0,1]\\n C[4,4]=C[3,3]\\n C[5,5]=C[3,3]\\n \\n #-- Hexagonal Structures --------------------------------------------------------------------------\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[2,2]=ci[0][3]\\n C[3,3]=ci[0][4]\\n C[1,1]=C[0,0]\\n C[1,2]=C[0,2]\\n C[4,4]=C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral I Structures ---------------------------------------------------------------------\\n if (LC == 'RI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral II Structures --------------------------------------------------------------------\\n if (LC == 'RII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[0,4]= ci[0][4]\\n C[2,2]= ci[0][5]\\n C[3,3]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[1,4]=-C[0,4]\\n C[3,5]=-C[0,4]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Tetragonal I Structures -----------------------------------------------------------------------\\n if (LC == 'TI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[2,2]= ci[0][3]\\n C[3,3]= ci[0][4]\\n C[5,5]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[4,4]= C[3,3]\\n \\n #-- Tetragonal II Structures ----------------------------------------------------------------------\\n if (LC == 'TII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,5]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[5,5]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,5]=-C[0,5]\\n C[4,4]= C[3,3]\\n \\n #-- Orthorhombic Structures -----------------------------------------------------------------------\\n if (LC == 'O'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[1,1]=ci[0][3]\\n C[1,2]=ci[0][4]\\n C[2,2]=ci[0][5]\\n C[3,3]=ci[0][6]\\n C[4,4]=ci[0][7]\\n C[5,5]=ci[0][8]\\n \\n #-- Monoclinic Structures -------------------------------------------------------------------------\\n if (LC == 'M'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,5]=ci[0][3]\\n C[1,1]=ci[0][4]\\n C[1,2]=ci[0][5]\\n C[1,5]=ci[0][6]\\n C[2,2]=ci[0][7]\\n C[2,5]=ci[0][8]\\n C[3,3]=ci[0][9]\\n C[3,4]=ci[0][10]\\n C[4,4]=ci[0][11]\\n C[5,5]=ci[0][12]\\n \\n #-- Triclinic Structures --------------------------------------------------------------------------\\n if (LC == 'N'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,3]=ci[0][3]\\n C[0,4]=ci[0][4]\\n C[0,5]=ci[0][5]\\n C[1,1]=ci[0][6]\\n C[1,2]=ci[0][7]\\n C[1,3]=ci[0][8]\\n C[1,4]=ci[0][9]\\n C[1,5]=ci[0][10]\\n C[2,2]=ci[0][11]\\n C[2,3]=ci[0][12]\\n C[2,4]=ci[0][13]\\n C[2,5]=ci[0][14]\\n C[3,3]=ci[0][15]\\n C[3,4]=ci[0][16]\\n C[3,5]=ci[0][17]\\n C[4,4]=ci[0][18]\\n C[4,5]=ci[0][19]\\n C[5,5]=ci[0][20]\\n #--------------------------------------------------------------------------------------------------\\n \\n \\n \\n for i in range(5):\\n for j in range(i+1,6):\\n C[j,i] = C[i,j] \\n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if self.__cod in ['espresso']: C = -C/10.\\n elif self.__cod in ['vasp','exciting','wien']: C = C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0\\n elif self.__cod in ['emto']: C = C*self.__ToGPa/self.__V0\\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\\n self.S = np.linalg.inv(C)\\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\\n self.BH = 0.50*(self.BV+self.BR)\\n self.GH = 0.50*(self.GV+self.GR)\\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\\n #--------------------------------------------------------------------------------------------------------------------------------\\n self.__C = C\\n \\n else:\\n Cs = []\\n for t in map(str,self.__T):#for t in range(len(self.__T)):\\n C = np.zeros((6,6))\\n \\n LC = self.__structures.items()[0][1].LC\\n if self.__mthd == 'Energy':\\n if type(etacalc)==list:\\n A2=[]\\n for i in range(len(etacalc)):\\n A2.append(self.__A2[t][i][etacalc[i]])\\n else:\\n if not etacalc in self.__A2[t][0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[t][0].keys()))\\n A2 = [a2[etacalc] for a2 in self.__A2[t]]\\n \\n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\\n C[1,1] = C[0,0]\\n C[2,2] = C[0,0]\\n C[3,3] = A2[2]/6.\\n C[4,4] = C[3,3]\\n C[5,5] = C[3,3]\\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\\n C[0,2] = C[0,1]\\n C[1,2] = C[0,1]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[2]\\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RI'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[1,4] =-C[0,4] \\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[3,5] =-C[0,4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TI'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[3]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TII'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\\n C[1,1] = C[0,0]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\\n C[1,2] = C[0,2]\\n C[1,5] =-C[0,5]\\n C[2,2] = 2.*A2[4]\\n C[3,3] = .5*A2[5]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[6]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'O'):\\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\\n C[1,1] = 2.*A2[4]\\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\\n C[2,2] = 2.*A2[5]\\n C[3,3] = .5*A2[6]\\n C[4,4] = .5*A2[7]\\n C[5,5] = .5*A2[8]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'M'):\\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\\n C[1,1] = 2.*A2[8]\\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\\n C[2,2] = 2.*A2[9]\\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\\n C[3,3] = .5*A2[10]\\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\\n C[4,4] = .5*A2[11]\\n C[5,5] = .5*A2[12]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'N'):\\n C[0,0] = 2.*A2[0]\\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \\n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\\n C[1,1] = 2.*A2[1]\\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\\n C[2,2] = 2.*A2[2] \\n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\\n C[3,3] = .5*A2[3]\\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\\n C[4,4] = .5*A2[4]\\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n elif self.__mthd == 'Stress':\\n \\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n Matrix = np.mat([[1.0, 5.0, 0.0],\\n [2.0, 4.0, 0.0],\\n [3.0, 3.0, 0.0],\\n [0.0, 0.0, 4.0],\\n [0.0, 0.0, 5.0],\\n [0.0, 0.0, 6.0]])\\n \\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\\n [ 2, 1, 3, 0, 0],\\n [ 0, 0, 3, 3, 0],\\n [ 0, 0, 0, 0, 4],\\n [ 0, 0, 0, 0, 5],\\n [ 3,-3, 0, 0, 0],\\n [ 3,-5,-1, 0, 0],\\n [-5, 3,-1, 0, 0],\\n [ 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 0, 6],\\n [ 0, 0, 0, 0, 2],\\n [-2, 2, 0, 0, 0]])\\n \\n if (LC == 'RI'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\\n [ 2, 1, 3,-4, 0, 0],\\n [ 0, 0, 3, 0, 3, 0],\\n [ 0, 0, 0,-1, 0, 4],\\n [ 0, 0, 0, 6, 0, 5],\\n [ 3,-3, 0, 5, 0, 0],\\n [ 3,-5,-1, 6, 0, 0],\\n [-5, 3,-1,-6, 0, 0],\\n [ 0, 0,-2, 0,-1, 0],\\n [ 0, 0, 0, 8, 0, 6],\\n [ 0, 0, 0,-4, 0, 2],\\n [-2, 2, 0, 2, 0, 0]])\\n \\n if (LC == 'RII'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\\n [ 2, 1, 3,-4,-5, 0, 0],\\n [ 0, 0, 3, 0, 0, 3, 0],\\n [ 0, 0, 0,-1,-6, 0, 4],\\n [ 0, 0, 0, 6,-1, 0, 5],\\n [ 3,-3, 0, 5,-4, 0, 0],\\n [ 3,-5,-1, 6, 2, 0, 0],\\n [-5, 3,-1,-6,-2, 0, 0],\\n [ 0, 0,-2, 0, 0,-1, 0],\\n [ 0, 0, 0, 8, 4, 0, 6],\\n [ 0, 0, 0,-4, 8, 0, 2],\\n [-2, 2, 0, 2,-6, 0, 0]])\\n \\n if (LC == 'TI'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\\n [ 2, 1, 3, 0, 0, 0],\\n [ 0, 0, 3, 3, 0, 0],\\n [ 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0, 0, 0, 6],\\n [ 3,-5,-1, 0, 0, 0],\\n [-5, 3,-1, 0, 0, 0],\\n [ 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 0, 0,-4]])\\n \\n if (LC == 'TII'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\\n [ 2, 1, 3,-6, 0, 0, 0],\\n [ 0, 0, 3, 0, 3, 0, 0],\\n [ 0, 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0,-1, 0, 0, 6],\\n [ 3,-5,-1,-4, 0, 0, 0],\\n [-5, 3,-1, 4, 0, 0, 0],\\n [ 0, 0,-2, 0,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 8, 0, 0,-4]])\\n \\n if (LC == 'O'):\\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\\n \\n if (LC == 'M'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\\n \\n if (LC == 'N'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\\n \\n sigma = np.array(self.__sigma[etacalc])\\n \\n ci = np.linalg.lstsq(Matrix,sigma)\\n \\n #-- Cubic structures ------------------------------------------------------------------------------\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n \\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[3,3]=ci[0][2]\\n C[1,1]=C[0,0]\\n C[2,2]=C[0,0]\\n C[0,2]=C[0,1]\\n C[1,2]=C[0,1]\\n C[4,4]=C[3,3]\\n C[5,5]=C[3,3]\\n \\n #-- Hexagonal Structures --------------------------------------------------------------------------\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[2,2]=ci[0][3]\\n C[3,3]=ci[0][4]\\n C[1,1]=C[0,0]\\n C[1,2]=C[0,2]\\n C[4,4]=C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral I Structures ---------------------------------------------------------------------\\n if (LC == 'RI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral II Structures --------------------------------------------------------------------\\n if (LC == 'RII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[0,4]= ci[0][4]\\n C[2,2]= ci[0][5]\\n C[3,3]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[1,4]=-C[0,4]\\n C[3,5]=-C[0,4]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Tetragonal I Structures -----------------------------------------------------------------------\\n if (LC == 'TI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[2,2]= ci[0][3]\\n C[3,3]= ci[0][4]\\n C[5,5]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[4,4]= C[3,3]\\n \\n #-- Tetragonal II Structures ----------------------------------------------------------------------\\n if (LC == 'TII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,5]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[5,5]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,5]=-C[0,5]\\n C[4,4]= C[3,3]\\n \\n #-- Orthorhombic Structures -----------------------------------------------------------------------\\n if (LC == 'O'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[1,1]=ci[0][3]\\n C[1,2]=ci[0][4]\\n C[2,2]=ci[0][5]\\n C[3,3]=ci[0][6]\\n C[4,4]=ci[0][7]\\n C[5,5]=ci[0][8]\\n \\n #-- Monoclinic Structures -------------------------------------------------------------------------\\n if (LC == 'M'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,5]=ci[0][3]\\n C[1,1]=ci[0][4]\\n C[1,2]=ci[0][5]\\n C[1,5]=ci[0][6]\\n C[2,2]=ci[0][7]\\n C[2,5]=ci[0][8]\\n C[3,3]=ci[0][9]\\n C[3,4]=ci[0][10]\\n C[4,4]=ci[0][11]\\n C[5,5]=ci[0][12]\\n \\n #-- Triclinic Structures --------------------------------------------------------------------------\\n if (LC == 'N'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,3]=ci[0][3]\\n C[0,4]=ci[0][4]\\n C[0,5]=ci[0][5]\\n C[1,1]=ci[0][6]\\n C[1,2]=ci[0][7]\\n C[1,3]=ci[0][8]\\n C[1,4]=ci[0][9]\\n C[1,5]=ci[0][10]\\n C[2,2]=ci[0][11]\\n C[2,3]=ci[0][12]\\n C[2,4]=ci[0][13]\\n C[2,5]=ci[0][14]\\n C[3,3]=ci[0][15]\\n C[3,4]=ci[0][16]\\n C[3,5]=ci[0][17]\\n C[4,4]=ci[0][18]\\n C[4,5]=ci[0][19]\\n C[5,5]=ci[0][20]\\n #--------------------------------------------------------------------------------------------------\\n \\n \\n \\n for i in range(5):\\n for j in range(i+1,6):\\n C[j,i] = C[i,j] \\n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if self.__cod == 'espresso': C = -C/10.\\n elif self.__cod in ['vasp','emto','exciting','wien']: C=C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0#C = C/4.\\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\\n self.S = np.linalg.inv(C)\\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\\n self.BH = 0.50*(self.BV+self.BR)\\n self.GH = 0.50*(self.GV+self.GR)\\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\\n #--------------------------------------------------------------------------------------------------------------------------------\\n Cs.append(C)\\n self.__C = Cs\",\n \"def NND_eta( eqCat, dConst, verbose = False, **kwargs):\\n #-------------------------------set args and kwargs----------------------------------------------- \\n rmax = 500 # in km\\n tmax = 20 # in years\\n M0 = 0\\n if 'M0' in kwargs.keys() and kwargs['M0'] is not None:\\n M0 = kwargs['M0']\\n if 'rmax' in kwargs.keys() and kwargs['rmax'] is not None:\\n rmax = kwargs['rmax']\\n if 'tmax' in kwargs.keys() and kwargs['tmax'] is not None:\\n tmax = kwargs['tmax']\\n #-----------------------------add small uncertainty to X in case events are colocated-------------------------- \\n if 'correct_co_located' in kwargs.keys() and kwargs['correct_co_located'] == True:\\n vUncer = np.random.randn( eqCat.size())*1e-10\\n eqCat.data['X'] += vUncer\\n eqCat.data['Time'] += abs( vUncer)#time has to stay positive otherwise parent-offspring gets switched\\n #------------------------------------------------------------------------------\\n aNND = np.zeros( eqCat.size())\\n vID_p = np.zeros( eqCat.size())\\n vID_c = np.zeros( eqCat.size())\\n deltaMag = (eqCat.data['Mag'] - M0)\\n \\n for jC in range( eqCat.size()):\\n if verbose == True:\\n print 'event %i of %i'%( jC+1, eqCat.size())\\n # interevent times: take events that happend before t_i \\n # child - parent > 0 \\n tau = eqCat.data['Time'][jC] - eqCat.data['Time']\\n sel_tau_par = tau > 0\\n if sel_tau_par.sum() > 0:\\n\\n vcurr_ID = np.arange( eqCat.size(), dtype = int)[sel_tau_par]\\n vR = np.sqrt( (eqCat.data['X'][jC] - eqCat.data['X'][vcurr_ID])**2 + (eqCat.data['Y'][jC] - eqCat.data['Y'][vcurr_ID])**2 )\\n # haversine distance\\n # = projUtils.haversine( eqCat.data['Lon'][jC], eqCat.data['Lat'][jC],eqCat.data['Lon'][curr_vID], eqCat.data['Lat'][curr_vID] ) \\n sel_r_par = vR < rmax\\n if sel_r_par.sum() > 0:\\n vcurr_ID = vcurr_ID[sel_r_par]\\n curr_Eta = tau[vcurr_ID]* (vR[sel_r_par]**dConst['D']) *( 10**(-dConst['b']*deltaMag[vcurr_ID]))\\n sel_min = curr_Eta == curr_Eta.min()\\n aNND[jC] = curr_Eta[sel_min][0]\\n vID_p[jC] = eqCat.data['N'][vcurr_ID][sel_min][0]\\n vID_c[jC] = eqCat.data['N'][jC]\\n #print 'parent', eqCat.data['N'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['N'][jC]\\n #print 'parent', eqCat.data['Time'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['Time'][jC]\\n\\n if sel_min.sum() > 1:\\n print aNND[jC], curr_Eta[sel_min], eqCat.data['N'][vcurr_ID][sel_min]\\n print eqCat.data['Lon'][vcurr_ID][sel_min], eqCat.data['Lat'][vcurr_ID][sel_min]\\n print eqCat.data['X'][vcurr_ID][sel_min], eqCat.data['Y'][vcurr_ID][sel_min]\\n sel2 = aNND > 0\\n if np.logical_not(sel2).sum() > 0:\\n print 'remove %i offspring without prior parents in catalog'%(np.logical_not(sel2).sum())\\n #raise ValueError, error_str\\n # remove events with aNND < 0; i.e. event at the beginning with no preceding parent\\n return { 'aNND' : aNND[sel2], 'aEqID_p' : vID_p[sel2], 'aEqID_c' : vID_c[sel2]}\",\n \"def connect_cells(dfte,vari):\\n # Create the variabel cell for mother, grand mother and grand grand mother\\n if 'g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\\n if 'g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_parent_cell')\\n if 'g_g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\\n #give unique index to all cells\\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\\n vac=[];sc=[];uid = []\\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\\n for c,idx in enumerate(dfte['cell'].unique()):\\n dau = dfte.loc[dfte['cell']==idx]\\n pc = dau['parent_cell'].iloc[0]\\n mum = dfte.loc[dfte['cell']==pc]\\n gpc = dau['g_parent_cell'].iloc[0]\\n gmum = dfte.loc[dfte['cell']==gpc]\\n ggpc = dau['g_g_parent_cell'].iloc[0]\\n ggmum = dfte.loc[dfte['cell']==ggpc]\\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\\n gggmum = dfte.loc[dfte['cell']==gggpc]\\n fte = lambda x: x[['{}'.format(vari),'uid']].values\\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\\n vac.append(tmp[:,0])\\n uid.append(tmp[:,1])\\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})\",\n \"def hr_egn(A, B, R, x0):\\n # A - Adjacency matrix, np.ndarray (N,N)\\n # B - A 2D or 3D matrix with all payoff matrices, np.ndarray (S,S,N)\\n # R - Relationship or preference matrix, np.ndarray (N,N)\\n # x0 - Initial state of our system, np.ndarray (N,S), must be double\\n\\n # Number of players\\n N = A[:, 0].size\\n # Number of strategies\\n S = x0[0, :].size\\n # Degree and degree of preferences\\n d = np.zeros([N, 2])\\n d[:, 0] = np.dot(A, np.ones(N))\\n\\n for v in range(N):\\n d[v, 1] = np.dot(np.ceil(np.abs(R[v, :])), A[v, :])\\n\\n # Player v neighborhood\\n k = np.zeros([N, S], dtype='double')\\n for v in range(N):\\n for u in range(N):\\n k[v, :] = np.add(k[v, :], np.multiply(A[v, u], x0[u, :]))\\n # Weights the neighborhood\\n k[v, :] = np.multiply(np.divide(1, d[v, 0]), k[v, :])\\n\\n # This variable is the increments that x0 receives, the derivative\\n x = np.zeros([N, S], dtype='double')\\n # This is the unit vector with 1 in some entry\\n es = np.zeros(S, dtype='int')\\n\\n # Phi and gamma\\n p = 0\\n g = 0\\n\\n # Auxiliary variables for better clarity\\n aux1 = 0\\n aux2 = 0\\n\\n # Here is the derivative calculation\\n # We first test if all payoffs are the same so we do less comparisons\\n if B.ndim == 2:\\n for v in range(N):\\n for s in range(S):\\n # Set es value\\n es[s] = 1\\n for u in range(N):\\n if v == u:\\n # Same payoff personal equation\\n # First we will do the dot products\\n # e_s*B*k_v\\n aux1 = np.dot(es, np.dot(B, k[v, :]))\\n # x_v*B*k_v\\n aux2 = np.dot(x0[v, :], np.dot(B, k[v, :]))\\n # Finally we subtract them to multiply by r_vv\\n p = np.multiply(R[v, u], np.subtract(aux1, aux2))\\n elif A[v, u] != 0:\\n # Same payoff social equation\\n # x_u*B*e_s\\n aux1 = np.dot(x0[u, :], np.dot(B, es))\\n # x_u*B*x_v\\n aux2 = np.dot(x0[u, :], np.dot(B, x0[v, :]))\\n # Subtract then multiply\\n aux1 = np.subtract(aux1, aux2)\\n aux2 = np.multiply(R[v, u], A[v, u])\\n g = np.add(g, np.multiply(aux2, aux1))\\n # Weights the social part\\n if d[v, 1] != 0:\\n g = np.multiply(np.divide(1, d[v, 1]), g)\\n # Estimates the derivative\\n x[v, s] = np.multiply(x0[v, s], np.add(p, g))\\n # Prepare variables to next iteration\\n p = 0\\n g = 0\\n es[s] = 0\\n else:\\n for v in range(N):\\n for s in range(S):\\n # Same thing as before, but now with individual payoffs\\n es[s] = 1\\n for u in range(N):\\n if v == u:\\n # Individual payoffs personal equation\\n # e_s*B_v*k_v\\n aux1 = np.dot(es, np.dot(B[:, :, v], k[v, :]))\\n # x_u*B_v*k_v\\n aux2 = np.dot(x0[v, :], np.dot(B[:, :, v], k[v, :]))\\n p = np.multiply(R[v, u], np.subtract(aux1, aux2))\\n elif A[v, u] != 0:\\n # Individual payoffs social equation\\n # x_u*B_u*e_s\\n aux1 = np.dot(x0[u, :], np.dot(B[:, :, u], es))\\n # x_u*B_u*x_v\\n aux2 = np.dot(x0[u, :], np.dot(B[:, :, u], x0[v, :]))\\n # Subtract then multiply\\n aux1 = np.subtract(aux1, aux2)\\n aux2 = np.multiply(R[v, u], A[v, u])\\n g = np.add(g, np.multiply(aux2, aux1))\\n # Weights the social part\\n if d[v, 1] != 0:\\n g = np.multiply(np.divide(1, d[v, 1]), g)\\n # Estimates the derivative\\n x[v, s] = np.multiply(x0[v, s], np.add(p, g))\\n # Prepare variables to next iteration\\n p = 0\\n g = 0\\n es[s] = 0\\n return x\",\n \"def tao_BC_and_LEM_lines(epics):\\n bc1_e0=epics.caget('SIOC:SYS0:ML00:AO483')*1e6\\n bc2_e0=epics.caget('SIOC:SYS0:ML00:AO489')*1e9\\n l3_e0 =epics.caget('SIOC:SYS0:ML00:AO500')*1e9\\n \\n # Charge in LTU\\n q_after_horn_cutting = epics.caget('SIOC:SYS0:ML00:CALC252')*1e-12 # pC -> C\\n bc1_offset=epics.caget('BMLN:LI21:235:MOTR')*1e-3\\n bc2_offset=epics.caget('BMLN:LI24:805:MOTR')*1e-3\\n \\n bc1_current=epics.caget('SIOC:SYS0:ML00:AO485')\\n bc2_current=epics.caget('SIOC:SYS0:ML00:AO195')\\n \\n # Catch bad settings\\n if bc1_current==0:\\n print('Warning: BC1 current is zero!')\\n bc1_sigma_z = 0\\n else:\\n # Assumes parabolic distribution\\n bc1_sigma_z = q_after_horn_cutting*299792458 / sqrt(10) / bc1_current\\n\\n if bc2_current==0:\\n print('Warning: BC1 current is zero!')\\n bc2_sigma_z = 0\\n else:\\n # Assumes Gaussian distribution\\n bc2_sigma_z = q_after_horn_cutting*299792458 / sqrt(12) / bc2_current \\n \\n lines = []\\n lines.append('set dat BC1.energy[1]|meas = '+str(bc1_e0))\\n lines.append('set dat BC2.energy[1]|meas = '+str(bc2_e0))\\n lines.append('set dat L3.energy[2]|meas = '+str(l3_e0))\\n lines.append('set dat BC1.offset[1]|meas = '+str(bc1_offset))\\n lines.append('set dat BC2.offset[1]|meas = '+str(bc2_offset))\\n \\n lines.append(f'! Charge after horn cutting: {q_after_horn_cutting*1e12:10.4} pC')\\n lines.append(f'! For BC1 current {bc1_current} A')\\n lines.append('set dat BC1.beam[1]|meas = '+str( bc1_sigma_z))\\n lines.append(f'! For BC2 current {bc2_current} A')\\n lines.append('set dat BC2.beam[1]|meas = '+str( bc2_sigma_z)) \\n\\n return lines\",\n \"def epidote():\\n\\n rho = 3465.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 211.5; C[0,1] = 65.6; C[0,2] = 43.2; C[0,3] = 0.; C[0,4] = -6.5; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 239.; C[1,2] = 43.6; C[1,3] = 0.; C[1,4] = -10.4; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 202.1; C[2,3] = 0.; C[2,4] = -20.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.1; C[3,4] = 0.; C[3,5] = -2.3\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 43.4; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.5\\n\\n return C, rho\",\n \"def add_electronetivity_and_lone_pair(df):\\n assert 'atom_0' in df.columns\\n assert 'atom_1' in df.columns\\n electro_df = get_electonegativity()\\n electro_df.index.name = 'atom_0'\\n df = df.join(electro_df, how='left', on='atom_0')\\n df.rename({\\n 'Electronegativity': 'Electronegativity_0'\\n },\\n axis=1,\\n inplace=True)\\n\\n electro_df.index.name = 'atom_1'\\n df = df.join(electro_df, how='left', on='atom_1')\\n df.rename({\\n 'Electronegativity': 'Electronegativity_1'\\n },\\n axis=1,\\n inplace=True)\\n df['Electronegativity_diff'] = df['Electronegativity_1'] - df[\\n 'Electronegativity_0']\\n\\n # lone pair\\n df['atom_0_lone_pair'] = df['atom_0'].map(get_lone_pair())\\n df['atom_1_lone_pair'] = df['atom_1'].map(get_lone_pair())\\n return df\",\n \"def getEnergyEvolution(self):\\n\\n\\t\\tEBefore = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleBefore]\\n\\t\\tEAfter = [0.5*np.sum(i**2)/self.__Nparticles for i in self.__XPEnsembleAfter]\\n\\n\\t\\treturn EBefore, EAfter\",\n \"def get_core_bonds(core_xyz, inp):\\n core_bonds = []\\n\\n if inp.core_en:\\n dists = cdist(core_xyz, core_xyz)\\n if inp.core_shape != \\\"shell\\\":\\n logger.info(\\\"\\\\tBuilding elastic network based on first neighbors...\\\")\\n close_dists = dists <= (2*inp.bead_radius+0.01)\\n for i in range(len(dists)):\\n ndx1 = i*1\\n close_ndxs = np.where(close_dists[i])[0]\\n if len(close_ndxs) == 1:\\n dists_sorted = np.argsort(dists[i])\\n close_ndxs = dists_sorted[[1,2,3,4,5,6]]\\n for ndx2 in close_ndxs:\\n if ndx2 != i and [ndx1, ndx2] not in core_bonds and [ndx2, ndx1] not in core_bonds:\\n core_bonds.append([ndx1, ndx2])\\n\\n else:\\n logger.info(\\\"\\\\tBuilding elastic network based on six nearest neighbours and one farthest neighbour...\\\")\\n neighboring_bonds = []\\n antipodal_bonds = []\\n dists_sorted = np.argsort(dists, axis=1)\\n for i in range(len(dists)):\\n ndx1 = i*1\\n close_ndxs = dists_sorted[i,[1,2,3,4,5,6]]\\n for ndx2 in close_ndxs:\\n if ndx2 != i and [ndx1, ndx2] not in core_bonds and [ndx2, ndx1] not in core_bonds:\\n neighboring_bonds.append([ndx1, ndx2])\\n antipodal_ndx = dists_sorted[i,-1]\\n if antipodal_ndx != i and [ndx1, antipodal_ndx] not in core_bonds and [antipodal_ndx, ndx1] not in core_bonds:\\n antipodal_bonds.append([ndx1, antipodal_ndx, \\\"antipodal\\\"])\\n core_bonds = neighboring_bonds + antipodal_bonds\\n\\n return core_bonds\",\n \"def cell_edges(self):\",\n \"def noe_analysis():\\n\\n files = ['f34k_7p2_noe_heights.txt', 'dphs_7p4_noe_heights.txt']\\n proteins = ['f34k','dphs']\\n\\n heights = [nmrfn.parse_noe(f) for f in files]\\n heights = pd.concat(heights, axis=1, keys=proteins)\\n\\n results = [noe_calcs(heights[i]) for i in proteins]\\n results = pd.concat(results, axis=1, keys=proteins)\\n\\n fig = plt.figure(figsize=(10,6))\\n val = plt.subplot(211)\\n dif = plt.subplot(212)\\n \\n seqmin = 6\\n seqmax = 144\\n noemin = 0.5\\n noemax = 0.95\\n\\n dy = results.dphs.noe - results.f34k.noe\\n dif_plot(val, dy.index, dy.ix[8:141], seqmin, seqmax)\\n noe_plot(proteins, results, seqmin, seqmax, noemin, noemax, xax=False)\\n\\n s1 = plt.Rectangle((0, 0), 1, 1, fc=proteins['f34k'])\\n s2 = plt.Rectangle((0, 0), 1, 1, fc=proteins['dphs'])\\n val.legend([s1, s2], ['F34K', '∆+PHS'], loc=8)\\n\\n title = 'HN NOE: F34K pH 7.2 compared with ∆+PHS pH 7.4'\\n savefile = 'f34k_7p2_noe_analysis.pdf'\\n save_plot(title, savefile)\",\n \"def interacting_conductivity(self,\\n sigma=0.04,\\n freq_range=(0.0, 6.0, 1000),\\n imag_dielectric=False,\\n broadening='lorentz'):\\n cell_area = np.abs(np.cross(self.a1, self.a2))*len(self.k_grid)\\n broad_fnc = get_broadening_function(broadening, sigma)\\n\\n frequency_grid = np.linspace(*freq_range)\\n output_grid = np.zeros(len(frequency_grid))\\n\\n energy_power = 1 + int(imag_dielectric)\\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\\n prefactor = prefactor/cell_area\\n\\n # Assuming x polarisation for now\\n position_dipole_matrix = self.construct_position_dipole_matrix()\\n\\n n_shift = self.n_spins*self.n_orbs\\n cutoff_band_min_maxes = self.exciton_obj.get_cutoff_band_min_maxes()\\n # Construct vector of velocity matrix elements to dot with the\\n # bse eigenvector.\\n velocity_element_lists = []\\n for s0 in range(self.n_spins):\\n velocity_elements = []\\n for idx_1, kpt in enumerate(self.k_grid):\\n velocity_matrix = self.load_velocity_matrix(idx_1)\\n bands = self.exciton_obj.get_number_conduction_valence_bands(\\n idx_1, s0\\n )\\n v_num, c_num = bands\\n v_min = 0\\n if self.exciton_obj.energy_cutoff is not None:\\n v_min = int(cutoff_band_min_maxes[s0][idx_1][0])\\n c_num = int(cutoff_band_min_maxes[s0][idx_1][1]) + 1\\n v_num = v_num - v_min\\n j1 = n_shift*idx_1 + s0*self.n_orbs\\n j2 = j1 + n_shift - (1 - s0)*self.n_orbs*(self.n_spins - 1)\\n eigvecs = np.array(\\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\\n )\\n if self.use_dipole_term:\\n eigvals = np.array(\\n self.file_storage['eigensystem']['eigenvalues'][idx_1]\\n )\\n else:\\n eigvals = None\\n for c, v in product(range(c_num), range(v_num)):\\n cb_vector = eigvecs[:, v_min + v_num + c]\\n vb_vector = eigvecs[:, v_min + v]\\n\\n matrix_elem = velocity_matrix_element(cb_vector,\\n vb_vector,\\n velocity_matrix)\\n if self.use_dipole_term:\\n cb_energy = eigvals[v_min + v_num + c]\\n vb_energy = eigvals[v_min + v]\\n position_dipole_term = get_position_dipole_element(\\n vb_vector,\\n cb_vector,\\n vb_energy,\\n cb_energy,\\n position_dipole_matrix\\n )\\n matrix_elem = matrix_elem + position_dipole_term\\n velocity_elements.append(matrix_elem)\\n velocity_elements = np.array(velocity_elements, dtype=complex)\\n velocity_element_lists.append(velocity_elements)\\n\\n bse_eigsys = self.exciton_obj.get_bse_eigensystem_direct(solve=True)\\n for s0 in range(self.n_spins):\\n for idx_1 in range(len(bse_eigsys[s0][0])):\\n bse_vector = bse_eigsys[s0][1][:, idx_1]\\n bse_value = bse_eigsys[s0][0][idx_1]\\n energy_denominator = bse_value**energy_power\\n interact_elem = np.dot(bse_vector, velocity_element_lists[s0])\\n for idx_2, omega in enumerate(frequency_grid):\\n smearing = broad_fnc(bse_value, omega)\\n main_term = np.abs(interact_elem)**2/energy_denominator\\n output = prefactor*main_term*smearing\\n output_grid[idx_2] += output\\n\\n return frequency_grid, output_grid\",\n \"def plot_resiliences(nodes, network_vals, er_vals, upa_vals):\\n node_vals = range(0, nodes)\\n\\n plt.plot(node_vals, network_vals, '-b', label='Network')\\n plt.plot(node_vals, er_vals, '-r', label='ER')\\n plt.plot(node_vals, upa_vals, '-g', label='UPA')\\n\\n plt.legend(loc='upper right')\\n plt.ylabel('Size of Largest Connected Component')\\n plt.xlabel('Number of Nodes Removed')\\n plt.grid(True)\\n plt.title('Comparison of Graph Resilience\\\\nMeasured by Largest Connected Component vs Nodes Removed by Target Attack\\\\n')\\n plt.show()\",\n \"def extended_euclidean(self):\\n self.a = gmpy2.invert(self.e1, self.e2)\\n self.b = (float(self.gcd(self.e1, self.e2)-(self.a*self.e1)))/float(self.e2)\",\n \"def TwoE(A, B, C, D, Rab2, Rcd2, Rpq2):\\n return 2.0 * (np.pi ** 2.5) / ((A + B) * (C + D) * np.sqrt(A + B + C + D)) * F0(\\n (A + B) * (C + D) * Rpq2 / (A + B + C + D)) * np.exp(-A * B * Rab2 / (A + B) - C * D * Rcd2 / (C + D))\",\n \"def get_nb_edit_operations(g1, g2, forward_map, backward_map):\\n n_vi = 0\\n n_vr = 0\\n n_vs = 0\\n n_ei = 0\\n n_er = 0\\n n_es = 0\\n\\n nodes1 = [n for n in g1.nodes()]\\n for i, map_i in enumerate(forward_map):\\n if map_i == np.inf:\\n n_vr += 1\\n elif g1.nodes[nodes1[i]]['atom_symbol'] != g2.nodes[map_i]['atom_symbol']:\\n n_vs += 1\\n for map_i in backward_map:\\n if map_i == np.inf:\\n n_vi += 1\\n\\n# idx_nodes1 = range(0, len(node1))\\n\\n edges1 = [e for e in g1.edges()]\\n nb_edges2_cnted = 0\\n for n1, n2 in edges1:\\n idx1 = nodes1.index(n1)\\n idx2 = nodes1.index(n2)\\n # one of the nodes is removed, thus the edge is removed.\\n if forward_map[idx1] == np.inf or forward_map[idx2] == np.inf:\\n n_er += 1\\n # corresponding edge is in g2. Edge label is not considered.\\n elif (forward_map[idx1], forward_map[idx2]) in g2.edges() or \\\\\\n (forward_map[idx2], forward_map[idx1]) in g2.edges():\\n nb_edges2_cnted += 1\\n # corresponding nodes are in g2, however the edge is removed.\\n else:\\n n_er += 1\\n n_ei = nx.number_of_edges(g2) - nb_edges2_cnted\\n\\n return n_vi, n_vr, n_vs, n_ei, n_er, n_es\",\n \"def non_interacting_conductivity(self,\\n sigma=0.04,\\n freq_range=(0.0, 6.0, 1000),\\n imag_dielectric=False,\\n broadening='lorentz'):\\n cell_area = np.abs(float(np.cross(self.a1, self.a2)))*len(self.k_grid)\\n broad_fnc = get_broadening_function(broadening, sigma)\\n\\n frequency_grid = np.linspace(*freq_range)\\n energy_increment = np.abs(frequency_grid[1] - frequency_grid[0])\\n num_freqs, min_freq = len(frequency_grid), min(frequency_grid)\\n output_grid = np.zeros(num_freqs)\\n\\n energy_power = 1 + int(imag_dielectric)\\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\\n prefactor = prefactor/cell_area\\n\\n # Assuming x polarisation for now\\n position_dipole_matrix = self.construct_position_dipole_matrix()\\n\\n n_shift = self.n_spins*self.n_orbs\\n for idx1, kpt in enumerate(self.k_grid):\\n for s0 in range(self.n_spins):\\n velocity_matrix = self.load_velocity_matrix(idx1)\\n bands = self.exciton_obj.get_number_conduction_valence_bands(\\n idx1, s0\\n )\\n v_num, c_num = bands\\n eigvals = np.array(\\n self.file_storage['eigensystem']['eigenvalues'][idx1]\\n )\\n\\n j1 = n_shift*idx1 + (self.n_spins - 1)*s0*self.n_orbs\\n j2 = j1 + self.n_orbs\\n eigvecs = np.array(\\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\\n )\\n for v, c in product(range(v_num), range(c_num)):\\n cb_vector = eigvecs[:, v_num + c]\\n vb_vector = eigvecs[:, v]\\n cb_energy, vb_energy = eigvals[v_num + c], eigvals[v]\\n energy_diff = (cb_energy - vb_energy)\\n\\n energy_diff_pow = energy_diff**energy_power\\n matrix_elem = velocity_matrix_element(vb_vector,\\n cb_vector,\\n velocity_matrix)\\n if self.use_dipole_term:\\n position_dipole_term = get_position_dipole_element(\\n vb_vector,\\n cb_vector,\\n vb_energy,\\n cb_energy,\\n position_dipole_matrix\\n )\\n matrix_elem = matrix_elem + position_dipole_term\\n\\n main_term = np.abs(matrix_elem)**2/energy_diff_pow\\n closest_idx = (energy_diff - min_freq)//energy_increment\\n idx_reach = self.reach_multiplier*(sigma//energy_increment)\\n\\n lower_idx = max([closest_idx - idx_reach, 0])\\n upper_idx = min([closest_idx + idx_reach, num_freqs - 1])\\n\\n for idx2 in range(int(lower_idx), int(upper_idx)):\\n smearing = broad_fnc(energy_diff, frequency_grid[idx2])\\n output = prefactor*main_term*smearing\\n output_grid[idx2] += output\\n\\n return frequency_grid, output_grid\",\n \"def eclogite_foliated():\\n\\n rho = 3300.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\\n\\n return C, rho\",\n \"def elemental_descriptor(A1_ion, A2_ion, B_ion):\\n ele_A1 = mg.Element(A1_ion)\\n ele_A2 = mg.Element(A2_ion)\\n ele_B = mg.Element(B_ion)\\n ele_O = mg.Element('O') \\n # A/B ion oxidation state \\n common_oxidation_states_A1 = ele_A1.common_oxidation_states[0]\\n common_oxidation_states_A2 = ele_A2.common_oxidation_states[0]\\n common_oxidation_states_A = np.mean(common_oxidation_states_A1 + common_oxidation_states_A2)\\n common_oxidation_states_B = ele_B.common_oxidation_states[0]\\n # ionic radius property\\n ionic_radius_A1 = float(str(ele_A1.average_ionic_radius)[:-4])\\n ionic_radius_A2 = float(str(ele_A2.average_ionic_radius)[:-4])\\n ionic_radius_A = (ionic_radius_A1+ ionic_radius_A2)/2\\n ionic_radius_B = float(str(ele_B.average_ionic_radius)[:-4])\\n ionic_radius_O = float(str(ele_O.average_ionic_radius)[:-4])\\n # Tolerance factor \\n TF = (ionic_radius_A + ionic_radius_O)/(np.sqrt(2)*(ionic_radius_B + ionic_radius_O))\\n # Octahedral factor\\n OF = ionic_radius_B/ionic_radius_O \\n # ionic_radius ratios\\n ionic_ration_AO = ionic_radius_A / ionic_radius_O\\n ionic_ration_BO = ionic_radius_B / ionic_radius_O\\n # averaged electronegativity for A and B atoms\\n Pauling_electronegativity_A1 = ele_A1.X\\n Pauling_electronegativity_A2 = ele_A2.X\\n Pauling_electronegativity_A = (Pauling_electronegativity_A1 + Pauling_electronegativity_A2)/2\\n Pauling_electronegativity_B = ele_B.X\\n Pauling_electronegativity_O = ele_O.X\\n # Difference in the electronegativity for A-O and B-O\\n Diff_A_O = Pauling_electronegativity_A - Pauling_electronegativity_O\\n Diff_B_O = Pauling_electronegativity_B - Pauling_electronegativity_O\\n return [common_oxidation_states_A, common_oxidation_states_B, Pauling_electronegativity_A, Pauling_electronegativity_B, TF, OF, ionic_ration_AO, ionic_ration_BO, Diff_A_O, Diff_B_O]\",\n \"def compute_edge_logits(self):\",\n \"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def test_superposition_cnot():\\n\\n program = dedent(\\n \\\"\\\"\\\"\\\\\\n register q0[0]\\n register q1[1]\\n H q0\\n CNOT q0 q1\\n \\\"\\\"\\\"\\n )\\n\\n result = run(program, run_gate_array, return_distribution=True)\\n assert isclose(result, [0.5, 0.0, 0.0, 0.5]).all()\",\n \"def plot_dereddening():\\n extinction_coefficients = {'2365-2764-1': np.array([0.2622, 0.844]), '4109-638-1': np.array([0.0524, 0.1576]),\\n '2058-56-1': np.array([0.0751, 0.248]), '3642-2459-1': np.array([0.1907, 0.608]),\\n '3999-1391-1': np.array([0.3911, 1.2480]), '2607-1448-1': np.array([0.0430, 0.1310])}\\n cepheids = {'2365-2764-1': np.array([0.959, 2.09]), '4109-638-1': np.array([0.705, 2.385]), '2058-56-1':\\n np.array([1.222, 1.333]), '3642-2459-1': np.array([1.088, 2.0518]), '3999-1391-1':\\n np.array([1.360, 1.2567]), '2607-1448-1': np.array([1.484, 0.6963])}\\n periods = {'2365-2764-1': 1.61, '4109-638-1': 15.31, '2058-56-1': 63.08, '3642-2459-1': 1.86, '3999-1391-1': 24.98,\\n '2607-1448-1': 8.54}\\n max_periods = max(periods.values())\\n\\n new_positions_bv_mv = [] # in M_V vs B-V space\\n colors = []\\n theoretical_position = []\\n for obj in extinction_coefficients.keys():\\n # new_positions_bv_mv.append(cepheids[obj]-extinction_coefficients[obj])\\n new_positions_bv_mv.append(cepheids[obj])\\n colors.append(periods[obj]/max_periods)\\n theoretical_position.append(-2.78*np.log10(periods[obj])-1.35)\\n\\n for pos in range(len(new_positions_bv_mv)):\\n plt.scatter(new_positions_bv_mv[pos][0], new_positions_bv_mv[pos][1], marker='^', facecolor='w', s=40)\\n plt.scatter(new_positions_bv_mv[pos][0], theoretical_position[pos], marker='o', facecolor='r', s=50)\\n return new_positions_bv_mv, colors\",\n \"def plot_links_2d_intra(epo1: mne.Epochs, epo2: mne.Epochs,\\n C1: np.ndarray, C2: np.ndarray,\\n threshold: str='auto', steps: int=2):\\n\\n # extract sensor infos and transform loc to fit with headmodel\\n loc1 = copy(np.array([ch['loc'][:3] for ch in epo1.info['chs']]))\\n loc1 = transform_2d_intra(loc1, traX=-0.178, traY=0.012, traZ=0, rotZ=(-np.pi/2))\\n\\n loc2 = copy(np.array([ch['loc'][:3] for ch in epo2.info['chs']]))\\n loc2 = transform_2d_intra(loc2, traX=0.178, traY=0.012, traZ=0, rotZ=(-np.pi/2))\\n\\n ctr1 = np.nanmean(loc1, 0)\\n ctr2 = np.nanmean(loc2, 0)\\n \\n # Calculate vmin and vmax for colormap as min and max [C1, C2]\\n Cmax1=np.nanmax(C1[:])\\n Cmax2=np.nanmax(C2[:])\\n Cmax=[]\\n Cmax=[Cmax1, Cmax2]\\n vmax=np.nanmax(Cmax)\\n Cmin1=np.nanmin(C1[:])\\n Cmin2=np.nanmin(C2[:])\\n Cmin=[]\\n Cmin=[Cmin1, Cmin2]\\n vmin=np.min(Cmin)\\n\\n # Calculate automatic threshold\\n if threshold == 'auto':\\n threshold = np.max([np.median(C1, 0),np.median(C2,0)])+np.max([np.std(C1, 0),np.std(C2, 0)])\\n else:\\n threshold = threshold\\n \\n\\n # Define colormap for both participant\\n cmap_p = matplotlib.cm.get_cmap('Reds')\\n norm_p = matplotlib.colors.Normalize(vmin=threshold, vmax=vmax)\\n cmap_n = matplotlib.cm.get_cmap('Blues_r')\\n norm_n = matplotlib.colors.Normalize(vmin=vmin, vmax=-threshold)\\n\\n # plot links\\n for e1 in range(len(loc1)):\\n x1 = loc1[e1, 0]\\n y1 = loc1[e1, 1]\\n for e2 in range(len(loc1)):\\n x2 = loc1[e2, 0]\\n y2 = loc1[e2, 1]\\n if C1[e1, e2] >= threshold:\\n color_p = cmap_p(norm_p(C1[e1, e2]))\\n if steps <= 2:\\n weight = 0.2 +1.6*((C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n plt.plot([loc1[e1, 0], loc1[e2, 0]],\\n [loc1[e1, 1], loc1[e2, 1]],\\n '-', color=color_p, linewidth=weight)\\n else:\\n alphas = np.linspace(0, 1, steps)\\n weight = 0.2 +1.6*((C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n for idx in range(len(alphas)-1):\\n a = alphas[idx]\\n b = alphas[idx+1]\\n xn = ((1-a)**3 * x1 +\\n 3 * (1-a)**2 * a * (2 * x1 - ctr1[0]) +\\n 3 * (1-a) * a**2 * (2 * x2 - ctr1[0]) +\\n a**3 * x2)\\n xnn = ((1-b)**3 * x1 +\\n 3 * (1-b)**2 * b * (2 * x1 - ctr1[0]) +\\n 3 * (1-b) * b**2 * (2 * x2 - ctr1[0]) +\\n b**3 * x2)\\n yn = ((1-a)**3 * y1 +\\n 3 * (1-a)**2 * a * (2 * y1 - ctr1[1]) +\\n 3 * (1-a) * a**2 * (2 * y2 - ctr1[1]) +\\n a**3 * y2)\\n ynn = ((1-b)**3 * y1 +\\n 3 * (1-b)**2 * b * (2 * y1 - ctr1[1]) +\\n 3 * (1-b) * b**2 * (2 * y2 - ctr1[1]) +\\n b**3 * y2)\\n plt.plot([xn, xnn], [yn, ynn],\\n '-', color=color_p, linewidth=weight)\\n if C1[e1, e2] <= -threshold:\\n color_n = cmap_n(norm_n(C1[e1, e2]))\\n if steps <= 2:\\n weight = 0.2 +1.6*((-C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n plt.plot([loc1[e1, 0], loc1[e2, 0]],\\n [loc1[e1, 1], loc1[e2, 1]],\\n '-', color=color_n, linewidth=weight)\\n else:\\n alphas = np.linspace(0, 1, steps)\\n weight = 0.2 +1.6*((-C1[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n for idx in range(len(alphas)-1):\\n a = alphas[idx]\\n b = alphas[idx+1]\\n xn = ((1-a)**3 * x1 +\\n 3 * (1-a)**2 * a * (2 * x1 - ctr1[0]) +\\n 3 * (1-a) * a**2 * (2 * x2 - ctr1[0]) +\\n a**3 * x2)\\n xnn = ((1-b)**3 * x1 +\\n 3 * (1-b)**2 * b * (2 * x1 - ctr1[0]) +\\n 3 * (1-b) * b**2 * (2 * x2 - ctr1[0]) +\\n b**3 * x2)\\n yn = ((1-a)**3 * y1 +\\n 3 * (1-a)**2 * a * (2 * y1 - ctr1[1]) +\\n 3 * (1-a) * a**2 * (2 * y2 - ctr1[1]) +\\n a**3 * y2)\\n ynn = ((1-b)**3 * y1 +\\n 3 * (1-b)**2 * b * (2 * y1 - ctr1[1]) +\\n 3 * (1-b) * b**2 * (2 * y2 - ctr1[1]) +\\n b**3 * y2)\\n plt.plot([xn, xnn], [yn, ynn],\\n '-', color=color_n, linewidth=weight)\\n\\n for e1 in range(len(loc2)):\\n x1 = loc2[e1, 0]\\n y1 = loc2[e1, 1]\\n for e2 in range(len(loc2)):\\n x2 = loc2[e2, 0]\\n y2 = loc2[e2, 1]\\n if C2[e1, e2] >= threshold:\\n color_p = cmap_p(norm_p(C2[e1, e2]))\\n if steps <= 2:\\n weight = 0.2 +1.6*((C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n plt.plot([loc2[e1, 0], loc2[e2, 0]],\\n [loc2[e1, 1], loc2[e2, 1]],\\n '-', color=color_p, linewidth=weight)\\n else:\\n alphas = np.linspace(0, 1, steps)\\n weight = 0.2 +1.6*((C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n for idx in range(len(alphas)-1):\\n a = alphas[idx]\\n b = alphas[idx+1]\\n xn = ((1-a)**3 * x1 +\\n 3 * (1-a)**2 * a * (2 * x1 - ctr2[0]) +\\n 3 * (1-a) * a**2 * (2 * x2 - ctr2[0]) +\\n a**3 * x2)\\n xnn = ((1-b)**3 * x1 +\\n 3 * (1-b)**2 * b * (2 * x1 - ctr2[0]) +\\n 3 * (1-b) * b**2 * (2 * x2 - ctr2[0]) +\\n b**3 * x2)\\n yn = ((1-a)**3 * y1 +\\n 3 * (1-a)**2 * a * (2 * y1 - ctr2[1]) +\\n 3 * (1-a) * a**2 * (2 * y2 - ctr2[1]) +\\n a**3 * y2)\\n ynn = ((1-b)**3 * y1 +\\n 3 * (1-b)**2 * b * (2 * y1 - ctr2[1]) +\\n 3 * (1-b) * b**2 * (2 * y2 - ctr2[1]) +\\n b**3 * y2)\\n plt.plot([xn, xnn], [yn, ynn],\\n '-', color=color_p, linewidth=weight)\\n if C2[e1, e2] <= -threshold:\\n color_n = cmap_n(norm_n(C2[e1, e2]))\\n if steps <= 2:\\n weight = 0.2 +1.6*((-C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n plt.plot([loc2[e1, 0], loc2[e2, 0]],\\n [loc2[e1, 1], loc2[e2, 1]],\\n '-', color=color_n, linewidth=weight)\\n else:\\n alphas = np.linspace(0, 1, steps)\\n weight = 0.2 +1.6*((-C2[e1, e2]-threshold)/(np.nanmax(vmax-threshold)))\\n for idx in range(len(alphas)-1):\\n a = alphas[idx]\\n b = alphas[idx+1]\\n xn = ((1-a)**3 * x1 +\\n 3 * (1-a)**2 * a * (2 * x1 - ctr2[0]) +\\n 3 * (1-a) * a**2 * (2 * x2 - ctr2[0]) +\\n a**3 * x2)\\n xnn = ((1-b)**3 * x1 +\\n 3 * (1-b)**2 * b * (2 * x1 - ctr2[0]) +\\n 3 * (1-b) * b**2 * (2 * x2 - ctr2[0]) +\\n b**3 * x2)\\n yn = ((1-a)**3 * y1 +\\n 3 * (1-a)**2 * a * (2 * y1 - ctr2[1]) +\\n 3 * (1-a) * a**2 * (2 * y2 - ctr2[1]) +\\n a**3 * y2)\\n ynn = ((1-b)**3 * y1 +\\n 3 * (1-b)**2 * b * (2 * y1 - ctr2[1]) +\\n 3 * (1-b) * b**2 * (2 * y2 - ctr2[1]) +\\n b**3 * y2)\\n plt.plot([xn, xnn], [yn, ynn],\\n '-', color=color_n, linewidth=weight)\",\n \"def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentNeofunctionalised= self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = conservedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\\n \\n parentGraph.name = 'Conserved metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def get_eulerian_graph_edges(bbox, source):\\n osm_graph = OSMGraph(bbox, source)\\n # input all nodes and get odd nodes, update node attributes\\n odd_nodes = get_odd_nodes(osm_graph.nodes_dict)\\n\\n # initialize all_pairs_list\\n all_pairs_list = []\\n\\n # if there are 6 or fewer odd nodes look for all possible options,\\n # otherwise look for just three basic pairing options\\n\\n if len(odd_nodes) <= 10:\\n print(\\\"ROBUST PAIRING FUNCTION\\\")\\n all_pairs_list = get_list_of_all_pairs_lists(odd_nodes)\\n\\n else:\\n print(\\\"CHEAP PAIRING FUNCTION\\\")\\n all_pairs_list = get_list_of_all_pairs_lists_short(odd_nodes)\\n\\n for item in all_pairs_list:\\n print(\\\"\\\\n\\\\nPair option:\\\", item)\\n print(\\\"Pair option len:\\\", len(item))\\n\\n dict_pairings_lists_lengths = get_dict_pairings_lists_lengths(\\n all_pairs_list, osm_graph\\n )\\n twice_traversals_edges = get_twice_traversals_edges(dict_pairings_lists_lengths)\\n updated_graph_instance = update_twice_traversal_edges(\\n twice_traversals_edges, osm_graph\\n )\\n return updated_graph_instance\",\n \"def setUpTwoElectricalSeries(self):\\n TestElectricalSeriesIO.make_electrode_table(self)\\n region1 = DynamicTableRegion(name='electrodes',\\n data=[0, 2],\\n description='the first and third electrodes',\\n table=self.table)\\n region2 = DynamicTableRegion(name='electrodes',\\n data=[1, 3],\\n description='the second and fourth electrodes',\\n table=self.table)\\n data1 = list(zip(range(10), range(10, 20)))\\n data2 = list(zip(reversed(range(10)), reversed(range(10, 20))))\\n timestamps = list(map(lambda x: x/10., range(10)))\\n es1 = ElectricalSeries(name='test_eS1', data=data1, electrodes=region1, timestamps=timestamps)\\n es2 = ElectricalSeries(name='test_eS2', data=data2, electrodes=region2, channel_conversion=[4., .4],\\n timestamps=timestamps)\\n return es1, es2\",\n \"def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n childGraph = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)\\n childGraph.name = 'Added metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return childGraph\",\n \"def plot_Epair_Etagm(run):\\n global fin\\n global pstags\\n global last_run\\n last_run = run\\n fin = ROOT.TXNetFile(xrootdir + f\\\"/PStagstudy2_{run}.root\\\")\\n pstags = fin.Get(\\\"pstags\\\")\\n get_from_ccdb(run)\\n pstags.Process(\\\"pstags\\\", \\\"\\\", 5000000)\\n h1 = ROOT.gROOT.FindObject(\\\"ttagm_pair\\\")\\n h2 = ROOT.gROOT.FindObject(\\\"Etagm_Epair\\\")\\n h3 = ROOT.gROOT.FindObject(\\\"Epair_Etagm\\\")\\n return h3, h2, h1\",\n \"def nsi_cross_edge_density(self, node_list1, node_list2):\\n W_j = sum(self.node_weights[node_list2])\\n return self.nsi_cross_mean_degree(node_list1, node_list2) / W_j\",\n \"def Eee2(neutron_spectrum):\\n neutron_spectrum.Fit('gaus')\\n results_fit = neutron_spectrum.GetFunction('gaus')\\n mean_energy = results_fit.GetParameter(1)\\n spread_energy = results_fit.GetParError(1)\\n return mean_energy, spread_energy\",\n \"def etable_atom_pair_energies(res1, atom_index_1, res2, atom_index_2, sfxn):\\n score_manager = rosetta.core.scoring.ScoringManager.get_instance()\\n etable_ptr = score_manager.etable(sfxn.energy_method_options().etable_type())\\n etable = etable_ptr.lock()\\n etable_energy = rosetta.core.scoring.etable.AnalyticEtableEnergy(\\n etable, sfxn.energy_method_options()\\n )\\n\\n # Construct coulomb class for calculating fa_elec energies\\n coulomb = rosetta.core.scoring.etable.coulomb.Coulomb(sfxn.energy_method_options())\\n\\n # Construct AtomPairEnergy container to hold computed energies.\\n ape = rosetta.core.scoring.etable.AtomPairEnergy()\\n\\n # Set all energies in the AtomPairEnergy to zero prior to calculation.\\n ape.attractive, ape.bead_bead_interaction, ape.repulsive, ape.solvation = [0.] * 4\\n\\n # Calculate the distance squared and set it in the AtomPairEnergy.\\n ape.distance_squared = res1.xyz(atom_index_1).distance_squared(\\n res2.xyz(atom_index_2)\\n )\\n\\n # Evaluate energies from pre-calculated etable, using a weight of 1.0\\n # in order to match the raw energies from eval_ci_2b.\\n atom1 = res1.atom(atom_index_1)\\n atom2 = res2.atom(atom_index_2)\\n etable_energy.atom_pair_energy(atom1, atom2, 1.0, ape)\\n\\n # Calculate atom-atom scores.\\n lj_atr = ape.attractive\\n lj_rep = ape.repulsive\\n solv = ape.solvation\\n fa_elec = coulomb.eval_atom_atom_fa_elecE(\\n res1.xyz(atom_index_1),\\n res1.atomic_charge(atom_index_1),\\n res2.xyz(atom_index_2),\\n res2.atomic_charge(atom_index_2),\\n )\\n\\n return lj_atr, lj_rep, solv, fa_elec\",\n \"def test_2():\\n h = iotbx.pdb.input(source_info=None, lines=test_pdb_2).construct_hierarchy()\\n asc = h.atom_selection_cache()\\n ncs_inp = iotbx.ncs.input(\\n hierarchy=h,\\n params=ncs_pars.ncs_search)\\n ncs_groups = ncs_inp.get_ncs_restraints_group_list()\\n assert len(ncs_groups) == 1\\n # group 1\\n assert ncs_groups[0].master_iselection.all_eq(\\n asc.selection(string = \\\"chain A\\\").iselection())\\n g1_c = ncs_groups[0].copies\\n assert len(g1_c)==1\\n assert g1_c[0].iselection.all_eq(\\n asc.selection(string = \\\"chain B\\\").iselection())\",\n \"def divergedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEcGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n \\n if colour is True:\\n lostGraph = GeneFunctionLoss.getGraph(parentCoreMetabolism, childCoreMetabolism)\\n lostEdges = lostGraph.getEdges()\\n \\n addedGraph = GeneFunctionAddition.getGraph(parentCoreMetabolism, childCoreMetabolism)\\n addedEdges = addedGraph.getEdges()\\n \\n graph = lostGraph.union(addedGraph, addCount = False, updateName = False) \\n \\n Export.addColourAttribute(graph, colour = Export.Colour.BLUE, nodes = False, edges = lostEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.RED, nodes = False, edges = addedEdges)\\n \\n else: \\n graph = GeneFunctionDivergence.getGraph(parentCoreMetabolism, childCoreMetabolism)\\n \\n graph.name = 'Diverged metabolism ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\",\n \"def divergedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = divergedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\\n childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\\n \\n parentGraph.name = 'Diverged metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def compute_cluster_similarities(emb_clusters1, emb_clusters2, compare, order, clmethod, plot):\\n def compute_sim(e, e1, cls, cls1):\\n sims = np.empty((20, 20))\\n xticks, yticks = [], []\\n for i, c in enumerate(cls):\\n yticks.append(', '.join(c[1]) + (f' {round(c[3], 5)}' if order == 'avgfreq' else ''))\\n for j, c1 in enumerate(cls1):\\n if len(xticks) < 20:\\n xticks.append(', '.join(c1[1]) + (f' {round(c1[3], 5)}' if order == 'avgfreq' else ''))\\n sims[i, j] = jaccard_similarity_score(c[2], c1[2])\\n jaccard_similarities[f'{e}-{e1}'] = sims\\n\\n if plot:\\n if order == 'clustermap':\\n similarity_clustermap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}')\\n elif order == 'default' or order == 'avgfreq':\\n similarity_heatmap(sims, xticks, yticks, f'{e}-{e1}_{clmethod}', order)\\n else:\\n pass\\n\\n jaccard_similarities = {}\\n if compare == 'cross':\\n for ie, (e, cls) in enumerate(emb_clusters1.items()):\\n for ie1, (e1, cls1) in enumerate(emb_clusters2.items()):\\n if ie < ie1:\\n compute_sim(e, e1, cls, cls1)\\n elif compare == 'dot':\\n for (e, cls), (e1, cls1) in zip(emb_clusters1.items(), emb_clusters2.items()):\\n compute_sim(e, e1, cls, cls1)\\n\\n return jaccard_similarities\",\n \"def show_dcr_results(dg):\\n cycle = dg.fileDB['cycle'].values[0]\\n df_dsp = pd.read_hdf(f'./temp_{cycle}.h5', 'opt_dcr')\\n # print(df_dsp.describe()) \\n\\n # compare DCR and A/E distributions\\n fig, (p0, p1) = plt.subplots(2, 1, figsize=(8, 8))\\n \\n elo, ehi, epb = 0, 25000, 100\\n \\n # aoe distribution\\n # ylo, yhi, ypb = -1, 2, 0.1\\n # ylo, yhi, ypb = -0.1, 0.3, 0.005\\n ylo, yhi, ypb = 0.05, 0.08, 0.0005\\n nbx = int((ehi-elo)/epb)\\n nby = int((yhi-ylo)/ypb)\\n h = p0.hist2d(df_dsp['trapEmax'], df_dsp['aoe'], bins=[nbx,nby],\\n range=[[elo, ehi], [ylo, yhi]], cmap='jet',\\n norm=LogNorm())\\n # p0.set_xlabel('Energy (uncal)', ha='right', x=1)\\n p0.set_ylabel('A/E', ha='right', y=1)\\n\\n # dcr distribution\\n # ylo, yhi, ypb = -20, 20, 1 # dcr_raw\\n # ylo, yhi, ypb = -5, 2.5, 0.1 # dcr = dcr_raw / trapEmax\\n # ylo, yhi, ypb = -3, 2, 0.1\\n ylo, yhi, ypb = 0.9, 1.08, 0.001\\n ylo, yhi, ypb = 1.034, 1.0425, 0.00005 # best for 64.4 us pz\\n # ylo, yhi, ypb = 1.05, 1.056, 0.00005 # best for 50 us pz\\n # ylo, yhi, ypb = 1.016, 1.022, 0.00005 # best for 100 us pz\\n nbx = int((ehi-elo)/epb)\\n nby = int((yhi-ylo)/ypb)\\n h = p1.hist2d(df_dsp['trapEmax'], df_dsp['dcr'], bins=[nbx,nby],\\n range=[[elo, ehi], [ylo, yhi]], cmap='jet',\\n norm=LogNorm())\\n p1.set_xlabel('Energy (uncal)', ha='right', x=1)\\n p1.set_ylabel('DCR', ha='right', y=1)\\n \\n # plt.show()\\n plt.savefig(f'./plots/dcr_cyc{cycle}.png', dpi=300)\\n plt.cla()\",\n \"def odd_decomposition(G) -> Tuple[Set[int], Set[int]]:\\n multi = isinstance(G, nx.MultiGraph)\\n\\n # create a MultiGraph copy of G\\n G = nx.MultiGraph(G)\\n\\n # remove isolated nodes from the graph, since they are irrelevant and we are working on a copy\\n G.remove_nodes_from(list(nx.isolates(G)))\\n\\n # if the base graph is already odd\\n if is_odd(G):\\n return (set(G.edges(keys=multi)), set())\\n\\n odd_subgraph = G.subgraph(odd_nodes(G))\\n even_subgraph = G.subgraph(even_nodes(G))\\n\\n odd_components = nx.connected_components(odd_subgraph)\\n even_components = nx.connected_components(even_subgraph)\\n\\n # use the same notation as in our source paper\\n X = list(odd_components)\\n Y, Z = partition(even_components, lambda x: len(x) % 2 == 0)\\n\\n lX, lY, lZ = len(X), len(Y), len(Z)\\n linear_system = Matrix(lY + lZ, lX + 1, bin_field)\\n\\n # create a linear system over GF(2) as described in our source paper\\n for i, Yi in enumerate(Y):\\n for j, Xi in enumerate(X):\\n if n_joining(G, Xi, Yi) % 2 == 1:\\n linear_system.set(i, j, 1)\\n else:\\n linear_system.set(i, j, 0)\\n linear_system.set(i, lX, 1)\\n\\n for i, Zi in enumerate(Z):\\n for j, Xi in enumerate(X):\\n if n_joining(G, Xi, Zi) % 2 == 1:\\n linear_system.set(i + lY, j, 1)\\n else:\\n linear_system.set(i + lY, j, 0)\\n linear_system.set(i + lY, lX, 0)\\n\\n # transform the system into RREF\\n linear_system.reduced_row_echelon_form()\\n red = set()\\n\\n # we only need one solution\\n # every non pivot is set to 0 (meaning blue)\\n # while every pivot matches the augmented value\\n # only create the red set since this is the only one we need\\n\\n for i in range(lY + lZ):\\n val = linear_system.get(i, lX)\\n # don't cross the last column (augmented part)\\n for j in range(lX):\\n v = linear_system.get(i, j)\\n if v == 1:\\n if val == 1:\\n red.add(j)\\n break\\n else:\\n if val == 1:\\n # if any of the lines has no pivot and 1 as the augmented value this system is not solvable\\n raise NotDecomposableError(\\\"The graph is not decomposable due to unsolvable system\\\")\\n else:\\n # this is a zero row ane there are no pivots after this row\\n break\\n\\n red_nodes = set.union(*[X[i] for i in red]) if red else set()\\n # edges adjacent to red nodes\\n red_edges = set(G.edges(nbunch=red_nodes, keys=True))\\n # calculate the red degree for every even node\\n degrees = dict()\\n for i in even_subgraph.nodes():\\n degrees[i] = 0\\n\\n for i, j, k in red_edges:\\n if i in degrees:\\n degrees[i] += 1\\n if j in degrees:\\n degrees[j] += 1\\n\\n # T set is the set of all nodes of even red degree\\n T = {i for i, deg in degrees.items() if deg % 2 == 0}\\n\\n # compute the T-join and mark the nodes as red/blue\\n red_join = T_join(even_subgraph, T)\\n\\n red_edges.update(red_join)\\n blue_edges = set(G.edges(keys=True)).difference(red_edges)\\n\\n if not multi:\\n red_edges = {(i, j) for i, j, k in red_edges}\\n blue_edges = {(i, j) for i, j, k in blue_edges}\\n\\n return (red_edges, blue_edges)\",\n \"def extended_euclides(self, a, b):\\n x = last_y = 0\\n y = last_x = 1\\n while b != 0:\\n quot = a // b\\n a, b = b, a % b\\n x, last_x = last_x - quot * x, x\\n y, last_y = last_y - quot * y, y\\n\\n return [last_x, last_y]\",\n \"def complex_network_mapping(graph):\\n vect = []\\n\\n n = nx.number_of_nodes(graph)\\n e = nx.number_of_edges(graph)\\n print n, e\\n\\n# adj = nx.adjacency_matrix(graph).toarray()\\n# adj_bin = np.where(adj > 0, 1., 0.)\\n# adj_conn = 1 - adj\\n adj_bin = nx.adjacency_matrix(graph).toarray()\\n adj_bin = np.array(adj_bin, dtype=np.float)\\n\\n # Node Betweenness binary\\n bt_bin = nx.betweenness_centrality(graph).values()\\n avg_btb = np.mean(bt_bin)\\n vect.append(avg_btb)\\n\\n # Edge betweenness\\n ebt = np.array(nx.edge_betweenness_centrality(graph).values())\\n vect.append(np.mean(ebt))\\n\\n # Eigen vector centrality binary\\n evc_bin = eigenvector_centrality_und(adj_bin)\\n avg_evcb = np.mean(evc_bin)\\n vect.append(avg_evcb)\\n\\n # Flow coefficient\\n _, flow_bin, _ = flow_coef_bd(adj_bin)\\n avg_flow = np.mean(flow_bin)\\n vect.append(avg_flow)\\n\\n # Kcoreness centrality\\n kcor_bin, _ = kcoreness_centrality_bu(adj_bin)\\n avg_kcor = np.mean(kcor_bin)\\n vect.append(avg_kcor)\\n\\n # Degree assortivity\\n dac = nx.degree_assortativity_coefficient(graph)\\n vect.append(dac)\\n\\n # Page rank centrality\\n# pgr_wei = pagerank_centrality(adj_bin, d=0.85)\\n# avg_pgr = np.mean(pgr_wei)\\n# vect.append(avg_pgr)\\n\\n # Rich club coefficient\\n# rcc = nx.rich_club_coefficient(graph).values()\\n# avg_rcc = np.mean(rcc)\\n# vect.append(avg_rcc)\\n\\n # Transitivity\\n tr = nx.transitivity(graph)\\n vect.append(tr)\\n\\n # average clustering\\n avg_clst = nx.average_clustering(graph)\\n vect.append(avg_clst)\\n\\n glb_ef = efficiency_bin(adj_bin)\\n vect.append(glb_ef)\\n\\n return vect\",\n \"def buildE(self, debug = False):\\n E = np.zeros([len(self.sta), len(self.dist)])\\n i = 0\\n for ikey, state in self.sta.items():\\n j = 0\\n c = state.c\\n for jkey, disturbance in self.dist.items():\\n if debug:\\n print(i, ' ', j)\\n if isinstance(disturbance, InputT) and disturbance in \\\\\\n self.rc.adj[state]:\\n # input is temperature and connected to this state\\n\\n E[i, j] = self.rc.adj[state][disturbance]['H'] / c\\n elif isinstance(disturbance, InputQ) and disturbance in \\\\\\n self.rc.adj[\\n state]: # input is heat flow and connected to state\\n E[i, j] = self.rc.adj[state][disturbance]['gain'] / c\\n j += 1\\n i += 1\\n\\n return E, list(self.sta.keys()), list(self.dist.keys())\",\n \"def es(d1, d2, verbose=False):\\n\\n d1 = assure_numpy_array(d1)\\n d2 = assure_numpy_array(d2)\\n\\n es, pvalue = stats.epps_singleton_2samp(d1, d2)\\n\\n return es, pvalue\",\n \"def figure_2_combined_cross_sections(crange=CRANGE, koffrange=KOFFRANGE,\\n scale_factor=alpha, label_style=0):\\n def combined_error_c(ctilde, z, scale_factor=scale_factor):\\n c = ctilde * c0\\n koff = z * KP\\n x = c * KON / koff\\n num = 2 * KP * x + koff * KP * T * (1 + x) ** 3 + koff ** 2 * T * x ** 2 * (1 + x) ** 3\\n den = koff ** 2 * KP * T ** 2 * x * (1 + x) ** 2\\n val = num / den\\n return scale_factor * val\\n def combined_error_koff(ctilde, z, scale_factor=scale_factor):\\n c = ctilde * c0\\n koff = z * KP\\n x = c * KON / koff\\n num = 2 * KP * x + koff * KP * T * (1 + x) ** 3 + koff**2 * T * (1 + x) ** 3\\n den = koff ** 2 * KP * T**2 * x * (1 + x) ** 2\\n val = num / den\\n return scale_factor * val\\n\\n def cross_section_combined_error_c():\\n arr = [combined_error_c(cval, 1) for cval in crange]\\n return dict({'xpts': crange, 'ypts':arr})\\n def cross_section_combined_error_koff():\\n arr = [combined_error_koff(c, 1) for c in crange]\\n return dict({'xpts': koffrange, 'ypts':arr})\\n\\n figname = 'combined_error_cross_sections'\\n curve1 = cross_section_combined_error_c()\\n curve2 = cross_section_combined_error_koff()\\n # plot\\n plt.figure(figsize=(3, 3))\\n ax1 = plt.gca()\\n ax2 = ax1.twiny()\\n\\n if label_style == 0:\\n ln1 = ax1.plot(curve1['xpts'], curve1['ypts'], color=cs['simple_fisher'], label=r'$c$', zorder=1)\\n ln2 = ax2.plot(curve2['xpts'], curve2['ypts'], color=cs['heuristic'], label=r'$k_{\\\\mathrm{off}}$', zorder=1)\\n #plt.title('Mode 2: MLE relative error comparison\\\\n' + r'($\\\\tilde{c}_0=10$, $\\\\alpha=1 \\\\times 10^4$, $k_{p}=10$)')\\n\\n elif label_style == 1:\\n ln1 = ax1.plot(curve1['xpts'], curve1['ypts'], color=cs['simple_fisher'], label=r'$\\\\delta c^{2}/c^{2}$', zorder=1)\\n ln2 = ax2.plot(curve2['xpts'], curve2['ypts'], color=cs['heuristic'],label=r'$\\\\delta k_{\\\\mathrm{off}}^{2}/k_{\\\\mathrm{off}}^{2}$', zorder=1)\\n plt.title('Mode 2: MLE relative error comparison\\\\n' + r'($k_p=10$, $t=100$, $k_{\\\\mathrm{off}}=k_{\\\\mathrm{on}}=1$)')\\n plt.ylabel(r'$\\\\langle\\\\delta (\\\\cdot)^{2}\\\\rangle$/$(\\\\cdot)^{2}$')\\n\\n # axis\\n ax1.set_xlabel(r'$k_{\\\\mathrm{on}}c/k_{p}$')\\n ax1.set_ylabel(r'$k_{p}t \\\\langle\\\\delta (\\\\cdot)^{2}\\\\rangle$/$(\\\\cdot)^{2}$')\\n #ax2.set_xlabel(r'$k_{\\\\mathrm{off}}$')\\n\\n ax1.set_xscale('log')\\n ax2.set_xscale('log')\\n ax1.set_xlim([1E-2, 1E2])\\n ax2.set_xlim([1E-2, 1E2])\\n plt.ylim([0, 0.01*alpha])\\n\\n lns = ln1 + ln2\\n labs = [l.get_label() for l in lns]\\n ax1.legend(lns, labs)\\n\\n plt.tight_layout()\\n # save figure\\n plt.savefig(DIR_OUTPUT + os.sep + figname + '.pdf', transparent=True)\\n plt.savefig(DIR_OUTPUT + os.sep + figname + '.eps')\",\n \"def eclogite_massive():\\n\\n rho = 3490.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\\n\\n return C, rho\",\n \"def test_pid_ccs2():\\n d = trivariates['anddup']\\n pid = PID_CCS(d, [[0], [1], [2]], [3])\\n for atom in pid._lattice:\\n if atom == ((0,), (1,), (2,)):\\n assert pid[atom] == pytest.approx(0.10375937481971094)\\n elif atom in [((0,), (2,)), ((1,),)]:\\n assert pid[atom] == pytest.approx(0.20751874963942191)\\n elif atom in [((2,), (0, 1)), ((0,), (1, 2)), ((0, 1), (0, 2)), ((0, 1), (1, 2)), ((0, 2), (1, 2)), ((0, 1, 2),)]:\\n assert pid[atom] == pytest.approx(0.14624062518028902)\\n elif atom in [((2,),), ((0,),), ((0, 1), (0, 2), (1, 2)), ((0, 2),)]:\\n assert pid[atom] == pytest.approx(-0.14624062518028902)\\n else:\\n assert pid[atom] == pytest.approx(0.0)\",\n \"def xsec_CEvNS(E_R, E_nu, N_p, N_n):\\n \\n A = N_p + N_n\\n Z = N_p\\n \\n m_A = A*0.9315 #Mass of target nucleus (in GeV)\\n q = np.sqrt(2.0*E_R*m_A) #Recoil momentum (in MeV)\\n #Note: m_A in GeV, E_R in keV, E_nu in MeV\\n \\n #Calculate SM contribution\\n Qv = (A-Z) - (1.0-4.0*SIN2THETAW)*Z #Coherence factor\\n \\n xsec_SM = (G_FERMI*G_FERMI/(4.0*np.pi))*Qv*Qv*m_A* \\\\\\n (1.0-(q*q)/(4.0*E_nu*E_nu))\\n \\n #Calculate New-Physics correction from Z' coupling\\n #Assume universal coupling to quarks (u and d)\\n #QvNP = 3.0*A*gsq\\n\\n #Factor of 1e6 from (GeV/MeV)^2\\n #G_V = 1 - 1e6*(SQRT2/G_FERMI)*(QvNP/Qv)*1.0/(q*q + m_med*m_med)\\n \\n #Convert from (GeV^-3) to (cm^2/keV)\\n #and multiply by form factor\\n return xsec_SM*1e-6*(1.98e-14)*(1.98e-14)*calcSIFormFactor(E_R, A)\",\n \"def test_cherenkov_instability( show=False ):\\n # Dictionary to record the final value of E\\n slope_Erms = {}\\n\\n for scheme in [ 'standard', 'galilean', 'pseudo-galilean']:\\n\\n # Choose the correct parameters for the scheme\\n if scheme == 'standard':\\n v_comoving = 0.\\n use_galilean = False\\n else:\\n v_comoving = 0.9999*c\\n if scheme == 'galilean':\\n use_galilean = True\\n else:\\n use_galilean = False\\n\\n # Initialize the simulation object\\n sim = Simulation( Nz, zmax, Nr, rmax, Nm, dt,\\n p_zmin, p_zmax, p_rmin, p_rmax, p_nz, p_nr, p_nt, n_e,\\n zmin=zmin, initialize_ions=True,\\n v_comoving=v_comoving, use_galilean=use_galilean,\\n boundaries={'z':'periodic', 'r':'reflective'}, use_cuda=use_cuda )\\n\\n # Give a relativistic velocity to the particle, with some noise\\n sim.ptcl[0].uz[:] = uz_m\\n sim.ptcl[0].inv_gamma[:] = 1./np.sqrt( 1 + sim.ptcl[0].uz**2 )\\n sim.ptcl[1].uz[:] = uz_m\\n sim.ptcl[1].inv_gamma[:] = 1./np.sqrt( 1 + sim.ptcl[1].uz**2 )\\n\\n # Perform the simulation;\\n # record the rms electric field every 50 timestep\\n Er_rms = np.zeros(int(N_step/30)+1)\\n t = np.zeros(int(N_step/30+1))\\n Er_rms[0] = get_Er_rms(sim)\\n t[0] += sim.time\\n for i in range(int(N_step/30)):\\n sim.step( 30, show_progress=False )\\n print('Checkpoint %d' %i)\\n Er_rms[i+1] = get_Er_rms(sim)\\n t[i+1] += sim.time\\n print('Calculated RMS')\\n\\n # Check/plot the results\\n if show:\\n import matplotlib.pyplot as plt\\n # Add a plot\\n plt.semilogy( t, Er_rms, '-', label=scheme )\\n plt.ylabel('RMS(Er)')\\n plt.xlabel('Time')\\n else:\\n # Registed the final value of the slope of the electric field\\n slope_Erms[scheme] = np.log( Er_rms[-1] ) - np.log(Er_rms[-2] )\\n\\n if show:\\n # Show the plot\\n plt.legend(loc=0)\\n plt.show()\\n else:\\n # Check that, in the standard case, the electric field is\\n # growing much faster, due to the Cherenkov instability\\n assert slope_Erms['standard'] > 3.5*slope_Erms['galilean']\\n assert slope_Erms['standard'] > 3.5*slope_Erms['pseudo-galilean']\",\n \"def etio_by_csa_dep(df):\\n\\n #TODO: need to scale subplots to same axes, spread subplots apart a little.\\n #Note: plan is to replace this with some seaborn graphs that are easier.\\n\\n # fig, axs = plt.subplots(4,1)\\n fig = plt.figure()\\n spec = gridspec.GridSpec(ncols=1, nrows=4, figure=fig)\\n\\n f_ax1 = fig.add_subplot(spec[0, 0])\\n f_ax2 = fig.add_subplot(spec[1, 0])\\n f_ax3 = fig.add_subplot(spec[2, 0])\\n f_ax4 = fig.add_subplot(spec[3, 0])\\n\\n OSA_pure = df.loc[df['BaseDx'] == \\\"Mainly OSA\\\"]\\n OSA_predom = df.loc[df['BaseDx'] == \\\"Combined OSA/CSA\\\"]\\n CSA_predom = df.loc[df['BaseDx'] == \\\"Predominantly CSA\\\"]\\n CSA_pure = df.loc[df['BaseDx'] == \\\"Pure CSA\\\"]\\n\\n fig.suptitle(\\\"Contributing etiologies grouped by percentage central events\\\")\\n\\n etio_subplot(OSA_pure, f_ax1, \\\"Pure OSA\\\", graph_color='forestgreen')\\n etio_subplot(OSA_predom, f_ax2, \\\"Predom OSA\\\", graph_color='aquamarine')\\n etio_subplot(CSA_predom, f_ax3, \\\"Predom CSA\\\", graph_color='lightskyblue')\\n etio_subplot(CSA_pure, f_ax4, \\\"Pure CSA\\\", graph_color='slategray')\\n\\n plt.show()\",\n \"def plot_2d(self):\\n fig = plt.figure(figsize=(10,8))\\n \\n d = int(len(self.a_scale.flat)**0.5)\\n a_scale = self.a_scale.reshape(d,d)\\n c_scale = self.c_scale.reshape(d,d)\\n E_coh = self.E_coh.reshape(d,d)\\n plt.pcolormesh(a_scale, c_scale, E_coh)\\n plt.xlabel('xy linear deformation coefficient')\\n plt.xlabel('z linear deformation coefficient')\\n cbar = plt.colorbar()\\n cbar.ax.set_ylabel('cohesive energy (eV/atom)',\\n fontsize='x-large')\\n plt.show()\\n \\n return fig\",\n \"def get_eddy_instances(parents, root_nodes, gate_dist):\\n\\tsnodes = []\\n\\te_instances = dict()\\n\\tchild_score = dict()\\n\\tfor parent in parents:\\n\\t\\tfor child in parent.children:\\n\\t\\t\\tif is_term(child):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tchild.score = parent.score + max(score(hyp, gate_dist) for hyp in child.tracks())\\n\\t\\t\\tchild_score[child] = (child.score, parent)\\n\\t\\t\\tif child.obj in e_instances:\\n\\t\\t\\t\\te_instances[child.obj].append(child)\\n\\t\\t\\telse:\\n\\t\\t\\t\\te_instances[child.obj] = [child]\\n\\tfor node in root_nodes:\\n\\t\\tnode.score = max(score(hyp, gate_dist) for hyp in node.tracks())\\n\\t\\tchild_score[node] = (node.score, None)\\n\\t\\tif node.obj in e_instances:\\n\\t\\t\\te_instances[node.obj].append(node)\\n\\t\\telse:\\n\\t\\t\\te_instances[node.obj] = [node]\\n\\treturn (e_instances, child_score)\",\n \"def calculate_E(self):\\n \\n E = 0\\n for i in xrange(self.size):\\n Ei = self.h[i]\\n Ei += 0.5*sum((1 if self.spins[j] else -1)*self.J[i,j] for j in self.adjacency[i])\\n if not self.spins[i]:\\n Ei *= -1\\n E += Ei\\n \\n return E\",\n \"def plot_gene_expression(data, tsne, genes):\\n\\n not_in_dataframe = set(genes).difference(data.columns)\\n if not_in_dataframe:\\n if len(not_in_dataframe) < len(genes):\\n print('The following genes were either not observed in the experiment, '\\n 'or the wrong gene symbol was used: {!r}'.format(not_in_dataframe))\\n else:\\n print('None of the listed genes were observed in the experiment, or the '\\n 'wrong symbols were used.')\\n return 0\\n\\n # remove genes missing from experiment\\n genes = set(genes).difference(not_in_dataframe)\\n\\n out_dir = '/Users/herman/dev/Bioinf/master-scrna/nextflow_scripts/results'\\n\\n # Plot\\n fig = FigureGrid(len(genes), 5)\\n points2 = None\\n\\n for g, ax in zip(genes, fig):\\n points2 = ax.scatter(tsne['x'], tsne['y'], c=(int(len(tsne['x'])/2)*[0] + int(len(tsne['x'])/2)*[1]), s=3,\\n cmap=matplotlib.cm.Spectral_r)\\n points = ax.scatter(tsne['x'], tsne['y'], c=data.loc[tsne.index, g], s=3,\\n cmap=matplotlib.cm.Spectral_r)\\n\\n #plt.colorbar(z1_plot,cax=ax1)\\n ax.set_axis_off()\\n ax.set_title(g)\\n\\n t = plt.colorbar(points2, label='expression', ax=ax)\\n plt.savefig(os.path.join(out_dir, 'test_expression.svg'))\",\n \"def cue_integration_output(self, cue1, cue2):\\r\\n pi_left = cue1[0:8]\\r\\n\\r\\n pi_l = np.zeros((self.num_neuron, int(self.nt)))\\r\\n pi_l[:, int(self.ni):] = np.repeat(pi_left.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\\r\\n\\r\\n pi_right = cue1[8:]\\r\\n pi_r = np.zeros((self.num_neuron, int(self.nt)))\\r\\n pi_r[:, int(self.ni):] = np.repeat(pi_right.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\\r\\n\\r\\n v_left = cue2[0:8]\\r\\n v_l = np.zeros((self.num_neuron, int(self.nt)))\\r\\n v_l[:, int(self.ni):] = np.repeat(v_left.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\\r\\n v_right = cue2[8:]\\r\\n v_r = np.zeros((self.num_neuron, int(self.nt)))\\r\\n v_r[:, int(self.ni):] = np.repeat(v_right.reshape(self.num_neuron, 1), int(self.nt - self.ni), axis=1)\\r\\n\\r\\n # generate the array for integration cells and the uniform inhibitory cell\\r\\n it_l = np.zeros((self.num_neuron, int(self.nt)))\\r\\n it_l[:, 0] = 0.1 * np.ones(self.num_neuron, )\\r\\n it_r = np.zeros((self.num_neuron, int(self.nt)))\\r\\n it_r[:, 0] = 0.1 * np.ones(self.num_neuron, )\\r\\n ul = np.zeros((1, int(self.nt)))\\r\\n ur = np.zeros((1, int(self.nt)))\\r\\n # iteration to the stable state\\r\\n for t in range(1, int(self.nt)):\\r\\n it_l[:, t] = it_l[:, t - 1] + (-it_l[:, t - 1] + np.max(\\r\\n [np.zeros((self.num_neuron,)),\\r\\n self.gammaE + np.dot(self.W_E_E, it_l[:, t - 1]) + self.W_E_I * ul[:, t - 1] + pi_l[:, t - 1]\\r\\n + v_l[:, t - 1]], axis=0)) * self.dt / self.tauE\\r\\n ul[:, t] = ul[:, t - 1] + (\\r\\n -ul[:, t - 1] + np.max([0, self.gammaI + self.W_I_E * np.sum(it_l[:, t - 1]) + self.W_I_I * ul[:, t - 1]],\\r\\n axis=0)) * self.dt / self.tauI\\r\\n\\r\\n it_r[:, t] = it_r[:, t - 1] + (-it_r[:, t - 1] + np.max(\\r\\n [np.zeros((self.num_neuron,)),\\r\\n self.gammaE + np.dot(self.W_E_E, it_r[:, t - 1]) + self.W_E_I * ur[:, t - 1] + pi_r[:, t - 1]\\r\\n + v_r[:, t - 1]], axis=0)) * self.dt / self.tauE\\r\\n ur[:, t] = ur[:, t - 1] + (\\r\\n -ur[:, t - 1] + np.max([0, self.gammaI + self.W_I_E * np.sum(it_r[:, t - 1]) + self.W_I_I * ur[:, t - 1]],\\r\\n axis=0)) * self.dt / self.tauI\\r\\n\\r\\n # get the final iteration as the output\\r\\n # update the neuron activation\\r\\n self.integration_neuron = np.hstack([it_l[:, -1], it_r[:, -1]])\\r\\n self.integration_neuron = noisy_sigmoid(self.integration_neuron, 5.0, 2.5, 0)\\r\\n self.inhibition_neuron = np.hstack([ul[-1], ur[-1]])\\r\\n\\r\\n return self.integration_neuron\",\n \"def divergedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childGeneDuplicated = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is True:\\n parentEdges = parentGeneDuplicated.getEdges()\\n childEdges = childGeneDuplicated.getEdges()\\n \\n graph = divergedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentGeneDuplicated.getEnzymes())\\n childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childGeneDuplicated.getEnzymes())\\n \\n parentGraph.name = 'Diverged metabolism gene-duplicated enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Diverged metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def punto2_1():\\r\\n\\tprint(\\\"2.1R/\\\")\\r\\n\\tn = 15\\r\\n\\tp = 0.2\\r\\n\\tseed = 12\\r\\n\\tvis = [False for i in range(n)]\\r\\n\\tG,graph=erdos_renyi_seed(n,p,seed)\\r\\n\\tnx.draw(graph,with_labels = True)\\r\\n\\tplt.show()\\t\\r\\n\\tm1 = tarjan(G)\\r\\n\\tnumCC = 0\\r\\n\\tfor u in range(n):\\r\\n\\t\\tif vis[u] == False:\\r\\n\\t\\t\\tnumCC+=1\\r\\n\\t\\t\\tdfsCC(G,vis,u)\\r\\n\\r\\n\\tavNumCC = n/numCC\\r\\n\\tnumAp,numB = m1[0],m1[1]\\r\\n\\tavnDeg = deg(G)\\r\\n\\tt = tripTrian(G)\\r\\n\\tnumTrian,numTrip = t[0],t[1]\\r\\n\\tprint(\\\"numero de componentes conectados: \\\" + str(numCC))\\r\\n\\tprint(\\\"tamaño promedio de componentes conectados: \\\" + str(avNumCC))\\r\\n\\tprint(\\\"numero de puntos de articulacion: \\\" + str(numAp))\\r\\n\\tprint(\\\"numero de puentes: \\\" + str(numB))\\r\\n\\tprint(\\\"Grado promedio por nodo: \\\" + str(avnDeg))\\r\\n\\tprint(\\\"numero de triangulos: \\\" + str(numTrian))\\r\\n\\tprint(\\\"numero de tripletas: \\\" + str(numTrip))\\r\\n\\treturn\",\n \"def testNonLinearity():\\n vis = VISinformation()\\n data = np.linspace(1, vis['fullwellcapacity'], 10000)\\n nonlin = CCDnonLinearityModel(data.copy())\\n\\n txt = '%s' % datetime.datetime.isoformat(datetime.datetime.now())\\n\\n fig = plt.figure(frameon=False)\\n\\n left, width = 0.1, 0.8\\n rect1 = [left, 0.3, width, 0.65]\\n rect2 = [left, 0.1, width, 0.2]\\n\\n ax1 = fig.add_axes(rect1, title='VIS Non-linearity Model')\\n ax2 = fig.add_axes(rect2) #left, bottom, width, height\\n\\n ax1.axhline(y=0, c='k', ls='--')\\n ax1.plot(data, (nonlin/data - 1.)*100, 'r-', label='Model')\\n\\n ax2.axhline(y=0, c='k', ls='--')\\n ax2.plot(data, (nonlin - data)/vis['gain'], 'g-')\\n\\n ax1.axvline(x=97, c='k', ls='--')\\n ax2.axvline(x=97, c='k', ls='--')\\n\\n ax1.set_xticklabels([])\\n ax2.set_xlabel('Real Charge [electrons]')\\n ax1.set_ylabel('(Output / Real - 1)*100')\\n ax2.set_ylabel('O - R [ADUs]')\\n\\n ax1.set_xlim(0, vis['fullwellcapacity'])\\n ax2.set_xlim(0, vis['fullwellcapacity'])\\n ax1.set_ylim(-.15, .2)\\n\\n ax1.text(0.83, 1.12, txt, ha='left', va='top', fontsize=9, transform=ax1.transAxes, alpha=0.2)\\n ax1.legend(shadow=True, fancybox=True, numpoints=1, scatterpoints=1, markerscale=1.0)\\n plt.savefig('NonlinearityModel.pdf')\\n\\n ax1.set_ylim(-.1, 8)\\n ax2.set_ylim(0, 2)\\n ax1.set_xlim(50, 800)\\n ax2.set_xlim(50, 800)\\n plt.savefig('NonlinearityModel2.pdf')\\n\\n plt.close()\",\n \"def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:\\n # get neofunctionalisations \\n neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)\\n \\n # filter core metabolism enzyme graph\\n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism) \\n neofunctionalisedMetabolism = neofunctionalisedEnzymes.filterGraph(enzymeGraph, minimumEcDifference = None)\\n \\n # colour core metabolism \\n if colour is not False:\\n \\n if colour is True:\\n colourToUse = Export.Colour.GREEN\\n else:\\n colourToUse = colour\\n \\n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\\n neofunctionalisedMetabolism = enzymeGraph\\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\\n \\n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism enzymes ' + ' '.join(self.ncbiNames)\\n \\n return neofunctionalisedMetabolism\",\n \"def RE2e(Type=\\\"DFA\\\"):\\n CC10, CC11 = state('CC10'), state('CC11')\\n for i in sigma:\\n CC10.transit[i] = CC10\\n CC11.transit[i] = CC11\\n for i in sigma_AW:\\n CC10.transit[i] = CC11\\n for i in sigma_B:\\n CC11.transit[i] = CC10\\n if Type == \\\"pDFA\\\":\\n CC1 = pDFA('CC1', sigma, [CC10, CC11], CC10, [CC10])\\n else:\\n CC1 = DFA('CC1', sigma, [CC10, CC11], CC10, [CC10])\\n if (SIZEOF):\\n EM_size[\\\"RE2e\\\"] = asizeof.asizeof(CC1)\\n return CC1\",\n \"def test_linear_buckling_iso_CCSS(plot_static=False, plot_lb=False):\\n # number of nodes\\n nx = 5 # along x\\n ny = 5 # along y\\n\\n # getting integration points\\n nint = 4\\n points, weights = get_points_weights(nint=nint)\\n\\n # geometry\\n a = 3 # along x\\n b = 3 # along y\\n\\n # material properties\\n E = 200e9\\n nu = 0.3\\n laminaprop = (E, E, nu)\\n stack = [0]\\n h = 0.001\\n lam = read_stack(stack=stack, plyt=h, laminaprop=laminaprop)\\n\\n # creating mesh\\n x = np.linspace(0, a, nx)\\n y = np.linspace(0, b, ny)\\n xmesh, ymesh = np.meshgrid(x, y)\\n\\n # node coordinates and position in the global matrix\\n ncoords = np.vstack((xmesh.T.flatten(), ymesh.T.flatten())).T\\n nids = 1 + np.arange(ncoords.shape[0])\\n nid_pos = dict(zip(nids, np.arange(len(nids))))\\n\\n # identifying nodal connectivity for plate elements\\n # similar than Nastran's CQUAD4\\n #\\n # ^ y\\n # |\\n #\\n # 4 ________ 3\\n # | |\\n # | | --> x\\n # | |\\n # |_______|\\n # 1 2\\n\\n\\n nids_mesh = nids.reshape(nx, ny)\\n n1s = nids_mesh[:-1, :-1].flatten()\\n n2s = nids_mesh[1:, :-1].flatten()\\n n3s = nids_mesh[1:, 1:].flatten()\\n n4s = nids_mesh[:-1, 1:].flatten()\\n\\n num_elements = len(n1s)\\n print('num_elements', num_elements)\\n\\n N = DOF*nx*ny\\n Kr = np.zeros(KC0_SPARSE_SIZE*num_elements, dtype=INT)\\n Kc = np.zeros(KC0_SPARSE_SIZE*num_elements, dtype=INT)\\n Kv = np.zeros(KC0_SPARSE_SIZE*num_elements, dtype=DOUBLE)\\n KGr = np.zeros(KG_SPARSE_SIZE*num_elements, dtype=INT)\\n KGc = np.zeros(KG_SPARSE_SIZE*num_elements, dtype=INT)\\n KGv = np.zeros(KG_SPARSE_SIZE*num_elements, dtype=DOUBLE)\\n init_k_KC0 = 0\\n init_k_KG = 0\\n\\n plates = []\\n for n1, n2, n3, n4 in zip(n1s, n2s, n3s, n4s):\\n plate = BFSPlate2D()\\n plate.n1 = n1\\n plate.n2 = n2\\n plate.n3 = n3\\n plate.n4 = n4\\n plate.c1 = DOF*nid_pos[n1]\\n plate.c2 = DOF*nid_pos[n2]\\n plate.c3 = DOF*nid_pos[n3]\\n plate.c4 = DOF*nid_pos[n4]\\n plate.ABD = lam.ABD\\n plate.lex = a/(nx - 1)\\n plate.ley = b/(ny - 1)\\n plate.init_k_KC0 = init_k_KC0\\n plate.init_k_KG = init_k_KG\\n update_KC0(plate, points, weights, Kr, Kc, Kv)\\n init_k_KC0 += KC0_SPARSE_SIZE\\n init_k_KG += KG_SPARSE_SIZE\\n plates.append(plate)\\n\\n KC0 = coo_matrix((Kv, (Kr, Kc)), shape=(N, N)).tocsc()\\n\\n # applying boundary conditions\\n\\n # locating nodes\\n bk = np.zeros(KC0.shape[0], dtype=bool) # constrained DOFs, can be used to prescribe displacements\\n\\n x = ncoords[:, 0]\\n y = ncoords[:, 1]\\n\\n # applying boundary conditions\\n # simply supported\\n check = isclose(x, 0) | isclose(x, a) | isclose(y, 0) | isclose(y, b)\\n bk[2::DOF] = check\\n check = isclose(x, 0) | isclose(x, a)\\n bk[3::DOF] = check\\n # point supports\\n check = isclose(x, a/2) & (isclose(y, 0) | isclose(y, b))\\n bk[0::DOF] = check\\n check = isclose(y, b/2) & (isclose(x, 0) | isclose(x, a))\\n bk[1::DOF] = check\\n\\n # unconstrained nodes\\n bu = ~bk # logical_not\\n\\n # defining external force vector\\n fext = np.zeros(KC0.shape[0], dtype=float)\\n\\n # applying unitary load along u at x=a\\n # nodes at vertices get 1/2 the force\\n for plate in plates:\\n pos1 = nid_pos[plate.n1]\\n pos2 = nid_pos[plate.n2]\\n pos3 = nid_pos[plate.n3]\\n pos4 = nid_pos[plate.n4]\\n if isclose(x[pos3], a):\\n Nxx = -1\\n xi = +1\\n elif isclose(x[pos1], 0):\\n Nxx = +1\\n xi = -1\\n else:\\n continue\\n lex = plate.lex\\n ley = plate.ley\\n indices = []\\n c1 = DOF*pos1\\n c2 = DOF*pos2\\n c3 = DOF*pos3\\n c4 = DOF*pos4\\n cs = [c1, c2, c3, c4]\\n for ci in cs:\\n for i in range(DOF):\\n indices.append(ci + i)\\n fe = np.zeros(4*DOF, dtype=float)\\n for j in range(nint):\\n eta = points[j]\\n plate.update_Nu(xi, eta)\\n Nu = np.asarray(plate.Nu)\\n fe += ley/2*weights[j]*Nu*Nxx\\n fext[indices] += fe\\n\\n Kuu = KC0[bu, :][:, bu]\\n fextu = fext[bu]\\n\\n # static solver\\n uu = spsolve(Kuu, fextu)\\n u = np.zeros(KC0.shape[0], dtype=float)\\n u[bu] = uu\\n\\n if plot_static:\\n import matplotlib\\n matplotlib.use('TkAgg')\\n import matplotlib.pyplot as plt\\n plt.gca().set_aspect('equal')\\n uplot = u[0::DOF].reshape(nx, ny).T\\n vplot = u[1::DOF].reshape(nx, ny).T\\n print('u extremes', uplot.min(), uplot.max())\\n print('v extremes', vplot.min(), vplot.max())\\n levels = np.linspace(uplot.min(), uplot.max(), 300)\\n plt.contourf(xmesh, ymesh, uplot, levels=levels)\\n plt.colorbar()\\n plt.show()\\n\\n # eigenvalue solver\\n\\n # getting integration points\\n for plate in plates:\\n update_KG(u, plate, points, weights, KGr, KGc, KGv)\\n KG = coo_matrix((KGv, (KGr, KGc)), shape=(N, N)).tocsc()\\n KGuu = KG[bu, :][:, bu]\\n\\n # solving modified generalized eigenvalue problem\\n # Original: (KC0 + lambda*KG)*v = 0\\n # Modified: (-1/lambda)*KC0*v = KG*v #NOTE here we find (-1/lambda)\\n num_eigenvalues = 5\\n eigvals, eigvecsu = eigsh(A=KGuu, k=num_eigenvalues, which='SM', M=Kuu,\\n tol=1e-6, sigma=1., mode='cayley')\\n eigvals = -1./eigvals\\n eigvecs = np.zeros((KC0.shape[0], num_eigenvalues), dtype=float)\\n eigvecs[bu, :] = eigvecsu\\n\\n if plot_lb:\\n import matplotlib\\n matplotlib.use('TkAgg')\\n import matplotlib.pyplot as plt\\n plt.gca().set_aspect('equal')\\n mode = 0\\n wplot = eigvecs[2::DOF, mode].reshape(nx, ny).T\\n levels = np.linspace(wplot.min(), wplot.max(), 300)\\n plt.contourf(xmesh, ymesh, wplot, levels=levels)\\n plt.colorbar()\\n plt.show()\\n\\n kc = eigvals[0]/(E*np.pi**2*(h/b)**2/(12*(1 - nu**2))*h)\\n assert isclose(kc, 6.6, rtol=0.05)\",\n \"def get_econs(self):\\n eham = self.beads.vpath*self.nm.omegan2 + self.nm.kin + self.forces.pot\\n eham += self.bias.pot # bias\\n for e in self._elist:\\n eham += e.get()\\n\\n return eham + self.eens\",\n \"def electoral_college_outcome(ec_rows, state_edges):\\r\\n ec_votes = {} # maps from state to number of electoral votes\\r\\n for row in ec_rows:\\r\\n ec_votes[row[\\\"State\\\"]] = float(row[\\\"Electors\\\"])\\r\\n\\r\\n outcome = {\\\"Dem\\\": 0, \\\"Rep\\\": 0}\\r\\n for state in state_edges:\\r\\n votes = ec_votes[state]\\r\\n if state_edges[state] > 0:\\r\\n outcome[\\\"Dem\\\"] += votes\\r\\n elif state_edges[state] < 0:\\r\\n outcome[\\\"Rep\\\"] += votes\\r\\n else:\\r\\n outcome[\\\"Dem\\\"] += votes/2.0\\r\\n outcome[\\\"Rep\\\"] += votes/2.0\\r\\n return outcome\",\n \"def get_second_eigenvector(graph):\\n ###TODO\\n pass\",\n \"def dual(self):\\n def set_twins():\\n for edge_idx in range(0, len(dual_dcel.edges), 2):\\n dual_dcel.edges[edge_idx].twin = dual_dcel.edges[edge_idx + 1]\\n dual_dcel.edges[edge_idx + 1].twin = dual_dcel.edges[edge_idx]\\n\\n def set_next_and_previous():\\n for face in dual_dcel.faces:\\n face_edges = [edge for edge in dual_dcel.edges if edge.incident_face == face]\\n for edge in face_edges:\\n if(not edge.get_destination().is_infinity()):\\n edge.nxt = [e for e in face_edges if e.origin == edge.get_destination()][0]\\n if(not edge.origin.is_infinity()):\\n edge.prev = [e for e in face_edges if edge.origin == e.get_destination()][0]\\n\\n dual_dcel = DCEL()\\n for edge in self.edges:\\n incident_face = dual_dcel.add_face(Face(circumcentre=edge.twin.origin.as_points()))\\n origin = dual_dcel.add_vertex(Vertex(coordinates=edge.incident_face.circumcentre))\\n dual_edge = HalfEdge(\\n origin=origin,\\n incident_face=incident_face\\n )\\n incident_face.outer_component = dual_edge\\n origin.incident_edge = dual_edge\\n dual_dcel.edges.append(dual_edge)\\n\\n set_twins()\\n set_next_and_previous()\\n return dual_dcel\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/27')\",\n \"def computemodes(self):\\n evals, modes = np.linalg.eig(self.A)\\n return evals, modes\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/26')\",\n \"def ehermite(a, b):\\n [c, d, g] = extgcd(a, b)\\n if g:\\n E = np.array([[c, d], [-b/g, a/g]])\\n else:\\n E = np.array([[1, 0], [0, 1]])\\n\\n return E\\n #Leila: check this \\\"http://www.ece.northwestern.edu/local-apps/matlabhelp/techdoc/ref/gcd.html\\\"\",\n \"def part1c_0():\\n xs = exampleInput\\n T = submission.computeEdgeMarginals(simpleCRF, xs)\\n for t in T:\\n grader.requireIsEqual( 1.0, sum(t.values()) )\",\n \"def eci(self):\\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\\n eci = coefs.copy()\\n eci = eci / self._subspace.function_total_multiplicities\\n return eci\",\n \"def get_core(evth, ncore=0):\\n x, y = (evth[98 + ncore], evth[118 + ncore])\\n return (x, y, sqrt((x * x) + (y * y)))\",\n \"def display_energy_levels_0d(diagram, num_atoms, atoms, h_poly):\\n h = eval_hamiltonian(num_atoms, h_poly, (1, 1))\\n\\n e, v = eigensystem(h)\\n\\n left = 0\\n bottom = 0\\n right = max([len(row) for row in diagram.split('\\\\n')])\\n top = len(diagram.split('\\\\n'))\\n\\n plot_rows = numpy.ceil(math.sqrt(num_atoms+1))\\n plot_cols = plot_rows\\n\\n for i in range(num_atoms):\\n matplotlib.pyplot.subplot(plot_rows, plot_cols, i+1, axisbg=\\\"#000000\\\")\\n y = [atom[0] for atom in atoms]\\n x = [atom[1] for atom in atoms]\\n c = numpy.abs(v[i]*v[i])\\n\\n matplotlib.pyplot.title('E = %f' % numpy.real(e[i]), fontsize = 10)\\n norm = matplotlib.colors.Normalize(vmin = min(c),\\n vmax = max(0.0001, max(c)))\\n #x = [0,0,1,1]\\n #y = [0,1,0,1]\\n #c = [1,2,3,4]\\n matplotlib.pyplot.hexbin(x, y, C = c,\\n gridsize = (right-left, top-bottom),\\n extent = (left, right, bottom, top),\\n cmap = matplotlib.pyplot.get_cmap(\\\"gray\\\"),\\n norm = norm\\n )\\n\\n matplotlib.pyplot.subplot(plot_rows, plot_cols, num_atoms+1)\\n matplotlib.pyplot.scatter(num_atoms*[0], e, s = 0.1)\",\n \"def euclid_ccl(Omega_c, sigma8=0.83):\\n\\n cosmo_fid = ccl.Cosmology(Omega_c=Omega_c, Omega_b=0.045, h=0.71, sigma8=sigma8, n_s=0.963)\\n\\n dNdzs = np.zeros((nbins, z.size))\\n shears = []\\n \\n for i in range(nbins):\\n # edges of 1 equal width redshift bins, between 0 and 2\\n zmin, zmax = i*(2./nbins), (i+1)*(2./nbins)\\n # generate dNdz per bin\\n dNdzs[i,:] = ccl.dNdz_tomog(z=z, zmin=zmin, zmax=zmax, pz_func=pz, dNdz_func = dNdz_true)\\n # calculate the shear per bin\\n gal_shapes = ccl.WeakLensingTracer(cosmo_fid, dndz=(z, dNdzs[i,:]))\\n shears.append(gal_shapes)\\n \\n print ('Shears calculated, calculating power spectra now...')\\n # calculate nbin*(nbin+1)/2 = 1 spectra from the shears\\n Cls = []\\n for i in range(nbins):\\n for j in range(0,i+1):\\n Cls.append(ccl.angular_cl(cosmo_fid, shears[i], shears[j], ells))\\n \\n return np.array(Cls), dNdzs\",\n \"def geneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:\\n \\n \\n \\n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)\\n \\n geneDuplicationModel = SimpleGeneDuplication\\n# geneDuplicationModel = SimpleGroupGeneDuplication(sameGroupOrganisms = self.group)\\n \\n # filter core metabolism enzyme graph \\n geneDuplicatedEnzymes = geneDuplicationModel.filterEnzymes(enzymeGraph, eValue = defaultEValue, ignoreDuplicatesOutsideSet = True, preCalculatedEnzymes = None)\\n \\n # colour core metabolism\\n if colour is not False:\\n \\n if colour is True:\\n colourToUse = Export.Colour.GREEN\\n else:\\n colourToUse = colour\\n \\n geneDuplicatedEnzymesOnly = geneDuplicatedEnzymes\\n geneDuplicatedEnzymes = enzymeGraph\\n Export.addColourAttribute(geneDuplicatedEnzymes, colourToUse, nodes = False, edges = geneDuplicatedEnzymesOnly.getEdges())\\n \\n geneDuplicatedEnzymes.name = 'Gene-duplicated core metabolism enzymes ' + ' '.join(self.ncbiNames)\\n \\n return geneDuplicatedEnzymes\",\n \"def main():\\n Nrep = 8 # number of repetition of EM steps\\n nm = 3 # number of mixed gaussians.\\n ns = 300 # number of samples.\\n \\n mu, sg, lm, lm_ind, smp, L_true = generate_synthetic_data(nm, ns)\\n plt.figure(1, figsize=(5,4))\\n plt.clf()\\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\\n \\n mue, sge, lme = generate_initial_state(nm, ns)\\n axi = 0 # subplot number\\n plt.figure(2, figsize=(12,9))\\n plt.clf()\\n for rep in range(Nrep):\\n # E-step\\n r, L_infer = e_step(smp, mue, sge, lme, nm, ns)\\n axi += 1 \\n ax = plt.subplot(Nrep/2, 6, axi)\\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\\n ax.set_title('E-step : %d' % (rep + 1))\\n ax.set_yticklabels([])\\n ax.set_xticklabels([])\\n ax.set_ylim((-0.1, 0.3))\\n\\n # M-step\\n mue, sge, lme = m_step(smp, r, nm, ns)\\n axi += 1 \\n ax = plt.subplot(Nrep/2, 6, axi)\\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\\n ax.set_title('M-step : %d' % (rep + 1))\\n ax.set_yticklabels([])\\n ax.set_xticklabels([])\\n ax.set_ylim((-0.1, 0.3))\\n\\n # plot the ground truth for comparison\\n axi += 1 \\n ax = plt.subplot(Nrep/2, 6, axi)\\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\\n ax.set_title('grn_truth')\\n ax.set_yticklabels([])\\n ax.set_xticklabels([])\\n ax.set_ylim((-0.1, 0.3))\\n\\n print('L_infer = %2.6f , L_true = %2.6f' % (L_infer, L_true))\",\n \"def electoral_college_outcome(ec_rows, state_edges):\\n ec_votes = {} # maps from state to number of electoral votes\\n for row in ec_rows:\\n ec_votes[row[\\\"State\\\"]] = float(row[\\\"Electors\\\"])\\n\\n outcome = {\\\"Dem\\\": 0, \\\"Rep\\\": 0}\\n for state in state_edges:\\n votes = ec_votes[state]\\n if state_edges[state] > 0:\\n outcome[\\\"Dem\\\"] += votes\\n elif state_edges[state] < 0:\\n outcome[\\\"Rep\\\"] += votes\\n else:\\n outcome[\\\"Dem\\\"] += votes/2.0\\n outcome[\\\"Rep\\\"] += votes/2.0\\n return outcome\",\n \"def conservedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childGeneDuplicated = self.childClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is True:\\n parentEdges = parentGeneDuplicated.getEdges()\\n childEdges = childGeneDuplicated.getEdges()\\n \\n graph = conservedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Conserved metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentGeneDuplicated.getEnzymes())\\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childGeneDuplicated.getEnzymes())\\n \\n parentGraph.name = 'Conserved metabolism gene-duplicated enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Conserved metabolism gene-duplicated enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def find_5sigma_diff(pop1, pop2):\\n\\n grb1 = pop1.g_tot\\n grb2 = pop2.g_tot\\n\\n first1 = True\\n first2 = True\\n\\n #----------------------\\n def show():\\n print(\\\" {:8s} {:5s} 1: sigmx={:5.1f} +/-{:5.1f} * CL={:5d} * vis={:2d} * err={:5d}\\\"\\n .format(g1[\\\"name\\\"], g1.loca, g1.sigmx, g1.esigmx, g1.d5s,g1.prpt,g1.err))\\n print(\\\" {:8s} {:5s} 2: {:5.1f} +/-{:5.1f} {:5d} * {:2d} * {:5d}\\\"\\n .format(\\\" \\\",\\\" \\\",g2.sigmx, g2.esigmx, g2.d5s,g2.prpt,g2.err))\\n return\\n #----------------------\\n\\n for (i1, g1), (i2, g2) in zip(grb1.iterrows(), grb2.iterrows()):\\n\\n if (g1.d5s >= pop1.eff_lvl and g2.d5s < pop2.eff_lvl) :\\n if (first1):\\n print(\\\"Error : detected in 1 not in 2\\\")\\n first1 = False\\n show()\\n\\n if (g1.d5s < pop1.eff_lvl and g2.d5s >= pop2.eff_lvl) :\\n if (first2):\\n print(\\\"Error : detected in 2 not in 1\\\")\\n first2 = False\\n show()\\n print(\\\"All done!\\\")\",\n \"def unifiedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph: \\n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is False:\\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\\n \\n else:\\n unifiedMetabolismEnzymes = self.unifiedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = True)\\n \\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = unifiedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6214158","0.60957927","0.59478533","0.58485806","0.57059896","0.5507641","0.5501157","0.54937583","0.54590195","0.5301212","0.52908236","0.52827936","0.5239076","0.5235464","0.5215645","0.52147555","0.52099645","0.5206502","0.5192779","0.5181326","0.5179808","0.5170709","0.51515466","0.5138134","0.51235694","0.5122695","0.51097876","0.5109335","0.50935864","0.5092792","0.5092435","0.50866646","0.5083372","0.5079754","0.50762784","0.50670075","0.50568396","0.50495994","0.50456417","0.50388676","0.5034526","0.502331","0.50185597","0.50158525","0.501382","0.5003884","0.49993983","0.49983305","0.49949545","0.4981292","0.4981128","0.49799097","0.4967996","0.49646378","0.4961987","0.49568817","0.49535275","0.49396518","0.4938819","0.49380732","0.49374318","0.4930586","0.4928232","0.4926096","0.49249777","0.49235103","0.49212906","0.49083894","0.49016055","0.48911336","0.48895645","0.48880783","0.48874238","0.48725867","0.48709145","0.4866491","0.48654976","0.48638126","0.48610336","0.48597556","0.48581675","0.4856427","0.4853607","0.4851965","0.4842021","0.48361874","0.4835946","0.48350853","0.4834981","0.48347077","0.48341426","0.48318547","0.48304644","0.4827786","0.48275733","0.4825927","0.48258895","0.48220938","0.48206475","0.4819296"],"string":"[\n \"0.6214158\",\n \"0.60957927\",\n \"0.59478533\",\n \"0.58485806\",\n \"0.57059896\",\n \"0.5507641\",\n \"0.5501157\",\n \"0.54937583\",\n \"0.54590195\",\n \"0.5301212\",\n \"0.52908236\",\n \"0.52827936\",\n \"0.5239076\",\n \"0.5235464\",\n \"0.5215645\",\n \"0.52147555\",\n \"0.52099645\",\n \"0.5206502\",\n \"0.5192779\",\n \"0.5181326\",\n \"0.5179808\",\n \"0.5170709\",\n \"0.51515466\",\n \"0.5138134\",\n \"0.51235694\",\n \"0.5122695\",\n \"0.51097876\",\n \"0.5109335\",\n \"0.50935864\",\n \"0.5092792\",\n \"0.5092435\",\n \"0.50866646\",\n \"0.5083372\",\n \"0.5079754\",\n \"0.50762784\",\n \"0.50670075\",\n \"0.50568396\",\n \"0.50495994\",\n \"0.50456417\",\n \"0.50388676\",\n \"0.5034526\",\n \"0.502331\",\n \"0.50185597\",\n \"0.50158525\",\n \"0.501382\",\n \"0.5003884\",\n \"0.49993983\",\n \"0.49983305\",\n \"0.49949545\",\n \"0.4981292\",\n \"0.4981128\",\n \"0.49799097\",\n \"0.4967996\",\n \"0.49646378\",\n \"0.4961987\",\n \"0.49568817\",\n \"0.49535275\",\n \"0.49396518\",\n \"0.4938819\",\n \"0.49380732\",\n \"0.49374318\",\n \"0.4930586\",\n \"0.4928232\",\n \"0.4926096\",\n \"0.49249777\",\n \"0.49235103\",\n \"0.49212906\",\n \"0.49083894\",\n \"0.49016055\",\n \"0.48911336\",\n \"0.48895645\",\n \"0.48880783\",\n \"0.48874238\",\n \"0.48725867\",\n \"0.48709145\",\n \"0.4866491\",\n \"0.48654976\",\n \"0.48638126\",\n \"0.48610336\",\n \"0.48597556\",\n \"0.48581675\",\n \"0.4856427\",\n \"0.4853607\",\n \"0.4851965\",\n \"0.4842021\",\n \"0.48361874\",\n \"0.4835946\",\n \"0.48350853\",\n \"0.4834981\",\n \"0.48347077\",\n \"0.48341426\",\n \"0.48318547\",\n \"0.48304644\",\n \"0.4827786\",\n \"0.48275733\",\n \"0.4825927\",\n \"0.48258895\",\n \"0.48220938\",\n \"0.48206475\",\n \"0.4819296\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61443126"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17443,"cells":{"query":{"kind":"string","value":"SubstanceEC graph of \"neofunctionalised\" EC numbers, derived from the unified core metabolisms."},"document":{"kind":"string","value":"def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is False:\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\n \n else:\n unifiedMetabolism = self.unifiedMetabolism(majorityPercentageCoreMetabolism, colour = True)\n \n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = unifiedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:\n # get neofunctionalisations \n neofunctionalisedECs = NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs))\n \n # filter core metabolism EC graph\n coreMetabolism = self.coreMetabolism(majorityPercentageCoreMetabolism)\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))\n \n neofunctionalisedMetabolism = neofunctionalisedECs.filterGraph(coreMetabolism, minimumEcDifference = None, minimumOrganismsCount = minimumOrganismsCount)\n \n # colour core metabolism\n if colour is not False:\n \n if colour is True:\n colourToUse = Export.Colour.GREEN\n else:\n colourToUse = colour\n \n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\n neofunctionalisedMetabolism = coreMetabolism\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\n \n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism ' + ' '.join(self.ncbiNames)\n \n return neofunctionalisedMetabolism","def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n childGraph = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(addedECs)\n childGraph.name = 'Added metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return childGraph","def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\n conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)\n \n parentNeofunctionalised= self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = conservedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = conservedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\n childGraph = conservedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\n \n parentGraph.name = 'Conserved metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def divergedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\n divergedMetabolism = self.divergedMetabolism(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = divergedMetabolism\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = divergedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\n childGraph = divergedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\n \n parentGraph.name = 'Diverged metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def lostMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n parentGraph = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(lostECs)\n parentGraph.name = 'Lost metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return parentGraph","def redundantECsForContributingNeofunctionalisation(self, \n majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, \n majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, \n eValue = defaultEValue, \n redundancyType: 'RedundancyType' = None,\n considerOnlyECs = None) -> Dict[Neofunctionalisation, Set[EcNumber]]:\n from FEV_KEGG.Robustness.Topology.Redundancy import Redundancy, RedundancyContribution, RedundancyType\n \n if redundancyType is None:\n redundancyType = RedundancyType.default\n \n #- calculate \"neofunctionalised\" ECs\n neofunctionalisedMetabolismSet = self.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs).getECs()\n neofunctionalisationsForFunctionChange = self.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs)\n \n #- calculate redundancy\n redundancy = Redundancy( self.coreMetabolism(majorityPercentageCoreMetabolism) )\n redundancyContribution = RedundancyContribution(redundancy, neofunctionalisedMetabolismSet)\n \n contributedECsForContributingNeofunctionalisedEC = redundancyContribution.getContributedKeysForSpecial(redundancyType)\n contributingNeofunctionalisedECs = set(contributedECsForContributingNeofunctionalisedEC.keys())\n \n #- REPEAT for each function change consisting of \"neofunctionalised\" ECs, which also contribute to redundancy\n contributingNeofunctionalisations = dict()\n \n for functionChange, neofunctionalisations in neofunctionalisationsForFunctionChange.items():\n #- report enzyme pairs of neofunctionalisations, which caused the EC to be considered \"neofunctionalised\", and are in return contributing to redundancy \n \n if functionChange.ecA in contributingNeofunctionalisedECs or functionChange.ecB in contributingNeofunctionalisedECs: # function change contributes to redundancy\n \n for neofunctionalisation in neofunctionalisations:\n currentSetOfContributedECs = contributingNeofunctionalisations.get(neofunctionalisation, None)\n \n if currentSetOfContributedECs is None:\n currentSetOfContributedECs = set()\n contributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs\n \n for ec in functionChange.ecPair:\n contributedECs = contributedECsForContributingNeofunctionalisedEC.get(ec, None)\n if contributedECs is not None:\n currentSetOfContributedECs.update(contributedECs)\n \n return contributingNeofunctionalisations","def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\n return graph","def __repr__(self):\n return \"EC(%s, %s)\" % (str(self.coefficient), repr(self.basefield))","def complex_network_mapping(graph):\n vect = []\n\n n = nx.number_of_nodes(graph)\n e = nx.number_of_edges(graph)\n print n, e\n\n# adj = nx.adjacency_matrix(graph).toarray()\n# adj_bin = np.where(adj > 0, 1., 0.)\n# adj_conn = 1 - adj\n adj_bin = nx.adjacency_matrix(graph).toarray()\n adj_bin = np.array(adj_bin, dtype=np.float)\n\n # Node Betweenness binary\n bt_bin = nx.betweenness_centrality(graph).values()\n avg_btb = np.mean(bt_bin)\n vect.append(avg_btb)\n\n # Edge betweenness\n ebt = np.array(nx.edge_betweenness_centrality(graph).values())\n vect.append(np.mean(ebt))\n\n # Eigen vector centrality binary\n evc_bin = eigenvector_centrality_und(adj_bin)\n avg_evcb = np.mean(evc_bin)\n vect.append(avg_evcb)\n\n # Flow coefficient\n _, flow_bin, _ = flow_coef_bd(adj_bin)\n avg_flow = np.mean(flow_bin)\n vect.append(avg_flow)\n\n # Kcoreness centrality\n kcor_bin, _ = kcoreness_centrality_bu(adj_bin)\n avg_kcor = np.mean(kcor_bin)\n vect.append(avg_kcor)\n\n # Degree assortivity\n dac = nx.degree_assortativity_coefficient(graph)\n vect.append(dac)\n\n # Page rank centrality\n# pgr_wei = pagerank_centrality(adj_bin, d=0.85)\n# avg_pgr = np.mean(pgr_wei)\n# vect.append(avg_pgr)\n\n # Rich club coefficient\n# rcc = nx.rich_club_coefficient(graph).values()\n# avg_rcc = np.mean(rcc)\n# vect.append(avg_rcc)\n\n # Transitivity\n tr = nx.transitivity(graph)\n vect.append(tr)\n\n # average clustering\n avg_clst = nx.average_clustering(graph)\n vect.append(avg_clst)\n\n glb_ef = efficiency_bin(adj_bin)\n vect.append(glb_ef)\n\n return vect","def cell_edges(self):","def c_edges(self):\n self.compute_c_edges(self)\n return self._c_edges","def get_ecg_graph():\n titles = ['ecg1', 'ecg2', 'ecg3']\n colors = ['rgb(240,0,0)', 'rgb(0,240,0)', 'rgb(0,0,240)']\n update()\n signames_ecg = queries['signames_ecg']\n signals = queries['signals']\n latesthr = queries['latesthr']\n return html.Div(className='ecg', children=[\n html.Div(style={'display': 'flex', 'height': '40vh'},\n children=[dcc.Graph(\n id=titles[i] + signame,\n style={'width': '100%'},\n figure={\n 'data': [\n {'x': signals[signame]['time'],\n 'y': signals[signame][titles[i]],\n 'mode': 'line', 'name': signame, 'line': {'color':colors[i]}}\n ],\n 'layout': {\n 'font': {'color':'#fff'},\n 'title': '{}-{}'.format(signame, titles[i]),\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\n 'yaxis': {'title': 'voltage (mv)', 'color': '#fff', 'showgrid': 'False', 'range': np.linspace(-2.5, 2.5, 10)},\n 'paper_bgcolor':'#000', 'plot_bgcolor':'#000'\n }\n }\n ) for i in range(len(titles))]\n +\n [html.Div(\n style={'justify-content': 'center', 'display': 'flex',\n 'align-items': 'center', 'width': '10vh', 'font-size': '30pt', 'color': 'white'},\n children=['{}'.format(latesthr[signame][0])])\n ]\n ) for signame in signames_ecg])","def set_ec(self, etacalc):\n if not self.__thermodyn:\n C = np.zeros((6,6))\n \n LC = self.__structures.items()[0][1].LC\n if self.__mthd == 'Energy':\n if type(etacalc)==list:\n A2=[]\n for i in range(len(etacalc)):\n A2.append(self.__A2[i][etacalc[i]])\n else:\n if not etacalc in self.__A2[0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[0].keys()))\n A2 = [a2[etacalc] for a2 in self.__A2]\n \n \n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'CI' or \\\n LC == 'CII'):\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\n C[1,1] = C[0,0]\n C[2,2] = C[0,0]\n C[3,3] = A2[2]/6.\n C[4,4] = C[3,3]\n C[5,5] = C[3,3]\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\n C[0,2] = C[0,1]\n C[1,2] = C[0,1]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[2]\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RI'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[1,4] =-C[0,4] \n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[3,5] =-C[0,4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TI'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[3]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TII'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\n C[1,1] = C[0,0]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\n C[1,2] = C[0,2]\n C[1,5] =-C[0,5]\n C[2,2] = 2.*A2[4]\n C[3,3] = .5*A2[5]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[6]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'O'):\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\n C[1,1] = 2.*A2[4]\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\n C[2,2] = 2.*A2[5]\n C[3,3] = .5*A2[6]\n C[4,4] = .5*A2[7]\n C[5,5] = .5*A2[8]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'M'):\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\n C[1,1] = 2.*A2[8]\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\n C[2,2] = 2.*A2[9]\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\n C[3,3] = .5*A2[10]\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\n C[4,4] = .5*A2[11]\n C[5,5] = .5*A2[12]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'N'):\n C[0,0] = 2.*A2[0]\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\n C[1,1] = 2.*A2[1]\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\n C[2,2] = 2.*A2[2] \n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\n C[3,3] = .5*A2[3]\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\n C[4,4] = .5*A2[4]\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n elif self.__mthd == 'Stress':\n \n if (LC == 'CI' or \\\n LC == 'CII'):\n Matrix = np.mat([[1.0, 5.0, 0.0],\n [2.0, 4.0, 0.0],\n [3.0, 3.0, 0.0],\n [0.0, 0.0, 4.0],\n [0.0, 0.0, 5.0],\n [0.0, 0.0, 6.0]])\n \n if (LC == 'HI' or \\\n LC == 'HII'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\n [ 2, 1, 3, 0, 0],\n [ 0, 0, 3, 3, 0],\n [ 0, 0, 0, 0, 4],\n [ 0, 0, 0, 0, 5],\n [ 3,-3, 0, 0, 0],\n [ 3,-5,-1, 0, 0],\n [-5, 3,-1, 0, 0],\n [ 0, 0,-2,-1, 0],\n [ 0, 0, 0, 0, 6],\n [ 0, 0, 0, 0, 2],\n [-2, 2, 0, 0, 0]])\n \n if (LC == 'RI'):\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\n [ 2, 1, 3,-4, 0, 0],\n [ 0, 0, 3, 0, 3, 0],\n [ 0, 0, 0,-1, 0, 4],\n [ 0, 0, 0, 6, 0, 5],\n [ 3,-3, 0, 5, 0, 0],\n [ 3,-5,-1, 6, 0, 0],\n [-5, 3,-1,-6, 0, 0],\n [ 0, 0,-2, 0,-1, 0],\n [ 0, 0, 0, 8, 0, 6],\n [ 0, 0, 0,-4, 0, 2],\n [-2, 2, 0, 2, 0, 0]])\n \n if (LC == 'RII'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\n [ 2, 1, 3,-4,-5, 0, 0],\n [ 0, 0, 3, 0, 0, 3, 0],\n [ 0, 0, 0,-1,-6, 0, 4],\n [ 0, 0, 0, 6,-1, 0, 5],\n [ 3,-3, 0, 5,-4, 0, 0],\n [ 3,-5,-1, 6, 2, 0, 0],\n [-5, 3,-1,-6,-2, 0, 0],\n [ 0, 0,-2, 0, 0,-1, 0],\n [ 0, 0, 0, 8, 4, 0, 6],\n [ 0, 0, 0,-4, 8, 0, 2],\n [-2, 2, 0, 2,-6, 0, 0]])\n \n if (LC == 'TI'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\n [ 2, 1, 3, 0, 0, 0],\n [ 0, 0, 3, 3, 0, 0],\n [ 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0, 0, 0, 6],\n [ 3,-5,-1, 0, 0, 0],\n [-5, 3,-1, 0, 0, 0],\n [ 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 0, 0,-4]])\n \n if (LC == 'TII'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\n [ 2, 1, 3,-6, 0, 0, 0],\n [ 0, 0, 3, 0, 3, 0, 0],\n [ 0, 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0,-1, 0, 0, 6],\n [ 3,-5,-1,-4, 0, 0, 0],\n [-5, 3,-1, 4, 0, 0, 0],\n [ 0, 0,-2, 0,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 8, 0, 0,-4]])\n \n if (LC == 'O'):\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\n \n if (LC == 'M'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\n \n if (LC == 'N'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\n \n sigma = np.array(self.__sigma[etacalc])\n \n ci = np.linalg.lstsq(Matrix,sigma)\n \n #-- Cubic structures ------------------------------------------------------------------------------\n if (LC == 'CI' or \\\n LC == 'CII'):\n \n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[3,3]=ci[0][2]\n C[1,1]=C[0,0]\n C[2,2]=C[0,0]\n C[0,2]=C[0,1]\n C[1,2]=C[0,1]\n C[4,4]=C[3,3]\n C[5,5]=C[3,3]\n \n #-- Hexagonal Structures --------------------------------------------------------------------------\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[2,2]=ci[0][3]\n C[3,3]=ci[0][4]\n C[1,1]=C[0,0]\n C[1,2]=C[0,2]\n C[4,4]=C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral I Structures ---------------------------------------------------------------------\n if (LC == 'RI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral II Structures --------------------------------------------------------------------\n if (LC == 'RII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[0,4]= ci[0][4]\n C[2,2]= ci[0][5]\n C[3,3]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[1,4]=-C[0,4]\n C[3,5]=-C[0,4]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Tetragonal I Structures -----------------------------------------------------------------------\n if (LC == 'TI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[2,2]= ci[0][3]\n C[3,3]= ci[0][4]\n C[5,5]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[4,4]= C[3,3]\n \n #-- Tetragonal II Structures ----------------------------------------------------------------------\n if (LC == 'TII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,5]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[5,5]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,5]=-C[0,5]\n C[4,4]= C[3,3]\n \n #-- Orthorhombic Structures -----------------------------------------------------------------------\n if (LC == 'O'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[1,1]=ci[0][3]\n C[1,2]=ci[0][4]\n C[2,2]=ci[0][5]\n C[3,3]=ci[0][6]\n C[4,4]=ci[0][7]\n C[5,5]=ci[0][8]\n \n #-- Monoclinic Structures -------------------------------------------------------------------------\n if (LC == 'M'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,5]=ci[0][3]\n C[1,1]=ci[0][4]\n C[1,2]=ci[0][5]\n C[1,5]=ci[0][6]\n C[2,2]=ci[0][7]\n C[2,5]=ci[0][8]\n C[3,3]=ci[0][9]\n C[3,4]=ci[0][10]\n C[4,4]=ci[0][11]\n C[5,5]=ci[0][12]\n \n #-- Triclinic Structures --------------------------------------------------------------------------\n if (LC == 'N'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,3]=ci[0][3]\n C[0,4]=ci[0][4]\n C[0,5]=ci[0][5]\n C[1,1]=ci[0][6]\n C[1,2]=ci[0][7]\n C[1,3]=ci[0][8]\n C[1,4]=ci[0][9]\n C[1,5]=ci[0][10]\n C[2,2]=ci[0][11]\n C[2,3]=ci[0][12]\n C[2,4]=ci[0][13]\n C[2,5]=ci[0][14]\n C[3,3]=ci[0][15]\n C[3,4]=ci[0][16]\n C[3,5]=ci[0][17]\n C[4,4]=ci[0][18]\n C[4,5]=ci[0][19]\n C[5,5]=ci[0][20]\n #--------------------------------------------------------------------------------------------------\n \n \n \n for i in range(5):\n for j in range(i+1,6):\n C[j,i] = C[i,j] \n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if self.__cod in ['espresso']: C = -C/10.\n elif self.__cod in ['vasp','exciting','wien']: C = C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0\n elif self.__cod in ['emto']: C = C*self.__ToGPa/self.__V0\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\n self.S = np.linalg.inv(C)\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\n self.BH = 0.50*(self.BV+self.BR)\n self.GH = 0.50*(self.GV+self.GR)\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\n #--------------------------------------------------------------------------------------------------------------------------------\n self.__C = C\n \n else:\n Cs = []\n for t in map(str,self.__T):#for t in range(len(self.__T)):\n C = np.zeros((6,6))\n \n LC = self.__structures.items()[0][1].LC\n if self.__mthd == 'Energy':\n if type(etacalc)==list:\n A2=[]\n for i in range(len(etacalc)):\n A2.append(self.__A2[t][i][etacalc[i]])\n else:\n if not etacalc in self.__A2[t][0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[t][0].keys()))\n A2 = [a2[etacalc] for a2 in self.__A2[t]]\n \n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'CI' or \\\n LC == 'CII'):\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\n C[1,1] = C[0,0]\n C[2,2] = C[0,0]\n C[3,3] = A2[2]/6.\n C[4,4] = C[3,3]\n C[5,5] = C[3,3]\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\n C[0,2] = C[0,1]\n C[1,2] = C[0,1]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[2]\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RI'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'RII'):\n C[0,0] = 2.*A2[3]\n C[0,1] = A2[1]- 2.*A2[3]\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[1,3] =-C[0,3]\n C[1,4] =-C[0,4] \n C[2,2] = 2.*A2[2]\n C[3,3] = .5*A2[4]\n C[3,5] =-C[0,4]\n C[4,4] = C[3,3]\n C[4,5] = C[0,3]\n C[5,5] = .5*(C[0,0] - C[0,1])\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TI'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\n C[1,1] = C[0,0]\n C[1,2] = C[0,2]\n C[2,2] = 2.*A2[3]\n C[3,3] = .5*A2[4]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'TII'):\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\n C[1,1] = C[0,0]\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\n C[1,2] = C[0,2]\n C[1,5] =-C[0,5]\n C[2,2] = 2.*A2[4]\n C[3,3] = .5*A2[5]\n C[4,4] = C[3,3]\n C[5,5] = .5*A2[6]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'O'):\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\n C[1,1] = 2.*A2[4]\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\n C[2,2] = 2.*A2[5]\n C[3,3] = .5*A2[6]\n C[4,4] = .5*A2[7]\n C[5,5] = .5*A2[8]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'M'):\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\n C[1,1] = 2.*A2[8]\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\n C[2,2] = 2.*A2[9]\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\n C[3,3] = .5*A2[10]\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\n C[4,4] = .5*A2[11]\n C[5,5] = .5*A2[12]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if (LC == 'N'):\n C[0,0] = 2.*A2[0]\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\n C[1,1] = 2.*A2[1]\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\n C[2,2] = 2.*A2[2] \n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\n C[3,3] = .5*A2[3]\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\n C[4,4] = .5*A2[4]\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\n C[5,5] = .5*A2[5]\n #--------------------------------------------------------------------------------------------------------------------------------\n \n elif self.__mthd == 'Stress':\n \n if (LC == 'CI' or \\\n LC == 'CII'):\n Matrix = np.mat([[1.0, 5.0, 0.0],\n [2.0, 4.0, 0.0],\n [3.0, 3.0, 0.0],\n [0.0, 0.0, 4.0],\n [0.0, 0.0, 5.0],\n [0.0, 0.0, 6.0]])\n \n if (LC == 'HI' or \\\n LC == 'HII'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\n [ 2, 1, 3, 0, 0],\n [ 0, 0, 3, 3, 0],\n [ 0, 0, 0, 0, 4],\n [ 0, 0, 0, 0, 5],\n [ 3,-3, 0, 0, 0],\n [ 3,-5,-1, 0, 0],\n [-5, 3,-1, 0, 0],\n [ 0, 0,-2,-1, 0],\n [ 0, 0, 0, 0, 6],\n [ 0, 0, 0, 0, 2],\n [-2, 2, 0, 0, 0]])\n \n if (LC == 'RI'):\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\n [ 2, 1, 3,-4, 0, 0],\n [ 0, 0, 3, 0, 3, 0],\n [ 0, 0, 0,-1, 0, 4],\n [ 0, 0, 0, 6, 0, 5],\n [ 3,-3, 0, 5, 0, 0],\n [ 3,-5,-1, 6, 0, 0],\n [-5, 3,-1,-6, 0, 0],\n [ 0, 0,-2, 0,-1, 0],\n [ 0, 0, 0, 8, 0, 6],\n [ 0, 0, 0,-4, 0, 2],\n [-2, 2, 0, 2, 0, 0]])\n \n if (LC == 'RII'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\n [ 2, 1, 3,-4,-5, 0, 0],\n [ 0, 0, 3, 0, 0, 3, 0],\n [ 0, 0, 0,-1,-6, 0, 4],\n [ 0, 0, 0, 6,-1, 0, 5],\n [ 3,-3, 0, 5,-4, 0, 0],\n [ 3,-5,-1, 6, 2, 0, 0],\n [-5, 3,-1,-6,-2, 0, 0],\n [ 0, 0,-2, 0, 0,-1, 0],\n [ 0, 0, 0, 8, 4, 0, 6],\n [ 0, 0, 0,-4, 8, 0, 2],\n [-2, 2, 0, 2,-6, 0, 0]])\n \n if (LC == 'TI'):\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\n [ 2, 1, 3, 0, 0, 0],\n [ 0, 0, 3, 3, 0, 0],\n [ 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0, 0, 0, 6],\n [ 3,-5,-1, 0, 0, 0],\n [-5, 3,-1, 0, 0, 0],\n [ 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 0, 0,-4]])\n \n if (LC == 'TII'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\n [ 2, 1, 3,-6, 0, 0, 0],\n [ 0, 0, 3, 0, 3, 0, 0],\n [ 0, 0, 0, 0, 0, 4, 0],\n [ 0, 0, 0, 0, 0, 5, 0],\n [ 0, 0, 0,-1, 0, 0, 6],\n [ 3,-5,-1,-4, 0, 0, 0],\n [-5, 3,-1, 4, 0, 0, 0],\n [ 0, 0,-2, 0,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 6, 0],\n [ 0, 0, 0, 0, 0, 2, 0],\n [ 0, 0, 0, 8, 0, 0,-4]])\n \n if (LC == 'O'):\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\n \n if (LC == 'M'):\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\n \n if (LC == 'N'):\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\n \n sigma = np.array(self.__sigma[etacalc])\n \n ci = np.linalg.lstsq(Matrix,sigma)\n \n #-- Cubic structures ------------------------------------------------------------------------------\n if (LC == 'CI' or \\\n LC == 'CII'):\n \n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[3,3]=ci[0][2]\n C[1,1]=C[0,0]\n C[2,2]=C[0,0]\n C[0,2]=C[0,1]\n C[1,2]=C[0,1]\n C[4,4]=C[3,3]\n C[5,5]=C[3,3]\n \n #-- Hexagonal Structures --------------------------------------------------------------------------\n if (LC == 'HI' or \\\n LC == 'HII'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[2,2]=ci[0][3]\n C[3,3]=ci[0][4]\n C[1,1]=C[0,0]\n C[1,2]=C[0,2]\n C[4,4]=C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral I Structures ---------------------------------------------------------------------\n if (LC == 'RI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Rhombohedral II Structures --------------------------------------------------------------------\n if (LC == 'RII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,3]= ci[0][3]\n C[0,4]= ci[0][4]\n C[2,2]= ci[0][5]\n C[3,3]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,3]=-C[0,3]\n C[4,5]= C[0,3]\n C[1,4]=-C[0,4]\n C[3,5]=-C[0,4]\n C[4,4]= C[3,3]\n C[5,5]=0.5*(C[0,0]-C[0,1])\n \n #-- Tetragonal I Structures -----------------------------------------------------------------------\n if (LC == 'TI'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[2,2]= ci[0][3]\n C[3,3]= ci[0][4]\n C[5,5]= ci[0][5]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[4,4]= C[3,3]\n \n #-- Tetragonal II Structures ----------------------------------------------------------------------\n if (LC == 'TII'):\n C[0,0]= ci[0][0]\n C[0,1]= ci[0][1]\n C[0,2]= ci[0][2]\n C[0,5]= ci[0][3]\n C[2,2]= ci[0][4]\n C[3,3]= ci[0][5]\n C[5,5]= ci[0][6]\n C[1,1]= C[0,0]\n C[1,2]= C[0,2]\n C[1,5]=-C[0,5]\n C[4,4]= C[3,3]\n \n #-- Orthorhombic Structures -----------------------------------------------------------------------\n if (LC == 'O'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[1,1]=ci[0][3]\n C[1,2]=ci[0][4]\n C[2,2]=ci[0][5]\n C[3,3]=ci[0][6]\n C[4,4]=ci[0][7]\n C[5,5]=ci[0][8]\n \n #-- Monoclinic Structures -------------------------------------------------------------------------\n if (LC == 'M'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,5]=ci[0][3]\n C[1,1]=ci[0][4]\n C[1,2]=ci[0][5]\n C[1,5]=ci[0][6]\n C[2,2]=ci[0][7]\n C[2,5]=ci[0][8]\n C[3,3]=ci[0][9]\n C[3,4]=ci[0][10]\n C[4,4]=ci[0][11]\n C[5,5]=ci[0][12]\n \n #-- Triclinic Structures --------------------------------------------------------------------------\n if (LC == 'N'):\n C[0,0]=ci[0][0]\n C[0,1]=ci[0][1]\n C[0,2]=ci[0][2]\n C[0,3]=ci[0][3]\n C[0,4]=ci[0][4]\n C[0,5]=ci[0][5]\n C[1,1]=ci[0][6]\n C[1,2]=ci[0][7]\n C[1,3]=ci[0][8]\n C[1,4]=ci[0][9]\n C[1,5]=ci[0][10]\n C[2,2]=ci[0][11]\n C[2,3]=ci[0][12]\n C[2,4]=ci[0][13]\n C[2,5]=ci[0][14]\n C[3,3]=ci[0][15]\n C[3,4]=ci[0][16]\n C[3,5]=ci[0][17]\n C[4,4]=ci[0][18]\n C[4,5]=ci[0][19]\n C[5,5]=ci[0][20]\n #--------------------------------------------------------------------------------------------------\n \n \n \n for i in range(5):\n for j in range(i+1,6):\n C[j,i] = C[i,j] \n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n if self.__cod == 'espresso': C = -C/10.\n elif self.__cod in ['vasp','emto','exciting','wien']: C=C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0#C = C/4.\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\n self.S = np.linalg.inv(C)\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\n self.BH = 0.50*(self.BV+self.BR)\n self.GH = 0.50*(self.GV+self.GR)\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\n #--------------------------------------------------------------------------------------------------------------------------------\n Cs.append(C)\n self.__C = Cs","def edges(self):\n return self.dovetails + self.containments + self.internals","def collectiveMetabolism(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes, addEcDescriptions = False) -> SubstanceEcGraph:\n graph = self.group.collectiveEcGraph(noMultifunctional = excludeMultifunctionalEnzymes, addCount = True, keepOnHeap = True, addEcDescriptions = addEcDescriptions)\n graph.name = 'Collective metabolism ECs ' + ' '.join(self.ncbiNames)\n return graph","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/26')","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/27')","def NND_eta( eqCat, dConst, verbose = False, **kwargs):\n #-------------------------------set args and kwargs----------------------------------------------- \n rmax = 500 # in km\n tmax = 20 # in years\n M0 = 0\n if 'M0' in kwargs.keys() and kwargs['M0'] is not None:\n M0 = kwargs['M0']\n if 'rmax' in kwargs.keys() and kwargs['rmax'] is not None:\n rmax = kwargs['rmax']\n if 'tmax' in kwargs.keys() and kwargs['tmax'] is not None:\n tmax = kwargs['tmax']\n #-----------------------------add small uncertainty to X in case events are colocated-------------------------- \n if 'correct_co_located' in kwargs.keys() and kwargs['correct_co_located'] == True:\n vUncer = np.random.randn( eqCat.size())*1e-10\n eqCat.data['X'] += vUncer\n eqCat.data['Time'] += abs( vUncer)#time has to stay positive otherwise parent-offspring gets switched\n #------------------------------------------------------------------------------\n aNND = np.zeros( eqCat.size())\n vID_p = np.zeros( eqCat.size())\n vID_c = np.zeros( eqCat.size())\n deltaMag = (eqCat.data['Mag'] - M0)\n \n for jC in range( eqCat.size()):\n if verbose == True:\n print 'event %i of %i'%( jC+1, eqCat.size())\n # interevent times: take events that happend before t_i \n # child - parent > 0 \n tau = eqCat.data['Time'][jC] - eqCat.data['Time']\n sel_tau_par = tau > 0\n if sel_tau_par.sum() > 0:\n\n vcurr_ID = np.arange( eqCat.size(), dtype = int)[sel_tau_par]\n vR = np.sqrt( (eqCat.data['X'][jC] - eqCat.data['X'][vcurr_ID])**2 + (eqCat.data['Y'][jC] - eqCat.data['Y'][vcurr_ID])**2 )\n # haversine distance\n # = projUtils.haversine( eqCat.data['Lon'][jC], eqCat.data['Lat'][jC],eqCat.data['Lon'][curr_vID], eqCat.data['Lat'][curr_vID] ) \n sel_r_par = vR < rmax\n if sel_r_par.sum() > 0:\n vcurr_ID = vcurr_ID[sel_r_par]\n curr_Eta = tau[vcurr_ID]* (vR[sel_r_par]**dConst['D']) *( 10**(-dConst['b']*deltaMag[vcurr_ID]))\n sel_min = curr_Eta == curr_Eta.min()\n aNND[jC] = curr_Eta[sel_min][0]\n vID_p[jC] = eqCat.data['N'][vcurr_ID][sel_min][0]\n vID_c[jC] = eqCat.data['N'][jC]\n #print 'parent', eqCat.data['N'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['N'][jC]\n #print 'parent', eqCat.data['Time'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['Time'][jC]\n\n if sel_min.sum() > 1:\n print aNND[jC], curr_Eta[sel_min], eqCat.data['N'][vcurr_ID][sel_min]\n print eqCat.data['Lon'][vcurr_ID][sel_min], eqCat.data['Lat'][vcurr_ID][sel_min]\n print eqCat.data['X'][vcurr_ID][sel_min], eqCat.data['Y'][vcurr_ID][sel_min]\n sel2 = aNND > 0\n if np.logical_not(sel2).sum() > 0:\n print 'remove %i offspring without prior parents in catalog'%(np.logical_not(sel2).sum())\n #raise ValueError, error_str\n # remove events with aNND < 0; i.e. event at the beginning with no preceding parent\n return { 'aNND' : aNND[sel2], 'aEqID_p' : vID_p[sel2], 'aEqID_c' : vID_c[sel2]}","def colored_edges(genome):\n edges = []\n for chromo in genome:\n nodes = [0] + chromosome_to_cycle(chromo)\n nodes.append(nodes[1])\n for j in range(1, len(chromo) + 1):\n edges.append((nodes[2 * j], nodes[2 * j + 1]))\n\n return edges","def neofunctionalisations(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None) -> Set[Neofunctionalisation]:\n # get neofunctionalisations \n return self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs).getNeofunctionalisations()","def all_subconstituents(self, compute=False):\n out = {}\n for i in range(self._.d+1):\n try:\n out[i] = self.subconstituent(i, compute=compute)\n except IndexError:\n pass\n return out","def display_energy_levels_0d(diagram, num_atoms, atoms, h_poly):\n h = eval_hamiltonian(num_atoms, h_poly, (1, 1))\n\n e, v = eigensystem(h)\n\n left = 0\n bottom = 0\n right = max([len(row) for row in diagram.split('\\n')])\n top = len(diagram.split('\\n'))\n\n plot_rows = numpy.ceil(math.sqrt(num_atoms+1))\n plot_cols = plot_rows\n\n for i in range(num_atoms):\n matplotlib.pyplot.subplot(plot_rows, plot_cols, i+1, axisbg=\"#000000\")\n y = [atom[0] for atom in atoms]\n x = [atom[1] for atom in atoms]\n c = numpy.abs(v[i]*v[i])\n\n matplotlib.pyplot.title('E = %f' % numpy.real(e[i]), fontsize = 10)\n norm = matplotlib.colors.Normalize(vmin = min(c),\n vmax = max(0.0001, max(c)))\n #x = [0,0,1,1]\n #y = [0,1,0,1]\n #c = [1,2,3,4]\n matplotlib.pyplot.hexbin(x, y, C = c,\n gridsize = (right-left, top-bottom),\n extent = (left, right, bottom, top),\n cmap = matplotlib.pyplot.get_cmap(\"gray\"),\n norm = norm\n )\n\n matplotlib.pyplot.subplot(plot_rows, plot_cols, num_atoms+1)\n matplotlib.pyplot.scatter(num_atoms*[0], e, s = 0.1)","def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n childGraph = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)\n childGraph.name = 'Added metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return childGraph","def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:\n # get neofunctionalisations \n neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)\n \n # filter core metabolism enzyme graph\n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism) \n neofunctionalisedMetabolism = neofunctionalisedEnzymes.filterGraph(enzymeGraph, minimumEcDifference = None)\n \n # colour core metabolism \n if colour is not False:\n \n if colour is True:\n colourToUse = Export.Colour.GREEN\n else:\n colourToUse = colour\n \n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\n neofunctionalisedMetabolism = enzymeGraph\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\n \n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism enzymes ' + ' '.join(self.ncbiNames)\n \n return neofunctionalisedMetabolism","def vnE(self):\n return np.array(\n [x for x in [self.nEx, self.nEy, self.nEz] if x is not None],\n dtype=int\n )","def get_band_edges():\n # Vacuum level energy from LOCPOT.\n locpot = Locpot.from_file('LOCPOT')\n evac = max(locpot.get_average_along_axis(2))\n\n vasprun = Vasprun('vasprun.xml')\n bs = vasprun.get_band_structure()\n eigenvals = vasprun.eigenvalues\n efermi = vasprun.efermi - evac\n\n if bs.is_metal():\n edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,\n 'efermi': efermi}\n\n elif bs.is_spin_polarized:\n up_cbm = min(\n [min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])\n for i in range(len(eigenvals[Spin.up]))]) - evac\n up_vbm = max(\n [max([e[0] for e in eigenvals[Spin.up][i] if e[1]])\n for i in range(len(eigenvals[Spin.up]))]) - evac\n dn_cbm = min(\n [min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])\n for i in range(len(eigenvals[Spin.down]))]) - evac\n dn_vbm = max(\n [max([e[0] for e in eigenvals[Spin.down][i] if e[1]])\n for i in range(len(eigenvals[Spin.down]))]) - evac\n edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,\n 'dn_vbm': dn_vbm, 'efermi': efermi}\n\n else:\n cbm = bs.get_cbm()['energy'] - evac\n vbm = bs.get_vbm()['energy'] - evac\n edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,\n 'efermi': efermi}\n\n return edges","def sage_graph(self):\n self.fe.load_cache()\n edges = []\n is_bipartite = self.variant.is_bipartite()\n for X in self.L:\n for Y in self.L:\n a = self.op_norm(X, Y)\n if not self.K.is_zero(a):\n for c in self.K.unit_group:\n d = a - c\n if X != Y or c < d or is_bipartite:\n edges.append(((X, c, False), (Y, d, is_bipartite)))\n if X == Y and not is_bipartite:\n break\n return sage.all.Graph(edges)","def _get_full_graph(self):","def nE(self):\n return int(self.vnE.sum())","def edges(self):\n return map(Edge, self._top_exp.edges())","def CS2e(Type=\"DFA\"):\n CC50, CC51, CC52, CC53 = state('CC50'), state('CC51'), state('CC52'), state('CC53')\n for i in sigma:\n CC52.transit[i] = CC52\n CC53.transit[i] = CC53\n for i in sigma_1:\n CC50.transit[i] = CC50\n for i in sigma_2:\n CC50.transit[i] = CC53\n CC51.transit[i] = CC51\n for i in sigma_cc:\n CC50.transit[i] = CC51\n CC51.transit[i] = CC51\n for i in sigma_ncc:\n CC51.transit[i] = CC50\n for i in sigma_B_A:\n CC51.transit[i] = CC50\n for i in sigma_e:\n CC51.transit[i] = CC53\n for i in sigma_A:\n CC51.transit[i] = CC52\n if Type == \"pDFA\":\n CC5 = pDFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\n else:\n CC5 = DFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\n if (SIZEOF):\n EM_size[\"CS2e\"] = asizeof.asizeof(CC5)\n return CC5","def _nelec(self):\n pd = self.particle_distribution(self._gam * mec2)\n return pd.to(1/mec2_unit).value","def final_components(self):\n DG = self.digraph()\n condensation = DG.strongly_connected_components_digraph()\n return [self.induced_sub_finite_state_machine([self.state(_) for _ in component])\n for component in condensation.vertices()\n if condensation.out_degree(component) == 0]","def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised= self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = conservedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\n \n parentGraph.name = 'Conserved metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def n_cs(self):\n pass","def plot_mcc_tree():\n t = ete2.Tree(\"mcct.nex\")\n ts = ete2.treeview.TreeStyle()\n ts.show_scale = False\n ts.show_leaf_name = False\n ts.show_branch_support = False\n ts.scale = 500\n margin = 10\n ts.margin_top = margin\n ts.margin_bottom = margin\n ts.margin_left = margin\n ts.margin_right = margin\n\n germ_style = ete2.NodeStyle()\n germ_style[\"bgcolor\"] = \"LightSteelBlue\"\n proto_germ = t.get_common_ancestor(\"Danish\", \"Norwegian\",\"Icelandic\",\"Swedish\", \"Dutch\", \"German\", \"English\")\n proto_germ.set_style(germ_style)\n\n bs_style = ete2.NodeStyle()\n bs_style[\"bgcolor\"] = \"Moccasin\"\n proto_bs = t.get_common_ancestor(\"Bulgarian\", \"Czech\",\"Polish\",\"Russian\")\n proto_bs.set_style(bs_style)\n\n ital_style = ete2.NodeStyle()\n ital_style[\"bgcolor\"] = \"DarkSeaGreen\"\n proto_ital = t.get_common_ancestor(\"French\", \"Romanian\", \"Italian\", \"Portuguese\", \"Spanish\")\n proto_ital.set_style(ital_style)\n\n t.render(\"mcct.eps\", style_func, tree_style=ts, dpi=600, units=\"px\", w=2250)","def coreMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:\n graph = self.group.collectiveEnzymeGraphByEcMajority(majorityPercentage = majorityPercentageCoreMetabolism, majorityTotal = None, noMultifunctional = excludeMultifunctionalEnzymes)\n graph.name = 'Core metabolism Enzymes ' + ' '.join(self.ncbiNames)\n return graph","def get_incident_nodes(self):\n # return the set of incident edges\n return \\\n {\n self.first_incident_node,\n self.second_incident_node\n }","def calEachCrossflowAllAxialNode():\n AxialNodeno = 14 # axial node number in CFD data\n Nodes = []\n base = 'Node'\n for i in range(0, AxialNodeno):\n Nodes.append(base+str(i))\n \n crossFlow = pd.read_csv('Data_crossflow.csv', index_col = 'Unnamed: 0')\n lateralFactors = []\n for node in Nodes:\n lateralFactors.append(crossFlow[node]/0.8)\n #need to judge the sign of lateral flow according to CTF rule!!\n gapsToFlip = [2,4,6,7,9,11,13,14,16,18,20,21] #gaps in y direction\n gapsToFlipIndex = [x - 1 for x in gapsToFlip]\n for factors in lateralFactors:\n for index in gapsToFlipIndex:\n factors[index] = -factors[index] \n #note: lateralFactors is a list of list\n \n #below calculate factors averaged over all subchannels\n crossFlowAveFactor = crossFlow.apply(abs).mean(axis = 0)/0.8\n lateralFactorsAvelist = []\n for i in range(0,14):\n base = []\n for j in range(0,24):\n base.append(crossFlowAveFactor[i])\n lateralFactorsAvelist.append(base)\n \n \n for i in range(0, 14):\n for j in range(0, 24):\n #note, in the original model there is only one sign for all source\n #terms in one sub-channel. therefore -- sign(crossFlow.iloc[j,2])\n lateralFactorsAvelist[i][j] = lateralFactorsAvelist[i][j] *sign(crossFlow.iloc[j,2]) \n for each in lateralFactorsAvelist:\n for index in gapsToFlipIndex:\n each[index] = -each[index] \n \n \n return lateralFactors, lateralFactorsAvelist","def components(self):\n comps = 0\n unexplored = self.nodes()\n while unexplored:\n comps += 1\n queue = {unexplored.pop()}\n while queue:\n new = queue.pop()\n unexplored.remove(new)\n for adjacent in new.parents() | new.children():\n if adjacent in unexplored:\n queue.add(adjacent)\n return comps","def eci(self):\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\n eci = coefs.copy()\n eci = eci / self._subspace.function_total_multiplicities\n return eci","def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:\n # get neofunctionalisations\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100)) \n return NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)).getNeofunctionalisationsForFunctionChange(minimumOrganismsCount = minimumOrganismsCount)","def get_edges(self):\n for i in self.gens:\n if self.active[i]:\n elist = set()\n H = (i,) # edge-stabilizing subgroup\n reps = set(self.word_generator(parabolic=H))\n reps = self.G.sort_words(reps)\n for word in reps:\n v1 = self.G.move(self.vtable, 0, word)\n v2 = self.G.move(self.vtable, 0, word + (i,))\n if v1 is not None and v2 is not None:\n if v1 > v2:\n v1, v2 = v2, v1\n if (v1, v2) not in elist:\n elist.add((v1, v2))\n\n self.edge_indices[i] = elist\n\n self.num_edges = sum(len(L) for L in self.edge_indices.values())","def discreteComplexDecomposeGraph(self,graph):\n s=[complex(*graph[i]) for i in range(len(graph))]\n N=len(s)\n M=self.coefficients_number\n d=0\n c=[]\n for k in range(-M//2,M//2):\n d+=sum([s[n]*cmath.exp(2j*cmath.pi*k*n/N) for n in range(N)])/N\n c.append(d)\n return c","def compute_edge_logits(self):","def connected_component(self):\n t1 = datetime.datetime.now()\n nodes = set(x.hex for x in self.agents)\n result = []\n while nodes:\n node = nodes.pop()\n # This set will contain the next group of nodes connected to each other.\n group = {node}\n # Build a queue with this node in it.\n queue = [node]\n # Iterate the queue.\n # When it's empty, we finished visiting a group of connected nodes.\n while queue:\n # Consume the next item from the queue.\n node = queue.pop(0)\n # Fetch the neighbors.\n neighbors = set(x for x in node.fon if x.is_occupied == 1)\n # Remove the neighbors we already visited.\n neighbors.difference_update(group)\n # Remove the remaining nodes from the global set.\n nodes.difference_update(neighbors)\n # Add them to the group of connected nodes.\n group.update(neighbors)\n # Add them to the queue, so we visit them in the next iterations.\n queue.extend(neighbors)\n\n # Add the group to the list of groups.\n result.append(len(group))\n td = datetime.datetime.now() - t1\n print(\"calculated {} connected components in {} seconds\".format(len(result),td.total_seconds()))\n return len(result), np.histogram(result, self.cluster_hist_breaks)[0]","def computemodes(self):\n evals, modes = np.linalg.eig(self.A)\n return evals, modes","def eclogite_massive():\n\n rho = 3490.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\n\n return C, rho","def Hasse_diagram_from_incidences(atom_to_coatoms, coatom_to_atoms,\n face_constructor=None,\n required_atoms=None,\n key = None,\n **kwds):\n from sage.graphs.all import DiGraph\n from sage.combinat.posets.posets import FinitePoset\n def default_face_constructor(atoms, coatoms, **kwds):\n return (atoms, coatoms)\n if face_constructor is None:\n face_constructor = default_face_constructor\n atom_to_coatoms = [frozenset(atc) for atc in atom_to_coatoms]\n A = frozenset(range(len(atom_to_coatoms))) # All atoms\n coatom_to_atoms = [frozenset(cta) for cta in coatom_to_atoms]\n C = frozenset(range(len(coatom_to_atoms))) # All coatoms\n # Comments with numbers correspond to steps in Section 2.5 of the article\n L = DiGraph(1) # 3: initialize L\n faces = dict()\n atoms = frozenset()\n coatoms = C\n faces[atoms, coatoms] = 0\n next_index = 1\n Q = [(atoms, coatoms)] # 4: initialize Q with the empty face\n while Q: # 5\n q_atoms, q_coatoms = Q.pop() # 6: remove some q from Q\n q = faces[q_atoms, q_coatoms]\n # 7: compute H = {closure(q+atom) : atom not in atoms of q}\n H = dict()\n candidates = set(A.difference(q_atoms))\n for atom in candidates:\n coatoms = q_coatoms.intersection(atom_to_coatoms[atom])\n atoms = A\n for coatom in coatoms:\n atoms = atoms.intersection(coatom_to_atoms[coatom])\n H[atom] = (atoms, coatoms)\n # 8: compute the set G of minimal sets in H\n minimals = set([])\n while candidates:\n candidate = candidates.pop()\n atoms = H[candidate][0]\n if atoms.isdisjoint(candidates) and atoms.isdisjoint(minimals):\n minimals.add(candidate)\n # Now G == {H[atom] : atom in minimals}\n for atom in minimals: # 9: for g in G:\n g_atoms, g_coatoms = H[atom]\n if not required_atoms is None:\n if g_atoms.isdisjoint(required_atoms):\n continue\n if (g_atoms, g_coatoms) in faces:\n g = faces[g_atoms, g_coatoms]\n else: # 11: if g was newly created\n g = next_index\n faces[g_atoms, g_coatoms] = g\n next_index += 1\n Q.append((g_atoms, g_coatoms)) # 12\n L.add_edge(q, g) # 14\n # End of algorithm, now construct a FinitePoset.\n # In principle, it is recommended to use Poset or in this case perhaps\n # even LatticePoset, but it seems to take several times more time\n # than the above computation, makes unnecessary copies, and crashes.\n # So for now we will mimic the relevant code from Poset.\n\n # Enumeration of graph vertices must be a linear extension of the poset\n new_order = L.topological_sort()\n # Make sure that coatoms are in the end in proper order\n tail = [faces[atoms, frozenset([coatom])]\n for coatom, atoms in enumerate(coatom_to_atoms)]\n tail.append(faces[A, frozenset()])\n new_order = [n for n in new_order if n not in tail] + tail\n # Make sure that atoms are in the beginning in proper order\n head = [0] # We know that the empty face has index 0\n head.extend(faces[frozenset([atom]), coatoms] \n for atom, coatoms in enumerate(atom_to_coatoms)\n if required_atoms is None or atom in required_atoms)\n new_order = head + [n for n in new_order if n not in head]\n # \"Invert\" this list to a dictionary\n labels = dict()\n for new, old in enumerate(new_order):\n labels[old] = new\n L.relabel(labels)\n # Construct the actual poset elements\n elements = [None] * next_index\n for face, index in faces.items():\n atoms, coatoms = face\n elements[labels[index]] = face_constructor(\n tuple(sorted(atoms)), tuple(sorted(coatoms)), **kwds)\n return FinitePoset(L, elements, key = key)","def neopentane():\n coords = [\n [0.000000, 0.0, 0.0],\n [0.881905, 0.881905, 0.881905],\n [-0.881905, -0.881905, 0.881905],\n [0.881905, -0.881905, -0.881905],\n [-0.881905, 0.881905, -0.881905],\n [-1.524077, 0.276170, -1.524077],\n [1.524077, 1.524077, 0.276170],\n [1.524077, -0.276170, -1.524077],\n [1.524077, 0.276170, 1.524077],\n [-1.524077, -0.276170, 1.524077],\n [1.524077, -1.524077, -0.276170],\n [-0.276170, 1.524077, -1.524077],\n [0.276170, 1.524077, 1.524077],\n [0.276170, -1.524077, -1.524077],\n [-0.276170, -1.524077, 1.524077],\n [-1.524077, 1.524077, -0.276170],\n [-1.524077, -1.524077, 0.276170],\n ]\n coords = [[float(j) / Bohr for j in i] for i in coords]\n\n symbols = [\n \"C\",\n \"C\",\n \"C\",\n \"C\",\n \"C\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n \"H\",\n ]\n\n atoms = []\n for i, _ in enumerate(coords):\n atoms.append(Atom(symbols[i], position=coords[i]))\n return Molecule(symbols=atoms)","def Excentricity_graph(self,ccenter,cradius,icenter):\n \n ellip = 0.\n q = 1. - ellip\n pa = 0.\n \n stamp = self['STAMP'].copy()\n mask = self['MASK'].copy()\n sky = self['BACKGROUND']\n Img = stamp - sky\n Img[num.where(mask != 0)] = 0.\n \n id = self._getGraphId()\n root = 'Excentricity_%s' % (id,)\n pngname = root + '.png' ; epsname = root + '.eps'\n jpgname = root + '.jpg'\n doStamp(Img,pngname,format='PNG')\n Convert(pngname,jpgname)\n \n Painted = Paint(jpgname)\n Painted.load()\n \n Painted.DrawEllipse(ccenter,cradius,q,pa,color='red',linewidth=2)\n Painted.DrawCross(ccenter,length=20,color='red')\n Painted.DrawCross(icenter,length=20,color='green')\n \n text = 'E=%.2f o/o' % self['M_E']\n \n # Painted.Graffiti(text,commtextpos)\n \n Painted.save(jpgname) \n Painted.release()\n \n Convert(jpgname,epsname)\n os.system('rm %s %s' % (pngname,jpgname))\n \n self['figures']['Excentricity'] = epsname\n self['figcomms']['Excentricity'] = text","def graph(self):\n ...","def extract_activities(ncfile):\n\n # Get current dimensions.\n (niterations, nstates) = ncfile.variables['energies'].shape\n\n # Extract energies.\n print \"Reading energies...\"\n energies = ncfile.variables['energies']\n u_kln_replica = zeros([nstates, nstates, niterations], float64)\n for n in range(niterations):\n u_kln_replica[:,:,n] = energies[n,:,:]\n print \"Done.\"\n\n # Deconvolute replicas\n print \"Deconvoluting replicas...\"\n u_kln = zeros([nstates, nstates, niterations], float64)\n for iteration in range(niterations):\n state_indices = ncfile.variables['states'][iteration,:]\n u_kln[state_indices,:,iteration] = energies[iteration,:,:]\n print \"Done.\"\n\n # Show all self-energies\n print 'all self-energies for all replicas'\n for iteration in range(niterations):\n for replica in range(nstates):\n state = int(ncfile.variables['states'][iteration,replica])\n print '%12.1f' % energies[iteration, replica, state],\n print ''\n\n # If no energies are 'nan', we're clean.\n if not any(isnan(energies[:,:,:])):\n return\n\n # There are some energies that are 'nan', so check if the first iteration has nans in their *own* energies:\n u_k = diag(energies[0,:,:])\n if any(isnan(u_k)):\n print \"First iteration has exploded replicas. Check to make sure structures are minimized before dynamics\"\n print \"Energies for all replicas after equilibration:\"\n print u_k\n sys.exit(1)\n\n # There are some energies that are 'nan' past the first iteration. Find the first instances for each replica and write PDB files.\n first_nan_k = zeros([nstates], int32)\n for iteration in range(niterations):\n for k in range(nstates):\n if isnan(energies[iteration,k,k]) and first_nan_k[k]==0:\n first_nan_k[k] = iteration\n if not all(first_nan_k == 0):\n print \"Some replicas exploded during the simulation.\"\n print \"Iterations where explosions were detected for each replica:\"\n print first_nan_k\n print \"Writing PDB files immediately before explosions were detected...\"\n for replica in range(nstates): \n if (first_nan_k[replica] > 0):\n state = ncfile.variables['states'][iteration,replica]\n iteration = first_nan_k[replica] - 1\n filename = 'replica-%d-before-explosion.pdb' % replica\n title = 'replica %d state %d iteration %d' % (replica, state, iteration)\n write_pdb(atoms, filename, iteration, replica, title, ncfile)\n filename = 'replica-%d-before-explosion.crd' % replica \n write_crd(filename, iteration, replica, title, ncfile)\n sys.exit(1)\n\n # There are some energies that are 'nan', but these are energies at foreign lambdas. We'll just have to be careful with MBAR.\n # Raise a warning.\n print \"WARNING: Some energies at foreign lambdas are 'nan'. This is recoverable.\"\n \n return","def edges(self):\r\n return self.__generate_edges()","def buildE(self, debug = False):\n E = np.zeros([len(self.sta), len(self.dist)])\n i = 0\n for ikey, state in self.sta.items():\n j = 0\n c = state.c\n for jkey, disturbance in self.dist.items():\n if debug:\n print(i, ' ', j)\n if isinstance(disturbance, InputT) and disturbance in \\\n self.rc.adj[state]:\n # input is temperature and connected to this state\n\n E[i, j] = self.rc.adj[state][disturbance]['H'] / c\n elif isinstance(disturbance, InputQ) and disturbance in \\\n self.rc.adj[\n state]: # input is heat flow and connected to state\n E[i, j] = self.rc.adj[state][disturbance]['gain'] / c\n j += 1\n i += 1\n\n return E, list(self.sta.keys()), list(self.dist.keys())","def num_of_subgraphs(self):\n \n G = self.to_undirected_graph()\n \n count = G.num_subgraph()\n \n print('The number of disconnected components in the graph is ', count)","def components_graph(geo, stereo=True):\n return automol.graph.connected_components(graph(geo, stereo=stereo))","def gen_graph(self):","def unifiedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph: \n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is False:\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\n \n else:\n unifiedMetabolismEnzymes = self.unifiedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = True)\n \n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = unifiedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph","def get_econs(self):\n eham = self.beads.vpath*self.nm.omegan2 + self.nm.kin + self.forces.pot\n eham += self.bias.pot # bias\n for e in self._elist:\n eham += e.get()\n\n return eham + self.eens","def system(Vg, Cs, Cd, Cg, Gs, Gd, num_e):\n myset = set.SET()\n # choose between the two lines below for metallic dot or quantum levels\n myset.add_metallic_dot('dot', num_e, -num_e, 0)\n #myset.add_quantum_dot('dot', [0, 1, 2, 3], [1, 1, 1, 1])\n myset.add_lead('source')\n myset.add_lead('drain')\n myset.add_gate('gate')\n #myset.add_link('dl', 'dot', 'source', 5.5e-18, 0.2)\n #myset.add_link('dl', 'dot', 'drain', 5.5e-18, 0.2)\n myset.add_link('dl', 'dot', 'drain', Cd,Gd)\n myset.add_link('dl', 'dot', 'source', Cs, Gs)\n #myset.add_link('dl', 'dot', 'drain', 1e-19, 10e-8)\n #myset.add_link('dl', 'dot', 'drain', Cg, Gg)\n myset.add_link('dg', 'dot', 'gate', Cg)\n return myset","def getSigma(E, opt=\"\"):\n # HG resolutions, from the energy unidoc.\n eRes = {\n 0 : {\"nat\": [1.260e-1, 1.790e-2, 2.370e-4], \"enr\": [1.500e-1, 1.750e-2, 2.820e-4], \"both\": [1.470e-1, 1.730e-2, 3.000e-4]},\n 1 : {\"nat\": [1.470e-1, 1.770e-2, 2.010e-4], \"enr\": [1.340e-1, 1.750e-2, 2.820e-4], \"both\": [1.360e-1, 1.740e-2, 2.800e-4]},\n 2 : {\"nat\": [1.410e-1, 1.800e-2, 1.680e-4], \"enr\": [1.420e-1, 1.720e-2, 2.860e-4], \"both\": [1.430e-1, 1.720e-2, 2.840e-4]},\n 3 : {\"nat\": [1.800e-1, 1.820e-2, 2.090e-4], \"enr\": [1.580e-1, 1.710e-2, 3.090e-4], \"both\": [1.620e-1, 1.720e-2, 2.970e-4]},\n 4 : {\"nat\": [2.140e-1, 1.540e-2, 3.970e-4], \"enr\": [2.170e-1, 1.490e-2, 3.190e-4], \"both\": [2.180e-1, 1.500e-2, 3.500e-4]},\n \"5A\" : {\"nat\": [2.248e-1, 1.894e-2, 2.794e-4], \"enr\": [2.660e-1, 2.215e-2, 2.868e-4], \"both\": [2.592e-1, 2.057e-2, 3.086e-4]},\n \"5B\" : {\"nat\": [1.650e-1, 1.760e-2, 2.828e-4], \"enr\": [1.815e-1, 1.705e-2, 3.153e-4], \"both\": [1.815e-1, 1.690e-2, 3.187e-4]},\n \"5C\" : {\"nat\": [1.565e-1, 1.810e-2, 2.201e-4], \"enr\": [1.361e-1, 1.740e-2, 2.829e-4], \"both\": [1.519e-1, 1.718e-2, 2.762e-4]}\n }\n\n if len(dsList)==1:\n p = eRes[dsList[0]][opt]\n return np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2)\n else:\n # weight the curve by exposure\n sig, expTot = 0, 0\n for ds in dsList:\n if opt==\"enr\": exp = dsExpo[ds][0]\n if opt==\"nat\": exp = dsExpo[ds][1]\n if opt==\"both\": exp = dsExpo[ds][0] + dsExpo[ds][1]\n p = eRes[ds][opt]\n sig += np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2) * exp\n expTot += exp\n sig /= expTot\n return sig","def line_graph_forbidden_subgraphs():\n from sage.graphs.all import Graph\n from sage.graphs.generators.basic import ClawGraph\n graphs = [ClawGraph()]\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3],\n 4: [2],\n 5: [3]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3, 4],\n 1: [2, 3, 4],\n 3: [4],\n 2: [5]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3],\n 4: [2, 3]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3],\n 4: [2],\n 5: [3, 4]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3, 4],\n 1: [2, 3, 4],\n 3: [4],\n 5: [2, 0, 1]\n }))\n\n graphs.append(Graph({\n 5: [0, 1, 2, 3, 4],\n 0: [1, 4],\n 2: [1, 3],\n 3: [4]\n }))\n\n graphs.append(Graph({\n 1: [0, 2, 3, 4],\n 3: [0, 4],\n 2: [4, 5],\n 4: [5]\n }))\n\n graphs.append(Graph({\n 0: [1, 2, 3],\n 1: [2, 3, 4],\n 2: [3, 4],\n 3: [4]\n }))\n\n return graphs","def connect_cells(dfte,vari):\n # Create the variabel cell for mother, grand mother and grand grand mother\n if 'g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\n if 'g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_parent_cell')\n if 'g_g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\n #give unique index to all cells\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\n vac=[];sc=[];uid = []\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\n for c,idx in enumerate(dfte['cell'].unique()):\n dau = dfte.loc[dfte['cell']==idx]\n pc = dau['parent_cell'].iloc[0]\n mum = dfte.loc[dfte['cell']==pc]\n gpc = dau['g_parent_cell'].iloc[0]\n gmum = dfte.loc[dfte['cell']==gpc]\n ggpc = dau['g_g_parent_cell'].iloc[0]\n ggmum = dfte.loc[dfte['cell']==ggpc]\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\n gggmum = dfte.loc[dfte['cell']==gggpc]\n fte = lambda x: x[['{}'.format(vari),'uid']].values\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\n vac.append(tmp[:,0])\n uid.append(tmp[:,1])\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})","def getNumNestedCVTerms(self):\n return _libsbml.CVTerm_getNumNestedCVTerms(self)","def cyclic_subgroups(C):\n ps = C.points()\n Gs = []\n \n for i in ps:\n P = Elliptic_Point(i[0],i[1],C)\n Gs.append(cyclic_subgroup(P))\n\n return Gs","def xsec_CEvNS(E_R, E_nu, N_p, N_n):\n \n A = N_p + N_n\n Z = N_p\n \n m_A = A*0.9315 #Mass of target nucleus (in GeV)\n q = np.sqrt(2.0*E_R*m_A) #Recoil momentum (in MeV)\n #Note: m_A in GeV, E_R in keV, E_nu in MeV\n \n #Calculate SM contribution\n Qv = (A-Z) - (1.0-4.0*SIN2THETAW)*Z #Coherence factor\n \n xsec_SM = (G_FERMI*G_FERMI/(4.0*np.pi))*Qv*Qv*m_A* \\\n (1.0-(q*q)/(4.0*E_nu*E_nu))\n \n #Calculate New-Physics correction from Z' coupling\n #Assume universal coupling to quarks (u and d)\n #QvNP = 3.0*A*gsq\n\n #Factor of 1e6 from (GeV/MeV)^2\n #G_V = 1 - 1e6*(SQRT2/G_FERMI)*(QvNP/Qv)*1.0/(q*q + m_med*m_med)\n \n #Convert from (GeV^-3) to (cm^2/keV)\n #and multiply by form factor\n return xsec_SM*1e-6*(1.98e-14)*(1.98e-14)*calcSIFormFactor(E_R, A)","def sub_graph_merging(self):","def edges(self):\r\n return self.capacity.keys()","def _get_graph_based_ic_dictionary(self):\n\n\t\t# TODO find the literature reference or presentation where this equation is from instead of just the presentation.\n\n\t\t#ic_dict = {}\n\t\t#num_terms_in_ontology = len(self)\n\t\t#for term in self.terms():\n\t\t#\tdepth = self._depth_dict[term.id]\n\t\t#\tnum_descendants = len(list(term.subclasses(with_self=False)))\n\t\t#\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\n\t\t#\tic_dict[term.id] = ic_value\n\t\t#return(ic_dict)\n\n\n\t\t# Getting the information content of each term in the ontology based on graph structure.\n\t\tic_dict = {}\n\t\tnum_terms_in_ontology = len(self)\n\t\tfor term in self.terms():\n\t\t\tdepth = self._depth_dict[term.id]\n\t\t\tnum_descendants = len(list(term.subclasses(with_self=False)))\n\t\t\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\n\t\t\tic_dict[term.id] = ic_value\n\n\n\t\t# Converting to weights based on information content rather than raw value.\n\t\tic_dict_as_weights = {}\n\t\tic_values = ic_dict.values()\n\t\tmin_ic = min(ic_values)\n\t\tmax_ic = max(ic_values)\n\t\tnew_max = 1.00\n\t\tnew_min = 0.00\n\t\tfor k,v in ic_dict.items():\n\t\t\told_range = max_ic-min_ic\n\t\t\tnew_range = new_max-new_min\n\t\t\tnew_value = (((v - min_ic) * new_range) / old_range) + new_min\n\t\t\tic_dict_as_weights[k] = new_value\n\n\t\treturn(ic_dict, ic_dict_as_weights)","def plot_eigenvector_centrality(net, label, outpath):\n _, eigenvector_centrality_values = networkit_util.get_eigenvector_centrality(net, label, outpath)\n unique_value, unique_cnt = np.unique(eigenvector_centrality_values, return_counts=True)\n unique_cumcnt = np.cumsum(unique_cnt) / sum(unique_cnt)\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(unique_value, unique_cumcnt, 'g.')\n # ax.set_title('Cumulative distribution of eigenvector centrality of nodes')\n ax.set_xlabel('eigenvector centrality e')\n ax.set_ylabel('p(x <= e)')\n plt.savefig(outpath + label + \"-eigenvector-distribution.eps\")","def connected_components(self) -> List[list]:\n for n in self.dw_graph.get_all_v().values():\n n.distance=0.0\n mega_list = []\n for n in self.dw_graph.get_all_v().values():\n if n.distance!=-10:\n mega_list.append(self.connected_component(n.node_id))\n return mega_list","def discreteComplexComposeGraph(self,S,n):\n N=len(S)\n M=N//2\n g=[]\n d=0\n for k in range(M):\n d+=S[k+M]*cmath.exp(2j*cmath.pi*k*n/N)+S[-k+M]*cmath.exp(-2j*cmath.pi*k*n/N)\n g.append((d.real,d.imag))\n return g","def edges(self):\n return self.generate_edges()","def generate_empirical_nodes(reduced_basis):\n \n # Get the index of the 0th empirical node T_0\n # from the maximum value of the 0th reduced basis.\n imax = np.argmax(np.abs(reduced_basis[0]))\n empirical_node_indices = [imax]\n \n # Iterate to find the other impirical nodes.\n for j in range(1, len(reduced_basis)):\n # The jth empirical node\n imax = generate_new_empirical_node(reduced_basis, empirical_node_indices)\n empirical_node_indices.append(imax)\n \n return empirical_node_indices","def divergedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\n \n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n \n if colour is True:\n parentEdges = parentNeofunctionalised.getEdges()\n childEdges = childNeofunctionalised.getEdges()\n \n graph = divergedMetabolismEnzymes\n \n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\n \n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return graph\n else:\n parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\n childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\n \n parentGraph.name = 'Diverged metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\n childGraph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\n \n return (parentGraph, childGraph)","def coaccessible_components(self):\n DG = self.digraph().reverse()\n coaccessible_states = DG.breadth_first_search(\n [_.label() for _ in self.iter_final_states()])\n return self.induced_sub_finite_state_machine(\n [self.state(_) for _ in coaccessible_states])","def ec_geometric_series(p0, T, n):\n if(n<=0):\n EC=np.zeros(T.shape)\n return EC\n else:\n R, D, L=rdl_decomposition(T)\n w=np.diagonal(D)\n L=np.transpose(L)\n\n D_sum=np.diag(geometric_series(w, n-1))\n T_sum=np.dot(np.dot(R, D_sum), np.conjugate(np.transpose(L)))\n p_sum=np.dot(p0, T_sum)\n EC=p_sum[:,np.newaxis]*T\n \"\"\"Truncate imginary part - which is zero, but we want real\n return values\"\"\"\n EC=EC.real\n return EC","def epidote():\n\n rho = 3465.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 211.5; C[0,1] = 65.6; C[0,2] = 43.2; C[0,3] = 0.; C[0,4] = -6.5; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 239.; C[1,2] = 43.6; C[1,3] = 0.; C[1,4] = -10.4; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 202.1; C[2,3] = 0.; C[2,4] = -20.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.1; C[3,4] = 0.; C[3,5] = -2.3\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 43.4; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.5\n\n return C, rho","def get_eulerian_graph_edges(bbox, source):\n osm_graph = OSMGraph(bbox, source)\n # input all nodes and get odd nodes, update node attributes\n odd_nodes = get_odd_nodes(osm_graph.nodes_dict)\n\n # initialize all_pairs_list\n all_pairs_list = []\n\n # if there are 6 or fewer odd nodes look for all possible options,\n # otherwise look for just three basic pairing options\n\n if len(odd_nodes) <= 10:\n print(\"ROBUST PAIRING FUNCTION\")\n all_pairs_list = get_list_of_all_pairs_lists(odd_nodes)\n\n else:\n print(\"CHEAP PAIRING FUNCTION\")\n all_pairs_list = get_list_of_all_pairs_lists_short(odd_nodes)\n\n for item in all_pairs_list:\n print(\"\\n\\nPair option:\", item)\n print(\"Pair option len:\", len(item))\n\n dict_pairings_lists_lengths = get_dict_pairings_lists_lengths(\n all_pairs_list, osm_graph\n )\n twice_traversals_edges = get_twice_traversals_edges(dict_pairings_lists_lengths)\n updated_graph_instance = update_twice_traversal_edges(\n twice_traversals_edges, osm_graph\n )\n return updated_graph_instance","def cell_edges(self):\n return self._cell_edges","def edges(self):\n return [(a, b) for a in self._consequences_of\n for b in self._consequences_of[a]]","def _generateEdges(self, separate_frames=False):\n\n # 3D edges\n Xe = []\n Ye = []\n Ze = []\n\n if not separate_frames:\n if self.showcuts:\n self.nxgraph.add_nodes_from(range(len(self.df)))\n for index, curr in self.df.iterrows():\n if curr['first']:\n self._symbol += ['circle']\n # skip root node\n if curr['number'] == 1:\n continue\n # found first LP solution of a new child node\n # parent is last LP of parent node\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\n else:\n # found an improving LP solution at the same node as before\n self._symbol += ['diamond']\n parent = self.df.iloc[index - 1]\n\n Xe += [float(parent['x']), curr['x'], None]\n Ye += [float(parent['y']), curr['y'], None]\n Ze += [float(parent['objval']), curr['objval'], None]\n self.nxgraph.add_edge(parent.name, curr.name)\n else:\n self.nxgraph.add_nodes_from(list(self.df['number']))\n for index, curr in self.df.iterrows():\n self._symbol += ['circle']\n if curr['number'] == 1:\n continue\n parent = self.df[self.df['number'] == curr['parent']]\n Xe += [float(parent['x']), curr['x'], None]\n Ye += [float(parent['y']), curr['y'], None]\n Ze += [float(parent['objval']), curr['objval'], None]\n self.nxgraph.add_edge(parent.iloc[0]['number'], curr['number'])\n\n else:\n max_age = self.df['age'].max()\n for i in range(1, max_age + 1):\n tmp = self.df[self.df['age'] == i]\n Xe_ = []\n Ye_ = []\n Ze_ = []\n for index, curr in tmp.iterrows():\n if curr['first']:\n self._symbol += ['circle']\n # skip root node\n if curr['number'] == 1:\n continue\n # found first LP solution of a new child node\n # parent is last LP of parent node\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\n else:\n # found an improving LP solution at the same node as before\n self._symbol += ['diamond']\n parent = self.df.iloc[index - 1]\n\n Xe_ += [float(parent['x']), curr['x'], None]\n Ye_ += [float(parent['y']), curr['y'], None]\n Ze_ += [float(parent['objval']), curr['objval'], None]\n Xe.append(Xe_)\n Ye.append(Ye_)\n Ze.append(Ze_)\n\n self.Xe = Xe\n self.Ye = Ye\n self.Ze = Ze","def life_expectancy_graph(country_name):\r\n dat = read_data(pre_file_name())\r\n MainDct = dat[0]\r\n # MetaDct = dat[1]\r\n f_life_expectancy = {}\r\n for idx in MainDct:\r\n if idx == country_name or MainDct[idx].country_code == country_name:\r\n for idx1 in MainDct[idx].values:\r\n if idx1[1] != 0:\r\n f_life_expectancy[idx1[0]] = idx1[1]\r\n return f_life_expectancy","def calculate_E(self):\n \n E = 0\n for i in xrange(self.size):\n Ei = self.h[i]\n Ei += 0.5*sum((1 if self.spins[j] else -1)*self.J[i,j] for j in self.adjacency[i])\n if not self.spins[i]:\n Ei *= -1\n E += Ei\n \n return E","def graph_eigenvalues(N, eigenvalues,interval):\n (a,b) =interval\n a=abs(a-b)\n n=[]\n E_numeric=[]\n E_anlytic=[]\n E_anlytic_harm=[]\n \n for i in range(N-1):\n n.append(i)\n E_numeric.append(eigenvalues[0,i])\n E_anlytic.append((((math.pi)**2)*(i**2))/(a**2))\n E_anlytic_harm.append(((1/2)**0.5)*((i)+(1/2)))\n plt.plot(n,E_numeric,'b')\n plt.plot(n,E_anlytic,'g')\n plt.plot(n,E_anlytic_harm,'r')\n plt.xlabel('n', fontsize=20, color='black')\n plt.ylabel('E', fontsize=20, color='black')\n plt.show()\n return None","def eclogite_foliated():\n\n rho = 3300.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\n\n return C, rho","def lostMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\n \n parentGraph = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(lostECs)\n parentGraph.name = 'Lost metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\n \n return parentGraph","def get_eddy_instances(parents, root_nodes, gate_dist):\n\tsnodes = []\n\te_instances = dict()\n\tchild_score = dict()\n\tfor parent in parents:\n\t\tfor child in parent.children:\n\t\t\tif is_term(child):\n\t\t\t\tcontinue\n\t\t\tchild.score = parent.score + max(score(hyp, gate_dist) for hyp in child.tracks())\n\t\t\tchild_score[child] = (child.score, parent)\n\t\t\tif child.obj in e_instances:\n\t\t\t\te_instances[child.obj].append(child)\n\t\t\telse:\n\t\t\t\te_instances[child.obj] = [child]\n\tfor node in root_nodes:\n\t\tnode.score = max(score(hyp, gate_dist) for hyp in node.tracks())\n\t\tchild_score[node] = (node.score, None)\n\t\tif node.obj in e_instances:\n\t\t\te_instances[node.obj].append(node)\n\t\telse:\n\t\t\te_instances[node.obj] = [node]\n\treturn (e_instances, child_score)","def plot_network(genome):\n g = genome.n\n # width = g.graph[\"size\"]\n # height = g.graph[\"size\"]\n\n # fig = plt.figure(figsize=(width,height))\n fig = plt.figure()\n fig.patch.set_facecolor('white')\n ax = fig.add_subplot(111, aspect='equal')\n # ax.set_axis_off()\n\n # collision_coords = find_collisions(genome)\n # das_coords = find_das_extended(genome)\n # slp_coords = find_slp(genome)\n slp_nodes = find_attacker_path(genome.n)\n\n # Plot the parent-child tree\n for n in g.nodes_iter():\n if g.node[n][\"parent\"] is not None:\n _line(g.node[n][\"coord\"], g.node[g.node[n][\"parent\"]][\"coord\"], zorder=0, color='k')\n\n for n in g.nodes_iter():\n coord = g.node[n][\"coord\"]\n shape = _circles\n colour = 'b'\n s = 0.4\n if n in slp_nodes:\n shape = _hexagons\n colour = 'y'\n s = 0.45\n if n == g.graph[\"source\"]:\n shape = _squares\n colour = 'g'\n if n == g.graph[\"sink\"]:\n shape = _octogons\n colour = 'k'\n s = 0.45\n shape(coord[0], coord[1], s, fc=\"white\", ec=colour)\n if(len(str(g.node[n][\"slot\"])) == 1):\n ax.text(coord[0]-0.15, coord[1]+0.15, str(g.node[n][\"slot\"]))\n elif(len(str(g.node[n][\"slot\"])) == 2):\n ax.text(coord[0]-0.25, coord[1]+0.15, str(g.node[n][\"slot\"]))\n elif(len(str(g.node[n][\"slot\"])) == 3):\n ax.text(coord[0]-0.4, coord[1]+0.15, str(g.node[n][\"slot\"]))\n else:\n ax.text(coord[0]-0.5, coord[1]+0.15, str(g.node[n][\"slot\"]))\n\n\n plt.gca().invert_yaxis()\n fig.show()","def get_34cohom_dim(v,l, even_e):\n op1 = OrdinaryGraphComplex.ContractEdgesGO.generate_operator(v,l, even_e)\n op2 = OrdinaryGraphComplex.ContractEdgesGO.generate_operator(v+1,l, even_e)\n fullvs = op1.domain\n fullvs2 = op2.domain\n \n vs34 = Valence34Mask(fullvs)\n vs342 = Valence34Mask(fullvs2)\n\n D34rank = 0\n if op1.is_valid():\n D = op1.get_matrix()\n # P34 = vs34.get_P34()\n # D34 = D * P34 \n i34 = vs34.get_34index_list()\n D34 = D[:, i34]\n D34rank = D34.rank()\n\n DD34rank = 0\n DD5rank = 0\n if op2.is_valid():\n DD = op2.get_matrix()\n # PP34 = vs342.get_P34()\n ii34 = vs342.get_34index_list()\n # DD34 = DD * PP34 \n DD34 = DD[:,ii34]\n DD34rank = DD34.rank()\n\n # P5 = vs34.get_P5()\n # DD5 = P5 * DD * PP34\n i5 = vs34.get_5index_list()\n DD5 = DD[i5, ii34]\n DD5rank = DD5.rank()\n\n\n return vs34.get_34dimension() - D34rank -DD34rank + DD5rank","def eccentricity(self):\n new_data = self._data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\n 'pl_masse', 'pl_orbeccen',\n 'pl_radj', 'pl_dens', 'st_teff',\n 'st_mass', 'st_rad']]\n new_data = new_data.dropna()\n\n features = new_data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\n 'pl_masse',\n 'pl_radj', 'pl_dens', 'st_teff',\n 'st_mass', 'st_rad']]\n labels = new_data['pl_orbeccen']\n\n features_train, features_test, labels_train, labels_test = \\\n train_test_split(features, labels, test_size=0.2)\n\n # Create an untrained model\n model = DecisionTreeRegressor()\n\n # Train it on the **training set**\n model.fit(features_train, labels_train)\n\n # Compute test accuracy\n test_predictions = model.predict(features_test)\n test_acc = mean_absolute_error(labels_test, test_predictions)\n test_acc_r2 = r2_score(labels_test, test_predictions)\n\n # Plot ML vs Actual\n fig, [ax1, ax2] = plt.subplots(2, figsize=(15, 12))\n\n sns.distplot(test_predictions, kde=False, ax=ax1)\n sns.distplot(labels_test, kde=False, ax=ax2)\n\n ax1.set_title('Distribution of Predicted Eccentricities of Orbits')\n ax1.set_xlabel('Eccentricity of Orbit')\n ax1.set_ylabel('Number of Planets')\n\n ax2.set_title('Distribution of Actual Eccentricities of Orbits')\n ax2.set_xlabel('Eccentricity of Orbit')\n ax2.set_ylabel('Number of Planets')\n\n plt.savefig('figures/ML_Eccentricity.png', bbox_inches='tight')\n\n return (test_acc, test_acc_r2)","def create_cpp_edgelist(euler_circuit):\n cpp_edgelist = {}\n\n for i, e in enumerate(euler_circuit):\n edge = frozenset([e[0], e[1]])\n\n if edge in cpp_edgelist:\n cpp_edgelist[edge][2]['sequence'] += ', ' + str(i)\n cpp_edgelist[edge][2]['visits'] += 1\n\n else:\n cpp_edgelist[edge] = e\n cpp_edgelist[edge][2]['sequence'] = str(i)\n cpp_edgelist[edge][2]['visits'] = 1\n\n return list(cpp_edgelist.values())","def n_e(self) -> u.m**-3:\n return np.sum(self._n_elem * self.ionic_fractions * self.charge_numbers)","def interacting_conductivity(self,\n sigma=0.04,\n freq_range=(0.0, 6.0, 1000),\n imag_dielectric=False,\n broadening='lorentz'):\n cell_area = np.abs(np.cross(self.a1, self.a2))*len(self.k_grid)\n broad_fnc = get_broadening_function(broadening, sigma)\n\n frequency_grid = np.linspace(*freq_range)\n output_grid = np.zeros(len(frequency_grid))\n\n energy_power = 1 + int(imag_dielectric)\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\n prefactor = prefactor/cell_area\n\n # Assuming x polarisation for now\n position_dipole_matrix = self.construct_position_dipole_matrix()\n\n n_shift = self.n_spins*self.n_orbs\n cutoff_band_min_maxes = self.exciton_obj.get_cutoff_band_min_maxes()\n # Construct vector of velocity matrix elements to dot with the\n # bse eigenvector.\n velocity_element_lists = []\n for s0 in range(self.n_spins):\n velocity_elements = []\n for idx_1, kpt in enumerate(self.k_grid):\n velocity_matrix = self.load_velocity_matrix(idx_1)\n bands = self.exciton_obj.get_number_conduction_valence_bands(\n idx_1, s0\n )\n v_num, c_num = bands\n v_min = 0\n if self.exciton_obj.energy_cutoff is not None:\n v_min = int(cutoff_band_min_maxes[s0][idx_1][0])\n c_num = int(cutoff_band_min_maxes[s0][idx_1][1]) + 1\n v_num = v_num - v_min\n j1 = n_shift*idx_1 + s0*self.n_orbs\n j2 = j1 + n_shift - (1 - s0)*self.n_orbs*(self.n_spins - 1)\n eigvecs = np.array(\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\n )\n if self.use_dipole_term:\n eigvals = np.array(\n self.file_storage['eigensystem']['eigenvalues'][idx_1]\n )\n else:\n eigvals = None\n for c, v in product(range(c_num), range(v_num)):\n cb_vector = eigvecs[:, v_min + v_num + c]\n vb_vector = eigvecs[:, v_min + v]\n\n matrix_elem = velocity_matrix_element(cb_vector,\n vb_vector,\n velocity_matrix)\n if self.use_dipole_term:\n cb_energy = eigvals[v_min + v_num + c]\n vb_energy = eigvals[v_min + v]\n position_dipole_term = get_position_dipole_element(\n vb_vector,\n cb_vector,\n vb_energy,\n cb_energy,\n position_dipole_matrix\n )\n matrix_elem = matrix_elem + position_dipole_term\n velocity_elements.append(matrix_elem)\n velocity_elements = np.array(velocity_elements, dtype=complex)\n velocity_element_lists.append(velocity_elements)\n\n bse_eigsys = self.exciton_obj.get_bse_eigensystem_direct(solve=True)\n for s0 in range(self.n_spins):\n for idx_1 in range(len(bse_eigsys[s0][0])):\n bse_vector = bse_eigsys[s0][1][:, idx_1]\n bse_value = bse_eigsys[s0][0][idx_1]\n energy_denominator = bse_value**energy_power\n interact_elem = np.dot(bse_vector, velocity_element_lists[s0])\n for idx_2, omega in enumerate(frequency_grid):\n smearing = broad_fnc(bse_value, omega)\n main_term = np.abs(interact_elem)**2/energy_denominator\n output = prefactor*main_term*smearing\n output_grid[idx_2] += output\n\n return frequency_grid, output_grid","def _compute_bare_spectrum_constant(self):\n eigendata = []\n for subsys in self._hilbertspace:\n if subsys not in self.subsys_update_list:\n evals_count = subsys.truncated_dim\n eigendata.append(subsys.eigensys(evals_count=evals_count))\n else:\n eigendata.append(None)\n return eigendata","def _sigma_ee_nonrel(self,gam,eps):\n s0 = 4 * r0**2 * alpha / (15 * eps)\n x = 4 * eps / (gam**2 - 1)\n sigma_nonrel = s0 * self._F(x,gam)\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\n return sigma_nonrel / mec2_unit","def GetCommonComponents(self):\n nr = 0\n comp = []\n for i in range(len(self.nodes)):\n comp.append(-1)\n for n in self.nodes:\n if comp[n.index-1] == -1:\n nr += 1\n comp[n.index-1] = nr\n self.Components_R(nr, n, comp)\n return self.GenerateCommonComponents(comp)","def test_5():\n h = iotbx.pdb.input(source_info=None, lines=test_pdb_5).construct_hierarchy()\n asc = h.atom_selection_cache()\n ncs_inp = iotbx.ncs.input(\n hierarchy=h,\n params=ncs_pars.ncs_search)\n ncs_groups = ncs_inp.get_ncs_restraints_group_list()\n assert len(ncs_groups) == 1\n # group 1\n assert ncs_groups[0].master_iselection.all_eq(\n asc.selection(string = \"chain A\").iselection())\n g1_c = ncs_groups[0].copies\n assert len(g1_c)==1\n assert g1_c[0].iselection.all_eq(\n asc.selection(string = \"chain B\").iselection())","def TNG_net(self): \n \n import h5py as h5\n filename = localpath+'input/yields/TNG/SNII.hdf5'\n # Read H5 file\n f = h5.File(filename, \"r\")\n \n # Define element indexing\t\t\t\n indexing = {}\n indexing['H'] = 'Hydrogen'\n indexing['He'] = 'Helium'\n indexing['C'] = 'Carbon'\n indexing['N']= 'Nitrogen'\n indexing['O'] = 'Oxygen'\n indexing['Ne'] = 'Neon'\n indexing['Mg'] = 'Magnesium'\n indexing['Si'] = 'Silicon'\n indexing['S'] = 'Sulphur' # Not used by TNG simulation\n indexing['Ca'] = 'Calcium' # Not used by TNG simulation\n indexing['Fe'] = 'Iron'\n \n self.elements = list(indexing.keys())\n \n self.table = {}\n \n # Define masses / metallicities\n self.metallicities = list(f['Metallicities'].value)\n self.masses = f['Masses'].value\n\n \n for z_index,z in enumerate(self.metallicities):\n \n yield_subtable = {}\n \n z_name = f['Yield_names'].value[z_index].decode('utf-8')\n z_data = f['Yields/'+z_name+'/Yield']\n \n ejecta_mass = f['Yields/'+z_name+'/Ejected_mass'].value\n \n yield_subtable['Mass'] = self.masses\n remnants = self.masses-ejecta_mass\n yield_subtable['mass_in_remnants'] = np.divide(remnants,self.masses)\n for el in list(indexing.keys()):\n yield_subtable[el] = np.zeros(len(self.masses))\n \n summed_yields = np.zeros(len(self.masses))\n \n for m_index,mass in enumerate(self.masses):\n for el_index,el in enumerate(self.elements):\n el_yield_fraction = z_data[el_index][m_index]/mass #(mass-remnants[m_index]) # Find fraction of ejecta per element\n yield_subtable[el][m_index] = el_yield_fraction\t\t\t\t\t\n summed_yields[m_index]+=el_yield_fraction # Compute total yield\n \n yield_subtable['unprocessed_mass_in_winds'] = 1.-summed_yields-yield_subtable['mass_in_remnants']\n \n # Restructure table\n all_keys = ['Mass','mass_in_remnants','unprocessed_mass_in_winds']+self.elements\n \n list_of_arrays = [yield_subtable[key] for key in all_keys]\n restructure_subtable = np.core.records.fromarrays(list_of_arrays,names=all_keys)\n \n self.table[z] = restructure_subtable"],"string":"[\n \"def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:\\n # get neofunctionalisations \\n neofunctionalisedECs = NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs))\\n \\n # filter core metabolism EC graph\\n coreMetabolism = self.coreMetabolism(majorityPercentageCoreMetabolism)\\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100))\\n \\n neofunctionalisedMetabolism = neofunctionalisedECs.filterGraph(coreMetabolism, minimumEcDifference = None, minimumOrganismsCount = minimumOrganismsCount)\\n \\n # colour core metabolism\\n if colour is not False:\\n \\n if colour is True:\\n colourToUse = Export.Colour.GREEN\\n else:\\n colourToUse = colour\\n \\n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\\n neofunctionalisedMetabolism = coreMetabolism\\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\\n \\n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism ' + ' '.join(self.ncbiNames)\\n \\n return neofunctionalisedMetabolism\",\n \"def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n childGraph = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(addedECs)\\n childGraph.name = 'Added metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return childGraph\",\n \"def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\\n conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)\\n \\n parentNeofunctionalised= self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = conservedMetabolism\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = conservedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\\n childGraph = conservedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\\n \\n parentGraph.name = 'Conserved metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Conserved metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def divergedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):\\n divergedMetabolism = self.divergedMetabolism(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentNeofunctionalised = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = divergedMetabolism\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = divergedMetabolism[0].removeAllECsExcept(parentNeofunctionalised.getECs())\\n childGraph = divergedMetabolism[1].removeAllECsExcept(childNeofunctionalised.getECs())\\n \\n parentGraph.name = 'Diverged metabolism neofunctionalised ECs *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Diverged metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def lostMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n parentGraph = self.parentClade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, colour = False).removeAllECsExcept(lostECs)\\n parentGraph.name = 'Lost metabolism neofunctionalised ECs ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return parentGraph\",\n \"def redundantECsForContributingNeofunctionalisation(self, \\n majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, \\n majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, \\n eValue = defaultEValue, \\n redundancyType: 'RedundancyType' = None,\\n considerOnlyECs = None) -> Dict[Neofunctionalisation, Set[EcNumber]]:\\n from FEV_KEGG.Robustness.Topology.Redundancy import Redundancy, RedundancyContribution, RedundancyType\\n \\n if redundancyType is None:\\n redundancyType = RedundancyType.default\\n \\n #- calculate \\\"neofunctionalised\\\" ECs\\n neofunctionalisedMetabolismSet = self.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs).getECs()\\n neofunctionalisationsForFunctionChange = self.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue, considerOnlyECs)\\n \\n #- calculate redundancy\\n redundancy = Redundancy( self.coreMetabolism(majorityPercentageCoreMetabolism) )\\n redundancyContribution = RedundancyContribution(redundancy, neofunctionalisedMetabolismSet)\\n \\n contributedECsForContributingNeofunctionalisedEC = redundancyContribution.getContributedKeysForSpecial(redundancyType)\\n contributingNeofunctionalisedECs = set(contributedECsForContributingNeofunctionalisedEC.keys())\\n \\n #- REPEAT for each function change consisting of \\\"neofunctionalised\\\" ECs, which also contribute to redundancy\\n contributingNeofunctionalisations = dict()\\n \\n for functionChange, neofunctionalisations in neofunctionalisationsForFunctionChange.items():\\n #- report enzyme pairs of neofunctionalisations, which caused the EC to be considered \\\"neofunctionalised\\\", and are in return contributing to redundancy \\n \\n if functionChange.ecA in contributingNeofunctionalisedECs or functionChange.ecB in contributingNeofunctionalisedECs: # function change contributes to redundancy\\n \\n for neofunctionalisation in neofunctionalisations:\\n currentSetOfContributedECs = contributingNeofunctionalisations.get(neofunctionalisation, None)\\n \\n if currentSetOfContributedECs is None:\\n currentSetOfContributedECs = set()\\n contributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs\\n \\n for ec in functionChange.ecPair:\\n contributedECs = contributedECsForContributingNeofunctionalisedEC.get(ec, None)\\n if contributedECs is not None:\\n currentSetOfContributedECs.update(contributedECs)\\n \\n return contributingNeofunctionalisations\",\n \"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\\n graph.name = 'Core metabolism ECs ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def __repr__(self):\\n return \\\"EC(%s, %s)\\\" % (str(self.coefficient), repr(self.basefield))\",\n \"def complex_network_mapping(graph):\\n vect = []\\n\\n n = nx.number_of_nodes(graph)\\n e = nx.number_of_edges(graph)\\n print n, e\\n\\n# adj = nx.adjacency_matrix(graph).toarray()\\n# adj_bin = np.where(adj > 0, 1., 0.)\\n# adj_conn = 1 - adj\\n adj_bin = nx.adjacency_matrix(graph).toarray()\\n adj_bin = np.array(adj_bin, dtype=np.float)\\n\\n # Node Betweenness binary\\n bt_bin = nx.betweenness_centrality(graph).values()\\n avg_btb = np.mean(bt_bin)\\n vect.append(avg_btb)\\n\\n # Edge betweenness\\n ebt = np.array(nx.edge_betweenness_centrality(graph).values())\\n vect.append(np.mean(ebt))\\n\\n # Eigen vector centrality binary\\n evc_bin = eigenvector_centrality_und(adj_bin)\\n avg_evcb = np.mean(evc_bin)\\n vect.append(avg_evcb)\\n\\n # Flow coefficient\\n _, flow_bin, _ = flow_coef_bd(adj_bin)\\n avg_flow = np.mean(flow_bin)\\n vect.append(avg_flow)\\n\\n # Kcoreness centrality\\n kcor_bin, _ = kcoreness_centrality_bu(adj_bin)\\n avg_kcor = np.mean(kcor_bin)\\n vect.append(avg_kcor)\\n\\n # Degree assortivity\\n dac = nx.degree_assortativity_coefficient(graph)\\n vect.append(dac)\\n\\n # Page rank centrality\\n# pgr_wei = pagerank_centrality(adj_bin, d=0.85)\\n# avg_pgr = np.mean(pgr_wei)\\n# vect.append(avg_pgr)\\n\\n # Rich club coefficient\\n# rcc = nx.rich_club_coefficient(graph).values()\\n# avg_rcc = np.mean(rcc)\\n# vect.append(avg_rcc)\\n\\n # Transitivity\\n tr = nx.transitivity(graph)\\n vect.append(tr)\\n\\n # average clustering\\n avg_clst = nx.average_clustering(graph)\\n vect.append(avg_clst)\\n\\n glb_ef = efficiency_bin(adj_bin)\\n vect.append(glb_ef)\\n\\n return vect\",\n \"def cell_edges(self):\",\n \"def c_edges(self):\\n self.compute_c_edges(self)\\n return self._c_edges\",\n \"def get_ecg_graph():\\n titles = ['ecg1', 'ecg2', 'ecg3']\\n colors = ['rgb(240,0,0)', 'rgb(0,240,0)', 'rgb(0,0,240)']\\n update()\\n signames_ecg = queries['signames_ecg']\\n signals = queries['signals']\\n latesthr = queries['latesthr']\\n return html.Div(className='ecg', children=[\\n html.Div(style={'display': 'flex', 'height': '40vh'},\\n children=[dcc.Graph(\\n id=titles[i] + signame,\\n style={'width': '100%'},\\n figure={\\n 'data': [\\n {'x': signals[signame]['time'],\\n 'y': signals[signame][titles[i]],\\n 'mode': 'line', 'name': signame, 'line': {'color':colors[i]}}\\n ],\\n 'layout': {\\n 'font': {'color':'#fff'},\\n 'title': '{}-{}'.format(signame, titles[i]),\\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\\n 'yaxis': {'title': 'voltage (mv)', 'color': '#fff', 'showgrid': 'False', 'range': np.linspace(-2.5, 2.5, 10)},\\n 'paper_bgcolor':'#000', 'plot_bgcolor':'#000'\\n }\\n }\\n ) for i in range(len(titles))]\\n +\\n [html.Div(\\n style={'justify-content': 'center', 'display': 'flex',\\n 'align-items': 'center', 'width': '10vh', 'font-size': '30pt', 'color': 'white'},\\n children=['{}'.format(latesthr[signame][0])])\\n ]\\n ) for signame in signames_ecg])\",\n \"def set_ec(self, etacalc):\\n if not self.__thermodyn:\\n C = np.zeros((6,6))\\n \\n LC = self.__structures.items()[0][1].LC\\n if self.__mthd == 'Energy':\\n if type(etacalc)==list:\\n A2=[]\\n for i in range(len(etacalc)):\\n A2.append(self.__A2[i][etacalc[i]])\\n else:\\n if not etacalc in self.__A2[0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[0].keys()))\\n A2 = [a2[etacalc] for a2 in self.__A2]\\n \\n \\n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\\n C[1,1] = C[0,0]\\n C[2,2] = C[0,0]\\n C[3,3] = A2[2]/6.\\n C[4,4] = C[3,3]\\n C[5,5] = C[3,3]\\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\\n C[0,2] = C[0,1]\\n C[1,2] = C[0,1]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[2]\\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RI'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[1,4] =-C[0,4] \\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[3,5] =-C[0,4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TI'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[3]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TII'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\\n C[1,1] = C[0,0]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\\n C[1,2] = C[0,2]\\n C[1,5] =-C[0,5]\\n C[2,2] = 2.*A2[4]\\n C[3,3] = .5*A2[5]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[6]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'O'):\\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\\n C[1,1] = 2.*A2[4]\\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\\n C[2,2] = 2.*A2[5]\\n C[3,3] = .5*A2[6]\\n C[4,4] = .5*A2[7]\\n C[5,5] = .5*A2[8]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'M'):\\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\\n C[1,1] = 2.*A2[8]\\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\\n C[2,2] = 2.*A2[9]\\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\\n C[3,3] = .5*A2[10]\\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\\n C[4,4] = .5*A2[11]\\n C[5,5] = .5*A2[12]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'N'):\\n C[0,0] = 2.*A2[0]\\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \\n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\\n C[1,1] = 2.*A2[1]\\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\\n C[2,2] = 2.*A2[2] \\n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\\n C[3,3] = .5*A2[3]\\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\\n C[4,4] = .5*A2[4]\\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n elif self.__mthd == 'Stress':\\n \\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n Matrix = np.mat([[1.0, 5.0, 0.0],\\n [2.0, 4.0, 0.0],\\n [3.0, 3.0, 0.0],\\n [0.0, 0.0, 4.0],\\n [0.0, 0.0, 5.0],\\n [0.0, 0.0, 6.0]])\\n \\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\\n [ 2, 1, 3, 0, 0],\\n [ 0, 0, 3, 3, 0],\\n [ 0, 0, 0, 0, 4],\\n [ 0, 0, 0, 0, 5],\\n [ 3,-3, 0, 0, 0],\\n [ 3,-5,-1, 0, 0],\\n [-5, 3,-1, 0, 0],\\n [ 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 0, 6],\\n [ 0, 0, 0, 0, 2],\\n [-2, 2, 0, 0, 0]])\\n \\n if (LC == 'RI'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\\n [ 2, 1, 3,-4, 0, 0],\\n [ 0, 0, 3, 0, 3, 0],\\n [ 0, 0, 0,-1, 0, 4],\\n [ 0, 0, 0, 6, 0, 5],\\n [ 3,-3, 0, 5, 0, 0],\\n [ 3,-5,-1, 6, 0, 0],\\n [-5, 3,-1,-6, 0, 0],\\n [ 0, 0,-2, 0,-1, 0],\\n [ 0, 0, 0, 8, 0, 6],\\n [ 0, 0, 0,-4, 0, 2],\\n [-2, 2, 0, 2, 0, 0]])\\n \\n if (LC == 'RII'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\\n [ 2, 1, 3,-4,-5, 0, 0],\\n [ 0, 0, 3, 0, 0, 3, 0],\\n [ 0, 0, 0,-1,-6, 0, 4],\\n [ 0, 0, 0, 6,-1, 0, 5],\\n [ 3,-3, 0, 5,-4, 0, 0],\\n [ 3,-5,-1, 6, 2, 0, 0],\\n [-5, 3,-1,-6,-2, 0, 0],\\n [ 0, 0,-2, 0, 0,-1, 0],\\n [ 0, 0, 0, 8, 4, 0, 6],\\n [ 0, 0, 0,-4, 8, 0, 2],\\n [-2, 2, 0, 2,-6, 0, 0]])\\n \\n if (LC == 'TI'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\\n [ 2, 1, 3, 0, 0, 0],\\n [ 0, 0, 3, 3, 0, 0],\\n [ 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0, 0, 0, 6],\\n [ 3,-5,-1, 0, 0, 0],\\n [-5, 3,-1, 0, 0, 0],\\n [ 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 0, 0,-4]])\\n \\n if (LC == 'TII'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\\n [ 2, 1, 3,-6, 0, 0, 0],\\n [ 0, 0, 3, 0, 3, 0, 0],\\n [ 0, 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0,-1, 0, 0, 6],\\n [ 3,-5,-1,-4, 0, 0, 0],\\n [-5, 3,-1, 4, 0, 0, 0],\\n [ 0, 0,-2, 0,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 8, 0, 0,-4]])\\n \\n if (LC == 'O'):\\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\\n \\n if (LC == 'M'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\\n \\n if (LC == 'N'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\\n \\n sigma = np.array(self.__sigma[etacalc])\\n \\n ci = np.linalg.lstsq(Matrix,sigma)\\n \\n #-- Cubic structures ------------------------------------------------------------------------------\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n \\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[3,3]=ci[0][2]\\n C[1,1]=C[0,0]\\n C[2,2]=C[0,0]\\n C[0,2]=C[0,1]\\n C[1,2]=C[0,1]\\n C[4,4]=C[3,3]\\n C[5,5]=C[3,3]\\n \\n #-- Hexagonal Structures --------------------------------------------------------------------------\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[2,2]=ci[0][3]\\n C[3,3]=ci[0][4]\\n C[1,1]=C[0,0]\\n C[1,2]=C[0,2]\\n C[4,4]=C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral I Structures ---------------------------------------------------------------------\\n if (LC == 'RI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral II Structures --------------------------------------------------------------------\\n if (LC == 'RII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[0,4]= ci[0][4]\\n C[2,2]= ci[0][5]\\n C[3,3]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[1,4]=-C[0,4]\\n C[3,5]=-C[0,4]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Tetragonal I Structures -----------------------------------------------------------------------\\n if (LC == 'TI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[2,2]= ci[0][3]\\n C[3,3]= ci[0][4]\\n C[5,5]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[4,4]= C[3,3]\\n \\n #-- Tetragonal II Structures ----------------------------------------------------------------------\\n if (LC == 'TII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,5]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[5,5]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,5]=-C[0,5]\\n C[4,4]= C[3,3]\\n \\n #-- Orthorhombic Structures -----------------------------------------------------------------------\\n if (LC == 'O'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[1,1]=ci[0][3]\\n C[1,2]=ci[0][4]\\n C[2,2]=ci[0][5]\\n C[3,3]=ci[0][6]\\n C[4,4]=ci[0][7]\\n C[5,5]=ci[0][8]\\n \\n #-- Monoclinic Structures -------------------------------------------------------------------------\\n if (LC == 'M'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,5]=ci[0][3]\\n C[1,1]=ci[0][4]\\n C[1,2]=ci[0][5]\\n C[1,5]=ci[0][6]\\n C[2,2]=ci[0][7]\\n C[2,5]=ci[0][8]\\n C[3,3]=ci[0][9]\\n C[3,4]=ci[0][10]\\n C[4,4]=ci[0][11]\\n C[5,5]=ci[0][12]\\n \\n #-- Triclinic Structures --------------------------------------------------------------------------\\n if (LC == 'N'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,3]=ci[0][3]\\n C[0,4]=ci[0][4]\\n C[0,5]=ci[0][5]\\n C[1,1]=ci[0][6]\\n C[1,2]=ci[0][7]\\n C[1,3]=ci[0][8]\\n C[1,4]=ci[0][9]\\n C[1,5]=ci[0][10]\\n C[2,2]=ci[0][11]\\n C[2,3]=ci[0][12]\\n C[2,4]=ci[0][13]\\n C[2,5]=ci[0][14]\\n C[3,3]=ci[0][15]\\n C[3,4]=ci[0][16]\\n C[3,5]=ci[0][17]\\n C[4,4]=ci[0][18]\\n C[4,5]=ci[0][19]\\n C[5,5]=ci[0][20]\\n #--------------------------------------------------------------------------------------------------\\n \\n \\n \\n for i in range(5):\\n for j in range(i+1,6):\\n C[j,i] = C[i,j] \\n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if self.__cod in ['espresso']: C = -C/10.\\n elif self.__cod in ['vasp','exciting','wien']: C = C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0\\n elif self.__cod in ['emto']: C = C*self.__ToGPa/self.__V0\\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\\n self.S = np.linalg.inv(C)\\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\\n self.BH = 0.50*(self.BV+self.BR)\\n self.GH = 0.50*(self.GV+self.GR)\\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\\n #--------------------------------------------------------------------------------------------------------------------------------\\n self.__C = C\\n \\n else:\\n Cs = []\\n for t in map(str,self.__T):#for t in range(len(self.__T)):\\n C = np.zeros((6,6))\\n \\n LC = self.__structures.items()[0][1].LC\\n if self.__mthd == 'Energy':\\n if type(etacalc)==list:\\n A2=[]\\n for i in range(len(etacalc)):\\n A2.append(self.__A2[t][i][etacalc[i]])\\n else:\\n if not etacalc in self.__A2[t][0].keys(): raise ValueError('Please coose one of %s'%(self.__A2[t][0].keys()))\\n A2 = [a2[etacalc] for a2 in self.__A2[t]]\\n \\n #%%%--- Cubic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n C[0,0] =-2.*(A2[0]-3.*A2[1])/3.\\n C[1,1] = C[0,0]\\n C[2,2] = C[0,0]\\n C[3,3] = A2[2]/6.\\n C[4,4] = C[3,3]\\n C[5,5] = C[3,3]\\n C[0,1] = (2.*A2[0]-3.*A2[1])/3.\\n C[0,2] = C[0,1]\\n C[1,2] = C[0,1]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Hexagonal structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = 2./3.*A2[0] + 4./3.*A2[1] - 2.*A2[2] - 2.*A2[3]\\n C[0,2] = 1./6.*A2[0] - 2./3.*A2[1] + 0.5*A2[2]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[2]\\n C[3,3] =-0.5*A2[2] + 0.5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RI'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Rhombohedral II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'RII'):\\n C[0,0] = 2.*A2[3]\\n C[0,1] = A2[1]- 2.*A2[3]\\n C[0,2] = .5*( A2[0] - A2[1] - A2[2])\\n C[0,3] = .5*(-A2[3] - A2[4] + A2[5])\\n C[0,4] = .5*(-A2[3] - A2[4] + A2[6])\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[1,3] =-C[0,3]\\n C[1,4] =-C[0,4] \\n C[2,2] = 2.*A2[2]\\n C[3,3] = .5*A2[4]\\n C[3,5] =-C[0,4]\\n C[4,4] = C[3,3]\\n C[4,5] = C[0,3]\\n C[5,5] = .5*(C[0,0] - C[0,1])\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal I structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TI'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[3]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[3]\\n C[0,2] = A2[0]/6.-2.*A2[1]/3.+.5*A2[3]\\n C[1,1] = C[0,0]\\n C[1,2] = C[0,2]\\n C[2,2] = 2.*A2[3]\\n C[3,3] = .5*A2[4]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Tetragonal II structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'TII'):\\n C[0,0] = (A2[0]+2.*A2[1])/3.+.5*A2[2]-A2[4]\\n C[1,1] = C[0,0]\\n C[0,1] = (A2[0]+2.*A2[1])/3.-.5*A2[2]-A2[4]\\n C[0,2] = A2[0]/6.-(2./3.)*A2[1]+.5*A2[4]\\n C[0,5] = (-A2[2]+A2[3]-A2[6])/4.\\n C[1,2] = C[0,2]\\n C[1,5] =-C[0,5]\\n C[2,2] = 2.*A2[4]\\n C[3,3] = .5*A2[5]\\n C[4,4] = C[3,3]\\n C[5,5] = .5*A2[6]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Orthorhombic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'O'):\\n C[0,0] = 2.*A2[0]/3.+4.*A2[1]/3.+A2[3]-2.*A2[4]-2.*A2[5]\\n C[0,1] = 1.*A2[0]/3.+2.*A2[1]/3.-.5*A2[3]-A2[5]\\n C[0,2] = 1.*A2[0]/3.-2.*A2[1]/3.+4.*A2[2]/3.-.5*A2[3]-A2[4]\\n C[1,1] = 2.*A2[4]\\n C[1,2] =-2.*A2[1]/3.-4.*A2[2]/3.+.5*A2[3]+A2[4]+A2[5]\\n C[2,2] = 2.*A2[5]\\n C[3,3] = .5*A2[6]\\n C[4,4] = .5*A2[7]\\n C[5,5] = .5*A2[8]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Monoclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'M'):\\n C[0,0] = 2.*A2[0]/3.+8.*(A2[1]+A2[2])/3.-2.*(A2[5]+A2[8]+A2[9])\\n C[0,1] = A2[0]/3.+4.*(A2[1]+A2[2])/3.-2.*A2[5]-A2[9]\\n C[0,2] =(A2[0]-4.*A2[2])/3.+A2[5]-A2[8]\\n C[0,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.+.5*(A2[5]+A2[7]+A2[8]+A2[9]-A2[12])\\n C[1,1] = 2.*A2[8]\\n C[1,2] =-4.*(2.*A2[1]+A2[2])/3.+2.*A2[5]+A2[8]+A2[9]+A2[12]\\n C[1,5] =-1.*A2[0]/6.-2.*(A2[1]+A2[2])/3.-.5*A2[3]+A2[5]+.5*(A2[7]+A2[8]+A2[9])\\n C[2,2] = 2.*A2[9]\\n C[2,5] =-1.*A2[0]/6.+2.*A2[1]/3.-.5*(A2[3]+A2[4]-A2[7]-A2[8]-A2[9]-A2[12])\\n C[3,3] = .5*A2[10]\\n C[3,4] = .25*(A2[6]-A2[10]-A2[11])\\n C[4,4] = .5*A2[11]\\n C[5,5] = .5*A2[12]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n #%%%--- Triclinic structures ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if (LC == 'N'):\\n C[0,0] = 2.*A2[0]\\n C[0,1] = 1.*(-A2[0]-A2[1]+A2[6])\\n C[0,2] = 1.*(-A2[0]-A2[2]+A2[7])\\n C[0,3] = .5*(-A2[0]-A2[3]+A2[8]) \\n C[0,4] = .5*(-A2[0]+A2[9]-A2[4])\\n C[0,5] = .5*(-A2[0]+A2[10]-A2[5])\\n C[1,1] = 2.*A2[1]\\n C[1,2] = 1.*(A2[11]-A2[1]-A2[2])\\n C[1,3] = .5*(A2[12]-A2[1]-A2[3])\\n C[1,4] = .5*(A2[13]-A2[1]-A2[4])\\n C[1,5] = .5*(A2[14]-A2[1]-A2[5])\\n C[2,2] = 2.*A2[2] \\n C[2,3] = .5*(A2[15]-A2[2]-A2[3])\\n C[2,4] = .5*(A2[16]-A2[2]-A2[4])\\n C[2,5] = .5*(A2[17]-A2[2]-A2[5])\\n C[3,3] = .5*A2[3]\\n C[3,4] = .25*(A2[18]-A2[3]-A2[4])\\n C[3,5] = .25*(A2[19]-A2[3]-A2[5])\\n C[4,4] = .5*A2[4]\\n C[4,5] = .25*(A2[20]-A2[4]-A2[5])\\n C[5,5] = .5*A2[5]\\n #--------------------------------------------------------------------------------------------------------------------------------\\n \\n elif self.__mthd == 'Stress':\\n \\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n Matrix = np.mat([[1.0, 5.0, 0.0],\\n [2.0, 4.0, 0.0],\\n [3.0, 3.0, 0.0],\\n [0.0, 0.0, 4.0],\\n [0.0, 0.0, 5.0],\\n [0.0, 0.0, 6.0]])\\n \\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0],\\n [ 2, 1, 3, 0, 0],\\n [ 0, 0, 3, 3, 0],\\n [ 0, 0, 0, 0, 4],\\n [ 0, 0, 0, 0, 5],\\n [ 3,-3, 0, 0, 0],\\n [ 3,-5,-1, 0, 0],\\n [-5, 3,-1, 0, 0],\\n [ 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 0, 6],\\n [ 0, 0, 0, 0, 2],\\n [-2, 2, 0, 0, 0]])\\n \\n if (LC == 'RI'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 0, 0],\\n [ 2, 1, 3,-4, 0, 0],\\n [ 0, 0, 3, 0, 3, 0],\\n [ 0, 0, 0,-1, 0, 4],\\n [ 0, 0, 0, 6, 0, 5],\\n [ 3,-3, 0, 5, 0, 0],\\n [ 3,-5,-1, 6, 0, 0],\\n [-5, 3,-1,-6, 0, 0],\\n [ 0, 0,-2, 0,-1, 0],\\n [ 0, 0, 0, 8, 0, 6],\\n [ 0, 0, 0,-4, 0, 2],\\n [-2, 2, 0, 2, 0, 0]])\\n \\n if (LC == 'RII'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 0, 0],\\n [ 2, 1, 3,-4,-5, 0, 0],\\n [ 0, 0, 3, 0, 0, 3, 0],\\n [ 0, 0, 0,-1,-6, 0, 4],\\n [ 0, 0, 0, 6,-1, 0, 5],\\n [ 3,-3, 0, 5,-4, 0, 0],\\n [ 3,-5,-1, 6, 2, 0, 0],\\n [-5, 3,-1,-6,-2, 0, 0],\\n [ 0, 0,-2, 0, 0,-1, 0],\\n [ 0, 0, 0, 8, 4, 0, 6],\\n [ 0, 0, 0,-4, 8, 0, 2],\\n [-2, 2, 0, 2,-6, 0, 0]])\\n \\n if (LC == 'TI'):\\n Matrix = np.mat([[ 1, 2, 3, 0, 0, 0],\\n [ 2, 1, 3, 0, 0, 0],\\n [ 0, 0, 3, 3, 0, 0],\\n [ 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0, 0, 0, 6],\\n [ 3,-5,-1, 0, 0, 0],\\n [-5, 3,-1, 0, 0, 0],\\n [ 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 0, 0,-4]])\\n \\n if (LC == 'TII'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0],\\n [ 2, 1, 3,-6, 0, 0, 0],\\n [ 0, 0, 3, 0, 3, 0, 0],\\n [ 0, 0, 0, 0, 0, 4, 0],\\n [ 0, 0, 0, 0, 0, 5, 0],\\n [ 0, 0, 0,-1, 0, 0, 6],\\n [ 3,-5,-1,-4, 0, 0, 0],\\n [-5, 3,-1, 4, 0, 0, 0],\\n [ 0, 0,-2, 0,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 6, 0],\\n [ 0, 0, 0, 0, 0, 2, 0],\\n [ 0, 0, 0, 8, 0, 0,-4]])\\n \\n if (LC == 'O'):\\n Matrix = np.mat([[1, 2, 3, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 2, 3, 0, 0, 0, 0],\\n [0, 0, 1, 0, 2, 3, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 5, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0, 6],\\n [3,-5,-1, 0, 0, 0, 0, 0, 0],\\n [0, 3, 0,-5,-1, 0, 0, 0, 0],\\n [0, 0, 3, 0,-5,-1, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 6, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-4],\\n [5, 4, 6, 0, 0, 0, 0, 0, 0],\\n [0, 5, 0, 4, 6, 0, 0, 0, 0],\\n [0, 0, 5, 0, 4, 6, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0,-2, 0, 0],\\n [0, 0, 0, 0, 0, 0, 0,-1, 0],\\n [0, 0, 0, 0, 0, 0, 0, 0,-3]])\\n \\n if (LC == 'M'):\\n Matrix = np.mat([[ 1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 2, 3, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 2, 0, 3, 6, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0],\\n [ 0, 0, 0, 1, 0, 0, 2, 0, 3, 0, 0, 0, 6],\\n [-2, 1, 4,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 1, 4,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 1, 0, 4,-5, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-3, 6, 0],\\n [ 0, 0, 0,-2, 0, 0, 1, 0, 4, 0, 0,-5, 0],\\n [ 3,-5,-1,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0,-5,-1,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0,-5, 0,-1,-4, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 0],\\n [ 0, 0, 0, 3, 0, 0,-5, 0,-1, 0, 0,-4, 0],\\n [-4,-6, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0,-6, 5, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0,-6, 0, 5, 2, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,-3, 0],\\n [ 0, 0, 0,-4, 0, 0,-6, 0, 5, 0, 0, 2, 0],\\n [ 5, 4, 6,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 4, 6,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 4, 0, 6,-3, 0, 0, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0, 0],\\n [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-2,-1, 0],\\n [ 0, 0, 0, 5, 0, 0, 4, 0, 6, 0, 0,-3, 0]])\\n \\n if (LC == 'N'):\\n Matrix = np.mat([[ 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 1, 0, 0, 0, 0, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 5, 6, 0, 0, 0],\\n [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6, 0],\\n [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 4, 0, 5, 6],\\n [-2, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-2, 0, 0, 0, 0, 1, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4,-3, 6,-5, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 6,-5, 0, 0, 0],\\n [ 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5, 0],\\n [ 0, 0, 0, 0, 0,-2, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0,-3, 0, 6,-5],\\n [ 3,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 3, 0, 0, 0, 0,-5,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 6, 2,-4, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 2,-4, 0, 0, 0],\\n [ 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4, 0],\\n [ 0, 0, 0, 0, 0, 3, 0, 0, 0, 0,-5, 0, 0, 0,-1, 0, 0, 6, 0, 2,-4],\\n [-4,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-4, 0, 0, 0, 0,-6, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 1,-3, 2, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1,-3, 2, 0, 0, 0],\\n [ 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2, 0],\\n [ 0, 0, 0, 0, 0,-4, 0, 0, 0, 0,-6, 0, 0, 0, 5, 0, 0, 1, 0,-3, 2],\\n [ 5, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 5, 0, 0, 0, 0, 4, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6,-2,-1,-3, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2,-1,-3, 0, 0, 0],\\n [ 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3, 0],\\n [ 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0,-2, 0,-1,-3],\\n [-6, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0,-6, 0, 0, 0, 0, 3,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\\n [ 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 5,-4, 1, 0, 0, 0, 0, 0, 0],\\n [ 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5,-4, 1, 0, 0, 0],\\n [ 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1, 0],\\n [ 0, 0, 0, 0, 0,-6, 0, 0, 0, 0, 3, 0, 0, 0,-2, 0, 0, 5, 0,-4, 1]])\\n \\n sigma = np.array(self.__sigma[etacalc])\\n \\n ci = np.linalg.lstsq(Matrix,sigma)\\n \\n #-- Cubic structures ------------------------------------------------------------------------------\\n if (LC == 'CI' or \\\\\\n LC == 'CII'):\\n \\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[3,3]=ci[0][2]\\n C[1,1]=C[0,0]\\n C[2,2]=C[0,0]\\n C[0,2]=C[0,1]\\n C[1,2]=C[0,1]\\n C[4,4]=C[3,3]\\n C[5,5]=C[3,3]\\n \\n #-- Hexagonal Structures --------------------------------------------------------------------------\\n if (LC == 'HI' or \\\\\\n LC == 'HII'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[2,2]=ci[0][3]\\n C[3,3]=ci[0][4]\\n C[1,1]=C[0,0]\\n C[1,2]=C[0,2]\\n C[4,4]=C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral I Structures ---------------------------------------------------------------------\\n if (LC == 'RI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Rhombohedral II Structures --------------------------------------------------------------------\\n if (LC == 'RII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,3]= ci[0][3]\\n C[0,4]= ci[0][4]\\n C[2,2]= ci[0][5]\\n C[3,3]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,3]=-C[0,3]\\n C[4,5]= C[0,3]\\n C[1,4]=-C[0,4]\\n C[3,5]=-C[0,4]\\n C[4,4]= C[3,3]\\n C[5,5]=0.5*(C[0,0]-C[0,1])\\n \\n #-- Tetragonal I Structures -----------------------------------------------------------------------\\n if (LC == 'TI'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[2,2]= ci[0][3]\\n C[3,3]= ci[0][4]\\n C[5,5]= ci[0][5]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[4,4]= C[3,3]\\n \\n #-- Tetragonal II Structures ----------------------------------------------------------------------\\n if (LC == 'TII'):\\n C[0,0]= ci[0][0]\\n C[0,1]= ci[0][1]\\n C[0,2]= ci[0][2]\\n C[0,5]= ci[0][3]\\n C[2,2]= ci[0][4]\\n C[3,3]= ci[0][5]\\n C[5,5]= ci[0][6]\\n C[1,1]= C[0,0]\\n C[1,2]= C[0,2]\\n C[1,5]=-C[0,5]\\n C[4,4]= C[3,3]\\n \\n #-- Orthorhombic Structures -----------------------------------------------------------------------\\n if (LC == 'O'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[1,1]=ci[0][3]\\n C[1,2]=ci[0][4]\\n C[2,2]=ci[0][5]\\n C[3,3]=ci[0][6]\\n C[4,4]=ci[0][7]\\n C[5,5]=ci[0][8]\\n \\n #-- Monoclinic Structures -------------------------------------------------------------------------\\n if (LC == 'M'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,5]=ci[0][3]\\n C[1,1]=ci[0][4]\\n C[1,2]=ci[0][5]\\n C[1,5]=ci[0][6]\\n C[2,2]=ci[0][7]\\n C[2,5]=ci[0][8]\\n C[3,3]=ci[0][9]\\n C[3,4]=ci[0][10]\\n C[4,4]=ci[0][11]\\n C[5,5]=ci[0][12]\\n \\n #-- Triclinic Structures --------------------------------------------------------------------------\\n if (LC == 'N'):\\n C[0,0]=ci[0][0]\\n C[0,1]=ci[0][1]\\n C[0,2]=ci[0][2]\\n C[0,3]=ci[0][3]\\n C[0,4]=ci[0][4]\\n C[0,5]=ci[0][5]\\n C[1,1]=ci[0][6]\\n C[1,2]=ci[0][7]\\n C[1,3]=ci[0][8]\\n C[1,4]=ci[0][9]\\n C[1,5]=ci[0][10]\\n C[2,2]=ci[0][11]\\n C[2,3]=ci[0][12]\\n C[2,4]=ci[0][13]\\n C[2,5]=ci[0][14]\\n C[3,3]=ci[0][15]\\n C[3,4]=ci[0][16]\\n C[3,5]=ci[0][17]\\n C[4,4]=ci[0][18]\\n C[4,5]=ci[0][19]\\n C[5,5]=ci[0][20]\\n #--------------------------------------------------------------------------------------------------\\n \\n \\n \\n for i in range(5):\\n for j in range(i+1,6):\\n C[j,i] = C[i,j] \\n #%%%--- Calculating the elastic moduli ---%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n if self.__cod == 'espresso': C = -C/10.\\n elif self.__cod in ['vasp','emto','exciting','wien']: C=C*self.__vToGPa/self.__V0#C = C/4.#C=C*self.__CONV/self.__V0#C = C/4.\\n self.BV = (C[0,0]+C[1,1]+C[2,2]+2*(C[0,1]+C[0,2]+C[1,2]))/9\\n self.GV = ((C[0,0]+C[1,1]+C[2,2])-(C[0,1]+C[0,2]+C[1,2])+3*(C[3,3]+C[4,4]+C[5,5]))/15\\n self.EV = (9*self.BV*self.GV)/(3*self.BV+self.GV)\\n self.nuV= (1.5*self.BV-self.GV)/(3*self.BV+self.GV)\\n self.S = np.linalg.inv(C)\\n self.BR = 1/(self.S[0,0]+self.S[1,1]+self.S[2,2]+2*(self.S[0,1]+self.S[0,2]+self.S[1,2]))\\n self.GR =15/(4*(self.S[0,0]+self.S[1,1]+self.S[2,2])-4*(self.S[0,1]+self.S[0,2]+self.S[1,2])+3*(self.S[3,3]+self.S[4,4]+self.S[5,5]))\\n self.ER = (9*self.BR*self.GR)/(3*self.BR+self.GR)\\n self.nuR= (1.5*self.BR-self.GR)/(3*self.BR+self.GR)\\n self.BH = 0.50*(self.BV+self.BR)\\n self.GH = 0.50*(self.GV+self.GR)\\n self.EH = (9.*self.BH*self.GH)/(3.*self.BH+self.GH)\\n self.nuH= (1.5*self.BH-self.GH)/(3.*self.BH+self.GH)\\n self.AVR= 100.*(self.GV-self.GR)/(self.GV+self.GR)\\n #--------------------------------------------------------------------------------------------------------------------------------\\n Cs.append(C)\\n self.__C = Cs\",\n \"def edges(self):\\n return self.dovetails + self.containments + self.internals\",\n \"def collectiveMetabolism(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes, addEcDescriptions = False) -> SubstanceEcGraph:\\n graph = self.group.collectiveEcGraph(noMultifunctional = excludeMultifunctionalEnzymes, addCount = True, keepOnHeap = True, addEcDescriptions = addEcDescriptions)\\n graph.name = 'Collective metabolism ECs ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/26')\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/27')\",\n \"def NND_eta( eqCat, dConst, verbose = False, **kwargs):\\n #-------------------------------set args and kwargs----------------------------------------------- \\n rmax = 500 # in km\\n tmax = 20 # in years\\n M0 = 0\\n if 'M0' in kwargs.keys() and kwargs['M0'] is not None:\\n M0 = kwargs['M0']\\n if 'rmax' in kwargs.keys() and kwargs['rmax'] is not None:\\n rmax = kwargs['rmax']\\n if 'tmax' in kwargs.keys() and kwargs['tmax'] is not None:\\n tmax = kwargs['tmax']\\n #-----------------------------add small uncertainty to X in case events are colocated-------------------------- \\n if 'correct_co_located' in kwargs.keys() and kwargs['correct_co_located'] == True:\\n vUncer = np.random.randn( eqCat.size())*1e-10\\n eqCat.data['X'] += vUncer\\n eqCat.data['Time'] += abs( vUncer)#time has to stay positive otherwise parent-offspring gets switched\\n #------------------------------------------------------------------------------\\n aNND = np.zeros( eqCat.size())\\n vID_p = np.zeros( eqCat.size())\\n vID_c = np.zeros( eqCat.size())\\n deltaMag = (eqCat.data['Mag'] - M0)\\n \\n for jC in range( eqCat.size()):\\n if verbose == True:\\n print 'event %i of %i'%( jC+1, eqCat.size())\\n # interevent times: take events that happend before t_i \\n # child - parent > 0 \\n tau = eqCat.data['Time'][jC] - eqCat.data['Time']\\n sel_tau_par = tau > 0\\n if sel_tau_par.sum() > 0:\\n\\n vcurr_ID = np.arange( eqCat.size(), dtype = int)[sel_tau_par]\\n vR = np.sqrt( (eqCat.data['X'][jC] - eqCat.data['X'][vcurr_ID])**2 + (eqCat.data['Y'][jC] - eqCat.data['Y'][vcurr_ID])**2 )\\n # haversine distance\\n # = projUtils.haversine( eqCat.data['Lon'][jC], eqCat.data['Lat'][jC],eqCat.data['Lon'][curr_vID], eqCat.data['Lat'][curr_vID] ) \\n sel_r_par = vR < rmax\\n if sel_r_par.sum() > 0:\\n vcurr_ID = vcurr_ID[sel_r_par]\\n curr_Eta = tau[vcurr_ID]* (vR[sel_r_par]**dConst['D']) *( 10**(-dConst['b']*deltaMag[vcurr_ID]))\\n sel_min = curr_Eta == curr_Eta.min()\\n aNND[jC] = curr_Eta[sel_min][0]\\n vID_p[jC] = eqCat.data['N'][vcurr_ID][sel_min][0]\\n vID_c[jC] = eqCat.data['N'][jC]\\n #print 'parent', eqCat.data['N'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['N'][jC]\\n #print 'parent', eqCat.data['Time'][vcurr_ID][sel_min][0], 'offspring', eqCat.data['Time'][jC]\\n\\n if sel_min.sum() > 1:\\n print aNND[jC], curr_Eta[sel_min], eqCat.data['N'][vcurr_ID][sel_min]\\n print eqCat.data['Lon'][vcurr_ID][sel_min], eqCat.data['Lat'][vcurr_ID][sel_min]\\n print eqCat.data['X'][vcurr_ID][sel_min], eqCat.data['Y'][vcurr_ID][sel_min]\\n sel2 = aNND > 0\\n if np.logical_not(sel2).sum() > 0:\\n print 'remove %i offspring without prior parents in catalog'%(np.logical_not(sel2).sum())\\n #raise ValueError, error_str\\n # remove events with aNND < 0; i.e. event at the beginning with no preceding parent\\n return { 'aNND' : aNND[sel2], 'aEqID_p' : vID_p[sel2], 'aEqID_c' : vID_c[sel2]}\",\n \"def colored_edges(genome):\\n edges = []\\n for chromo in genome:\\n nodes = [0] + chromosome_to_cycle(chromo)\\n nodes.append(nodes[1])\\n for j in range(1, len(chromo) + 1):\\n edges.append((nodes[2 * j], nodes[2 * j + 1]))\\n\\n return edges\",\n \"def neofunctionalisations(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None) -> Set[Neofunctionalisation]:\\n # get neofunctionalisations \\n return self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs).getNeofunctionalisations()\",\n \"def all_subconstituents(self, compute=False):\\n out = {}\\n for i in range(self._.d+1):\\n try:\\n out[i] = self.subconstituent(i, compute=compute)\\n except IndexError:\\n pass\\n return out\",\n \"def display_energy_levels_0d(diagram, num_atoms, atoms, h_poly):\\n h = eval_hamiltonian(num_atoms, h_poly, (1, 1))\\n\\n e, v = eigensystem(h)\\n\\n left = 0\\n bottom = 0\\n right = max([len(row) for row in diagram.split('\\\\n')])\\n top = len(diagram.split('\\\\n'))\\n\\n plot_rows = numpy.ceil(math.sqrt(num_atoms+1))\\n plot_cols = plot_rows\\n\\n for i in range(num_atoms):\\n matplotlib.pyplot.subplot(plot_rows, plot_cols, i+1, axisbg=\\\"#000000\\\")\\n y = [atom[0] for atom in atoms]\\n x = [atom[1] for atom in atoms]\\n c = numpy.abs(v[i]*v[i])\\n\\n matplotlib.pyplot.title('E = %f' % numpy.real(e[i]), fontsize = 10)\\n norm = matplotlib.colors.Normalize(vmin = min(c),\\n vmax = max(0.0001, max(c)))\\n #x = [0,0,1,1]\\n #y = [0,1,0,1]\\n #c = [1,2,3,4]\\n matplotlib.pyplot.hexbin(x, y, C = c,\\n gridsize = (right-left, top-bottom),\\n extent = (left, right, bottom, top),\\n cmap = matplotlib.pyplot.get_cmap(\\\"gray\\\"),\\n norm = norm\\n )\\n\\n matplotlib.pyplot.subplot(plot_rows, plot_cols, num_atoms+1)\\n matplotlib.pyplot.scatter(num_atoms*[0], e, s = 0.1)\",\n \"def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n addedECs = GeneFunctionAddition.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n childGraph = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(addedECs)\\n childGraph.name = 'Added metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return childGraph\",\n \"def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:\\n # get neofunctionalisations \\n neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)\\n \\n # filter core metabolism enzyme graph\\n enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism) \\n neofunctionalisedMetabolism = neofunctionalisedEnzymes.filterGraph(enzymeGraph, minimumEcDifference = None)\\n \\n # colour core metabolism \\n if colour is not False:\\n \\n if colour is True:\\n colourToUse = Export.Colour.GREEN\\n else:\\n colourToUse = colour\\n \\n neofunctionalisedMetabolismOnly = neofunctionalisedMetabolism\\n neofunctionalisedMetabolism = enzymeGraph\\n Export.addColourAttribute(neofunctionalisedMetabolism, colourToUse, nodes = False, edges = neofunctionalisedMetabolismOnly.getEdges())\\n \\n neofunctionalisedMetabolism.name = 'Neofunctionalised core metabolism enzymes ' + ' '.join(self.ncbiNames)\\n \\n return neofunctionalisedMetabolism\",\n \"def vnE(self):\\n return np.array(\\n [x for x in [self.nEx, self.nEy, self.nEz] if x is not None],\\n dtype=int\\n )\",\n \"def get_band_edges():\\n # Vacuum level energy from LOCPOT.\\n locpot = Locpot.from_file('LOCPOT')\\n evac = max(locpot.get_average_along_axis(2))\\n\\n vasprun = Vasprun('vasprun.xml')\\n bs = vasprun.get_band_structure()\\n eigenvals = vasprun.eigenvalues\\n efermi = vasprun.efermi - evac\\n\\n if bs.is_metal():\\n edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,\\n 'efermi': efermi}\\n\\n elif bs.is_spin_polarized:\\n up_cbm = min(\\n [min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])\\n for i in range(len(eigenvals[Spin.up]))]) - evac\\n up_vbm = max(\\n [max([e[0] for e in eigenvals[Spin.up][i] if e[1]])\\n for i in range(len(eigenvals[Spin.up]))]) - evac\\n dn_cbm = min(\\n [min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])\\n for i in range(len(eigenvals[Spin.down]))]) - evac\\n dn_vbm = max(\\n [max([e[0] for e in eigenvals[Spin.down][i] if e[1]])\\n for i in range(len(eigenvals[Spin.down]))]) - evac\\n edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,\\n 'dn_vbm': dn_vbm, 'efermi': efermi}\\n\\n else:\\n cbm = bs.get_cbm()['energy'] - evac\\n vbm = bs.get_vbm()['energy'] - evac\\n edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,\\n 'efermi': efermi}\\n\\n return edges\",\n \"def sage_graph(self):\\n self.fe.load_cache()\\n edges = []\\n is_bipartite = self.variant.is_bipartite()\\n for X in self.L:\\n for Y in self.L:\\n a = self.op_norm(X, Y)\\n if not self.K.is_zero(a):\\n for c in self.K.unit_group:\\n d = a - c\\n if X != Y or c < d or is_bipartite:\\n edges.append(((X, c, False), (Y, d, is_bipartite)))\\n if X == Y and not is_bipartite:\\n break\\n return sage.all.Graph(edges)\",\n \"def _get_full_graph(self):\",\n \"def nE(self):\\n return int(self.vnE.sum())\",\n \"def edges(self):\\n return map(Edge, self._top_exp.edges())\",\n \"def CS2e(Type=\\\"DFA\\\"):\\n CC50, CC51, CC52, CC53 = state('CC50'), state('CC51'), state('CC52'), state('CC53')\\n for i in sigma:\\n CC52.transit[i] = CC52\\n CC53.transit[i] = CC53\\n for i in sigma_1:\\n CC50.transit[i] = CC50\\n for i in sigma_2:\\n CC50.transit[i] = CC53\\n CC51.transit[i] = CC51\\n for i in sigma_cc:\\n CC50.transit[i] = CC51\\n CC51.transit[i] = CC51\\n for i in sigma_ncc:\\n CC51.transit[i] = CC50\\n for i in sigma_B_A:\\n CC51.transit[i] = CC50\\n for i in sigma_e:\\n CC51.transit[i] = CC53\\n for i in sigma_A:\\n CC51.transit[i] = CC52\\n if Type == \\\"pDFA\\\":\\n CC5 = pDFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\\n else:\\n CC5 = DFA('CC5', sigma, [CC50, CC51, CC52, CC53], CC50, [CC52])\\n if (SIZEOF):\\n EM_size[\\\"CS2e\\\"] = asizeof.asizeof(CC5)\\n return CC5\",\n \"def _nelec(self):\\n pd = self.particle_distribution(self._gam * mec2)\\n return pd.to(1/mec2_unit).value\",\n \"def final_components(self):\\n DG = self.digraph()\\n condensation = DG.strongly_connected_components_digraph()\\n return [self.induced_sub_finite_state_machine([self.state(_) for _ in component])\\n for component in condensation.vertices()\\n if condensation.out_degree(component) == 0]\",\n \"def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\\n conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentNeofunctionalised= self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = conservedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = conservedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\\n childGraph = conservedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\\n \\n parentGraph.name = 'Conserved metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Conserved metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def n_cs(self):\\n pass\",\n \"def plot_mcc_tree():\\n t = ete2.Tree(\\\"mcct.nex\\\")\\n ts = ete2.treeview.TreeStyle()\\n ts.show_scale = False\\n ts.show_leaf_name = False\\n ts.show_branch_support = False\\n ts.scale = 500\\n margin = 10\\n ts.margin_top = margin\\n ts.margin_bottom = margin\\n ts.margin_left = margin\\n ts.margin_right = margin\\n\\n germ_style = ete2.NodeStyle()\\n germ_style[\\\"bgcolor\\\"] = \\\"LightSteelBlue\\\"\\n proto_germ = t.get_common_ancestor(\\\"Danish\\\", \\\"Norwegian\\\",\\\"Icelandic\\\",\\\"Swedish\\\", \\\"Dutch\\\", \\\"German\\\", \\\"English\\\")\\n proto_germ.set_style(germ_style)\\n\\n bs_style = ete2.NodeStyle()\\n bs_style[\\\"bgcolor\\\"] = \\\"Moccasin\\\"\\n proto_bs = t.get_common_ancestor(\\\"Bulgarian\\\", \\\"Czech\\\",\\\"Polish\\\",\\\"Russian\\\")\\n proto_bs.set_style(bs_style)\\n\\n ital_style = ete2.NodeStyle()\\n ital_style[\\\"bgcolor\\\"] = \\\"DarkSeaGreen\\\"\\n proto_ital = t.get_common_ancestor(\\\"French\\\", \\\"Romanian\\\", \\\"Italian\\\", \\\"Portuguese\\\", \\\"Spanish\\\")\\n proto_ital.set_style(ital_style)\\n\\n t.render(\\\"mcct.eps\\\", style_func, tree_style=ts, dpi=600, units=\\\"px\\\", w=2250)\",\n \"def coreMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:\\n graph = self.group.collectiveEnzymeGraphByEcMajority(majorityPercentage = majorityPercentageCoreMetabolism, majorityTotal = None, noMultifunctional = excludeMultifunctionalEnzymes)\\n graph.name = 'Core metabolism Enzymes ' + ' '.join(self.ncbiNames)\\n return graph\",\n \"def get_incident_nodes(self):\\n # return the set of incident edges\\n return \\\\\\n {\\n self.first_incident_node,\\n self.second_incident_node\\n }\",\n \"def calEachCrossflowAllAxialNode():\\n AxialNodeno = 14 # axial node number in CFD data\\n Nodes = []\\n base = 'Node'\\n for i in range(0, AxialNodeno):\\n Nodes.append(base+str(i))\\n \\n crossFlow = pd.read_csv('Data_crossflow.csv', index_col = 'Unnamed: 0')\\n lateralFactors = []\\n for node in Nodes:\\n lateralFactors.append(crossFlow[node]/0.8)\\n #need to judge the sign of lateral flow according to CTF rule!!\\n gapsToFlip = [2,4,6,7,9,11,13,14,16,18,20,21] #gaps in y direction\\n gapsToFlipIndex = [x - 1 for x in gapsToFlip]\\n for factors in lateralFactors:\\n for index in gapsToFlipIndex:\\n factors[index] = -factors[index] \\n #note: lateralFactors is a list of list\\n \\n #below calculate factors averaged over all subchannels\\n crossFlowAveFactor = crossFlow.apply(abs).mean(axis = 0)/0.8\\n lateralFactorsAvelist = []\\n for i in range(0,14):\\n base = []\\n for j in range(0,24):\\n base.append(crossFlowAveFactor[i])\\n lateralFactorsAvelist.append(base)\\n \\n \\n for i in range(0, 14):\\n for j in range(0, 24):\\n #note, in the original model there is only one sign for all source\\n #terms in one sub-channel. therefore -- sign(crossFlow.iloc[j,2])\\n lateralFactorsAvelist[i][j] = lateralFactorsAvelist[i][j] *sign(crossFlow.iloc[j,2]) \\n for each in lateralFactorsAvelist:\\n for index in gapsToFlipIndex:\\n each[index] = -each[index] \\n \\n \\n return lateralFactors, lateralFactorsAvelist\",\n \"def components(self):\\n comps = 0\\n unexplored = self.nodes()\\n while unexplored:\\n comps += 1\\n queue = {unexplored.pop()}\\n while queue:\\n new = queue.pop()\\n unexplored.remove(new)\\n for adjacent in new.parents() | new.children():\\n if adjacent in unexplored:\\n queue.add(adjacent)\\n return comps\",\n \"def eci(self):\\n num_ext_terms = len(self._subspace.external_terms) # check for extra terms\\n coefs = self.coefs[:-num_ext_terms] if num_ext_terms else self.coefs[:]\\n eci = coefs.copy()\\n eci = eci / self._subspace.function_total_multiplicities\\n return eci\",\n \"def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:\\n # get neofunctionalisations\\n minimumOrganismsCount = math.ceil(self.organismsCount * (majorityPercentageNeofunctionalisation / 100)) \\n return NeofunctionalisedECs(self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, considerOnlyECs)).getNeofunctionalisationsForFunctionChange(minimumOrganismsCount = minimumOrganismsCount)\",\n \"def get_edges(self):\\n for i in self.gens:\\n if self.active[i]:\\n elist = set()\\n H = (i,) # edge-stabilizing subgroup\\n reps = set(self.word_generator(parabolic=H))\\n reps = self.G.sort_words(reps)\\n for word in reps:\\n v1 = self.G.move(self.vtable, 0, word)\\n v2 = self.G.move(self.vtable, 0, word + (i,))\\n if v1 is not None and v2 is not None:\\n if v1 > v2:\\n v1, v2 = v2, v1\\n if (v1, v2) not in elist:\\n elist.add((v1, v2))\\n\\n self.edge_indices[i] = elist\\n\\n self.num_edges = sum(len(L) for L in self.edge_indices.values())\",\n \"def discreteComplexDecomposeGraph(self,graph):\\n s=[complex(*graph[i]) for i in range(len(graph))]\\n N=len(s)\\n M=self.coefficients_number\\n d=0\\n c=[]\\n for k in range(-M//2,M//2):\\n d+=sum([s[n]*cmath.exp(2j*cmath.pi*k*n/N) for n in range(N)])/N\\n c.append(d)\\n return c\",\n \"def compute_edge_logits(self):\",\n \"def connected_component(self):\\n t1 = datetime.datetime.now()\\n nodes = set(x.hex for x in self.agents)\\n result = []\\n while nodes:\\n node = nodes.pop()\\n # This set will contain the next group of nodes connected to each other.\\n group = {node}\\n # Build a queue with this node in it.\\n queue = [node]\\n # Iterate the queue.\\n # When it's empty, we finished visiting a group of connected nodes.\\n while queue:\\n # Consume the next item from the queue.\\n node = queue.pop(0)\\n # Fetch the neighbors.\\n neighbors = set(x for x in node.fon if x.is_occupied == 1)\\n # Remove the neighbors we already visited.\\n neighbors.difference_update(group)\\n # Remove the remaining nodes from the global set.\\n nodes.difference_update(neighbors)\\n # Add them to the group of connected nodes.\\n group.update(neighbors)\\n # Add them to the queue, so we visit them in the next iterations.\\n queue.extend(neighbors)\\n\\n # Add the group to the list of groups.\\n result.append(len(group))\\n td = datetime.datetime.now() - t1\\n print(\\\"calculated {} connected components in {} seconds\\\".format(len(result),td.total_seconds()))\\n return len(result), np.histogram(result, self.cluster_hist_breaks)[0]\",\n \"def computemodes(self):\\n evals, modes = np.linalg.eig(self.A)\\n return evals, modes\",\n \"def eclogite_massive():\\n\\n rho = 3490.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 238.85; C[0,1] = 82.01; C[0,2] = 81.44; C[0,3] = 0.3; C[0,4] = -0.02; C[0,5] = 0.5\\n C[1,0] = C[0,1]; C[1,1] = 242.12; C[1,2] = 81.11; C[1,3] = -0.66; C[1,4] = 0.33; C[1,5] = 0.12\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 235.57; C[2,3] = -0.28; C[2,4] = 0.22; C[2,5] = 0.31\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 78.72; C[3,4] = 0.27; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 78.37; C[4,5] = 0.25\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 77.91\\n\\n return C, rho\",\n \"def Hasse_diagram_from_incidences(atom_to_coatoms, coatom_to_atoms,\\n face_constructor=None,\\n required_atoms=None,\\n key = None,\\n **kwds):\\n from sage.graphs.all import DiGraph\\n from sage.combinat.posets.posets import FinitePoset\\n def default_face_constructor(atoms, coatoms, **kwds):\\n return (atoms, coatoms)\\n if face_constructor is None:\\n face_constructor = default_face_constructor\\n atom_to_coatoms = [frozenset(atc) for atc in atom_to_coatoms]\\n A = frozenset(range(len(atom_to_coatoms))) # All atoms\\n coatom_to_atoms = [frozenset(cta) for cta in coatom_to_atoms]\\n C = frozenset(range(len(coatom_to_atoms))) # All coatoms\\n # Comments with numbers correspond to steps in Section 2.5 of the article\\n L = DiGraph(1) # 3: initialize L\\n faces = dict()\\n atoms = frozenset()\\n coatoms = C\\n faces[atoms, coatoms] = 0\\n next_index = 1\\n Q = [(atoms, coatoms)] # 4: initialize Q with the empty face\\n while Q: # 5\\n q_atoms, q_coatoms = Q.pop() # 6: remove some q from Q\\n q = faces[q_atoms, q_coatoms]\\n # 7: compute H = {closure(q+atom) : atom not in atoms of q}\\n H = dict()\\n candidates = set(A.difference(q_atoms))\\n for atom in candidates:\\n coatoms = q_coatoms.intersection(atom_to_coatoms[atom])\\n atoms = A\\n for coatom in coatoms:\\n atoms = atoms.intersection(coatom_to_atoms[coatom])\\n H[atom] = (atoms, coatoms)\\n # 8: compute the set G of minimal sets in H\\n minimals = set([])\\n while candidates:\\n candidate = candidates.pop()\\n atoms = H[candidate][0]\\n if atoms.isdisjoint(candidates) and atoms.isdisjoint(minimals):\\n minimals.add(candidate)\\n # Now G == {H[atom] : atom in minimals}\\n for atom in minimals: # 9: for g in G:\\n g_atoms, g_coatoms = H[atom]\\n if not required_atoms is None:\\n if g_atoms.isdisjoint(required_atoms):\\n continue\\n if (g_atoms, g_coatoms) in faces:\\n g = faces[g_atoms, g_coatoms]\\n else: # 11: if g was newly created\\n g = next_index\\n faces[g_atoms, g_coatoms] = g\\n next_index += 1\\n Q.append((g_atoms, g_coatoms)) # 12\\n L.add_edge(q, g) # 14\\n # End of algorithm, now construct a FinitePoset.\\n # In principle, it is recommended to use Poset or in this case perhaps\\n # even LatticePoset, but it seems to take several times more time\\n # than the above computation, makes unnecessary copies, and crashes.\\n # So for now we will mimic the relevant code from Poset.\\n\\n # Enumeration of graph vertices must be a linear extension of the poset\\n new_order = L.topological_sort()\\n # Make sure that coatoms are in the end in proper order\\n tail = [faces[atoms, frozenset([coatom])]\\n for coatom, atoms in enumerate(coatom_to_atoms)]\\n tail.append(faces[A, frozenset()])\\n new_order = [n for n in new_order if n not in tail] + tail\\n # Make sure that atoms are in the beginning in proper order\\n head = [0] # We know that the empty face has index 0\\n head.extend(faces[frozenset([atom]), coatoms] \\n for atom, coatoms in enumerate(atom_to_coatoms)\\n if required_atoms is None or atom in required_atoms)\\n new_order = head + [n for n in new_order if n not in head]\\n # \\\"Invert\\\" this list to a dictionary\\n labels = dict()\\n for new, old in enumerate(new_order):\\n labels[old] = new\\n L.relabel(labels)\\n # Construct the actual poset elements\\n elements = [None] * next_index\\n for face, index in faces.items():\\n atoms, coatoms = face\\n elements[labels[index]] = face_constructor(\\n tuple(sorted(atoms)), tuple(sorted(coatoms)), **kwds)\\n return FinitePoset(L, elements, key = key)\",\n \"def neopentane():\\n coords = [\\n [0.000000, 0.0, 0.0],\\n [0.881905, 0.881905, 0.881905],\\n [-0.881905, -0.881905, 0.881905],\\n [0.881905, -0.881905, -0.881905],\\n [-0.881905, 0.881905, -0.881905],\\n [-1.524077, 0.276170, -1.524077],\\n [1.524077, 1.524077, 0.276170],\\n [1.524077, -0.276170, -1.524077],\\n [1.524077, 0.276170, 1.524077],\\n [-1.524077, -0.276170, 1.524077],\\n [1.524077, -1.524077, -0.276170],\\n [-0.276170, 1.524077, -1.524077],\\n [0.276170, 1.524077, 1.524077],\\n [0.276170, -1.524077, -1.524077],\\n [-0.276170, -1.524077, 1.524077],\\n [-1.524077, 1.524077, -0.276170],\\n [-1.524077, -1.524077, 0.276170],\\n ]\\n coords = [[float(j) / Bohr for j in i] for i in coords]\\n\\n symbols = [\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"C\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n \\\"H\\\",\\n ]\\n\\n atoms = []\\n for i, _ in enumerate(coords):\\n atoms.append(Atom(symbols[i], position=coords[i]))\\n return Molecule(symbols=atoms)\",\n \"def Excentricity_graph(self,ccenter,cradius,icenter):\\n \\n ellip = 0.\\n q = 1. - ellip\\n pa = 0.\\n \\n stamp = self['STAMP'].copy()\\n mask = self['MASK'].copy()\\n sky = self['BACKGROUND']\\n Img = stamp - sky\\n Img[num.where(mask != 0)] = 0.\\n \\n id = self._getGraphId()\\n root = 'Excentricity_%s' % (id,)\\n pngname = root + '.png' ; epsname = root + '.eps'\\n jpgname = root + '.jpg'\\n doStamp(Img,pngname,format='PNG')\\n Convert(pngname,jpgname)\\n \\n Painted = Paint(jpgname)\\n Painted.load()\\n \\n Painted.DrawEllipse(ccenter,cradius,q,pa,color='red',linewidth=2)\\n Painted.DrawCross(ccenter,length=20,color='red')\\n Painted.DrawCross(icenter,length=20,color='green')\\n \\n text = 'E=%.2f o/o' % self['M_E']\\n \\n # Painted.Graffiti(text,commtextpos)\\n \\n Painted.save(jpgname) \\n Painted.release()\\n \\n Convert(jpgname,epsname)\\n os.system('rm %s %s' % (pngname,jpgname))\\n \\n self['figures']['Excentricity'] = epsname\\n self['figcomms']['Excentricity'] = text\",\n \"def graph(self):\\n ...\",\n \"def extract_activities(ncfile):\\n\\n # Get current dimensions.\\n (niterations, nstates) = ncfile.variables['energies'].shape\\n\\n # Extract energies.\\n print \\\"Reading energies...\\\"\\n energies = ncfile.variables['energies']\\n u_kln_replica = zeros([nstates, nstates, niterations], float64)\\n for n in range(niterations):\\n u_kln_replica[:,:,n] = energies[n,:,:]\\n print \\\"Done.\\\"\\n\\n # Deconvolute replicas\\n print \\\"Deconvoluting replicas...\\\"\\n u_kln = zeros([nstates, nstates, niterations], float64)\\n for iteration in range(niterations):\\n state_indices = ncfile.variables['states'][iteration,:]\\n u_kln[state_indices,:,iteration] = energies[iteration,:,:]\\n print \\\"Done.\\\"\\n\\n # Show all self-energies\\n print 'all self-energies for all replicas'\\n for iteration in range(niterations):\\n for replica in range(nstates):\\n state = int(ncfile.variables['states'][iteration,replica])\\n print '%12.1f' % energies[iteration, replica, state],\\n print ''\\n\\n # If no energies are 'nan', we're clean.\\n if not any(isnan(energies[:,:,:])):\\n return\\n\\n # There are some energies that are 'nan', so check if the first iteration has nans in their *own* energies:\\n u_k = diag(energies[0,:,:])\\n if any(isnan(u_k)):\\n print \\\"First iteration has exploded replicas. Check to make sure structures are minimized before dynamics\\\"\\n print \\\"Energies for all replicas after equilibration:\\\"\\n print u_k\\n sys.exit(1)\\n\\n # There are some energies that are 'nan' past the first iteration. Find the first instances for each replica and write PDB files.\\n first_nan_k = zeros([nstates], int32)\\n for iteration in range(niterations):\\n for k in range(nstates):\\n if isnan(energies[iteration,k,k]) and first_nan_k[k]==0:\\n first_nan_k[k] = iteration\\n if not all(first_nan_k == 0):\\n print \\\"Some replicas exploded during the simulation.\\\"\\n print \\\"Iterations where explosions were detected for each replica:\\\"\\n print first_nan_k\\n print \\\"Writing PDB files immediately before explosions were detected...\\\"\\n for replica in range(nstates): \\n if (first_nan_k[replica] > 0):\\n state = ncfile.variables['states'][iteration,replica]\\n iteration = first_nan_k[replica] - 1\\n filename = 'replica-%d-before-explosion.pdb' % replica\\n title = 'replica %d state %d iteration %d' % (replica, state, iteration)\\n write_pdb(atoms, filename, iteration, replica, title, ncfile)\\n filename = 'replica-%d-before-explosion.crd' % replica \\n write_crd(filename, iteration, replica, title, ncfile)\\n sys.exit(1)\\n\\n # There are some energies that are 'nan', but these are energies at foreign lambdas. We'll just have to be careful with MBAR.\\n # Raise a warning.\\n print \\\"WARNING: Some energies at foreign lambdas are 'nan'. This is recoverable.\\\"\\n \\n return\",\n \"def edges(self):\\r\\n return self.__generate_edges()\",\n \"def buildE(self, debug = False):\\n E = np.zeros([len(self.sta), len(self.dist)])\\n i = 0\\n for ikey, state in self.sta.items():\\n j = 0\\n c = state.c\\n for jkey, disturbance in self.dist.items():\\n if debug:\\n print(i, ' ', j)\\n if isinstance(disturbance, InputT) and disturbance in \\\\\\n self.rc.adj[state]:\\n # input is temperature and connected to this state\\n\\n E[i, j] = self.rc.adj[state][disturbance]['H'] / c\\n elif isinstance(disturbance, InputQ) and disturbance in \\\\\\n self.rc.adj[\\n state]: # input is heat flow and connected to state\\n E[i, j] = self.rc.adj[state][disturbance]['gain'] / c\\n j += 1\\n i += 1\\n\\n return E, list(self.sta.keys()), list(self.dist.keys())\",\n \"def num_of_subgraphs(self):\\n \\n G = self.to_undirected_graph()\\n \\n count = G.num_subgraph()\\n \\n print('The number of disconnected components in the graph is ', count)\",\n \"def components_graph(geo, stereo=True):\\n return automol.graph.connected_components(graph(geo, stereo=stereo))\",\n \"def gen_graph(self):\",\n \"def unifiedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph: \\n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is False:\\n graph = parentNeofunctionalised.union(childNeofunctionalised, addCount = False, updateName = False)\\n \\n else:\\n unifiedMetabolismEnzymes = self.unifiedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = True)\\n \\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = unifiedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\",\n \"def get_econs(self):\\n eham = self.beads.vpath*self.nm.omegan2 + self.nm.kin + self.forces.pot\\n eham += self.bias.pot # bias\\n for e in self._elist:\\n eham += e.get()\\n\\n return eham + self.eens\",\n \"def system(Vg, Cs, Cd, Cg, Gs, Gd, num_e):\\n myset = set.SET()\\n # choose between the two lines below for metallic dot or quantum levels\\n myset.add_metallic_dot('dot', num_e, -num_e, 0)\\n #myset.add_quantum_dot('dot', [0, 1, 2, 3], [1, 1, 1, 1])\\n myset.add_lead('source')\\n myset.add_lead('drain')\\n myset.add_gate('gate')\\n #myset.add_link('dl', 'dot', 'source', 5.5e-18, 0.2)\\n #myset.add_link('dl', 'dot', 'drain', 5.5e-18, 0.2)\\n myset.add_link('dl', 'dot', 'drain', Cd,Gd)\\n myset.add_link('dl', 'dot', 'source', Cs, Gs)\\n #myset.add_link('dl', 'dot', 'drain', 1e-19, 10e-8)\\n #myset.add_link('dl', 'dot', 'drain', Cg, Gg)\\n myset.add_link('dg', 'dot', 'gate', Cg)\\n return myset\",\n \"def getSigma(E, opt=\\\"\\\"):\\n # HG resolutions, from the energy unidoc.\\n eRes = {\\n 0 : {\\\"nat\\\": [1.260e-1, 1.790e-2, 2.370e-4], \\\"enr\\\": [1.500e-1, 1.750e-2, 2.820e-4], \\\"both\\\": [1.470e-1, 1.730e-2, 3.000e-4]},\\n 1 : {\\\"nat\\\": [1.470e-1, 1.770e-2, 2.010e-4], \\\"enr\\\": [1.340e-1, 1.750e-2, 2.820e-4], \\\"both\\\": [1.360e-1, 1.740e-2, 2.800e-4]},\\n 2 : {\\\"nat\\\": [1.410e-1, 1.800e-2, 1.680e-4], \\\"enr\\\": [1.420e-1, 1.720e-2, 2.860e-4], \\\"both\\\": [1.430e-1, 1.720e-2, 2.840e-4]},\\n 3 : {\\\"nat\\\": [1.800e-1, 1.820e-2, 2.090e-4], \\\"enr\\\": [1.580e-1, 1.710e-2, 3.090e-4], \\\"both\\\": [1.620e-1, 1.720e-2, 2.970e-4]},\\n 4 : {\\\"nat\\\": [2.140e-1, 1.540e-2, 3.970e-4], \\\"enr\\\": [2.170e-1, 1.490e-2, 3.190e-4], \\\"both\\\": [2.180e-1, 1.500e-2, 3.500e-4]},\\n \\\"5A\\\" : {\\\"nat\\\": [2.248e-1, 1.894e-2, 2.794e-4], \\\"enr\\\": [2.660e-1, 2.215e-2, 2.868e-4], \\\"both\\\": [2.592e-1, 2.057e-2, 3.086e-4]},\\n \\\"5B\\\" : {\\\"nat\\\": [1.650e-1, 1.760e-2, 2.828e-4], \\\"enr\\\": [1.815e-1, 1.705e-2, 3.153e-4], \\\"both\\\": [1.815e-1, 1.690e-2, 3.187e-4]},\\n \\\"5C\\\" : {\\\"nat\\\": [1.565e-1, 1.810e-2, 2.201e-4], \\\"enr\\\": [1.361e-1, 1.740e-2, 2.829e-4], \\\"both\\\": [1.519e-1, 1.718e-2, 2.762e-4]}\\n }\\n\\n if len(dsList)==1:\\n p = eRes[dsList[0]][opt]\\n return np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2)\\n else:\\n # weight the curve by exposure\\n sig, expTot = 0, 0\\n for ds in dsList:\\n if opt==\\\"enr\\\": exp = dsExpo[ds][0]\\n if opt==\\\"nat\\\": exp = dsExpo[ds][1]\\n if opt==\\\"both\\\": exp = dsExpo[ds][0] + dsExpo[ds][1]\\n p = eRes[ds][opt]\\n sig += np.sqrt(p[0]**2 + p[1]**2 * E + p[2]**2 * E**2) * exp\\n expTot += exp\\n sig /= expTot\\n return sig\",\n \"def line_graph_forbidden_subgraphs():\\n from sage.graphs.all import Graph\\n from sage.graphs.generators.basic import ClawGraph\\n graphs = [ClawGraph()]\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3],\\n 4: [2],\\n 5: [3]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3, 4],\\n 1: [2, 3, 4],\\n 3: [4],\\n 2: [5]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3],\\n 4: [2, 3]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3],\\n 4: [2],\\n 5: [3, 4]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3, 4],\\n 1: [2, 3, 4],\\n 3: [4],\\n 5: [2, 0, 1]\\n }))\\n\\n graphs.append(Graph({\\n 5: [0, 1, 2, 3, 4],\\n 0: [1, 4],\\n 2: [1, 3],\\n 3: [4]\\n }))\\n\\n graphs.append(Graph({\\n 1: [0, 2, 3, 4],\\n 3: [0, 4],\\n 2: [4, 5],\\n 4: [5]\\n }))\\n\\n graphs.append(Graph({\\n 0: [1, 2, 3],\\n 1: [2, 3, 4],\\n 2: [3, 4],\\n 3: [4]\\n }))\\n\\n return graphs\",\n \"def connect_cells(dfte,vari):\\n # Create the variabel cell for mother, grand mother and grand grand mother\\n if 'g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\\n if 'g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_parent_cell')\\n if 'g_g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\\n #give unique index to all cells\\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\\n vac=[];sc=[];uid = []\\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\\n for c,idx in enumerate(dfte['cell'].unique()):\\n dau = dfte.loc[dfte['cell']==idx]\\n pc = dau['parent_cell'].iloc[0]\\n mum = dfte.loc[dfte['cell']==pc]\\n gpc = dau['g_parent_cell'].iloc[0]\\n gmum = dfte.loc[dfte['cell']==gpc]\\n ggpc = dau['g_g_parent_cell'].iloc[0]\\n ggmum = dfte.loc[dfte['cell']==ggpc]\\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\\n gggmum = dfte.loc[dfte['cell']==gggpc]\\n fte = lambda x: x[['{}'.format(vari),'uid']].values\\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\\n vac.append(tmp[:,0])\\n uid.append(tmp[:,1])\\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})\",\n \"def getNumNestedCVTerms(self):\\n return _libsbml.CVTerm_getNumNestedCVTerms(self)\",\n \"def cyclic_subgroups(C):\\n ps = C.points()\\n Gs = []\\n \\n for i in ps:\\n P = Elliptic_Point(i[0],i[1],C)\\n Gs.append(cyclic_subgroup(P))\\n\\n return Gs\",\n \"def xsec_CEvNS(E_R, E_nu, N_p, N_n):\\n \\n A = N_p + N_n\\n Z = N_p\\n \\n m_A = A*0.9315 #Mass of target nucleus (in GeV)\\n q = np.sqrt(2.0*E_R*m_A) #Recoil momentum (in MeV)\\n #Note: m_A in GeV, E_R in keV, E_nu in MeV\\n \\n #Calculate SM contribution\\n Qv = (A-Z) - (1.0-4.0*SIN2THETAW)*Z #Coherence factor\\n \\n xsec_SM = (G_FERMI*G_FERMI/(4.0*np.pi))*Qv*Qv*m_A* \\\\\\n (1.0-(q*q)/(4.0*E_nu*E_nu))\\n \\n #Calculate New-Physics correction from Z' coupling\\n #Assume universal coupling to quarks (u and d)\\n #QvNP = 3.0*A*gsq\\n\\n #Factor of 1e6 from (GeV/MeV)^2\\n #G_V = 1 - 1e6*(SQRT2/G_FERMI)*(QvNP/Qv)*1.0/(q*q + m_med*m_med)\\n \\n #Convert from (GeV^-3) to (cm^2/keV)\\n #and multiply by form factor\\n return xsec_SM*1e-6*(1.98e-14)*(1.98e-14)*calcSIFormFactor(E_R, A)\",\n \"def sub_graph_merging(self):\",\n \"def edges(self):\\r\\n return self.capacity.keys()\",\n \"def _get_graph_based_ic_dictionary(self):\\n\\n\\t\\t# TODO find the literature reference or presentation where this equation is from instead of just the presentation.\\n\\n\\t\\t#ic_dict = {}\\n\\t\\t#num_terms_in_ontology = len(self)\\n\\t\\t#for term in self.terms():\\n\\t\\t#\\tdepth = self._depth_dict[term.id]\\n\\t\\t#\\tnum_descendants = len(list(term.subclasses(with_self=False)))\\n\\t\\t#\\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\\n\\t\\t#\\tic_dict[term.id] = ic_value\\n\\t\\t#return(ic_dict)\\n\\n\\n\\t\\t# Getting the information content of each term in the ontology based on graph structure.\\n\\t\\tic_dict = {}\\n\\t\\tnum_terms_in_ontology = len(self)\\n\\t\\tfor term in self.terms():\\n\\t\\t\\tdepth = self._depth_dict[term.id]\\n\\t\\t\\tnum_descendants = len(list(term.subclasses(with_self=False)))\\n\\t\\t\\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\\n\\t\\t\\tic_dict[term.id] = ic_value\\n\\n\\n\\t\\t# Converting to weights based on information content rather than raw value.\\n\\t\\tic_dict_as_weights = {}\\n\\t\\tic_values = ic_dict.values()\\n\\t\\tmin_ic = min(ic_values)\\n\\t\\tmax_ic = max(ic_values)\\n\\t\\tnew_max = 1.00\\n\\t\\tnew_min = 0.00\\n\\t\\tfor k,v in ic_dict.items():\\n\\t\\t\\told_range = max_ic-min_ic\\n\\t\\t\\tnew_range = new_max-new_min\\n\\t\\t\\tnew_value = (((v - min_ic) * new_range) / old_range) + new_min\\n\\t\\t\\tic_dict_as_weights[k] = new_value\\n\\n\\t\\treturn(ic_dict, ic_dict_as_weights)\",\n \"def plot_eigenvector_centrality(net, label, outpath):\\n _, eigenvector_centrality_values = networkit_util.get_eigenvector_centrality(net, label, outpath)\\n unique_value, unique_cnt = np.unique(eigenvector_centrality_values, return_counts=True)\\n unique_cumcnt = np.cumsum(unique_cnt) / sum(unique_cnt)\\n fig = plt.figure()\\n ax = fig.add_subplot(1, 1, 1)\\n ax.plot(unique_value, unique_cumcnt, 'g.')\\n # ax.set_title('Cumulative distribution of eigenvector centrality of nodes')\\n ax.set_xlabel('eigenvector centrality e')\\n ax.set_ylabel('p(x <= e)')\\n plt.savefig(outpath + label + \\\"-eigenvector-distribution.eps\\\")\",\n \"def connected_components(self) -> List[list]:\\n for n in self.dw_graph.get_all_v().values():\\n n.distance=0.0\\n mega_list = []\\n for n in self.dw_graph.get_all_v().values():\\n if n.distance!=-10:\\n mega_list.append(self.connected_component(n.node_id))\\n return mega_list\",\n \"def discreteComplexComposeGraph(self,S,n):\\n N=len(S)\\n M=N//2\\n g=[]\\n d=0\\n for k in range(M):\\n d+=S[k+M]*cmath.exp(2j*cmath.pi*k*n/N)+S[-k+M]*cmath.exp(-2j*cmath.pi*k*n/N)\\n g.append((d.real,d.imag))\\n return g\",\n \"def edges(self):\\n return self.generate_edges()\",\n \"def generate_empirical_nodes(reduced_basis):\\n \\n # Get the index of the 0th empirical node T_0\\n # from the maximum value of the 0th reduced basis.\\n imax = np.argmax(np.abs(reduced_basis[0]))\\n empirical_node_indices = [imax]\\n \\n # Iterate to find the other impirical nodes.\\n for j in range(1, len(reduced_basis)):\\n # The jth empirical node\\n imax = generate_new_empirical_node(reduced_basis, empirical_node_indices)\\n empirical_node_indices.append(imax)\\n \\n return empirical_node_indices\",\n \"def divergedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):\\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)\\n \\n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n childNeofunctionalised = self.childClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\\n \\n if colour is True:\\n parentEdges = parentNeofunctionalised.getEdges()\\n childEdges = childNeofunctionalised.getEdges()\\n \\n graph = divergedMetabolismEnzymes\\n \\n Export.addColourAttribute(graph, colour = Export.Colour.GREEN, nodes = False, edges = parentEdges)\\n Export.addColourAttribute(graph, colour = Export.Colour.YELLOW, nodes = False, edges = childEdges)\\n \\n graph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return graph\\n else:\\n parentGraph = divergedMetabolismEnzymes[0].removeAllEnzymesExcept(parentNeofunctionalised.getEnzymes())\\n childGraph = divergedMetabolismEnzymes[1].removeAllEnzymesExcept(childNeofunctionalised.getEnzymes())\\n \\n parentGraph.name = 'Diverged metabolism neofunctionalised enzymes *' + ' '.join(self.parentNCBInames) + '* -> ' + ' '.join(self.childNCBInames)\\n childGraph.name = 'Diverged metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> *' + ' '.join(self.childNCBInames) + '*'\\n \\n return (parentGraph, childGraph)\",\n \"def coaccessible_components(self):\\n DG = self.digraph().reverse()\\n coaccessible_states = DG.breadth_first_search(\\n [_.label() for _ in self.iter_final_states()])\\n return self.induced_sub_finite_state_machine(\\n [self.state(_) for _ in coaccessible_states])\",\n \"def ec_geometric_series(p0, T, n):\\n if(n<=0):\\n EC=np.zeros(T.shape)\\n return EC\\n else:\\n R, D, L=rdl_decomposition(T)\\n w=np.diagonal(D)\\n L=np.transpose(L)\\n\\n D_sum=np.diag(geometric_series(w, n-1))\\n T_sum=np.dot(np.dot(R, D_sum), np.conjugate(np.transpose(L)))\\n p_sum=np.dot(p0, T_sum)\\n EC=p_sum[:,np.newaxis]*T\\n \\\"\\\"\\\"Truncate imginary part - which is zero, but we want real\\n return values\\\"\\\"\\\"\\n EC=EC.real\\n return EC\",\n \"def epidote():\\n\\n rho = 3465.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 211.5; C[0,1] = 65.6; C[0,2] = 43.2; C[0,3] = 0.; C[0,4] = -6.5; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 239.; C[1,2] = 43.6; C[1,3] = 0.; C[1,4] = -10.4; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 202.1; C[2,3] = 0.; C[2,4] = -20.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 39.1; C[3,4] = 0.; C[3,5] = -2.3\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 43.4; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 79.5\\n\\n return C, rho\",\n \"def get_eulerian_graph_edges(bbox, source):\\n osm_graph = OSMGraph(bbox, source)\\n # input all nodes and get odd nodes, update node attributes\\n odd_nodes = get_odd_nodes(osm_graph.nodes_dict)\\n\\n # initialize all_pairs_list\\n all_pairs_list = []\\n\\n # if there are 6 or fewer odd nodes look for all possible options,\\n # otherwise look for just three basic pairing options\\n\\n if len(odd_nodes) <= 10:\\n print(\\\"ROBUST PAIRING FUNCTION\\\")\\n all_pairs_list = get_list_of_all_pairs_lists(odd_nodes)\\n\\n else:\\n print(\\\"CHEAP PAIRING FUNCTION\\\")\\n all_pairs_list = get_list_of_all_pairs_lists_short(odd_nodes)\\n\\n for item in all_pairs_list:\\n print(\\\"\\\\n\\\\nPair option:\\\", item)\\n print(\\\"Pair option len:\\\", len(item))\\n\\n dict_pairings_lists_lengths = get_dict_pairings_lists_lengths(\\n all_pairs_list, osm_graph\\n )\\n twice_traversals_edges = get_twice_traversals_edges(dict_pairings_lists_lengths)\\n updated_graph_instance = update_twice_traversal_edges(\\n twice_traversals_edges, osm_graph\\n )\\n return updated_graph_instance\",\n \"def cell_edges(self):\\n return self._cell_edges\",\n \"def edges(self):\\n return [(a, b) for a in self._consequences_of\\n for b in self._consequences_of[a]]\",\n \"def _generateEdges(self, separate_frames=False):\\n\\n # 3D edges\\n Xe = []\\n Ye = []\\n Ze = []\\n\\n if not separate_frames:\\n if self.showcuts:\\n self.nxgraph.add_nodes_from(range(len(self.df)))\\n for index, curr in self.df.iterrows():\\n if curr['first']:\\n self._symbol += ['circle']\\n # skip root node\\n if curr['number'] == 1:\\n continue\\n # found first LP solution of a new child node\\n # parent is last LP of parent node\\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\\n else:\\n # found an improving LP solution at the same node as before\\n self._symbol += ['diamond']\\n parent = self.df.iloc[index - 1]\\n\\n Xe += [float(parent['x']), curr['x'], None]\\n Ye += [float(parent['y']), curr['y'], None]\\n Ze += [float(parent['objval']), curr['objval'], None]\\n self.nxgraph.add_edge(parent.name, curr.name)\\n else:\\n self.nxgraph.add_nodes_from(list(self.df['number']))\\n for index, curr in self.df.iterrows():\\n self._symbol += ['circle']\\n if curr['number'] == 1:\\n continue\\n parent = self.df[self.df['number'] == curr['parent']]\\n Xe += [float(parent['x']), curr['x'], None]\\n Ye += [float(parent['y']), curr['y'], None]\\n Ze += [float(parent['objval']), curr['objval'], None]\\n self.nxgraph.add_edge(parent.iloc[0]['number'], curr['number'])\\n\\n else:\\n max_age = self.df['age'].max()\\n for i in range(1, max_age + 1):\\n tmp = self.df[self.df['age'] == i]\\n Xe_ = []\\n Ye_ = []\\n Ze_ = []\\n for index, curr in tmp.iterrows():\\n if curr['first']:\\n self._symbol += ['circle']\\n # skip root node\\n if curr['number'] == 1:\\n continue\\n # found first LP solution of a new child node\\n # parent is last LP of parent node\\n parent = self.df[self.df['number'] == curr['parent']].iloc[-1]\\n else:\\n # found an improving LP solution at the same node as before\\n self._symbol += ['diamond']\\n parent = self.df.iloc[index - 1]\\n\\n Xe_ += [float(parent['x']), curr['x'], None]\\n Ye_ += [float(parent['y']), curr['y'], None]\\n Ze_ += [float(parent['objval']), curr['objval'], None]\\n Xe.append(Xe_)\\n Ye.append(Ye_)\\n Ze.append(Ze_)\\n\\n self.Xe = Xe\\n self.Ye = Ye\\n self.Ze = Ze\",\n \"def life_expectancy_graph(country_name):\\r\\n dat = read_data(pre_file_name())\\r\\n MainDct = dat[0]\\r\\n # MetaDct = dat[1]\\r\\n f_life_expectancy = {}\\r\\n for idx in MainDct:\\r\\n if idx == country_name or MainDct[idx].country_code == country_name:\\r\\n for idx1 in MainDct[idx].values:\\r\\n if idx1[1] != 0:\\r\\n f_life_expectancy[idx1[0]] = idx1[1]\\r\\n return f_life_expectancy\",\n \"def calculate_E(self):\\n \\n E = 0\\n for i in xrange(self.size):\\n Ei = self.h[i]\\n Ei += 0.5*sum((1 if self.spins[j] else -1)*self.J[i,j] for j in self.adjacency[i])\\n if not self.spins[i]:\\n Ei *= -1\\n E += Ei\\n \\n return E\",\n \"def graph_eigenvalues(N, eigenvalues,interval):\\n (a,b) =interval\\n a=abs(a-b)\\n n=[]\\n E_numeric=[]\\n E_anlytic=[]\\n E_anlytic_harm=[]\\n \\n for i in range(N-1):\\n n.append(i)\\n E_numeric.append(eigenvalues[0,i])\\n E_anlytic.append((((math.pi)**2)*(i**2))/(a**2))\\n E_anlytic_harm.append(((1/2)**0.5)*((i)+(1/2)))\\n plt.plot(n,E_numeric,'b')\\n plt.plot(n,E_anlytic,'g')\\n plt.plot(n,E_anlytic_harm,'r')\\n plt.xlabel('n', fontsize=20, color='black')\\n plt.ylabel('E', fontsize=20, color='black')\\n plt.show()\\n return None\",\n \"def eclogite_foliated():\\n\\n rho = 3300.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 203.45; C[0,1] = 67.76; C[0,2] = 64.47; C[0,3] = 0.08; C[0,4] = 1.9; C[0,5] = -0.4\\n C[1,0] = C[0,1]; C[1,1] = 220.58; C[1,2] = 63.65; C[1,3] = 0.46; C[1,4] = 0.59; C[1,5] = 0.06\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 189.75; C[2,3] = 0.13; C[2,4] = 0.95; C[2,5] = -0.2\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 66.32; C[3,4] = -0.27; C[3,5] = 0.73\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.77; C[4,5] = -0.02\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 70.75\\n\\n return C, rho\",\n \"def lostMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)\\n lostECs = GeneFunctionLoss.getECs(parentCoreMetabolism, childCoreMetabolism)\\n \\n parentGraph = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False).keepEnzymesByEC(lostECs)\\n parentGraph.name = 'Lost metabolism neofunctionalised enzymes ' + ' '.join(self.parentNCBInames) + ' -> ' + ' '.join(self.childNCBInames)\\n \\n return parentGraph\",\n \"def get_eddy_instances(parents, root_nodes, gate_dist):\\n\\tsnodes = []\\n\\te_instances = dict()\\n\\tchild_score = dict()\\n\\tfor parent in parents:\\n\\t\\tfor child in parent.children:\\n\\t\\t\\tif is_term(child):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tchild.score = parent.score + max(score(hyp, gate_dist) for hyp in child.tracks())\\n\\t\\t\\tchild_score[child] = (child.score, parent)\\n\\t\\t\\tif child.obj in e_instances:\\n\\t\\t\\t\\te_instances[child.obj].append(child)\\n\\t\\t\\telse:\\n\\t\\t\\t\\te_instances[child.obj] = [child]\\n\\tfor node in root_nodes:\\n\\t\\tnode.score = max(score(hyp, gate_dist) for hyp in node.tracks())\\n\\t\\tchild_score[node] = (node.score, None)\\n\\t\\tif node.obj in e_instances:\\n\\t\\t\\te_instances[node.obj].append(node)\\n\\t\\telse:\\n\\t\\t\\te_instances[node.obj] = [node]\\n\\treturn (e_instances, child_score)\",\n \"def plot_network(genome):\\n g = genome.n\\n # width = g.graph[\\\"size\\\"]\\n # height = g.graph[\\\"size\\\"]\\n\\n # fig = plt.figure(figsize=(width,height))\\n fig = plt.figure()\\n fig.patch.set_facecolor('white')\\n ax = fig.add_subplot(111, aspect='equal')\\n # ax.set_axis_off()\\n\\n # collision_coords = find_collisions(genome)\\n # das_coords = find_das_extended(genome)\\n # slp_coords = find_slp(genome)\\n slp_nodes = find_attacker_path(genome.n)\\n\\n # Plot the parent-child tree\\n for n in g.nodes_iter():\\n if g.node[n][\\\"parent\\\"] is not None:\\n _line(g.node[n][\\\"coord\\\"], g.node[g.node[n][\\\"parent\\\"]][\\\"coord\\\"], zorder=0, color='k')\\n\\n for n in g.nodes_iter():\\n coord = g.node[n][\\\"coord\\\"]\\n shape = _circles\\n colour = 'b'\\n s = 0.4\\n if n in slp_nodes:\\n shape = _hexagons\\n colour = 'y'\\n s = 0.45\\n if n == g.graph[\\\"source\\\"]:\\n shape = _squares\\n colour = 'g'\\n if n == g.graph[\\\"sink\\\"]:\\n shape = _octogons\\n colour = 'k'\\n s = 0.45\\n shape(coord[0], coord[1], s, fc=\\\"white\\\", ec=colour)\\n if(len(str(g.node[n][\\\"slot\\\"])) == 1):\\n ax.text(coord[0]-0.15, coord[1]+0.15, str(g.node[n][\\\"slot\\\"]))\\n elif(len(str(g.node[n][\\\"slot\\\"])) == 2):\\n ax.text(coord[0]-0.25, coord[1]+0.15, str(g.node[n][\\\"slot\\\"]))\\n elif(len(str(g.node[n][\\\"slot\\\"])) == 3):\\n ax.text(coord[0]-0.4, coord[1]+0.15, str(g.node[n][\\\"slot\\\"]))\\n else:\\n ax.text(coord[0]-0.5, coord[1]+0.15, str(g.node[n][\\\"slot\\\"]))\\n\\n\\n plt.gca().invert_yaxis()\\n fig.show()\",\n \"def get_34cohom_dim(v,l, even_e):\\n op1 = OrdinaryGraphComplex.ContractEdgesGO.generate_operator(v,l, even_e)\\n op2 = OrdinaryGraphComplex.ContractEdgesGO.generate_operator(v+1,l, even_e)\\n fullvs = op1.domain\\n fullvs2 = op2.domain\\n \\n vs34 = Valence34Mask(fullvs)\\n vs342 = Valence34Mask(fullvs2)\\n\\n D34rank = 0\\n if op1.is_valid():\\n D = op1.get_matrix()\\n # P34 = vs34.get_P34()\\n # D34 = D * P34 \\n i34 = vs34.get_34index_list()\\n D34 = D[:, i34]\\n D34rank = D34.rank()\\n\\n DD34rank = 0\\n DD5rank = 0\\n if op2.is_valid():\\n DD = op2.get_matrix()\\n # PP34 = vs342.get_P34()\\n ii34 = vs342.get_34index_list()\\n # DD34 = DD * PP34 \\n DD34 = DD[:,ii34]\\n DD34rank = DD34.rank()\\n\\n # P5 = vs34.get_P5()\\n # DD5 = P5 * DD * PP34\\n i5 = vs34.get_5index_list()\\n DD5 = DD[i5, ii34]\\n DD5rank = DD5.rank()\\n\\n\\n return vs34.get_34dimension() - D34rank -DD34rank + DD5rank\",\n \"def eccentricity(self):\\n new_data = self._data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\\n 'pl_masse', 'pl_orbeccen',\\n 'pl_radj', 'pl_dens', 'st_teff',\\n 'st_mass', 'st_rad']]\\n new_data = new_data.dropna()\\n\\n features = new_data[['pl_pnum', 'pl_orbper', 'pl_orbsmax',\\n 'pl_masse',\\n 'pl_radj', 'pl_dens', 'st_teff',\\n 'st_mass', 'st_rad']]\\n labels = new_data['pl_orbeccen']\\n\\n features_train, features_test, labels_train, labels_test = \\\\\\n train_test_split(features, labels, test_size=0.2)\\n\\n # Create an untrained model\\n model = DecisionTreeRegressor()\\n\\n # Train it on the **training set**\\n model.fit(features_train, labels_train)\\n\\n # Compute test accuracy\\n test_predictions = model.predict(features_test)\\n test_acc = mean_absolute_error(labels_test, test_predictions)\\n test_acc_r2 = r2_score(labels_test, test_predictions)\\n\\n # Plot ML vs Actual\\n fig, [ax1, ax2] = plt.subplots(2, figsize=(15, 12))\\n\\n sns.distplot(test_predictions, kde=False, ax=ax1)\\n sns.distplot(labels_test, kde=False, ax=ax2)\\n\\n ax1.set_title('Distribution of Predicted Eccentricities of Orbits')\\n ax1.set_xlabel('Eccentricity of Orbit')\\n ax1.set_ylabel('Number of Planets')\\n\\n ax2.set_title('Distribution of Actual Eccentricities of Orbits')\\n ax2.set_xlabel('Eccentricity of Orbit')\\n ax2.set_ylabel('Number of Planets')\\n\\n plt.savefig('figures/ML_Eccentricity.png', bbox_inches='tight')\\n\\n return (test_acc, test_acc_r2)\",\n \"def create_cpp_edgelist(euler_circuit):\\n cpp_edgelist = {}\\n\\n for i, e in enumerate(euler_circuit):\\n edge = frozenset([e[0], e[1]])\\n\\n if edge in cpp_edgelist:\\n cpp_edgelist[edge][2]['sequence'] += ', ' + str(i)\\n cpp_edgelist[edge][2]['visits'] += 1\\n\\n else:\\n cpp_edgelist[edge] = e\\n cpp_edgelist[edge][2]['sequence'] = str(i)\\n cpp_edgelist[edge][2]['visits'] = 1\\n\\n return list(cpp_edgelist.values())\",\n \"def n_e(self) -> u.m**-3:\\n return np.sum(self._n_elem * self.ionic_fractions * self.charge_numbers)\",\n \"def interacting_conductivity(self,\\n sigma=0.04,\\n freq_range=(0.0, 6.0, 1000),\\n imag_dielectric=False,\\n broadening='lorentz'):\\n cell_area = np.abs(np.cross(self.a1, self.a2))*len(self.k_grid)\\n broad_fnc = get_broadening_function(broadening, sigma)\\n\\n frequency_grid = np.linspace(*freq_range)\\n output_grid = np.zeros(len(frequency_grid))\\n\\n energy_power = 1 + int(imag_dielectric)\\n prefactor = e_charge_2_over_epsilon0 if imag_dielectric else 1\\n prefactor = prefactor/cell_area\\n\\n # Assuming x polarisation for now\\n position_dipole_matrix = self.construct_position_dipole_matrix()\\n\\n n_shift = self.n_spins*self.n_orbs\\n cutoff_band_min_maxes = self.exciton_obj.get_cutoff_band_min_maxes()\\n # Construct vector of velocity matrix elements to dot with the\\n # bse eigenvector.\\n velocity_element_lists = []\\n for s0 in range(self.n_spins):\\n velocity_elements = []\\n for idx_1, kpt in enumerate(self.k_grid):\\n velocity_matrix = self.load_velocity_matrix(idx_1)\\n bands = self.exciton_obj.get_number_conduction_valence_bands(\\n idx_1, s0\\n )\\n v_num, c_num = bands\\n v_min = 0\\n if self.exciton_obj.energy_cutoff is not None:\\n v_min = int(cutoff_band_min_maxes[s0][idx_1][0])\\n c_num = int(cutoff_band_min_maxes[s0][idx_1][1]) + 1\\n v_num = v_num - v_min\\n j1 = n_shift*idx_1 + s0*self.n_orbs\\n j2 = j1 + n_shift - (1 - s0)*self.n_orbs*(self.n_spins - 1)\\n eigvecs = np.array(\\n self.file_storage['eigensystem']['eigenvectors'][j1:j2, :]\\n )\\n if self.use_dipole_term:\\n eigvals = np.array(\\n self.file_storage['eigensystem']['eigenvalues'][idx_1]\\n )\\n else:\\n eigvals = None\\n for c, v in product(range(c_num), range(v_num)):\\n cb_vector = eigvecs[:, v_min + v_num + c]\\n vb_vector = eigvecs[:, v_min + v]\\n\\n matrix_elem = velocity_matrix_element(cb_vector,\\n vb_vector,\\n velocity_matrix)\\n if self.use_dipole_term:\\n cb_energy = eigvals[v_min + v_num + c]\\n vb_energy = eigvals[v_min + v]\\n position_dipole_term = get_position_dipole_element(\\n vb_vector,\\n cb_vector,\\n vb_energy,\\n cb_energy,\\n position_dipole_matrix\\n )\\n matrix_elem = matrix_elem + position_dipole_term\\n velocity_elements.append(matrix_elem)\\n velocity_elements = np.array(velocity_elements, dtype=complex)\\n velocity_element_lists.append(velocity_elements)\\n\\n bse_eigsys = self.exciton_obj.get_bse_eigensystem_direct(solve=True)\\n for s0 in range(self.n_spins):\\n for idx_1 in range(len(bse_eigsys[s0][0])):\\n bse_vector = bse_eigsys[s0][1][:, idx_1]\\n bse_value = bse_eigsys[s0][0][idx_1]\\n energy_denominator = bse_value**energy_power\\n interact_elem = np.dot(bse_vector, velocity_element_lists[s0])\\n for idx_2, omega in enumerate(frequency_grid):\\n smearing = broad_fnc(bse_value, omega)\\n main_term = np.abs(interact_elem)**2/energy_denominator\\n output = prefactor*main_term*smearing\\n output_grid[idx_2] += output\\n\\n return frequency_grid, output_grid\",\n \"def _compute_bare_spectrum_constant(self):\\n eigendata = []\\n for subsys in self._hilbertspace:\\n if subsys not in self.subsys_update_list:\\n evals_count = subsys.truncated_dim\\n eigendata.append(subsys.eigensys(evals_count=evals_count))\\n else:\\n eigendata.append(None)\\n return eigendata\",\n \"def _sigma_ee_nonrel(self,gam,eps):\\n s0 = 4 * r0**2 * alpha / (15 * eps)\\n x = 4 * eps / (gam**2 - 1)\\n sigma_nonrel = s0 * self._F(x,gam)\\n sigma_nonrel[np.where(eps >= 0.25*(gam**2 - 1.))] = 0.0\\n sigma_nonrel[np.where(gam*np.ones_like(eps) < 1.0)] = 0.0\\n return sigma_nonrel / mec2_unit\",\n \"def GetCommonComponents(self):\\n nr = 0\\n comp = []\\n for i in range(len(self.nodes)):\\n comp.append(-1)\\n for n in self.nodes:\\n if comp[n.index-1] == -1:\\n nr += 1\\n comp[n.index-1] = nr\\n self.Components_R(nr, n, comp)\\n return self.GenerateCommonComponents(comp)\",\n \"def test_5():\\n h = iotbx.pdb.input(source_info=None, lines=test_pdb_5).construct_hierarchy()\\n asc = h.atom_selection_cache()\\n ncs_inp = iotbx.ncs.input(\\n hierarchy=h,\\n params=ncs_pars.ncs_search)\\n ncs_groups = ncs_inp.get_ncs_restraints_group_list()\\n assert len(ncs_groups) == 1\\n # group 1\\n assert ncs_groups[0].master_iselection.all_eq(\\n asc.selection(string = \\\"chain A\\\").iselection())\\n g1_c = ncs_groups[0].copies\\n assert len(g1_c)==1\\n assert g1_c[0].iselection.all_eq(\\n asc.selection(string = \\\"chain B\\\").iselection())\",\n \"def TNG_net(self): \\n \\n import h5py as h5\\n filename = localpath+'input/yields/TNG/SNII.hdf5'\\n # Read H5 file\\n f = h5.File(filename, \\\"r\\\")\\n \\n # Define element indexing\\t\\t\\t\\n indexing = {}\\n indexing['H'] = 'Hydrogen'\\n indexing['He'] = 'Helium'\\n indexing['C'] = 'Carbon'\\n indexing['N']= 'Nitrogen'\\n indexing['O'] = 'Oxygen'\\n indexing['Ne'] = 'Neon'\\n indexing['Mg'] = 'Magnesium'\\n indexing['Si'] = 'Silicon'\\n indexing['S'] = 'Sulphur' # Not used by TNG simulation\\n indexing['Ca'] = 'Calcium' # Not used by TNG simulation\\n indexing['Fe'] = 'Iron'\\n \\n self.elements = list(indexing.keys())\\n \\n self.table = {}\\n \\n # Define masses / metallicities\\n self.metallicities = list(f['Metallicities'].value)\\n self.masses = f['Masses'].value\\n\\n \\n for z_index,z in enumerate(self.metallicities):\\n \\n yield_subtable = {}\\n \\n z_name = f['Yield_names'].value[z_index].decode('utf-8')\\n z_data = f['Yields/'+z_name+'/Yield']\\n \\n ejecta_mass = f['Yields/'+z_name+'/Ejected_mass'].value\\n \\n yield_subtable['Mass'] = self.masses\\n remnants = self.masses-ejecta_mass\\n yield_subtable['mass_in_remnants'] = np.divide(remnants,self.masses)\\n for el in list(indexing.keys()):\\n yield_subtable[el] = np.zeros(len(self.masses))\\n \\n summed_yields = np.zeros(len(self.masses))\\n \\n for m_index,mass in enumerate(self.masses):\\n for el_index,el in enumerate(self.elements):\\n el_yield_fraction = z_data[el_index][m_index]/mass #(mass-remnants[m_index]) # Find fraction of ejecta per element\\n yield_subtable[el][m_index] = el_yield_fraction\\t\\t\\t\\t\\t\\n summed_yields[m_index]+=el_yield_fraction # Compute total yield\\n \\n yield_subtable['unprocessed_mass_in_winds'] = 1.-summed_yields-yield_subtable['mass_in_remnants']\\n \\n # Restructure table\\n all_keys = ['Mass','mass_in_remnants','unprocessed_mass_in_winds']+self.elements\\n \\n list_of_arrays = [yield_subtable[key] for key in all_keys]\\n restructure_subtable = np.core.records.fromarrays(list_of_arrays,names=all_keys)\\n \\n self.table[z] = restructure_subtable\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6325263","0.6321526","0.62352514","0.6154268","0.6030487","0.5650482","0.5579346","0.55359584","0.55139494","0.5471832","0.5470082","0.54345083","0.54121524","0.53877515","0.53301024","0.53161114","0.5314621","0.5297274","0.5284816","0.5276731","0.526976","0.5254203","0.5225095","0.5213531","0.5208045","0.5200646","0.51907206","0.5168163","0.5167861","0.51625115","0.5161207","0.5160836","0.51461244","0.5134645","0.5132685","0.51239675","0.5101673","0.50964504","0.5093826","0.5085103","0.5082036","0.50802094","0.50715524","0.5067297","0.5067148","0.5062024","0.505403","0.50535107","0.50509155","0.50440043","0.5036664","0.50330645","0.50328326","0.5032326","0.50279665","0.50272596","0.5027071","0.5024442","0.5022611","0.50153494","0.5014804","0.5008047","0.5005861","0.50052553","0.5003085","0.50012183","0.49974728","0.49940726","0.49909168","0.49876502","0.49770096","0.4973105","0.49727544","0.49704826","0.49650455","0.4961688","0.49605003","0.4954616","0.49494472","0.49377862","0.49376482","0.49341202","0.4932122","0.49311438","0.4927528","0.4926875","0.49209356","0.49175853","0.49133417","0.4912572","0.4906751","0.49057394","0.49056867","0.4901732","0.49015224","0.48993304","0.4895365","0.48901865","0.48893288","0.4885905"],"string":"[\n \"0.6325263\",\n \"0.6321526\",\n \"0.62352514\",\n \"0.6154268\",\n \"0.6030487\",\n \"0.5650482\",\n \"0.5579346\",\n \"0.55359584\",\n \"0.55139494\",\n \"0.5471832\",\n \"0.5470082\",\n \"0.54345083\",\n \"0.54121524\",\n \"0.53877515\",\n \"0.53301024\",\n \"0.53161114\",\n \"0.5314621\",\n \"0.5297274\",\n \"0.5284816\",\n \"0.5276731\",\n \"0.526976\",\n \"0.5254203\",\n \"0.5225095\",\n \"0.5213531\",\n \"0.5208045\",\n \"0.5200646\",\n \"0.51907206\",\n \"0.5168163\",\n \"0.5167861\",\n \"0.51625115\",\n \"0.5161207\",\n \"0.5160836\",\n \"0.51461244\",\n \"0.5134645\",\n \"0.5132685\",\n \"0.51239675\",\n \"0.5101673\",\n \"0.50964504\",\n \"0.5093826\",\n \"0.5085103\",\n \"0.5082036\",\n \"0.50802094\",\n \"0.50715524\",\n \"0.5067297\",\n \"0.5067148\",\n \"0.5062024\",\n \"0.505403\",\n \"0.50535107\",\n \"0.50509155\",\n \"0.50440043\",\n \"0.5036664\",\n \"0.50330645\",\n \"0.50328326\",\n \"0.5032326\",\n \"0.50279665\",\n \"0.50272596\",\n \"0.5027071\",\n \"0.5024442\",\n \"0.5022611\",\n \"0.50153494\",\n \"0.5014804\",\n \"0.5008047\",\n \"0.5005861\",\n \"0.50052553\",\n \"0.5003085\",\n \"0.50012183\",\n \"0.49974728\",\n \"0.49940726\",\n \"0.49909168\",\n \"0.49876502\",\n \"0.49770096\",\n \"0.4973105\",\n \"0.49727544\",\n \"0.49704826\",\n \"0.49650455\",\n \"0.4961688\",\n \"0.49605003\",\n \"0.4954616\",\n \"0.49494472\",\n \"0.49377862\",\n \"0.49376482\",\n \"0.49341202\",\n \"0.4932122\",\n \"0.49311438\",\n \"0.4927528\",\n \"0.4926875\",\n \"0.49209356\",\n \"0.49175853\",\n \"0.49133417\",\n \"0.4912572\",\n \"0.4906751\",\n \"0.49057394\",\n \"0.49056867\",\n \"0.4901732\",\n \"0.49015224\",\n \"0.48993304\",\n \"0.4895365\",\n \"0.48901865\",\n \"0.48893288\",\n \"0.4885905\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6479725"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17444,"cells":{"query":{"kind":"string","value":"Two clades in NCBI taxonomy, 'child' is assumed younger and must be nested somewhere inside 'parent'."},"document":{"kind":"string","value":"def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified):\n # read first NCBI name from Clade object, if necessary\n if isinstance(parent, Clade):\n parentNCBIname = parent.ncbiNames[0]\n elif not isinstance(parent, str):\n # must be iterable, else fail\n parentNCBIname = parent[0]\n \n if isinstance(child, Clade):\n childNCBIname = child.ncbiNames[0]\n elif not isinstance(child, str):\n # must be iterable, else fail\n childNCBIname = child[0]\n \n # check if child is really a child of parent\n taxonomy = NCBI.getTaxonomy()\n parentNode = taxonomy.searchNodesByPath(parentNCBIname, exceptPaths=('unclassified' if excludeUnclassified else None))\n if parentNode is None or len(parentNode) == 0:\n raise ValueError(\"No clade of this path found: \" + parentNCBIname)\n else: # only consider first element\n parentNode = parentNode[0]\n \n childNode = taxonomy.searchNodesByPath(childNCBIname, exceptPaths=('unclassified' if excludeUnclassified else None))\n if childNode is None or len(childNode) == 0:\n raise ValueError(\"No clade of this path found: \" + childNCBIname)\n else: # only consider first element\n childNode = childNode[0]\n \n foundParent = False\n for ancestor in childNode.ancestors:\n if Taxonomy.nodePath2String(ancestor) == Taxonomy.nodePath2String(parentNode):\n foundParent = True\n break\n \n if foundParent == False:\n raise ValueError(\"Child is not a descendant of parent.\")\n \n super().__init__(parent, child, excludeUnclassified)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified, oneOrganismPerSpecies = defaultOneOrganismPerSpecies):\n # read NCBI names from Clade object, if necessary\n if isinstance(parent, Clade):\n self.parentClade = parent\n else:\n self.parentClade = Clade(parent, excludeUnclassified, oneOrganismPerSpecies=oneOrganismPerSpecies)\n \n if isinstance(child, Clade):\n self.childClade = child\n else:\n self.childClade = Clade(child, excludeUnclassified, oneOrganismPerSpecies=oneOrganismPerSpecies)","def children(self, taxon, taxonomy):\n\n c = set()\n for taxon_id, taxa in taxonomy.items():\n if taxon in taxa:\n\n if taxon.startswith('s__'):\n c.add(taxon_id)\n else:\n taxon_index = taxa.index(taxon)\n for child in taxa[taxon_index + 1:]:\n if len(child) > 3: # not just an empty prefix\n c.add(child)\n\n return c","def add_hierarchy(self, parent, edge, child): # XXX DEPRECATED\n if type(parent) != rdflib.URIRef:\n parent = self.check_thing(parent)\n\n if type(edge) != rdflib.URIRef:\n edge = self.check_thing(edge)\n\n if type(child) != infixowl.Class:\n if type(child) != rdflib.URIRef:\n child = self.check_thing(child)\n child = infixowl.Class(child, graph=self.g)\n\n restriction = infixowl.Restriction(edge, graph=self.g, someValuesFrom=parent)\n child.subClassOf = [restriction] + [c for c in child.subClassOf]","def test_make_taxonomy(self):\n basic_test_runner(self, 'taxonomy')","def add_relatives(self, child2parent, idx2word):\n for child, parent in child2parent.items():\n if parent not in (0, -1):\n parent_word = idx2word[parent]\n parent_word.add_child(child)\n child.parent = parent_word","def is_allowed_to_have_child_terms(self):\n return self._is_allowed_to_have_child_terms","def _is_child(self, parent, child): # type: (str, str) -> bool\n return child != parent and child.startswith(parent + \".\")","def is_allowed_to_have_child_terms(self, is_allowed_to_have_child_terms):\n self._is_allowed_to_have_child_terms = is_allowed_to_have_child_terms","def Children(self) -> _n_1_t_2:","def findDiscripancies(taxonomy):\n i = 0\n for entry in taxonomy:\n if entry['parentName'] != None:\n print entry['nodeName']\n if entry['nodeName'].lower() == entry['parentName'].lower():\n i += 1\n print \"No of same nodes = {} \" .format(i)","def taxon_children(self, taxonomy):\n\n taxon_children = defaultdict(set)\n for taxon_id, taxa in taxonomy.items():\n for i, taxon in enumerate(taxa):\n if len(taxon) == 3:\n continue # just rank prefix\n\n if len(taxa) > i + 1 and len(taxa[i + 1]) != 3:\n taxon_children[taxon].add(taxa[i + 1])\n\n if len(taxa) > self.rank_index['s__']:\n taxon = taxa[self.rank_index['s__']]\n if taxon != 's__':\n taxon_children[taxon].add(taxon_id)\n\n return taxon_children","def test_process_label_in_node(self):\n tree = Node(children=[\n Node(\"Defining secret phrase.\", label=['AB', 'a']),\n Node(\"Has secret phrase. Then some other content\", \n label=['AB', 'b'])\n ], label=['AB'])\n t = Terms(tree)\n t.scoped_terms = {\n ('AB',): [Ref(\"secret phrase\", \"AB-a\", (9,22))]\n }\n # Term is defined in the first child\n self.assertEqual([], t.process(tree.children[0]))\n self.assertEqual(1, len(t.process(tree.children[1])))","def cu_for_new_child(self,instance,undo=True):\n\t\tself.utility.increment_counts(instance)\n\t\tself.create_new_child(instance)\n\t\tcu = self.utility.category_utility()\n\t\tif undo:\n\t\t\tself.tree.children.pop()\n\t\t\tself.utility.decrement_counts(instance)\n\t\treturn cu","def gen_child(self, g, ng, child):\n with About(child.debug, self.relation):\n self.remap_node((g, child), g, child, ng, ng.apply())","def test_field_resolution_multiple_inheritance_with_child_field_defined(self):\n child_field = ChildWithMultipleParentsAndTheSameField()._get_fields()[\"name\"]\n self.assertEqual(child_field.default, \"C\")","def is_child_of(self, *args):\n return _ida_hexrays.cexpr_t_is_child_of(self, *args)","def __post_init__(self) -> None:\n arity = self.root.arity\n length = len(self.children)\n if arity != length:\n raise ValueError(\n 'Incorrect number of child terms: '\n f'Expected {arity}, found {length}'\n )","def _del_node_two_children(self, parent, node):\n succ = self._get_successor(node)\n self.delete(succ._data)\n succ._rkid = node._rkid\n succ._lkid = node._lkid\n if node._rkid:\n node._rkid._parent = succ\n if node._lkid:\n node._lkid._parent = succ\n if node is not self._root:\n if parent._rkid is node:\n parent._rkid = succ\n succ._parent = parent\n else:\n parent._lkid = succ\n succ._parent = parent\n else:\n self._root = succ\n succ._parent = None","def search_category(self):\n return _(self.child_class)","def test_grandchildren():\n\n # note c.upto(\"status\").desired.grandchildren\n # this is the same as *c.upto(\"status\").desired in python3.5+\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.upto(\"status\").desired.grandchildren))\n assert \"type\" in res\n assert \"reason\" in res\n assert \"version\" in res\n assert \"image\" in res\n assert \"force\" in res","def get_code_per_child(self, obj, child):\n return []","def set_child(self,b):\n if b.isChecked() == True:\n self.mother = False\n self.child = True\n else:\n self.mother = True\n self.child = False","def get_taxonomy(): # noqa: E501\n return 'do some magic!'","def test_environmentReverseInheritance(self):\n # In the child only\n node = create_node(\"somewhere\", \"myservice\", \"parent:child\")\n disco = create_disco()\n disco.onMessage(None, NodeActive(node))\n # Parent can't find it\n self.assertEqual(resolve(disco, \"myservice\", \"1.0\", \"parent\"), None)","def children(self): # noqa: ANN201","def __init__(self, child_type = None):\r\n super().__init__()\r\n self.__child_dict = collections.OrderedDict()\r\n self.__child_type = child_type\r\n self.__mykeys = ()\r\n self.__parent = None","def create_child(self, value, fun=None):\n child = TaxonomyTree(value, self.weights, fun)\n self.children[value] = child\n child.parent = self\n child.level = self.level + 1\n child.__root = self.__root\n self.__root.__fast_find.append((child.fun, child))\n return child","def cladistic(tree, taxa):\n tips = []\n taxa = set(taxa)\n for tip in tree.tips():\n if tip.name in taxa:\n tips.append(tip)\n n = len(taxa)\n if len(tips) < n:\n raise ValueError('Taxa not found in the tree.')\n return ('uni' if n == 1 else\n ('mono' if len(tree.lca(tips).subset()) == n else\n 'poly'))","def testMotherChild(self):\n attr = self.session.create_visit_attr()\n\n self.util.stringTypeTest(self, attr, \"mother_child\")\n\n self.util.stringPropertyTest(self, attr, \"mother_child\")","def childNCBInames(self):\n return self.childClade.ncbiNames","def test_make_taxa_long(self):\n basic_test_runner(self, 'taxonomy', rank='all')","def __init__(self, l_child, r_child):\n self.l_child = l_child\n self.r_child = r_child","def is_cousin(parent_db, A, B):\n parent_dict = {}\n for item in parent_db:\n if item[0] in parent_dict: #If parent is already in the dictionary, add this child to value (set of children)\n parent_dict[item[0]].add(item[1])\n else:\n parent_dict[item[0]] = {item[1]}\n\n child_dict = {}\n for item in parent_db:\n if item[1] in child_dict: #If child is already in the dictionary, add this parent to value (set of parents)\n child_dict[item[1]].add(item[0])\n else:\n child_dict[item[1]] = {item[0]}\n\n if A==B:\n return None\n\n for parent in parent_dict:\n if A in parent_dict[parent] and B in parent_dict[parent]: #Checking if they share the same parent\n return None\n\n grandparents_A = set()\n for parent in child_dict[A]: #Iterating through parents of A\n for grandparent in child_dict[parent]: #Iterating through parents of parents of A (grandparents of A)\n grandparents_A.add(grandparent)\n\n for parent in child_dict[B]: #Iterating through parents of B\n for grandparent in child_dict[parent]: #Iterating through parents of parents of B (grandparents of B)\n if grandparent in grandparents_A:\n return grandparent\n\n return None","def parse_taxonomy( seq_id, lineage, key_dictionary ):\n\tif seq_id in sti_dict:\n\t\ttax_id = sti_dict[ seq_id ]\n\t\ttax_names = [ tax_id ] #list of taxon names\n\telse:\n\t\ttax_id = str( seq_id )\n\t\ttax_names = [ tax_id ] #list of taxon names\n\ttax_numbers = [ seq_id ]\n\tis_A_list = [] #store is_A relationships\n\n\twhile lineage != '1': #forces traversal through the tri file until we get to the root of taxonomy\n\t\t#print lineage\n\t\tif lineage == '0': #need this to process the root in the tri file. \n\t\t\tbreak\n\t\tis_A_list = [lineage] + is_A_list\n\t\ttax_numbers = [lineage] + tax_numbers\n\t\tif lineage in sti_dict: #we have the next taxonomic representative in the sti file\n\t\t\ttax_id = sti_dict[ lineage ]\n\t\t\ttax_names = [tax_id] + tax_names #append tax_id to front of list\n\t\telse: #the taxon does not have a sequence representative. \n\t\t\ttax_id = str( lineage ) \n\t\t\ttax_names = [tax_id] + tax_names\n\t\t#now process to next lineage\n\t\tlineage = tri_dict[ lineage ] \n\n\n\ttax_names = ['root'] + tax_names #append tax_id to front of list\n\ttax_numbers = [lineage] + tax_numbers\n\tis_A_list = ['0'] + [lineage] + is_A_list\n\n\t#now append all of these reuslts to the final dictionary, which will be keyed \n\t#off of the tax_numbers list (unique IDs for each taxonomic level.\n\n\tfor i in xrange( len( tax_numbers ) ):\n\t\tid = tax_numbers[i]\n\t\tif id in key_dictionary:\n\t\t\tpass\n\t\telse:\n\t\t\tparent = is_A_list[i]\n\t\t\tlevel = i #taxonomic level (how far down in levels are we?)\n\t\t\tnames = process_names( tax_names[:i+1] )\n\t\t\tkey_dictionary[ id ] = [ parent, level, names ]\n\n\treturn( key_dictionary )","def getChild(self, child, request):\n request.postpath.insert(0, request.prepath.pop())\n return self.leaf","def fix(child, roots):\n link, root_url = child\n root = get_root(root_url)\n if len(roots[roots.root == root].children > 0) == 1 and (roots[roots.root == root].children > 0).values[0]:\n check = fix_url(link, root_url)\n if check != '' and check is not None:\n return (check,\n roots.loc[roots[roots.root == root].index].root.values[0],\n roots.loc[roots[roots.root == root].index].category.values[0])\n return","def __init__(self, label=\"\"):\n super(Tree, self).__init__(Tree) \n self.mother = None\n self.name = label","def __init__(self, concept_ref='', is_parent=False):\n self.concept_ref = concept_ref\n self.is_parent = is_parent","def taxonomic_consistency(self, taxonomy, report_errors=True):\n\n expected_parent = {}\n for genome_id, taxa in taxonomy.items():\n if taxa[0] == 'd__Viruses' or '[P]' in taxa[0]:\n # *** This is a HACK. It would be far better to enforce\n # a taxonomically consistent taxonomy, but\n # the viral taxonomy at IMG is currently not consistent\n continue\n\n for r in range(1, len(taxa)):\n if len(taxa[r]) == 3:\n break\n\n if taxa[r] in expected_parent:\n if report_errors:\n if taxa[r - 1] != expected_parent[taxa[r]]:\n self.logger.warning('Provided taxonomy is not taxonomically consistent.')\n self.logger.warning(\n 'Genome %s indicates the parent of %s is %s.' % (genome_id, taxa[r], taxa[r - 1]))\n self.logger.warning(\n 'The parent of this taxa was previously indicated as %s.' % (expected_parent[taxa[r]]))\n # return None\n\n expected_parent[taxa[r]] = taxa[r - 1]\n\n return expected_parent","def delete_child(self, child):\n for i, c in enumerate(self.children): #For each child in the Node's children\n if c is child and c.is_leaf(): #If that child is the one we're looking for and it has no children\n del self.children[i] #Delete it.","def create_taxonomy(dataset_name, attr, dataset=[]):\n #path = os.getcwd()\n\n path_in = os.getcwd()\n pattern = '^.*/thesis-data-anonymisation/'\n path_top = re.search(pattern, path_in).group(0)\n\n path = path_top +'data'\n\n if len(dataset_name) > 0:\n prefix = '../data/'+dataset_name+'/hierarchy_'\n else:\n prefix = '../data/hierarchy_'\n\n postfix = '.csv'\n\n try:\n file = open(path + '/' + prefix + attr + postfix, 'r')\n except FileNotFoundError:\n if len(dataset_name) > 0:\n prefix = '/data/'+dataset_name+'/hierarchy_'\n else:\n prefix = '/data/hierarchy_'\n file = open(path+prefix + attr + postfix, 'r')\n\n taxonomy = {}\n #dataset_group = dataset.groupby(attr).groups\n\n lines_in = file.readlines()\n file.close()\n lines = [line.strip().split(';') for line in lines_in]\n max_height = max([len(line) for line in lines])\n try:\n float(lines[0][0])\n is_numeric = True\n except ValueError:\n is_numeric = False\n for line in lines:\n #try:\n # if is_numeric:\n # dataset_group[int(line[0])]\n # else:\n # dataset_group[line[0]]\n #except KeyError:\n # continue\n line.reverse()\n for i, val in enumerate(line):\n is_leaf = False\n if val == '*':\n node = TaxNode(val, None, is_numeric, is_leaf)\n else:\n if i == len(line) - 1:\n is_leaf = True\n\n node = TaxNode(val, taxonomy[line[i - 1]][-1], is_numeric, is_leaf)\n try:\n current_nodes = taxonomy[val]\n already_added = False\n for current_node in current_nodes:\n if current_node.parent is None:\n already_added = True\n elif current_node.parent.value == node.parent.value:\n already_added = True\n if not already_added:\n taxonomy[val].append(node)\n except KeyError:\n taxonomy[val] = [node] # Saves the nodes in a list in case of several parents (only valid for nodes with several parents!!!)\n hierarchy = Taxonomy(taxonomy, max_height)\n\n return hierarchy","def fm_append_member(cls, parent, child):\n parent.fm_append(child, cls.CHILD)\n child.fm_append(parent, cls.PARENT)","def child_classes(self):\n response = check_defined(self, inspect.stack()[0][3])\n if not response:\n return response\n children = self.se.full_class_only_graph.successors(self.uri)\n result = restructure_output(self,\n children,\n inspect.stack()[0][3],\n self.output_type)\n return result","def child(self, v, c):\n # method here","def needs_parens(parent, child, posn):\n return False","def test_parse_taxonomy(self):\r\n example_tax = \\\r\n \"\"\"412 PC.635_647\tRoot;Bacteria;Firmicutes; \"Clostridia\";Clostridiales\t0.930\r\n319 PC.355_281\tRoot;Bacteria;Bacteroidetes \t0.970\r\n353 PC.634_154\tRoot; Bacteria ; Bacteroidetes\t0.830\r\n17 PC.607_302\tRoot;Bacteria;Bacteroidetes\t0.960\r\n13 PC.481_1214\tRoot;Bacteria;Firmicutes;\"Clostridia\";Clostridiales\t0.870\r\n338 PC.593_1314\tRoot; Bacteria \t0.990\t42556\tAdditional fields ignored\"\"\"\r\n res = parse_taxonomy(example_tax.split('\\n'))\r\n self.assertEqual(res['412'],\r\n [\"Root\", \"Bacteria\", \"Firmicutes\", \"\\\"Clostridia\\\"\", \"Clostridiales\"])\r\n self.assertEqual(res['338'],\r\n [\"Root\", \"Bacteria\"])","def set_child(self, c, line_number=0):\n if isinstance(self.child, set):\n self.child = self.child | {c}\n self._child_line = self._child_line | {line_number}\n else:\n self.child = {c} if (c and c != 'NA') else 'NA'\n self._child_line = {line_number}","def __init__ (self, parent, child, length=None):\n self.parent = parent\n self.child = child\n self.length = length","def test_child_link(self):\n def compare_func(obj, node):\n child_nodes = self.get_children_of_node(node)\n\n for (child_obj, child_node) in zip(obj.children, child_nodes):\n # parent-child link (children must have obj as their parent)\n self.assertEqual(child_obj.parent, obj)\n\n self.recursively_compare_tree_against_html(compare_func)","def test_add_child_category(self):\n self.add_success(self.test_data['pants'])\n rv = self.get('pants')\n assert in_response(rv, 'This category is empty.')\n self.add_success(self.test_data['jeans'])\n rv = self.get('pants')\n assert not in_response(rv, 'This category is empty.')\n assert in_response(rv, 'Jeans')","def _newChild(self, child):\n self._testKeySubNsAdd()\n self._getSubNsList().append(child)","def __missing__(self, key):\n child = super(Tree, self).__missing__(key)\n child.name = key\n child.mother = self\n return child","def test_get_child():\n \n root_ts = TrackSegment(flow_dict=flow_dict) \n \n # ROOT MODULE\n start = root_ts.get_child(\"start\")\n # check depth\n assert(start.depth == root_ts.depth+1)\n # check parent\n assert(start.parent is root_ts)\n # check module_id\n assert(start.module_id == 'start')\n \n # CHILD (1,2,3)\n root_ts_get_child_result = root_ts.get_child((1,2,3))\n # check depth\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\n # check parent\n assert(root_ts_get_child_result.parent is root_ts)\n \n # CHILD (2,3,4)\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\n # check depth\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\n # check parent \n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\n \n print(\"TEST GET_CHILD: success!\")","def testParentageChange(self):\n cc2 = cdl_convert.ColorCorrectionRef('001')\n cc2.parent = 'bob'\n mr2 = cdl_convert.MediaRef('sdhjd.dpx')\n mr2.parent = 'jim'\n\n self.cd.cc = cc2\n self.cd.media_ref = mr2\n\n self.assertEqual(\n self.cd,\n cc2.parent\n )\n\n self.assertEqual(\n self.cd,\n mr2.parent\n )","def child(self, index):\n raise AttributeError, \"Cannot retrieve children from leaf nodes! Attempted on leaf:\\n\\n%s\" % self.prettyPrint()","def test_ontology_term_inheritance(ontology):\n\tassert len(ontology.inherited(\"TO:0000001\")) == 1\n\tassert len(ontology.inherited(\"TO:0000002\")) == 2\n\tassert len(ontology.inherited(\"TO:0000003\")) == 2\n\tassert len(ontology.inherited(\"TO:0000004\")) == 4\n\tassert len(ontology.inherited(\"TO:0000005\")) == 5\n\tassert len(ontology.inherited(\"TO:0000006\")) == 5\n\tassert len(ontology.inherited(\"TO:0000007\")) == 2\n\tassert len(ontology.inherited(\"TO:0000008\")) == 3\n\tassert len(ontology.inherited(\"TO:0000009\")) == 4\n\n\tassert len(ontology.inherited([\"TO:0000002\",\"TO:0000003\"])) == 3\n\tassert len(ontology.inherited([\"TO:0000009\",\"TO:0000005\"])) == 8\n\tassert len(ontology.inherited([\"TO:0000004\",\"TO:0000003\"])) == 4\n\tassert len(ontology.inherited([\"TO:0000002\"])) == 2\n\tassert len(ontology.inherited([])) == 0","def make_forest (self, doclets):\n\n for o in doclets:\n if o.memberof is None:\n continue\n if o.memberof in self.longnames:\n o.parent = self.longnames[o.memberof]\n o.parent.children.append (o)\n continue\n if o.doc ():\n if o.memberof == '<anonymous>':\n o.error (\"\"\"Could not link up object %s to %s.\n Try giving the anonymous object an @alias.\"\"\"\n % (o.longname, o.memberof))\n else:\n o.error (\"Could not link up object %s to %s\" % (o.longname, o.memberof))","def _child_types(self, children):\n\n # Recursively determine types on each child node\n child_types = []\n for child in children:\n child_types.append(self.expression_type(child))\n return child_types","def parse_taxonomy_set(self, taxonomy_xml):\n\n set_name = taxonomy_xml.findtext('taxonomy_name')\n\n def _parse_node(parent_node, parents=None):\n \"\"\"The actual recursion function (generator). This iterates all of the child nodes of the element passed.\n It yields the node data, and node keys as it finds. It keeps a few lists in memory to collate the\n child/parent information as it is being parsed\n \"\"\"\n for node in parent_node.findall('node'):\n node_name = node.findtext('node_name')\n if node_name is None:\n # We cannot proceed without a name\n log.warn('Taxonomy Source has a node missing a node name')\n continue\n node_key = node_name.strip().lower().replace(' ', '_')\n node_data = {\n 'name': node_name,\n 'parents': parents,\n 'children': [],\n }\n # capture the integer properties of the node\n for property in self.INT_PROPERTIES:\n if property in node.attrib:\n node_data[property] = int(node.get(property)) if len(node.get(property)) > 0 else None\n # Loop the children nodes\n parents.append(node_key)\n for child_key, child_data in _parse_node(node, parents[:]):\n if child_data['parents'][-1] == node_key:\n node_data['children'].append(child_key)\n yield child_key, child_data\n parents.pop()\n\n yield node_key, node_data\n\n set_data = {key: data for key, data in _parse_node(taxonomy_xml, [])}\n self._sets[set_name] = [key for key in set_data.keys()]\n self.update(set_data)","def children(node):\n\n return snd(node)","def __init__(self):\n self.children = dict()\n self.isWord = False","def _init_child(self,child,path):\n pass","def _addEntity(self, pid, chunks):\n parent = chunks[pid]\n sub = None\n # Find subject\n for i in range(len(parent.children)):\n child = chunks[parent.children[i]]\n if child.func in SubDict:\n sub = child\n if child.func == \"では\":\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\n pass\n else:\n sub = None\n if sub:\n self._addNode(parent, sub=sub.main)\n self._addEdge(sub.main, parent.main, label=\"陳述\", etype=\"stat\")\n else:\n self._addNode(parent)\n \n # Lopp through all children\n for i in range(len(parent.children)):\n child = chunks[parent.children[i]]\n # If child is noun\n if child.func in SubDict:\n if child.func == \"では\":\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\n pass\n else:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=child.func, etype=\"attr\")\n elif child.type == 0 and child.func in [\"と\", \"などと\"] and child.id + 1 == parent.id and preprocessText(chunks[parent.parent].main) not in [\"交代\", \"交換\"]:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=\"並列\", etype=\"para\")\n self._addEdge(parent.main, child.main, label=\"並列\", etype=\"para\")\n self.para.append([child.main, parent.main])\n elif child.type == 0 and child.func in ParallelDict and child.id + 1 == parent.id:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=\"並列\", etype=\"para\")\n self._addEdge(parent.main, child.main, label=\"並列\", etype=\"para\")\n self.para.append([child.main, parent.main])\n else:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=child.func, etype=\"attr\")","def visit_Compound(self, node):\n for child in node.children:\n self.visit(child)","def create_hierarchy(parent_path, child_path):\n if parent_path:\n parent_skill_id = database_controller.get_skill(parent_path).id\n child_skill_id = database_controller.get_skill(child_path).id\n new_hierarchy = Hierarchy(parent_skill_id=parent_skill_id, child_skill_id=child_skill_id)\n db.session.add(new_hierarchy)\n db.session.commit()\n else:\n child_skill_id = database_controller.get_skill(child_path).id\n new_hierarchy = Hierarchy(child_skill_id=child_skill_id)\n db.session.add(new_hierarchy)\n db.session.commit()\n ''' \n new_hierarchy = Hierarchy()\n if parent_path:\n parent_skill = database_controller.get_skill(parent_path)\n new_hierarchy.parent_skill_assoc = parent_skill\n child_skill = database_controller.get_skill(child_path)\n new_hierarchy.child_skill_assoc = child_skill\n db.session.add(new_hierarchy)\n db.session.commit()\n '''","def classify(self, document, tree):\n if type(tree) is ClassTreeNode:\n return tree.c\n else:\n if tree.word in document.bag_of_words:\n return self.classify(document, tree.children[0])\n else:\n return self.classify(document, tree.children[1])","def test_subsample_taxonomy(self):\n basic_test_runner(self, 'taxonomy', nrows=6, niter=3, normalize='subsample')","def visit_tertiary_node(self, node, children):\n return {node.rule_name: children[0]}","def has_child(self, term):\n for parent in self.children:\n if parent.id == term or parent.has_child(term):\n return True\n return False","def mutate(self, child):\n return child","def classify(tree, input):\n\n #if this is a leaf node, return its value\n if tree in [True, False]:\n return tree\n\n #otherwise this tree consists of an attribute to split on\n #and a dict whose keys are values of that attribute\n #and whose values are subtrees to consider next\n attribute, subtree_dict = tree\n\n subtree_key = input.get(attribute) #None if input is missing\n\n if subtree_key not in subtree_dict: #if no subtree for key, use None\n subtree_key = None\n\n subtree = subtree_dict[subtree_key] # choose the appropriate subtree\n return classify(subtree, input) # and use it to classify the input","def set_categories(post, old, new):\n c = None\n if new:\n c = Category(name=new.pop())\n parent = c\n son = None\n while new:\n son = parent\n parent = Category(name=new.pop())\n son.parent = parent\n db.session.add(parent)\n if old:\n old_c = Category.query.get(old)\n parent.parent = old_c\n elif old:\n c = Category.query.get(old)\n if c is not None:\n db.session.add(c)\n post.category = c","def merge_nodes(self, parent, child):\n parent.key += child.key\n parent.real = child.real\n parent.value = child.value\n parent.children = child.children","def cousins(word, level):\r\n parents = [get_synset(word)]\r\n for _ in range(level):\r\n parent_hypernyms = [parent.hypernyms() for parent in parents]\r\n parents = list(chain(*parent_hypernyms))\r\n\r\n children = parents\r\n for _ in range(level):\r\n children_hyponyms = [child.hyponyms() for child in children]\r\n children = list(chain(*children_hyponyms))\r\n\r\n children = set(children)\r\n children -= {get_synset(word)}\r\n return set(map(synset_name, children))","def test_closest_child(self):\n self.assertEqual(self.vectors.closest_child('dog.n.01'), 'terrier.n.01')\n self.assertEqual(self.vectors.closest_child('harbor_porpoise.n.01'), None)","def is_child(self, kid, mother): \n mom_node = self.names_to_nodes[mother] \n child_node = self.names_to_nodes[kid]\n return mom_node.is_child(child_node)","def add_child(self, character, child_node):\n # make sure character is uppercase\n character = character.upper()\n # TODO: Should I consider check if char is english letter?\n if not character.isalpha():\n raise ValueError(f'Child can only be English letters!')\n\n if not self.has_child(character):\n # create a new node for this character\n # child_node = PrefixTreeNode(character)\n index = self._get_index(character)\n # create empty list for children\n # self.children = [None] * 26\n # place the new child into right position\n self.children[index] = child_node\n else:\n raise ValueError(f'Child exists for character {character!r}')","def young_child(self, parent):\n return 2 * parent","def two_best_children(self,instance):\n\t\tif len(self.tree.children) == 0:\n\t\t\traise Exception(\"No children!\")\n\t\t\n\t\tself.utility.increment_counts(instance)\n\t\tchildren_cu = []\n\t\tfor i in range(len(self.tree.children)):\n\t\t\tself.tree.children[i].utility.increment_counts(instance)\n\t\t\tchildren_cu.append((self.utility.category_utility(self.tree.children[i]),i))\n\t\t\tself.tree.children[i].utility.decrement_counts(instance)\n\t\tself.utility.decrement_counts(instance)\n\t\tchildren_cu.sort(reverse=True)\n\n\t\tif len(self.tree.children) == 1:\n\t\t\treturn children_cu[0], None \n\n\t\treturn children_cu[0], children_cu[1]","def get_mds_children(node):\n children=node.getChildren()\n return {get_mds_shortname(child):child for child in children}","def set_child(self, child):\n if child.tags is None:\n raise Exception(\"Missing tags when: Component {0} tried ta add_child \"\n \"component: {1} to its children. \".format(str(self), str(child)))\n if child.component_type is None:\n raise Exception(\"Missing component_type when: Component {0} tried ta add_child \"\n \"component: {1} to its children. \".format(str(self), str(child)))\n if not child._parent is None:\n raise Exception(\"Component {0} tried ta add_child \"\n \"component: {1} to its children. \"\n \"But it already \"\n \"had parent: {2}.\".format(str(self), str(child), str(child.parent)))\n if self.has(child.component_type):\n self.remove_component_of_type(child.component_type)\n self._children[child.component_type] = child\n self._add_child_to_tag_table(child)\n child.parent = self","def children(word, word_dict):\n res = []\n for i in range(len(word)):\n child = word[:i]+word[i+1:]\n if child in word_dict:\n res.append(child)\n return res","def test_diamond(self):\n b_child_of_a = InheritanceFactory.create(child__name='b', parent__name='a')\n a = b_child_of_a.parent\n b = b_child_of_a.child\n\n c = InheritanceFactory.create(parent=a, child__name='c').child\n d = InheritanceFactory.create(parent=b, child__name='d').child\n InheritanceFactory.create(parent=c, child=d, order=2)\n\n mro = d.get_all_ancestors()\n self.assertSequenceEqual(mro, [b, c, a])","def get_related_taxonomy(taxonomy_list=None):\n if not taxonomy_list:\n taxonomy_list = []\n\n taxonomies = []\n for taxonomy in taxonomy_list:\n list = __get_related_list__(taxonomy)\n\n for child_taxonomy in list:\n if child_taxonomy not in taxonomies:\n taxonomies.append(child_taxonomy)\n\n return taxonomies","def test_taxonomy_process_prefs(self):\r\n taxonomy_levels = [2, 3, 4]\r\n exp1 = {}\r\n exp1['level_2'] = {'column': '2'}\r\n exp1['level_3'] = {'column': 3, 'colors':\r\n {'a': ('red', (0, 100, 100))}}\r\n obs1 = taxonomy_process_prefs(taxonomy_levels, exp1)\r\n\r\n exp2 = {}\r\n exp2['2'] = {'column': '2', 'colors': {}}\r\n exp2['3'] = {'column': '3', 'colors': {'a': ('red', (0, 100, 100))}}\r\n exp2['4'] = {'column': '4', 'colors': {}}\r\n self.assertEqual(obs1, exp2)","def _del_node_one_child(self, parent, node):\n if parent:\n if parent._rkid == node:\n if node._rkid:\n parent._rkid = node._rkid\n node._rkid._parent = parent\n else:\n parent._rkid = node._lkid\n node._lkid._parent = parent\n elif node._rkid:\n parent._lkid = node._rkid\n node._rkid._parent = parent\n else:\n parent._lkid = node._lkid\n node._lkid._parent = parent\n else:\n if node._rkid:\n self._root = node._rkid\n node._rkid._parent = None\n else:\n self._root = node._lkid\n node._lkid._parent = None","def new_child(self,instance):\n\t\treturn self.cu_for_new_child(instance,False)","def child_constr(self):\n\t\tself.append_child(Bond((258.0, 0.0, 2950.0), (0.0, 0.0, 1.0), 0.0))\n\t\tself.append_child(Bond((-258.0, 0.0, 2950.0), (0.0, 0.0, 1.0), 180.0))\n\t\tself.append_child(Bond((0.0, 258.0, 2950.0), (0.0, 0.0, 1.0), 90.0))\n\t\tself.append_child(Bond((0.0, -258.0, 2950.0), (0.0, 0.0, 1.0), -90.0))\n\t\tself.append_child(Column_Stock((0.0, 0.0, 3000.0), (0.0, 0.0, 1.0), -90.0))","def __init__(self, word):\n TreeNode.__init__(self)\n self.word = word\n self.children = []","def visit_cname_place(self, node, children):\n # If a value is supplied it will be a single time list, so extract with [0]\n # If no value is supplied for an optional item, default must also be a single item list [default_value]\n w = children.results.get('wrap')\n wrap_value = 1 if not w else w[0]['wrap']\n cplace = {'cname': children.results['name'][0], # Required component\n 'dir': children.results.get('dir', [1])[0], # many optional components with default values\n 'bend': children.results.get('bend', [1])[0],\n 'notch': children.results.get('notch', [0])[0],\n 'wrap': wrap_value,\n }\n return cplace","def is_subhalo(self, childid, parentid):\n if (childid in self._halos[parentid].properties['children']):\n return True\n else:\n return False","def add_child_info(conll_tokens, child_funcs, child_strings, lex):\n\tfor child_id in child_funcs:\n\t\tif child_id > len(conll_tokens)-1:\n\t\t\tcontinue\n\t\tfor func in child_funcs[child_id]:\n\t\t\tif func not in conll_tokens[child_id].child_funcs:\n\t\t\t\tconll_tokens[child_id].child_funcs.append(func)\n\t\t\t\tif lex.filters[\"neg_func\"].match(func):\n\t\t\t\t\tconll_tokens[child_id].negated = True\n\t\tfor tok_text in child_strings[child_id]:\n\t\t\tif tok_text not in conll_tokens[child_id].child_strings:\n\t\t\t\tconll_tokens[child_id].child_strings.append(tok_text)","def test_hasChild(self):\n bag = Bag('faded plum', [(2, 'wavy cyan'), (1, 'navy turquoise')])\n res = bag.hasChild('wavy cyan')\n self.assertTrue(res)\n bag1 = Bag('gaded plum', [(2, 'wavy cyan'), (1, 'navy turquoise')])\n res1 = bag1.hasChild('gaded plum')\n self.assertFalse(res1)","def archetype(self):\n return_value = 0\n if self.thesclass is not None:\n try:\n parent = [a for a in self.thesclass.ancestors() if\n a.label is not None and a.label][0]\n except IndexError:\n pass\n else:\n label = parent.label\n if label in (self.lemma, 'a ' + self.lemma, 'an ' + self.lemma):\n return_value = 2\n else:\n target = ' ' + self.lemma + ' '\n label = label.replace(',', ' ').replace(';', ' ')\n label = ' ' + label + ' '\n if target in label:\n return_value = 1\n return return_value","def crossover_binary(parent1: numpy.ndarray, parent2: numpy.ndarray, eta: float):\n # Ref: https://github.com/Chrispresso/SnakeAI/blob/master/genetic_algorithm/crossover.py\n rand = numpy.random.random(size=parent1.shape)\n gamma = numpy.empty(shape=parent1.shape)\n gamma[rand <= 0.5] = (2 * rand[rand <= 0.5]) ** (1.0 / (eta + 1)) # First case of equation 9.11\n gamma[rand > 0.5] = (1.0 / (2.0 * (1.0 - rand[rand > 0.5]))) ** (1.0 / (eta + 1)) # Second case\n\n # Calculate Child 1 chromosome (Eq. 9.9)\n chromosome1 = 0.5 * ((1 + gamma) * parent1 + (1 - gamma) * parent2)\n # Calculate Child 2 chromosome (Eq. 9.10)\n chromosome2 = 0.5 * ((1 - gamma) * parent1 + (1 + gamma) * parent2)\n\n return chromosome1, chromosome2","def get_subname(get, node):\n if isinstance(node, ast.Attribute) and get(node.value) == 'parent':\n return node.attr","def test_update_child_category(self):\n self.add_success(self.test_data['pants'])\n self.add_success(self.test_data['shirts'])\n\n self.add_success(self.test_data['jeans'])\n rv = self.get('pants')\n assert not in_response(rv, 'This category is empty.')\n assert in_response(rv, 'Jeans')\n\n self.edit_success('jeans', self.test_data['t-shirts'])\n rv = self.get('pants')\n assert in_response(rv, 'This category is empty.')\n assert not in_response(rv, 'Jeans')\n assert not in_response(rv, 'T-Shirts')\n rv = self.get('shirts')\n assert not in_response(rv, 'This category is empty.')\n assert in_response(rv, 'T-Shirts')\n assert not in_response(rv, 'Jeans')","def test_append_children_category(self):\n category = Category(catname='olympic games')\n category1 = Category(catname='Tennis')\n category.parents.append(category1)\n category.save()\n assert category.parents","def visit_ClassDef(self, node):\n self.recurse(node)","def make_child(self, key):\n child = Bar(key)\n child.__parent__ = self\n return child"],"string":"[\n \"def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified, oneOrganismPerSpecies = defaultOneOrganismPerSpecies):\\n # read NCBI names from Clade object, if necessary\\n if isinstance(parent, Clade):\\n self.parentClade = parent\\n else:\\n self.parentClade = Clade(parent, excludeUnclassified, oneOrganismPerSpecies=oneOrganismPerSpecies)\\n \\n if isinstance(child, Clade):\\n self.childClade = child\\n else:\\n self.childClade = Clade(child, excludeUnclassified, oneOrganismPerSpecies=oneOrganismPerSpecies)\",\n \"def children(self, taxon, taxonomy):\\n\\n c = set()\\n for taxon_id, taxa in taxonomy.items():\\n if taxon in taxa:\\n\\n if taxon.startswith('s__'):\\n c.add(taxon_id)\\n else:\\n taxon_index = taxa.index(taxon)\\n for child in taxa[taxon_index + 1:]:\\n if len(child) > 3: # not just an empty prefix\\n c.add(child)\\n\\n return c\",\n \"def add_hierarchy(self, parent, edge, child): # XXX DEPRECATED\\n if type(parent) != rdflib.URIRef:\\n parent = self.check_thing(parent)\\n\\n if type(edge) != rdflib.URIRef:\\n edge = self.check_thing(edge)\\n\\n if type(child) != infixowl.Class:\\n if type(child) != rdflib.URIRef:\\n child = self.check_thing(child)\\n child = infixowl.Class(child, graph=self.g)\\n\\n restriction = infixowl.Restriction(edge, graph=self.g, someValuesFrom=parent)\\n child.subClassOf = [restriction] + [c for c in child.subClassOf]\",\n \"def test_make_taxonomy(self):\\n basic_test_runner(self, 'taxonomy')\",\n \"def add_relatives(self, child2parent, idx2word):\\n for child, parent in child2parent.items():\\n if parent not in (0, -1):\\n parent_word = idx2word[parent]\\n parent_word.add_child(child)\\n child.parent = parent_word\",\n \"def is_allowed_to_have_child_terms(self):\\n return self._is_allowed_to_have_child_terms\",\n \"def _is_child(self, parent, child): # type: (str, str) -> bool\\n return child != parent and child.startswith(parent + \\\".\\\")\",\n \"def is_allowed_to_have_child_terms(self, is_allowed_to_have_child_terms):\\n self._is_allowed_to_have_child_terms = is_allowed_to_have_child_terms\",\n \"def Children(self) -> _n_1_t_2:\",\n \"def findDiscripancies(taxonomy):\\n i = 0\\n for entry in taxonomy:\\n if entry['parentName'] != None:\\n print entry['nodeName']\\n if entry['nodeName'].lower() == entry['parentName'].lower():\\n i += 1\\n print \\\"No of same nodes = {} \\\" .format(i)\",\n \"def taxon_children(self, taxonomy):\\n\\n taxon_children = defaultdict(set)\\n for taxon_id, taxa in taxonomy.items():\\n for i, taxon in enumerate(taxa):\\n if len(taxon) == 3:\\n continue # just rank prefix\\n\\n if len(taxa) > i + 1 and len(taxa[i + 1]) != 3:\\n taxon_children[taxon].add(taxa[i + 1])\\n\\n if len(taxa) > self.rank_index['s__']:\\n taxon = taxa[self.rank_index['s__']]\\n if taxon != 's__':\\n taxon_children[taxon].add(taxon_id)\\n\\n return taxon_children\",\n \"def test_process_label_in_node(self):\\n tree = Node(children=[\\n Node(\\\"Defining secret phrase.\\\", label=['AB', 'a']),\\n Node(\\\"Has secret phrase. Then some other content\\\", \\n label=['AB', 'b'])\\n ], label=['AB'])\\n t = Terms(tree)\\n t.scoped_terms = {\\n ('AB',): [Ref(\\\"secret phrase\\\", \\\"AB-a\\\", (9,22))]\\n }\\n # Term is defined in the first child\\n self.assertEqual([], t.process(tree.children[0]))\\n self.assertEqual(1, len(t.process(tree.children[1])))\",\n \"def cu_for_new_child(self,instance,undo=True):\\n\\t\\tself.utility.increment_counts(instance)\\n\\t\\tself.create_new_child(instance)\\n\\t\\tcu = self.utility.category_utility()\\n\\t\\tif undo:\\n\\t\\t\\tself.tree.children.pop()\\n\\t\\t\\tself.utility.decrement_counts(instance)\\n\\t\\treturn cu\",\n \"def gen_child(self, g, ng, child):\\n with About(child.debug, self.relation):\\n self.remap_node((g, child), g, child, ng, ng.apply())\",\n \"def test_field_resolution_multiple_inheritance_with_child_field_defined(self):\\n child_field = ChildWithMultipleParentsAndTheSameField()._get_fields()[\\\"name\\\"]\\n self.assertEqual(child_field.default, \\\"C\\\")\",\n \"def is_child_of(self, *args):\\n return _ida_hexrays.cexpr_t_is_child_of(self, *args)\",\n \"def __post_init__(self) -> None:\\n arity = self.root.arity\\n length = len(self.children)\\n if arity != length:\\n raise ValueError(\\n 'Incorrect number of child terms: '\\n f'Expected {arity}, found {length}'\\n )\",\n \"def _del_node_two_children(self, parent, node):\\n succ = self._get_successor(node)\\n self.delete(succ._data)\\n succ._rkid = node._rkid\\n succ._lkid = node._lkid\\n if node._rkid:\\n node._rkid._parent = succ\\n if node._lkid:\\n node._lkid._parent = succ\\n if node is not self._root:\\n if parent._rkid is node:\\n parent._rkid = succ\\n succ._parent = parent\\n else:\\n parent._lkid = succ\\n succ._parent = parent\\n else:\\n self._root = succ\\n succ._parent = None\",\n \"def search_category(self):\\n return _(self.child_class)\",\n \"def test_grandchildren():\\n\\n # note c.upto(\\\"status\\\").desired.grandchildren\\n # this is the same as *c.upto(\\\"status\\\").desired in python3.5+\\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.upto(\\\"status\\\").desired.grandchildren))\\n assert \\\"type\\\" in res\\n assert \\\"reason\\\" in res\\n assert \\\"version\\\" in res\\n assert \\\"image\\\" in res\\n assert \\\"force\\\" in res\",\n \"def get_code_per_child(self, obj, child):\\n return []\",\n \"def set_child(self,b):\\n if b.isChecked() == True:\\n self.mother = False\\n self.child = True\\n else:\\n self.mother = True\\n self.child = False\",\n \"def get_taxonomy(): # noqa: E501\\n return 'do some magic!'\",\n \"def test_environmentReverseInheritance(self):\\n # In the child only\\n node = create_node(\\\"somewhere\\\", \\\"myservice\\\", \\\"parent:child\\\")\\n disco = create_disco()\\n disco.onMessage(None, NodeActive(node))\\n # Parent can't find it\\n self.assertEqual(resolve(disco, \\\"myservice\\\", \\\"1.0\\\", \\\"parent\\\"), None)\",\n \"def children(self): # noqa: ANN201\",\n \"def __init__(self, child_type = None):\\r\\n super().__init__()\\r\\n self.__child_dict = collections.OrderedDict()\\r\\n self.__child_type = child_type\\r\\n self.__mykeys = ()\\r\\n self.__parent = None\",\n \"def create_child(self, value, fun=None):\\n child = TaxonomyTree(value, self.weights, fun)\\n self.children[value] = child\\n child.parent = self\\n child.level = self.level + 1\\n child.__root = self.__root\\n self.__root.__fast_find.append((child.fun, child))\\n return child\",\n \"def cladistic(tree, taxa):\\n tips = []\\n taxa = set(taxa)\\n for tip in tree.tips():\\n if tip.name in taxa:\\n tips.append(tip)\\n n = len(taxa)\\n if len(tips) < n:\\n raise ValueError('Taxa not found in the tree.')\\n return ('uni' if n == 1 else\\n ('mono' if len(tree.lca(tips).subset()) == n else\\n 'poly'))\",\n \"def testMotherChild(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.stringTypeTest(self, attr, \\\"mother_child\\\")\\n\\n self.util.stringPropertyTest(self, attr, \\\"mother_child\\\")\",\n \"def childNCBInames(self):\\n return self.childClade.ncbiNames\",\n \"def test_make_taxa_long(self):\\n basic_test_runner(self, 'taxonomy', rank='all')\",\n \"def __init__(self, l_child, r_child):\\n self.l_child = l_child\\n self.r_child = r_child\",\n \"def is_cousin(parent_db, A, B):\\n parent_dict = {}\\n for item in parent_db:\\n if item[0] in parent_dict: #If parent is already in the dictionary, add this child to value (set of children)\\n parent_dict[item[0]].add(item[1])\\n else:\\n parent_dict[item[0]] = {item[1]}\\n\\n child_dict = {}\\n for item in parent_db:\\n if item[1] in child_dict: #If child is already in the dictionary, add this parent to value (set of parents)\\n child_dict[item[1]].add(item[0])\\n else:\\n child_dict[item[1]] = {item[0]}\\n\\n if A==B:\\n return None\\n\\n for parent in parent_dict:\\n if A in parent_dict[parent] and B in parent_dict[parent]: #Checking if they share the same parent\\n return None\\n\\n grandparents_A = set()\\n for parent in child_dict[A]: #Iterating through parents of A\\n for grandparent in child_dict[parent]: #Iterating through parents of parents of A (grandparents of A)\\n grandparents_A.add(grandparent)\\n\\n for parent in child_dict[B]: #Iterating through parents of B\\n for grandparent in child_dict[parent]: #Iterating through parents of parents of B (grandparents of B)\\n if grandparent in grandparents_A:\\n return grandparent\\n\\n return None\",\n \"def parse_taxonomy( seq_id, lineage, key_dictionary ):\\n\\tif seq_id in sti_dict:\\n\\t\\ttax_id = sti_dict[ seq_id ]\\n\\t\\ttax_names = [ tax_id ] #list of taxon names\\n\\telse:\\n\\t\\ttax_id = str( seq_id )\\n\\t\\ttax_names = [ tax_id ] #list of taxon names\\n\\ttax_numbers = [ seq_id ]\\n\\tis_A_list = [] #store is_A relationships\\n\\n\\twhile lineage != '1': #forces traversal through the tri file until we get to the root of taxonomy\\n\\t\\t#print lineage\\n\\t\\tif lineage == '0': #need this to process the root in the tri file. \\n\\t\\t\\tbreak\\n\\t\\tis_A_list = [lineage] + is_A_list\\n\\t\\ttax_numbers = [lineage] + tax_numbers\\n\\t\\tif lineage in sti_dict: #we have the next taxonomic representative in the sti file\\n\\t\\t\\ttax_id = sti_dict[ lineage ]\\n\\t\\t\\ttax_names = [tax_id] + tax_names #append tax_id to front of list\\n\\t\\telse: #the taxon does not have a sequence representative. \\n\\t\\t\\ttax_id = str( lineage ) \\n\\t\\t\\ttax_names = [tax_id] + tax_names\\n\\t\\t#now process to next lineage\\n\\t\\tlineage = tri_dict[ lineage ] \\n\\n\\n\\ttax_names = ['root'] + tax_names #append tax_id to front of list\\n\\ttax_numbers = [lineage] + tax_numbers\\n\\tis_A_list = ['0'] + [lineage] + is_A_list\\n\\n\\t#now append all of these reuslts to the final dictionary, which will be keyed \\n\\t#off of the tax_numbers list (unique IDs for each taxonomic level.\\n\\n\\tfor i in xrange( len( tax_numbers ) ):\\n\\t\\tid = tax_numbers[i]\\n\\t\\tif id in key_dictionary:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tparent = is_A_list[i]\\n\\t\\t\\tlevel = i #taxonomic level (how far down in levels are we?)\\n\\t\\t\\tnames = process_names( tax_names[:i+1] )\\n\\t\\t\\tkey_dictionary[ id ] = [ parent, level, names ]\\n\\n\\treturn( key_dictionary )\",\n \"def getChild(self, child, request):\\n request.postpath.insert(0, request.prepath.pop())\\n return self.leaf\",\n \"def fix(child, roots):\\n link, root_url = child\\n root = get_root(root_url)\\n if len(roots[roots.root == root].children > 0) == 1 and (roots[roots.root == root].children > 0).values[0]:\\n check = fix_url(link, root_url)\\n if check != '' and check is not None:\\n return (check,\\n roots.loc[roots[roots.root == root].index].root.values[0],\\n roots.loc[roots[roots.root == root].index].category.values[0])\\n return\",\n \"def __init__(self, label=\\\"\\\"):\\n super(Tree, self).__init__(Tree) \\n self.mother = None\\n self.name = label\",\n \"def __init__(self, concept_ref='', is_parent=False):\\n self.concept_ref = concept_ref\\n self.is_parent = is_parent\",\n \"def taxonomic_consistency(self, taxonomy, report_errors=True):\\n\\n expected_parent = {}\\n for genome_id, taxa in taxonomy.items():\\n if taxa[0] == 'd__Viruses' or '[P]' in taxa[0]:\\n # *** This is a HACK. It would be far better to enforce\\n # a taxonomically consistent taxonomy, but\\n # the viral taxonomy at IMG is currently not consistent\\n continue\\n\\n for r in range(1, len(taxa)):\\n if len(taxa[r]) == 3:\\n break\\n\\n if taxa[r] in expected_parent:\\n if report_errors:\\n if taxa[r - 1] != expected_parent[taxa[r]]:\\n self.logger.warning('Provided taxonomy is not taxonomically consistent.')\\n self.logger.warning(\\n 'Genome %s indicates the parent of %s is %s.' % (genome_id, taxa[r], taxa[r - 1]))\\n self.logger.warning(\\n 'The parent of this taxa was previously indicated as %s.' % (expected_parent[taxa[r]]))\\n # return None\\n\\n expected_parent[taxa[r]] = taxa[r - 1]\\n\\n return expected_parent\",\n \"def delete_child(self, child):\\n for i, c in enumerate(self.children): #For each child in the Node's children\\n if c is child and c.is_leaf(): #If that child is the one we're looking for and it has no children\\n del self.children[i] #Delete it.\",\n \"def create_taxonomy(dataset_name, attr, dataset=[]):\\n #path = os.getcwd()\\n\\n path_in = os.getcwd()\\n pattern = '^.*/thesis-data-anonymisation/'\\n path_top = re.search(pattern, path_in).group(0)\\n\\n path = path_top +'data'\\n\\n if len(dataset_name) > 0:\\n prefix = '../data/'+dataset_name+'/hierarchy_'\\n else:\\n prefix = '../data/hierarchy_'\\n\\n postfix = '.csv'\\n\\n try:\\n file = open(path + '/' + prefix + attr + postfix, 'r')\\n except FileNotFoundError:\\n if len(dataset_name) > 0:\\n prefix = '/data/'+dataset_name+'/hierarchy_'\\n else:\\n prefix = '/data/hierarchy_'\\n file = open(path+prefix + attr + postfix, 'r')\\n\\n taxonomy = {}\\n #dataset_group = dataset.groupby(attr).groups\\n\\n lines_in = file.readlines()\\n file.close()\\n lines = [line.strip().split(';') for line in lines_in]\\n max_height = max([len(line) for line in lines])\\n try:\\n float(lines[0][0])\\n is_numeric = True\\n except ValueError:\\n is_numeric = False\\n for line in lines:\\n #try:\\n # if is_numeric:\\n # dataset_group[int(line[0])]\\n # else:\\n # dataset_group[line[0]]\\n #except KeyError:\\n # continue\\n line.reverse()\\n for i, val in enumerate(line):\\n is_leaf = False\\n if val == '*':\\n node = TaxNode(val, None, is_numeric, is_leaf)\\n else:\\n if i == len(line) - 1:\\n is_leaf = True\\n\\n node = TaxNode(val, taxonomy[line[i - 1]][-1], is_numeric, is_leaf)\\n try:\\n current_nodes = taxonomy[val]\\n already_added = False\\n for current_node in current_nodes:\\n if current_node.parent is None:\\n already_added = True\\n elif current_node.parent.value == node.parent.value:\\n already_added = True\\n if not already_added:\\n taxonomy[val].append(node)\\n except KeyError:\\n taxonomy[val] = [node] # Saves the nodes in a list in case of several parents (only valid for nodes with several parents!!!)\\n hierarchy = Taxonomy(taxonomy, max_height)\\n\\n return hierarchy\",\n \"def fm_append_member(cls, parent, child):\\n parent.fm_append(child, cls.CHILD)\\n child.fm_append(parent, cls.PARENT)\",\n \"def child_classes(self):\\n response = check_defined(self, inspect.stack()[0][3])\\n if not response:\\n return response\\n children = self.se.full_class_only_graph.successors(self.uri)\\n result = restructure_output(self,\\n children,\\n inspect.stack()[0][3],\\n self.output_type)\\n return result\",\n \"def child(self, v, c):\\n # method here\",\n \"def needs_parens(parent, child, posn):\\n return False\",\n \"def test_parse_taxonomy(self):\\r\\n example_tax = \\\\\\r\\n \\\"\\\"\\\"412 PC.635_647\\tRoot;Bacteria;Firmicutes; \\\"Clostridia\\\";Clostridiales\\t0.930\\r\\n319 PC.355_281\\tRoot;Bacteria;Bacteroidetes \\t0.970\\r\\n353 PC.634_154\\tRoot; Bacteria ; Bacteroidetes\\t0.830\\r\\n17 PC.607_302\\tRoot;Bacteria;Bacteroidetes\\t0.960\\r\\n13 PC.481_1214\\tRoot;Bacteria;Firmicutes;\\\"Clostridia\\\";Clostridiales\\t0.870\\r\\n338 PC.593_1314\\tRoot; Bacteria \\t0.990\\t42556\\tAdditional fields ignored\\\"\\\"\\\"\\r\\n res = parse_taxonomy(example_tax.split('\\\\n'))\\r\\n self.assertEqual(res['412'],\\r\\n [\\\"Root\\\", \\\"Bacteria\\\", \\\"Firmicutes\\\", \\\"\\\\\\\"Clostridia\\\\\\\"\\\", \\\"Clostridiales\\\"])\\r\\n self.assertEqual(res['338'],\\r\\n [\\\"Root\\\", \\\"Bacteria\\\"])\",\n \"def set_child(self, c, line_number=0):\\n if isinstance(self.child, set):\\n self.child = self.child | {c}\\n self._child_line = self._child_line | {line_number}\\n else:\\n self.child = {c} if (c and c != 'NA') else 'NA'\\n self._child_line = {line_number}\",\n \"def __init__ (self, parent, child, length=None):\\n self.parent = parent\\n self.child = child\\n self.length = length\",\n \"def test_child_link(self):\\n def compare_func(obj, node):\\n child_nodes = self.get_children_of_node(node)\\n\\n for (child_obj, child_node) in zip(obj.children, child_nodes):\\n # parent-child link (children must have obj as their parent)\\n self.assertEqual(child_obj.parent, obj)\\n\\n self.recursively_compare_tree_against_html(compare_func)\",\n \"def test_add_child_category(self):\\n self.add_success(self.test_data['pants'])\\n rv = self.get('pants')\\n assert in_response(rv, 'This category is empty.')\\n self.add_success(self.test_data['jeans'])\\n rv = self.get('pants')\\n assert not in_response(rv, 'This category is empty.')\\n assert in_response(rv, 'Jeans')\",\n \"def _newChild(self, child):\\n self._testKeySubNsAdd()\\n self._getSubNsList().append(child)\",\n \"def __missing__(self, key):\\n child = super(Tree, self).__missing__(key)\\n child.name = key\\n child.mother = self\\n return child\",\n \"def test_get_child():\\n \\n root_ts = TrackSegment(flow_dict=flow_dict) \\n \\n # ROOT MODULE\\n start = root_ts.get_child(\\\"start\\\")\\n # check depth\\n assert(start.depth == root_ts.depth+1)\\n # check parent\\n assert(start.parent is root_ts)\\n # check module_id\\n assert(start.module_id == 'start')\\n \\n # CHILD (1,2,3)\\n root_ts_get_child_result = root_ts.get_child((1,2,3))\\n # check depth\\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\\n # check parent\\n assert(root_ts_get_child_result.parent is root_ts)\\n \\n # CHILD (2,3,4)\\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\\n # check depth\\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\\n # check parent \\n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\\n \\n print(\\\"TEST GET_CHILD: success!\\\")\",\n \"def testParentageChange(self):\\n cc2 = cdl_convert.ColorCorrectionRef('001')\\n cc2.parent = 'bob'\\n mr2 = cdl_convert.MediaRef('sdhjd.dpx')\\n mr2.parent = 'jim'\\n\\n self.cd.cc = cc2\\n self.cd.media_ref = mr2\\n\\n self.assertEqual(\\n self.cd,\\n cc2.parent\\n )\\n\\n self.assertEqual(\\n self.cd,\\n mr2.parent\\n )\",\n \"def child(self, index):\\n raise AttributeError, \\\"Cannot retrieve children from leaf nodes! Attempted on leaf:\\\\n\\\\n%s\\\" % self.prettyPrint()\",\n \"def test_ontology_term_inheritance(ontology):\\n\\tassert len(ontology.inherited(\\\"TO:0000001\\\")) == 1\\n\\tassert len(ontology.inherited(\\\"TO:0000002\\\")) == 2\\n\\tassert len(ontology.inherited(\\\"TO:0000003\\\")) == 2\\n\\tassert len(ontology.inherited(\\\"TO:0000004\\\")) == 4\\n\\tassert len(ontology.inherited(\\\"TO:0000005\\\")) == 5\\n\\tassert len(ontology.inherited(\\\"TO:0000006\\\")) == 5\\n\\tassert len(ontology.inherited(\\\"TO:0000007\\\")) == 2\\n\\tassert len(ontology.inherited(\\\"TO:0000008\\\")) == 3\\n\\tassert len(ontology.inherited(\\\"TO:0000009\\\")) == 4\\n\\n\\tassert len(ontology.inherited([\\\"TO:0000002\\\",\\\"TO:0000003\\\"])) == 3\\n\\tassert len(ontology.inherited([\\\"TO:0000009\\\",\\\"TO:0000005\\\"])) == 8\\n\\tassert len(ontology.inherited([\\\"TO:0000004\\\",\\\"TO:0000003\\\"])) == 4\\n\\tassert len(ontology.inherited([\\\"TO:0000002\\\"])) == 2\\n\\tassert len(ontology.inherited([])) == 0\",\n \"def make_forest (self, doclets):\\n\\n for o in doclets:\\n if o.memberof is None:\\n continue\\n if o.memberof in self.longnames:\\n o.parent = self.longnames[o.memberof]\\n o.parent.children.append (o)\\n continue\\n if o.doc ():\\n if o.memberof == '<anonymous>':\\n o.error (\\\"\\\"\\\"Could not link up object %s to %s.\\n Try giving the anonymous object an @alias.\\\"\\\"\\\"\\n % (o.longname, o.memberof))\\n else:\\n o.error (\\\"Could not link up object %s to %s\\\" % (o.longname, o.memberof))\",\n \"def _child_types(self, children):\\n\\n # Recursively determine types on each child node\\n child_types = []\\n for child in children:\\n child_types.append(self.expression_type(child))\\n return child_types\",\n \"def parse_taxonomy_set(self, taxonomy_xml):\\n\\n set_name = taxonomy_xml.findtext('taxonomy_name')\\n\\n def _parse_node(parent_node, parents=None):\\n \\\"\\\"\\\"The actual recursion function (generator). This iterates all of the child nodes of the element passed.\\n It yields the node data, and node keys as it finds. It keeps a few lists in memory to collate the\\n child/parent information as it is being parsed\\n \\\"\\\"\\\"\\n for node in parent_node.findall('node'):\\n node_name = node.findtext('node_name')\\n if node_name is None:\\n # We cannot proceed without a name\\n log.warn('Taxonomy Source has a node missing a node name')\\n continue\\n node_key = node_name.strip().lower().replace(' ', '_')\\n node_data = {\\n 'name': node_name,\\n 'parents': parents,\\n 'children': [],\\n }\\n # capture the integer properties of the node\\n for property in self.INT_PROPERTIES:\\n if property in node.attrib:\\n node_data[property] = int(node.get(property)) if len(node.get(property)) > 0 else None\\n # Loop the children nodes\\n parents.append(node_key)\\n for child_key, child_data in _parse_node(node, parents[:]):\\n if child_data['parents'][-1] == node_key:\\n node_data['children'].append(child_key)\\n yield child_key, child_data\\n parents.pop()\\n\\n yield node_key, node_data\\n\\n set_data = {key: data for key, data in _parse_node(taxonomy_xml, [])}\\n self._sets[set_name] = [key for key in set_data.keys()]\\n self.update(set_data)\",\n \"def children(node):\\n\\n return snd(node)\",\n \"def __init__(self):\\n self.children = dict()\\n self.isWord = False\",\n \"def _init_child(self,child,path):\\n pass\",\n \"def _addEntity(self, pid, chunks):\\n parent = chunks[pid]\\n sub = None\\n # Find subject\\n for i in range(len(parent.children)):\\n child = chunks[parent.children[i]]\\n if child.func in SubDict:\\n sub = child\\n if child.func == \\\"では\\\":\\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\\n pass\\n else:\\n sub = None\\n if sub:\\n self._addNode(parent, sub=sub.main)\\n self._addEdge(sub.main, parent.main, label=\\\"陳述\\\", etype=\\\"stat\\\")\\n else:\\n self._addNode(parent)\\n \\n # Lopp through all children\\n for i in range(len(parent.children)):\\n child = chunks[parent.children[i]]\\n # If child is noun\\n if child.func in SubDict:\\n if child.func == \\\"では\\\":\\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\\n pass\\n else:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=child.func, etype=\\\"attr\\\")\\n elif child.type == 0 and child.func in [\\\"と\\\", \\\"などと\\\"] and child.id + 1 == parent.id and preprocessText(chunks[parent.parent].main) not in [\\\"交代\\\", \\\"交換\\\"]:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self._addEdge(parent.main, child.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self.para.append([child.main, parent.main])\\n elif child.type == 0 and child.func in ParallelDict and child.id + 1 == parent.id:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self._addEdge(parent.main, child.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self.para.append([child.main, parent.main])\\n else:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=child.func, etype=\\\"attr\\\")\",\n \"def visit_Compound(self, node):\\n for child in node.children:\\n self.visit(child)\",\n \"def create_hierarchy(parent_path, child_path):\\n if parent_path:\\n parent_skill_id = database_controller.get_skill(parent_path).id\\n child_skill_id = database_controller.get_skill(child_path).id\\n new_hierarchy = Hierarchy(parent_skill_id=parent_skill_id, child_skill_id=child_skill_id)\\n db.session.add(new_hierarchy)\\n db.session.commit()\\n else:\\n child_skill_id = database_controller.get_skill(child_path).id\\n new_hierarchy = Hierarchy(child_skill_id=child_skill_id)\\n db.session.add(new_hierarchy)\\n db.session.commit()\\n ''' \\n new_hierarchy = Hierarchy()\\n if parent_path:\\n parent_skill = database_controller.get_skill(parent_path)\\n new_hierarchy.parent_skill_assoc = parent_skill\\n child_skill = database_controller.get_skill(child_path)\\n new_hierarchy.child_skill_assoc = child_skill\\n db.session.add(new_hierarchy)\\n db.session.commit()\\n '''\",\n \"def classify(self, document, tree):\\n if type(tree) is ClassTreeNode:\\n return tree.c\\n else:\\n if tree.word in document.bag_of_words:\\n return self.classify(document, tree.children[0])\\n else:\\n return self.classify(document, tree.children[1])\",\n \"def test_subsample_taxonomy(self):\\n basic_test_runner(self, 'taxonomy', nrows=6, niter=3, normalize='subsample')\",\n \"def visit_tertiary_node(self, node, children):\\n return {node.rule_name: children[0]}\",\n \"def has_child(self, term):\\n for parent in self.children:\\n if parent.id == term or parent.has_child(term):\\n return True\\n return False\",\n \"def mutate(self, child):\\n return child\",\n \"def classify(tree, input):\\n\\n #if this is a leaf node, return its value\\n if tree in [True, False]:\\n return tree\\n\\n #otherwise this tree consists of an attribute to split on\\n #and a dict whose keys are values of that attribute\\n #and whose values are subtrees to consider next\\n attribute, subtree_dict = tree\\n\\n subtree_key = input.get(attribute) #None if input is missing\\n\\n if subtree_key not in subtree_dict: #if no subtree for key, use None\\n subtree_key = None\\n\\n subtree = subtree_dict[subtree_key] # choose the appropriate subtree\\n return classify(subtree, input) # and use it to classify the input\",\n \"def set_categories(post, old, new):\\n c = None\\n if new:\\n c = Category(name=new.pop())\\n parent = c\\n son = None\\n while new:\\n son = parent\\n parent = Category(name=new.pop())\\n son.parent = parent\\n db.session.add(parent)\\n if old:\\n old_c = Category.query.get(old)\\n parent.parent = old_c\\n elif old:\\n c = Category.query.get(old)\\n if c is not None:\\n db.session.add(c)\\n post.category = c\",\n \"def merge_nodes(self, parent, child):\\n parent.key += child.key\\n parent.real = child.real\\n parent.value = child.value\\n parent.children = child.children\",\n \"def cousins(word, level):\\r\\n parents = [get_synset(word)]\\r\\n for _ in range(level):\\r\\n parent_hypernyms = [parent.hypernyms() for parent in parents]\\r\\n parents = list(chain(*parent_hypernyms))\\r\\n\\r\\n children = parents\\r\\n for _ in range(level):\\r\\n children_hyponyms = [child.hyponyms() for child in children]\\r\\n children = list(chain(*children_hyponyms))\\r\\n\\r\\n children = set(children)\\r\\n children -= {get_synset(word)}\\r\\n return set(map(synset_name, children))\",\n \"def test_closest_child(self):\\n self.assertEqual(self.vectors.closest_child('dog.n.01'), 'terrier.n.01')\\n self.assertEqual(self.vectors.closest_child('harbor_porpoise.n.01'), None)\",\n \"def is_child(self, kid, mother): \\n mom_node = self.names_to_nodes[mother] \\n child_node = self.names_to_nodes[kid]\\n return mom_node.is_child(child_node)\",\n \"def add_child(self, character, child_node):\\n # make sure character is uppercase\\n character = character.upper()\\n # TODO: Should I consider check if char is english letter?\\n if not character.isalpha():\\n raise ValueError(f'Child can only be English letters!')\\n\\n if not self.has_child(character):\\n # create a new node for this character\\n # child_node = PrefixTreeNode(character)\\n index = self._get_index(character)\\n # create empty list for children\\n # self.children = [None] * 26\\n # place the new child into right position\\n self.children[index] = child_node\\n else:\\n raise ValueError(f'Child exists for character {character!r}')\",\n \"def young_child(self, parent):\\n return 2 * parent\",\n \"def two_best_children(self,instance):\\n\\t\\tif len(self.tree.children) == 0:\\n\\t\\t\\traise Exception(\\\"No children!\\\")\\n\\t\\t\\n\\t\\tself.utility.increment_counts(instance)\\n\\t\\tchildren_cu = []\\n\\t\\tfor i in range(len(self.tree.children)):\\n\\t\\t\\tself.tree.children[i].utility.increment_counts(instance)\\n\\t\\t\\tchildren_cu.append((self.utility.category_utility(self.tree.children[i]),i))\\n\\t\\t\\tself.tree.children[i].utility.decrement_counts(instance)\\n\\t\\tself.utility.decrement_counts(instance)\\n\\t\\tchildren_cu.sort(reverse=True)\\n\\n\\t\\tif len(self.tree.children) == 1:\\n\\t\\t\\treturn children_cu[0], None \\n\\n\\t\\treturn children_cu[0], children_cu[1]\",\n \"def get_mds_children(node):\\n children=node.getChildren()\\n return {get_mds_shortname(child):child for child in children}\",\n \"def set_child(self, child):\\n if child.tags is None:\\n raise Exception(\\\"Missing tags when: Component {0} tried ta add_child \\\"\\n \\\"component: {1} to its children. \\\".format(str(self), str(child)))\\n if child.component_type is None:\\n raise Exception(\\\"Missing component_type when: Component {0} tried ta add_child \\\"\\n \\\"component: {1} to its children. \\\".format(str(self), str(child)))\\n if not child._parent is None:\\n raise Exception(\\\"Component {0} tried ta add_child \\\"\\n \\\"component: {1} to its children. \\\"\\n \\\"But it already \\\"\\n \\\"had parent: {2}.\\\".format(str(self), str(child), str(child.parent)))\\n if self.has(child.component_type):\\n self.remove_component_of_type(child.component_type)\\n self._children[child.component_type] = child\\n self._add_child_to_tag_table(child)\\n child.parent = self\",\n \"def children(word, word_dict):\\n res = []\\n for i in range(len(word)):\\n child = word[:i]+word[i+1:]\\n if child in word_dict:\\n res.append(child)\\n return res\",\n \"def test_diamond(self):\\n b_child_of_a = InheritanceFactory.create(child__name='b', parent__name='a')\\n a = b_child_of_a.parent\\n b = b_child_of_a.child\\n\\n c = InheritanceFactory.create(parent=a, child__name='c').child\\n d = InheritanceFactory.create(parent=b, child__name='d').child\\n InheritanceFactory.create(parent=c, child=d, order=2)\\n\\n mro = d.get_all_ancestors()\\n self.assertSequenceEqual(mro, [b, c, a])\",\n \"def get_related_taxonomy(taxonomy_list=None):\\n if not taxonomy_list:\\n taxonomy_list = []\\n\\n taxonomies = []\\n for taxonomy in taxonomy_list:\\n list = __get_related_list__(taxonomy)\\n\\n for child_taxonomy in list:\\n if child_taxonomy not in taxonomies:\\n taxonomies.append(child_taxonomy)\\n\\n return taxonomies\",\n \"def test_taxonomy_process_prefs(self):\\r\\n taxonomy_levels = [2, 3, 4]\\r\\n exp1 = {}\\r\\n exp1['level_2'] = {'column': '2'}\\r\\n exp1['level_3'] = {'column': 3, 'colors':\\r\\n {'a': ('red', (0, 100, 100))}}\\r\\n obs1 = taxonomy_process_prefs(taxonomy_levels, exp1)\\r\\n\\r\\n exp2 = {}\\r\\n exp2['2'] = {'column': '2', 'colors': {}}\\r\\n exp2['3'] = {'column': '3', 'colors': {'a': ('red', (0, 100, 100))}}\\r\\n exp2['4'] = {'column': '4', 'colors': {}}\\r\\n self.assertEqual(obs1, exp2)\",\n \"def _del_node_one_child(self, parent, node):\\n if parent:\\n if parent._rkid == node:\\n if node._rkid:\\n parent._rkid = node._rkid\\n node._rkid._parent = parent\\n else:\\n parent._rkid = node._lkid\\n node._lkid._parent = parent\\n elif node._rkid:\\n parent._lkid = node._rkid\\n node._rkid._parent = parent\\n else:\\n parent._lkid = node._lkid\\n node._lkid._parent = parent\\n else:\\n if node._rkid:\\n self._root = node._rkid\\n node._rkid._parent = None\\n else:\\n self._root = node._lkid\\n node._lkid._parent = None\",\n \"def new_child(self,instance):\\n\\t\\treturn self.cu_for_new_child(instance,False)\",\n \"def child_constr(self):\\n\\t\\tself.append_child(Bond((258.0, 0.0, 2950.0), (0.0, 0.0, 1.0), 0.0))\\n\\t\\tself.append_child(Bond((-258.0, 0.0, 2950.0), (0.0, 0.0, 1.0), 180.0))\\n\\t\\tself.append_child(Bond((0.0, 258.0, 2950.0), (0.0, 0.0, 1.0), 90.0))\\n\\t\\tself.append_child(Bond((0.0, -258.0, 2950.0), (0.0, 0.0, 1.0), -90.0))\\n\\t\\tself.append_child(Column_Stock((0.0, 0.0, 3000.0), (0.0, 0.0, 1.0), -90.0))\",\n \"def __init__(self, word):\\n TreeNode.__init__(self)\\n self.word = word\\n self.children = []\",\n \"def visit_cname_place(self, node, children):\\n # If a value is supplied it will be a single time list, so extract with [0]\\n # If no value is supplied for an optional item, default must also be a single item list [default_value]\\n w = children.results.get('wrap')\\n wrap_value = 1 if not w else w[0]['wrap']\\n cplace = {'cname': children.results['name'][0], # Required component\\n 'dir': children.results.get('dir', [1])[0], # many optional components with default values\\n 'bend': children.results.get('bend', [1])[0],\\n 'notch': children.results.get('notch', [0])[0],\\n 'wrap': wrap_value,\\n }\\n return cplace\",\n \"def is_subhalo(self, childid, parentid):\\n if (childid in self._halos[parentid].properties['children']):\\n return True\\n else:\\n return False\",\n \"def add_child_info(conll_tokens, child_funcs, child_strings, lex):\\n\\tfor child_id in child_funcs:\\n\\t\\tif child_id > len(conll_tokens)-1:\\n\\t\\t\\tcontinue\\n\\t\\tfor func in child_funcs[child_id]:\\n\\t\\t\\tif func not in conll_tokens[child_id].child_funcs:\\n\\t\\t\\t\\tconll_tokens[child_id].child_funcs.append(func)\\n\\t\\t\\t\\tif lex.filters[\\\"neg_func\\\"].match(func):\\n\\t\\t\\t\\t\\tconll_tokens[child_id].negated = True\\n\\t\\tfor tok_text in child_strings[child_id]:\\n\\t\\t\\tif tok_text not in conll_tokens[child_id].child_strings:\\n\\t\\t\\t\\tconll_tokens[child_id].child_strings.append(tok_text)\",\n \"def test_hasChild(self):\\n bag = Bag('faded plum', [(2, 'wavy cyan'), (1, 'navy turquoise')])\\n res = bag.hasChild('wavy cyan')\\n self.assertTrue(res)\\n bag1 = Bag('gaded plum', [(2, 'wavy cyan'), (1, 'navy turquoise')])\\n res1 = bag1.hasChild('gaded plum')\\n self.assertFalse(res1)\",\n \"def archetype(self):\\n return_value = 0\\n if self.thesclass is not None:\\n try:\\n parent = [a for a in self.thesclass.ancestors() if\\n a.label is not None and a.label][0]\\n except IndexError:\\n pass\\n else:\\n label = parent.label\\n if label in (self.lemma, 'a ' + self.lemma, 'an ' + self.lemma):\\n return_value = 2\\n else:\\n target = ' ' + self.lemma + ' '\\n label = label.replace(',', ' ').replace(';', ' ')\\n label = ' ' + label + ' '\\n if target in label:\\n return_value = 1\\n return return_value\",\n \"def crossover_binary(parent1: numpy.ndarray, parent2: numpy.ndarray, eta: float):\\n # Ref: https://github.com/Chrispresso/SnakeAI/blob/master/genetic_algorithm/crossover.py\\n rand = numpy.random.random(size=parent1.shape)\\n gamma = numpy.empty(shape=parent1.shape)\\n gamma[rand <= 0.5] = (2 * rand[rand <= 0.5]) ** (1.0 / (eta + 1)) # First case of equation 9.11\\n gamma[rand > 0.5] = (1.0 / (2.0 * (1.0 - rand[rand > 0.5]))) ** (1.0 / (eta + 1)) # Second case\\n\\n # Calculate Child 1 chromosome (Eq. 9.9)\\n chromosome1 = 0.5 * ((1 + gamma) * parent1 + (1 - gamma) * parent2)\\n # Calculate Child 2 chromosome (Eq. 9.10)\\n chromosome2 = 0.5 * ((1 - gamma) * parent1 + (1 + gamma) * parent2)\\n\\n return chromosome1, chromosome2\",\n \"def get_subname(get, node):\\n if isinstance(node, ast.Attribute) and get(node.value) == 'parent':\\n return node.attr\",\n \"def test_update_child_category(self):\\n self.add_success(self.test_data['pants'])\\n self.add_success(self.test_data['shirts'])\\n\\n self.add_success(self.test_data['jeans'])\\n rv = self.get('pants')\\n assert not in_response(rv, 'This category is empty.')\\n assert in_response(rv, 'Jeans')\\n\\n self.edit_success('jeans', self.test_data['t-shirts'])\\n rv = self.get('pants')\\n assert in_response(rv, 'This category is empty.')\\n assert not in_response(rv, 'Jeans')\\n assert not in_response(rv, 'T-Shirts')\\n rv = self.get('shirts')\\n assert not in_response(rv, 'This category is empty.')\\n assert in_response(rv, 'T-Shirts')\\n assert not in_response(rv, 'Jeans')\",\n \"def test_append_children_category(self):\\n category = Category(catname='olympic games')\\n category1 = Category(catname='Tennis')\\n category.parents.append(category1)\\n category.save()\\n assert category.parents\",\n \"def visit_ClassDef(self, node):\\n self.recurse(node)\",\n \"def make_child(self, key):\\n child = Bar(key)\\n child.__parent__ = self\\n return child\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5975638","0.58271253","0.5484291","0.5396265","0.5390652","0.53713834","0.53584284","0.53490835","0.5338156","0.53251886","0.5263821","0.5227132","0.51992923","0.5184101","0.51213396","0.5080763","0.50803787","0.5078497","0.50769305","0.5075885","0.50741947","0.5056136","0.50552154","0.50527424","0.50440645","0.50069416","0.49847037","0.4984668","0.4952457","0.49428636","0.49380222","0.49248993","0.49102858","0.49060124","0.48977083","0.48935714","0.4887358","0.48835054","0.48834538","0.48712793","0.4855589","0.48501235","0.4816588","0.47865066","0.47853628","0.4777044","0.4769141","0.47509375","0.4736678","0.47275728","0.47237462","0.47228438","0.47203556","0.47093606","0.47074112","0.47026742","0.47012562","0.4696927","0.46964717","0.46854317","0.46845835","0.46798038","0.4676964","0.46759427","0.46751803","0.4670078","0.46696478","0.46619558","0.46577054","0.4655504","0.46372947","0.46362802","0.4635823","0.4635247","0.46342775","0.46320233","0.46267244","0.46108857","0.46024227","0.45998845","0.45981959","0.45968935","0.45964697","0.45924672","0.4590994","0.45902866","0.4587706","0.45866606","0.458588","0.45793006","0.4575962","0.45750818","0.4572282","0.4571978","0.4568506","0.45622784","0.45617443","0.45604107","0.45585185","0.45493075"],"string":"[\n \"0.5975638\",\n \"0.58271253\",\n \"0.5484291\",\n \"0.5396265\",\n \"0.5390652\",\n \"0.53713834\",\n \"0.53584284\",\n \"0.53490835\",\n \"0.5338156\",\n \"0.53251886\",\n \"0.5263821\",\n \"0.5227132\",\n \"0.51992923\",\n \"0.5184101\",\n \"0.51213396\",\n \"0.5080763\",\n \"0.50803787\",\n \"0.5078497\",\n \"0.50769305\",\n \"0.5075885\",\n \"0.50741947\",\n \"0.5056136\",\n \"0.50552154\",\n \"0.50527424\",\n \"0.50440645\",\n \"0.50069416\",\n \"0.49847037\",\n \"0.4984668\",\n \"0.4952457\",\n \"0.49428636\",\n \"0.49380222\",\n \"0.49248993\",\n \"0.49102858\",\n \"0.49060124\",\n \"0.48977083\",\n \"0.48935714\",\n \"0.4887358\",\n \"0.48835054\",\n \"0.48834538\",\n \"0.48712793\",\n \"0.4855589\",\n \"0.48501235\",\n \"0.4816588\",\n \"0.47865066\",\n \"0.47853628\",\n \"0.4777044\",\n \"0.4769141\",\n \"0.47509375\",\n \"0.4736678\",\n \"0.47275728\",\n \"0.47237462\",\n \"0.47228438\",\n \"0.47203556\",\n \"0.47093606\",\n \"0.47074112\",\n \"0.47026742\",\n \"0.47012562\",\n \"0.4696927\",\n \"0.46964717\",\n \"0.46854317\",\n \"0.46845835\",\n \"0.46798038\",\n \"0.4676964\",\n \"0.46759427\",\n \"0.46751803\",\n \"0.4670078\",\n \"0.46696478\",\n \"0.46619558\",\n \"0.46577054\",\n \"0.4655504\",\n \"0.46372947\",\n \"0.46362802\",\n \"0.4635823\",\n \"0.4635247\",\n \"0.46342775\",\n \"0.46320233\",\n \"0.46267244\",\n \"0.46108857\",\n \"0.46024227\",\n \"0.45998845\",\n \"0.45981959\",\n \"0.45968935\",\n \"0.45964697\",\n \"0.45924672\",\n \"0.4590994\",\n \"0.45902866\",\n \"0.4587706\",\n \"0.45866606\",\n \"0.458588\",\n \"0.45793006\",\n \"0.4575962\",\n \"0.45750818\",\n \"0.4572282\",\n \"0.4571978\",\n \"0.4568506\",\n \"0.45622784\",\n \"0.45617443\",\n \"0.45604107\",\n \"0.45585185\",\n \"0.45493075\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68073165"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17445,"cells":{"query":{"kind":"string","value":"Placeholders for input NB images are expressed in terms of vector and not matrices."},"document":{"kind":"string","value":"def main(restore_save=False):\n x = tf.placeholder(tf.float32, shape=[None, 784])\n\n # Placeholder for targets\n targets = tf.placeholder(tf.float32, shape=[None, 10])\n\n # Placeholder for discerning train/eval mode\n is_training = tf.placeholder(dtype=tf.bool)\n\n # Define global step to indicize checkpoint saves\n global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name=\"global_step\")\n\n mnist = input_data.read_data_sets('/tmp/mnist', one_hot=True)\n\n model = MultiLayerPerceptron([256, 64, 10], x, targets, is_training, step=global_step, learning_rate=0.0000975)\n init_op = tf.global_variables_initializer()\n\n writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n saver = tf.train.Saver(model.get_vars_to_save())\n with tf.Session() as sess:\n writer = tf.summary.FileWriter(logdir, sess.graph)\n\n if(restore_save):\n # check if there is checkpoint\n ckpt = tf.train.get_checkpoint_state(os.path.dirname('checkpoints/checkpoint'))\n\n # check if there is a valid checkpoint path\n if ckpt and ckpt.model_checkpoint_path:\n saver.restore(sess, ckpt.model_checkpoint_path)\n print(\"Checkpoint restored: {}\".format(ckpt.model_checkpoint_path))\n\n #sess = tf_debug.LocalCLIDebugWrapperSession(sess)\n\n # Initialize all variables\n sess.run(init_op)\n\n # Training parameters\n training_epochs = 50\n batch_size = 128\n\n # Number of batches to process to see whole dataset\n batches_each_epoch = mnist.train.num_examples // batch_size\n\n for epoch in range(training_epochs):\n\n # During training measure accuracy on validation set to have an idea of what's happening\n val_accuracy = sess.run(fetches=model.accuracy,\n feed_dict={x: mnist.validation.images,\n targets: mnist.validation.labels,\n is_training: False})\n \n saver.save(sess, \n 'checkpoints/mlp.ckpt',\n global_step=global_step)\n\n print(\n 'Epoch: {:06d} - VAL accuracy: {:.03f} %'.format(epoch, val_accuracy * 100))\n\n for index in range(batches_each_epoch):\n\n # Load a batch of training data\n x_batch, target_batch = mnist.train.next_batch(batch_size)\n\n # Actually run one training step here\n _, summary = sess.run(fetches=[model.optimize, model.summary],\n feed_dict={x: x_batch, targets: target_batch, is_training: True})\n writer.add_summary(summary, global_step=index * (epoch + 1))\n\n test_accuracy = sess.run(fetches=model.accuracy,\n feed_dict={x: mnist.test.images,\n targets: mnist.test.labels,\n is_training: False})\n print('*' * 50)\n print('Training ended. TEST accuracy: {:.03f} %'.format(\n test_accuracy * 100))\n writer.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def logits_placeholder(self):","def neural_net_image_input(image_shape):\n return tf.placeholder(tf.float32, shape=[None]+list(image_shape), name=\"x\")","def neural_net_image_input(image_shape):\n # TODO: Implement Function\n n_input_1 = image_shape[0]\n n_input_2 = image_shape[1]\n n_input_3 = image_shape[2]\n return tf.placeholder(tf.float32,[None, n_input_1, n_input_2, n_input_3], name='x')","def dummy_inputs(self):\n input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32)\n batch_size, seq_len = input_ids.shape\n\n VISION_DUMMY_INPUTS = tf.random.uniform(\n shape=(\n batch_size,\n self.config.vision_config.num_channels,\n self.config.vision_config.image_size,\n self.config.vision_config.image_size,\n ),\n dtype=tf.float32,\n )\n pixel_values = tf.constant(VISION_DUMMY_INPUTS)\n dummy = {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n return dummy","def neural_net_image_input(image_shape):\n # TODO: Implement Function\n return tf.placeholder(tf.float32, shape=[None, *image_shape], name=\"x\")","def neural_net_image_input(image_shape):\n return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')","def neural_net_image_input(image_shape):\n # TODO: Implement Function\n shape = (None,image_shape[0],image_shape[1],image_shape[2])\n x = tf.placeholder(tf.float32,shape,name = 'x')\n return x","def neural_net_image_input(image_shape):\n image_height = image_shape[0]\n image_width = image_shape[1]\n color_channels = image_shape[2]\n imagetensor = tf.placeholder(tf.float32, shape=[None, image_height, image_width, color_channels],name='x')\n return imagetensor","def _setup_images_and_labels(self):\n if FLAGS.dataset == 'cifar10':\n self.num_classes = 10\n else:\n self.num_classes = 100\n self.images = tf.placeholder(tf.float32, [self.batch_size, 32, 32, 3])\n self.labels = tf.placeholder(tf.float32,\n [self.batch_size, self.num_classes])","def placeholder_inputs(batch_size):\n images_ph = tf.placeholder(tf.float32, shape=(batch_size, FLAGS.img_s, FLAGS.img_s, 3), name='images_placeholder') \n labels_ph = tf.placeholder(tf.float32, shape=(batch_size, FLAGS.n_classes), name='labels_placeholder')\n keep_prob_ph = tf.placeholder(tf.float32, shape=(), name='keep_prob_placeholder')\n\n return images_ph, labels_ph, keep_prob_ph","def placeholder_inputs(batch_size):\n # Note that the shapes of the placeholders match the shapes of the full\n # image and label tensors, except the first dimension is now batch_size\n # rather than the full size of the train or test data sets.\n images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,\n c3d_model.NUM_FRAMES_PER_CLIP,\n c3d_model.CROP_SIZE,\n c3d_model.CROP_SIZE,\n c3d_model.CHANNELS))\n labels_placeholder = tf.placeholder(tf.int64, shape=(batch_size))\n return images_placeholder, labels_placeholder","def __init__(self, input):\n try:\n img = Image.open(input).resize(ImageVector.size)\n self.pixels = img.getdata()\n except:\n self.pixels = input","def MNIST_data():\n\n # Pobieramy macierze numpy z cyframi\n # images[i,j,k] <=> piksel (j,k) z i-tego obrazka w zbiorze danych\n images, labels = get_MNIST_dataset(range(10), \"training\") #pierwszy argument to\n\n # a) Ilosc przykladow i rozmiary danych\n print \"Raw training data dimensions \", images.shape\n print \"Labels dimensions \",labels.shape\n\n # b) Ile jest cyfr 2?\n print \"Counting 2 in training dataset \",len(filter(lambda x: x == 2, labels))\n\n # c) Jaki jest sredni obrazek 2 ? (Usrednienie wszystkich macierzy ktore sa 2)\n\n #1. Pobierzmy wszystkie dwojki, fajny sposob indeksowania\n print labels == 2\n only_2 = images[labels == 2, :, :]\n print \"Checking number of 2s \", only_2.shape\n\n #2. TODO: Usrednienie (matrix.mean moze byc przydatne)\n\n #3. TODO: narysowanie usrednionej cyfry (zobacz pl.imshow)\n\n # d) Ostatnie - przetworzmy ostatnia cyfre do 1 wymiarowego wektora\n vectorized = np.reshape(images[-1], newshape=(images[-1].shape[0]*images[-1].shape[1]))\n print \"Vectorized last digit \", vectorized","def testShapesNotKnown(self, use_bias):\n\n batch_size = 5\n in_length = 32\n in_channels = out_channels = 5\n kernel_shape = 3\n\n inputs = tf.placeholder(\n tf.float32,\n shape=[None, None, in_channels],\n name=\"inputs\")\n\n conv1 = snt.Conv1D(\n name=\"conv1\",\n output_channels=out_channels,\n kernel_shape=kernel_shape,\n padding=snt.SAME,\n stride=1,\n use_bias=use_bias)\n\n output = conv1(inputs)\n\n with self.test_session():\n tf.variables_initializer(\n [conv1.w, conv1.b] if use_bias else [conv1.w]).run()\n\n output_eval = output.eval({\n inputs: np.zeros([batch_size, in_length, in_channels])})\n\n self.assertEqual(\n output_eval.shape,\n (batch_size, in_length, out_channels))","def imagenet_preprocess(image, label):\n i = image\n i = tf.cast(i, tf.float32)\n i = tf.image.resize_with_crop_or_pad(i, 224, 224)\n if model_name == 'ResNet50' or model_name == 'ResNet152':\n i = tf.keras.applications.resnet.preprocess_input(i)\n else:\n i = tf.keras.applications.densenet.preprocess_input(i)\n return (i, label)","def _check_neb_inputs(self):\n\n incar = self.ctx.inputs.parameters\n\n images = incar.get('images')\n\n if not images:\n raise InputValidationError('IMAGES parameters is not set in the INCAR inputs')\n\n nimages = len(self.ctx.inputs.neb_images)\n\n if nimages != images:\n raise InputValidationError('Mismatch between IMAGES and actual number supplied input structures.')\n\n # Check for NEB tags\n iopt = incar.get('iopt', 0)\n ibrion = incar.get('ibrion')\n potim = incar.get('potim')\n\n # Check the sanity of parameters\n if ibrion != 3:\n self.report('WARNING: IBRION should be set to 3 for VTST runs, proceed with caution.')\n elif potim != 0:\n self.report(\n 'WARNING: Using VTST optimisors with IBRION=3, but POTIM is not set to zero, proceed with caution.'\n )\n if iopt == 0:\n self.report('WARNING: IOPT not set.')\n\n if ibrion == 2:\n raise InputValidationError('IBRION=2 should not be used for NEB optimization!!')\n\n # Check the displacement of atoms between the frames\n # the hope is that this may detect simple errors such as atoms going across the PBC or\n # the order of atoms are changed between different frames\n\n tmp = list(self.ctx.inputs.neb_images.items())\n tmp.sort(key=lambda x: x[0])\n frames = [x[1].get_ase() for x in tmp]\n frames = [self.ctx.inputs.initial_structure.get_ase()] + frames + [self.ctx.inputs.final_structure.get_ase()]\n\n last_frame = frames[0]\n # Function for computing the distance using the scaled positions\n rel_dist = np.vectorize(lambda x: x if x < 0.5 else 1. - x)\n for iframe, frame in enumerate(frames[1:]):\n # Relative displacements\n disp = abs(frame.get_scaled_positions() - last_frame.get_scaled_positions()) % 1.0\n # Apply convention\n disp = rel_dist(disp)\n # Convert back to absolute displacement\n disp = disp @ frame.cell\n norm_disp = np.linalg.norm(disp, axis=1)\n sort_idx = np.argsort(norm_disp)\n if norm_disp[sort_idx[-1]] > self._norm_disp_threshold:\n raise InputValidationError(\n 'Large displacement detected for atom {} at frame {} - please check the inputs images'.format(\n sort_idx[-1], iframe + 1\n )\n )\n last_frame = frame","def preprocess_image(self, inputs):\n raise NotImplementedError('preprocess_image method not implemented.')","def create_model_input_default(self, rgb_images, all_boxes, all_masks):\n box_regions = []\n for i in range(all_boxes.size(0)):\n for j in range(all_boxes.size(1)):\n box = all_boxes[i][j]\n if is_dummy_box(box):\n continue\n image = rgb_images[i].cpu()\n\n # Apply object mask to the image\n if self.use_masks:\n image = image.clone()\n mask = all_masks[i][j].cpu()\n image = image * mask\n\n box_region = get_patch_from_image(box, image)\n box_regions.append(box_region)\n\n t = T.Compose([T.ToPILImage(), T.Resize((self.reduced_size, self.reduced_size)), T.ToTensor()])\n box_regions = [t(box.cpu()) for box in box_regions]\n return torch.stack(box_regions)","def testShapesNotKnownAtrous(self, use_bias):\n\n inputs = tf.placeholder(\n tf.float32,\n shape=[None, None, None, 5],\n name=\"inputs\")\n\n conv1 = snt.Conv2D(\n name=\"conv1\",\n output_channels=5,\n kernel_shape=[3, 3],\n padding=snt.SAME,\n stride=1,\n rate=2,\n use_bias=use_bias)\n\n conv1(inputs)","def preprocess_image(self, batched_inputs):\n images = [x.to(self.device) for x in batched_inputs]\n norms = [self.normalizer(x) for x in images]\n size = (norms[0].shape[1],norms[0].shape[2])\n images = ImageList.from_tensors(norms, self.backbone.size_divisibility)\n return images, size","def nits(self):","def input_shape(self):\n return [None, 32, 32, 1]","def input_shape(self):\n return [None, 32, 32, 1]","def input_shape(self):\n return [None, 32, 32, 1]","def inference_input():\n # Decode image into float range [0,1]\n jpegs = tf.placeholder(tf.string, shape=(1), name='input')\n image_buffer = tf.squeeze(jpegs, [0])\n image = tf.image.decode_jpeg(image_buffer, channels=3)\n image = tf.image.convert_image_dtype(image, dtype=tf.float32)\n image = tf.image.central_crop(image, central_fraction=0.875)\n image = tf.expand_dims(image, 0)\n image = tf.image.resize_bilinear(image, [FLAGS.image_size, FLAGS.image_size], align_corners=False)\n image = tf.squeeze(image, [0])\n\n # Rescale the image to [-1,-1]\n image = tf.sub(image, 0.5)\n image = tf.mul(image, 2.0)\n images = tf.expand_dims(image, 0)\n\n return images, jpegs","def testShapesNotKnown(self, use_bias):\n\n batch_size = 5\n in_height = in_width = 32\n in_channels = out_channels = 5\n kernel_shape_h = kernel_shape_w = 3\n\n inputs = tf.placeholder(\n tf.float32,\n shape=[None, None, None, in_channels],\n name=\"inputs\")\n\n conv1 = snt.Conv2D(\n name=\"conv1\",\n output_channels=out_channels,\n kernel_shape=[kernel_shape_h, kernel_shape_w],\n padding=snt.SAME,\n stride=1,\n use_bias=use_bias)\n\n output = conv1(inputs)\n\n with self.test_session():\n tf.variables_initializer(\n [conv1.w, conv1.b] if use_bias else [conv1.w]).run()\n\n output_eval = output.eval({\n inputs: np.zeros([batch_size, in_height, in_width, in_channels])})\n\n self.assertEqual(\n output_eval.shape,\n (batch_size, in_height, in_width, out_channels))","def testShapesNotKnown(self, use_bias):\n\n inputs = tf.placeholder(\n tf.float32, shape=[None, None, self.in_channels], name=\"inputs\")\n\n conv1 = snt.SeparableConv1D(\n output_channels=self.out_channels_dw,\n channel_multiplier=1,\n kernel_shape=self.kernel_shape,\n padding=snt.SAME,\n use_bias=use_bias)\n output = conv1(inputs)\n\n with self.test_session() as session:\n tf.variables_initializer(\n [conv1.w_dw, conv1.w_pw, conv1.b] if use_bias else\n [conv1.w_dw, conv1.w_pw]).run()\n output_eval = session.run(output, {inputs: np.zeros(self.input_shape)})\n self.assertEqual(output_eval.shape, tuple(self.output_shape))","def neural_net_image_input(self, image_shape):\n assert len(image_shape) == 3, 'image shape is not correct'\n features = tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], 'x')\n return features","def test_missing_info(self):\r\n self.validate(None, None,\r\n N_image_shape=(3, 2, 8, 8),\r\n N_filter_shape=(4, 2, 5, 5))\r\n self.validate((3, 2, None, None), None,\r\n N_image_shape=(3, 2, 8, 8),\r\n N_filter_shape=(4, 2, 5, 5))\r\n self.validate((None, 2, None, None), (None, 2, 5, 5),\r\n N_image_shape=(3, 2, 8, 8),\r\n N_filter_shape=(4, 2, 5, 5))","def preprocess(self, resized_inputs):\n return (2.0 / 255.0) * resized_inputs - 1.0","def testShapesNotKnown(self, use_bias):\n\n inputs = tf.placeholder(\n tf.float32, shape=[None, None, None, self.in_channels], name=\"inputs\")\n\n conv1 = snt.SeparableConv2D(\n output_channels=self.out_channels_dw,\n channel_multiplier=1,\n kernel_shape=self.kernel_shape,\n padding=snt.SAME,\n use_bias=use_bias)\n output = conv1(inputs)\n\n with self.test_session():\n tf.variables_initializer(\n [conv1.w_dw, conv1.w_pw, conv1.b] if use_bias else\n [conv1.w_dw, conv1.w_pw]).run()\n output_eval = output.eval({inputs: np.zeros(self.input_shape)})\n self.assertEqual(output_eval.shape, tuple(self.output_shape))","def test_nparray_input(self):\n\n vgg16 = VGG16()\n data = np.ones((224,224,3))\n output = vgg16.input_handler(data)\n\n self.assertEqual(len(output.shape), 4) \n self.assertEqual(output.shape[1], 224)\n self.assertEqual(output.shape[2], 224)\n self.assertEqual(output.shape[3], 3)\n\n data = np.random.rand(120,75,3)\n output = vgg16.input_handler(data)\n\n self.assertEqual(len(output.shape), 4) \n self.assertEqual(output.shape[1], 224)\n self.assertEqual(output.shape[2], 224)\n self.assertEqual(output.shape[3], 3)","def __init__(self, nvim):\n self.nvim = nvim","def image_special_func(img):\n return myimg.image_special_func(img.tolist())","def add_placeholders(self):\n ### YOUR CODE HERE\n self.input_placeholder=tf.placeholder(dtype=tf.float32,shape=(None, Config.n_features),name='input_placeholder')\n self.labels_placeholder=tf.placeholder(dtype=tf.int32,shape=(None, Config.n_classes), name='labels_placeholder')\n ### END YOUR CODE","def sanitise_inputs(self):\n img_paths_var_names = ('dataset_img_path', 'atlas_img_path', 'brain_of_atlas_img_path')\n for img_path_var_name in img_paths_var_names:\n img_path = getattr(self, img_path_var_name)\n if not os.path.exists(img_path):\n sys.exit('Cannot perform registration, image {} not found'.format(img_path))\n if not img_path.endswith('.nii'):\n if img_path.endswith(('.tiff', '.tif')):\n nii_path = '{}{}'.format(os.path.splitext(img_path)[0], '.nii')\n bio.tiff_to_nii(img_path, nii_path)\n setattr(self, img_path_var_name, nii_path)\n else:\n raise RegistrationError('Cannot perform registration, image {} not in supported format'\n .format(img_path))","def testMissingImage(self):\n self.assertNotIn('no_image', self.data)","def _create_placeholders(self):\n # \"None\" means the batches may have a variable batch size and length.\n self.x = tf.placeholder(tf.int64, shape=[None, None])","def test_to_tensor(dummy_input):\n # Test the 2D image: B, H, W, C\n image, label = dummy_input(image_size=(512, 512, 3),\n label_size=(512, 512, 1))\n transform = ToTensor()\n _image, _label = transform(image, label, dtypes=[torch.float, torch.long])\n assert _image.dtype == torch.float\n assert _label.dtype == torch.long\n\n # Test the 3D image: B, H, W, D, C\n image, label = dummy_input(image_size=(512, 512, 20, 3),\n label_size=(512, 512, 20, 1))\n transform = ToTensor()\n _image, _label = transform(image, label, dtypes=[torch.float, torch.long])\n assert _image.dtype == torch.float\n assert _label.dtype == torch.long","def test_imibread(self):\n gen = imibread(TEST_MIB)\n arr = next(gen)\n self.assertEqual(arr.shape, (256, 256))\n self.assertEqual(arr.dtype, np.dtype(\">u2\"))","def prepare_n_mnist(filename, is_filter, num_spikes, step_factor=1):\n td = ev.read_dataset(filename)\n # td.show_td(100)\n td.data = stabilize(td)\n td.data = td.extract_roi([3, 3], [28, 28], True)\n images = make_td_images(td, num_spikes, step_factor)\n\n if is_filter:\n images = ndimage.median_filter(images, 3)\n\n # for image in images:\n # cv2.imshow('img', image)\n # cv2.waitKey(70)\n return images","def use_mnist_model(self):\n\n\t\t# load the model\n\t\tnumber_recognizer_MNIST = load_model('models/MNIST_digits_recognition.h5', compile=False)\n\n\t\t# create empty ndarray\n\t\tnumbers_mnist = np.ones(shape=(self.sudoku_size, self.sudoku_size))\n\n\t\tpics = deepcopy(self.list_of_number_pictures)\n\t\tfor i in range(self.sudoku_size):\n\t\t\tfor j in range(self.sudoku_size):\n\t\t\t\tpics[i][j] = self.preprocess_cell(pics[i][j], mnist=True, resize=True, clean_remains=True)\n\t\t\t\tif self.empty_cells[i][j] != 0:\n\t\t\t\t\tnumbers_mnist[i][j] = np.argmax(number_recognizer_MNIST.predict([[pics[i][j].reshape(28,28,1)]]))\n\n\t\treturn numbers_mnist","def norm_input(image, label):\n cropped_image = tf.image.resize_image_with_crop_or_pad(image, FLAGS.image_size, FLAGS.image_size)\n\n norm_image = tf.image.per_image_standardization(cropped_image)\n\n return norm_image, label","def __init__(self,bits):\n self.image_bits = bits","def inputs(self) -> Sequence[jnp.ndarray]:\n pass","def __init__(self, im, lab):\n\n #self.ROOT = root\n self.images = im\n self.labels = lab","def __init__(self):\r\n # A dummy layer does nothing\r\n self.weights = np.zeros(shape=(input.shape[1], 10))\r\n bias = np.zeros(shape=(10,))\r\n pass","def preprocess_image(self, batched_inputs):\n images = [x[\"image\"].float().to(self.device) for x in batched_inputs]\n images = [self.normalizer(img) for img in images]\n images = ImageList.from_tensors(images, self.backbone.size_divisibility)\n return images","def mobile_input():\n # Decode image into float range [0,1]\n jpegs = tf.placeholder(tf.float32, shape=(1, FLAGS.image_size, FLAGS.image_size, 3),\n name='input')\n return jpegs, jpegs","def __init__(self, real: float, img: np.ndarray):\n if img.shape != (3,):\n raise TypeError('imaginary part should be a vector with 3 elements')\n self.__real = real # real part, qw\n self.__img = img.astype(dtype=np.float) # imaginary part, qv = [x, y, z]^T","def image_batch():\n return np.zeros((2, 1, 4, 4))","def add_placeholders(self):\n ### YOUR CODE HERE (~4-6 lines)\n self.input_placeholder = tf.placeholder(tf.int32, [None, self.max_length, Config.n_features])\n self.labels_placeholder = tf.placeholder(tf.int32, [None, self.max_length])\n self.length_placeholder = tf.placeholder(tf.int32, [None])\n self.mask_placeholder = tf.placeholder(tf.bool, [None, self.max_length])\n self.dropout_placeholder = tf.placeholder(tf.float32)\n ### END YOUR CODE","def n_inputs(self):","def _create_train_input(self, input_batch):\n self.raw_image = input_batch\n self.image = tf.reshape(self.raw_image, (-1, self._im_size[0], self._im_size[1]))\n self.lr = tf.placeholder(tf.float32, name='lr')\n self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')","def test_npy_inference():\n input_path = os.path.join(MAIN_PATH, \"data/test_cases/\")\n input_path += os.listdir(input_path)[0]\n\n model = InferenceModel()\n metadata1 = model.nifti2json(input_path, \"\")\n\n img = nib.load(input_path)\n pixel_spacings = img.header.get_zooms()\n X = img.get_fdata(dtype=np.float32)\n metadata2 = model.npy2json(X, \"\", pixel_spacings=pixel_spacings)\n\n assert metadata1 == metadata2","def testZeroInput(self):\n self.assertRaises(TypeError, nb.rscale,)","def preprocess_nib(nib_img, is_mask=False):\n if not is_mask:\n img_data = nib_img.get_fdata()\n if img_data.max() > 256:\n mask256 = img_data < 257\n img_data = img_data * mask256\n img_data = img_data.astype('float32')\n mean = np.mean(img_data)\n std = np.std(img_data)\n img_data /= img_data.max() # intensity normalization\n img_data -= mean # data centering\n img_data /= std # data normalization\n\n return np.expand_dims(img_data, axis=3)\n else:\n img_data = np.array(nib_img.get_fdata(), dtype='float32')\n\n return np.expand_dims(img_data, axis=3)","def __init__(self, n=5, name=None):\n super().__init__(name=name)\n self.n = n\n self._images = []\n self._sigmas = []","def build_filler_images(self):","def imgFinite(inImg):\n arr = sitk.GetArrayFromImage(inImg)\n arr[~np.isfinite(arr)]=0\n outImg = sitk.GetImageFromArray(arr)\n outImg.SetSpacing(inImg.GetSpacing())\n outImg.SetOrigin(inImg.GetOrigin())\n outImg.SetDirection(inImg.GetDirection())\n return outImg","def __init__(self, images, labels, tf=None):\r\n self.images = images\r\n self.labels = labels\r\n self.tf = tf","def _add_images(self, epoch, train_batch, train_outputs, valid_batch, valid_outputs):\n raise NotImplementedError","def __init__(self, observation_space, name=None):\r\n is_image = len(observation_space.shape) == 3\r\n inpt, self.processed_inpt = observation_input(observation_space, name=name, scale=is_image)\r\n super().__init__(inpt)","def _basic_input(dst_embed_i, _):\n return dst_embed_i.squeeze(0)","def process_images(image, label):\n # Normalize images to have a mean of 0 and standard deviation of 1\n # per_image_standardization is preferred, which normalize the entire image to mean zero and std 1.\n # It also make learning fast.\n image = tf.image.per_image_standardization(image)\n # Resize images from 32x32 to 277x277\n image = tf.image.resize(image, (227,227))\n return image, label","def check_niimg(\n niimg,\n ensure_ndim=None,\n atleast_4d=False,\n dtype=None,\n return_iterator=False,\n wildcards=True,\n):\n from ..image import new_img_like # avoid circular imports\n\n niimg = stringify_path(niimg)\n\n if isinstance(niimg, str):\n if wildcards and ni.EXPAND_PATH_WILDCARDS:\n # Ascending sorting + expand user path\n filenames = sorted(glob.glob(os.path.expanduser(niimg)))\n\n # processing filenames matching globbing expression\n if len(filenames) >= 1 and glob.has_magic(niimg):\n niimg = filenames # iterable case\n # niimg is an existing filename\n elif [niimg] == filenames:\n niimg = filenames[0]\n # No files found by glob\n elif glob.has_magic(niimg):\n # No files matching the glob expression, warn the user\n message = (\n \"No files matching the entered niimg expression: \"\n \"'%s'.\\n You may have left wildcards usage \"\n \"activated: please set the global constant \"\n \"'nilearn.EXPAND_PATH_WILDCARDS' to False to \"\n \"deactivate this behavior.\"\n ) % niimg\n raise ValueError(message)\n else:\n raise ValueError(f\"File not found: '{niimg}'\")\n elif not os.path.exists(niimg):\n raise ValueError(f\"File not found: '{niimg}'\")\n\n # in case of an iterable\n if hasattr(niimg, \"__iter__\") and not isinstance(niimg, str):\n if return_iterator:\n return _iter_check_niimg(\n niimg, ensure_ndim=ensure_ndim, dtype=dtype\n )\n return concat_niimgs(niimg, ensure_ndim=ensure_ndim, dtype=dtype)\n\n # Otherwise, it should be a filename or a SpatialImage, we load it\n niimg = load_niimg(niimg, dtype=dtype)\n\n if ensure_ndim == 3 and len(niimg.shape) == 4 and niimg.shape[3] == 1:\n # \"squeeze\" the image.\n data = _safe_get_data(niimg)\n affine = niimg.affine\n niimg = new_img_like(niimg, data[:, :, :, 0], affine)\n if atleast_4d and len(niimg.shape) == 3:\n data = _get_data(niimg).view()\n data.shape = data.shape + (1,)\n niimg = new_img_like(niimg, data, niimg.affine)\n\n if ensure_ndim is not None and len(niimg.shape) != ensure_ndim:\n raise DimensionError(len(niimg.shape), ensure_ndim)\n\n if return_iterator:\n return (_index_img(niimg, i) for i in range(niimg.shape[3]))\n\n return niimg","def test_empty_img():\n assert detected_boxes[-1] == ground_truth_boxes[-1]","def set_input_tensor(interpreter, image):\n tensor_index = interpreter.get_input_details()[0]['index']\n input_tensor = interpreter.tensor(tensor_index)()[0]\n input_tensor[:, :] = image","def test_lm_bisenet_post_process():\n tf.InteractiveSession()\n\n image_size = [96, 64]\n batch_size = 2\n classes = Camvid.classes\n data_format = \"NHWC\"\n\n model = LMBiSeNet(\n image_size=image_size,\n batch_size=batch_size,\n classes=classes,\n data_format=data_format,\n )\n\n post_process = Sequence([\n Bilinear(\n size=image_size,\n data_format=data_format,\n compatible_tensorflow_v1=True,\n ),\n Softmax()\n ])\n\n shape = (batch_size, image_size[0]//8, image_size[1]//8, len(classes))\n np_output = np.random.uniform(-10., 10., size=shape).astype(np.float32)\n output = tf.constant(np_output)\n\n output = model.post_process(output)\n\n expected = post_process(outputs=np_output)[\"outputs\"]\n\n assert np.allclose(output.eval(), expected, atol=1e-5, rtol=1e-5)","def preprocess_input(img):\n img /= 255.\n img -= 0.5\n img *= 2.\n return img","def _create_placeholders(self, n_features, n_classes):\n\n self.input_data = tf.placeholder(\"float\", [None, n_features], name='x-input')\n self.input_labels = tf.placeholder(\"float\", [None, n_classes], name='y-input')","def make_default_inputs(filtered_dot_file):\n# w_kernel = pickle.load(open(\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-3.p\", \"rb\" ))\n# w_kernel = pickle.load(open(\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-2.p\", \"rb\" ))\n# w_kernel = pickle.load(open(\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-4.p\", \"rb\" ))\n w_kernel = pickle.load(open(\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-new-2.p\", \"rb\" ))\n filtered = pickle.load(open(filtered_dot_file, \"rb\" ))\n \n n_inputs = 200\n centres = np.random.randn(n_inputs,2)\n mag = np.sum(centres**2, axis=1)**(1/2)\n centres = 155 * centres.T / mag\n centres = centres * np.random.rand(n_inputs)**(1/2)\n centres = np.round(centres).astype('int')\n centres = centres + 199 #put in image centre\n \n inputs = get_inputs(w_kernel, filtered, centres.T)\n \n pickle.dump(inputs, open(filtered_dot_file.replace('-filt.p', '-inputs.p'), \"wb\" ))","def __init__(self, images, is_training, num_points):\n\n self.images = images\n self.is_training = is_training\n self.num_points = num_points\n self.weight_decay = 0.00004\n self.stddev = 0.1\n self.dropout_keep_prob = 0.8\n self.use_batch_norm = True\n self.batch_norm_params = None\n self.add_summaries = True\n self.scope = \"InceptionV3\"","def no_bin(image, *args, **kwargs):\n return image","def create_model_input_blacked(self, rgb_images, all_boxes, all_masks):\n images = []\n for i in range(all_boxes.size(0)):\n for j in range(all_boxes.size(1)):\n box = all_boxes[i][j]\n if is_dummy_box(box):\n continue\n\n image = rgb_images[i].cpu()\n\n # Apply object mask to the image\n if self.use_masks:\n mask = all_masks[i][j].cpu()\n image = image * mask\n\n image = TF.to_pil_image(image)\n image = blacken_image(image, box)\n image = TF.resize(image, (self.reduced_size, self.reduced_size))\n image = TF.to_tensor(image)\n images.append(image)\n return torch.stack(images)","def inv_preprocess(imgs, num_images, img_mean):\n n, h, w, c = imgs.shape\n assert (n >= num_images), 'Batch size %d should be greater or equal than number of images to save %d.' % (\n n, num_images)\n outputs = np.zeros((num_images, h, w, c), dtype=np.uint8)\n for i in range(num_images):\n outputs[i] = (imgs[i] + img_mean)[:, :, ::-1].astype(np.uint8)\n return outputs","def __init__(self, root: str, size: torch.Size, exclude_imagenet1k=True, *args, **kwargs):\n super(MyImageNet22K, self).__init__(root, *args, **kwargs)\n\n self.exclude_imagenet1k = exclude_imagenet1k\n self.shuffle_idxs = False\n self.size = size\n\n if exclude_imagenet1k:\n self.samples = [s for s in self.samples if not any([idx in s[0] for idx in self.imagenet1k_idxs])]","def set_input_tensor(interpreter, image):\n tensor_index = interpreter.get_input_details()[0][\"index\"]\n input_tensor = interpreter.tensor(tensor_index)()[0]\n input_tensor[:, :] = image","def set_input_tensor(interpreter, image):\n tensor_index = interpreter.get_input_details()[0][\"index\"]\n input_tensor = interpreter.tensor(tensor_index)()[0]\n input_tensor[:, :] = image","def set_input_tensor(interpreter, image):\n tensor_index = interpreter.get_input_details()[0][\"index\"]\n input_tensor = interpreter.tensor(tensor_index)()[0]\n input_tensor[:, :] = image","def set_default_parameters(self):\n super().set_default_parameters()\n if not \"n_sub_images\" in vars(self):\n self.n_sub_images = -1 # do all-sub-images","def MNIST_normalize_input(inputs: [int]) -> [float]:\n return [i/256 for i in inputs] # simple for MNIST","def stage_one(self, imgs, threshold, factor, minsize, nms_threshold):\n\n width = imgs.shape[-2]\n height = imgs.shape[-1]\n num_img = imgs.shape[0]\n\n # Compute valid scales\n scales = []\n cur_width = width\n cur_height = height\n cur_factor = 1\n while cur_width >= 12 and cur_height >= 12:\n if 12 / cur_factor >= minsize: # Ignore boxes that smaller than minsize\n\n w = cur_width\n h = cur_height\n scales.append((w, h, cur_factor))\n\n cur_factor *= factor\n cur_width = math.ceil(cur_width * factor)\n cur_height = math.ceil(cur_height * factor)\n\n # Get candidate boxesi ph\n candidate_boxes = torch.empty(0, dtype=torch.int32, device=self.device)\n candidate_scores = torch.empty(0, device=self.device)\n candidate_offsets = torch.empty(\n 0, dtype=torch.float32, device=self.device)\n all_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)\n for w, h, f in scales:\n resize_img = torch.nn.functional.interpolate(\n imgs, size=(w, h), mode='bilinear')\n p_distribution, box_regs, _ = self.pnet(resize_img)\n\n candidate, scores, offsets, img_labels = self._generate_bboxes(\n p_distribution, box_regs, f, threshold)\n\n candidate_boxes = torch.cat([candidate_boxes, candidate])\n candidate_scores = torch.cat([candidate_scores, scores])\n candidate_offsets = torch.cat([candidate_offsets, offsets])\n all_img_labels = torch.cat([all_img_labels, img_labels])\n\n \n if candidate_boxes.shape[0] != 0:\n candidate_boxes = self._calibrate_box(\n candidate_boxes, candidate_offsets)\n candidate_boxes = self._convert_to_square(candidate_boxes)\n candidate_boxes = self._refine_boxes(\n candidate_boxes, width, height)\n \n final_boxes = torch.empty(0, dtype=torch.int32, device=self.device)\n final_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)\n for i in range(num_img):\n mask = all_img_labels == i\n keep = func.nms(candidate_boxes[mask].cpu().numpy(),\n candidate_scores[mask].cpu().numpy(), nms_threshold)\n final_boxes = torch.cat([final_boxes, candidate_boxes[mask][keep]])\n final_img_labels = torch.cat([final_img_labels, all_img_labels[mask][keep]])\n\n return torch.cat([final_boxes, final_img_labels.unsqueeze(1 )], -1)\n else:\n return candidate_boxes","def fake_data(num_images):\n data = np.ndarray(\n shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),\n dtype=np.float32)\n labels = np.zeros(shape=(num_images,), dtype=np.int64)\n for image in xrange(num_images):\n label = image % 2\n data[image, :, :, 0] = label - 0.5\n labels[image] = label\n return data, labels","def testShapesNotKnown(self, use_bias):\n\n inputs = tf.placeholder(\n tf.float32, shape=[None, None, None, self.in_channels], name=\"inputs\")\n\n conv1 = snt.DepthwiseConv2D(\n channel_multiplier=self.channel_multiplier,\n kernel_shape=self.kernel_shape,\n padding=snt.SAME,\n stride=1,\n use_bias=use_bias)\n output = conv1(inputs)\n\n with self.test_session():\n tf.variables_initializer(\n [conv1.w, conv1.b] if use_bias else [conv1.w]).run()\n output_eval = output.eval({inputs: np.zeros(self.input_shape)})\n self.assertEqual(output_eval.shape, tuple(self.output_shape))","def test_train_image_dims_content(self):\n iterator = self._dataset.get_train()\n sample = next(iterator)\n image, label = sample['image'], sample['label']\n\n with self.subTest(name='DataShape'):\n self.assertTupleEqual(image.shape, (self._batch_size, 32, 32, 3))\n\n with self.subTest(name='DataType'):\n self.assertTrue(np.issubdtype(image.dtype, float))\n\n with self.subTest(name='DataValues'):\n # Normalized by stddev., expect nothing to fall outside 3 stddev.\n self.assertTrue((image >= -3.).all() and (image <= 3.).all())\n\n with self.subTest(name='LabelShape'):\n self.assertLen(label, self._batch_size)\n\n with self.subTest(name='LabelType'):\n self.assertTrue(np.issubdtype(label.dtype, int))\n\n with self.subTest(name='LabelValues'):\n self.assertTrue((label >= 0).all() and\n (label <= self._dataset.num_classes).all())","def _fake_only_visualize(fake_img, real_img, caption, num, num_per_caption, num_per_row, model):\n fake_img = _post_process(fake_img, model)\n grid = _make_grid(fake_img, cols=num_per_row)\n grid = tf.convert_to_tensor(grid, dtype=tf.uint8)\n return fake_img, grid","def add_placeholders(self):\n ### YOUR CODE HERE\n #print self.config.num_steps\n self.input_placeholder = tf.placeholder(tf.int32, shape=(None, self.config.num_steps))\n self.labels_placeholder = tf.placeholder(tf.int32, shape=[None, self.config.num_steps])\n self.dropout_placeholder = tf.placeholder(tf.float32)\n ### END YOUR CODE","def find_input():\n\n mat = [[5,0],[0,0],[0,0]]\n num_range = [0,2]\n sub = 3 #Number of pixels to be substituted.\n\n input = [mat, num_range, sub]\n\n return input","def test_2d_inputs():\n reseed()\n\n base_img1 = np.array([[0, 0, 1, 1],\n [0, 0, 1, 1],\n [0, 1, 1, 1]], dtype=np.uint8)\n base_img2 = np.array([[0, 0, 1, 1],\n [0, 1, 1, 1],\n [0, 1, 0, 0]], dtype=np.uint8)\n\n base_img1_flipped = np.array([[1, 1, 0, 0],\n [1, 1, 0, 0],\n [1, 1, 1, 0]], dtype=np.uint8)\n base_img2_flipped = np.array([[1, 1, 0, 0],\n [1, 1, 1, 0],\n [0, 0, 1, 0]], dtype=np.uint8)\n\n images = np.array([base_img1, base_img2])\n images_flipped = np.array([base_img1_flipped, base_img2_flipped])\n images_list = [base_img1, base_img2]\n images_flipped_list = [base_img1_flipped, base_img2_flipped]\n images_list2d3d = [base_img1, base_img2[:, :, np.newaxis]]\n images_flipped_list2d3d = [base_img1_flipped, base_img2_flipped[:, :, np.newaxis]]\n\n aug = iaa.Fliplr(1.0)\n noaug = iaa.Fliplr(0.0)\n\n # one numpy array as input\n observed = aug.augment_images(images)\n assert np.array_equal(observed, images_flipped)\n\n observed = noaug.augment_images(images)\n assert np.array_equal(observed, images)\n\n # list of 2d images\n observed = aug.augment_images(images_list)\n assert array_equal_lists(observed, images_flipped_list)\n\n observed = noaug.augment_images(images_list)\n assert array_equal_lists(observed, images_list)\n\n # list of images, one 2d and one 3d\n observed = aug.augment_images(images_list2d3d)\n assert array_equal_lists(observed, images_flipped_list2d3d)\n\n observed = noaug.augment_images(images_list2d3d)\n assert array_equal_lists(observed, images_list2d3d)","def init_predictions(nb_rows, nb_cols, dtype=np.float64):\n return np.zeros((nb_rows, nb_cols), dtype=dtype)","def iou_train(self, boxA, boxB, realBox):\n iou1 = tf.reshape(iou_train_unit(boxA, realBox), [-1, 7, 7, 1])\n iou2 = tf.reshape(iou_train_unit(boxB, realBox), [-1, 7, 7, 1])\n return tf.concat([iou1, iou2], 3)","def test_image_input(self):\n \n vgg16 = VGG16()\n data_dir = os.path.dirname(os.path.abspath(__file__))\n data = os.path.join(data_dir, 'data/elephant.jpg')\n output = vgg16.input_handler(data)\n\n self.assertEqual(len(output.shape), 4) \n self.assertEqual(output.shape[1], 224)\n self.assertEqual(output.shape[2], 224)\n self.assertEqual(output.shape[3], 3)","def handle_origin_image(image, gt_box):\n x = image.width\n y = image.height\n im_max = max(x, y)\n im_min = min(x, y)\n scale = cfg.TRAIN.MIN_SIZE / im_min\n if scale * im_max > cfg.TRAIN.MAX_SIZE:\n scale = cfg.TRAIN.MAX_SIZE / im_max\n width = round(round(x * scale) / 32) * 32\n height = round(round(y * scale) / 32) * 32\n im = image.resize((width, height))\n box = [round(gt_box[0] * width / x), round(gt_box[1] * height / y), round(gt_box[2] * width / x),\n round(gt_box[3] * height / y)]\n # make sure there really tiny flaw still have box to predict\n if (box[3] - box[1]) * (box[2] - box[0]) < 100:\n box = [box[0] - 3, box[1] - 3, box[2] + 3, box[3] + 3]\n return np.array(im), box","def setUp(self):\n self.gray_image = np.ndarray((100, 200), dtype=np.uint8)\n self.rgb_image = np.ndarray((100, 200, 3), dtype=np.uint8)","def __init__(self, n_in, n_out):\n self.W = np.random.randn(n_in, n_out) * 0.1\n self.b = np.zeros(n_out)","def __init__(self, env, height=80, width=80, grayscale=True,\n crop=lambda img: img):\n super(PreprocessImage, self).__init__(env)\n self.img_size = (height, width)\n self.grayscale = grayscale\n self.crop = crop\n\n n_colors = 1 if self.grayscale else 3\n self.observation_space = Box(0.0, 1.0, [n_colors, height, width])","def __call__(self, src, label):\r\n # img = mx.nd.image.to_tensor(src)\r\n # img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\r\n src = mx.nd.array(src)\r\n img = mx.nd.image.to_tensor(src)\r\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\r\n return img, mx.nd.array(label, dtype=img.dtype)","def test_init_ops(self):\n tf.reset_default_graph()\n tf.set_random_seed(42)\n self.imagenet_inception_v3.set_up()\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n num_param = [\n np.prod(v.get_shape().as_list())\n for v in tf.trainable_variables()\n ]\n # Check if number of parameters per \"layer\" is equal to what we expect\n # We will write them in the following form:\n # - Conv layer: [input_filter*output_filter*kernel[0]*kernel[1]]\n # - Batch norm: [input, input] (for beta and gamma)\n # - Fully connected: [input*output]\n # - Bias: [dim]\n self.assertEqual(\n num_param, [\n 3 * 32 * 3 * 3, 32, 32, 32 * 32 * 3 * 3, 32, 32,\n 32 * 64 * 3 * 3, 64, 64, 64 * 80 * 1 * 1, 80, 80,\n 80 * 192 * 3 * 3, 192, 192, 192 * 64 * 1 * 1, 64, 64,\n 192 * 32 * 1 * 1, 32, 32, 192 * 48 * 1 * 1, 48, 48,\n 48 * 64 * 5 * 5, 64, 64, 192 * 64 * 1 * 1,\n 64, 64, 64 * 96 * 3 * 3, 96, 96, 96 * 96 * 3 * 3, 96, 96,\n (64 + 32 + 64 + 96) * 64 * 1 * 1, 64, 64,\n (64 + 32 + 64 + 96) * 64 * 1 * 1, 64, 64,\n (64 + 32 + 64 + 96) * 48 * 1 * 1, 48, 48, 48 * 64 * 5 * 5,\n 64, 64, (64 + 32 + 64 + 96) * 64 * 1 * 1, 64, 64,\n 64 * 96 * 3 * 3, 96, 96, 96 * 96 * 3 * 3, 96, 96,\n (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64,\n (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64,\n (64 + 64 + 64 + 96) * 48 * 1 * 1, 48, 48, 48 * 64 * 5 * 5,\n 64, 64, (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64,\n 64 * 96 * 3 * 3, 96, 96, 96 * 96 * 3 * 3, 96, 96,\n (64 + 64 + 64 + 96) * 384 * 3 * 3, 384, 384,\n (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64, 64 * 96 * 3 * 3,\n 96, 96, 96 * 96 * 3 * 3, 96, 96,\n ((64 + 64 + 64 + 96) + 384 + 96) * 192 * 1 * 1, 192, 192,\n ((64 + 64 + 64 + 96) + 384 + 96) * 192 * 1 * 1, 192, 192,\n ((64 + 64 + 64 + 96) + 384 + 96) * 128 * 1 * 1, 128, 128,\n 128 * 128 * 1 * 7, 128, 128, 128 * 192 * 7 * 1, 192, 192,\n ((64 + 64 + 64 + 96) + 384 + 96) * 128 * 1 * 1, 128, 128,\n 128 * 128 * 7 * 1, 128, 128, 128 * 128 * 1 * 7, 128, 128,\n 128 * 128 * 7 * 1, 128, 128, 128 * 192 * 1 * 7, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\n 160 * 160 * 1 * 7, 160, 160, 160 * 192 * 7 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\n 160 * 160 * 7 * 1, 160, 160, 160 * 160 * 1 * 7, 160, 160,\n 160 * 160 * 7 * 1, 160, 160, 160 * 192 * 1 * 7, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\n 160 * 160 * 1 * 7, 160, 160, 160 * 192 * 7 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\n 160 * 160 * 7 * 1, 160, 160, 160 * 160 * 1 * 7, 160, 160,\n 160 * 160 * 7 * 1, 160, 160, 160 * 192 * 1 * 7, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n 192 * 192 * 1 * 7, 192, 192, 192 * 192 * 7 * 1, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n 192 * 192 * 7 * 1, 192, 192, 192 * 192 * 1 * 7, 192, 192,\n 192 * 192 * 7 * 1, 192, 192, 192 * 192 * 1 * 7, 192, 192,\n (192 + 192 + 192 + 192) * 128 * 1 * 1, 128, 128,\n 128 * 768 * 5 * 5, 768, 768, 768 * 1001, 1001,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n 192 * 192 * 1 * 7, 192, 192, 192 * 192 * 7 * 1,\n 192, 192, 192 * 192 * 3 * 3, 192, 192,\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\n 192 * 320 * 3 * 3, 320, 320,\n (4 * 192 + 192 + 320) * 320 * 1 * 1, 320, 320,\n (4 * 192 + 192 + 320) * 384 * 1 * 1, 384, 384,\n 384 * 384 * 1 * 3, 384, 384, 384 * 384 * 3 * 1, 384, 384,\n (4 * 192 + 192 + 320) * 448 * 1 * 1, 448, 448,\n 448 * 384 * 3 * 3, 384, 384,\n 384 * 384 * 1 * 3, 384, 384, 384 * 384 * 3 * 1, 384, 384,\n (4 * 192 + 192 + 320) * 192, 192, 192,\n (320 + 384 * 2 + 384 * 2 + 192) * 320 * 1 * 1, 320, 320,\n (320 + 384 * 2 + 384 * 2 + 192) * 384 * 1 * 1, 384, 384,\n 384 * 384 * 1 * 3, 384, 384, 384 * 384 * 3 * 1, 384, 384,\n (320 + 384 * 2 + 384 * 2 + 192) * 448 * 1 * 1, 448, 448,\n 448 * 384 * 3 * 3, 384, 384, 384 * 384 * 1 * 3, 384,\n 384, 384 * 384 * 3 * 1, 384, 384,\n (320 + 384 * 2 + 384 * 2 + 192) * 192, 192, 192,\n 2048 * 1001, 1001\n ])\n for init_op in [\n self.imagenet_inception_v3.train_init_op,\n self.imagenet_inception_v3.test_init_op,\n self.imagenet_inception_v3.train_eval_init_op\n ]:\n sess.run(init_op)\n losses_, regularizer_, accuracy_ = sess.run([\n self.imagenet_inception_v3.losses,\n self.imagenet_inception_v3.regularizer,\n self.imagenet_inception_v3.accuracy\n ])\n self.assertEqual(losses_.shape, (self.batch_size, ))\n self.assertIsInstance(regularizer_, np.float32)\n self.assertIsInstance(accuracy_, np.float32)","def _real_only_visualize(fake_img, real_img, caption, num, num_per_caption, num_per_row, model):\n real_img = _post_process(real_img, model)\n grid = _make_grid(real_img, cols=num_per_row)\n grid = tf.convert_to_tensor(grid, dtype=tf.uint8)\n return real_img, grid","def fake_data(num_images):\n data = np.ndarray(\n shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),\n dtype=np.float32)\n labels = np.zeros(shape=(num_images,), dtype=np.int64)\n for image in xrange(num_images):\n label = image % 2\n data[image, :, :, 0] = label - 0.5\n labels[image] = label\n return data, labels"],"string":"[\n \"def logits_placeholder(self):\",\n \"def neural_net_image_input(image_shape):\\n return tf.placeholder(tf.float32, shape=[None]+list(image_shape), name=\\\"x\\\")\",\n \"def neural_net_image_input(image_shape):\\n # TODO: Implement Function\\n n_input_1 = image_shape[0]\\n n_input_2 = image_shape[1]\\n n_input_3 = image_shape[2]\\n return tf.placeholder(tf.float32,[None, n_input_1, n_input_2, n_input_3], name='x')\",\n \"def dummy_inputs(self):\\n input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32)\\n batch_size, seq_len = input_ids.shape\\n\\n VISION_DUMMY_INPUTS = tf.random.uniform(\\n shape=(\\n batch_size,\\n self.config.vision_config.num_channels,\\n self.config.vision_config.image_size,\\n self.config.vision_config.image_size,\\n ),\\n dtype=tf.float32,\\n )\\n pixel_values = tf.constant(VISION_DUMMY_INPUTS)\\n dummy = {\\\"pixel_values\\\": pixel_values, \\\"input_ids\\\": input_ids}\\n return dummy\",\n \"def neural_net_image_input(image_shape):\\n # TODO: Implement Function\\n return tf.placeholder(tf.float32, shape=[None, *image_shape], name=\\\"x\\\")\",\n \"def neural_net_image_input(image_shape):\\n return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], name='x')\",\n \"def neural_net_image_input(image_shape):\\n # TODO: Implement Function\\n shape = (None,image_shape[0],image_shape[1],image_shape[2])\\n x = tf.placeholder(tf.float32,shape,name = 'x')\\n return x\",\n \"def neural_net_image_input(image_shape):\\n image_height = image_shape[0]\\n image_width = image_shape[1]\\n color_channels = image_shape[2]\\n imagetensor = tf.placeholder(tf.float32, shape=[None, image_height, image_width, color_channels],name='x')\\n return imagetensor\",\n \"def _setup_images_and_labels(self):\\n if FLAGS.dataset == 'cifar10':\\n self.num_classes = 10\\n else:\\n self.num_classes = 100\\n self.images = tf.placeholder(tf.float32, [self.batch_size, 32, 32, 3])\\n self.labels = tf.placeholder(tf.float32,\\n [self.batch_size, self.num_classes])\",\n \"def placeholder_inputs(batch_size):\\n images_ph = tf.placeholder(tf.float32, shape=(batch_size, FLAGS.img_s, FLAGS.img_s, 3), name='images_placeholder') \\n labels_ph = tf.placeholder(tf.float32, shape=(batch_size, FLAGS.n_classes), name='labels_placeholder')\\n keep_prob_ph = tf.placeholder(tf.float32, shape=(), name='keep_prob_placeholder')\\n\\n return images_ph, labels_ph, keep_prob_ph\",\n \"def placeholder_inputs(batch_size):\\n # Note that the shapes of the placeholders match the shapes of the full\\n # image and label tensors, except the first dimension is now batch_size\\n # rather than the full size of the train or test data sets.\\n images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,\\n c3d_model.NUM_FRAMES_PER_CLIP,\\n c3d_model.CROP_SIZE,\\n c3d_model.CROP_SIZE,\\n c3d_model.CHANNELS))\\n labels_placeholder = tf.placeholder(tf.int64, shape=(batch_size))\\n return images_placeholder, labels_placeholder\",\n \"def __init__(self, input):\\n try:\\n img = Image.open(input).resize(ImageVector.size)\\n self.pixels = img.getdata()\\n except:\\n self.pixels = input\",\n \"def MNIST_data():\\n\\n # Pobieramy macierze numpy z cyframi\\n # images[i,j,k] <=> piksel (j,k) z i-tego obrazka w zbiorze danych\\n images, labels = get_MNIST_dataset(range(10), \\\"training\\\") #pierwszy argument to\\n\\n # a) Ilosc przykladow i rozmiary danych\\n print \\\"Raw training data dimensions \\\", images.shape\\n print \\\"Labels dimensions \\\",labels.shape\\n\\n # b) Ile jest cyfr 2?\\n print \\\"Counting 2 in training dataset \\\",len(filter(lambda x: x == 2, labels))\\n\\n # c) Jaki jest sredni obrazek 2 ? (Usrednienie wszystkich macierzy ktore sa 2)\\n\\n #1. Pobierzmy wszystkie dwojki, fajny sposob indeksowania\\n print labels == 2\\n only_2 = images[labels == 2, :, :]\\n print \\\"Checking number of 2s \\\", only_2.shape\\n\\n #2. TODO: Usrednienie (matrix.mean moze byc przydatne)\\n\\n #3. TODO: narysowanie usrednionej cyfry (zobacz pl.imshow)\\n\\n # d) Ostatnie - przetworzmy ostatnia cyfre do 1 wymiarowego wektora\\n vectorized = np.reshape(images[-1], newshape=(images[-1].shape[0]*images[-1].shape[1]))\\n print \\\"Vectorized last digit \\\", vectorized\",\n \"def testShapesNotKnown(self, use_bias):\\n\\n batch_size = 5\\n in_length = 32\\n in_channels = out_channels = 5\\n kernel_shape = 3\\n\\n inputs = tf.placeholder(\\n tf.float32,\\n shape=[None, None, in_channels],\\n name=\\\"inputs\\\")\\n\\n conv1 = snt.Conv1D(\\n name=\\\"conv1\\\",\\n output_channels=out_channels,\\n kernel_shape=kernel_shape,\\n padding=snt.SAME,\\n stride=1,\\n use_bias=use_bias)\\n\\n output = conv1(inputs)\\n\\n with self.test_session():\\n tf.variables_initializer(\\n [conv1.w, conv1.b] if use_bias else [conv1.w]).run()\\n\\n output_eval = output.eval({\\n inputs: np.zeros([batch_size, in_length, in_channels])})\\n\\n self.assertEqual(\\n output_eval.shape,\\n (batch_size, in_length, out_channels))\",\n \"def imagenet_preprocess(image, label):\\n i = image\\n i = tf.cast(i, tf.float32)\\n i = tf.image.resize_with_crop_or_pad(i, 224, 224)\\n if model_name == 'ResNet50' or model_name == 'ResNet152':\\n i = tf.keras.applications.resnet.preprocess_input(i)\\n else:\\n i = tf.keras.applications.densenet.preprocess_input(i)\\n return (i, label)\",\n \"def _check_neb_inputs(self):\\n\\n incar = self.ctx.inputs.parameters\\n\\n images = incar.get('images')\\n\\n if not images:\\n raise InputValidationError('IMAGES parameters is not set in the INCAR inputs')\\n\\n nimages = len(self.ctx.inputs.neb_images)\\n\\n if nimages != images:\\n raise InputValidationError('Mismatch between IMAGES and actual number supplied input structures.')\\n\\n # Check for NEB tags\\n iopt = incar.get('iopt', 0)\\n ibrion = incar.get('ibrion')\\n potim = incar.get('potim')\\n\\n # Check the sanity of parameters\\n if ibrion != 3:\\n self.report('WARNING: IBRION should be set to 3 for VTST runs, proceed with caution.')\\n elif potim != 0:\\n self.report(\\n 'WARNING: Using VTST optimisors with IBRION=3, but POTIM is not set to zero, proceed with caution.'\\n )\\n if iopt == 0:\\n self.report('WARNING: IOPT not set.')\\n\\n if ibrion == 2:\\n raise InputValidationError('IBRION=2 should not be used for NEB optimization!!')\\n\\n # Check the displacement of atoms between the frames\\n # the hope is that this may detect simple errors such as atoms going across the PBC or\\n # the order of atoms are changed between different frames\\n\\n tmp = list(self.ctx.inputs.neb_images.items())\\n tmp.sort(key=lambda x: x[0])\\n frames = [x[1].get_ase() for x in tmp]\\n frames = [self.ctx.inputs.initial_structure.get_ase()] + frames + [self.ctx.inputs.final_structure.get_ase()]\\n\\n last_frame = frames[0]\\n # Function for computing the distance using the scaled positions\\n rel_dist = np.vectorize(lambda x: x if x < 0.5 else 1. - x)\\n for iframe, frame in enumerate(frames[1:]):\\n # Relative displacements\\n disp = abs(frame.get_scaled_positions() - last_frame.get_scaled_positions()) % 1.0\\n # Apply convention\\n disp = rel_dist(disp)\\n # Convert back to absolute displacement\\n disp = disp @ frame.cell\\n norm_disp = np.linalg.norm(disp, axis=1)\\n sort_idx = np.argsort(norm_disp)\\n if norm_disp[sort_idx[-1]] > self._norm_disp_threshold:\\n raise InputValidationError(\\n 'Large displacement detected for atom {} at frame {} - please check the inputs images'.format(\\n sort_idx[-1], iframe + 1\\n )\\n )\\n last_frame = frame\",\n \"def preprocess_image(self, inputs):\\n raise NotImplementedError('preprocess_image method not implemented.')\",\n \"def create_model_input_default(self, rgb_images, all_boxes, all_masks):\\n box_regions = []\\n for i in range(all_boxes.size(0)):\\n for j in range(all_boxes.size(1)):\\n box = all_boxes[i][j]\\n if is_dummy_box(box):\\n continue\\n image = rgb_images[i].cpu()\\n\\n # Apply object mask to the image\\n if self.use_masks:\\n image = image.clone()\\n mask = all_masks[i][j].cpu()\\n image = image * mask\\n\\n box_region = get_patch_from_image(box, image)\\n box_regions.append(box_region)\\n\\n t = T.Compose([T.ToPILImage(), T.Resize((self.reduced_size, self.reduced_size)), T.ToTensor()])\\n box_regions = [t(box.cpu()) for box in box_regions]\\n return torch.stack(box_regions)\",\n \"def testShapesNotKnownAtrous(self, use_bias):\\n\\n inputs = tf.placeholder(\\n tf.float32,\\n shape=[None, None, None, 5],\\n name=\\\"inputs\\\")\\n\\n conv1 = snt.Conv2D(\\n name=\\\"conv1\\\",\\n output_channels=5,\\n kernel_shape=[3, 3],\\n padding=snt.SAME,\\n stride=1,\\n rate=2,\\n use_bias=use_bias)\\n\\n conv1(inputs)\",\n \"def preprocess_image(self, batched_inputs):\\n images = [x.to(self.device) for x in batched_inputs]\\n norms = [self.normalizer(x) for x in images]\\n size = (norms[0].shape[1],norms[0].shape[2])\\n images = ImageList.from_tensors(norms, self.backbone.size_divisibility)\\n return images, size\",\n \"def nits(self):\",\n \"def input_shape(self):\\n return [None, 32, 32, 1]\",\n \"def input_shape(self):\\n return [None, 32, 32, 1]\",\n \"def input_shape(self):\\n return [None, 32, 32, 1]\",\n \"def inference_input():\\n # Decode image into float range [0,1]\\n jpegs = tf.placeholder(tf.string, shape=(1), name='input')\\n image_buffer = tf.squeeze(jpegs, [0])\\n image = tf.image.decode_jpeg(image_buffer, channels=3)\\n image = tf.image.convert_image_dtype(image, dtype=tf.float32)\\n image = tf.image.central_crop(image, central_fraction=0.875)\\n image = tf.expand_dims(image, 0)\\n image = tf.image.resize_bilinear(image, [FLAGS.image_size, FLAGS.image_size], align_corners=False)\\n image = tf.squeeze(image, [0])\\n\\n # Rescale the image to [-1,-1]\\n image = tf.sub(image, 0.5)\\n image = tf.mul(image, 2.0)\\n images = tf.expand_dims(image, 0)\\n\\n return images, jpegs\",\n \"def testShapesNotKnown(self, use_bias):\\n\\n batch_size = 5\\n in_height = in_width = 32\\n in_channels = out_channels = 5\\n kernel_shape_h = kernel_shape_w = 3\\n\\n inputs = tf.placeholder(\\n tf.float32,\\n shape=[None, None, None, in_channels],\\n name=\\\"inputs\\\")\\n\\n conv1 = snt.Conv2D(\\n name=\\\"conv1\\\",\\n output_channels=out_channels,\\n kernel_shape=[kernel_shape_h, kernel_shape_w],\\n padding=snt.SAME,\\n stride=1,\\n use_bias=use_bias)\\n\\n output = conv1(inputs)\\n\\n with self.test_session():\\n tf.variables_initializer(\\n [conv1.w, conv1.b] if use_bias else [conv1.w]).run()\\n\\n output_eval = output.eval({\\n inputs: np.zeros([batch_size, in_height, in_width, in_channels])})\\n\\n self.assertEqual(\\n output_eval.shape,\\n (batch_size, in_height, in_width, out_channels))\",\n \"def testShapesNotKnown(self, use_bias):\\n\\n inputs = tf.placeholder(\\n tf.float32, shape=[None, None, self.in_channels], name=\\\"inputs\\\")\\n\\n conv1 = snt.SeparableConv1D(\\n output_channels=self.out_channels_dw,\\n channel_multiplier=1,\\n kernel_shape=self.kernel_shape,\\n padding=snt.SAME,\\n use_bias=use_bias)\\n output = conv1(inputs)\\n\\n with self.test_session() as session:\\n tf.variables_initializer(\\n [conv1.w_dw, conv1.w_pw, conv1.b] if use_bias else\\n [conv1.w_dw, conv1.w_pw]).run()\\n output_eval = session.run(output, {inputs: np.zeros(self.input_shape)})\\n self.assertEqual(output_eval.shape, tuple(self.output_shape))\",\n \"def neural_net_image_input(self, image_shape):\\n assert len(image_shape) == 3, 'image shape is not correct'\\n features = tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], 'x')\\n return features\",\n \"def test_missing_info(self):\\r\\n self.validate(None, None,\\r\\n N_image_shape=(3, 2, 8, 8),\\r\\n N_filter_shape=(4, 2, 5, 5))\\r\\n self.validate((3, 2, None, None), None,\\r\\n N_image_shape=(3, 2, 8, 8),\\r\\n N_filter_shape=(4, 2, 5, 5))\\r\\n self.validate((None, 2, None, None), (None, 2, 5, 5),\\r\\n N_image_shape=(3, 2, 8, 8),\\r\\n N_filter_shape=(4, 2, 5, 5))\",\n \"def preprocess(self, resized_inputs):\\n return (2.0 / 255.0) * resized_inputs - 1.0\",\n \"def testShapesNotKnown(self, use_bias):\\n\\n inputs = tf.placeholder(\\n tf.float32, shape=[None, None, None, self.in_channels], name=\\\"inputs\\\")\\n\\n conv1 = snt.SeparableConv2D(\\n output_channels=self.out_channels_dw,\\n channel_multiplier=1,\\n kernel_shape=self.kernel_shape,\\n padding=snt.SAME,\\n use_bias=use_bias)\\n output = conv1(inputs)\\n\\n with self.test_session():\\n tf.variables_initializer(\\n [conv1.w_dw, conv1.w_pw, conv1.b] if use_bias else\\n [conv1.w_dw, conv1.w_pw]).run()\\n output_eval = output.eval({inputs: np.zeros(self.input_shape)})\\n self.assertEqual(output_eval.shape, tuple(self.output_shape))\",\n \"def test_nparray_input(self):\\n\\n vgg16 = VGG16()\\n data = np.ones((224,224,3))\\n output = vgg16.input_handler(data)\\n\\n self.assertEqual(len(output.shape), 4) \\n self.assertEqual(output.shape[1], 224)\\n self.assertEqual(output.shape[2], 224)\\n self.assertEqual(output.shape[3], 3)\\n\\n data = np.random.rand(120,75,3)\\n output = vgg16.input_handler(data)\\n\\n self.assertEqual(len(output.shape), 4) \\n self.assertEqual(output.shape[1], 224)\\n self.assertEqual(output.shape[2], 224)\\n self.assertEqual(output.shape[3], 3)\",\n \"def __init__(self, nvim):\\n self.nvim = nvim\",\n \"def image_special_func(img):\\n return myimg.image_special_func(img.tolist())\",\n \"def add_placeholders(self):\\n ### YOUR CODE HERE\\n self.input_placeholder=tf.placeholder(dtype=tf.float32,shape=(None, Config.n_features),name='input_placeholder')\\n self.labels_placeholder=tf.placeholder(dtype=tf.int32,shape=(None, Config.n_classes), name='labels_placeholder')\\n ### END YOUR CODE\",\n \"def sanitise_inputs(self):\\n img_paths_var_names = ('dataset_img_path', 'atlas_img_path', 'brain_of_atlas_img_path')\\n for img_path_var_name in img_paths_var_names:\\n img_path = getattr(self, img_path_var_name)\\n if not os.path.exists(img_path):\\n sys.exit('Cannot perform registration, image {} not found'.format(img_path))\\n if not img_path.endswith('.nii'):\\n if img_path.endswith(('.tiff', '.tif')):\\n nii_path = '{}{}'.format(os.path.splitext(img_path)[0], '.nii')\\n bio.tiff_to_nii(img_path, nii_path)\\n setattr(self, img_path_var_name, nii_path)\\n else:\\n raise RegistrationError('Cannot perform registration, image {} not in supported format'\\n .format(img_path))\",\n \"def testMissingImage(self):\\n self.assertNotIn('no_image', self.data)\",\n \"def _create_placeholders(self):\\n # \\\"None\\\" means the batches may have a variable batch size and length.\\n self.x = tf.placeholder(tf.int64, shape=[None, None])\",\n \"def test_to_tensor(dummy_input):\\n # Test the 2D image: B, H, W, C\\n image, label = dummy_input(image_size=(512, 512, 3),\\n label_size=(512, 512, 1))\\n transform = ToTensor()\\n _image, _label = transform(image, label, dtypes=[torch.float, torch.long])\\n assert _image.dtype == torch.float\\n assert _label.dtype == torch.long\\n\\n # Test the 3D image: B, H, W, D, C\\n image, label = dummy_input(image_size=(512, 512, 20, 3),\\n label_size=(512, 512, 20, 1))\\n transform = ToTensor()\\n _image, _label = transform(image, label, dtypes=[torch.float, torch.long])\\n assert _image.dtype == torch.float\\n assert _label.dtype == torch.long\",\n \"def test_imibread(self):\\n gen = imibread(TEST_MIB)\\n arr = next(gen)\\n self.assertEqual(arr.shape, (256, 256))\\n self.assertEqual(arr.dtype, np.dtype(\\\">u2\\\"))\",\n \"def prepare_n_mnist(filename, is_filter, num_spikes, step_factor=1):\\n td = ev.read_dataset(filename)\\n # td.show_td(100)\\n td.data = stabilize(td)\\n td.data = td.extract_roi([3, 3], [28, 28], True)\\n images = make_td_images(td, num_spikes, step_factor)\\n\\n if is_filter:\\n images = ndimage.median_filter(images, 3)\\n\\n # for image in images:\\n # cv2.imshow('img', image)\\n # cv2.waitKey(70)\\n return images\",\n \"def use_mnist_model(self):\\n\\n\\t\\t# load the model\\n\\t\\tnumber_recognizer_MNIST = load_model('models/MNIST_digits_recognition.h5', compile=False)\\n\\n\\t\\t# create empty ndarray\\n\\t\\tnumbers_mnist = np.ones(shape=(self.sudoku_size, self.sudoku_size))\\n\\n\\t\\tpics = deepcopy(self.list_of_number_pictures)\\n\\t\\tfor i in range(self.sudoku_size):\\n\\t\\t\\tfor j in range(self.sudoku_size):\\n\\t\\t\\t\\tpics[i][j] = self.preprocess_cell(pics[i][j], mnist=True, resize=True, clean_remains=True)\\n\\t\\t\\t\\tif self.empty_cells[i][j] != 0:\\n\\t\\t\\t\\t\\tnumbers_mnist[i][j] = np.argmax(number_recognizer_MNIST.predict([[pics[i][j].reshape(28,28,1)]]))\\n\\n\\t\\treturn numbers_mnist\",\n \"def norm_input(image, label):\\n cropped_image = tf.image.resize_image_with_crop_or_pad(image, FLAGS.image_size, FLAGS.image_size)\\n\\n norm_image = tf.image.per_image_standardization(cropped_image)\\n\\n return norm_image, label\",\n \"def __init__(self,bits):\\n self.image_bits = bits\",\n \"def inputs(self) -> Sequence[jnp.ndarray]:\\n pass\",\n \"def __init__(self, im, lab):\\n\\n #self.ROOT = root\\n self.images = im\\n self.labels = lab\",\n \"def __init__(self):\\r\\n # A dummy layer does nothing\\r\\n self.weights = np.zeros(shape=(input.shape[1], 10))\\r\\n bias = np.zeros(shape=(10,))\\r\\n pass\",\n \"def preprocess_image(self, batched_inputs):\\n images = [x[\\\"image\\\"].float().to(self.device) for x in batched_inputs]\\n images = [self.normalizer(img) for img in images]\\n images = ImageList.from_tensors(images, self.backbone.size_divisibility)\\n return images\",\n \"def mobile_input():\\n # Decode image into float range [0,1]\\n jpegs = tf.placeholder(tf.float32, shape=(1, FLAGS.image_size, FLAGS.image_size, 3),\\n name='input')\\n return jpegs, jpegs\",\n \"def __init__(self, real: float, img: np.ndarray):\\n if img.shape != (3,):\\n raise TypeError('imaginary part should be a vector with 3 elements')\\n self.__real = real # real part, qw\\n self.__img = img.astype(dtype=np.float) # imaginary part, qv = [x, y, z]^T\",\n \"def image_batch():\\n return np.zeros((2, 1, 4, 4))\",\n \"def add_placeholders(self):\\n ### YOUR CODE HERE (~4-6 lines)\\n self.input_placeholder = tf.placeholder(tf.int32, [None, self.max_length, Config.n_features])\\n self.labels_placeholder = tf.placeholder(tf.int32, [None, self.max_length])\\n self.length_placeholder = tf.placeholder(tf.int32, [None])\\n self.mask_placeholder = tf.placeholder(tf.bool, [None, self.max_length])\\n self.dropout_placeholder = tf.placeholder(tf.float32)\\n ### END YOUR CODE\",\n \"def n_inputs(self):\",\n \"def _create_train_input(self, input_batch):\\n self.raw_image = input_batch\\n self.image = tf.reshape(self.raw_image, (-1, self._im_size[0], self._im_size[1]))\\n self.lr = tf.placeholder(tf.float32, name='lr')\\n self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')\",\n \"def test_npy_inference():\\n input_path = os.path.join(MAIN_PATH, \\\"data/test_cases/\\\")\\n input_path += os.listdir(input_path)[0]\\n\\n model = InferenceModel()\\n metadata1 = model.nifti2json(input_path, \\\"\\\")\\n\\n img = nib.load(input_path)\\n pixel_spacings = img.header.get_zooms()\\n X = img.get_fdata(dtype=np.float32)\\n metadata2 = model.npy2json(X, \\\"\\\", pixel_spacings=pixel_spacings)\\n\\n assert metadata1 == metadata2\",\n \"def testZeroInput(self):\\n self.assertRaises(TypeError, nb.rscale,)\",\n \"def preprocess_nib(nib_img, is_mask=False):\\n if not is_mask:\\n img_data = nib_img.get_fdata()\\n if img_data.max() > 256:\\n mask256 = img_data < 257\\n img_data = img_data * mask256\\n img_data = img_data.astype('float32')\\n mean = np.mean(img_data)\\n std = np.std(img_data)\\n img_data /= img_data.max() # intensity normalization\\n img_data -= mean # data centering\\n img_data /= std # data normalization\\n\\n return np.expand_dims(img_data, axis=3)\\n else:\\n img_data = np.array(nib_img.get_fdata(), dtype='float32')\\n\\n return np.expand_dims(img_data, axis=3)\",\n \"def __init__(self, n=5, name=None):\\n super().__init__(name=name)\\n self.n = n\\n self._images = []\\n self._sigmas = []\",\n \"def build_filler_images(self):\",\n \"def imgFinite(inImg):\\n arr = sitk.GetArrayFromImage(inImg)\\n arr[~np.isfinite(arr)]=0\\n outImg = sitk.GetImageFromArray(arr)\\n outImg.SetSpacing(inImg.GetSpacing())\\n outImg.SetOrigin(inImg.GetOrigin())\\n outImg.SetDirection(inImg.GetDirection())\\n return outImg\",\n \"def __init__(self, images, labels, tf=None):\\r\\n self.images = images\\r\\n self.labels = labels\\r\\n self.tf = tf\",\n \"def _add_images(self, epoch, train_batch, train_outputs, valid_batch, valid_outputs):\\n raise NotImplementedError\",\n \"def __init__(self, observation_space, name=None):\\r\\n is_image = len(observation_space.shape) == 3\\r\\n inpt, self.processed_inpt = observation_input(observation_space, name=name, scale=is_image)\\r\\n super().__init__(inpt)\",\n \"def _basic_input(dst_embed_i, _):\\n return dst_embed_i.squeeze(0)\",\n \"def process_images(image, label):\\n # Normalize images to have a mean of 0 and standard deviation of 1\\n # per_image_standardization is preferred, which normalize the entire image to mean zero and std 1.\\n # It also make learning fast.\\n image = tf.image.per_image_standardization(image)\\n # Resize images from 32x32 to 277x277\\n image = tf.image.resize(image, (227,227))\\n return image, label\",\n \"def check_niimg(\\n niimg,\\n ensure_ndim=None,\\n atleast_4d=False,\\n dtype=None,\\n return_iterator=False,\\n wildcards=True,\\n):\\n from ..image import new_img_like # avoid circular imports\\n\\n niimg = stringify_path(niimg)\\n\\n if isinstance(niimg, str):\\n if wildcards and ni.EXPAND_PATH_WILDCARDS:\\n # Ascending sorting + expand user path\\n filenames = sorted(glob.glob(os.path.expanduser(niimg)))\\n\\n # processing filenames matching globbing expression\\n if len(filenames) >= 1 and glob.has_magic(niimg):\\n niimg = filenames # iterable case\\n # niimg is an existing filename\\n elif [niimg] == filenames:\\n niimg = filenames[0]\\n # No files found by glob\\n elif glob.has_magic(niimg):\\n # No files matching the glob expression, warn the user\\n message = (\\n \\\"No files matching the entered niimg expression: \\\"\\n \\\"'%s'.\\\\n You may have left wildcards usage \\\"\\n \\\"activated: please set the global constant \\\"\\n \\\"'nilearn.EXPAND_PATH_WILDCARDS' to False to \\\"\\n \\\"deactivate this behavior.\\\"\\n ) % niimg\\n raise ValueError(message)\\n else:\\n raise ValueError(f\\\"File not found: '{niimg}'\\\")\\n elif not os.path.exists(niimg):\\n raise ValueError(f\\\"File not found: '{niimg}'\\\")\\n\\n # in case of an iterable\\n if hasattr(niimg, \\\"__iter__\\\") and not isinstance(niimg, str):\\n if return_iterator:\\n return _iter_check_niimg(\\n niimg, ensure_ndim=ensure_ndim, dtype=dtype\\n )\\n return concat_niimgs(niimg, ensure_ndim=ensure_ndim, dtype=dtype)\\n\\n # Otherwise, it should be a filename or a SpatialImage, we load it\\n niimg = load_niimg(niimg, dtype=dtype)\\n\\n if ensure_ndim == 3 and len(niimg.shape) == 4 and niimg.shape[3] == 1:\\n # \\\"squeeze\\\" the image.\\n data = _safe_get_data(niimg)\\n affine = niimg.affine\\n niimg = new_img_like(niimg, data[:, :, :, 0], affine)\\n if atleast_4d and len(niimg.shape) == 3:\\n data = _get_data(niimg).view()\\n data.shape = data.shape + (1,)\\n niimg = new_img_like(niimg, data, niimg.affine)\\n\\n if ensure_ndim is not None and len(niimg.shape) != ensure_ndim:\\n raise DimensionError(len(niimg.shape), ensure_ndim)\\n\\n if return_iterator:\\n return (_index_img(niimg, i) for i in range(niimg.shape[3]))\\n\\n return niimg\",\n \"def test_empty_img():\\n assert detected_boxes[-1] == ground_truth_boxes[-1]\",\n \"def set_input_tensor(interpreter, image):\\n tensor_index = interpreter.get_input_details()[0]['index']\\n input_tensor = interpreter.tensor(tensor_index)()[0]\\n input_tensor[:, :] = image\",\n \"def test_lm_bisenet_post_process():\\n tf.InteractiveSession()\\n\\n image_size = [96, 64]\\n batch_size = 2\\n classes = Camvid.classes\\n data_format = \\\"NHWC\\\"\\n\\n model = LMBiSeNet(\\n image_size=image_size,\\n batch_size=batch_size,\\n classes=classes,\\n data_format=data_format,\\n )\\n\\n post_process = Sequence([\\n Bilinear(\\n size=image_size,\\n data_format=data_format,\\n compatible_tensorflow_v1=True,\\n ),\\n Softmax()\\n ])\\n\\n shape = (batch_size, image_size[0]//8, image_size[1]//8, len(classes))\\n np_output = np.random.uniform(-10., 10., size=shape).astype(np.float32)\\n output = tf.constant(np_output)\\n\\n output = model.post_process(output)\\n\\n expected = post_process(outputs=np_output)[\\\"outputs\\\"]\\n\\n assert np.allclose(output.eval(), expected, atol=1e-5, rtol=1e-5)\",\n \"def preprocess_input(img):\\n img /= 255.\\n img -= 0.5\\n img *= 2.\\n return img\",\n \"def _create_placeholders(self, n_features, n_classes):\\n\\n self.input_data = tf.placeholder(\\\"float\\\", [None, n_features], name='x-input')\\n self.input_labels = tf.placeholder(\\\"float\\\", [None, n_classes], name='y-input')\",\n \"def make_default_inputs(filtered_dot_file):\\n# w_kernel = pickle.load(open(\\\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-3.p\\\", \\\"rb\\\" ))\\n# w_kernel = pickle.load(open(\\\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-2.p\\\", \\\"rb\\\" ))\\n# w_kernel = pickle.load(open(\\\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-4.p\\\", \\\"rb\\\" ))\\n w_kernel = pickle.load(open(\\\"/Users/bptripp/code/nengo-FPGA/v1/dog-gabor-weights-new-2.p\\\", \\\"rb\\\" ))\\n filtered = pickle.load(open(filtered_dot_file, \\\"rb\\\" ))\\n \\n n_inputs = 200\\n centres = np.random.randn(n_inputs,2)\\n mag = np.sum(centres**2, axis=1)**(1/2)\\n centres = 155 * centres.T / mag\\n centres = centres * np.random.rand(n_inputs)**(1/2)\\n centres = np.round(centres).astype('int')\\n centres = centres + 199 #put in image centre\\n \\n inputs = get_inputs(w_kernel, filtered, centres.T)\\n \\n pickle.dump(inputs, open(filtered_dot_file.replace('-filt.p', '-inputs.p'), \\\"wb\\\" ))\",\n \"def __init__(self, images, is_training, num_points):\\n\\n self.images = images\\n self.is_training = is_training\\n self.num_points = num_points\\n self.weight_decay = 0.00004\\n self.stddev = 0.1\\n self.dropout_keep_prob = 0.8\\n self.use_batch_norm = True\\n self.batch_norm_params = None\\n self.add_summaries = True\\n self.scope = \\\"InceptionV3\\\"\",\n \"def no_bin(image, *args, **kwargs):\\n return image\",\n \"def create_model_input_blacked(self, rgb_images, all_boxes, all_masks):\\n images = []\\n for i in range(all_boxes.size(0)):\\n for j in range(all_boxes.size(1)):\\n box = all_boxes[i][j]\\n if is_dummy_box(box):\\n continue\\n\\n image = rgb_images[i].cpu()\\n\\n # Apply object mask to the image\\n if self.use_masks:\\n mask = all_masks[i][j].cpu()\\n image = image * mask\\n\\n image = TF.to_pil_image(image)\\n image = blacken_image(image, box)\\n image = TF.resize(image, (self.reduced_size, self.reduced_size))\\n image = TF.to_tensor(image)\\n images.append(image)\\n return torch.stack(images)\",\n \"def inv_preprocess(imgs, num_images, img_mean):\\n n, h, w, c = imgs.shape\\n assert (n >= num_images), 'Batch size %d should be greater or equal than number of images to save %d.' % (\\n n, num_images)\\n outputs = np.zeros((num_images, h, w, c), dtype=np.uint8)\\n for i in range(num_images):\\n outputs[i] = (imgs[i] + img_mean)[:, :, ::-1].astype(np.uint8)\\n return outputs\",\n \"def __init__(self, root: str, size: torch.Size, exclude_imagenet1k=True, *args, **kwargs):\\n super(MyImageNet22K, self).__init__(root, *args, **kwargs)\\n\\n self.exclude_imagenet1k = exclude_imagenet1k\\n self.shuffle_idxs = False\\n self.size = size\\n\\n if exclude_imagenet1k:\\n self.samples = [s for s in self.samples if not any([idx in s[0] for idx in self.imagenet1k_idxs])]\",\n \"def set_input_tensor(interpreter, image):\\n tensor_index = interpreter.get_input_details()[0][\\\"index\\\"]\\n input_tensor = interpreter.tensor(tensor_index)()[0]\\n input_tensor[:, :] = image\",\n \"def set_input_tensor(interpreter, image):\\n tensor_index = interpreter.get_input_details()[0][\\\"index\\\"]\\n input_tensor = interpreter.tensor(tensor_index)()[0]\\n input_tensor[:, :] = image\",\n \"def set_input_tensor(interpreter, image):\\n tensor_index = interpreter.get_input_details()[0][\\\"index\\\"]\\n input_tensor = interpreter.tensor(tensor_index)()[0]\\n input_tensor[:, :] = image\",\n \"def set_default_parameters(self):\\n super().set_default_parameters()\\n if not \\\"n_sub_images\\\" in vars(self):\\n self.n_sub_images = -1 # do all-sub-images\",\n \"def MNIST_normalize_input(inputs: [int]) -> [float]:\\n return [i/256 for i in inputs] # simple for MNIST\",\n \"def stage_one(self, imgs, threshold, factor, minsize, nms_threshold):\\n\\n width = imgs.shape[-2]\\n height = imgs.shape[-1]\\n num_img = imgs.shape[0]\\n\\n # Compute valid scales\\n scales = []\\n cur_width = width\\n cur_height = height\\n cur_factor = 1\\n while cur_width >= 12 and cur_height >= 12:\\n if 12 / cur_factor >= minsize: # Ignore boxes that smaller than minsize\\n\\n w = cur_width\\n h = cur_height\\n scales.append((w, h, cur_factor))\\n\\n cur_factor *= factor\\n cur_width = math.ceil(cur_width * factor)\\n cur_height = math.ceil(cur_height * factor)\\n\\n # Get candidate boxesi ph\\n candidate_boxes = torch.empty(0, dtype=torch.int32, device=self.device)\\n candidate_scores = torch.empty(0, device=self.device)\\n candidate_offsets = torch.empty(\\n 0, dtype=torch.float32, device=self.device)\\n all_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)\\n for w, h, f in scales:\\n resize_img = torch.nn.functional.interpolate(\\n imgs, size=(w, h), mode='bilinear')\\n p_distribution, box_regs, _ = self.pnet(resize_img)\\n\\n candidate, scores, offsets, img_labels = self._generate_bboxes(\\n p_distribution, box_regs, f, threshold)\\n\\n candidate_boxes = torch.cat([candidate_boxes, candidate])\\n candidate_scores = torch.cat([candidate_scores, scores])\\n candidate_offsets = torch.cat([candidate_offsets, offsets])\\n all_img_labels = torch.cat([all_img_labels, img_labels])\\n\\n \\n if candidate_boxes.shape[0] != 0:\\n candidate_boxes = self._calibrate_box(\\n candidate_boxes, candidate_offsets)\\n candidate_boxes = self._convert_to_square(candidate_boxes)\\n candidate_boxes = self._refine_boxes(\\n candidate_boxes, width, height)\\n \\n final_boxes = torch.empty(0, dtype=torch.int32, device=self.device)\\n final_img_labels = torch.empty(0, dtype=torch.int32, device=self.device)\\n for i in range(num_img):\\n mask = all_img_labels == i\\n keep = func.nms(candidate_boxes[mask].cpu().numpy(),\\n candidate_scores[mask].cpu().numpy(), nms_threshold)\\n final_boxes = torch.cat([final_boxes, candidate_boxes[mask][keep]])\\n final_img_labels = torch.cat([final_img_labels, all_img_labels[mask][keep]])\\n\\n return torch.cat([final_boxes, final_img_labels.unsqueeze(1 )], -1)\\n else:\\n return candidate_boxes\",\n \"def fake_data(num_images):\\n data = np.ndarray(\\n shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),\\n dtype=np.float32)\\n labels = np.zeros(shape=(num_images,), dtype=np.int64)\\n for image in xrange(num_images):\\n label = image % 2\\n data[image, :, :, 0] = label - 0.5\\n labels[image] = label\\n return data, labels\",\n \"def testShapesNotKnown(self, use_bias):\\n\\n inputs = tf.placeholder(\\n tf.float32, shape=[None, None, None, self.in_channels], name=\\\"inputs\\\")\\n\\n conv1 = snt.DepthwiseConv2D(\\n channel_multiplier=self.channel_multiplier,\\n kernel_shape=self.kernel_shape,\\n padding=snt.SAME,\\n stride=1,\\n use_bias=use_bias)\\n output = conv1(inputs)\\n\\n with self.test_session():\\n tf.variables_initializer(\\n [conv1.w, conv1.b] if use_bias else [conv1.w]).run()\\n output_eval = output.eval({inputs: np.zeros(self.input_shape)})\\n self.assertEqual(output_eval.shape, tuple(self.output_shape))\",\n \"def test_train_image_dims_content(self):\\n iterator = self._dataset.get_train()\\n sample = next(iterator)\\n image, label = sample['image'], sample['label']\\n\\n with self.subTest(name='DataShape'):\\n self.assertTupleEqual(image.shape, (self._batch_size, 32, 32, 3))\\n\\n with self.subTest(name='DataType'):\\n self.assertTrue(np.issubdtype(image.dtype, float))\\n\\n with self.subTest(name='DataValues'):\\n # Normalized by stddev., expect nothing to fall outside 3 stddev.\\n self.assertTrue((image >= -3.).all() and (image <= 3.).all())\\n\\n with self.subTest(name='LabelShape'):\\n self.assertLen(label, self._batch_size)\\n\\n with self.subTest(name='LabelType'):\\n self.assertTrue(np.issubdtype(label.dtype, int))\\n\\n with self.subTest(name='LabelValues'):\\n self.assertTrue((label >= 0).all() and\\n (label <= self._dataset.num_classes).all())\",\n \"def _fake_only_visualize(fake_img, real_img, caption, num, num_per_caption, num_per_row, model):\\n fake_img = _post_process(fake_img, model)\\n grid = _make_grid(fake_img, cols=num_per_row)\\n grid = tf.convert_to_tensor(grid, dtype=tf.uint8)\\n return fake_img, grid\",\n \"def add_placeholders(self):\\n ### YOUR CODE HERE\\n #print self.config.num_steps\\n self.input_placeholder = tf.placeholder(tf.int32, shape=(None, self.config.num_steps))\\n self.labels_placeholder = tf.placeholder(tf.int32, shape=[None, self.config.num_steps])\\n self.dropout_placeholder = tf.placeholder(tf.float32)\\n ### END YOUR CODE\",\n \"def find_input():\\n\\n mat = [[5,0],[0,0],[0,0]]\\n num_range = [0,2]\\n sub = 3 #Number of pixels to be substituted.\\n\\n input = [mat, num_range, sub]\\n\\n return input\",\n \"def test_2d_inputs():\\n reseed()\\n\\n base_img1 = np.array([[0, 0, 1, 1],\\n [0, 0, 1, 1],\\n [0, 1, 1, 1]], dtype=np.uint8)\\n base_img2 = np.array([[0, 0, 1, 1],\\n [0, 1, 1, 1],\\n [0, 1, 0, 0]], dtype=np.uint8)\\n\\n base_img1_flipped = np.array([[1, 1, 0, 0],\\n [1, 1, 0, 0],\\n [1, 1, 1, 0]], dtype=np.uint8)\\n base_img2_flipped = np.array([[1, 1, 0, 0],\\n [1, 1, 1, 0],\\n [0, 0, 1, 0]], dtype=np.uint8)\\n\\n images = np.array([base_img1, base_img2])\\n images_flipped = np.array([base_img1_flipped, base_img2_flipped])\\n images_list = [base_img1, base_img2]\\n images_flipped_list = [base_img1_flipped, base_img2_flipped]\\n images_list2d3d = [base_img1, base_img2[:, :, np.newaxis]]\\n images_flipped_list2d3d = [base_img1_flipped, base_img2_flipped[:, :, np.newaxis]]\\n\\n aug = iaa.Fliplr(1.0)\\n noaug = iaa.Fliplr(0.0)\\n\\n # one numpy array as input\\n observed = aug.augment_images(images)\\n assert np.array_equal(observed, images_flipped)\\n\\n observed = noaug.augment_images(images)\\n assert np.array_equal(observed, images)\\n\\n # list of 2d images\\n observed = aug.augment_images(images_list)\\n assert array_equal_lists(observed, images_flipped_list)\\n\\n observed = noaug.augment_images(images_list)\\n assert array_equal_lists(observed, images_list)\\n\\n # list of images, one 2d and one 3d\\n observed = aug.augment_images(images_list2d3d)\\n assert array_equal_lists(observed, images_flipped_list2d3d)\\n\\n observed = noaug.augment_images(images_list2d3d)\\n assert array_equal_lists(observed, images_list2d3d)\",\n \"def init_predictions(nb_rows, nb_cols, dtype=np.float64):\\n return np.zeros((nb_rows, nb_cols), dtype=dtype)\",\n \"def iou_train(self, boxA, boxB, realBox):\\n iou1 = tf.reshape(iou_train_unit(boxA, realBox), [-1, 7, 7, 1])\\n iou2 = tf.reshape(iou_train_unit(boxB, realBox), [-1, 7, 7, 1])\\n return tf.concat([iou1, iou2], 3)\",\n \"def test_image_input(self):\\n \\n vgg16 = VGG16()\\n data_dir = os.path.dirname(os.path.abspath(__file__))\\n data = os.path.join(data_dir, 'data/elephant.jpg')\\n output = vgg16.input_handler(data)\\n\\n self.assertEqual(len(output.shape), 4) \\n self.assertEqual(output.shape[1], 224)\\n self.assertEqual(output.shape[2], 224)\\n self.assertEqual(output.shape[3], 3)\",\n \"def handle_origin_image(image, gt_box):\\n x = image.width\\n y = image.height\\n im_max = max(x, y)\\n im_min = min(x, y)\\n scale = cfg.TRAIN.MIN_SIZE / im_min\\n if scale * im_max > cfg.TRAIN.MAX_SIZE:\\n scale = cfg.TRAIN.MAX_SIZE / im_max\\n width = round(round(x * scale) / 32) * 32\\n height = round(round(y * scale) / 32) * 32\\n im = image.resize((width, height))\\n box = [round(gt_box[0] * width / x), round(gt_box[1] * height / y), round(gt_box[2] * width / x),\\n round(gt_box[3] * height / y)]\\n # make sure there really tiny flaw still have box to predict\\n if (box[3] - box[1]) * (box[2] - box[0]) < 100:\\n box = [box[0] - 3, box[1] - 3, box[2] + 3, box[3] + 3]\\n return np.array(im), box\",\n \"def setUp(self):\\n self.gray_image = np.ndarray((100, 200), dtype=np.uint8)\\n self.rgb_image = np.ndarray((100, 200, 3), dtype=np.uint8)\",\n \"def __init__(self, n_in, n_out):\\n self.W = np.random.randn(n_in, n_out) * 0.1\\n self.b = np.zeros(n_out)\",\n \"def __init__(self, env, height=80, width=80, grayscale=True,\\n crop=lambda img: img):\\n super(PreprocessImage, self).__init__(env)\\n self.img_size = (height, width)\\n self.grayscale = grayscale\\n self.crop = crop\\n\\n n_colors = 1 if self.grayscale else 3\\n self.observation_space = Box(0.0, 1.0, [n_colors, height, width])\",\n \"def __call__(self, src, label):\\r\\n # img = mx.nd.image.to_tensor(src)\\r\\n # img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\\r\\n src = mx.nd.array(src)\\r\\n img = mx.nd.image.to_tensor(src)\\r\\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\\r\\n return img, mx.nd.array(label, dtype=img.dtype)\",\n \"def test_init_ops(self):\\n tf.reset_default_graph()\\n tf.set_random_seed(42)\\n self.imagenet_inception_v3.set_up()\\n with tf.Session() as sess:\\n sess.run(tf.global_variables_initializer())\\n num_param = [\\n np.prod(v.get_shape().as_list())\\n for v in tf.trainable_variables()\\n ]\\n # Check if number of parameters per \\\"layer\\\" is equal to what we expect\\n # We will write them in the following form:\\n # - Conv layer: [input_filter*output_filter*kernel[0]*kernel[1]]\\n # - Batch norm: [input, input] (for beta and gamma)\\n # - Fully connected: [input*output]\\n # - Bias: [dim]\\n self.assertEqual(\\n num_param, [\\n 3 * 32 * 3 * 3, 32, 32, 32 * 32 * 3 * 3, 32, 32,\\n 32 * 64 * 3 * 3, 64, 64, 64 * 80 * 1 * 1, 80, 80,\\n 80 * 192 * 3 * 3, 192, 192, 192 * 64 * 1 * 1, 64, 64,\\n 192 * 32 * 1 * 1, 32, 32, 192 * 48 * 1 * 1, 48, 48,\\n 48 * 64 * 5 * 5, 64, 64, 192 * 64 * 1 * 1,\\n 64, 64, 64 * 96 * 3 * 3, 96, 96, 96 * 96 * 3 * 3, 96, 96,\\n (64 + 32 + 64 + 96) * 64 * 1 * 1, 64, 64,\\n (64 + 32 + 64 + 96) * 64 * 1 * 1, 64, 64,\\n (64 + 32 + 64 + 96) * 48 * 1 * 1, 48, 48, 48 * 64 * 5 * 5,\\n 64, 64, (64 + 32 + 64 + 96) * 64 * 1 * 1, 64, 64,\\n 64 * 96 * 3 * 3, 96, 96, 96 * 96 * 3 * 3, 96, 96,\\n (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64,\\n (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64,\\n (64 + 64 + 64 + 96) * 48 * 1 * 1, 48, 48, 48 * 64 * 5 * 5,\\n 64, 64, (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64,\\n 64 * 96 * 3 * 3, 96, 96, 96 * 96 * 3 * 3, 96, 96,\\n (64 + 64 + 64 + 96) * 384 * 3 * 3, 384, 384,\\n (64 + 64 + 64 + 96) * 64 * 1 * 1, 64, 64, 64 * 96 * 3 * 3,\\n 96, 96, 96 * 96 * 3 * 3, 96, 96,\\n ((64 + 64 + 64 + 96) + 384 + 96) * 192 * 1 * 1, 192, 192,\\n ((64 + 64 + 64 + 96) + 384 + 96) * 192 * 1 * 1, 192, 192,\\n ((64 + 64 + 64 + 96) + 384 + 96) * 128 * 1 * 1, 128, 128,\\n 128 * 128 * 1 * 7, 128, 128, 128 * 192 * 7 * 1, 192, 192,\\n ((64 + 64 + 64 + 96) + 384 + 96) * 128 * 1 * 1, 128, 128,\\n 128 * 128 * 7 * 1, 128, 128, 128 * 128 * 1 * 7, 128, 128,\\n 128 * 128 * 7 * 1, 128, 128, 128 * 192 * 1 * 7, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\\n 160 * 160 * 1 * 7, 160, 160, 160 * 192 * 7 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\\n 160 * 160 * 7 * 1, 160, 160, 160 * 160 * 1 * 7, 160, 160,\\n 160 * 160 * 7 * 1, 160, 160, 160 * 192 * 1 * 7, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\\n 160 * 160 * 1 * 7, 160, 160, 160 * 192 * 7 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 160 * 1 * 1, 160, 160,\\n 160 * 160 * 7 * 1, 160, 160, 160 * 160 * 1 * 7, 160, 160,\\n 160 * 160 * 7 * 1, 160, 160, 160 * 192 * 1 * 7, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n 192 * 192 * 1 * 7, 192, 192, 192 * 192 * 7 * 1, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n 192 * 192 * 7 * 1, 192, 192, 192 * 192 * 1 * 7, 192, 192,\\n 192 * 192 * 7 * 1, 192, 192, 192 * 192 * 1 * 7, 192, 192,\\n (192 + 192 + 192 + 192) * 128 * 1 * 1, 128, 128,\\n 128 * 768 * 5 * 5, 768, 768, 768 * 1001, 1001,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n 192 * 192 * 1 * 7, 192, 192, 192 * 192 * 7 * 1,\\n 192, 192, 192 * 192 * 3 * 3, 192, 192,\\n (192 + 192 + 192 + 192) * 192 * 1 * 1, 192, 192,\\n 192 * 320 * 3 * 3, 320, 320,\\n (4 * 192 + 192 + 320) * 320 * 1 * 1, 320, 320,\\n (4 * 192 + 192 + 320) * 384 * 1 * 1, 384, 384,\\n 384 * 384 * 1 * 3, 384, 384, 384 * 384 * 3 * 1, 384, 384,\\n (4 * 192 + 192 + 320) * 448 * 1 * 1, 448, 448,\\n 448 * 384 * 3 * 3, 384, 384,\\n 384 * 384 * 1 * 3, 384, 384, 384 * 384 * 3 * 1, 384, 384,\\n (4 * 192 + 192 + 320) * 192, 192, 192,\\n (320 + 384 * 2 + 384 * 2 + 192) * 320 * 1 * 1, 320, 320,\\n (320 + 384 * 2 + 384 * 2 + 192) * 384 * 1 * 1, 384, 384,\\n 384 * 384 * 1 * 3, 384, 384, 384 * 384 * 3 * 1, 384, 384,\\n (320 + 384 * 2 + 384 * 2 + 192) * 448 * 1 * 1, 448, 448,\\n 448 * 384 * 3 * 3, 384, 384, 384 * 384 * 1 * 3, 384,\\n 384, 384 * 384 * 3 * 1, 384, 384,\\n (320 + 384 * 2 + 384 * 2 + 192) * 192, 192, 192,\\n 2048 * 1001, 1001\\n ])\\n for init_op in [\\n self.imagenet_inception_v3.train_init_op,\\n self.imagenet_inception_v3.test_init_op,\\n self.imagenet_inception_v3.train_eval_init_op\\n ]:\\n sess.run(init_op)\\n losses_, regularizer_, accuracy_ = sess.run([\\n self.imagenet_inception_v3.losses,\\n self.imagenet_inception_v3.regularizer,\\n self.imagenet_inception_v3.accuracy\\n ])\\n self.assertEqual(losses_.shape, (self.batch_size, ))\\n self.assertIsInstance(regularizer_, np.float32)\\n self.assertIsInstance(accuracy_, np.float32)\",\n \"def _real_only_visualize(fake_img, real_img, caption, num, num_per_caption, num_per_row, model):\\n real_img = _post_process(real_img, model)\\n grid = _make_grid(real_img, cols=num_per_row)\\n grid = tf.convert_to_tensor(grid, dtype=tf.uint8)\\n return real_img, grid\",\n \"def fake_data(num_images):\\n data = np.ndarray(\\n shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),\\n dtype=np.float32)\\n labels = np.zeros(shape=(num_images,), dtype=np.int64)\\n for image in xrange(num_images):\\n label = image % 2\\n data[image, :, :, 0] = label - 0.5\\n labels[image] = label\\n return data, labels\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6088899","0.59365016","0.5740839","0.573186","0.5731295","0.5677845","0.56754416","0.56236225","0.55761564","0.5560068","0.5500983","0.5489259","0.5475015","0.5471705","0.54692245","0.54186827","0.5388585","0.53639835","0.5345162","0.5341476","0.53397614","0.53364605","0.53364605","0.53364605","0.5333801","0.5332035","0.5309034","0.5269681","0.5269565","0.5264732","0.5258058","0.52535063","0.525234","0.52517164","0.5251385","0.52507144","0.521391","0.5212524","0.52039707","0.5203835","0.51913035","0.5187684","0.51863587","0.5185883","0.5183215","0.5176887","0.51726717","0.51686823","0.51575416","0.51557463","0.5154525","0.51491827","0.5147053","0.51452446","0.51437783","0.51405436","0.5127884","0.5122402","0.5118903","0.5118386","0.51169384","0.51140666","0.5111875","0.5102211","0.5095333","0.50901175","0.5086695","0.5076467","0.5075364","0.5075349","0.506842","0.5055991","0.505331","0.50410676","0.50310576","0.5028585","0.5023331","0.502295","0.502295","0.502295","0.5021777","0.50045174","0.49924234","0.49841422","0.49834785","0.49797198","0.49720562","0.49715295","0.49710205","0.4966292","0.49652657","0.49573138","0.4953629","0.4953528","0.49487618","0.49453905","0.49440783","0.4934044","0.4933025","0.4932212","0.4927909"],"string":"[\n \"0.6088899\",\n \"0.59365016\",\n \"0.5740839\",\n \"0.573186\",\n \"0.5731295\",\n \"0.5677845\",\n \"0.56754416\",\n \"0.56236225\",\n \"0.55761564\",\n \"0.5560068\",\n \"0.5500983\",\n \"0.5489259\",\n \"0.5475015\",\n \"0.5471705\",\n \"0.54692245\",\n \"0.54186827\",\n \"0.5388585\",\n \"0.53639835\",\n \"0.5345162\",\n \"0.5341476\",\n \"0.53397614\",\n \"0.53364605\",\n \"0.53364605\",\n \"0.53364605\",\n \"0.5333801\",\n \"0.5332035\",\n \"0.5309034\",\n \"0.5269681\",\n \"0.5269565\",\n \"0.5264732\",\n \"0.5258058\",\n \"0.52535063\",\n \"0.525234\",\n \"0.52517164\",\n \"0.5251385\",\n \"0.52507144\",\n \"0.521391\",\n \"0.5212524\",\n \"0.52039707\",\n \"0.5203835\",\n \"0.51913035\",\n \"0.5187684\",\n \"0.51863587\",\n \"0.5185883\",\n \"0.5183215\",\n \"0.5176887\",\n \"0.51726717\",\n \"0.51686823\",\n \"0.51575416\",\n \"0.51557463\",\n \"0.5154525\",\n \"0.51491827\",\n \"0.5147053\",\n \"0.51452446\",\n \"0.51437783\",\n \"0.51405436\",\n \"0.5127884\",\n \"0.5122402\",\n \"0.5118903\",\n \"0.5118386\",\n \"0.51169384\",\n \"0.51140666\",\n \"0.5111875\",\n \"0.5102211\",\n \"0.5095333\",\n \"0.50901175\",\n \"0.5086695\",\n \"0.5076467\",\n \"0.5075364\",\n \"0.5075349\",\n \"0.506842\",\n \"0.5055991\",\n \"0.505331\",\n \"0.50410676\",\n \"0.50310576\",\n \"0.5028585\",\n \"0.5023331\",\n \"0.502295\",\n \"0.502295\",\n \"0.502295\",\n \"0.5021777\",\n \"0.50045174\",\n \"0.49924234\",\n \"0.49841422\",\n \"0.49834785\",\n \"0.49797198\",\n \"0.49720562\",\n \"0.49715295\",\n \"0.49710205\",\n \"0.4966292\",\n \"0.49652657\",\n \"0.49573138\",\n \"0.4953629\",\n \"0.4953528\",\n \"0.49487618\",\n \"0.49453905\",\n \"0.49440783\",\n \"0.4934044\",\n \"0.4933025\",\n \"0.4932212\",\n \"0.4927909\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17446,"cells":{"query":{"kind":"string","value":"Update the current file metadata with component versions on the database"},"document":{"kind":"string","value":"def checkVersions():\n item = Item(fromScene=True)\n\n for ns, componentMData in item.components.iteritems():\n if ns == 'cam':\n # todo tratar versoes da camera\n continue\n\n if componentMData['assembleMode'] == 'reference':\n refComponent = ReferenceComponent(ns, componentMData, parent=item)\n refComponent.checkDBForNewVersion()\n\n elif componentMData['assembleMode'] == 'xlo':\n xloComponent = XloComponent(ns, componentMData, parent=item)\n xloComponent.checkDBForNewVersion()\n xloComponent.checkDBForNewCacheVersion()\n\n elif componentMData['assembleMode'] == 'cache':\n cacheComponent = CacheComponent(ns, componentMData, parent=item)\n cacheComponent.checkDBForNewVersion()\n\n item.putDataToDB()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_version(self, version):\n self._metadata['version'] = version\n\n if self._type == '.json':\n with open(self._filename, 'w') as f:\n f.write(json.dumps(self._metadata, indent=2))\n\n dof_filename = os.path.join(self.path, self.name + '.dof')\n if os.path.isfile(dof_filename):\n dof_file = DOFFile(dof_filename)\n dof_file.update_version(version)","def update_metadata(self, file_id, metadata):\n pass","def update_version_files (component):\n\n vprint (\"Updating version files for \" + component)\n\n retval = []\n\n ## Update component/VERSION.txt\n path = get_path(component, \"VERSION.txt\")\n with open (path, \"r+\") as version_file:\n new_version = re.sub (component + \" version .*\",\n \"%s version %s, released %s\" % (component,\n comp_versions[component + \"_version\"],\n release_date),\n version_file.read ())\n if opts.take_action:\n version_file.seek (0)\n version_file.truncate (0)\n version_file.write (new_version)\n else:\n print (\"New version file for \" + component)\n print (new_version)\n\n vprint (\"Updating Version.h for \" + component)\n\n retval.append(path)\n\n ## Update COMPONENT/component/Version.h\n comp_l = len(component + \"_\")\n parts = {k[comp_l:]:v for (k, v) in comp_versions.items() if k.startswith(component)}\n parts[\"comp\"] = component\n version_header = \"\"\"\n// -*- C++ -*-\n// This is file was automatically generated by $ACE_ROOT/bin/make_release.py\n\n#define {comp}_MAJOR_VERSION {major}\n#define {comp}_MINOR_VERSION {minor}\n#define {comp}_MICRO_VERSION {micro}\n#define {comp}_VERSION \\\"{version}\\\"\n#define {comp}_VERSION_CODE 0x{code:x}\n#define {comp}_MAKE_VERSION_CODE(a,b,c) (((a) << 16) + ((b) << 8) + (c))\n\"\"\".format(**parts)\n\n path = get_path(component, component.lower (), \"Version.h\")\n if opts.take_action:\n with open (path, 'w+') as version_h:\n version_h.write (version_header)\n else:\n print (\"New Version.h for \" + component)\n print (version_header)\n\n retval.append(path)\n\n # Update component/PROBLEM-REPORT-FORM\n vprint (\"Updating PRF for \" + component)\n\n version_line_re = re.compile (r\"^\\s*(\\w+) +VERSION ?:\")\n path = get_path(component, \"PROBLEM-REPORT-FORM\")\n\n with open (path, 'r+') as prf:\n new_prf = \"\"\n for line in prf.readlines ():\n match = version_line_re.search (line)\n if match is not None:\n vprint (\"Found PRF Version for \" + match.group (1))\n new_version = comp_versions[match.group(1) + \"_version\"]\n line = version_re.sub (new_version, line)\n\n new_prf += line\n\n if opts.take_action:\n prf.seek (0)\n prf.truncate (0)\n prf.writelines (new_prf)\n else:\n print (\"New PRF for \" + component)\n print (\"\".join (new_prf))\n\n retval.append(path)\n\n return retval","def updateFileInfo(self, data, pid):\n self.db.updateLinkInfo(data)\n self.evm.dispatchEvent(\"packageUpdated\", pid)","def updateDictFile(self):\n if self.dictFile.vdata.get('version',0): return\n #--Update to version 1\n for name in self.data.keys():\n installer = self.data[name]\n if isinstance(installer,Installer):\n self.data[name] = installer.__copy__(InstallerArchive)\n self.dictFile.vdata['version'] = 1","def updateVersions(self):\r\n f = open('../versions.pckl', 'wb')\r\n pickle.dump(self.versions, f)\r\n f.close()","def refresh(self):\r\n self.metadata = self.db.read(self.path).json()","def _update_version(self) -> None:\n # Implement in child class.\n raise NotImplementedError","def test_update_metadata(self):\n pass","def update(self):\n\n if not self.db: self.validate()\n\n self.logging.debug( \"update(%s)\" % (self.db) )\n\n for name in self.tables:\n self.dbs_tables[name]['md5'] = get_md5( self.dbs_tables[name]['path'] )\n\n self._get_magnitudes()\n self._get_events()","def update(self):\n self.content = self.get_content()\n self.dependencies = self.content['requirements']['run']\n self.pythonversion = self.content['extra']['pythonversion']\n self.package_name = self.content['package']['name']","def _volume_metadata_set(self, volume_path, data):\n data['compat_version'] = 1\n data['version'] = self.version\n return self._metadata_set(self._volume_metadata_path(volume_path), data)","def _metadata_update_pulp():\n items = db.firmware.get_all()\n files_to_scan = []\n files_to_scan.append('firmware.xml.gz')\n files_to_scan.append('firmware.xml.gz.asc')\n for item in items:\n if item.target != 'stable':\n continue\n files_to_scan.append(item.filename)\n\n # for each file in stable plus metadata\n data = []\n download_dir = app.config['DOWNLOAD_DIR']\n for f in files_to_scan:\n fn = os.path.join(download_dir, f)\n if not os.path.exists(fn):\n continue\n\n # filename,sha256,size\n sha256 = _hashfile(open(fn, 'rb'), hashlib.sha256())\n fn_sz = os.path.getsize(fn)\n data.append('%s,%s,%i\\n' % (f, sha256, fn_sz))\n\n # write file\n filename = os.path.join(download_dir, 'PULP_MANIFEST')\n f = open(filename, 'w')\n f.writelines(data)\n f.close()\n\n # upload to CDN\n blob = open(filename, 'rb').read()\n _upload_to_cdn(filename, blob)\n return","def run_sql_file(filename, connection, version, lastversion):\n cursor = connection.cursor()\n for line in open(filename):\n cursor.execute(line)\n connection.commit()\n cursor.execute(\n \"update ecs.versionTable SET ecs.versionTable.version='{}' \"\n \"where ecs.versionTable.version ='{}';\".format(version, lastversion))\n connection.commit()\n print(\"VersionTable updated. Current version is now: {}\".format(version))","def set_version(self, bundle, ctx, filename, version):","def update_pkg_metadata(self, pkg, version=None, **kwargs):\n pass","def update_channel_metadata():\n from .channel_import import (\n import_channel_from_local_db,\n InvalidSchemaVersionError,\n FutureSchemaError,\n )\n\n channel_ids = get_channel_ids_for_content_database_dir(\n get_content_database_dir_path()\n )\n for channel_id in channel_ids:\n if not ChannelMetadata.objects.filter(id=channel_id).exists():\n try:\n import_channel_from_local_db(channel_id)\n annotate_content(channel_id)\n except (InvalidSchemaVersionError, FutureSchemaError):\n logger.warning(\n \"Tried to import channel {channel_id}, but database file was incompatible\".format(\n channel_id=channel_id\n )\n )\n except DatabaseError:\n logger.warning(\n \"Tried to import channel {channel_id}, but database file was corrupted.\".format(\n channel_id=channel_id\n )\n )\n fix_multiple_trees_with_id_one()\n connection.close()","def test_update_metadata1(self):\n pass","def versions(self):\n raise Exception(\"mcapi.Datafile.versions is not implemented\")","def metadata_update(self, _):\n self.details.original_widget = YesNoWidget('Update metadata files?', self.__metadata_update)","def upgrade(self):","def upgrade(self):","def update_metadata(self):\n parser = GenericParser(\n fn_re='{}/(e\\d+s\\d+)_.*/Production.nc'.format(self.data_folder),\n group_names=['sim'],\n group_transforms=[lambda x: x],\n top_fn='',\n step_ps=self.timestep\n )\n meta = gather_metadata('{}/e*/*nc'.format(self.data_folder), parser)\n meta['top_fn'] = sorted(glob('{}/e*/structure.prmtop'.format(self.input_folder)))\n self.meta = meta","def updateMdrizVerHistory(self,build,versions):\n _plist = self.assoc.parlist[0]\n if build == True: _output = _plist['output']\n else: _output = _plist['outdata']\n \n fhdu = pyfits.open(_output,mode='update')\n prihdr = fhdu[0].header\n \n ver_str = \"MultiDrizzle product generated using: \"\n prihdr.add_history(ver_str)\n \n for key in versions:\n if versions[key].find('\\n') < 0:\n prihdr.add_history(key+versions[key])\n else:\n # This will accomodate multi-line comments\n _ver_str = versions[key].split('\\n')\n prihdr.add_history(key)\n for val in _ver_str:\n if val.strip() != '':\n prihdr.add_history(val)\n \n #ver_str = ' MultiDrizzle Version '+str(version)\n #prihdr.add_history(ver_str)\n \n fhdu.close()\n del fhdu","def update_library_metadata(self, id, version=None):\r\n\r\n #### Begin functionality here\r\n if debug: eprint(f\"INFO: Updating the library entry with id {id}\")\r\n session = self.session\r\n library = session.query(LibraryRecord).filter(LibraryRecord.library_record_id==id).first()\r\n if library is not None:\r\n if version is not None:\r\n print(f\" Updating version to {version}\")\r\n library.version = version\r\n session.flush()\r\n session.commit()\r\n else:\r\n print(f\"ERROR: Library entry with id {id} not found\")","def update_metadata(cube_list, infile_history):\n\n equalise_attributes(cube_list)\n\n for cube in cube_list:\n cube.attributes['history'] = gio.write_metadata(file_info=infile_history)\n cube.data = numpy.array(cube.data) #removes _FillValue attribute\n\n return cube_list","def _update_fileinfo(self, metadata_filename):\n \n # In case we delayed loading the metadata and didn't do it in\n # __init__ (such as with delegated metadata), then get the file\n # info now.\n \n # Save the path to the current metadata file for 'metadata_filename'.\n current_filepath = os.path.join(self.metadata_directory['current'],\n metadata_filename)\n # If the path is invalid, simply return and leave fileinfo unset.\n if not os.path.exists(current_filepath):\n self.fileinfo[current_filepath] = None\n return\n \n # Extract the file information from the actual file and save it\n # to the fileinfo store.\n file_length, hashes = tuf.util.get_file_details(current_filepath)\n metadata_fileinfo = tuf.formats.make_fileinfo(file_length, hashes)\n self.fileinfo[metadata_filename] = metadata_fileinfo","def preCommitFixup(self):\n log_method_call(self, self.name)\n # UEFI firmware/bootloader cannot read 1.1 or 1.2 metadata arrays\n if getattr(self.format, \"mountpoint\", None) == \"/boot/efi\":\n self.metadataVersion = \"1.0\"","def get_and_update_versions ():\n\n try:\n get_comp_versions (\"ACE\")\n get_comp_versions (\"TAO\")\n\n if opts.update:\n files = []\n files += update_version_files (\"ACE\")\n files += update_version_files (\"TAO\")\n files += create_changelog (\"ACE\")\n files += create_changelog (\"TAO\")\n files += update_spec_file ()\n files += update_debianbuild ()\n\n commit (files)\n\n except:\n print (\"Fatal error in get_and_update_versions.\")\n raise","def _store_package_metadata(self):","def update_versions(self, reference_resolution):\n raise NotImplementedError(\"update_versions is not implemented\")","def sync_metadata_to_db(self):\n metadata_file_path = os.path.join(\n self.plugin_path, self.plugin_metadata)\n\n metadata = self._load_config(metadata_file_path) or {}\n Plugin.update(self.plugin, metadata)","def _update_filesystem_metadata(self, metadata):\n directory, fname = os.path.split(self.fname)\n fbase = os.path.splitext(fname)[0]\n \n # Test for presence and size of zip file\n zip_file = fbase + '.zip'\n zip_path = os.path.join(directory, zip_file)\n \n if os.path.isfile(zip_path):\n location = 'on_disk'\n data_file_size = os.path.getsize(zip_path)\n else:\n location = 'on_tape'\n data_file_size = 0\n \n # Test for presence of quick look PNG file\n quicklook_file = fbase + '.png'\n quicklook_path = os.path.join(directory, quicklook_file)\n \n if not os.path.isfile(quicklook_path):\n quicklook_file = ''\n\n # Add to metadata dictionary\n item_map = {'directory': directory, 'metadata_file': fname,\n 'data_file': zip_file, 'location': location, \n 'data_file_size': data_file_size, 'quicklook_file': quicklook_file}\n \n for key, value in item_map.items():\n metadata[key] = value","def updateVersionTag(deploymentPath, revision):\n fluidinfoPath = os.path.join(deploymentPath, revision, 'fluidinfo')\n with cd(fluidinfoPath):\n with open('deployment/api-version.txt') as versionFile:\n version = versionFile.read()\n sudo('bin/python bin/fluidinfo update-version-tag '\n 'postgres:///fluidinfo '\n '%s' % version,\n user='fluidinfo')","def update_metadata(self, metadata):\n return self.manager.update_metadata(self, metadata)","async def update_version(self, version: int):\n async with open(self.__file_name, mode=\"r\") as auth_file:\n tag_data = json.loads(await auth_file.read())\n await auth_file.close()\n async with open(self.__file_name, mode=\"w\") as auth:\n tag_data[\"version\"] = version\n await auth.write(json.dumps(tag_data, indent=2, sort_keys=True))\n await auth.close()\n self.__version = version","def update_database():\n\n # We obtain the data from the official database\n df = getData.extractData()\n\n # We save the dataframe for later use in the API\n auxiliary.saveToCsv(df, 'app/resources')","def updateFileData(self):\n with open(pagePath(self.pageName)) as f:\n self.fileData = f.read()\n self.lastUpdated = time.time()","def update_metadata(self):\n self.data[\"keywords\"] = self.repo.topics(self.data.get(\"keywords\", []))\n self.data[\"description\"] = self.data.get(\"description\") or self.repo.description\n self.data[\"codeRepository\"] = (\n self.data.get(\"codeRepository\") or self.repo.html_url\n )\n self.data[\"name\"] = self.data.get(\"name\") or self.repo.name\n self.data[\"issueTracker\"] = (\n self.data.get(\"issueTracker\") or self.repo.issues_url\n )\n self.data[\"license\"] = self.data.get(\"license\") or self.repo.license","def __metadata_update(self, value):\n if value:\n try:\n self.details.original_widget = TextWidget('Updating metadata files. Please, wait...')\n self.execution_manager.close()\n except QMapError as e:\n self.details.original_widget = TextWidget(e)\n self.__back_to_main()","def updateDoc(self, path):\n self.db.setDb(self.db_file)\n \n if not self.authd:\n self._authorize()\n \n db_row = self.db.getRowFromPath(path)\n if not db_row:\n return False\n \n resource_id = db_row[0]\n etag = db_row[1]\n title = db_row[2]\n \n ms = gdata.data.MediaSource(file_path=path, content_type=MIMETYPES['ODT'])\n doc = self.client.GetDoc(resource_id.replace(':', '%3A'))\n new_version = self.client.Update(doc, media_source=ms)\n print 'Document pushed:', new_version.GetAlternateLink().href\n \n self.db.resetEtag(new_version)","def test_update_meta_file(self):\n\n df_expected = pd.DataFrame({self.meta_date_col: [\n self.test_date], self.meta_timestamp_col: [datetime.today().strftime(self.meta_timestamp_format)]})\n # method execution\n MetaFile.update_meta_file(self.test_date, self.trg_bucket_connector)\n df_result = self.trg_bucket_connector.read_meta_file()\n self.assertTrue(df_result.equals(df_expected))","def sync_data_files(self, version, tag='stable'):\n data_folder = self._generate_data_folder(version, tag)\n self.sync_files(data_folder)","def _update_pyrex_file(self, lines, filename):\n found_version_line = False\n for lineno, line in enumerate(lines):\n if line.startswith('__version__'):\n found_version_line = True\n break\n if found_version_line:\n if self.Verbose:\n print 'Version string found on line %d' % lineno\n lines[lineno] = '__version__ = \"%s\"\\n' % str(self.VersionTuple)\n else:\n print \"No version string found in %s\" % filename\n return (lines, found_version_line)","def refresh(self):\n\n # The timestamp role does not have signed metadata about it; otherwise we\n # would need an infinite regress of metadata. Therefore, we use some\n # default, sane metadata about it.\n DEFAULT_TIMESTAMP_FILEINFO = {\n 'hashes':None,\n 'length': tuf.conf.DEFAULT_TIMESTAMP_REQUIRED_LENGTH\n }\n\n # Update the top-level metadata. The _update_metadata_if_changed() and\n # _update_metadata() calls below do NOT perform an update if there\n # is insufficient trusted signatures for the specified metadata.\n # Raise 'tuf.NoWorkingMirrorError' if an update fails.\n\n # Use default but sane information for timestamp metadata, and do not\n # require strict checks on its required length.\n self._update_metadata('timestamp', DEFAULT_TIMESTAMP_FILEINFO)\n\n self._update_metadata_if_changed('release', referenced_metadata='timestamp')\n\n self._update_metadata_if_changed('root')\n\n self._update_metadata_if_changed('targets')\n\n # Updated the top-level metadata (which all had valid signatures), however,\n # have they expired? Raise 'tuf.ExpiredMetadataError' if any of the metadata\n # has expired.\n for metadata_role in ['timestamp', 'root', 'release', 'targets']:\n self._ensure_not_expired(metadata_role)","def update_version(filename, new_version, vers_attr='__version__'):\n lines = []\n with open(filename, 'r') as handle:\n lines = handle.readlines()\n\n updated = False\n for lineno, line in enumerate(lines):\n if line.startswith(vers_attr):\n lines[lineno] = \"{0} = \\\"{1}\\\"\\n\".format(vers_attr, new_version)\n updated = True\n break\n if not updated:\n lines.append(\"{0} = \\\"{1}\\\"\\n\".format(vers_attr, new_version))\n\n with open(filename, 'w') as handle:\n handle.writelines(lines)","def test_partial_update_metadata(self):\n pass","def report_version(self, data):\n self.firmata_version.append(data[0]) # add major\n self.firmata_version.append(data[1]) # add minor","def refresh(self):\n self.update_from_file()\n self.update_from_env()","def do_upgrade(env, ver, cursor):\n tables = [\n ('attachment', {'time': ('int', 'int64')}),\n ('wiki', {'time': ('int', 'int64')}),\n ('revision', {'time': ('int', 'int64')}),\n ('ticket', {'time': ('int', 'int64'),\n 'changetime': ('int', 'int64')}),\n ('ticket_change', {'time': ('int', 'int64')}),\n ('milestone', {'due': ('int', 'int64'),\n 'completed': ('int', 'int64')}),\n ('version', {'time': ('int', 'int64')}),\n ]\n\n db_connector, _ = DatabaseManager(env).get_connector()\n for table, columns in tables:\n # Alter column types\n for sql in db_connector.alter_column_types(table, columns):\n cursor.execute(sql)\n\n # Convert timestamps to microseconds\n cursor.execute(\"UPDATE %s SET %s\" % (table,\n ', '.join(\"%s=%s*1000000\" % (column, column)\n for column in columns)))\n\n # Convert comment edit timestamps to microseconds\n db = env.get_read_db()\n cursor.execute(\"\"\"\n UPDATE ticket_change SET newvalue=%s*1000000\n WHERE field %s\"\"\" % (db.cast('newvalue', 'int64'), db.like()),\n ('_comment%',))","def update_setupcfg_version(filename, version):\n\n setup_cfg = open(filename).readlines()\n current_section = None\n updated = False\n\n for idx, line in enumerate(setup_cfg):\n m = ConfigParser.SECTCRE.match(line)\n if m:\n if current_section == 'metadata':\n # We already parsed the entire metadata section without finding\n # a version line, and are now moving into a new section\n break\n current_section = m.group('header')\n continue\n\n if '=' not in line:\n continue\n\n opt, val = line.split('=', 1)\n opt, val = opt.strip(), val.strip()\n if current_section == 'metadata' and opt == 'version':\n setup_cfg[idx] = 'version = %s\\n' % version\n updated = True\n break\n\n if updated:\n open(filename, 'w').writelines(setup_cfg)\n logger.info(\"Set %s's version to %r\" % (os.path.basename(filename),\n version))","def metadata_update(self, new_metadata=None):\n if new_metadata is None:\n self.metadata_set(self.t.metadata())","def updateFluidDB(serverName):\n # Upload and set up the code.\n deploymentPath = os.path.join('/srv', serverName)\n revision = datetime.utcnow().strftime('%Y%m%d-%H%M')\n revisionPath = os.path.join(deploymentPath, revision)\n\n sudo('mkdir -p {0}'.format(revisionPath))\n\n local('git archive --prefix=fluidinfo/ -v --format tar HEAD | '\n 'bzip2 > fluidinfo.tar.bz2')\n put('fluidinfo.tar.bz2', revisionPath, use_sudo=True)\n\n with cd(revisionPath):\n sudo('mkdir -p var/log var/log/trace var/run var/tmp')\n sudo('chown -R fluidinfo {0}'.format(revisionPath))\n sudo('tar jxvf fluidinfo.tar.bz2', user='fluidinfo')\n\n with cd(os.path.join(revisionPath, 'fluidinfo')):\n sudo('virtualenv .', user='fluidinfo')\n sudo('mkdir -p /var/lib/fluidinfo/pip-cache', user='fluidinfo')\n sudo('./bin/pip install --use-mirrors '\n '--download-cache=/var/lib/fluidinfo/pip-cache '\n '--log /tmp/pip.log '\n '-r requirements.txt', user='fluidinfo')\n # On successful completion, clean up /tmp\n sudo('rm -f /tmp/pip.log')\n\n get(os.path.join(deploymentPath, 'current', 'fluidinfo-api.conf'),\n 'fluidinfo-api.conf')\n config = RawConfigParser()\n with open('fluidinfo-api.conf', 'r') as configFile:\n config.readfp(configFile)\n\n # Copy and set up configuration files.\n deployConfigFiles(\n {'deployment-path': deploymentPath,\n 'server-name': serverName,\n 'revision-path': revisionPath,\n 'solr-url': config.get('index', 'url'),\n 'solr-shards': config.get('index', 'shards'),\n 'postgres-uri': config.get('store', 'main-uri')},\n\n ('fluidinfo/fluidinfo-api.conf.template',\n '{revision-path}/fluidinfo-api.conf'))\n\n local('rm fluidinfo-api.conf')\n\n # TODO: check patches.\n # TODO: update version tag.\n\n with cd(deploymentPath):\n sudo('rm current')\n sudo('ln -fs {0} current'.format(revision))\n\n for port in range(9001, 9009):\n sudo('restart fluidinfo-api-node PORT=%d' % port)","def update_tables(tables):\n # First we ensure we can find the file format version number\n # in top-level metadata. Then we proceed to fix up the tables as necessary.\n if not (isinstance(tables.metadata, dict) and 'SLiM' in tables.metadata):\n # Old versions kept information in provenance, not top-level metadata.\n # Note this uses defaults on keys not present in provenance,\n # which prior to 0.5 was everything but generation and model_type.\n values = default_slim_metadata('tree_sequence')['SLiM']\n prov = None\n file_version = 'unknown'\n # use only the last SLiM provenance\n for p in tables.provenances:\n is_slim, this_file_version = slim_provenance_version(p) \n if is_slim:\n prov = p\n file_version = this_file_version\n values['file_version'] = file_version\n try:\n record = json.loads(prov.record)\n if file_version == \"0.1\":\n values['model_type'] = record['model_type']\n values['tick'] = record['generation']\n values['cycle'] = record['generation']\n else:\n if 'generation' in record['slim']:\n values['tick'] = record['slim']['generation']\n values['cycle'] = record['slim']['generation']\n for k in values:\n if k in record['parameters']:\n values[k] = record['parameters'][k]\n if k in record['slim']:\n values[k] = record['slim'][k]\n except:\n raise ValueError(\"Failed to obtain metadata from provenance.\")\n set_tree_sequence_metadata(tables, **values)\n\n file_version = tables.metadata['SLiM']['file_version']\n if file_version != slim_file_version:\n warnings.warn(\"This is a version {} SLiM tree sequence.\".format(file_version) +\n \" When you write this out, \" +\n \"it will be converted to version {}.\".format(slim_file_version))\n\n # the only tables to have metadata schema changed thus far\n # are populations, individuals, mutations, and top-level:\n old_schema = _old_metadata_schema(\"tree_sequence\", file_version)\n if old_schema is not None:\n md = tables.metadata\n new_schema = slim_metadata_schemas[\"tree_sequence\"]\n new_properties = new_schema.asdict()['properties']['SLiM']['required']\n tables.metadata_schema = new_schema\n defaults = default_slim_metadata(\"tree_sequence\")\n for k in new_properties:\n if k not in md['SLiM']:\n if k == \"tick\":\n md['SLiM']['tick'] = md['SLiM']['generation']\n md['SLiM']['cycle'] = md['SLiM']['generation']\n else:\n md['SLiM'][k] = defaults['SLiM'][k]\n tables.metadata = md\n\n old_schema = _old_metadata_schema(\"population\", file_version)\n if old_schema is not None:\n pops = tables.populations.copy()\n tables.populations.clear()\n if pops.metadata_schema == tskit.MetadataSchema(None):\n pops.metadata_schema = old_schema\n new_schema = slim_metadata_schemas[\"population\"]\n tables.populations.metadata_schema = new_schema\n defaults = default_slim_metadata(\"population\")\n # just needs recoding\n for pop in pops:\n tables.populations.append(pop)\n\n old_schema = _old_metadata_schema(\"individual\", file_version)\n if old_schema is not None:\n inds = tables.individuals.copy()\n tables.individuals.clear()\n if inds.metadata_schema == tskit.MetadataSchema(None):\n inds.metadata_schema = old_schema\n new_schema = slim_metadata_schemas[\"individual\"]\n tables.individuals.metadata_schema = new_schema\n defaults = default_slim_metadata(\"individual\")\n d = {}\n for k in [\"pedigree_p1\", \"pedigree_p2\"]:\n d[k] = defaults[k]\n for ind in inds:\n md = ind.metadata\n md.update(d)\n tables.individuals.append(ind.replace(metadata=md))\n\n old_schema = _old_metadata_schema(\"mutation\", file_version)\n if old_schema is not None:\n muts = tables.mutations.copy()\n tables.mutations.clear()\n if muts.metadata_schema == tskit.MetadataSchema(None):\n muts.metadata_schema = old_schema\n tables.mutations.metadata_schema = slim_metadata_schemas[\"mutation\"]\n for mut in muts:\n md = mut.metadata\n for ml in md['mutation_list']:\n ml['nucleotide'] = -1\n tables.mutations.append(mut.replace(metadata=md))\n\n if file_version == \"0.1\":\n # shift times\n slim_generation = tables.metadata['SLiM']['tick']\n node_times = tables.nodes.time + slim_generation\n tables.nodes.set_columns(\n flags=tables.nodes.flags,\n time=node_times,\n population=tables.nodes.population,\n individual=tables.nodes.individual,\n metadata=tables.nodes.metadata,\n metadata_offset=tables.nodes.metadata_offset)\n migration_times = tables.migrations.time + slim_generation\n tables.migrations.set_columns(\n left=tables.migrations.left,\n right=tables.migrations.right,\n node=tables.migrations.node,\n source=tables.migrations.source,\n dest=tables.migrations.dest,\n time=migration_times)\n\n new_record = {\n \"schema_version\": \"1.0.0\",\n \"software\": {\n \"name\": \"pyslim\",\n \"version\": pyslim_version,\n },\n \"parameters\": {\n \"command\": [\"updrade_tables\"],\n \"old_file_version\": file_version,\n \"new_file_version\": slim_file_version,\n },\n \"environment\": get_environment(),\n }\n tskit.validate_provenance(new_record)\n tables.provenances.add_row(json.dumps(new_record))\n\n set_metadata_schemas(tables)\n md = tables.metadata\n md['SLiM']['file_version'] = slim_file_version\n tables.metadata = md","def _WriteChromeVersionToMetadata(self):\n self._run.attrs.metadata.UpdateKeyDictWithDict(\n 'version',\n {'chrome': self._run.attrs.chrome_version})\n self.UploadMetadata(filename=constants.PARTIAL_METADATA_JSON)","def _update_python_file(self, lines, filename):\n found_version_line = False\n for lineno, line in enumerate(lines):\n if line.startswith('__version__'):\n found_version_line = True\n break\n if found_version_line:\n if self.Verbose:\n print 'Version string found on line %d' % lineno\n lines[lineno] = '__version__ = \"%s\"\\n' % self.Version\n else:\n print \"No version string found in %s\" % filename\n return (lines, found_version_line)","def updateVersionNumber(self, *args):\n file = '//corp-nas01/DC/dc_art/character/GEPPETTO/CharacterTracker.csv'\n \n character = []\n character = cmds.textScrollList(self.UIElements['characterName'] , q=True, si=True)\n \n key = character[0]\n characterInfo = self.trackerInfo[key]\n versionNumber = characterInfo[2][1]\n \n index = 0\n \"\"\" Open the file for reading \"\"\"\n with open(file, 'rb') as f:\n reader = csv.reader(f, delimiter=',', quotechar='\"')\n for row in reader:\n newIndex = index+1\n index = newIndex\n if row[0] == character[0]:\n numLine = index\n versionNumber = int(row[2])\n newVersion = versionNumber + 1\n \n row[2] = newVersion\n print row\n \n \n \n line_to_override = {numLine:[row]} \n\n \"\"\" Open the csv in maya for writing \"\"\"\n writer = csv.writer(open(file, 'wb'), delimiter=',')\n\n data = line_to_override.get(numLine, row)\n writer.writerow(data)\n \n #writer.writerow(version)","def publish_proto_metadata_update(self):\n metadata = load_mpe_service_metadata(self.args.metadata_file)\n ipfs_hash_base58 = ipfs_utils.publish_proto_in_ipfs(\n self._get_ipfs_client(), self.args.protodir)\n metadata.set_simple_field(\"model_ipfs_hash\", ipfs_hash_base58)\n metadata.save_pretty(self.args.metadata_file)","def update_metadata(state, name, wid, file_id, metadata_file):\n\n client = state.api_client\n\n # Get the workspace details\n w_details = helpers.workspace.details(client, wid, name)\n if w_details is None:\n # Can only happen when the name is used and there are no results. Not\n # with the wid option because it would raise a 404 QuetzalAPIException\n raise click.ClickException(f'Workspace named \"{name}\" does not exist.')\n\n metadata_contents = json.load(metadata_file)\n response = client.workspace_file_update_metadata(wid=w_details.id, uuid=file_id,\n body=metadata_contents)\n click.secho(f'Metadata for file {file_id} successfully changed.', fg='green')\n click.secho('Updated metadata:')\n click.secho(json.dumps(response, indent=2), fg='blue')","def set_media_versions(self):\n\n # access to .multimedia_map is slow\n previous_version = self._get_version_comparison_build()\n prev_multimedia_map = previous_version.multimedia_map if previous_version else {}\n\n for path, map_item in self.multimedia_map.items():\n prev_map_item = prev_multimedia_map.get(path, None)\n if prev_map_item and prev_map_item.unique_id:\n # Re-use the id so CommCare knows it's the same resource\n map_item.unique_id = prev_map_item.unique_id\n if (prev_map_item and prev_map_item.version\n and prev_map_item.multimedia_id == map_item.multimedia_id):\n map_item.version = prev_map_item.version\n else:\n map_item.version = self.version","async def async_update(self):\n self.update_file_path()","def test_partial_update_metadata1(self):\n pass","def _upgrade(cls, contents):\n metadata = contents.get(\"metadata\", {})\n version = metadata.get(\"cache_version\")\n if version == \"1\":\n metadata.pop(\"cache_version\", None)\n return {\"metadata\": cls.METADATA,\n **{cls.LANG_PREFIX + \"coq\":\n {\"driver\": contents.pop(\"generator\"),\n \"metadata\": contents.pop(\"metadata\"),\n **contents}}}\n return contents","def db_update_files():\n _populate_table_files(File)\n _populate_table_files(Software)\n return redirect(url_for('view_index'))","def update_metadata_csv(self, source):\n timestamp = os.path.getmtime(source)\n filedate = datetime.datetime.fromtimestamp(timestamp)\n return self.update_metadata_date(filedate)","def update_metadata(self, metadata):\n return self.parent.update_metadata_for_node(self, metadata)","def test_update_meta_file_meta_file_exists(self):\n # Expected results\n date_old = '2021-09-13'\n date_new = '2021-09-14'\n dates_expected = [date_old, date_new]\n\n meta_content = f\"{self.meta_date_col},{self.meta_timestamp_col}\\n{date_old},{datetime.today().strftime(self.meta_timestamp_format)}\"\n self.bucket.put_object(Body=meta_content, Key=self.meta_key)\n # method execution\n MetaFile.update_meta_file(date_new, self.trg_bucket_connector)\n # read meta file\n df_result = self.trg_bucket_connector.read_meta_file()\n dates_result = list(df_result[\n self.meta_date_col])\n self.assertEqual(dates_expected, dates_result)","def test_changeVersionInFile(self):\n # The version numbers are arbitrary, the name is only kind of\n # arbitrary.\n packageName = 'foo'\n oldVersion = Version(packageName, 2, 5, 0)\n file = self.makeFile('README',\n \"Hello and welcome to %s.\" % oldVersion.base())\n\n newVersion = Version(packageName, 7, 6, 0)\n _changeVersionInFile(oldVersion, newVersion, file.path)\n\n self.assertEqual(file.getContent(),\n \"Hello and welcome to %s.\" % newVersion.base())","def snapshot_info(self) -> MetaFile:\n raise NotImplementedError","def bumpversion(path=\"setup.cfg\"):\n config = ConfigParser()\n config.read(path)\n cfg = open(path, 'w')\n new_version = \"0.0.0\"\n if config.has_option('metadata', 'version'):\n old_version = config.get('metadata', 'version')\n major, minor, patch = old_version.split(\".\")\n new_version = \"%s.%s.%s\" % (major, minor, int(patch) + 1)\n if not config.has_section('metadata'):\n config.add_section('metadata')\n config.set('metadata', 'version', new_version)\n config.write(cfg)\n cfg.close()\n return new_version","def update_h5store(fileh):\n logger.debug(\"Running update_h5store\")\n root = fileh.root\n\n version = root._v_attrs['version'] if 'version' in root._v_attrs else 0\n \n if version < 1:\n # No version, or new file\n # Ensure that the major nodes exist\n logger.debug('updating to version 1')\n for node in ['spaces', 'datasets', 'tasksets', 'results']:\n if not hasattr(root, node):\n fileh.createGroup( root, node )\n # Check that the dataset nodes are well-formed\n for dsnode in root.datasets:\n if not hasattr(dsnode, 'tokenstreams'):\n logger.debug('Node %s did not have tokenstreams node; adding.', dsnode._v_name)\n fileh.createGroup( dsnode, \"tokenstreams\" )\n if not hasattr(dsnode, 'sequence'):\n logger.debug('Node %s did not have sequence node; adding.', dsnode._v_name)\n fileh.createGroup( dsnode, \"sequence\" )\n if version < 2:\n # In version 2, we introduce the concept of instance spaces, detaching the instance\n # identifiers from the dataset nodes and instead attaching them to the space nodes\n logger.debug('updating to version 2')\n for dsnode in root.datasets:\n # Move the instance id node to spaces\n id_node = dsnode.instance_id\n id_node._v_attrs['size'] = len(dsnode.instance_id)\n id_node._v_attrs['type'] = 'instance'\n id_node._v_attrs['name'] = dsnode._v_name\n id_node._v_attrs['encoding'] = 'utf8' # to be safe, in case we had e.g. utf8 filenames\n fileh.moveNode(dsnode.instance_id, root.spaces, dsnode._v_name)\n # Unless otherwise specified, the instance space is the dataset name\n dsnode._v_attrs['instance_space'] = dsnode._v_name\n\n # Add the instance space metadata to all tasksets\n for tsnode in root.tasksets:\n tsnode._v_attrs.instance_space = tsnode._v_attrs.dataset\n for t in tsnode:\n t._v_attrs.instance_space = t._v_attrs.dataset\n \n # Add the instance space metadata to all results\n for rnode in root.results:\n rnode._v_attrs.instance_space = rnode._v_attrs.dataset\n if hasattr(rnode._v_attrs, 'eval_dataset'):\n rnode._v_attrs.eval_space = rnode._v_attrs.eval_dataset\n for node in rnode:\n if node._v_name == 'summary':\n for summary in node:\n summary._v_attrs.instance_space = summary._v_attrs.dataset\n if hasattr(summary._v_attrs, 'eval_dataset'):\n summary._v_attrs.eval_space = summary._v_attrs.eval_dataset\n else:\n node._v_attrs.instance_space = node._v_attrs.dataset\n if hasattr(node._v_attrs, 'eval_dataset'):\n node._v_attrs.eval_space = node._v_attrs.eval_dataset\n if version < 3:\n # In version 3, we add weights associated with task nodes\n for tsnode in root.tasksets:\n for t in tsnode:\n fileh.createGroup(t, 'weights')\n if version < 4:\n # In version 4, we introduced a node to store splits in datasets\n for dsnode in root.datasets:\n if not hasattr(dsnode, 'split'):\n logger.debug('Node %s did not have split node; adding.', dsnode._v_name)\n fileh.createGroup( dsnode, \"split\" )\n # TODO:\n # Replace all boolean maps for tasks with their equivalent flatnonzero indices\n # Eliminate UUID from taskset and result metadata\n # Get rid of all date attrs\n # Ensure all TSR nodes have a summary node\n \n\n logger.debug(\"updated store from version %d to %d\", version, STORE_VERSION)\n root._v_attrs['version'] = STORE_VERSION\n fileh.flush()","def sync_metadata_to_db(self):\n super(PluginAdapterV3, self).sync_metadata_to_db()\n\n db_config_metadata_mapping = {\n 'attributes_metadata': self.environment_config_name,\n 'roles_metadata': self.node_roles_config_name,\n 'volumes_metadata': self.volumes_config_name,\n 'network_roles_metadata': self.network_roles_config_name,\n 'deployment_tasks': self.deployment_tasks_config_name,\n 'tasks': self.task_config_name\n }\n\n self._update_plugin(db_config_metadata_mapping)","def _helperUpdateAnnoByMetadata(syn,temp,metaDf,refCol,cols2Add,fileExts):\n \n print \"Updating annotations by metadata...\"\n \n exts = ')|('.join(fileExts)\n exts = r'(' + exts + ')'\n tempName = re.sub(exts,\"\",temp.name)\n \n row = df.loc[df[refCol] == tempName]\n if row.empty:\n print \"missing metadata\"\n else:\n for colName in cols2Add:\n print \"> %s \" % colName\n temp[colName] = map(str,row[colName])[0]\n temp = syn.store(temp, forceVersion = False)\n print \"\"","def set_form_versions(self):\n def _hash(val):\n return hashlib.md5(val).hexdigest()\n\n latest_build = self._get_version_comparison_build()\n if not latest_build:\n return\n force_new_version = self.build_profiles != latest_build.build_profiles\n for form_stuff in self.get_forms(bare=False):\n filename = 'files/%s' % self.get_form_filename(**form_stuff)\n current_form = form_stuff[\"form\"]\n if not force_new_version:\n try:\n previous_form = latest_build.get_form(current_form.unique_id)\n # take the previous version's compiled form as-is\n # (generation code may have changed since last build)\n previous_source = latest_build.fetch_attachment(filename)\n except (ResourceNotFound, FormNotFoundException):\n current_form.version = None\n else:\n previous_hash = _hash(previous_source)\n\n # set form version to previous version, and only update if content has changed\n current_form.version = previous_form.get_version()\n current_form = current_form.validate_form()\n current_hash = _hash(current_form.render_xform())\n if previous_hash != current_hash:\n current_form.version = None\n # clear cache since render_xform was called with a mutated form set to the previous version\n current_form.render_xform.reset_cache(current_form)\n else:\n current_form.version = None","def store_pkg_metadata(self, pkg, version):\n pass","def set_version_db(apps, schema_editor):\n Version = apps.get_model(\"reversion\", \"Version\")\n content_types = Version.objects.values_list(\"content_type\", flat=True).distinct()\n for content_type in content_types:\n model_class = content_type.model_class()\n db = router.db_for_write(model_class)\n Version.objects.filter(content_type=content_type).update(db=db)","def test_updateTwistedVersionInformation(self):\n baseDirectory = FilePath(self.mktemp())\n baseDirectory.createDirectory()\n now = date.today()\n\n projectName = 'foo'\n oldVersion = Version(projectName, 2, 5, 0)\n newVersion = getNextVersion(oldVersion, now=now)\n\n project = self.makeProject(oldVersion, baseDirectory)\n\n updateTwistedVersionInformation(baseDirectory, now=now)\n\n self.assertEqual(project.getVersion(), newVersion)\n self.assertEqual(\n project.directory.child('topfiles').child('README').getContent(),\n newVersion.base())","def update(self):\n \n dbpath, config = self._start()\n \n self.config.obo = check_file(config.obo, dbpath, \"obo\") \n desc_file = check_file(config.model_descriptions, dbpath,\n \"model_descriptions\", allow_none=True) \n phen_file = check_file(config.model_phenotypes, dbpath,\n \"model_phenotypes\", allow_none=True)\n \n summary = self._update(desc_file, phen_file) \n if len(summary[\"incorrect_ids\"]) == 0 and not config.skip_compute:\n self._compute(models=summary[\"new_phenotypes\"])\n \n self._end()","def __update_version(self):\r\n\r\n db_version = self.__get_db_version_int()\r\n if db_version == SCHEMA_VERSION:\r\n return\r\n\r\n #\r\n # Define functions for upgrading between schema versions\r\n #\r\n def update_2xto30():\r\n \"\"\"Incremental update of database from Freeseer 2.x and older to 3.0\r\n\r\n SCHEMA_VERSION is 300\r\n \"\"\"\r\n if db_version > 300:\r\n log.debug('Database newer than schema version 300.')\r\n return # No update needed\r\n\r\n log.debug('Updating to schema 300.')\r\n QtSql.QSqlQuery('ALTER TABLE presentations RENAME TO presentations_old') # temporary table\r\n self.__create_presentations_table(PRESENTATIONS_SCHEMA_300)\r\n QtSql.QSqlQuery(\"\"\"INSERT INTO presentations\r\n SELECT Id, Title, Speaker, Description, Level, Event, Room, Time FROM presentations_old\"\"\")\r\n QtSql.QSqlQuery('DROP TABLE presentations_old')\r\n\r\n def update_30to31():\r\n \"\"\"Performs incremental update of database from 3.0 and older to 3.1.\"\"\"\r\n QtSql.QSqlQuery('ALTER TABLE presentations RENAME TO presentations_old')\r\n self.__create_presentations_table(PRESENTATIONS_SCHEMA_310)\r\n QtSql.QSqlQuery(\"\"\"INSERT INTO presentations\r\n SELECT Id, Title, Speaker, Description, Level, Event, Room, Time, Time, Time\r\n FROM presentations_old\"\"\")\r\n QtSql.QSqlQuery('DROP TABLE presentations_old')\r\n\r\n #\r\n # Perform the upgrade\r\n #\r\n updaters = [update_2xto30, update_30to31]\r\n for updater in updaters:\r\n updater()\r\n\r\n QtSql.QSqlQuery('PRAGMA user_version = %i' % SCHEMA_VERSION)\r\n log.info('Upgraded presentations database from version {} to {}'.format(db_version, SCHEMA_VERSION))","def update_version(self, new_version):\n if new_version is not None:\n self.version_details = json.loads(new_version)\n\n # Update port file.\n http_port = self.version_details['appscaleExtensions']['httpPort']\n version_key = VERSION_PATH_SEPARATOR.join(\n [self.project_id, self.service_id, self.version_id])\n port_file_location = os.path.join(\n CONFIG_DIR, 'port-{}.txt'.format(version_key))\n with open(port_file_location, 'w') as port_file:\n port_file.write(str(http_port))\n\n logger.info('Updated version details: {}'.format(version_key))\n if self.callback is not None:\n self.callback()","def _update_properties_file(self, lines, filename):\n found_version_line = False\n if filename.endswith('cogent-requirements.txt'):\n for lineno, line in enumerate(lines):\n if 'packages/source/c/cogent' in line:\n found_version_line = True\n break\n if found_version_line:\n if self.Verbose:\n print 'Version string found on line %d' % lineno\n http_base = lines[lineno].rsplit('/',1)[0]\n lines[lineno] = '%s/PyCogent-%s.tgz\\n' % (http_base, self.Version)\n else:\n print \"No version string found in %s\" % filename\n return (lines, found_version_line)","def update(self, revision, data, user=None, save=True):\n version = super(FigshareFile, self).update(None, data, user=user, save=save)\n\n # Draft files are not renderable\n if data['extra']['status'] == 'drafts':\n return (version, u\"\"\"\n <style>\n .file-download{{display: none;}}\n .file-share{{display: none;}}\n </style>\n <div class=\"alert alert-info\" role=\"alert\">\n The file \"{name}\" is still a draft on figshare. <br>\n To view it on the OSF\n <a href=\"https://support.figshare.com/support/solutions\">publish</a>\n it on figshare.\n </div>\n \"\"\".format(name=markupsafe.escape(self.name)))\n\n return version","def update():","def update():","def version_updater(logging_level=logging.WARNING):\n # connect to db\n do_db_setup()\n\n # set Qt lib\n set_qt_lib()\n\n from anima.ui import version_updater, models\n from anima.env import mayaEnv\n reload(mayaEnv)\n reload(version_updater)\n reload(models)\n m = Maya()\n import pymel\n m.name = \"Maya\" + str(pymel.versions.current())[0:4]\n\n logger.setLevel(logging_level)\n\n # generate a reference_resolution\n version_updater.UI(environment=m)","def upgrade(self):\n import re\n currversion = getattr(self, '_version', 0)\n if currversion == self.version:\n return 'already running latest version (%s)' % currversion\n # a slightly modified version of propertyitems\n propertyitems = map(lambda i,s=self: (i['id'],getattr(s, i['id'], None)),\n self._properties)\n for propertyitem in propertyitems:\n if not propertyitem[1]:\n pass\n elif propertyitem[0] in ('stylesheet_paths', 'xslt_paths'):\n self.transform_paths = propertyitem[1]\n elif propertyitem[0][:4] == 'xslt':\n transform = getattr(self, propertyitem[1], None)\n if not transform:\n for obj in self._get_path_objs(self.transform_paths):\n transform = getattr(obj, propertyitem[1], None)\n if transform:\n break\n if transform:\n mtype = re.sub('template|renderer', '', transform.meta_type.lower()).strip() \n setattr(self,\n 'transform_%s' % propertyitem[0][5:],\n '%s (%s)' % (propertyitem[1], mtype))\n elif propertyitem[0][:10] == 'stylesheet':\n setattr(self, 'template_%s' % propertyitem[0][11:], propertyitem[1])\n else:\n continue\n try:\n delattr(self, propertyitem[0])\n except:\n pass\n try:\n self._delProperty(propertyitem[0])\n except:\n pass\n\n self._properties = self._def_properties\n self.setup_transformProperties()\n self.setup_schemaProperties()\n \n self._version = self.version\n \n return 'upgraded %s to version %s from version %s' % (self.getId(),\n self._version,\n currversion)","def _update_aea_version(cls, agent_config: AgentConfig):\n for item in agent_config.package_dependencies:\n type_ = item.component_type.to_plural()\n dotted_path = f\"vendor.{item.author}.{type_}.{item.name}.aea_version\"\n path = os.path.join(\"vendor\", item.author, type_, item.name)\n cls.nested_set_config(dotted_path, cls.AEA_VERSION_SPECIFIER)\n cls.run_cli_command(\"fingerprint\", \"by-path\", path, cwd=cls._get_cwd())","def _load_meta(self, db, metadata, source_name) -> None:\n db.metadata.put_item(Item={\n 'src_name': source_name,\n 'data_license': metadata.data_license,\n 'data_license_url': metadata.data_license_url,\n 'version': metadata.version,\n 'data_url': metadata.data_url,\n 'rdp_url': metadata.rdp_url,\n 'data_license_attributes': metadata.data_license_attributes,\n 'genome_assemblies': metadata.genome_assemblies\n })","def _writeVersionToMetadata(config):\n if not config.has_section(GenericMetadata.ECOHYDROLIB_SECION):\n config.add_section(GenericMetadata.ECOHYDROLIB_SECION)\n \n if not config.has_option(GenericMetadata.ECOHYDROLIB_SECION, \\\n GenericMetadata.VERSION_KEY):\n config.set(GenericMetadata.ECOHYDROLIB_SECION, \\\n GenericMetadata.VERSION_KEY, GenericMetadata._ecohydrolibVersion)\n return\n \n metadataVersion = config.get(GenericMetadata.ECOHYDROLIB_SECION, \\\n GenericMetadata.VERSION_KEY)\n if metadataVersion != GenericMetadata._ecohydrolibVersion:\n raise MetadataVersionError(metadataVersion)","def export(uri: str, version: str, date: str, file: str, update: bool = False):\n con = cx_Oracle.connect(uri)\n cur = con.cursor()\n\n cur.execute(\"SELECT COUNT(*) FROM INTERPRO.ENTRY WHERE CHECKED = 'Y'\")\n num_interpro_entries, = cur.fetchone()\n\n cur.execute(\"SELECT VERSION FROM INTERPRO.DB_VERSION WHERE DBCODE = 'I'\")\n prod_version, = cur.fetchone()\n\n cur.execute(\"SELECT DISTINCT DBCODE FROM INTERPRO.METHOD\")\n signature_dbcodes = {dbcode for dbcode, in cur}\n\n cur.execute(\"SELECT DISTINCT DBCODE FROM INTERPRO.FEATURE_METHOD\")\n feature_dbcodes = {dbcode for dbcode, in cur}\n\n if prod_version == version:\n # DB_VERSION is already up-to-date\n use_db_version = True\n elif update:\n # DB_VERSION is outdated, but will be up-to-date\n use_db_version = True\n cur.execute(\n \"\"\"\n UPDATE INTERPRO.DB_VERSION\n SET VERSION = :1,\n FILE_DATE = :2,\n ENTRY_COUNT = :3\n WHERE DBCODE = 'I'\n \"\"\", (version, datetime.strptime(date, \"%Y-%m-%d\"),\n num_interpro_entries)\n )\n con.commit()\n else:\n # DB_VERSION is outdated and will stay outdated\n # This run is a test done on the production database (SRSLY?!!111)\n use_db_version = False\n\n # Get all releases\n all_releases = {}\n cur.execute(\n \"\"\"\n SELECT DBCODE, VERSION, FILE_DATE\n FROM INTERPRO.DB_VERSION_AUDIT\n ORDER BY TIMESTAMP DESC\n \"\"\"\n )\n\n for dbcode, dbversion, dbdate in cur:\n try:\n all_releases[dbcode].append((dbversion, dbdate))\n except KeyError:\n all_releases[dbcode] = [(dbversion, dbdate)]\n\n cur.execute(\n \"\"\"\n SELECT\n DB.DBCODE, DB.DBSHORT, DB.DBNAME, DB.DESCRIPTION, V.VERSION, \n V.FILE_DATE, V.ENTRY_COUNT\n FROM INTERPRO.CV_DATABASE DB\n LEFT OUTER JOIN INTERPRO.DB_VERSION V ON DB.DBCODE = V.DBCODE\n \"\"\"\n )\n\n databases = {}\n for rec in cur:\n dbcode = rec[0]\n short_name = rec[1]\n name = rec[2]\n description = rec[3]\n release_version = rec[4]\n release_date = rec[5]\n num_entries = rec[6]\n prev_release_version = prev_release_date = None\n\n try:\n prev_releases = all_releases[dbcode]\n except KeyError:\n pass\n else:\n for dbversion, dbdate in prev_releases:\n if dbversion != release_version:\n prev_release_version = dbversion\n prev_release_date = dbdate\n break\n\n if dbcode == 'I' or dbcode in signature_dbcodes:\n db_type = \"entry\"\n elif dbcode in feature_dbcodes:\n db_type = \"feature\"\n elif dbcode in ['S', 'T', 'u']:\n if dbcode == 'S':\n short_name = \"reviewed\"\n elif dbcode == 'T':\n short_name = \"unreviewed\"\n\n db_type = \"protein\"\n else:\n db_type = \"other\"\n\n if dbcode == 'I' and not use_db_version:\n # DB_VERSION is outdated:\n # it contains info for the live release (soon to be 'previous')\n num_entries = num_interpro_entries\n prev_release_version = release_version\n prev_release_date = release_date\n release_version = version\n release_date = datetime.strptime(date, \"%Y-%m-%d\")\n\n databases[short_name] = {\n \"name\": name,\n \"description\": description,\n \"type\": db_type,\n \"entries\": num_entries,\n \"release\": {\n \"version\": release_version,\n \"date\": release_date,\n },\n \"previous_release\": {\n \"version\": prev_release_version,\n \"date\": prev_release_date,\n },\n }\n\n cur.close()\n con.close()\n\n with open(file, \"wb\") as fh:\n pickle.dump(databases, fh)","def updateLastCommitFile(self):\n f = open(self.last_released, 'w')\n f.write(self.new_rev)\n f.close()","def update(self, filename):\n\n cam_info, cam_header = self.db.read_data(filename, config.CAM_HEADER, 'tuple', None)\n\n with open('updated_camera_list.csv', mode='w') as csv_file:\n cam_writer = csv.writer(csv_file, delimiter=',')\n cam_writer.writerow(cam_header)\n\n for info in cam_info:\n cam = Camera(camera_id=info[0], ip_address=info[8], image_path=info[10], video_path=info[11])\n cam.capture = cv2.VideoCapture(cam.video_path)\n cam.get_image()\n\n if cam.newImage is not None:\n img_w = cam.newImage.shape[0]\n img_h = cam.newImage.shape[1]\n\n cam_writer.writerow([info[0], info[1], info[2], info[3], info[4], info[5],\n img_w, img_h, info[8], info[9], info[10], info[11]])\n cam.capture.release()\n print('updated camera: {}'.format(info[0]))\n\n csv_file.close()","def update(self):\n current = LazyRegister(self.db)\n current.render()\n cur = self.db.cursor()\n for table in self.tables:\n if table in current.tables:\n additions, removals = current.tables[table].migrate(self.tables[table])\n for addition in additions:\n cur.execute(\"\"\"ALTER TABLE %s ADD COLUMN %s\"\"\" % (\n table, addition[1].get_sql()\n ))\n print('Added column: ', addition[0])\n for removal in removals:\n #cur.execute(\"\"\"ALTER TABLE %s DROP COLUMN %s\"\"\" % (\n # table, removal[0]\n #))\n #print('Removed column: ', removal[0])\n print('Did not removed column: ', removal[0])\n else:\n schema = self.tables[table].get_create_table_sql()\n cur.execute(schema)\n print('Added table %s' % table)","def update_version(self, version):\n self.version = CPE.escape_for_cpe23_fs(version)","def update(self) -> None:\n ...","def test_updateVersion(self):\n project = self.makeProject(Version(\"bar\", 2, 1, 0))\n newVersion = Version(\"bar\", 3, 2, 9)\n project.updateVersion(newVersion)\n self.assertEquals(project.getVersion(), newVersion)\n self.assertEquals(\n project.directory.child(\"topfiles\").child(\"README\").getContent(),\n \"3.2.9\")","def update(self):\n self.getDbRecord().update()","def save(self, **kwargs):\n super(ProjectCurrentSerializer, self).save(**kwargs)\n \n if hasattr(self, 'uploaded_pot_file'):\n previous_version = self.object.get_current_version()\n current_version = create_new_version(self.object, previous_version.version+1, self.uploaded_pot_file)\n update_catalogs(self.object, previous_version, current_version)\n self.object.save()\n \n return self.object","def _update(self, desc_file, phen_file): \n \n self.logger.msg1(\"Update summary\")\n \n stamp = self.config.stamp\n summary = update_model_descriptions(self.dbpath, desc_file, stamp)\n for k, v in summary.items():\n kstr = k.replace(\"_\", \" \") + \": \"\n self.logger.msg2(\"models with \" + kstr + str(len(v)))\n \n summary = add_model_phenotypes(self.dbpath, phen_file, stamp)\n for k, v in summary.items():\n kstr = k.replace(\"_\", \" \") + \": \"\n self.logger.msg2(\"models with \" + kstr + str(len(v)))\n \n return summary","def _update_metadata(self, container_name: Union[str, list] = None, multibeam_file_list: list = None, crs: str = None,\n vertical_reference: str = None):\n\n if container_name:\n if isinstance(container_name, str): # only one converted data instance provided\n container_name = [container_name]\n if isinstance(multibeam_file_list[0], str): # only one converted data instance provided\n multibeam_file_list = [multibeam_file_list]\n for contname, mfiles in zip(container_name, multibeam_file_list):\n if mfiles:\n if contname in self.sources and (set(mfiles) & set(self.sources[contname])):\n raise ValueError('QuadManager: Found some of these new lines in the existing lines metadata for container: {}'.format(contname))\n elif contname in self.sources:\n self.sources[contname].extend(mfiles)\n else:\n self.sources[contname] = mfiles\n else:\n self.sources[contname] = ['unknown']\n if self.crs and (self.crs != crs):\n raise ValueError('QuadManager: Found existing coordinate system {}, new coordinate system {} must match'.format(self.crs,\n crs))\n if self.vertical_reference and (self.vertical_reference != vertical_reference):\n raise ValueError('QuadManager: Found existing vertical reference {}, new vertical reference {} must match'.format(self.vertical_reference,\n vertical_reference))\n self.crs = crs\n self.vertical_reference = vertical_reference"],"string":"[\n \"def update_version(self, version):\\n self._metadata['version'] = version\\n\\n if self._type == '.json':\\n with open(self._filename, 'w') as f:\\n f.write(json.dumps(self._metadata, indent=2))\\n\\n dof_filename = os.path.join(self.path, self.name + '.dof')\\n if os.path.isfile(dof_filename):\\n dof_file = DOFFile(dof_filename)\\n dof_file.update_version(version)\",\n \"def update_metadata(self, file_id, metadata):\\n pass\",\n \"def update_version_files (component):\\n\\n vprint (\\\"Updating version files for \\\" + component)\\n\\n retval = []\\n\\n ## Update component/VERSION.txt\\n path = get_path(component, \\\"VERSION.txt\\\")\\n with open (path, \\\"r+\\\") as version_file:\\n new_version = re.sub (component + \\\" version .*\\\",\\n \\\"%s version %s, released %s\\\" % (component,\\n comp_versions[component + \\\"_version\\\"],\\n release_date),\\n version_file.read ())\\n if opts.take_action:\\n version_file.seek (0)\\n version_file.truncate (0)\\n version_file.write (new_version)\\n else:\\n print (\\\"New version file for \\\" + component)\\n print (new_version)\\n\\n vprint (\\\"Updating Version.h for \\\" + component)\\n\\n retval.append(path)\\n\\n ## Update COMPONENT/component/Version.h\\n comp_l = len(component + \\\"_\\\")\\n parts = {k[comp_l:]:v for (k, v) in comp_versions.items() if k.startswith(component)}\\n parts[\\\"comp\\\"] = component\\n version_header = \\\"\\\"\\\"\\n// -*- C++ -*-\\n// This is file was automatically generated by $ACE_ROOT/bin/make_release.py\\n\\n#define {comp}_MAJOR_VERSION {major}\\n#define {comp}_MINOR_VERSION {minor}\\n#define {comp}_MICRO_VERSION {micro}\\n#define {comp}_VERSION \\\\\\\"{version}\\\\\\\"\\n#define {comp}_VERSION_CODE 0x{code:x}\\n#define {comp}_MAKE_VERSION_CODE(a,b,c) (((a) << 16) + ((b) << 8) + (c))\\n\\\"\\\"\\\".format(**parts)\\n\\n path = get_path(component, component.lower (), \\\"Version.h\\\")\\n if opts.take_action:\\n with open (path, 'w+') as version_h:\\n version_h.write (version_header)\\n else:\\n print (\\\"New Version.h for \\\" + component)\\n print (version_header)\\n\\n retval.append(path)\\n\\n # Update component/PROBLEM-REPORT-FORM\\n vprint (\\\"Updating PRF for \\\" + component)\\n\\n version_line_re = re.compile (r\\\"^\\\\s*(\\\\w+) +VERSION ?:\\\")\\n path = get_path(component, \\\"PROBLEM-REPORT-FORM\\\")\\n\\n with open (path, 'r+') as prf:\\n new_prf = \\\"\\\"\\n for line in prf.readlines ():\\n match = version_line_re.search (line)\\n if match is not None:\\n vprint (\\\"Found PRF Version for \\\" + match.group (1))\\n new_version = comp_versions[match.group(1) + \\\"_version\\\"]\\n line = version_re.sub (new_version, line)\\n\\n new_prf += line\\n\\n if opts.take_action:\\n prf.seek (0)\\n prf.truncate (0)\\n prf.writelines (new_prf)\\n else:\\n print (\\\"New PRF for \\\" + component)\\n print (\\\"\\\".join (new_prf))\\n\\n retval.append(path)\\n\\n return retval\",\n \"def updateFileInfo(self, data, pid):\\n self.db.updateLinkInfo(data)\\n self.evm.dispatchEvent(\\\"packageUpdated\\\", pid)\",\n \"def updateDictFile(self):\\n if self.dictFile.vdata.get('version',0): return\\n #--Update to version 1\\n for name in self.data.keys():\\n installer = self.data[name]\\n if isinstance(installer,Installer):\\n self.data[name] = installer.__copy__(InstallerArchive)\\n self.dictFile.vdata['version'] = 1\",\n \"def updateVersions(self):\\r\\n f = open('../versions.pckl', 'wb')\\r\\n pickle.dump(self.versions, f)\\r\\n f.close()\",\n \"def refresh(self):\\r\\n self.metadata = self.db.read(self.path).json()\",\n \"def _update_version(self) -> None:\\n # Implement in child class.\\n raise NotImplementedError\",\n \"def test_update_metadata(self):\\n pass\",\n \"def update(self):\\n\\n if not self.db: self.validate()\\n\\n self.logging.debug( \\\"update(%s)\\\" % (self.db) )\\n\\n for name in self.tables:\\n self.dbs_tables[name]['md5'] = get_md5( self.dbs_tables[name]['path'] )\\n\\n self._get_magnitudes()\\n self._get_events()\",\n \"def update(self):\\n self.content = self.get_content()\\n self.dependencies = self.content['requirements']['run']\\n self.pythonversion = self.content['extra']['pythonversion']\\n self.package_name = self.content['package']['name']\",\n \"def _volume_metadata_set(self, volume_path, data):\\n data['compat_version'] = 1\\n data['version'] = self.version\\n return self._metadata_set(self._volume_metadata_path(volume_path), data)\",\n \"def _metadata_update_pulp():\\n items = db.firmware.get_all()\\n files_to_scan = []\\n files_to_scan.append('firmware.xml.gz')\\n files_to_scan.append('firmware.xml.gz.asc')\\n for item in items:\\n if item.target != 'stable':\\n continue\\n files_to_scan.append(item.filename)\\n\\n # for each file in stable plus metadata\\n data = []\\n download_dir = app.config['DOWNLOAD_DIR']\\n for f in files_to_scan:\\n fn = os.path.join(download_dir, f)\\n if not os.path.exists(fn):\\n continue\\n\\n # filename,sha256,size\\n sha256 = _hashfile(open(fn, 'rb'), hashlib.sha256())\\n fn_sz = os.path.getsize(fn)\\n data.append('%s,%s,%i\\\\n' % (f, sha256, fn_sz))\\n\\n # write file\\n filename = os.path.join(download_dir, 'PULP_MANIFEST')\\n f = open(filename, 'w')\\n f.writelines(data)\\n f.close()\\n\\n # upload to CDN\\n blob = open(filename, 'rb').read()\\n _upload_to_cdn(filename, blob)\\n return\",\n \"def run_sql_file(filename, connection, version, lastversion):\\n cursor = connection.cursor()\\n for line in open(filename):\\n cursor.execute(line)\\n connection.commit()\\n cursor.execute(\\n \\\"update ecs.versionTable SET ecs.versionTable.version='{}' \\\"\\n \\\"where ecs.versionTable.version ='{}';\\\".format(version, lastversion))\\n connection.commit()\\n print(\\\"VersionTable updated. Current version is now: {}\\\".format(version))\",\n \"def set_version(self, bundle, ctx, filename, version):\",\n \"def update_pkg_metadata(self, pkg, version=None, **kwargs):\\n pass\",\n \"def update_channel_metadata():\\n from .channel_import import (\\n import_channel_from_local_db,\\n InvalidSchemaVersionError,\\n FutureSchemaError,\\n )\\n\\n channel_ids = get_channel_ids_for_content_database_dir(\\n get_content_database_dir_path()\\n )\\n for channel_id in channel_ids:\\n if not ChannelMetadata.objects.filter(id=channel_id).exists():\\n try:\\n import_channel_from_local_db(channel_id)\\n annotate_content(channel_id)\\n except (InvalidSchemaVersionError, FutureSchemaError):\\n logger.warning(\\n \\\"Tried to import channel {channel_id}, but database file was incompatible\\\".format(\\n channel_id=channel_id\\n )\\n )\\n except DatabaseError:\\n logger.warning(\\n \\\"Tried to import channel {channel_id}, but database file was corrupted.\\\".format(\\n channel_id=channel_id\\n )\\n )\\n fix_multiple_trees_with_id_one()\\n connection.close()\",\n \"def test_update_metadata1(self):\\n pass\",\n \"def versions(self):\\n raise Exception(\\\"mcapi.Datafile.versions is not implemented\\\")\",\n \"def metadata_update(self, _):\\n self.details.original_widget = YesNoWidget('Update metadata files?', self.__metadata_update)\",\n \"def upgrade(self):\",\n \"def upgrade(self):\",\n \"def update_metadata(self):\\n parser = GenericParser(\\n fn_re='{}/(e\\\\d+s\\\\d+)_.*/Production.nc'.format(self.data_folder),\\n group_names=['sim'],\\n group_transforms=[lambda x: x],\\n top_fn='',\\n step_ps=self.timestep\\n )\\n meta = gather_metadata('{}/e*/*nc'.format(self.data_folder), parser)\\n meta['top_fn'] = sorted(glob('{}/e*/structure.prmtop'.format(self.input_folder)))\\n self.meta = meta\",\n \"def updateMdrizVerHistory(self,build,versions):\\n _plist = self.assoc.parlist[0]\\n if build == True: _output = _plist['output']\\n else: _output = _plist['outdata']\\n \\n fhdu = pyfits.open(_output,mode='update')\\n prihdr = fhdu[0].header\\n \\n ver_str = \\\"MultiDrizzle product generated using: \\\"\\n prihdr.add_history(ver_str)\\n \\n for key in versions:\\n if versions[key].find('\\\\n') < 0:\\n prihdr.add_history(key+versions[key])\\n else:\\n # This will accomodate multi-line comments\\n _ver_str = versions[key].split('\\\\n')\\n prihdr.add_history(key)\\n for val in _ver_str:\\n if val.strip() != '':\\n prihdr.add_history(val)\\n \\n #ver_str = ' MultiDrizzle Version '+str(version)\\n #prihdr.add_history(ver_str)\\n \\n fhdu.close()\\n del fhdu\",\n \"def update_library_metadata(self, id, version=None):\\r\\n\\r\\n #### Begin functionality here\\r\\n if debug: eprint(f\\\"INFO: Updating the library entry with id {id}\\\")\\r\\n session = self.session\\r\\n library = session.query(LibraryRecord).filter(LibraryRecord.library_record_id==id).first()\\r\\n if library is not None:\\r\\n if version is not None:\\r\\n print(f\\\" Updating version to {version}\\\")\\r\\n library.version = version\\r\\n session.flush()\\r\\n session.commit()\\r\\n else:\\r\\n print(f\\\"ERROR: Library entry with id {id} not found\\\")\",\n \"def update_metadata(cube_list, infile_history):\\n\\n equalise_attributes(cube_list)\\n\\n for cube in cube_list:\\n cube.attributes['history'] = gio.write_metadata(file_info=infile_history)\\n cube.data = numpy.array(cube.data) #removes _FillValue attribute\\n\\n return cube_list\",\n \"def _update_fileinfo(self, metadata_filename):\\n \\n # In case we delayed loading the metadata and didn't do it in\\n # __init__ (such as with delegated metadata), then get the file\\n # info now.\\n \\n # Save the path to the current metadata file for 'metadata_filename'.\\n current_filepath = os.path.join(self.metadata_directory['current'],\\n metadata_filename)\\n # If the path is invalid, simply return and leave fileinfo unset.\\n if not os.path.exists(current_filepath):\\n self.fileinfo[current_filepath] = None\\n return\\n \\n # Extract the file information from the actual file and save it\\n # to the fileinfo store.\\n file_length, hashes = tuf.util.get_file_details(current_filepath)\\n metadata_fileinfo = tuf.formats.make_fileinfo(file_length, hashes)\\n self.fileinfo[metadata_filename] = metadata_fileinfo\",\n \"def preCommitFixup(self):\\n log_method_call(self, self.name)\\n # UEFI firmware/bootloader cannot read 1.1 or 1.2 metadata arrays\\n if getattr(self.format, \\\"mountpoint\\\", None) == \\\"/boot/efi\\\":\\n self.metadataVersion = \\\"1.0\\\"\",\n \"def get_and_update_versions ():\\n\\n try:\\n get_comp_versions (\\\"ACE\\\")\\n get_comp_versions (\\\"TAO\\\")\\n\\n if opts.update:\\n files = []\\n files += update_version_files (\\\"ACE\\\")\\n files += update_version_files (\\\"TAO\\\")\\n files += create_changelog (\\\"ACE\\\")\\n files += create_changelog (\\\"TAO\\\")\\n files += update_spec_file ()\\n files += update_debianbuild ()\\n\\n commit (files)\\n\\n except:\\n print (\\\"Fatal error in get_and_update_versions.\\\")\\n raise\",\n \"def _store_package_metadata(self):\",\n \"def update_versions(self, reference_resolution):\\n raise NotImplementedError(\\\"update_versions is not implemented\\\")\",\n \"def sync_metadata_to_db(self):\\n metadata_file_path = os.path.join(\\n self.plugin_path, self.plugin_metadata)\\n\\n metadata = self._load_config(metadata_file_path) or {}\\n Plugin.update(self.plugin, metadata)\",\n \"def _update_filesystem_metadata(self, metadata):\\n directory, fname = os.path.split(self.fname)\\n fbase = os.path.splitext(fname)[0]\\n \\n # Test for presence and size of zip file\\n zip_file = fbase + '.zip'\\n zip_path = os.path.join(directory, zip_file)\\n \\n if os.path.isfile(zip_path):\\n location = 'on_disk'\\n data_file_size = os.path.getsize(zip_path)\\n else:\\n location = 'on_tape'\\n data_file_size = 0\\n \\n # Test for presence of quick look PNG file\\n quicklook_file = fbase + '.png'\\n quicklook_path = os.path.join(directory, quicklook_file)\\n \\n if not os.path.isfile(quicklook_path):\\n quicklook_file = ''\\n\\n # Add to metadata dictionary\\n item_map = {'directory': directory, 'metadata_file': fname,\\n 'data_file': zip_file, 'location': location, \\n 'data_file_size': data_file_size, 'quicklook_file': quicklook_file}\\n \\n for key, value in item_map.items():\\n metadata[key] = value\",\n \"def updateVersionTag(deploymentPath, revision):\\n fluidinfoPath = os.path.join(deploymentPath, revision, 'fluidinfo')\\n with cd(fluidinfoPath):\\n with open('deployment/api-version.txt') as versionFile:\\n version = versionFile.read()\\n sudo('bin/python bin/fluidinfo update-version-tag '\\n 'postgres:///fluidinfo '\\n '%s' % version,\\n user='fluidinfo')\",\n \"def update_metadata(self, metadata):\\n return self.manager.update_metadata(self, metadata)\",\n \"async def update_version(self, version: int):\\n async with open(self.__file_name, mode=\\\"r\\\") as auth_file:\\n tag_data = json.loads(await auth_file.read())\\n await auth_file.close()\\n async with open(self.__file_name, mode=\\\"w\\\") as auth:\\n tag_data[\\\"version\\\"] = version\\n await auth.write(json.dumps(tag_data, indent=2, sort_keys=True))\\n await auth.close()\\n self.__version = version\",\n \"def update_database():\\n\\n # We obtain the data from the official database\\n df = getData.extractData()\\n\\n # We save the dataframe for later use in the API\\n auxiliary.saveToCsv(df, 'app/resources')\",\n \"def updateFileData(self):\\n with open(pagePath(self.pageName)) as f:\\n self.fileData = f.read()\\n self.lastUpdated = time.time()\",\n \"def update_metadata(self):\\n self.data[\\\"keywords\\\"] = self.repo.topics(self.data.get(\\\"keywords\\\", []))\\n self.data[\\\"description\\\"] = self.data.get(\\\"description\\\") or self.repo.description\\n self.data[\\\"codeRepository\\\"] = (\\n self.data.get(\\\"codeRepository\\\") or self.repo.html_url\\n )\\n self.data[\\\"name\\\"] = self.data.get(\\\"name\\\") or self.repo.name\\n self.data[\\\"issueTracker\\\"] = (\\n self.data.get(\\\"issueTracker\\\") or self.repo.issues_url\\n )\\n self.data[\\\"license\\\"] = self.data.get(\\\"license\\\") or self.repo.license\",\n \"def __metadata_update(self, value):\\n if value:\\n try:\\n self.details.original_widget = TextWidget('Updating metadata files. Please, wait...')\\n self.execution_manager.close()\\n except QMapError as e:\\n self.details.original_widget = TextWidget(e)\\n self.__back_to_main()\",\n \"def updateDoc(self, path):\\n self.db.setDb(self.db_file)\\n \\n if not self.authd:\\n self._authorize()\\n \\n db_row = self.db.getRowFromPath(path)\\n if not db_row:\\n return False\\n \\n resource_id = db_row[0]\\n etag = db_row[1]\\n title = db_row[2]\\n \\n ms = gdata.data.MediaSource(file_path=path, content_type=MIMETYPES['ODT'])\\n doc = self.client.GetDoc(resource_id.replace(':', '%3A'))\\n new_version = self.client.Update(doc, media_source=ms)\\n print 'Document pushed:', new_version.GetAlternateLink().href\\n \\n self.db.resetEtag(new_version)\",\n \"def test_update_meta_file(self):\\n\\n df_expected = pd.DataFrame({self.meta_date_col: [\\n self.test_date], self.meta_timestamp_col: [datetime.today().strftime(self.meta_timestamp_format)]})\\n # method execution\\n MetaFile.update_meta_file(self.test_date, self.trg_bucket_connector)\\n df_result = self.trg_bucket_connector.read_meta_file()\\n self.assertTrue(df_result.equals(df_expected))\",\n \"def sync_data_files(self, version, tag='stable'):\\n data_folder = self._generate_data_folder(version, tag)\\n self.sync_files(data_folder)\",\n \"def _update_pyrex_file(self, lines, filename):\\n found_version_line = False\\n for lineno, line in enumerate(lines):\\n if line.startswith('__version__'):\\n found_version_line = True\\n break\\n if found_version_line:\\n if self.Verbose:\\n print 'Version string found on line %d' % lineno\\n lines[lineno] = '__version__ = \\\"%s\\\"\\\\n' % str(self.VersionTuple)\\n else:\\n print \\\"No version string found in %s\\\" % filename\\n return (lines, found_version_line)\",\n \"def refresh(self):\\n\\n # The timestamp role does not have signed metadata about it; otherwise we\\n # would need an infinite regress of metadata. Therefore, we use some\\n # default, sane metadata about it.\\n DEFAULT_TIMESTAMP_FILEINFO = {\\n 'hashes':None,\\n 'length': tuf.conf.DEFAULT_TIMESTAMP_REQUIRED_LENGTH\\n }\\n\\n # Update the top-level metadata. The _update_metadata_if_changed() and\\n # _update_metadata() calls below do NOT perform an update if there\\n # is insufficient trusted signatures for the specified metadata.\\n # Raise 'tuf.NoWorkingMirrorError' if an update fails.\\n\\n # Use default but sane information for timestamp metadata, and do not\\n # require strict checks on its required length.\\n self._update_metadata('timestamp', DEFAULT_TIMESTAMP_FILEINFO)\\n\\n self._update_metadata_if_changed('release', referenced_metadata='timestamp')\\n\\n self._update_metadata_if_changed('root')\\n\\n self._update_metadata_if_changed('targets')\\n\\n # Updated the top-level metadata (which all had valid signatures), however,\\n # have they expired? Raise 'tuf.ExpiredMetadataError' if any of the metadata\\n # has expired.\\n for metadata_role in ['timestamp', 'root', 'release', 'targets']:\\n self._ensure_not_expired(metadata_role)\",\n \"def update_version(filename, new_version, vers_attr='__version__'):\\n lines = []\\n with open(filename, 'r') as handle:\\n lines = handle.readlines()\\n\\n updated = False\\n for lineno, line in enumerate(lines):\\n if line.startswith(vers_attr):\\n lines[lineno] = \\\"{0} = \\\\\\\"{1}\\\\\\\"\\\\n\\\".format(vers_attr, new_version)\\n updated = True\\n break\\n if not updated:\\n lines.append(\\\"{0} = \\\\\\\"{1}\\\\\\\"\\\\n\\\".format(vers_attr, new_version))\\n\\n with open(filename, 'w') as handle:\\n handle.writelines(lines)\",\n \"def test_partial_update_metadata(self):\\n pass\",\n \"def report_version(self, data):\\n self.firmata_version.append(data[0]) # add major\\n self.firmata_version.append(data[1]) # add minor\",\n \"def refresh(self):\\n self.update_from_file()\\n self.update_from_env()\",\n \"def do_upgrade(env, ver, cursor):\\n tables = [\\n ('attachment', {'time': ('int', 'int64')}),\\n ('wiki', {'time': ('int', 'int64')}),\\n ('revision', {'time': ('int', 'int64')}),\\n ('ticket', {'time': ('int', 'int64'),\\n 'changetime': ('int', 'int64')}),\\n ('ticket_change', {'time': ('int', 'int64')}),\\n ('milestone', {'due': ('int', 'int64'),\\n 'completed': ('int', 'int64')}),\\n ('version', {'time': ('int', 'int64')}),\\n ]\\n\\n db_connector, _ = DatabaseManager(env).get_connector()\\n for table, columns in tables:\\n # Alter column types\\n for sql in db_connector.alter_column_types(table, columns):\\n cursor.execute(sql)\\n\\n # Convert timestamps to microseconds\\n cursor.execute(\\\"UPDATE %s SET %s\\\" % (table,\\n ', '.join(\\\"%s=%s*1000000\\\" % (column, column)\\n for column in columns)))\\n\\n # Convert comment edit timestamps to microseconds\\n db = env.get_read_db()\\n cursor.execute(\\\"\\\"\\\"\\n UPDATE ticket_change SET newvalue=%s*1000000\\n WHERE field %s\\\"\\\"\\\" % (db.cast('newvalue', 'int64'), db.like()),\\n ('_comment%',))\",\n \"def update_setupcfg_version(filename, version):\\n\\n setup_cfg = open(filename).readlines()\\n current_section = None\\n updated = False\\n\\n for idx, line in enumerate(setup_cfg):\\n m = ConfigParser.SECTCRE.match(line)\\n if m:\\n if current_section == 'metadata':\\n # We already parsed the entire metadata section without finding\\n # a version line, and are now moving into a new section\\n break\\n current_section = m.group('header')\\n continue\\n\\n if '=' not in line:\\n continue\\n\\n opt, val = line.split('=', 1)\\n opt, val = opt.strip(), val.strip()\\n if current_section == 'metadata' and opt == 'version':\\n setup_cfg[idx] = 'version = %s\\\\n' % version\\n updated = True\\n break\\n\\n if updated:\\n open(filename, 'w').writelines(setup_cfg)\\n logger.info(\\\"Set %s's version to %r\\\" % (os.path.basename(filename),\\n version))\",\n \"def metadata_update(self, new_metadata=None):\\n if new_metadata is None:\\n self.metadata_set(self.t.metadata())\",\n \"def updateFluidDB(serverName):\\n # Upload and set up the code.\\n deploymentPath = os.path.join('/srv', serverName)\\n revision = datetime.utcnow().strftime('%Y%m%d-%H%M')\\n revisionPath = os.path.join(deploymentPath, revision)\\n\\n sudo('mkdir -p {0}'.format(revisionPath))\\n\\n local('git archive --prefix=fluidinfo/ -v --format tar HEAD | '\\n 'bzip2 > fluidinfo.tar.bz2')\\n put('fluidinfo.tar.bz2', revisionPath, use_sudo=True)\\n\\n with cd(revisionPath):\\n sudo('mkdir -p var/log var/log/trace var/run var/tmp')\\n sudo('chown -R fluidinfo {0}'.format(revisionPath))\\n sudo('tar jxvf fluidinfo.tar.bz2', user='fluidinfo')\\n\\n with cd(os.path.join(revisionPath, 'fluidinfo')):\\n sudo('virtualenv .', user='fluidinfo')\\n sudo('mkdir -p /var/lib/fluidinfo/pip-cache', user='fluidinfo')\\n sudo('./bin/pip install --use-mirrors '\\n '--download-cache=/var/lib/fluidinfo/pip-cache '\\n '--log /tmp/pip.log '\\n '-r requirements.txt', user='fluidinfo')\\n # On successful completion, clean up /tmp\\n sudo('rm -f /tmp/pip.log')\\n\\n get(os.path.join(deploymentPath, 'current', 'fluidinfo-api.conf'),\\n 'fluidinfo-api.conf')\\n config = RawConfigParser()\\n with open('fluidinfo-api.conf', 'r') as configFile:\\n config.readfp(configFile)\\n\\n # Copy and set up configuration files.\\n deployConfigFiles(\\n {'deployment-path': deploymentPath,\\n 'server-name': serverName,\\n 'revision-path': revisionPath,\\n 'solr-url': config.get('index', 'url'),\\n 'solr-shards': config.get('index', 'shards'),\\n 'postgres-uri': config.get('store', 'main-uri')},\\n\\n ('fluidinfo/fluidinfo-api.conf.template',\\n '{revision-path}/fluidinfo-api.conf'))\\n\\n local('rm fluidinfo-api.conf')\\n\\n # TODO: check patches.\\n # TODO: update version tag.\\n\\n with cd(deploymentPath):\\n sudo('rm current')\\n sudo('ln -fs {0} current'.format(revision))\\n\\n for port in range(9001, 9009):\\n sudo('restart fluidinfo-api-node PORT=%d' % port)\",\n \"def update_tables(tables):\\n # First we ensure we can find the file format version number\\n # in top-level metadata. Then we proceed to fix up the tables as necessary.\\n if not (isinstance(tables.metadata, dict) and 'SLiM' in tables.metadata):\\n # Old versions kept information in provenance, not top-level metadata.\\n # Note this uses defaults on keys not present in provenance,\\n # which prior to 0.5 was everything but generation and model_type.\\n values = default_slim_metadata('tree_sequence')['SLiM']\\n prov = None\\n file_version = 'unknown'\\n # use only the last SLiM provenance\\n for p in tables.provenances:\\n is_slim, this_file_version = slim_provenance_version(p) \\n if is_slim:\\n prov = p\\n file_version = this_file_version\\n values['file_version'] = file_version\\n try:\\n record = json.loads(prov.record)\\n if file_version == \\\"0.1\\\":\\n values['model_type'] = record['model_type']\\n values['tick'] = record['generation']\\n values['cycle'] = record['generation']\\n else:\\n if 'generation' in record['slim']:\\n values['tick'] = record['slim']['generation']\\n values['cycle'] = record['slim']['generation']\\n for k in values:\\n if k in record['parameters']:\\n values[k] = record['parameters'][k]\\n if k in record['slim']:\\n values[k] = record['slim'][k]\\n except:\\n raise ValueError(\\\"Failed to obtain metadata from provenance.\\\")\\n set_tree_sequence_metadata(tables, **values)\\n\\n file_version = tables.metadata['SLiM']['file_version']\\n if file_version != slim_file_version:\\n warnings.warn(\\\"This is a version {} SLiM tree sequence.\\\".format(file_version) +\\n \\\" When you write this out, \\\" +\\n \\\"it will be converted to version {}.\\\".format(slim_file_version))\\n\\n # the only tables to have metadata schema changed thus far\\n # are populations, individuals, mutations, and top-level:\\n old_schema = _old_metadata_schema(\\\"tree_sequence\\\", file_version)\\n if old_schema is not None:\\n md = tables.metadata\\n new_schema = slim_metadata_schemas[\\\"tree_sequence\\\"]\\n new_properties = new_schema.asdict()['properties']['SLiM']['required']\\n tables.metadata_schema = new_schema\\n defaults = default_slim_metadata(\\\"tree_sequence\\\")\\n for k in new_properties:\\n if k not in md['SLiM']:\\n if k == \\\"tick\\\":\\n md['SLiM']['tick'] = md['SLiM']['generation']\\n md['SLiM']['cycle'] = md['SLiM']['generation']\\n else:\\n md['SLiM'][k] = defaults['SLiM'][k]\\n tables.metadata = md\\n\\n old_schema = _old_metadata_schema(\\\"population\\\", file_version)\\n if old_schema is not None:\\n pops = tables.populations.copy()\\n tables.populations.clear()\\n if pops.metadata_schema == tskit.MetadataSchema(None):\\n pops.metadata_schema = old_schema\\n new_schema = slim_metadata_schemas[\\\"population\\\"]\\n tables.populations.metadata_schema = new_schema\\n defaults = default_slim_metadata(\\\"population\\\")\\n # just needs recoding\\n for pop in pops:\\n tables.populations.append(pop)\\n\\n old_schema = _old_metadata_schema(\\\"individual\\\", file_version)\\n if old_schema is not None:\\n inds = tables.individuals.copy()\\n tables.individuals.clear()\\n if inds.metadata_schema == tskit.MetadataSchema(None):\\n inds.metadata_schema = old_schema\\n new_schema = slim_metadata_schemas[\\\"individual\\\"]\\n tables.individuals.metadata_schema = new_schema\\n defaults = default_slim_metadata(\\\"individual\\\")\\n d = {}\\n for k in [\\\"pedigree_p1\\\", \\\"pedigree_p2\\\"]:\\n d[k] = defaults[k]\\n for ind in inds:\\n md = ind.metadata\\n md.update(d)\\n tables.individuals.append(ind.replace(metadata=md))\\n\\n old_schema = _old_metadata_schema(\\\"mutation\\\", file_version)\\n if old_schema is not None:\\n muts = tables.mutations.copy()\\n tables.mutations.clear()\\n if muts.metadata_schema == tskit.MetadataSchema(None):\\n muts.metadata_schema = old_schema\\n tables.mutations.metadata_schema = slim_metadata_schemas[\\\"mutation\\\"]\\n for mut in muts:\\n md = mut.metadata\\n for ml in md['mutation_list']:\\n ml['nucleotide'] = -1\\n tables.mutations.append(mut.replace(metadata=md))\\n\\n if file_version == \\\"0.1\\\":\\n # shift times\\n slim_generation = tables.metadata['SLiM']['tick']\\n node_times = tables.nodes.time + slim_generation\\n tables.nodes.set_columns(\\n flags=tables.nodes.flags,\\n time=node_times,\\n population=tables.nodes.population,\\n individual=tables.nodes.individual,\\n metadata=tables.nodes.metadata,\\n metadata_offset=tables.nodes.metadata_offset)\\n migration_times = tables.migrations.time + slim_generation\\n tables.migrations.set_columns(\\n left=tables.migrations.left,\\n right=tables.migrations.right,\\n node=tables.migrations.node,\\n source=tables.migrations.source,\\n dest=tables.migrations.dest,\\n time=migration_times)\\n\\n new_record = {\\n \\\"schema_version\\\": \\\"1.0.0\\\",\\n \\\"software\\\": {\\n \\\"name\\\": \\\"pyslim\\\",\\n \\\"version\\\": pyslim_version,\\n },\\n \\\"parameters\\\": {\\n \\\"command\\\": [\\\"updrade_tables\\\"],\\n \\\"old_file_version\\\": file_version,\\n \\\"new_file_version\\\": slim_file_version,\\n },\\n \\\"environment\\\": get_environment(),\\n }\\n tskit.validate_provenance(new_record)\\n tables.provenances.add_row(json.dumps(new_record))\\n\\n set_metadata_schemas(tables)\\n md = tables.metadata\\n md['SLiM']['file_version'] = slim_file_version\\n tables.metadata = md\",\n \"def _WriteChromeVersionToMetadata(self):\\n self._run.attrs.metadata.UpdateKeyDictWithDict(\\n 'version',\\n {'chrome': self._run.attrs.chrome_version})\\n self.UploadMetadata(filename=constants.PARTIAL_METADATA_JSON)\",\n \"def _update_python_file(self, lines, filename):\\n found_version_line = False\\n for lineno, line in enumerate(lines):\\n if line.startswith('__version__'):\\n found_version_line = True\\n break\\n if found_version_line:\\n if self.Verbose:\\n print 'Version string found on line %d' % lineno\\n lines[lineno] = '__version__ = \\\"%s\\\"\\\\n' % self.Version\\n else:\\n print \\\"No version string found in %s\\\" % filename\\n return (lines, found_version_line)\",\n \"def updateVersionNumber(self, *args):\\n file = '//corp-nas01/DC/dc_art/character/GEPPETTO/CharacterTracker.csv'\\n \\n character = []\\n character = cmds.textScrollList(self.UIElements['characterName'] , q=True, si=True)\\n \\n key = character[0]\\n characterInfo = self.trackerInfo[key]\\n versionNumber = characterInfo[2][1]\\n \\n index = 0\\n \\\"\\\"\\\" Open the file for reading \\\"\\\"\\\"\\n with open(file, 'rb') as f:\\n reader = csv.reader(f, delimiter=',', quotechar='\\\"')\\n for row in reader:\\n newIndex = index+1\\n index = newIndex\\n if row[0] == character[0]:\\n numLine = index\\n versionNumber = int(row[2])\\n newVersion = versionNumber + 1\\n \\n row[2] = newVersion\\n print row\\n \\n \\n \\n line_to_override = {numLine:[row]} \\n\\n \\\"\\\"\\\" Open the csv in maya for writing \\\"\\\"\\\"\\n writer = csv.writer(open(file, 'wb'), delimiter=',')\\n\\n data = line_to_override.get(numLine, row)\\n writer.writerow(data)\\n \\n #writer.writerow(version)\",\n \"def publish_proto_metadata_update(self):\\n metadata = load_mpe_service_metadata(self.args.metadata_file)\\n ipfs_hash_base58 = ipfs_utils.publish_proto_in_ipfs(\\n self._get_ipfs_client(), self.args.protodir)\\n metadata.set_simple_field(\\\"model_ipfs_hash\\\", ipfs_hash_base58)\\n metadata.save_pretty(self.args.metadata_file)\",\n \"def update_metadata(state, name, wid, file_id, metadata_file):\\n\\n client = state.api_client\\n\\n # Get the workspace details\\n w_details = helpers.workspace.details(client, wid, name)\\n if w_details is None:\\n # Can only happen when the name is used and there are no results. Not\\n # with the wid option because it would raise a 404 QuetzalAPIException\\n raise click.ClickException(f'Workspace named \\\"{name}\\\" does not exist.')\\n\\n metadata_contents = json.load(metadata_file)\\n response = client.workspace_file_update_metadata(wid=w_details.id, uuid=file_id,\\n body=metadata_contents)\\n click.secho(f'Metadata for file {file_id} successfully changed.', fg='green')\\n click.secho('Updated metadata:')\\n click.secho(json.dumps(response, indent=2), fg='blue')\",\n \"def set_media_versions(self):\\n\\n # access to .multimedia_map is slow\\n previous_version = self._get_version_comparison_build()\\n prev_multimedia_map = previous_version.multimedia_map if previous_version else {}\\n\\n for path, map_item in self.multimedia_map.items():\\n prev_map_item = prev_multimedia_map.get(path, None)\\n if prev_map_item and prev_map_item.unique_id:\\n # Re-use the id so CommCare knows it's the same resource\\n map_item.unique_id = prev_map_item.unique_id\\n if (prev_map_item and prev_map_item.version\\n and prev_map_item.multimedia_id == map_item.multimedia_id):\\n map_item.version = prev_map_item.version\\n else:\\n map_item.version = self.version\",\n \"async def async_update(self):\\n self.update_file_path()\",\n \"def test_partial_update_metadata1(self):\\n pass\",\n \"def _upgrade(cls, contents):\\n metadata = contents.get(\\\"metadata\\\", {})\\n version = metadata.get(\\\"cache_version\\\")\\n if version == \\\"1\\\":\\n metadata.pop(\\\"cache_version\\\", None)\\n return {\\\"metadata\\\": cls.METADATA,\\n **{cls.LANG_PREFIX + \\\"coq\\\":\\n {\\\"driver\\\": contents.pop(\\\"generator\\\"),\\n \\\"metadata\\\": contents.pop(\\\"metadata\\\"),\\n **contents}}}\\n return contents\",\n \"def db_update_files():\\n _populate_table_files(File)\\n _populate_table_files(Software)\\n return redirect(url_for('view_index'))\",\n \"def update_metadata_csv(self, source):\\n timestamp = os.path.getmtime(source)\\n filedate = datetime.datetime.fromtimestamp(timestamp)\\n return self.update_metadata_date(filedate)\",\n \"def update_metadata(self, metadata):\\n return self.parent.update_metadata_for_node(self, metadata)\",\n \"def test_update_meta_file_meta_file_exists(self):\\n # Expected results\\n date_old = '2021-09-13'\\n date_new = '2021-09-14'\\n dates_expected = [date_old, date_new]\\n\\n meta_content = f\\\"{self.meta_date_col},{self.meta_timestamp_col}\\\\n{date_old},{datetime.today().strftime(self.meta_timestamp_format)}\\\"\\n self.bucket.put_object(Body=meta_content, Key=self.meta_key)\\n # method execution\\n MetaFile.update_meta_file(date_new, self.trg_bucket_connector)\\n # read meta file\\n df_result = self.trg_bucket_connector.read_meta_file()\\n dates_result = list(df_result[\\n self.meta_date_col])\\n self.assertEqual(dates_expected, dates_result)\",\n \"def test_changeVersionInFile(self):\\n # The version numbers are arbitrary, the name is only kind of\\n # arbitrary.\\n packageName = 'foo'\\n oldVersion = Version(packageName, 2, 5, 0)\\n file = self.makeFile('README',\\n \\\"Hello and welcome to %s.\\\" % oldVersion.base())\\n\\n newVersion = Version(packageName, 7, 6, 0)\\n _changeVersionInFile(oldVersion, newVersion, file.path)\\n\\n self.assertEqual(file.getContent(),\\n \\\"Hello and welcome to %s.\\\" % newVersion.base())\",\n \"def snapshot_info(self) -> MetaFile:\\n raise NotImplementedError\",\n \"def bumpversion(path=\\\"setup.cfg\\\"):\\n config = ConfigParser()\\n config.read(path)\\n cfg = open(path, 'w')\\n new_version = \\\"0.0.0\\\"\\n if config.has_option('metadata', 'version'):\\n old_version = config.get('metadata', 'version')\\n major, minor, patch = old_version.split(\\\".\\\")\\n new_version = \\\"%s.%s.%s\\\" % (major, minor, int(patch) + 1)\\n if not config.has_section('metadata'):\\n config.add_section('metadata')\\n config.set('metadata', 'version', new_version)\\n config.write(cfg)\\n cfg.close()\\n return new_version\",\n \"def update_h5store(fileh):\\n logger.debug(\\\"Running update_h5store\\\")\\n root = fileh.root\\n\\n version = root._v_attrs['version'] if 'version' in root._v_attrs else 0\\n \\n if version < 1:\\n # No version, or new file\\n # Ensure that the major nodes exist\\n logger.debug('updating to version 1')\\n for node in ['spaces', 'datasets', 'tasksets', 'results']:\\n if not hasattr(root, node):\\n fileh.createGroup( root, node )\\n # Check that the dataset nodes are well-formed\\n for dsnode in root.datasets:\\n if not hasattr(dsnode, 'tokenstreams'):\\n logger.debug('Node %s did not have tokenstreams node; adding.', dsnode._v_name)\\n fileh.createGroup( dsnode, \\\"tokenstreams\\\" )\\n if not hasattr(dsnode, 'sequence'):\\n logger.debug('Node %s did not have sequence node; adding.', dsnode._v_name)\\n fileh.createGroup( dsnode, \\\"sequence\\\" )\\n if version < 2:\\n # In version 2, we introduce the concept of instance spaces, detaching the instance\\n # identifiers from the dataset nodes and instead attaching them to the space nodes\\n logger.debug('updating to version 2')\\n for dsnode in root.datasets:\\n # Move the instance id node to spaces\\n id_node = dsnode.instance_id\\n id_node._v_attrs['size'] = len(dsnode.instance_id)\\n id_node._v_attrs['type'] = 'instance'\\n id_node._v_attrs['name'] = dsnode._v_name\\n id_node._v_attrs['encoding'] = 'utf8' # to be safe, in case we had e.g. utf8 filenames\\n fileh.moveNode(dsnode.instance_id, root.spaces, dsnode._v_name)\\n # Unless otherwise specified, the instance space is the dataset name\\n dsnode._v_attrs['instance_space'] = dsnode._v_name\\n\\n # Add the instance space metadata to all tasksets\\n for tsnode in root.tasksets:\\n tsnode._v_attrs.instance_space = tsnode._v_attrs.dataset\\n for t in tsnode:\\n t._v_attrs.instance_space = t._v_attrs.dataset\\n \\n # Add the instance space metadata to all results\\n for rnode in root.results:\\n rnode._v_attrs.instance_space = rnode._v_attrs.dataset\\n if hasattr(rnode._v_attrs, 'eval_dataset'):\\n rnode._v_attrs.eval_space = rnode._v_attrs.eval_dataset\\n for node in rnode:\\n if node._v_name == 'summary':\\n for summary in node:\\n summary._v_attrs.instance_space = summary._v_attrs.dataset\\n if hasattr(summary._v_attrs, 'eval_dataset'):\\n summary._v_attrs.eval_space = summary._v_attrs.eval_dataset\\n else:\\n node._v_attrs.instance_space = node._v_attrs.dataset\\n if hasattr(node._v_attrs, 'eval_dataset'):\\n node._v_attrs.eval_space = node._v_attrs.eval_dataset\\n if version < 3:\\n # In version 3, we add weights associated with task nodes\\n for tsnode in root.tasksets:\\n for t in tsnode:\\n fileh.createGroup(t, 'weights')\\n if version < 4:\\n # In version 4, we introduced a node to store splits in datasets\\n for dsnode in root.datasets:\\n if not hasattr(dsnode, 'split'):\\n logger.debug('Node %s did not have split node; adding.', dsnode._v_name)\\n fileh.createGroup( dsnode, \\\"split\\\" )\\n # TODO:\\n # Replace all boolean maps for tasks with their equivalent flatnonzero indices\\n # Eliminate UUID from taskset and result metadata\\n # Get rid of all date attrs\\n # Ensure all TSR nodes have a summary node\\n \\n\\n logger.debug(\\\"updated store from version %d to %d\\\", version, STORE_VERSION)\\n root._v_attrs['version'] = STORE_VERSION\\n fileh.flush()\",\n \"def sync_metadata_to_db(self):\\n super(PluginAdapterV3, self).sync_metadata_to_db()\\n\\n db_config_metadata_mapping = {\\n 'attributes_metadata': self.environment_config_name,\\n 'roles_metadata': self.node_roles_config_name,\\n 'volumes_metadata': self.volumes_config_name,\\n 'network_roles_metadata': self.network_roles_config_name,\\n 'deployment_tasks': self.deployment_tasks_config_name,\\n 'tasks': self.task_config_name\\n }\\n\\n self._update_plugin(db_config_metadata_mapping)\",\n \"def _helperUpdateAnnoByMetadata(syn,temp,metaDf,refCol,cols2Add,fileExts):\\n \\n print \\\"Updating annotations by metadata...\\\"\\n \\n exts = ')|('.join(fileExts)\\n exts = r'(' + exts + ')'\\n tempName = re.sub(exts,\\\"\\\",temp.name)\\n \\n row = df.loc[df[refCol] == tempName]\\n if row.empty:\\n print \\\"missing metadata\\\"\\n else:\\n for colName in cols2Add:\\n print \\\"> %s \\\" % colName\\n temp[colName] = map(str,row[colName])[0]\\n temp = syn.store(temp, forceVersion = False)\\n print \\\"\\\"\",\n \"def set_form_versions(self):\\n def _hash(val):\\n return hashlib.md5(val).hexdigest()\\n\\n latest_build = self._get_version_comparison_build()\\n if not latest_build:\\n return\\n force_new_version = self.build_profiles != latest_build.build_profiles\\n for form_stuff in self.get_forms(bare=False):\\n filename = 'files/%s' % self.get_form_filename(**form_stuff)\\n current_form = form_stuff[\\\"form\\\"]\\n if not force_new_version:\\n try:\\n previous_form = latest_build.get_form(current_form.unique_id)\\n # take the previous version's compiled form as-is\\n # (generation code may have changed since last build)\\n previous_source = latest_build.fetch_attachment(filename)\\n except (ResourceNotFound, FormNotFoundException):\\n current_form.version = None\\n else:\\n previous_hash = _hash(previous_source)\\n\\n # set form version to previous version, and only update if content has changed\\n current_form.version = previous_form.get_version()\\n current_form = current_form.validate_form()\\n current_hash = _hash(current_form.render_xform())\\n if previous_hash != current_hash:\\n current_form.version = None\\n # clear cache since render_xform was called with a mutated form set to the previous version\\n current_form.render_xform.reset_cache(current_form)\\n else:\\n current_form.version = None\",\n \"def store_pkg_metadata(self, pkg, version):\\n pass\",\n \"def set_version_db(apps, schema_editor):\\n Version = apps.get_model(\\\"reversion\\\", \\\"Version\\\")\\n content_types = Version.objects.values_list(\\\"content_type\\\", flat=True).distinct()\\n for content_type in content_types:\\n model_class = content_type.model_class()\\n db = router.db_for_write(model_class)\\n Version.objects.filter(content_type=content_type).update(db=db)\",\n \"def test_updateTwistedVersionInformation(self):\\n baseDirectory = FilePath(self.mktemp())\\n baseDirectory.createDirectory()\\n now = date.today()\\n\\n projectName = 'foo'\\n oldVersion = Version(projectName, 2, 5, 0)\\n newVersion = getNextVersion(oldVersion, now=now)\\n\\n project = self.makeProject(oldVersion, baseDirectory)\\n\\n updateTwistedVersionInformation(baseDirectory, now=now)\\n\\n self.assertEqual(project.getVersion(), newVersion)\\n self.assertEqual(\\n project.directory.child('topfiles').child('README').getContent(),\\n newVersion.base())\",\n \"def update(self):\\n \\n dbpath, config = self._start()\\n \\n self.config.obo = check_file(config.obo, dbpath, \\\"obo\\\") \\n desc_file = check_file(config.model_descriptions, dbpath,\\n \\\"model_descriptions\\\", allow_none=True) \\n phen_file = check_file(config.model_phenotypes, dbpath,\\n \\\"model_phenotypes\\\", allow_none=True)\\n \\n summary = self._update(desc_file, phen_file) \\n if len(summary[\\\"incorrect_ids\\\"]) == 0 and not config.skip_compute:\\n self._compute(models=summary[\\\"new_phenotypes\\\"])\\n \\n self._end()\",\n \"def __update_version(self):\\r\\n\\r\\n db_version = self.__get_db_version_int()\\r\\n if db_version == SCHEMA_VERSION:\\r\\n return\\r\\n\\r\\n #\\r\\n # Define functions for upgrading between schema versions\\r\\n #\\r\\n def update_2xto30():\\r\\n \\\"\\\"\\\"Incremental update of database from Freeseer 2.x and older to 3.0\\r\\n\\r\\n SCHEMA_VERSION is 300\\r\\n \\\"\\\"\\\"\\r\\n if db_version > 300:\\r\\n log.debug('Database newer than schema version 300.')\\r\\n return # No update needed\\r\\n\\r\\n log.debug('Updating to schema 300.')\\r\\n QtSql.QSqlQuery('ALTER TABLE presentations RENAME TO presentations_old') # temporary table\\r\\n self.__create_presentations_table(PRESENTATIONS_SCHEMA_300)\\r\\n QtSql.QSqlQuery(\\\"\\\"\\\"INSERT INTO presentations\\r\\n SELECT Id, Title, Speaker, Description, Level, Event, Room, Time FROM presentations_old\\\"\\\"\\\")\\r\\n QtSql.QSqlQuery('DROP TABLE presentations_old')\\r\\n\\r\\n def update_30to31():\\r\\n \\\"\\\"\\\"Performs incremental update of database from 3.0 and older to 3.1.\\\"\\\"\\\"\\r\\n QtSql.QSqlQuery('ALTER TABLE presentations RENAME TO presentations_old')\\r\\n self.__create_presentations_table(PRESENTATIONS_SCHEMA_310)\\r\\n QtSql.QSqlQuery(\\\"\\\"\\\"INSERT INTO presentations\\r\\n SELECT Id, Title, Speaker, Description, Level, Event, Room, Time, Time, Time\\r\\n FROM presentations_old\\\"\\\"\\\")\\r\\n QtSql.QSqlQuery('DROP TABLE presentations_old')\\r\\n\\r\\n #\\r\\n # Perform the upgrade\\r\\n #\\r\\n updaters = [update_2xto30, update_30to31]\\r\\n for updater in updaters:\\r\\n updater()\\r\\n\\r\\n QtSql.QSqlQuery('PRAGMA user_version = %i' % SCHEMA_VERSION)\\r\\n log.info('Upgraded presentations database from version {} to {}'.format(db_version, SCHEMA_VERSION))\",\n \"def update_version(self, new_version):\\n if new_version is not None:\\n self.version_details = json.loads(new_version)\\n\\n # Update port file.\\n http_port = self.version_details['appscaleExtensions']['httpPort']\\n version_key = VERSION_PATH_SEPARATOR.join(\\n [self.project_id, self.service_id, self.version_id])\\n port_file_location = os.path.join(\\n CONFIG_DIR, 'port-{}.txt'.format(version_key))\\n with open(port_file_location, 'w') as port_file:\\n port_file.write(str(http_port))\\n\\n logger.info('Updated version details: {}'.format(version_key))\\n if self.callback is not None:\\n self.callback()\",\n \"def _update_properties_file(self, lines, filename):\\n found_version_line = False\\n if filename.endswith('cogent-requirements.txt'):\\n for lineno, line in enumerate(lines):\\n if 'packages/source/c/cogent' in line:\\n found_version_line = True\\n break\\n if found_version_line:\\n if self.Verbose:\\n print 'Version string found on line %d' % lineno\\n http_base = lines[lineno].rsplit('/',1)[0]\\n lines[lineno] = '%s/PyCogent-%s.tgz\\\\n' % (http_base, self.Version)\\n else:\\n print \\\"No version string found in %s\\\" % filename\\n return (lines, found_version_line)\",\n \"def update(self, revision, data, user=None, save=True):\\n version = super(FigshareFile, self).update(None, data, user=user, save=save)\\n\\n # Draft files are not renderable\\n if data['extra']['status'] == 'drafts':\\n return (version, u\\\"\\\"\\\"\\n <style>\\n .file-download{{display: none;}}\\n .file-share{{display: none;}}\\n </style>\\n <div class=\\\"alert alert-info\\\" role=\\\"alert\\\">\\n The file \\\"{name}\\\" is still a draft on figshare. <br>\\n To view it on the OSF\\n <a href=\\\"https://support.figshare.com/support/solutions\\\">publish</a>\\n it on figshare.\\n </div>\\n \\\"\\\"\\\".format(name=markupsafe.escape(self.name)))\\n\\n return version\",\n \"def update():\",\n \"def update():\",\n \"def version_updater(logging_level=logging.WARNING):\\n # connect to db\\n do_db_setup()\\n\\n # set Qt lib\\n set_qt_lib()\\n\\n from anima.ui import version_updater, models\\n from anima.env import mayaEnv\\n reload(mayaEnv)\\n reload(version_updater)\\n reload(models)\\n m = Maya()\\n import pymel\\n m.name = \\\"Maya\\\" + str(pymel.versions.current())[0:4]\\n\\n logger.setLevel(logging_level)\\n\\n # generate a reference_resolution\\n version_updater.UI(environment=m)\",\n \"def upgrade(self):\\n import re\\n currversion = getattr(self, '_version', 0)\\n if currversion == self.version:\\n return 'already running latest version (%s)' % currversion\\n # a slightly modified version of propertyitems\\n propertyitems = map(lambda i,s=self: (i['id'],getattr(s, i['id'], None)),\\n self._properties)\\n for propertyitem in propertyitems:\\n if not propertyitem[1]:\\n pass\\n elif propertyitem[0] in ('stylesheet_paths', 'xslt_paths'):\\n self.transform_paths = propertyitem[1]\\n elif propertyitem[0][:4] == 'xslt':\\n transform = getattr(self, propertyitem[1], None)\\n if not transform:\\n for obj in self._get_path_objs(self.transform_paths):\\n transform = getattr(obj, propertyitem[1], None)\\n if transform:\\n break\\n if transform:\\n mtype = re.sub('template|renderer', '', transform.meta_type.lower()).strip() \\n setattr(self,\\n 'transform_%s' % propertyitem[0][5:],\\n '%s (%s)' % (propertyitem[1], mtype))\\n elif propertyitem[0][:10] == 'stylesheet':\\n setattr(self, 'template_%s' % propertyitem[0][11:], propertyitem[1])\\n else:\\n continue\\n try:\\n delattr(self, propertyitem[0])\\n except:\\n pass\\n try:\\n self._delProperty(propertyitem[0])\\n except:\\n pass\\n\\n self._properties = self._def_properties\\n self.setup_transformProperties()\\n self.setup_schemaProperties()\\n \\n self._version = self.version\\n \\n return 'upgraded %s to version %s from version %s' % (self.getId(),\\n self._version,\\n currversion)\",\n \"def _update_aea_version(cls, agent_config: AgentConfig):\\n for item in agent_config.package_dependencies:\\n type_ = item.component_type.to_plural()\\n dotted_path = f\\\"vendor.{item.author}.{type_}.{item.name}.aea_version\\\"\\n path = os.path.join(\\\"vendor\\\", item.author, type_, item.name)\\n cls.nested_set_config(dotted_path, cls.AEA_VERSION_SPECIFIER)\\n cls.run_cli_command(\\\"fingerprint\\\", \\\"by-path\\\", path, cwd=cls._get_cwd())\",\n \"def _load_meta(self, db, metadata, source_name) -> None:\\n db.metadata.put_item(Item={\\n 'src_name': source_name,\\n 'data_license': metadata.data_license,\\n 'data_license_url': metadata.data_license_url,\\n 'version': metadata.version,\\n 'data_url': metadata.data_url,\\n 'rdp_url': metadata.rdp_url,\\n 'data_license_attributes': metadata.data_license_attributes,\\n 'genome_assemblies': metadata.genome_assemblies\\n })\",\n \"def _writeVersionToMetadata(config):\\n if not config.has_section(GenericMetadata.ECOHYDROLIB_SECION):\\n config.add_section(GenericMetadata.ECOHYDROLIB_SECION)\\n \\n if not config.has_option(GenericMetadata.ECOHYDROLIB_SECION, \\\\\\n GenericMetadata.VERSION_KEY):\\n config.set(GenericMetadata.ECOHYDROLIB_SECION, \\\\\\n GenericMetadata.VERSION_KEY, GenericMetadata._ecohydrolibVersion)\\n return\\n \\n metadataVersion = config.get(GenericMetadata.ECOHYDROLIB_SECION, \\\\\\n GenericMetadata.VERSION_KEY)\\n if metadataVersion != GenericMetadata._ecohydrolibVersion:\\n raise MetadataVersionError(metadataVersion)\",\n \"def export(uri: str, version: str, date: str, file: str, update: bool = False):\\n con = cx_Oracle.connect(uri)\\n cur = con.cursor()\\n\\n cur.execute(\\\"SELECT COUNT(*) FROM INTERPRO.ENTRY WHERE CHECKED = 'Y'\\\")\\n num_interpro_entries, = cur.fetchone()\\n\\n cur.execute(\\\"SELECT VERSION FROM INTERPRO.DB_VERSION WHERE DBCODE = 'I'\\\")\\n prod_version, = cur.fetchone()\\n\\n cur.execute(\\\"SELECT DISTINCT DBCODE FROM INTERPRO.METHOD\\\")\\n signature_dbcodes = {dbcode for dbcode, in cur}\\n\\n cur.execute(\\\"SELECT DISTINCT DBCODE FROM INTERPRO.FEATURE_METHOD\\\")\\n feature_dbcodes = {dbcode for dbcode, in cur}\\n\\n if prod_version == version:\\n # DB_VERSION is already up-to-date\\n use_db_version = True\\n elif update:\\n # DB_VERSION is outdated, but will be up-to-date\\n use_db_version = True\\n cur.execute(\\n \\\"\\\"\\\"\\n UPDATE INTERPRO.DB_VERSION\\n SET VERSION = :1,\\n FILE_DATE = :2,\\n ENTRY_COUNT = :3\\n WHERE DBCODE = 'I'\\n \\\"\\\"\\\", (version, datetime.strptime(date, \\\"%Y-%m-%d\\\"),\\n num_interpro_entries)\\n )\\n con.commit()\\n else:\\n # DB_VERSION is outdated and will stay outdated\\n # This run is a test done on the production database (SRSLY?!!111)\\n use_db_version = False\\n\\n # Get all releases\\n all_releases = {}\\n cur.execute(\\n \\\"\\\"\\\"\\n SELECT DBCODE, VERSION, FILE_DATE\\n FROM INTERPRO.DB_VERSION_AUDIT\\n ORDER BY TIMESTAMP DESC\\n \\\"\\\"\\\"\\n )\\n\\n for dbcode, dbversion, dbdate in cur:\\n try:\\n all_releases[dbcode].append((dbversion, dbdate))\\n except KeyError:\\n all_releases[dbcode] = [(dbversion, dbdate)]\\n\\n cur.execute(\\n \\\"\\\"\\\"\\n SELECT\\n DB.DBCODE, DB.DBSHORT, DB.DBNAME, DB.DESCRIPTION, V.VERSION, \\n V.FILE_DATE, V.ENTRY_COUNT\\n FROM INTERPRO.CV_DATABASE DB\\n LEFT OUTER JOIN INTERPRO.DB_VERSION V ON DB.DBCODE = V.DBCODE\\n \\\"\\\"\\\"\\n )\\n\\n databases = {}\\n for rec in cur:\\n dbcode = rec[0]\\n short_name = rec[1]\\n name = rec[2]\\n description = rec[3]\\n release_version = rec[4]\\n release_date = rec[5]\\n num_entries = rec[6]\\n prev_release_version = prev_release_date = None\\n\\n try:\\n prev_releases = all_releases[dbcode]\\n except KeyError:\\n pass\\n else:\\n for dbversion, dbdate in prev_releases:\\n if dbversion != release_version:\\n prev_release_version = dbversion\\n prev_release_date = dbdate\\n break\\n\\n if dbcode == 'I' or dbcode in signature_dbcodes:\\n db_type = \\\"entry\\\"\\n elif dbcode in feature_dbcodes:\\n db_type = \\\"feature\\\"\\n elif dbcode in ['S', 'T', 'u']:\\n if dbcode == 'S':\\n short_name = \\\"reviewed\\\"\\n elif dbcode == 'T':\\n short_name = \\\"unreviewed\\\"\\n\\n db_type = \\\"protein\\\"\\n else:\\n db_type = \\\"other\\\"\\n\\n if dbcode == 'I' and not use_db_version:\\n # DB_VERSION is outdated:\\n # it contains info for the live release (soon to be 'previous')\\n num_entries = num_interpro_entries\\n prev_release_version = release_version\\n prev_release_date = release_date\\n release_version = version\\n release_date = datetime.strptime(date, \\\"%Y-%m-%d\\\")\\n\\n databases[short_name] = {\\n \\\"name\\\": name,\\n \\\"description\\\": description,\\n \\\"type\\\": db_type,\\n \\\"entries\\\": num_entries,\\n \\\"release\\\": {\\n \\\"version\\\": release_version,\\n \\\"date\\\": release_date,\\n },\\n \\\"previous_release\\\": {\\n \\\"version\\\": prev_release_version,\\n \\\"date\\\": prev_release_date,\\n },\\n }\\n\\n cur.close()\\n con.close()\\n\\n with open(file, \\\"wb\\\") as fh:\\n pickle.dump(databases, fh)\",\n \"def updateLastCommitFile(self):\\n f = open(self.last_released, 'w')\\n f.write(self.new_rev)\\n f.close()\",\n \"def update(self, filename):\\n\\n cam_info, cam_header = self.db.read_data(filename, config.CAM_HEADER, 'tuple', None)\\n\\n with open('updated_camera_list.csv', mode='w') as csv_file:\\n cam_writer = csv.writer(csv_file, delimiter=',')\\n cam_writer.writerow(cam_header)\\n\\n for info in cam_info:\\n cam = Camera(camera_id=info[0], ip_address=info[8], image_path=info[10], video_path=info[11])\\n cam.capture = cv2.VideoCapture(cam.video_path)\\n cam.get_image()\\n\\n if cam.newImage is not None:\\n img_w = cam.newImage.shape[0]\\n img_h = cam.newImage.shape[1]\\n\\n cam_writer.writerow([info[0], info[1], info[2], info[3], info[4], info[5],\\n img_w, img_h, info[8], info[9], info[10], info[11]])\\n cam.capture.release()\\n print('updated camera: {}'.format(info[0]))\\n\\n csv_file.close()\",\n \"def update(self):\\n current = LazyRegister(self.db)\\n current.render()\\n cur = self.db.cursor()\\n for table in self.tables:\\n if table in current.tables:\\n additions, removals = current.tables[table].migrate(self.tables[table])\\n for addition in additions:\\n cur.execute(\\\"\\\"\\\"ALTER TABLE %s ADD COLUMN %s\\\"\\\"\\\" % (\\n table, addition[1].get_sql()\\n ))\\n print('Added column: ', addition[0])\\n for removal in removals:\\n #cur.execute(\\\"\\\"\\\"ALTER TABLE %s DROP COLUMN %s\\\"\\\"\\\" % (\\n # table, removal[0]\\n #))\\n #print('Removed column: ', removal[0])\\n print('Did not removed column: ', removal[0])\\n else:\\n schema = self.tables[table].get_create_table_sql()\\n cur.execute(schema)\\n print('Added table %s' % table)\",\n \"def update_version(self, version):\\n self.version = CPE.escape_for_cpe23_fs(version)\",\n \"def update(self) -> None:\\n ...\",\n \"def test_updateVersion(self):\\n project = self.makeProject(Version(\\\"bar\\\", 2, 1, 0))\\n newVersion = Version(\\\"bar\\\", 3, 2, 9)\\n project.updateVersion(newVersion)\\n self.assertEquals(project.getVersion(), newVersion)\\n self.assertEquals(\\n project.directory.child(\\\"topfiles\\\").child(\\\"README\\\").getContent(),\\n \\\"3.2.9\\\")\",\n \"def update(self):\\n self.getDbRecord().update()\",\n \"def save(self, **kwargs):\\n super(ProjectCurrentSerializer, self).save(**kwargs)\\n \\n if hasattr(self, 'uploaded_pot_file'):\\n previous_version = self.object.get_current_version()\\n current_version = create_new_version(self.object, previous_version.version+1, self.uploaded_pot_file)\\n update_catalogs(self.object, previous_version, current_version)\\n self.object.save()\\n \\n return self.object\",\n \"def _update(self, desc_file, phen_file): \\n \\n self.logger.msg1(\\\"Update summary\\\")\\n \\n stamp = self.config.stamp\\n summary = update_model_descriptions(self.dbpath, desc_file, stamp)\\n for k, v in summary.items():\\n kstr = k.replace(\\\"_\\\", \\\" \\\") + \\\": \\\"\\n self.logger.msg2(\\\"models with \\\" + kstr + str(len(v)))\\n \\n summary = add_model_phenotypes(self.dbpath, phen_file, stamp)\\n for k, v in summary.items():\\n kstr = k.replace(\\\"_\\\", \\\" \\\") + \\\": \\\"\\n self.logger.msg2(\\\"models with \\\" + kstr + str(len(v)))\\n \\n return summary\",\n \"def _update_metadata(self, container_name: Union[str, list] = None, multibeam_file_list: list = None, crs: str = None,\\n vertical_reference: str = None):\\n\\n if container_name:\\n if isinstance(container_name, str): # only one converted data instance provided\\n container_name = [container_name]\\n if isinstance(multibeam_file_list[0], str): # only one converted data instance provided\\n multibeam_file_list = [multibeam_file_list]\\n for contname, mfiles in zip(container_name, multibeam_file_list):\\n if mfiles:\\n if contname in self.sources and (set(mfiles) & set(self.sources[contname])):\\n raise ValueError('QuadManager: Found some of these new lines in the existing lines metadata for container: {}'.format(contname))\\n elif contname in self.sources:\\n self.sources[contname].extend(mfiles)\\n else:\\n self.sources[contname] = mfiles\\n else:\\n self.sources[contname] = ['unknown']\\n if self.crs and (self.crs != crs):\\n raise ValueError('QuadManager: Found existing coordinate system {}, new coordinate system {} must match'.format(self.crs,\\n crs))\\n if self.vertical_reference and (self.vertical_reference != vertical_reference):\\n raise ValueError('QuadManager: Found existing vertical reference {}, new vertical reference {} must match'.format(self.vertical_reference,\\n vertical_reference))\\n self.crs = crs\\n self.vertical_reference = vertical_reference\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6678135","0.6618736","0.6559671","0.65394354","0.6385025","0.6356499","0.627543","0.61180854","0.6045036","0.60443014","0.6028333","0.601643","0.59997","0.59620434","0.5956937","0.59510905","0.5949729","0.59445655","0.59294254","0.5921162","0.591343","0.591343","0.5856004","0.583472","0.5829332","0.5827197","0.5815541","0.5811213","0.5808108","0.5790052","0.57821804","0.57733595","0.57100314","0.57032895","0.56958866","0.56801844","0.5679093","0.5661654","0.56605244","0.5660172","0.56571794","0.56417567","0.56342983","0.56323","0.562887","0.562431","0.56111836","0.5596479","0.55924803","0.55853564","0.5580677","0.55744374","0.55723816","0.55691785","0.5564526","0.5560757","0.5554553","0.5535151","0.55300367","0.5523094","0.5510665","0.54979765","0.5484611","0.5483453","0.5482497","0.5472282","0.54714954","0.5462273","0.5460722","0.5460118","0.5459712","0.54488266","0.5448818","0.54386497","0.54347485","0.5432008","0.54315937","0.54281557","0.5410799","0.53956676","0.5391009","0.5390993","0.53908676","0.53908676","0.5388453","0.538384","0.53838104","0.53816724","0.53803706","0.53780013","0.53772634","0.53667134","0.53661287","0.53649896","0.5363351","0.53551507","0.53419673","0.53356624","0.53304225","0.5316937"],"string":"[\n \"0.6678135\",\n \"0.6618736\",\n \"0.6559671\",\n \"0.65394354\",\n \"0.6385025\",\n \"0.6356499\",\n \"0.627543\",\n \"0.61180854\",\n \"0.6045036\",\n \"0.60443014\",\n \"0.6028333\",\n \"0.601643\",\n \"0.59997\",\n \"0.59620434\",\n \"0.5956937\",\n \"0.59510905\",\n \"0.5949729\",\n \"0.59445655\",\n \"0.59294254\",\n \"0.5921162\",\n \"0.591343\",\n \"0.591343\",\n \"0.5856004\",\n \"0.583472\",\n \"0.5829332\",\n \"0.5827197\",\n \"0.5815541\",\n \"0.5811213\",\n \"0.5808108\",\n \"0.5790052\",\n \"0.57821804\",\n \"0.57733595\",\n \"0.57100314\",\n \"0.57032895\",\n \"0.56958866\",\n \"0.56801844\",\n \"0.5679093\",\n \"0.5661654\",\n \"0.56605244\",\n \"0.5660172\",\n \"0.56571794\",\n \"0.56417567\",\n \"0.56342983\",\n \"0.56323\",\n \"0.562887\",\n \"0.562431\",\n \"0.56111836\",\n \"0.5596479\",\n \"0.55924803\",\n \"0.55853564\",\n \"0.5580677\",\n \"0.55744374\",\n \"0.55723816\",\n \"0.55691785\",\n \"0.5564526\",\n \"0.5560757\",\n \"0.5554553\",\n \"0.5535151\",\n \"0.55300367\",\n \"0.5523094\",\n \"0.5510665\",\n \"0.54979765\",\n \"0.5484611\",\n \"0.5483453\",\n \"0.5482497\",\n \"0.5472282\",\n \"0.54714954\",\n \"0.5462273\",\n \"0.5460722\",\n \"0.5460118\",\n \"0.5459712\",\n \"0.54488266\",\n \"0.5448818\",\n \"0.54386497\",\n \"0.54347485\",\n \"0.5432008\",\n \"0.54315937\",\n \"0.54281557\",\n \"0.5410799\",\n \"0.53956676\",\n \"0.5391009\",\n \"0.5390993\",\n \"0.53908676\",\n \"0.53908676\",\n \"0.5388453\",\n \"0.538384\",\n \"0.53838104\",\n \"0.53816724\",\n \"0.53803706\",\n \"0.53780013\",\n \"0.53772634\",\n \"0.53667134\",\n \"0.53661287\",\n \"0.53649896\",\n \"0.5363351\",\n \"0.53551507\",\n \"0.53419673\",\n \"0.53356624\",\n \"0.53304225\",\n \"0.5316937\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6078741"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":17447,"cells":{"query":{"kind":"string","value":"Compare the current scene references versions with metadata and update as needed"},"document":{"kind":"string","value":"def sceneRefCheck(silent=False):\n uptodate = True\n logger.debug('init sceneChecking...')\n currentProject = database.getCurrentProject()\n projName = pm.fileInfo.get('projectName')\n\n if currentProject != projName:\n logger.error('This file is from a project different from the current project')\n return\n\n item = Item(fromScene=True) # get current scene metadata\n\n\n # compare references and metadata and create lists of references to add, delete, update and replace\n logger.debug('creating lists of changes...')\n refOnSceneList = pm.getReferences()\n toDelete = [x for x in refOnSceneList if x not in item.components]\n toAdd = [x for x in item.components if x not in refOnSceneList and x != 'cam']\n toReplace = [x for x in item.components if item.components[x]['task'] != item.components[x]['proxyMode']]\n refToCheckUpdate = [x for x in refOnSceneList if x not in toDelete and x not in toReplace]\n toUpdate = {}\n\n # create the list of references to update depending on the assemble mode\n logger.debug('check update...')\n for ns in refToCheckUpdate:\n logger.info('updating ns:%s' % ns)\n if item.components[ns]['assembleMode'] == 'camera':\n continue\n\n if item.components[ns]['assembleMode'] == 'reference':\n logger.debug('reference')\n start_time = time.time()\n component = ReferenceComponent(ns, item.components[ns], parent=item)\n toUpdate[ns] = component.updateVersion(refOnSceneList[ns])\n elapsed_time = time.time () - start_time\n logger.debug ('%s Total info' % elapsed_time)\n\n if item.components[ns]['assembleMode'] == 'xlo':\n component = XloComponent(ns, item.components[ns], parent=item)\n toUpdate[ns] = component.updateVersion(refOnSceneList[ns])\n\n if item.components[ns]['assembleMode'] == 'cache':\n cache = CacheComponent(ns, item.components[ns], parent=item)\n toUpdate[ns] = cache.updateVersion(refOnSceneList[ns])\n\n # If not in silent mode, show dialogs to the user choose which references should be processed\n logger.debug('prompt if needed')\n if not silent:\n if toDelete:\n uptodate = False\n toDelete = pm.layoutDialog(ui=lambda: refCheckPrompt(toDelete, 'delete')).split(',')\n\n if toAdd:\n uptodate = False\n toAdd = pm.layoutDialog(ui=lambda: refCheckPrompt(toAdd, 'add')).split(',')\n\n if toReplace:\n uptodate = False\n toReplace = pm.layoutDialog(ui=lambda: refCheckPrompt(toReplace, 'replace')).split(',')\n\n upDateList = [x for x, y in toUpdate.iteritems() if y]\n if upDateList:\n uptodate = False\n upDateList = pm.layoutDialog(ui=lambda: refCheckPrompt(upDateList, 'update')).split(',')\n toUpdate = {x: y for x, y in toUpdate.iteritems() if x in upDateList}\n else:\n toUpdate = {}\n\n if uptodate:\n pm.confirmDialog(title='Scene Check', ma='center',\n message='Versions ok!',\n button=['OK'], defaultButton='OK', dismissString='OK')\n\n\n logger.debug('processing...')\n # Do the processing\n # delete\n logger.debug('toDelete:%s' % toDelete)\n for ns in toDelete:\n refOnSceneList[ns].remove()\n\n # add\n logger.debug('toAdd:%s' % toAdd)\n for ns in toAdd:\n if item.components[ns]['assembleMode'] == 'camera':\n continue\n\n if item.components[ns]['assembleMode'] == 'reference':\n component = ReferenceComponent(ns, item.components[ns], parent=item)\n component.addToScene()\n\n elif item.components[ns]['assembleMode'] == 'xlo':\n component = XloComponent(ns, item.components[ns], parent=item)\n component.addToScene()\n\n cache = CacheComponent(ns, item.components[ns], parent=item)\n cache.importCache()\n\n elif item.components[ns]['assembleMode'] == 'cache':\n cache = CacheComponent(ns, item.components[ns], parent=item)\n cache.addToScene()\n\n #update versions\n for ns, versions in toUpdate.iteritems():\n if item.components[ns]['assembleMode'] == 'camera':\n continue\n\n if item.components[ns]['assembleMode'] == 'reference':\n component = ReferenceComponent(ns, item.components[ns], parent=item)\n componentPath = component.getPublishPath()\n refOnSceneList[ns].replaceWith(componentPath)\n\n if item.components[ns]['assembleMode'] == 'xlo':\n if 'ver' in versions:\n component = XloComponent(ns, item.components[ns], parent=item)\n componentPath = component.getPublishPath()\n refOnSceneList[ns].replaceWith(componentPath)\n\n if 'cacheVer' in versions:\n #todo check if need to delete old cache node\n cache = CacheComponent(ns, item.components[ns], parent=item)\n cache.importCache()\n\n if item.components[ns]['assembleMode'] == 'cache':\n component = CacheComponent(ns, item.components[ns], parent=item)\n componentPath = component.getPublishPath()\n refOnSceneList[ns].replaceWith(componentPath)\n\n # Replace\n for ns in toReplace:\n if item.components[ns]['assembleMode'] == 'reference':\n oldProxyMode = item.components[ns]['task']\n\n item.components[ns]['task'] = item.components[ns]['proxyMode']\n component = ReferenceComponent(ns, item.components[ns], parent=item)\n componentItem = component.getItem()\n if componentItem.publishVer > 0:\n refOnSceneList[ns].replaceWith(component.getPublishPath())\n else:\n item.components[ns]['task'] = oldProxyMode\n\n item.putDataToDB()\n\n logger.info('done sceneChecking!')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def checkVersions():\n item = Item(fromScene=True)\n\n for ns, componentMData in item.components.iteritems():\n if ns == 'cam':\n # todo tratar versoes da camera\n continue\n\n if componentMData['assembleMode'] == 'reference':\n refComponent = ReferenceComponent(ns, componentMData, parent=item)\n refComponent.checkDBForNewVersion()\n\n elif componentMData['assembleMode'] == 'xlo':\n xloComponent = XloComponent(ns, componentMData, parent=item)\n xloComponent.checkDBForNewVersion()\n xloComponent.checkDBForNewCacheVersion()\n\n elif componentMData['assembleMode'] == 'cache':\n cacheComponent = CacheComponent(ns, componentMData, parent=item)\n cacheComponent.checkDBForNewVersion()\n\n item.putDataToDB()","def update_versions(self, reference_resolution):\n raise NotImplementedError(\"update_versions is not implemented\")","def set_media_versions(self):\n\n # access to .multimedia_map is slow\n previous_version = self._get_version_comparison_build()\n prev_multimedia_map = previous_version.multimedia_map if previous_version else {}\n\n for path, map_item in self.multimedia_map.items():\n prev_map_item = prev_multimedia_map.get(path, None)\n if prev_map_item and prev_map_item.unique_id:\n # Re-use the id so CommCare knows it's the same resource\n map_item.unique_id = prev_map_item.unique_id\n if (prev_map_item and prev_map_item.version\n and prev_map_item.multimedia_id == map_item.multimedia_id):\n map_item.version = prev_map_item.version\n else:\n map_item.version = self.version","def check_referenced_versions(self, pdm=None):\n if not pdm:\n pdm = ProgressManagerFactory.get_progress_manager()\n\n caller = pdm.register(\n 3, \"%s.check_referenced_versions() prepare data\" % self.__class__.__name__\n )\n\n # deeply get which file is referencing which other files\n self.deep_version_inputs_update()\n if caller:\n caller.step()\n\n from anima.dcc import empty_reference_resolution\n\n reference_resolution = empty_reference_resolution(\n root=self.get_referenced_versions()\n )\n\n if caller:\n caller.step()\n\n # reverse walk in DFS\n dfs_version_references = []\n\n version = self.get_current_version()\n if not version:\n return reference_resolution\n\n for v in version.walk_inputs():\n dfs_version_references.append(v)\n\n if caller:\n caller.step()\n\n # pop the first element which is the current scene\n dfs_version_references.pop(0)\n\n caller.end_progress()\n\n # register a new caller\n caller = pdm.register(\n len(dfs_version_references),\n \"%s.check_referenced_versions()\" % self.__class__.__name__,\n )\n\n # iterate back in the list\n for v in reversed(dfs_version_references):\n # check inputs first\n to_be_updated_list = []\n for ref_v in v.inputs:\n if not ref_v.is_latest_published_version():\n to_be_updated_list.append(ref_v)\n\n if to_be_updated_list:\n action = \"create\"\n # check if there is a new published version of this version\n # that is using all the updated versions of the references\n latest_published_version = v.latest_published_version\n if latest_published_version and not v.is_latest_published_version():\n # so there is a new published version\n # check if its children needs any update\n # and the updated child versions are already\n # referenced to the this published version\n if all(\n [\n ref_v.latest_published_version\n in latest_published_version.inputs\n for ref_v in to_be_updated_list\n ]\n ):\n # so all new versions are referenced to this published\n # version, just update to this latest published version\n action = \"update\"\n else:\n # not all references are in the inputs\n # so we need to create a new version as usual\n # and update the references to the latest versions\n action = \"create\"\n else:\n # nothing needs to be updated,\n # so check if this version has a new version,\n # also there could be no reference under this referenced\n # version\n if v.is_latest_published_version():\n # do nothing\n action = \"leave\"\n else:\n # update to latest published version\n action = \"update\"\n\n # before setting the action check all the inputs in\n # resolution_dictionary, if any of them are update, or create\n # then set this one to 'create'\n if any(\n rev_v in reference_resolution[\"update\"]\n or rev_v in reference_resolution[\"create\"]\n for rev_v in v.inputs\n ):\n action = \"create\"\n\n # so append this v to the related action list\n reference_resolution[action].append(v)\n\n # from stalker import Version\n # assert isinstance(v, Version)\n caller.step(message=v.nice_name)\n\n caller.end_progress()\n\n return reference_resolution","def check_if_previous_version_references(progress_controller=None):\n if progress_controller is None:\n progress_controller = ProgressControllerBase()\n\n from anima.dcc.mayaEnv import Maya\n\n m = Maya()\n ver = m.get_current_version()\n\n if ver is None:\n progress_controller.complete()\n return\n\n same_version_references = []\n all_references = pm.listReferences()\n progress_controller.maximum = len(all_references)\n for ref in all_references: # check only 1st level references\n ref_version = m.get_version_from_full_path(ref.path)\n if ref_version:\n if ref_version.task == ver.task and ref_version.take_name == ver.take_name:\n same_version_references.append(ref)\n progress_controller.increment()\n\n progress_controller.complete()\n if len(same_version_references):\n print(\"The following nodes are references to an older version of this \" \"scene\")\n print(\"\\n\".join(map(lambda x: x.refNode.name(), same_version_references)))\n raise PublishError(\n \"The current scene contains a <b>reference</b> to a<br>\"\n \"<b>previous version</b> of itself.<br><br>\"\n \"Please remove it!!!\"\n )","def saveVersion(self, makeReference=True, versionNotes=\"\", sceneFormat=\"mb\", *args, **kwargs):\n logger.debug(\"Func: saveVersion\")\n\n\n\n now = datetime.datetime.now().strftime(\"%d/%m/%Y-%H:%M\")\n completeNote = \"[%s] on %s\\n%s\\n\" % (self.currentUser, now, versionNotes)\n\n sceneName = self.getSceneFile()\n if not sceneName:\n msg = \"This is not a base scene (Untitled)\"\n self._exception(360, msg)\n return -1, msg\n\n sceneInfo = self.getOpenSceneInfo()\n\n if sceneInfo: ## getCurrentJson returns None if the resolved json path is missing\n jsonFile = sceneInfo[\"jsonFile\"]\n jsonInfo = self._loadJson(jsonFile)\n\n currentVersion = len(jsonInfo[\"Versions\"]) + 1\n sceneName = \"{0}_{1}_{2}_v{3}\".format(jsonInfo[\"Name\"], jsonInfo[\"Category\"], self._usersDict[self.currentUser],\n str(currentVersion).zfill(3))\n relSceneFile = os.path.join(jsonInfo[\"Path\"], \"{0}.{1}\".format(sceneName, sceneFormat))\n\n sceneFile = os.path.join(sceneInfo[\"projectPath\"], relSceneFile)\n\n # killTurtle()\n # TODO // cmds?\n # pm.saveAs(sceneFile)\n\n nuke.scriptSaveAs(sceneFile)\n\n thumbPath = self.createThumbnail(dbPath=jsonFile, versionInt=currentVersion)\n\n jsonInfo[\"Versions\"].append(\n # PATH => Notes => User Initials => Machine ID => Playblast => Thumbnail\n # TODO : ref => Dict\n {\"RelativePath\": relSceneFile,\n \"Note\": completeNote,\n \"User\": self._usersDict[self.currentUser],\n \"Workstation\": socket.gethostname(),\n \"Preview\": {},\n \"Thumb\": thumbPath,\n \"Ranges\": self._getTimelineRanges()\n }\n )\n\n if makeReference:\n referenceName = \"{0}_{1}_forReference\".format(jsonInfo[\"Name\"], jsonInfo[\"Category\"])\n relReferenceFile = os.path.join(jsonInfo[\"Path\"], \"{0}.{1}\".format(referenceName, sceneFormat))\n referenceFile = os.path.join(sceneInfo[\"projectPath\"], relReferenceFile)\n\n shutil.copyfile(sceneFile, referenceFile)\n jsonInfo[\"ReferenceFile\"] = relReferenceFile\n jsonInfo[\"ReferencedVersion\"] = currentVersion\n self._dumpJson(jsonInfo, jsonFile)\n else:\n msg = \"This is not a base scene (Json file cannot be found)\"\n self._exception(360, msg)\n return -1, msg\n return jsonInfo","def version_updater(logging_level=logging.WARNING):\n # connect to db\n do_db_setup()\n\n # set Qt lib\n set_qt_lib()\n\n from anima.ui import version_updater, models\n from anima.env import mayaEnv\n reload(mayaEnv)\n reload(version_updater)\n reload(models)\n m = Maya()\n import pymel\n m.name = \"Maya\" + str(pymel.versions.current())[0:4]\n\n logger.setLevel(logging_level)\n\n # generate a reference_resolution\n version_updater.UI(environment=m)","def populateSceneRefs(*args):\n pi.referenceDictionary = {}\n cmds.textScrollList(widgets[\"shotAssListTSL\"], e=True, ra=True)\n\n #get reference paths\n refs = cmds.file(q=True, r=True)\n\n buff = []\n # loaded = []\n for ref in refs:\n #get the associated namespace\n ns = cmds.file(ref, q=True, ns=True)\n pi.referenceDictionary[ns] = ref\n\n # put files in buffer list to sort\n for g in pi.referenceDictionary.keys():\n buff.append(g)\n buff.sort()\n\n # now put the sorted namespaces in the list\n for b in buff:\n cmds.textScrollList(widgets[\"shotAssListTSL\"], e=True, append=b, dcc = selectRefs)\n\n # if ref is deferred(not loaded), change it's font\n for ref in refs:\n if cmds.file(ref, q=True, deferReference=True):\n ns = cmds.file(ref, q=True, ns=True) # get the namespace in order to get the item name\n cmds.textScrollList(widgets[\"shotAssListTSL\"], e=True, selectItem=ns) # sel the item in order to query it\n index = cmds.textScrollList(widgets[\"shotAssListTSL\"], q=True, selectIndexedItem=True)[0] # query the index of sel\n cmds.textScrollList(widgets[\"shotAssListTSL\"], e=True, lineFont = [index, \"obliqueLabelFont\"])\n cmds.textScrollList(widgets[\"shotAssListTSL\"], e=True, deselectAll=True)\n\n # if we're in a lgt file, look through current refs and for each one of type \"anm\", check the frame rates, etc. and give option to change\n curr = paths.PathManager(cmds.file(q=True, sn=True))\n if curr.shotType == \"lgt\":\n for ref in refs:\n p=paths.PathManager(ref)\n if p.shotType == \"anm\":\n dict = cFuncs.getFileFrameInfo(cFuncs.fixPath(ref))\n csi.compareSceneInfo(dict)","def test_master_versions(self):\n m = self.d.master(4242)\n r = self.d.release(79)\n v = m.versions\n\n self.assertEqual(len(v), 2)\n self.assertTrue(r in v)\n self.assertEqual(r.master, m)\n\n r2 = self.d.release(3329867)\n self.assertTrue(r2.master is None)","def compareVersions(self):\n logger.debug(\"Func: compareVersions\")\n\n cMajorV = nuke.NUKE_VERSION_MAJOR\n cMinorV = nuke.NUKE_VERSION_MINOR\n currentVersion = float(\"{0}.{1}\".format(cMajorV, cMinorV))\n\n dbMajorV = self._currentSceneInfo[\"NukeVersion\"][0]\n dbMinorV = self._currentSceneInfo[\"NukeVersion\"][1]\n databaseVersion = float(\"{0}.{1}\".format(dbMajorV, dbMinorV))\n\n messageList = []\n\n\n if currentVersion == databaseVersion:\n pass\n\n if currentVersion < databaseVersion: # version compare\n message = \"Base Scene is created with a HIGHER Nuke version ({0}). Are you sure you want to continue?\".format(databaseVersion)\n messageList.append(message)\n\n if currentVersion > databaseVersion:\n message = \"Base Scene is created with a LOWER Nuke version ({0}). Are you sure you want to continue?\".format(databaseVersion)\n messageList.append(message)\n\n message=\"\"\n for x in messageList:\n message = message + \"\\n\" + str(x)\n\n if messageList == []:\n return 0, message\n else:\n return -1, message","def test_ls_returns_sorted_versions():\n with pipeline.fixture(assets=[\"Asset1\"], subsets=[\"animRig\"], versions=1):\n for asset in pipeline.ls():\n previous_version = 0\n for subset in asset[\"subsets\"]:\n for version in subset[\"versions\"]:\n version = version[\"version\"]\n assert version > previous_version\n previous_version = version","def do_snapshot(\n self, force: bool = False\n ) -> Tuple[bool, Dict[str, MetaFile]]:\n\n # Snapshot update is needed if\n # * any targets files are not yet in snapshot or\n # * any targets version is incorrect\n update_version = force\n removed: Dict[str, MetaFile] = {}\n\n with self.edit_snapshot() as snapshot:\n for keyname, new_meta in self.targets_infos.items():\n if keyname not in snapshot.meta:\n update_version = True\n snapshot.meta[keyname] = deepcopy(new_meta)\n continue\n\n old_meta = snapshot.meta[keyname]\n if new_meta.version < old_meta.version:\n raise ValueError(f\"{keyname} version rollback\")\n if new_meta.version > old_meta.version:\n update_version = True\n snapshot.meta[keyname] = deepcopy(new_meta)\n removed[keyname] = old_meta\n\n if not update_version:\n # prevent edit_snapshot() from storing a new version\n raise AbortEdit(\"Skip snapshot: No targets version changes\")\n\n if not update_version:\n # this is reachable as edit_snapshot() handles AbortEdit\n logger.debug(\"Snapshot update not needed\") # type: ignore[unreachable]\n else:\n logger.debug(\"Snapshot v%d\", snapshot.version)\n\n return update_version, removed","def _update_metadata_if_changed(self, metadata_role, referenced_metadata='release'):\n \n uncompressed_metadata_filename = metadata_role + '.txt'\n\n # Ensure the referenced metadata has been loaded. The 'root' role may be\n # updated without having 'release' available. \n if referenced_metadata not in self.metadata['current']:\n message = 'Cannot update '+repr(metadata_role)+' because ' \\\n +referenced_metadata+' is missing.'\n raise tuf.RepositoryError(message)\n # The referenced metadata has been loaded. Extract the new\n # fileinfo for 'metadata_role' from it. \n else:\n message = repr(metadata_role)+' referenced in '+\\\n repr(referenced_metadata)+'. '+repr(metadata_role)+' may be updated.'\n logger.debug(message)\n \n # There might be a compressed version of 'release.txt' or Targets\n # metadata available for download. Check the 'meta' field of\n # 'referenced_metadata' to see if it is listed when 'metadata_role'\n # is 'release'. The full rolename for delegated Targets metadata\n # must begin with 'targets/'. The Release role lists all the Targets\n # metadata available on the repository, including any that may be in\n # compressed form.\n compression = None\n\n # Extract the fileinfo of the uncompressed version of 'metadata_role'.\n uncompressed_fileinfo = self.metadata['current'][referenced_metadata] \\\n ['meta'] \\\n [uncompressed_metadata_filename]\n\n # Check for availability of compressed versions of 'release.txt',\n # 'targets.txt', and delegated Targets, which also start with 'targets'.\n # For 'targets.txt' and delegated metadata, 'referenced_metata'\n # should always be 'release'. 'release.txt' specifies all roles\n # provided by a repository, including their file sizes and hashes.\n if metadata_role == 'release' or metadata_role.startswith('targets'):\n gzip_metadata_filename = uncompressed_metadata_filename + '.gz'\n if gzip_metadata_filename in self.metadata['current'] \\\n [referenced_metadata]['meta']:\n compression = 'gzip'\n compressed_fileinfo = self.metadata['current'][referenced_metadata] \\\n ['meta'][gzip_metadata_filename]\n # NOTE: When we download the compressed file, we care about its\n # compressed length. However, we check the hash of the uncompressed\n # file; therefore we use the hashes of the uncompressed file.\n fileinfo = {'length': compressed_fileinfo['length'],\n 'hashes': uncompressed_fileinfo['hashes']}\n logger.debug('Compressed version of '+\\\n repr(uncompressed_metadata_filename)+' is available at '+\\\n repr(gzip_metadata_filename)+'.')\n else:\n logger.debug('Compressed version of '+\\\n repr(uncompressed_metadata_filename)+' not available.')\n fileinfo = uncompressed_fileinfo\n else:\n fileinfo = uncompressed_fileinfo\n\n # Simply return if the file has not changed, according to the metadata\n # about the uncompressed file provided by the referenced metadata.\n if not self._fileinfo_has_changed(uncompressed_metadata_filename,\n uncompressed_fileinfo):\n return\n\n logger.debug('Metadata '+repr(uncompressed_metadata_filename)+\\\n ' has changed.')\n\n try:\n self._update_metadata(metadata_role, fileinfo=fileinfo,\n compression=compression)\n except:\n # The current metadata we have is not current but we couldn't\n # get new metadata. We shouldn't use the old metadata anymore.\n # This will get rid of in-memory knowledge of the role and\n # delegated roles, but will leave delegated metadata files as\n # current files on disk.\n # TODO: Should we get rid of the delegated metadata files?\n # We shouldn't need to, but we need to check the trust\n # implications of the current implementation.\n self._delete_metadata(metadata_role)\n logger.error('Metadata for '+str(metadata_role)+' could not be updated')\n raise\n else:\n # We need to remove delegated roles because the delegated roles\n # may not be trusted anymore.\n if metadata_role == 'targets' or metadata_role.startswith('targets/'):\n logger.debug('Removing delegated roles of '+repr(metadata_role)+'.')\n # TODO: Should we also remove the keys of the delegated roles?\n tuf.roledb.remove_delegated_roles(metadata_role)\n self._import_delegations(metadata_role)","def _recalculate_versions(self):\n versions = self._get_local_resource_versions()\n for versions_dict in self._versions_by_consumer.values():\n for res_type, res_version in versions_dict.items():\n versions[res_type].add(res_version)\n self._versions = versions","def load_referenced_versions(self):\n raise NotImplementedError(\"load_referenced_versions is not implemented\")","def updateVersions(self):\r\n f = open('../versions.pckl', 'wb')\r\n pickle.dump(self.versions, f)\r\n f.close()","def testMetacommunityReferencesStorage(self):\n community_dict1 = self.t2.get_community_parameters(1)\n community_dict2 = self.t2.get_community_parameters(2)\n community_dict3 = self.t2.get_community_parameters(3)\n community_dict4 = self.t2.get_community_parameters(4)\n comparison_dict1 = {\"speciation_rate\": 0.5, \"time\": 0.0, \"fragments\": 0, \"metacommunity_reference\": 0}\n comparison_dict2 = {\"speciation_rate\": 0.5, \"time\": 0.0, \"fragments\": 0, \"metacommunity_reference\": 1}\n comparison_dict3 = {\"speciation_rate\": 0.5, \"time\": 0.0, \"fragments\": 0, \"metacommunity_reference\": 2}\n comparison_dict4 = {\"speciation_rate\": 0.5, \"time\": 0.0, \"fragments\": 0, \"metacommunity_reference\": 3}\n self.assertDictEqual(comparison_dict1, community_dict1)\n self.assertDictEqual(comparison_dict2, community_dict2)\n self.assertDictEqual(comparison_dict3, community_dict3)\n self.assertDictEqual(comparison_dict4, community_dict4)","def addon_matrix(self):\n print(\"Checking riot-web version\")\n repo = self.github.get_repo('vector-im/riot-web')\n releases = list(repo.get_releases())\n index = 0\n while True:\n remote_version = releases[index].tag_name\n if 'b' in remote_version:\n index = index + 1\n else:\n break\n file = \"{}/Dockerfile\".format(self.name)\n remote_file = self.get_file_obj(file)\n masterfile = self.repoupdater.get_file_content(remote_file)\n file_version = masterfile.split('releases/download/')[1]\n file_version = file_version.split('/')[0]\n if self.verbose:\n print(\"Current version\", file_version)\n print(\"Available version\", remote_version)\n if remote_version != file_version:\n msg = COMMIT_MSG.format('riot-web', remote_version)\n new_content = self.repoupdater.get_file_content(remote_file)\n new_content = new_content.replace(file_version, remote_version)\n self.repoupdater.commit(file, msg, new_content, remote_file.sha)\n else:\n print(\"riot-web already have the newest version\", file_version)","def makeReference(self):\n logger.debug(\"Func: makeReference\")\n\n if self._currentVersionIndex == -1:\n msg = \"Cursor is not on a Base Scene Version. Cancelling\"\n # logger.warning(msg)\n # raise Exception([101, msg])\n self._exception(101, msg)\n return\n # return\n\n absVersionFile = os.path.join(self.projectDir, self._currentSceneInfo[\"Versions\"][self._currentVersionIndex-1][\"RelativePath\"])\n name = os.path.split(absVersionFile)[1]\n filename, extension = os.path.splitext(name)\n referenceName = \"{0}_{1}_forReference\".format(self._currentSceneInfo[\"Name\"], self._currentSceneInfo[\"Category\"])\n relReferenceFile = os.path.join(self._currentSceneInfo[\"Path\"], \"{0}{1}\".format(referenceName, extension))\n absReferenceFile = os.path.join(self.projectDir, relReferenceFile)\n shutil.copyfile(absVersionFile, absReferenceFile)\n self._currentSceneInfo[\"ReferenceFile\"] = relReferenceFile\n # SET the referenced version as the 'VISUAL INDEX NUMBER' starting from 1\n self._currentSceneInfo[\"ReferencedVersion\"] = self._currentVersionIndex\n\n self._dumpJson(self._currentSceneInfo, self._baseScenesInCategory[self.currentBaseSceneName])","def testMultipleOldRefs(self):\n self.mr2 = cdl_convert.MediaRef('hello')\n\n self.assertEqual(\n {'hello': [self.mr, self.mr2]},\n cdl_convert.MediaRef.members\n )\n\n self.mr._filename = 'goodbye'\n self.mr._change_membership(old_ref='hello')\n\n self.assertEqual(\n {'hello': [self.mr2],'goodbye': [self.mr]},\n cdl_convert.MediaRef.members\n )","def updateMdrizVerHistory(self,build,versions):\n _plist = self.assoc.parlist[0]\n if build == True: _output = _plist['output']\n else: _output = _plist['outdata']\n \n fhdu = pyfits.open(_output,mode='update')\n prihdr = fhdu[0].header\n \n ver_str = \"MultiDrizzle product generated using: \"\n prihdr.add_history(ver_str)\n \n for key in versions:\n if versions[key].find('\\n') < 0:\n prihdr.add_history(key+versions[key])\n else:\n # This will accomodate multi-line comments\n _ver_str = versions[key].split('\\n')\n prihdr.add_history(key)\n for val in _ver_str:\n if val.strip() != '':\n prihdr.add_history(val)\n \n #ver_str = ' MultiDrizzle Version '+str(version)\n #prihdr.add_history(ver_str)\n \n fhdu.close()\n del fhdu","def updateToLatest(self):\n # Determine the newest stamp in each dependency\n latest = {}\n for item in self.spec:\n if isinstance(item, CachedResource):\n latest[item] = item.getLatestStamp()\n if not latest:\n return\n\n # Our new stamp is the greatest out of all deps' stamps\n stamp = max(latest.itervalues())\n\n # Update only if we need to\n if self.getLatestStamp() >= stamp:\n return\n self.updateStamp(latest, stamp)\n\n # Clean old versions if that was successful\n self.cleanStamps(lambda s: s < stamp)","def getVersion(self):\n self.getDocumentedObject().getVersion()","def get_and_update_versions ():\n\n try:\n get_comp_versions (\"ACE\")\n get_comp_versions (\"TAO\")\n\n if opts.update:\n files = []\n files += update_version_files (\"ACE\")\n files += update_version_files (\"TAO\")\n files += create_changelog (\"ACE\")\n files += create_changelog (\"TAO\")\n files += update_spec_file ()\n files += update_debianbuild ()\n\n commit (files)\n\n except:\n print (\"Fatal error in get_and_update_versions.\")\n raise","def compare_version_objects(version1, version2):\n if version1.epoch < version2.epoch:\n return -1\n if version1.epoch > version2.epoch:\n return 1\n result = compare_strings(version1.upstream, version2.upstream)\n if result != 0:\n return result\n if version1.revision or version2.revision:\n return compare_strings(version1.revision, version2.revision)\n return 0","def test_changeAllProjectVersionsPreRelease(self):\n root = FilePath(self.mktemp())\n root.createDirectory()\n coreNews = (\"Twisted Core 1.0.0 (2009-12-25)\\n\"\n \"===============================\\n\"\n \"\\n\")\n webNews = (\"Twisted Web 1.0.0pre1 (2009-12-25)\\n\"\n \"==================================\\n\"\n \"\\n\")\n structure = {\n \"README\": \"Hi this is 1.0.0.\",\n \"NEWS\": coreNews + webNews,\n \"twisted\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.0\",\n \"NEWS\": coreNews},\n \"_version.py\":\n genVersion(\"twisted\", 1, 0, 0),\n \"web\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.0pre1\",\n \"NEWS\": webNews},\n \"_version.py\": genVersion(\"twisted.web\", 1, 0, 0, 1)\n }}}\n self.createStructure(root, structure)\n changeAllProjectVersions(root, Version(\"lol\", 1, 0, 2), '2010-01-01')\n coreNews = (\n \"Twisted Core 1.0.0 (2009-12-25)\\n\"\n \"===============================\\n\"\n \"\\n\")\n webNews = (\"Twisted Web 1.0.2 (2010-01-01)\\n\"\n \"==============================\\n\"\n \"\\n\")\n outStructure = {\n \"README\": \"Hi this is 1.0.2.\",\n \"NEWS\": coreNews + webNews,\n \"twisted\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.2\",\n \"NEWS\": coreNews},\n \"_version.py\":\n genVersion(\"twisted\", 1, 0, 2),\n \"web\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.2\",\n \"NEWS\": webNews},\n \"_version.py\": genVersion(\"twisted.web\", 1, 0, 2),\n }}}\n self.assertStructure(root, outStructure)","def versions(self):\n raise Exception(\"mcapi.Datafile.versions is not implemented\")","def test_ls():\n\n with pipeline.fixture(assets=[\"Asset1\"],\n subsets=[\"animRig\"],\n versions=1) as root:\n asset = next(pipeline.ls())\n\n reference = {\n \"schema\": \"pyblish-mindbender:asset-1.0\",\n \"name\": \"Asset1\",\n \"subsets\": [\n {\n \"schema\": \"pyblish-mindbender:subset-1.0\",\n \"name\": \"animRig\",\n \"versions\": [\n {\n \"schema\": \"pyblish-mindbender:version-1.0\",\n \"version\": 1,\n \"path\": os.path.join(\n root,\n \"Asset1\",\n \"publish\",\n \"animRig\",\n \"v001\"\n ),\n \"source\": os.path.join(\n \"{project}\",\n \"maya\",\n \"scenes\",\n \"scene.ma\"\n ),\n \"representations\": [\n {\n \"schema\": (\"pyblish-mindbender:\"\n \"representation-1.0\"),\n \"format\": \".ma\",\n \"path\": os.path.join(\n \"{dirname}\",\n \"Asset1{format}\"\n ),\n }\n ],\n \"time\": \"\",\n \"author\": \"mottosso\",\n },\n ]\n }\n ]\n }\n\n # Printed on error\n print(\"# Comparing result:\")\n print(json.dumps(asset, indent=4, sort_keys=True))\n print(\"# With reference:\")\n print(json.dumps(reference, indent=4, sort_keys=True))\n\n assert_equals(asset, reference)","def saveCallback(self):\n\n ## TODO // TEST IT\n self._pathsDict[\"sceneFile\"] = self.getSceneFile()\n try:\n openSceneInfo = self.getOpenSceneInfo()\n if not openSceneInfo:\n return\n except TypeError:\n return\n if openSceneInfo[\"jsonFile\"]:\n jsonInfo = self._loadJson(openSceneInfo[\"jsonFile\"])\n if jsonInfo[\"ReferenceFile\"]:\n absRefFile = os.path.join(self._pathsDict[\"projectDir\"], jsonInfo[\"ReferenceFile\"])\n # TODO : ref => Dict\n absBaseSceneVersion = os.path.join(self._pathsDict[\"projectDir\"], jsonInfo[\"Versions\"][int(jsonInfo[\"ReferencedVersion\"]) - 1][\"RelativePath\"])\n # if the refererenced scene file is the saved file (saved or saved as)\n if self._pathsDict[\"sceneFile\"] == absBaseSceneVersion:\n # copy over the forReference file\n try:\n shutil.copyfile(self._pathsDict[\"sceneFile\"], absRefFile)\n print \"Scene Manager Update:\\nReference File Updated\"\n except:\n pass","def set_form_versions(self):\n def _hash(val):\n return hashlib.md5(val).hexdigest()\n\n latest_build = self._get_version_comparison_build()\n if not latest_build:\n return\n force_new_version = self.build_profiles != latest_build.build_profiles\n for form_stuff in self.get_forms(bare=False):\n filename = 'files/%s' % self.get_form_filename(**form_stuff)\n current_form = form_stuff[\"form\"]\n if not force_new_version:\n try:\n previous_form = latest_build.get_form(current_form.unique_id)\n # take the previous version's compiled form as-is\n # (generation code may have changed since last build)\n previous_source = latest_build.fetch_attachment(filename)\n except (ResourceNotFound, FormNotFoundException):\n current_form.version = None\n else:\n previous_hash = _hash(previous_source)\n\n # set form version to previous version, and only update if content has changed\n current_form.version = previous_form.get_version()\n current_form = current_form.validate_form()\n current_hash = _hash(current_form.render_xform())\n if previous_hash != current_hash:\n current_form.version = None\n # clear cache since render_xform was called with a mutated form set to the previous version\n current_form.render_xform.reset_cache(current_form)\n else:\n current_form.version = None","def update():\n as3s = AS3Schema()\n as3s.updateschemas()\n as3s_new = AS3Schema()\n\n if as3s.version != as3s_new.version:\n click.echo(\n f\"Updated AS3 JSON Schemas from version:{as3s.version} to:{as3s_new.version}\",\n )\n else:\n click.echo(\n f\"AS3 JSON Schemas are up-to-date, current version:{as3s.version}\",\n )","def _do_maya_post_publish(self, work_template, progress_cb): \n import maya.cmds as cmds\n \n progress_cb(0, \"Versioning up the scene file\")\n \n # get the current scene path:\n scene_path = os.path.abspath(cmds.file(query=True, sn=True))\n \n # increment version and construct new file name:\n progress_cb(25, \"Finding next version number\")\n fields = work_template.get_fields(scene_path)\n next_version = self._get_next_work_file_version(work_template, fields)\n fields[\"version\"] = next_version \n new_scene_path = work_template.apply_fields(fields)\n \n # log info\n self.parent.log_debug(\"Version up work file %s --> %s...\" % (scene_path, new_scene_path))\n \n # rename and save the file\n progress_cb(50, \"Saving the scene file\")\n cmds.file(rename=new_scene_path)\n cmds.file(save=True)\n \n #updating shotgun task status to cmpt---\n \n progress_cb(100)","def update_versions(consumer, resource_versions):\n _get_cached_tracker().update_versions(consumer, resource_versions)","def check_representations(progress_controller=None):\n if progress_controller is None:\n progress_controller = ProgressControllerBase()\n\n ref_reprs = []\n wrong_reprs = []\n\n v = staging.get(\"version\")\n if v:\n r = Representation(version=v)\n current_repr = r.repr\n\n # For **Base** representation\n # allow any type of representation to be present in the scene\n if r.is_base():\n progress_controller.complete()\n return\n\n references = pm.listReferences()\n progress_controller.maximum = len(references)\n for ref in references:\n ref_repr = ref.repr\n if ref_repr is None:\n # skip this one this is not related to a Stalker Version\n progress_controller.increment()\n continue\n\n ref_reprs.append([ref, ref_repr])\n if ref_repr != current_repr:\n wrong_reprs.append(ref)\n progress_controller.increment()\n else:\n progress_controller.complete()\n return\n\n progress_controller.complete()\n if len(wrong_reprs):\n ref_repr_labels = []\n for ref_repr in ref_reprs:\n ref = ref_repr[0]\n repr_name = ref_repr[1]\n\n color = \"red\" if current_repr != repr_name else \"green\"\n\n ref_repr_labels.append(\n '<span style=\"color: %(color)s\">%(repr_name)s</span> -> '\n \"%(ref)s\"\n % {\"color\": color, \"repr_name\": repr_name, \"ref\": ref.refNode.name()}\n )\n\n raise PublishError(\n \"You are saving as the <b>%s</b> representation<br>\"\n \"for the current scene, but the following references<br>\"\n \"are not <b>%s</b> representations of their versions:<br><br>\"\n \"%s\"\n % (\n current_repr,\n current_repr,\n \"<br>\".join(ref_repr_labels[:MAX_NODE_DISPLAY]),\n )\n )","def run():\n scene = lm.scene_object()\n copy_latest_low()\n copy_latest_high()","def getVersions(self):\n logger.debug(\"Func: getVersions\")\n\n try:\n return self._currentSceneInfo[\"Versions\"]\n except:\n return []","def test_list_versions(self):\n self.metadata.create_or_update(data=self.create)\n\n # Find by name\n res_name = self.metadata.get_by_name(\n entity=Dashboard, fqn=self.entity.fullyQualifiedName\n )\n\n res = self.metadata.get_list_entity_versions(\n entity=Dashboard, entity_id=res_name.id.__root__\n )\n assert res","def scene_is_modified():\n\n pass","def test_item_revision_history(testapp, registry):\n objv1 = {\n 'title': \"Testing1\",\n 'description': \"This is testing object 1\",\n }\n objv2 = {\n 'title': \"Testing2\",\n 'description': \"This is testing object 2\",\n }\n objv3 = {\n 'title': \"Testing3\",\n 'description': \"This is testing object 3\",\n }\n item_uuid = testapp.post_json('/embedding-tests', objv1, status=201).json['@graph'][0]['uuid']\n testapp.patch_json('/' + item_uuid, objv2, status=200)\n testapp.patch_json('/' + item_uuid, objv3, status=200)\n\n # now get revision history\n revisions = testapp.get('/' + item_uuid + '/@@revision-history').json['revisions']\n assert len(revisions) == 3 # we made 3 edits\n\n # lets make some more\n testapp.patch_json('/' + item_uuid, objv2, status=200)\n testapp.patch_json('/' + item_uuid, objv1, status=200)\n revisions = testapp.get('/' + item_uuid + '/@@revision-history').json['revisions']\n assert len(revisions) == 5 # now we made 5 edits\n # they should be ordered by sid, recall the patch order above\n for patched_metadata, revision in zip([objv1, objv2, objv3, objv2, objv1], revisions):\n assert revision['title'] == patched_metadata['title']","def revision(self):\n self.r3.reuse = self.r2\n self.r3.case = self.case\n self.r3.revise()\n self.r2.predictionGenre = self.case.playlist_genre\n self.r2.predictionSubGenre =self.case.playlist_subgenre\n print()","def replace_old(self):\n # Tell the client the index is ready to be used and to no longer show it as building.\n self.db.indexes.find_one_and_update({\"_id\": self.params[\"index_id\"]}, {\n \"$set\": {\n \"ready\": True\n }\n })\n\n self.dispatch(\"indexes\", \"update\", [self.params[\"index_id\"]])\n\n active_indexes = virtool.db.sync.get_active_index_ids(self.db, self.params[\"ref_id\"])\n\n remove_unused_index_files(self.params[\"reference_path\"], active_indexes)\n\n query = {\n \"_id\": {\n \"$not\": {\n \"$in\": active_indexes\n }\n }\n }\n\n self.db.indexes.update_many(query, {\n \"$set\": {\n \"has_files\": False\n }\n })\n\n id_list = self.db.indexes.distinct(\"_id\", query)\n\n self.dispatch(\"indexes\", \"update\", id_list)\n\n # Find OTUs with changes.\n pipeline = [\n {\"$project\": {\n \"reference\": True,\n \"version\": True,\n \"last_indexed_version\": True,\n \"comp\": {\"$cmp\": [\"$version\", \"$last_indexed_version\"]}\n }},\n {\"$match\": {\n \"reference.id\": self.params[\"ref_id\"],\n \"comp\": {\"$ne\": 0}\n }},\n {\"$group\": {\n \"_id\": \"$version\",\n \"id_list\": {\n \"$addToSet\": \"$_id\"\n }\n }}\n ]\n\n id_version_key = {agg[\"_id\"]: agg[\"id_list\"] for agg in self.db.otus.aggregate(pipeline)}\n\n # For each version number\n for version, id_list in id_version_key.items():\n self.db.otus.update_many({\"_id\": {\"$in\": id_list}}, {\n \"$set\": {\n \"last_indexed_version\": version\n }\n })","def _sceneChanged(self, oldScene, newScene):\n pass","def test_update_revision_from_history_with_diffsets(self):\n repository = self.create_repository(tool_name='Test')\n diffset_history = DiffSetHistory.objects.create()\n diffset_history.diffsets.add(\n self.create_diffset(repository=repository))\n\n diffset = DiffSet()\n diffset.update_revision_from_history(diffset_history)\n\n self.assertEqual(diffset.revision, 2)","def update_version_inputs(self, parent_ref=None):\n logger.debug(\"parent_ref: %s\" % parent_ref)\n\n logger.debug(\"get a version\")\n if not parent_ref:\n logger.debug(\"got no parent_ref\")\n version = self.get_current_version()\n else:\n logger.debug(\"have a parent_ref\")\n version = self.get_version_from_full_path(parent_ref.path)\n\n if version:\n logger.debug(\"got a version: %s\" % version.absolute_full_path)\n # use the original version if it is a Repr version\n from anima.representation import Representation\n\n if Representation.repr_separator in version.take_name and version.parent:\n version = version.parent\n logger.debug(\n \"this is a representation switching to its parent: %s\" % version\n )\n\n # update the reference list\n referenced_versions = self.get_referenced_versions(parent_ref)\n version.inputs = referenced_versions\n\n # commit data to the database\n from stalker.db.session import DBSession\n\n DBSession.add(version)\n DBSession.commit()","def update(dl_url, force_update=False):\n def compare_versions(vA, vB):\n \"\"\"\nCompares two version number strings\n@param vA: first version string to compare\n@param vB: second version string to compare\n@author <a href=\"http_stream://sebthom.de/136-comparing-version-numbers-in-jython-pytho/\">Sebastian Thomschke</a>\n@return negative if vA < vB, zero if vA == vB, positive if vA > vB.\n\"\"\"\n if vA == vB:\n return 0\n\n def num(s):\n if s.isdigit():\n return int(s)\n return s\n\n seqA = map(num, re.findall('\\d+|\\w+', vA.replace('-SNAPSHOT', '')))\n seqB = map(num, re.findall('\\d+|\\w+', vB.replace('-SNAPSHOT', '')))\n\n # this is to ensure that 1.0 == 1.0.0 in cmp(..)\n lenA, lenB = len(seqA), len(seqB)\n for i in range(lenA, lenB):\n seqA += (0,)\n for i in range(lenB, lenA):\n seqB += (0,)\n\n rc = cmp(seqA, seqB)\n\n if rc == 0:\n if vA.endswith('-SNAPSHOT'):\n return -1\n if vB.endswith('-SNAPSHOT'):\n return 1\n return rc\n\n # dl the first 256 bytes and parse it for version number\n try:\n http_stream = urllib.urlopen(dl_url)\n # update_file = http_stream.read(256)\n update_file = http_stream.read(300)\n http_stream.close()\n\n except IOError, (errno, strerror):\n logging.info(\"Unable to retrieve version data\")\n logging.info(\"Error %s: %s\" % (errno, strerror))\n return\n\n match_regex = re.search(r'__version__ *= *\"(\\S+)\"', update_file)\n if not match_regex:\n logging.info(\"No version info could be found\")\n return\n update_version = match_regex.group(1)\n\n if not update_version:\n logging.info(\"Unable to parse version data\")\n return\n\n if force_update:\n logging.info(\"Forcing update, downloading version %s...\" %\n update_version)\n\n else:\n cmp_result = compare_versions(__version__, update_version)\n if cmp_result < 0:\n logging.info(\"Newer version %s available, downloading...\" %\n update_version)\n elif cmp_result > 0:\n logging.info(\"Local version %s newer then available %s, not updating.\" % (\n __version__, update_version))\n return\n else:\n logging.info(\"You already have the latest version.\")\n return\n\n # dl, backup, and save the updated script\n app_path = os.path.realpath(sys.argv[0])\n # if __asModule__ == True:\n # app_path = __file__\n\n if not os.access(app_path, os.W_OK):\n logging.info(\"Cannot update -- unable to write to %s\" % app_path)\n\n dl_path = app_path + \".new\"\n backup_path = app_path + \".old\"\n try:\n dl_file = open(dl_path, 'w')\n http_stream = urllib.urlopen(dl_url)\n total_size = None\n bytes_so_far = 0\n chunk_size = 8192\n try:\n total_size = int(http_stream.info().getheader(\n 'Content-Length').strip())\n except:\n # The header is improper or missing Content-Length, just download\n dl_file.write(http_stream.read())\n\n while total_size:\n chunk = http_stream.read(chunk_size)\n dl_file.write(chunk)\n bytes_so_far += len(chunk)\n\n if not chunk:\n break\n\n percent = float(bytes_so_far) / total_size\n percent = round(percent*100, 2)\n sys.stdout.write(\"Downloaded %d of %d bytes (%0.2f%%)\\r\" %\n (bytes_so_far, total_size, percent))\n\n if bytes_so_far >= total_size:\n sys.stdout.write('\\n')\n\n http_stream.close()\n dl_file.close()\n except IOError, (errno, strerror):\n logging.info(\"Download failed\")\n logging.info(\"Error %s: %s\" % (errno, strerror))\n return\n\n try:\n os.rename(app_path, backup_path)\n except OSError, (errno, strerror):\n logging.info(\"Unable to rename %s to %s: (%d) %s\" %\n (app_path, backup_path, errno, strerror))\n return\n\n try:\n os.rename(dl_path, app_path)\n except OSError, (errno, strerror):\n logging.info(\"Unable to rename %s to %s: (%d) %s\" %\n (dl_path, app_path, errno, strerror))\n return\n\n try:\n import shutil\n shutil.copymode(backup_path, app_path)\n except:\n os.chmod(app_path, 0755)\n\n logging.info(\"New version installed as %s\" % app_path)\n logging.info(\"(previous version backed up to %s)\" % (backup_path))\n return True","def test_updateTwistedVersionInformation(self):\n baseDirectory = FilePath(self.mktemp())\n baseDirectory.createDirectory()\n now = date.today()\n\n projectName = 'foo'\n oldVersion = Version(projectName, 2, 5, 0)\n newVersion = getNextVersion(oldVersion, now=now)\n\n project = self.makeProject(oldVersion, baseDirectory)\n\n updateTwistedVersionInformation(baseDirectory, now=now)\n\n self.assertEqual(project.getVersion(), newVersion)\n self.assertEqual(\n project.directory.child('topfiles').child('README').getContent(),\n newVersion.base())","def refresh(self):\n\n # The timestamp role does not have signed metadata about it; otherwise we\n # would need an infinite regress of metadata. Therefore, we use some\n # default, sane metadata about it.\n DEFAULT_TIMESTAMP_FILEINFO = {\n 'hashes':None,\n 'length': tuf.conf.DEFAULT_TIMESTAMP_REQUIRED_LENGTH\n }\n\n # Update the top-level metadata. The _update_metadata_if_changed() and\n # _update_metadata() calls below do NOT perform an update if there\n # is insufficient trusted signatures for the specified metadata.\n # Raise 'tuf.NoWorkingMirrorError' if an update fails.\n\n # Use default but sane information for timestamp metadata, and do not\n # require strict checks on its required length.\n self._update_metadata('timestamp', DEFAULT_TIMESTAMP_FILEINFO)\n\n self._update_metadata_if_changed('release', referenced_metadata='timestamp')\n\n self._update_metadata_if_changed('root')\n\n self._update_metadata_if_changed('targets')\n\n # Updated the top-level metadata (which all had valid signatures), however,\n # have they expired? Raise 'tuf.ExpiredMetadataError' if any of the metadata\n # has expired.\n for metadata_role in ['timestamp', 'root', 'release', 'targets']:\n self._ensure_not_expired(metadata_role)","def compare_and_update(self):\n\n # first read in the old cites\n try:\n with open(\"cites.json\") as f:\n cites_json = f.read()\n old_cites = json.loads(cites_json)\n except OSError:\n old_cites = {}\n\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\n print(\"number of citations = \", num_cites)\n\n h_index = 0\n for n, p in enumerate(self.mypapers):\n if p.citation_count >= n+1:\n h_index = n + 1\n print(\"h-index = \", h_index)\n\n # now create a dict of the cites and update the stored JSON\n cites = {}\n for p in self.mypapers:\n cites[p.bibcode] = (p.title[0], p.citation_count)\n\n cite_json = json.dumps(cites, indent=4)\n with open(\"cites.json\", \"w\") as f:\n f.write(cite_json)\n\n # now look for differences in cites\n for key, value in cites.items():\n if key not in old_cites:\n print(f\"new paper: {value[0]}\")\n else:\n if value[1] != old_cites[key][1]:\n print(f\"change of {value[1] - old_cites[key][1]:+d} for paper: {value[0]}\")","def update_previous_all_versions():\n\n # get all the ids\n version_ids = m.meta.Session.query(distinct(tst.TestVersion.id)).filter_by(archived=False).\\\n join('methods').filter_by(short_name='Online').\\\n join('test','type').filter_by(short_name='RC').all()\n\n for version_id in version_ids:\n update_previous(version_id)","def compare_versions(vA, vB):\n if vA == vB:\n return 0\n\n def num(s):\n if s.isdigit():\n return int(s)\n return s\n\n seqA = map(num, re.findall('\\d+|\\w+', vA.replace('-SNAPSHOT', '')))\n seqB = map(num, re.findall('\\d+|\\w+', vB.replace('-SNAPSHOT', '')))\n\n # this is to ensure that 1.0 == 1.0.0 in cmp(..)\n lenA, lenB = len(seqA), len(seqB)\n for i in range(lenA, lenB):\n seqA += (0,)\n for i in range(lenB, lenA):\n seqB += (0,)\n\n rc = cmp(seqA, seqB)\n\n if rc == 0:\n if vA.endswith('-SNAPSHOT'):\n return -1\n if vB.endswith('-SNAPSHOT'):\n return 1\n return rc","def compare_versions(vA, vB):\n if vA == vB: return 0\n\n def num(s):\n if s.isdigit(): return int(s)\n return s\n\n seqA = map(num, re.findall('\\d+|\\w+', vA.replace('-SNAPSHOT', '')))\n seqB = map(num, re.findall('\\d+|\\w+', vB.replace('-SNAPSHOT', '')))\n\n # this is to ensure that 1.0 == 1.0.0 in cmp(..)\n lenA, lenB = len(seqA), len(seqB)\n for i in range(lenA, lenB): seqA += (0,)\n for i in range(lenB, lenA): seqB += (0,)\n\n rc = cmp(seqA, seqB)\n\n if rc == 0:\n if vA.endswith('-SNAPSHOT'): return -1\n if vB.endswith('-SNAPSHOT'): return 1\n return rc","def versions():\n result = timeline.versions()\n if result:\n click.echo('\\n'.join(result))","def compare_version(self, ref):\n if not self.version:\n print 'No version for package %s' % self.package\n if not ref.version:\n print 'No version for package %s' % ref.package\n if not self.parsed_version:\n self.parsed_version = parse_version(self.version)\n if not ref.parsed_version:\n ref.parsed_version = parse_version(ref.version)\n return self.parsed_version.compare(ref.parsed_version)","def _check_accessors_for_version_problems(self):\n\n accessors_needing_saving = []\n for accessor in self._cache_accessors:\n old_prov = accessor.load_provenance()\n\n if old_prov is None:\n continue\n\n new_prov = accessor.query.provenance\n\n if old_prov.exactly_matches(new_prov):\n continue\n accessors_needing_saving.append(accessor)\n\n if old_prov.code_version_minor == new_prov.code_version_minor:\n if old_prov.bytecode_hash != new_prov.bytecode_hash:\n raise CodeVersioningError(\n oneline(\n f\"\"\"\n Found a cached artifact with the same\n descriptor ({accessor.query.dnode.to_descriptor()!r}) and\n version (major={old_prov.code_version_major!r},\n minor={old_prov.code_version_minor!r}),\n But created by different code\n (old hash {old_prov.bytecode_hash!r},\n new hash {new_prov.bytecode_hash!r}).\n Did you change your code but not update the\n version number?\n Change @version(major=) to indicate that your\n function's behavior has changed, or @version(minor=)\n to indicate that it has *not* changed.\"\"\"\n )\n )\n\n for accessor in accessors_needing_saving:\n accessor.update_provenance()","def testMultipleNewRefs(self):\n self.mr2 = cdl_convert.MediaRef('goodbye')\n\n self.assertEqual(\n {'hello': [self.mr], 'goodbye': [self.mr2]},\n cdl_convert.MediaRef.members\n )\n\n self.mr._filename = 'goodbye'\n self.mr._change_membership(old_ref='hello')\n\n self.assertEqual(\n {'goodbye': [self.mr2, self.mr]},\n cdl_convert.MediaRef.members\n )","def _current_versions(self, job):\n raise NotImplementedError","def reference_scene(file_path, **kwargs):\n\n pass","def main():\n compare_versions(Reactome())","def test_version():\n with open(\"pyproject.toml\") as f:\n tomllines = f.read().splitlines()\n tomlversion = set([l for l in tomllines if \"version =\" in l])\n initversion = set([f'version = \"{mei2volpiano.__version__}\"'])\n # set is there to catch any duplicate/additional entries\n assert initversion == tomlversion","def upgrade_to_21():\n\n def update_project_template(template):\n new_template = {'acquisitions': []}\n for a in template.get('acquisitions', []):\n new_a = {'minimum': a['minimum']}\n properties = a['schema']['properties']\n if 'measurement' in properties:\n m_req = properties['measurement']['pattern']\n m_req = re.sub('^\\(\\?i\\)', '', m_req)\n new_a['files']=[{'measurement': m_req, 'minimum': 1}]\n if 'label' in properties:\n l_req = properties['label']['pattern']\n l_req = re.sub('^\\(\\?i\\)', '', l_req)\n new_a['label'] = l_req\n new_template['acquisitions'].append(new_a)\n\n return new_template\n\n def dm_v2_updates(cont_list, cont_name):\n for container in cont_list:\n\n query = {'_id': container['_id']}\n update = {'$rename': {'metadata': 'info'}}\n\n if cont_name == 'projects' and container.get('template'):\n new_template = update_project_template(json.loads(container.get('template')))\n update['$set'] = {'template': new_template}\n\n\n if cont_name == 'sessions':\n update['$rename'].update({'subject.metadata': 'subject.info'})\n\n\n measurement = None\n modality = None\n info = None\n if cont_name == 'acquisitions':\n update['$unset'] = {'instrument': '', 'measurement': ''}\n measurement = container.get('measurement', None)\n modality = container.get('instrument', None)\n info = container.get('metadata', None)\n if info:\n config.db.acquisitions.update_one(query, {'$set': {'metadata': {}}})\n\n\n # From mongo docs: '$rename does not work if these fields are in array elements.'\n files = container.get('files')\n if files is not None:\n updated_files = []\n for file_ in files:\n file_['info'] = {}\n if 'metadata' in file_:\n file_['info'] = file_.pop('metadata', None)\n if 'instrument' in file_:\n file_['modality'] = file_.pop('instrument', None)\n if measurement:\n # Move the acquisition's measurement to all files\n if file_.get('measurements'):\n file_['measurements'].append(measurement)\n else:\n file_['measurements'] = [measurement]\n if info and file_.get('type', '') == 'dicom':\n # This is going to be the dicom header info\n updated_info = info\n updated_info.update(file_['info'])\n file_['info'] = updated_info\n if modality and not file_.get('modality'):\n file_['modality'] = modality\n\n updated_files.append(file_)\n if update.get('$set'):\n update['$set']['files'] = updated_files\n else:\n update['$set'] = {'files': updated_files}\n\n result = config.db[cont_name].update_one(query, update)\n\n query = {'$or':[{'files.metadata': { '$exists': True}},\n {'metadata': { '$exists': True}},\n {'files.instrument': { '$exists': True}}]}\n\n dm_v2_updates(config.db.collections.find(query), 'collections')\n\n query['$or'].append({'template': { '$exists': True}})\n dm_v2_updates(config.db.projects.find({}), 'projects')\n\n query['$or'].append({'subject': { '$exists': True}})\n dm_v2_updates(config.db.sessions.find(query), 'sessions')\n\n query['$or'].append({'instrument': { '$exists': True}})\n query['$or'].append({'measurement': { '$exists': True}})\n dm_v2_updates(config.db.acquisitions.find(query), 'acquisitions')","def compareTwoReco(reference, new, histos, debug=1):\n\n # Tracks with index False are the ones that have been matched to the reference track collection\n new_valid = [True for i in new]\n\n # Tracks with index False are the ones that have been matched to the comparison track collection\n original_valid = [True for i in reference]\n print \" \".join(\"%10s\" % k for k in variables)\n debug_verbose = checkDebug(debug, 'Verbose')\n debug_ordinary = checkDebug(debug, 'Ordinary')\n debug_recovery = checkDebug(debug, 'Recovery')\n debug_lost = checkDebug(debug, 'Lost')\n debug_fake = checkDebug(debug, 'Fake')\n\n for original_index, original in enumerate(reference):\n # Fill in cumulative plots for the reference sample first\n histos['reference_hits_vs_algo'].Fill(original.algo, original.hits)\n histos['reference_hits_vs_orialgo'].Fill(original.orialgo, original.hits)\n histos['reference_hits_vs_pt'].Fill(original.pt, original.hits)\n histos['den'].Fill(original.pt)\n histos['den_eta'].Fill(original.eta)\n histos['den_phi'].Fill(original.phi)\n histos['den_hits'].Fill(original.hits)\n histos['den_algo'].Fill(original.algo)\n histos['den_orialgo'].Fill(original.orialgo)\n\n # Now start to look for a matching track in the comparison track collection\n window_depth = 400 # elements to span to look for best candidate\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = -1, 100, 100\n if debug_verbose:\n print original\n for i,j in enumerate(new):\n if new_valid[i] == True:\n if debug_verbose:\n print \" \", i, j\n if window_depth == 0:\n break\n dr_squared, dPt_over_pt = match(original, j)\n if dr_squared < bestDeltaRMatch*bestDeltaRMatch and dPt_over_pt < DELTA_PT_OVER_PT_CUT:\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = i, dr_squared, dPt_over_pt\n if debug_verbose:\n print \" \", window_depth, iBest, bestDeltaRMatch, dr_squared, bestDeltaPt_over_PtMatch, dPt_over_pt\n if bestDeltaRMatch <= 0.0001 or bestDeltaPt_over_PtMatch == 0.0001:\n break\n window_depth -= 1\n if iBest != -1 and bestDeltaRMatch < DELTA_R_CUT:\n # These are the tracks in the reference track collection\n # that have been matched to a track in the comparison\n # track collection\n new_valid[iBest] = False\n original_valid[original_index] = False\n assert original.run == new[iBest].run, \"run mismatch\"\n assert original.ls == new[iBest].ls, \"ls mismatch\"\n assert original.event == new[iBest].event, \"event mismatch\"\n if debug_ordinary:\n print original\n print new[iBest]\n print iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch, '\\n'\n histos['num'].Fill(original.pt)\n histos['num_eta'].Fill(original.eta)\n histos['num_phi'].Fill(original.phi)\n histos['num_hits'].Fill(original.hits)\n histos['num_algo'].Fill(original.algo)\n histos['num_orialgo'].Fill(original.orialgo)\n histos['fake_num'].Fill(new[iBest].pt)\n histos['fake_num_eta'].Fill(new[iBest].eta)\n histos['fake_num_phi'].Fill(new[iBest].phi)\n histos['fake_num_hits'].Fill(new[iBest].hits)\n histos['fake_num_algo'].Fill(new[iBest].algo)\n histos['fake_num_orialgo'].Fill(new[iBest].orialgo)\n histos['comparison_algo_vs_reference_algo'].Fill(original.algo, new[iBest].algo)\n histos['comparison_orialgo_vs_reference_orialgo'].Fill(original.orialgo, new[iBest].orialgo)\n histos['comparison_hits_vs_reference_hits'].Fill(original.hits, new[iBest].hits)\n\n # Let's try a recovery loop with somewhat lesser stringent cuts\n for original_index, original in enumerate(reference):\n if original_valid[original_index]:\n # Now start to look for a matching track in the comparison track collection\n window_depth = 300 # elements to span to look for best candidate\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = -1, 100, 100\n if debug_verbose:\n print \"Recovery \", original\n for i,j in enumerate(new):\n if new_valid[i] == True:\n if debug_verbose:\n print \"Recovery \", i, j\n if window_depth == 0:\n break\n dr_squared, dPt_over_pt = match(original, j)\n if dr_squared < bestDeltaRMatch*bestDeltaRMatch and dPt_over_pt < DELTA_PT_OVER_PT_CUT*6:\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = i, dr_squared, dPt_over_pt\n if debug_verbose:\n print \"Recovery \", window_depth, iBest, bestDeltaRMatch, dr_squared, bestDeltaPt_over_PtMatch, dPt_over_pt\n if bestDeltaRMatch <= 0.0001 or bestDeltaPt_over_PtMatch == 0.0001:\n break\n window_depth -= 1\n if iBest != -1 and bestDeltaRMatch < DELTA_R_CUT*10: # inflate cut on DeltaR to recover some good-medium matching\n # These are the tracks in the reference track collection\n # that have been matched to a track in the comparison\n # track collection\n new_valid[iBest] = False\n original_valid[original_index] = False\n if debug_recovery:\n print \"Recovery \", original\n print \"Recovery \", new[iBest]\n print \"Recovery \", iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch\n histos['num'].Fill(original.pt)\n histos['num_eta'].Fill(original.eta)\n histos['num_phi'].Fill(original.phi)\n histos['num_hits'].Fill(original.hits)\n histos['num_algo'].Fill(original.algo)\n histos['num_orialgo'].Fill(original.orialgo)\n histos['fake_num'].Fill(new[iBest].pt)\n histos['fake_num_eta'].Fill(new[iBest].eta)\n histos['fake_num_hits'].Fill(new[iBest].hits)\n histos['fake_num_algo'].Fill(new[iBest].algo)\n histos['fake_num_orialgo'].Fill(new[iBest].orialgo)\n histos['comparison_algo_vs_reference_algo'].Fill(original.algo, new[iBest].algo)\n histos['comparison_orialgo_vs_reference_orialgo'].Fill(original.orialgo, new[iBest].orialgo)\n histos['comparison_hits_vs_reference_hits'].Fill(original.hits, new[iBest].hits)\n\n\n # These are the tracks in the reference track collection\n # that have *not* been associated to any track in the\n # comparison collection == > LOST TRACKS\n reference_not_assigned = [j for i,j in enumerate(reference) if original_valid[i]]\n reference_not_assigned.sort(key=lambda tr: tr.algo)\n if debug_lost:\n print \"**** Lost tracks **** %d\" % len(reference_not_assigned)\n for j in reference_not_assigned:\n histos['lost_hits_vs_algo'].Fill(j.algo, j.hits)\n histos['lost_hits_vs_orialgo'].Fill(j.orialgo, j.hits)\n histos['lost_hits_vs_pt'].Fill(j.pt, j.hits)\n histos['lost_eta'].Fill(j.eta)\n if debug:\n print j\n if debug_lost:\n print \"**** End of Lost tracks ****\"\n\n # Fake Tracks\n for i, j in enumerate(new):\n # Fill in the cumulative plots related to tracks in the comparison track collection\n histos['comparison_hits_vs_algo'].Fill(j.algo, j.hits)\n histos['comparison_hits_vs_orialgo'].Fill(j.orialgo, j.hits)\n histos['comparison_hits_vs_pt'].Fill(j.pt, j.hits)\n histos['fake_den'].Fill(j.pt)\n histos['fake_den_eta'].Fill(j.eta)\n histos['fake_den_phi'].Fill(j.phi)\n histos['fake_den_hits'].Fill(j.hits)\n histos['fake_den_algo'].Fill(j.algo)\n histos['fake_den_orialgo'].Fill(j.orialgo)\n\n # These are the tracks in the comparison track collection\n # that have *not* been associated to any track in the\n # reference collection ==> FAKE TRACKS\n new_not_assigned = [j for i,j in enumerate(new) if new_valid[i]]\n new_not_assigned.sort(key=lambda tr: tr.algo)\n if debug_fake:\n print \"**** Fake tracks **** %d\" % len(new_not_assigned)\n for j in new_not_assigned:\n histos['fake_hits_vs_algo'].Fill(j.algo, j.hits)\n histos['fake_hits_vs_orialgo'].Fill(j.orialgo, j.hits)\n histos['fake_hits_vs_pt'].Fill(j.pt, j.hits)\n if debug:\n print j\n if debug_fake:\n print \"**** End of Fake tracks ****\"","def open(self, version, force=False, representation=None,\n reference_depth=0, skip_update_check=False):\n version_full_path = os.path.normpath(version.absolute_full_path)\n\n # delete all the comps and open new one\n #comps = self.fusion.GetCompList().values()\n #for comp_ in comps:\n # comp_.Close()\n\n self.fusion.LoadComp(version_full_path.encode())\n\n rfm = RecentFileManager()\n rfm.add(self.name, version.absolute_full_path)\n\n # set the project_directory\n #self.project_directory = os.path.dirname(version.absolute_path)\n\n # TODO: file paths in different OS'es should be replaced with the current one\n # Check if the file paths are starting with a string matching one of\n # the OS'es project_directory path and replace them with a relative one\n # matching the current OS\n\n # replace paths\n #self.replace_external_paths()\n\n # return True to specify everything was ok and an empty list\n # for the versions those needs to be updated\n return empty_reference_resolution()","def isSceneModified(self):\n logger.debug(\"Func: isSceneModified\")\n return nuke.modified()","def version(self):","def updateEditedRefImages(self, recipe):\n oldProperties = recipe.getProperties()\n\n show = oldProperties['show']\n sequence = oldProperties['sequence']\n oldMode = Mode(show, sequence)\n updatedPoses = self.__getUpdatedPoses(recipe)\n\n newRecipeXML = CopySetup.localizeSetup(oldProperties, show, sequence,\n renderCallback=self.__renderCallback,\n setupCallback=self.feedReloadSetupsMultiTracks,\n multiTrackCallback=self.feedReloadSetupsMultiTracks)\n\n newRecipe = Recipe.fromXMLElement(newRecipeXML)\n\n self.__storeUpdatedPoses(newRecipe, updatedPoses)\n self.__updatePosesInSetup(newRecipe, updatedPoses)\n\n oldProperties = recipe.getProperties()\n existingRecipeEditingPath = oldMode.get(\"[poseEditingFile]\", oldProperties).replace(\".psd\", \".xml\")\n\n # if a setup has been send to flix already we will now use the new recipe version\n if self.fileServiceLocal.exists(existingRecipeEditingPath):\n oldProperties = self.fileServiceLocal.loadXMLFile(existingRecipeEditingPath).find(\"Properties\").attrib\n\n\n recipiesXML = ET.fromstring('<Recipies/>')\n newSetupXML = newRecipe.getMasterXML()\n\n setupXML = ET.fromstring('<OldSetup show=\"%s\" sequence=\"%s\" beat=\"%s\" setup=\"%s\" version=\"%s\" />'\\\n % (oldProperties[\"show\"],\n oldProperties[\"sequence\"],\n oldProperties[\"beat\"],\n oldProperties[\"setup\"],\n oldProperties[\"version\"]))\n\n\n setupXML.append(newSetupXML)\n recipiesXML.append(setupXML)\n self.addFeedback(\"replaceSetupsMultiTracks\", recipiesXML)\n\n FlixNuke().fromRecipe(newRecipe)\n FlixNuke().compRecipe(newRecipe, renderCallback=self.__renderCallback)\n\n newProperties = newRecipe.getProperties()\n mode = Mode(newProperties.get('show', None), newProperties.get('sequence', None))\n newMultitrackFile = mode.get('[recipeMultiTrackFile]', newProperties)\n newMultitrack = self.fileServiceLocal.loadTextFile(newMultitrackFile)\n\n data = []\n data.append('<Recipies>')\n data.append(\n \"\"\"<Setup\n show=\"%(show)s\"\n sequence=\"%(sequence)s\"\n beat=\"%(beat)s\"\n setup=\"%(setup)s\"\n version=\"%(version)s\">'\"\"\" % newProperties)\n data.append(newMultitrack + \"</Setup>\" + \"</Recipies>\")\n dataString = \"\".join(data)\n\n self.feedReloadSetupsMultiTracks(dataString)\n\n# FlixNuke().compRecipe(newRecipe, fileOutNodes='fileOut_master_png')\n\n return newRecipe","def notesChanged(self):\n if self.controller:\n self.versionProp.updateVersion(self.controller.current_version)","def _update_aea_version(cls, agent_config: AgentConfig):\n for item in agent_config.package_dependencies:\n type_ = item.component_type.to_plural()\n dotted_path = f\"vendor.{item.author}.{type_}.{item.name}.aea_version\"\n path = os.path.join(\"vendor\", item.author, type_, item.name)\n cls.nested_set_config(dotted_path, cls.AEA_VERSION_SPECIFIER)\n cls.run_cli_command(\"fingerprint\", \"by-path\", path, cwd=cls._get_cwd())","def getOpenSceneInfo(self):\n logger.debug(\"Func: getOpenSceneInfo\")\n\n self._pathsDict[\"sceneFile\"] = self.getSceneFile()\n if not self._pathsDict[\"sceneFile\"]:\n return None\n\n # get name of the upper directory to find out base name\n sceneDir = os.path.abspath(os.path.join(self._pathsDict[\"sceneFile\"], os.pardir))\n baseSceneName = os.path.basename(sceneDir)\n\n upperSceneDir = os.path.abspath(os.path.join(sceneDir, os.pardir))\n upperSceneDirName = os.path.basename(upperSceneDir)\n\n if upperSceneDirName in self._subProjectsList:\n subProjectDir = upperSceneDir\n subProject = upperSceneDirName\n categoryDir = os.path.abspath(os.path.join(subProjectDir, os.pardir))\n category = os.path.basename(categoryDir)\n\n dbCategoryPath = os.path.normpath(os.path.join(self._pathsDict[\"databaseDir\"], category))\n dbPath = os.path.normpath(os.path.join(dbCategoryPath, subProject))\n\n pbCategoryPath = os.path.normpath(os.path.join(self._pathsDict[\"previewsDir\"], category))\n pbSubPath = os.path.normpath(os.path.join(pbCategoryPath, subProject))\n pbPath = os.path.normpath(os.path.join(pbSubPath, baseSceneName))\n\n else:\n subProject = self._subProjectsList[0]\n categoryDir = upperSceneDir\n category = upperSceneDirName\n dbPath = os.path.normpath(os.path.join(self._pathsDict[\"databaseDir\"], category))\n pbCategoryPath = os.path.normpath(os.path.join(self._pathsDict[\"previewsDir\"], category))\n pbPath = os.path.normpath(os.path.join(pbCategoryPath, baseSceneName))\n\n jsonFile = os.path.join(dbPath, \"{}.json\".format(baseSceneName))\n if os.path.isfile(jsonFile):\n version = (self.niceName(self._pathsDict[\"sceneFile\"])[-4:])\n self._openSceneInfo = {\n \"jsonFile\":jsonFile,\n \"projectPath\":self._pathsDict[\"projectDir\"],\n \"subProject\":subProject,\n \"category\":category,\n \"shotName\":baseSceneName,\n \"version\":version,\n \"previewPath\":pbPath\n }\n return self._openSceneInfo\n else:\n return None","def update_versions(self, new_versions_list):\n to_stop = [version for version in self if version not in new_versions_list]\n for version_id in to_stop:\n del self[version_id]\n\n for version_id in new_versions_list:\n if version_id not in self:\n self[version_id] = VersionManager(\n self.zk_client, self.project_id, self.service_id, version_id,\n self.callback)\n\n self[version_id].ensure_watch()","def _update_version(self) -> None:\n # Implement in child class.\n raise NotImplementedError","def referenceAsset(*args):\n\n #check that we have the correct tab open\n path = \"\"\n path = getSelectMasteredAsset()\n\n if path:\n print \"shotWin.referenceAsset: referencing in - - -\", path\n cFuncs.referenceInWithNamespace(path)\n # now refresh. . .\n populateMasteredAssets()\n else:\n cmds.warning(\"You must select an asset from the lists to reference in!\")","def update(self):\n self.content = self.get_content()\n self.dependencies = self.content['requirements']['run']\n self.pythonversion = self.content['extra']['pythonversion']\n self.package_name = self.content['package']['name']","def get_comp_versions (component):\n vprint (\"Detecting current version for \" + component)\n\n regex = re.compile (r\"version \" + version_restr)\n major = component + \"_major\"\n minor = component + \"_minor\"\n micro = component + \"_micro\"\n\n\n version = (None, None, None)\n with open (doc_root + \"/ACE_TAO/\" + component + \"/VERSION.txt\") as version_file:\n for line in version_file:\n match = regex.search (line)\n if match is not None:\n version = match.groups(default=0)\n\n vprint (\"Detected version %s.%s.%s\" % version)\n\n comp_versions[major] = int (version[0])\n comp_versions[minor] = int (version[1])\n comp_versions[micro] = int (version[2])\n\n break\n\n print (\"FATAL ERROR: Unable to locate current version for \" + component)\n raise Exception\n\n # Also store the current release (old from now)\n old_comp_versions[major] = comp_versions[major]\n old_comp_versions[minor] = comp_versions[minor]\n old_comp_versions[micro] = comp_versions[micro]\n\n if opts.update:\n if opts.release_type == ReleaseType.major:\n comp_versions[major] += 1\n comp_versions[minor] = 0\n comp_versions[micro] = 0\n elif opts.release_type == ReleaseType.minor:\n comp_versions[minor] += 1\n comp_versions[micro] = 0\n elif opts.release_type == ReleaseType.micro:\n comp_versions[micro] += 1\n\n def make_version (versions, joiner):\n return joiner.join ([\n str (versions[component + '_' + x]) for x in ReleaseType.__members__.keys ()\n ])\n\n comp_versions [component + \"_version\"] = make_version (comp_versions, '.')\n comp_versions [component + \"_version_\"] = make_version (comp_versions, '_')\n\n comp_versions [component + \"_code\"] = \\\n (comp_versions[major] << 16) + \\\n (comp_versions[minor] << 8) + \\\n comp_versions[micro]\n\n old_comp_versions [component + \"_version\"] = make_version (old_comp_versions, '.')\n old_comp_versions [component + \"_version_\"] = make_version (old_comp_versions, '_')\n\n if opts.update:\n vprint (\"Updating from version %s to version %s\" %\n (old_comp_versions [component + \"_version\"], comp_versions [component + \"_version\"]))\n else:\n vprint (\"Found version %s\" %\n (comp_versions [component + \"_version\"]))\n\n # else:\n # comp_versions [component + \"_version\"] = \\\n # str (comp_versions[major]) + '.' + \\\n # str (comp_versions[minor])","def _compare_variants(self, all_revisions):\n previous = None\n previous = self._compare(previous, '.xml', all_revisions)\n previous = self._compare(previous, '-utf16.xml', all_revisions)\n previous = self._compare(previous, '.xml.bz2', all_revisions)\n self._compare(previous, '-utf16.xml.bz2', all_revisions)","def compare():\n body: t.Any = request.json\n check_error({'input': {'old': {}, 'new': {}}}, body)\n response_new = rpc_search({'input': body['input']['new']})\n response_old = rpc_search({'input': body['input']['old']})\n\n modules_new = response_new['yang-catalog:modules']['module']\n modules_old = response_old['yang-catalog:modules']['module']\n\n if len(modules_new) == 0 or len(modules_old) == 0:\n abort(404, description='No hits found either in old or new input')\n\n new_mods = []\n for mod_new in modules_new:\n new_rev = mod_new['revision']\n new_name = mod_new['name']\n found = False\n new_rev_found = False\n for mod_old in modules_old:\n old_rev = mod_old['revision']\n old_name = mod_old['name']\n if new_name == old_name and new_rev == old_rev:\n found = True\n break\n if new_name == old_name and new_rev != old_rev:\n new_rev_found = True\n if not found:\n mod_new['reason-to-show'] = 'New module'\n new_mods.append(mod_new)\n if new_rev_found:\n mod_new['reason-to-show'] = 'Different revision'\n new_mods.append(mod_new)\n if len(new_mods) == 0:\n abort(404, description='No new modules or modules with different revisions found')\n output = {'output': new_mods}\n return output","def _compare(self, previous, variant, all_revisions):\n entries = get_entries('article-pyrus' + variant,\n allrevisions=all_revisions)\n result = [entry.__dict__ for entry in entries]\n if previous:\n self.assertEqual(previous, result)\n return result","def test_check_version_release(self):\n with self.assertLogs(\"dakara_feeder.version\", \"DEBUG\") as logger:\n with patch.multiple(\n \"dakara_feeder.version\", __version__=\"0.0.0\", __date__=\"1970-01-01\"\n ):\n check_version()\n\n # assert effect on logs\n self.assertListEqual(\n logger.output,\n [\"INFO:dakara_feeder.version:\" \"Dakara feeder 0.0.0 (1970-01-01)\"],\n )","def _update_pyrex_file(self, lines, filename):\n found_version_line = False\n for lineno, line in enumerate(lines):\n if line.startswith('__version__'):\n found_version_line = True\n break\n if found_version_line:\n if self.Verbose:\n print 'Version string found on line %d' % lineno\n lines[lineno] = '__version__ = \"%s\"\\n' % str(self.VersionTuple)\n else:\n print \"No version string found in %s\" % filename\n return (lines, found_version_line)","def test_update_refruns_info(self):\n run_number = 321123\n mixer.blend(\n RunReconstruction,\n reconstruction=RunReconstruction.EXPRESS,\n run=mixer.blend(OmsRun, run_number=run_number),\n is_reference=True,\n )\n req = RequestFactory().get(reverse(\"addrefrun:update_refruns_info\"))\n req.user = mixer.blend(User, user_privilege=User.SHIFTLEADER)\n resp = views.update_refruns_info(req)\n\n # Response is a JSON with a \"success\" key\n data = json.loads(resp.content.decode(\"utf-8\"))\n assert data[\"success\"] is True","def compare_versions(version1, version2):\n version1 = coerce_version(version1)\n version2 = coerce_version(version2)\n return compare_version_objects(version1, version2)","def test_above_24_latest_version(self):\n self.data['version'] = ''\n self.data['appVersion'] = '28.0'\n\n up = self.get(self.data)\n rdf = up.get_rdf()\n assert rdf.find('20202020.01') > -1","def _does_require_force_update(self):\n\n if self.current_version[0][0] > self.version_yaml[0]:\n # The current version first index is greater than the one we have in the\n # current version.yaml.\n\n # We return True.\n return True\n\n # We return False, we do not need to force the update for\n # the current version number.\n return False","def version_check(self):\n # anchor_matcher --> matcher\n if hasattr(self, \"anchor_matcher\"):\n self.matcher = self.anchor_matcher\n if hasattr(self, \"head_in_features\"):\n self.in_features = self.head_in_features\n if hasattr(self, \"test_topk_candidates\"):\n self.topk_candidates = self.test_topk_candidates\n if hasattr(self, \"test_score_thresh\"):\n self.score_threshold = self.test_score_thresh","def test_video_update(self):\n updated = timezone.now()\n v1 = make_video(media_id='1234', updated=updated)\n v2 = make_video(media_id='5678', updated=updated)\n set_resources_and_sync([v1, v2])\n i1 = mpmodels.MediaItem.objects.get(jwp__key=v1.key)\n i2 = mpmodels.MediaItem.objects.get(jwp__key=v2.key)\n\n # Update video 1 but not 2\n v1['updated'] += 1\n v1['title'] += 'new title'\n set_resources_and_sync([v1, v2])\n\n new_i1 = mpmodels.MediaItem.objects.get(jwp__key=v1.key)\n new_i2 = mpmodels.MediaItem.objects.get(jwp__key=v2.key)\n\n # Only video 1 was updated\n self.assertGreater(new_i1.updated_at, i1.updated_at)\n self.assertEqual(new_i1.title, v1['title'])\n self.assertEqual(new_i2.updated_at, i2.updated_at)","def test_dump_major_update_exists(self, tmpdir):\n filename = tmpdir / 'exists.json'\n with open(str(filename), 'w') as tmp_fh:\n json.dump([self.dummy_metadata1], tmp_fh)\n\n schema_utils.dump_major_update(\n self.dummy_metadata2,\n filename,\n )\n\n with open(str(filename), 'r') as tmp_fh:\n saved_data = json.load(tmp_fh)\n\n assert saved_data[1] == self.dummy_metadata2","def test_get_entity_version(self):\n self.metadata.create_or_update(data=self.create)\n\n # Find by name\n res_name = self.metadata.get_by_name(\n entity=Dashboard, fqn=self.entity.fullyQualifiedName\n )\n res = self.metadata.get_entity_version(\n entity=Dashboard, entity_id=res_name.id.__root__, version=0.1\n )\n\n # check we get the correct version requested and the correct entity ID\n assert res.version.__root__ == 0.1\n assert res.id == res_name.id","def next_available_version(self):\n pattern = \"{descriptor}_{task}_v*{ext}\".format(\n descriptor=self.descriptor, task=self.task, ext=self.extension)\n matching_scenefiles = []\n try:\n for file_ in self.folder_path.files():\n if file_.name.fnmatch(pattern):\n matching_scenefiles.append(file_)\n if not matching_scenefiles:\n return 1\n except OSError as err:\n return 1\n matching_scenefiles.sort()\n latest_scenefile = matching_scenefiles[-1]\n latest_version = latest_scenefile.name.stripext().split(\"_v\")[-1]\n return int(latest_version) + 1","def test_updates_static_version(self):\n scripts.update_static_asset_version.main()","def test_get_versions_cached(self):\n versions = {\"foo-1.0.tar.gz\": \"../../packages/foo-1.0.tar.gz\"}\n self.index._save_index(\"foo\", versions)\n with patch(\"cheddar.index.remote.get\") as mocked:\n result = self.index.get_versions(\"foo\")\n eq_(result, versions)\n eq_(mocked.call_count, 0)","def update_metadata(cube_list, infile_history):\n\n equalise_attributes(cube_list)\n\n for cube in cube_list:\n cube.attributes['history'] = gio.write_metadata(file_info=infile_history)\n cube.data = numpy.array(cube.data) #removes _FillValue attribute\n\n return cube_list","def upgrade(self):","def upgrade(self):","def _do_3dsmax_post_publish(self, work_template, progress_cb): \n from Py3dsMax import mxs\n \n progress_cb(0, \"Versioning up the scene file\")\n \n # get the current scene path:\n scene_path = os.path.abspath(os.path.join(mxs.maxFilePath, mxs.maxFileName))\n \n # increment version and construct new file name:\n progress_cb(25, \"Finding next version number\")\n fields = work_template.get_fields(scene_path)\n next_version = self._get_next_work_file_version(work_template, fields)\n fields[\"version\"] = next_version \n new_scene_path = work_template.apply_fields(fields)\n \n # log info\n self.parent.log_debug(\"Version up work file %s --> %s...\" % (scene_path, new_scene_path))\n \n # rename and save the file\n progress_cb(50, \"Saving the scene file\")\n mxs.saveMaxFile(new_scene_path)\n \n progress_cb(100)","def test_changeAllProjectVersions(self):\n root = FilePath(self.mktemp())\n root.createDirectory()\n structure = {\n \"README\": \"Hi this is 1.0.0.\",\n \"twisted\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.0\"},\n \"_version.py\":\n genVersion(\"twisted\", 1, 0, 0),\n \"web\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.0\"},\n \"_version.py\": genVersion(\"twisted.web\", 1, 0, 0)\n }}}\n self.createStructure(root, structure)\n changeAllProjectVersions(root, Version(\"lol\", 1, 0, 2))\n outStructure = {\n \"README\": \"Hi this is 1.0.2.\",\n \"twisted\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.2\"},\n \"_version.py\":\n genVersion(\"twisted\", 1, 0, 2),\n \"web\": {\n \"topfiles\": {\n \"README\": \"Hi this is 1.0.2\"},\n \"_version.py\": genVersion(\"twisted.web\", 1, 0, 2),\n }}}\n self.assertStructure(root, outStructure)","def update_scenes(self) -> None:\n self.scenes.update(\n {\n f\"{group.id}_{scene.id}\": scene\n for group in self.groups.values() # type: ignore\n for scene in group.scenes.values()\n if f\"{group.id}_{scene.id}\" not in self.scenes\n }\n )","def single_node_changed(self, old_version, new_version):\n self.scene().update_scene_single_node_change(self.controller,\n old_version,\n new_version)\n if self.controller and self.controller.reset_version_view:\n self.scene().fitToAllViews()\n if self.controller:\n # self.versionProp.updateVersion(self.controller.current_version)\n self.versionProp.updateVersion(self.controller.current_version)\n self.emit(QtCore.SIGNAL(\"vistrailChanged()\"))","async def check_new_version(now):\n result = await get_newest_version(hass, huuid, include_components)\n\n if result is None:\n return\n\n newest, releasenotes, android, apt = result\n\n # Load data from supervisor on hass.io\n if hass.components.hassio.is_hassio():\n newest = hass.components.hassio.get_homeassistant_version()\n\n # Validate version\n if StrictVersion(newest) > StrictVersion(current_version):\n _LOGGER.info(\"The latest available version is %s\", newest)\n info = 'Dostępna jest nowa wersja ' + newest + '. ' + releasenotes\n hass.states.async_set(\n ENTITY_ID, info, {\n ATTR_FRIENDLY_NAME: 'Aktualizacja',\n \"icon\": \"mdi:update\",\n \"reinstall_dom_app\": True,\n \"reinstall_android_app\": android,\n \"apt\": apt\n }\n )\n # add all entities to keep the order\n # hass.async_add_job(\n # hass.services.async_call(\n # 'group',\n # 'set', {\n # \"object_id\": \"dom_system_version\",\n # \"entities\": [\n # \"sensor.version_info\",\n # \"script.ais_update_system\",\n # \"camera.remote_access\",\n # \"input_boolean.ais_remote_access\",\n # \"sensor.ais_secure_android_id_dom\",\n # \"script.ais_scan_network_devices\",\n # \"script.ais_restart_system\",\n # \"script.ais_stop_system\"]}))\n\n hass.states.async_set(\n 'script.ais_update_system', 'off', {\n ATTR_FRIENDLY_NAME: ' Zainstaluj aktualizację',\n \"icon\": \"mdi:download\"\n }\n )\n\n else:\n info = 'Twój system jest aktualny, wersja ' + newest + '. '\n info += releasenotes\n hass.states.async_set(\n ENTITY_ID, info, {\n ATTR_FRIENDLY_NAME: 'Wersja',\n \"icon\": \"mdi:update\",\n \"reinstall_dom_app\": False,\n \"reinstall_android_app\": False,\n \"apt\": apt\n }\n )\n hass.states.async_set(\n 'script.ais_update_system', 'off', {\n ATTR_FRIENDLY_NAME: ' Sprawdź dostępność aktualizacji',\n \"icon\": \"mdi:refresh\"\n }\n )\n _LOGGER.info(\n \"You are on the latest version (%s) of Assystent domowy\", newest)","def test_update_metadata1(self):\n pass","def _update_metadata(self, container_name: Union[str, list] = None, multibeam_file_list: list = None, crs: str = None,\n vertical_reference: str = None):\n\n if container_name:\n if isinstance(container_name, str): # only one converted data instance provided\n container_name = [container_name]\n if isinstance(multibeam_file_list[0], str): # only one converted data instance provided\n multibeam_file_list = [multibeam_file_list]\n for contname, mfiles in zip(container_name, multibeam_file_list):\n if mfiles:\n if contname in self.sources and (set(mfiles) & set(self.sources[contname])):\n raise ValueError('QuadManager: Found some of these new lines in the existing lines metadata for container: {}'.format(contname))\n elif contname in self.sources:\n self.sources[contname].extend(mfiles)\n else:\n self.sources[contname] = mfiles\n else:\n self.sources[contname] = ['unknown']\n if self.crs and (self.crs != crs):\n raise ValueError('QuadManager: Found existing coordinate system {}, new coordinate system {} must match'.format(self.crs,\n crs))\n if self.vertical_reference and (self.vertical_reference != vertical_reference):\n raise ValueError('QuadManager: Found existing vertical reference {}, new vertical reference {} must match'.format(self.vertical_reference,\n vertical_reference))\n self.crs = crs\n self.vertical_reference = vertical_reference","def test_update_meta_file_meta_file_exists(self):\n # Expected results\n date_old = '2021-09-13'\n date_new = '2021-09-14'\n dates_expected = [date_old, date_new]\n\n meta_content = f\"{self.meta_date_col},{self.meta_timestamp_col}\\n{date_old},{datetime.today().strftime(self.meta_timestamp_format)}\"\n self.bucket.put_object(Body=meta_content, Key=self.meta_key)\n # method execution\n MetaFile.update_meta_file(date_new, self.trg_bucket_connector)\n # read meta file\n df_result = self.trg_bucket_connector.read_meta_file()\n dates_result = list(df_result[\n self.meta_date_col])\n self.assertEqual(dates_expected, dates_result)"],"string":"[\n \"def checkVersions():\\n item = Item(fromScene=True)\\n\\n for ns, componentMData in item.components.iteritems():\\n if ns == 'cam':\\n # todo tratar versoes da camera\\n continue\\n\\n if componentMData['assembleMode'] == 'reference':\\n refComponent = ReferenceComponent(ns, componentMData, parent=item)\\n refComponent.checkDBForNewVersion()\\n\\n elif componentMData['assembleMode'] == 'xlo':\\n xloComponent = XloComponent(ns, componentMData, parent=item)\\n xloComponent.checkDBForNewVersion()\\n xloComponent.checkDBForNewCacheVersion()\\n\\n elif componentMData['assembleMode'] == 'cache':\\n cacheComponent = CacheComponent(ns, componentMData, parent=item)\\n cacheComponent.checkDBForNewVersion()\\n\\n item.putDataToDB()\",\n \"def update_versions(self, reference_resolution):\\n raise NotImplementedError(\\\"update_versions is not implemented\\\")\",\n \"def set_media_versions(self):\\n\\n # access to .multimedia_map is slow\\n previous_version = self._get_version_comparison_build()\\n prev_multimedia_map = previous_version.multimedia_map if previous_version else {}\\n\\n for path, map_item in self.multimedia_map.items():\\n prev_map_item = prev_multimedia_map.get(path, None)\\n if prev_map_item and prev_map_item.unique_id:\\n # Re-use the id so CommCare knows it's the same resource\\n map_item.unique_id = prev_map_item.unique_id\\n if (prev_map_item and prev_map_item.version\\n and prev_map_item.multimedia_id == map_item.multimedia_id):\\n map_item.version = prev_map_item.version\\n else:\\n map_item.version = self.version\",\n \"def check_referenced_versions(self, pdm=None):\\n if not pdm:\\n pdm = ProgressManagerFactory.get_progress_manager()\\n\\n caller = pdm.register(\\n 3, \\\"%s.check_referenced_versions() prepare data\\\" % self.__class__.__name__\\n )\\n\\n # deeply get which file is referencing which other files\\n self.deep_version_inputs_update()\\n if caller:\\n caller.step()\\n\\n from anima.dcc import empty_reference_resolution\\n\\n reference_resolution = empty_reference_resolution(\\n root=self.get_referenced_versions()\\n )\\n\\n if caller:\\n caller.step()\\n\\n # reverse walk in DFS\\n dfs_version_references = []\\n\\n version = self.get_current_version()\\n if not version:\\n return reference_resolution\\n\\n for v in version.walk_inputs():\\n dfs_version_references.append(v)\\n\\n if caller:\\n caller.step()\\n\\n # pop the first element which is the current scene\\n dfs_version_references.pop(0)\\n\\n caller.end_progress()\\n\\n # register a new caller\\n caller = pdm.register(\\n len(dfs_version_references),\\n \\\"%s.check_referenced_versions()\\\" % self.__class__.__name__,\\n )\\n\\n # iterate back in the list\\n for v in reversed(dfs_version_references):\\n # check inputs first\\n to_be_updated_list = []\\n for ref_v in v.inputs:\\n if not ref_v.is_latest_published_version():\\n to_be_updated_list.append(ref_v)\\n\\n if to_be_updated_list:\\n action = \\\"create\\\"\\n # check if there is a new published version of this version\\n # that is using all the updated versions of the references\\n latest_published_version = v.latest_published_version\\n if latest_published_version and not v.is_latest_published_version():\\n # so there is a new published version\\n # check if its children needs any update\\n # and the updated child versions are already\\n # referenced to the this published version\\n if all(\\n [\\n ref_v.latest_published_version\\n in latest_published_version.inputs\\n for ref_v in to_be_updated_list\\n ]\\n ):\\n # so all new versions are referenced to this published\\n # version, just update to this latest published version\\n action = \\\"update\\\"\\n else:\\n # not all references are in the inputs\\n # so we need to create a new version as usual\\n # and update the references to the latest versions\\n action = \\\"create\\\"\\n else:\\n # nothing needs to be updated,\\n # so check if this version has a new version,\\n # also there could be no reference under this referenced\\n # version\\n if v.is_latest_published_version():\\n # do nothing\\n action = \\\"leave\\\"\\n else:\\n # update to latest published version\\n action = \\\"update\\\"\\n\\n # before setting the action check all the inputs in\\n # resolution_dictionary, if any of them are update, or create\\n # then set this one to 'create'\\n if any(\\n rev_v in reference_resolution[\\\"update\\\"]\\n or rev_v in reference_resolution[\\\"create\\\"]\\n for rev_v in v.inputs\\n ):\\n action = \\\"create\\\"\\n\\n # so append this v to the related action list\\n reference_resolution[action].append(v)\\n\\n # from stalker import Version\\n # assert isinstance(v, Version)\\n caller.step(message=v.nice_name)\\n\\n caller.end_progress()\\n\\n return reference_resolution\",\n \"def check_if_previous_version_references(progress_controller=None):\\n if progress_controller is None:\\n progress_controller = ProgressControllerBase()\\n\\n from anima.dcc.mayaEnv import Maya\\n\\n m = Maya()\\n ver = m.get_current_version()\\n\\n if ver is None:\\n progress_controller.complete()\\n return\\n\\n same_version_references = []\\n all_references = pm.listReferences()\\n progress_controller.maximum = len(all_references)\\n for ref in all_references: # check only 1st level references\\n ref_version = m.get_version_from_full_path(ref.path)\\n if ref_version:\\n if ref_version.task == ver.task and ref_version.take_name == ver.take_name:\\n same_version_references.append(ref)\\n progress_controller.increment()\\n\\n progress_controller.complete()\\n if len(same_version_references):\\n print(\\\"The following nodes are references to an older version of this \\\" \\\"scene\\\")\\n print(\\\"\\\\n\\\".join(map(lambda x: x.refNode.name(), same_version_references)))\\n raise PublishError(\\n \\\"The current scene contains a <b>reference</b> to a<br>\\\"\\n \\\"<b>previous version</b> of itself.<br><br>\\\"\\n \\\"Please remove it!!!\\\"\\n )\",\n \"def saveVersion(self, makeReference=True, versionNotes=\\\"\\\", sceneFormat=\\\"mb\\\", *args, **kwargs):\\n logger.debug(\\\"Func: saveVersion\\\")\\n\\n\\n\\n now = datetime.datetime.now().strftime(\\\"%d/%m/%Y-%H:%M\\\")\\n completeNote = \\\"[%s] on %s\\\\n%s\\\\n\\\" % (self.currentUser, now, versionNotes)\\n\\n sceneName = self.getSceneFile()\\n if not sceneName:\\n msg = \\\"This is not a base scene (Untitled)\\\"\\n self._exception(360, msg)\\n return -1, msg\\n\\n sceneInfo = self.getOpenSceneInfo()\\n\\n if sceneInfo: ## getCurrentJson returns None if the resolved json path is missing\\n jsonFile = sceneInfo[\\\"jsonFile\\\"]\\n jsonInfo = self._loadJson(jsonFile)\\n\\n currentVersion = len(jsonInfo[\\\"Versions\\\"]) + 1\\n sceneName = \\\"{0}_{1}_{2}_v{3}\\\".format(jsonInfo[\\\"Name\\\"], jsonInfo[\\\"Category\\\"], self._usersDict[self.currentUser],\\n str(currentVersion).zfill(3))\\n relSceneFile = os.path.join(jsonInfo[\\\"Path\\\"], \\\"{0}.{1}\\\".format(sceneName, sceneFormat))\\n\\n sceneFile = os.path.join(sceneInfo[\\\"projectPath\\\"], relSceneFile)\\n\\n # killTurtle()\\n # TODO // cmds?\\n # pm.saveAs(sceneFile)\\n\\n nuke.scriptSaveAs(sceneFile)\\n\\n thumbPath = self.createThumbnail(dbPath=jsonFile, versionInt=currentVersion)\\n\\n jsonInfo[\\\"Versions\\\"].append(\\n # PATH => Notes => User Initials => Machine ID => Playblast => Thumbnail\\n # TODO : ref => Dict\\n {\\\"RelativePath\\\": relSceneFile,\\n \\\"Note\\\": completeNote,\\n \\\"User\\\": self._usersDict[self.currentUser],\\n \\\"Workstation\\\": socket.gethostname(),\\n \\\"Preview\\\": {},\\n \\\"Thumb\\\": thumbPath,\\n \\\"Ranges\\\": self._getTimelineRanges()\\n }\\n )\\n\\n if makeReference:\\n referenceName = \\\"{0}_{1}_forReference\\\".format(jsonInfo[\\\"Name\\\"], jsonInfo[\\\"Category\\\"])\\n relReferenceFile = os.path.join(jsonInfo[\\\"Path\\\"], \\\"{0}.{1}\\\".format(referenceName, sceneFormat))\\n referenceFile = os.path.join(sceneInfo[\\\"projectPath\\\"], relReferenceFile)\\n\\n shutil.copyfile(sceneFile, referenceFile)\\n jsonInfo[\\\"ReferenceFile\\\"] = relReferenceFile\\n jsonInfo[\\\"ReferencedVersion\\\"] = currentVersion\\n self._dumpJson(jsonInfo, jsonFile)\\n else:\\n msg = \\\"This is not a base scene (Json file cannot be found)\\\"\\n self._exception(360, msg)\\n return -1, msg\\n return jsonInfo\",\n \"def version_updater(logging_level=logging.WARNING):\\n # connect to db\\n do_db_setup()\\n\\n # set Qt lib\\n set_qt_lib()\\n\\n from anima.ui import version_updater, models\\n from anima.env import mayaEnv\\n reload(mayaEnv)\\n reload(version_updater)\\n reload(models)\\n m = Maya()\\n import pymel\\n m.name = \\\"Maya\\\" + str(pymel.versions.current())[0:4]\\n\\n logger.setLevel(logging_level)\\n\\n # generate a reference_resolution\\n version_updater.UI(environment=m)\",\n \"def populateSceneRefs(*args):\\n pi.referenceDictionary = {}\\n cmds.textScrollList(widgets[\\\"shotAssListTSL\\\"], e=True, ra=True)\\n\\n #get reference paths\\n refs = cmds.file(q=True, r=True)\\n\\n buff = []\\n # loaded = []\\n for ref in refs:\\n #get the associated namespace\\n ns = cmds.file(ref, q=True, ns=True)\\n pi.referenceDictionary[ns] = ref\\n\\n # put files in buffer list to sort\\n for g in pi.referenceDictionary.keys():\\n buff.append(g)\\n buff.sort()\\n\\n # now put the sorted namespaces in the list\\n for b in buff:\\n cmds.textScrollList(widgets[\\\"shotAssListTSL\\\"], e=True, append=b, dcc = selectRefs)\\n\\n # if ref is deferred(not loaded), change it's font\\n for ref in refs:\\n if cmds.file(ref, q=True, deferReference=True):\\n ns = cmds.file(ref, q=True, ns=True) # get the namespace in order to get the item name\\n cmds.textScrollList(widgets[\\\"shotAssListTSL\\\"], e=True, selectItem=ns) # sel the item in order to query it\\n index = cmds.textScrollList(widgets[\\\"shotAssListTSL\\\"], q=True, selectIndexedItem=True)[0] # query the index of sel\\n cmds.textScrollList(widgets[\\\"shotAssListTSL\\\"], e=True, lineFont = [index, \\\"obliqueLabelFont\\\"])\\n cmds.textScrollList(widgets[\\\"shotAssListTSL\\\"], e=True, deselectAll=True)\\n\\n # if we're in a lgt file, look through current refs and for each one of type \\\"anm\\\", check the frame rates, etc. and give option to change\\n curr = paths.PathManager(cmds.file(q=True, sn=True))\\n if curr.shotType == \\\"lgt\\\":\\n for ref in refs:\\n p=paths.PathManager(ref)\\n if p.shotType == \\\"anm\\\":\\n dict = cFuncs.getFileFrameInfo(cFuncs.fixPath(ref))\\n csi.compareSceneInfo(dict)\",\n \"def test_master_versions(self):\\n m = self.d.master(4242)\\n r = self.d.release(79)\\n v = m.versions\\n\\n self.assertEqual(len(v), 2)\\n self.assertTrue(r in v)\\n self.assertEqual(r.master, m)\\n\\n r2 = self.d.release(3329867)\\n self.assertTrue(r2.master is None)\",\n \"def compareVersions(self):\\n logger.debug(\\\"Func: compareVersions\\\")\\n\\n cMajorV = nuke.NUKE_VERSION_MAJOR\\n cMinorV = nuke.NUKE_VERSION_MINOR\\n currentVersion = float(\\\"{0}.{1}\\\".format(cMajorV, cMinorV))\\n\\n dbMajorV = self._currentSceneInfo[\\\"NukeVersion\\\"][0]\\n dbMinorV = self._currentSceneInfo[\\\"NukeVersion\\\"][1]\\n databaseVersion = float(\\\"{0}.{1}\\\".format(dbMajorV, dbMinorV))\\n\\n messageList = []\\n\\n\\n if currentVersion == databaseVersion:\\n pass\\n\\n if currentVersion < databaseVersion: # version compare\\n message = \\\"Base Scene is created with a HIGHER Nuke version ({0}). Are you sure you want to continue?\\\".format(databaseVersion)\\n messageList.append(message)\\n\\n if currentVersion > databaseVersion:\\n message = \\\"Base Scene is created with a LOWER Nuke version ({0}). Are you sure you want to continue?\\\".format(databaseVersion)\\n messageList.append(message)\\n\\n message=\\\"\\\"\\n for x in messageList:\\n message = message + \\\"\\\\n\\\" + str(x)\\n\\n if messageList == []:\\n return 0, message\\n else:\\n return -1, message\",\n \"def test_ls_returns_sorted_versions():\\n with pipeline.fixture(assets=[\\\"Asset1\\\"], subsets=[\\\"animRig\\\"], versions=1):\\n for asset in pipeline.ls():\\n previous_version = 0\\n for subset in asset[\\\"subsets\\\"]:\\n for version in subset[\\\"versions\\\"]:\\n version = version[\\\"version\\\"]\\n assert version > previous_version\\n previous_version = version\",\n \"def do_snapshot(\\n self, force: bool = False\\n ) -> Tuple[bool, Dict[str, MetaFile]]:\\n\\n # Snapshot update is needed if\\n # * any targets files are not yet in snapshot or\\n # * any targets version is incorrect\\n update_version = force\\n removed: Dict[str, MetaFile] = {}\\n\\n with self.edit_snapshot() as snapshot:\\n for keyname, new_meta in self.targets_infos.items():\\n if keyname not in snapshot.meta:\\n update_version = True\\n snapshot.meta[keyname] = deepcopy(new_meta)\\n continue\\n\\n old_meta = snapshot.meta[keyname]\\n if new_meta.version < old_meta.version:\\n raise ValueError(f\\\"{keyname} version rollback\\\")\\n if new_meta.version > old_meta.version:\\n update_version = True\\n snapshot.meta[keyname] = deepcopy(new_meta)\\n removed[keyname] = old_meta\\n\\n if not update_version:\\n # prevent edit_snapshot() from storing a new version\\n raise AbortEdit(\\\"Skip snapshot: No targets version changes\\\")\\n\\n if not update_version:\\n # this is reachable as edit_snapshot() handles AbortEdit\\n logger.debug(\\\"Snapshot update not needed\\\") # type: ignore[unreachable]\\n else:\\n logger.debug(\\\"Snapshot v%d\\\", snapshot.version)\\n\\n return update_version, removed\",\n \"def _update_metadata_if_changed(self, metadata_role, referenced_metadata='release'):\\n \\n uncompressed_metadata_filename = metadata_role + '.txt'\\n\\n # Ensure the referenced metadata has been loaded. The 'root' role may be\\n # updated without having 'release' available. \\n if referenced_metadata not in self.metadata['current']:\\n message = 'Cannot update '+repr(metadata_role)+' because ' \\\\\\n +referenced_metadata+' is missing.'\\n raise tuf.RepositoryError(message)\\n # The referenced metadata has been loaded. Extract the new\\n # fileinfo for 'metadata_role' from it. \\n else:\\n message = repr(metadata_role)+' referenced in '+\\\\\\n repr(referenced_metadata)+'. '+repr(metadata_role)+' may be updated.'\\n logger.debug(message)\\n \\n # There might be a compressed version of 'release.txt' or Targets\\n # metadata available for download. Check the 'meta' field of\\n # 'referenced_metadata' to see if it is listed when 'metadata_role'\\n # is 'release'. The full rolename for delegated Targets metadata\\n # must begin with 'targets/'. The Release role lists all the Targets\\n # metadata available on the repository, including any that may be in\\n # compressed form.\\n compression = None\\n\\n # Extract the fileinfo of the uncompressed version of 'metadata_role'.\\n uncompressed_fileinfo = self.metadata['current'][referenced_metadata] \\\\\\n ['meta'] \\\\\\n [uncompressed_metadata_filename]\\n\\n # Check for availability of compressed versions of 'release.txt',\\n # 'targets.txt', and delegated Targets, which also start with 'targets'.\\n # For 'targets.txt' and delegated metadata, 'referenced_metata'\\n # should always be 'release'. 'release.txt' specifies all roles\\n # provided by a repository, including their file sizes and hashes.\\n if metadata_role == 'release' or metadata_role.startswith('targets'):\\n gzip_metadata_filename = uncompressed_metadata_filename + '.gz'\\n if gzip_metadata_filename in self.metadata['current'] \\\\\\n [referenced_metadata]['meta']:\\n compression = 'gzip'\\n compressed_fileinfo = self.metadata['current'][referenced_metadata] \\\\\\n ['meta'][gzip_metadata_filename]\\n # NOTE: When we download the compressed file, we care about its\\n # compressed length. However, we check the hash of the uncompressed\\n # file; therefore we use the hashes of the uncompressed file.\\n fileinfo = {'length': compressed_fileinfo['length'],\\n 'hashes': uncompressed_fileinfo['hashes']}\\n logger.debug('Compressed version of '+\\\\\\n repr(uncompressed_metadata_filename)+' is available at '+\\\\\\n repr(gzip_metadata_filename)+'.')\\n else:\\n logger.debug('Compressed version of '+\\\\\\n repr(uncompressed_metadata_filename)+' not available.')\\n fileinfo = uncompressed_fileinfo\\n else:\\n fileinfo = uncompressed_fileinfo\\n\\n # Simply return if the file has not changed, according to the metadata\\n # about the uncompressed file provided by the referenced metadata.\\n if not self._fileinfo_has_changed(uncompressed_metadata_filename,\\n uncompressed_fileinfo):\\n return\\n\\n logger.debug('Metadata '+repr(uncompressed_metadata_filename)+\\\\\\n ' has changed.')\\n\\n try:\\n self._update_metadata(metadata_role, fileinfo=fileinfo,\\n compression=compression)\\n except:\\n # The current metadata we have is not current but we couldn't\\n # get new metadata. We shouldn't use the old metadata anymore.\\n # This will get rid of in-memory knowledge of the role and\\n # delegated roles, but will leave delegated metadata files as\\n # current files on disk.\\n # TODO: Should we get rid of the delegated metadata files?\\n # We shouldn't need to, but we need to check the trust\\n # implications of the current implementation.\\n self._delete_metadata(metadata_role)\\n logger.error('Metadata for '+str(metadata_role)+' could not be updated')\\n raise\\n else:\\n # We need to remove delegated roles because the delegated roles\\n # may not be trusted anymore.\\n if metadata_role == 'targets' or metadata_role.startswith('targets/'):\\n logger.debug('Removing delegated roles of '+repr(metadata_role)+'.')\\n # TODO: Should we also remove the keys of the delegated roles?\\n tuf.roledb.remove_delegated_roles(metadata_role)\\n self._import_delegations(metadata_role)\",\n \"def _recalculate_versions(self):\\n versions = self._get_local_resource_versions()\\n for versions_dict in self._versions_by_consumer.values():\\n for res_type, res_version in versions_dict.items():\\n versions[res_type].add(res_version)\\n self._versions = versions\",\n \"def load_referenced_versions(self):\\n raise NotImplementedError(\\\"load_referenced_versions is not implemented\\\")\",\n \"def updateVersions(self):\\r\\n f = open('../versions.pckl', 'wb')\\r\\n pickle.dump(self.versions, f)\\r\\n f.close()\",\n \"def testMetacommunityReferencesStorage(self):\\n community_dict1 = self.t2.get_community_parameters(1)\\n community_dict2 = self.t2.get_community_parameters(2)\\n community_dict3 = self.t2.get_community_parameters(3)\\n community_dict4 = self.t2.get_community_parameters(4)\\n comparison_dict1 = {\\\"speciation_rate\\\": 0.5, \\\"time\\\": 0.0, \\\"fragments\\\": 0, \\\"metacommunity_reference\\\": 0}\\n comparison_dict2 = {\\\"speciation_rate\\\": 0.5, \\\"time\\\": 0.0, \\\"fragments\\\": 0, \\\"metacommunity_reference\\\": 1}\\n comparison_dict3 = {\\\"speciation_rate\\\": 0.5, \\\"time\\\": 0.0, \\\"fragments\\\": 0, \\\"metacommunity_reference\\\": 2}\\n comparison_dict4 = {\\\"speciation_rate\\\": 0.5, \\\"time\\\": 0.0, \\\"fragments\\\": 0, \\\"metacommunity_reference\\\": 3}\\n self.assertDictEqual(comparison_dict1, community_dict1)\\n self.assertDictEqual(comparison_dict2, community_dict2)\\n self.assertDictEqual(comparison_dict3, community_dict3)\\n self.assertDictEqual(comparison_dict4, community_dict4)\",\n \"def addon_matrix(self):\\n print(\\\"Checking riot-web version\\\")\\n repo = self.github.get_repo('vector-im/riot-web')\\n releases = list(repo.get_releases())\\n index = 0\\n while True:\\n remote_version = releases[index].tag_name\\n if 'b' in remote_version:\\n index = index + 1\\n else:\\n break\\n file = \\\"{}/Dockerfile\\\".format(self.name)\\n remote_file = self.get_file_obj(file)\\n masterfile = self.repoupdater.get_file_content(remote_file)\\n file_version = masterfile.split('releases/download/')[1]\\n file_version = file_version.split('/')[0]\\n if self.verbose:\\n print(\\\"Current version\\\", file_version)\\n print(\\\"Available version\\\", remote_version)\\n if remote_version != file_version:\\n msg = COMMIT_MSG.format('riot-web', remote_version)\\n new_content = self.repoupdater.get_file_content(remote_file)\\n new_content = new_content.replace(file_version, remote_version)\\n self.repoupdater.commit(file, msg, new_content, remote_file.sha)\\n else:\\n print(\\\"riot-web already have the newest version\\\", file_version)\",\n \"def makeReference(self):\\n logger.debug(\\\"Func: makeReference\\\")\\n\\n if self._currentVersionIndex == -1:\\n msg = \\\"Cursor is not on a Base Scene Version. Cancelling\\\"\\n # logger.warning(msg)\\n # raise Exception([101, msg])\\n self._exception(101, msg)\\n return\\n # return\\n\\n absVersionFile = os.path.join(self.projectDir, self._currentSceneInfo[\\\"Versions\\\"][self._currentVersionIndex-1][\\\"RelativePath\\\"])\\n name = os.path.split(absVersionFile)[1]\\n filename, extension = os.path.splitext(name)\\n referenceName = \\\"{0}_{1}_forReference\\\".format(self._currentSceneInfo[\\\"Name\\\"], self._currentSceneInfo[\\\"Category\\\"])\\n relReferenceFile = os.path.join(self._currentSceneInfo[\\\"Path\\\"], \\\"{0}{1}\\\".format(referenceName, extension))\\n absReferenceFile = os.path.join(self.projectDir, relReferenceFile)\\n shutil.copyfile(absVersionFile, absReferenceFile)\\n self._currentSceneInfo[\\\"ReferenceFile\\\"] = relReferenceFile\\n # SET the referenced version as the 'VISUAL INDEX NUMBER' starting from 1\\n self._currentSceneInfo[\\\"ReferencedVersion\\\"] = self._currentVersionIndex\\n\\n self._dumpJson(self._currentSceneInfo, self._baseScenesInCategory[self.currentBaseSceneName])\",\n \"def testMultipleOldRefs(self):\\n self.mr2 = cdl_convert.MediaRef('hello')\\n\\n self.assertEqual(\\n {'hello': [self.mr, self.mr2]},\\n cdl_convert.MediaRef.members\\n )\\n\\n self.mr._filename = 'goodbye'\\n self.mr._change_membership(old_ref='hello')\\n\\n self.assertEqual(\\n {'hello': [self.mr2],'goodbye': [self.mr]},\\n cdl_convert.MediaRef.members\\n )\",\n \"def updateMdrizVerHistory(self,build,versions):\\n _plist = self.assoc.parlist[0]\\n if build == True: _output = _plist['output']\\n else: _output = _plist['outdata']\\n \\n fhdu = pyfits.open(_output,mode='update')\\n prihdr = fhdu[0].header\\n \\n ver_str = \\\"MultiDrizzle product generated using: \\\"\\n prihdr.add_history(ver_str)\\n \\n for key in versions:\\n if versions[key].find('\\\\n') < 0:\\n prihdr.add_history(key+versions[key])\\n else:\\n # This will accomodate multi-line comments\\n _ver_str = versions[key].split('\\\\n')\\n prihdr.add_history(key)\\n for val in _ver_str:\\n if val.strip() != '':\\n prihdr.add_history(val)\\n \\n #ver_str = ' MultiDrizzle Version '+str(version)\\n #prihdr.add_history(ver_str)\\n \\n fhdu.close()\\n del fhdu\",\n \"def updateToLatest(self):\\n # Determine the newest stamp in each dependency\\n latest = {}\\n for item in self.spec:\\n if isinstance(item, CachedResource):\\n latest[item] = item.getLatestStamp()\\n if not latest:\\n return\\n\\n # Our new stamp is the greatest out of all deps' stamps\\n stamp = max(latest.itervalues())\\n\\n # Update only if we need to\\n if self.getLatestStamp() >= stamp:\\n return\\n self.updateStamp(latest, stamp)\\n\\n # Clean old versions if that was successful\\n self.cleanStamps(lambda s: s < stamp)\",\n \"def getVersion(self):\\n self.getDocumentedObject().getVersion()\",\n \"def get_and_update_versions ():\\n\\n try:\\n get_comp_versions (\\\"ACE\\\")\\n get_comp_versions (\\\"TAO\\\")\\n\\n if opts.update:\\n files = []\\n files += update_version_files (\\\"ACE\\\")\\n files += update_version_files (\\\"TAO\\\")\\n files += create_changelog (\\\"ACE\\\")\\n files += create_changelog (\\\"TAO\\\")\\n files += update_spec_file ()\\n files += update_debianbuild ()\\n\\n commit (files)\\n\\n except:\\n print (\\\"Fatal error in get_and_update_versions.\\\")\\n raise\",\n \"def compare_version_objects(version1, version2):\\n if version1.epoch < version2.epoch:\\n return -1\\n if version1.epoch > version2.epoch:\\n return 1\\n result = compare_strings(version1.upstream, version2.upstream)\\n if result != 0:\\n return result\\n if version1.revision or version2.revision:\\n return compare_strings(version1.revision, version2.revision)\\n return 0\",\n \"def test_changeAllProjectVersionsPreRelease(self):\\n root = FilePath(self.mktemp())\\n root.createDirectory()\\n coreNews = (\\\"Twisted Core 1.0.0 (2009-12-25)\\\\n\\\"\\n \\\"===============================\\\\n\\\"\\n \\\"\\\\n\\\")\\n webNews = (\\\"Twisted Web 1.0.0pre1 (2009-12-25)\\\\n\\\"\\n \\\"==================================\\\\n\\\"\\n \\\"\\\\n\\\")\\n structure = {\\n \\\"README\\\": \\\"Hi this is 1.0.0.\\\",\\n \\\"NEWS\\\": coreNews + webNews,\\n \\\"twisted\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.0\\\",\\n \\\"NEWS\\\": coreNews},\\n \\\"_version.py\\\":\\n genVersion(\\\"twisted\\\", 1, 0, 0),\\n \\\"web\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.0pre1\\\",\\n \\\"NEWS\\\": webNews},\\n \\\"_version.py\\\": genVersion(\\\"twisted.web\\\", 1, 0, 0, 1)\\n }}}\\n self.createStructure(root, structure)\\n changeAllProjectVersions(root, Version(\\\"lol\\\", 1, 0, 2), '2010-01-01')\\n coreNews = (\\n \\\"Twisted Core 1.0.0 (2009-12-25)\\\\n\\\"\\n \\\"===============================\\\\n\\\"\\n \\\"\\\\n\\\")\\n webNews = (\\\"Twisted Web 1.0.2 (2010-01-01)\\\\n\\\"\\n \\\"==============================\\\\n\\\"\\n \\\"\\\\n\\\")\\n outStructure = {\\n \\\"README\\\": \\\"Hi this is 1.0.2.\\\",\\n \\\"NEWS\\\": coreNews + webNews,\\n \\\"twisted\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.2\\\",\\n \\\"NEWS\\\": coreNews},\\n \\\"_version.py\\\":\\n genVersion(\\\"twisted\\\", 1, 0, 2),\\n \\\"web\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.2\\\",\\n \\\"NEWS\\\": webNews},\\n \\\"_version.py\\\": genVersion(\\\"twisted.web\\\", 1, 0, 2),\\n }}}\\n self.assertStructure(root, outStructure)\",\n \"def versions(self):\\n raise Exception(\\\"mcapi.Datafile.versions is not implemented\\\")\",\n \"def test_ls():\\n\\n with pipeline.fixture(assets=[\\\"Asset1\\\"],\\n subsets=[\\\"animRig\\\"],\\n versions=1) as root:\\n asset = next(pipeline.ls())\\n\\n reference = {\\n \\\"schema\\\": \\\"pyblish-mindbender:asset-1.0\\\",\\n \\\"name\\\": \\\"Asset1\\\",\\n \\\"subsets\\\": [\\n {\\n \\\"schema\\\": \\\"pyblish-mindbender:subset-1.0\\\",\\n \\\"name\\\": \\\"animRig\\\",\\n \\\"versions\\\": [\\n {\\n \\\"schema\\\": \\\"pyblish-mindbender:version-1.0\\\",\\n \\\"version\\\": 1,\\n \\\"path\\\": os.path.join(\\n root,\\n \\\"Asset1\\\",\\n \\\"publish\\\",\\n \\\"animRig\\\",\\n \\\"v001\\\"\\n ),\\n \\\"source\\\": os.path.join(\\n \\\"{project}\\\",\\n \\\"maya\\\",\\n \\\"scenes\\\",\\n \\\"scene.ma\\\"\\n ),\\n \\\"representations\\\": [\\n {\\n \\\"schema\\\": (\\\"pyblish-mindbender:\\\"\\n \\\"representation-1.0\\\"),\\n \\\"format\\\": \\\".ma\\\",\\n \\\"path\\\": os.path.join(\\n \\\"{dirname}\\\",\\n \\\"Asset1{format}\\\"\\n ),\\n }\\n ],\\n \\\"time\\\": \\\"\\\",\\n \\\"author\\\": \\\"mottosso\\\",\\n },\\n ]\\n }\\n ]\\n }\\n\\n # Printed on error\\n print(\\\"# Comparing result:\\\")\\n print(json.dumps(asset, indent=4, sort_keys=True))\\n print(\\\"# With reference:\\\")\\n print(json.dumps(reference, indent=4, sort_keys=True))\\n\\n assert_equals(asset, reference)\",\n \"def saveCallback(self):\\n\\n ## TODO // TEST IT\\n self._pathsDict[\\\"sceneFile\\\"] = self.getSceneFile()\\n try:\\n openSceneInfo = self.getOpenSceneInfo()\\n if not openSceneInfo:\\n return\\n except TypeError:\\n return\\n if openSceneInfo[\\\"jsonFile\\\"]:\\n jsonInfo = self._loadJson(openSceneInfo[\\\"jsonFile\\\"])\\n if jsonInfo[\\\"ReferenceFile\\\"]:\\n absRefFile = os.path.join(self._pathsDict[\\\"projectDir\\\"], jsonInfo[\\\"ReferenceFile\\\"])\\n # TODO : ref => Dict\\n absBaseSceneVersion = os.path.join(self._pathsDict[\\\"projectDir\\\"], jsonInfo[\\\"Versions\\\"][int(jsonInfo[\\\"ReferencedVersion\\\"]) - 1][\\\"RelativePath\\\"])\\n # if the refererenced scene file is the saved file (saved or saved as)\\n if self._pathsDict[\\\"sceneFile\\\"] == absBaseSceneVersion:\\n # copy over the forReference file\\n try:\\n shutil.copyfile(self._pathsDict[\\\"sceneFile\\\"], absRefFile)\\n print \\\"Scene Manager Update:\\\\nReference File Updated\\\"\\n except:\\n pass\",\n \"def set_form_versions(self):\\n def _hash(val):\\n return hashlib.md5(val).hexdigest()\\n\\n latest_build = self._get_version_comparison_build()\\n if not latest_build:\\n return\\n force_new_version = self.build_profiles != latest_build.build_profiles\\n for form_stuff in self.get_forms(bare=False):\\n filename = 'files/%s' % self.get_form_filename(**form_stuff)\\n current_form = form_stuff[\\\"form\\\"]\\n if not force_new_version:\\n try:\\n previous_form = latest_build.get_form(current_form.unique_id)\\n # take the previous version's compiled form as-is\\n # (generation code may have changed since last build)\\n previous_source = latest_build.fetch_attachment(filename)\\n except (ResourceNotFound, FormNotFoundException):\\n current_form.version = None\\n else:\\n previous_hash = _hash(previous_source)\\n\\n # set form version to previous version, and only update if content has changed\\n current_form.version = previous_form.get_version()\\n current_form = current_form.validate_form()\\n current_hash = _hash(current_form.render_xform())\\n if previous_hash != current_hash:\\n current_form.version = None\\n # clear cache since render_xform was called with a mutated form set to the previous version\\n current_form.render_xform.reset_cache(current_form)\\n else:\\n current_form.version = None\",\n \"def update():\\n as3s = AS3Schema()\\n as3s.updateschemas()\\n as3s_new = AS3Schema()\\n\\n if as3s.version != as3s_new.version:\\n click.echo(\\n f\\\"Updated AS3 JSON Schemas from version:{as3s.version} to:{as3s_new.version}\\\",\\n )\\n else:\\n click.echo(\\n f\\\"AS3 JSON Schemas are up-to-date, current version:{as3s.version}\\\",\\n )\",\n \"def _do_maya_post_publish(self, work_template, progress_cb): \\n import maya.cmds as cmds\\n \\n progress_cb(0, \\\"Versioning up the scene file\\\")\\n \\n # get the current scene path:\\n scene_path = os.path.abspath(cmds.file(query=True, sn=True))\\n \\n # increment version and construct new file name:\\n progress_cb(25, \\\"Finding next version number\\\")\\n fields = work_template.get_fields(scene_path)\\n next_version = self._get_next_work_file_version(work_template, fields)\\n fields[\\\"version\\\"] = next_version \\n new_scene_path = work_template.apply_fields(fields)\\n \\n # log info\\n self.parent.log_debug(\\\"Version up work file %s --> %s...\\\" % (scene_path, new_scene_path))\\n \\n # rename and save the file\\n progress_cb(50, \\\"Saving the scene file\\\")\\n cmds.file(rename=new_scene_path)\\n cmds.file(save=True)\\n \\n #updating shotgun task status to cmpt---\\n \\n progress_cb(100)\",\n \"def update_versions(consumer, resource_versions):\\n _get_cached_tracker().update_versions(consumer, resource_versions)\",\n \"def check_representations(progress_controller=None):\\n if progress_controller is None:\\n progress_controller = ProgressControllerBase()\\n\\n ref_reprs = []\\n wrong_reprs = []\\n\\n v = staging.get(\\\"version\\\")\\n if v:\\n r = Representation(version=v)\\n current_repr = r.repr\\n\\n # For **Base** representation\\n # allow any type of representation to be present in the scene\\n if r.is_base():\\n progress_controller.complete()\\n return\\n\\n references = pm.listReferences()\\n progress_controller.maximum = len(references)\\n for ref in references:\\n ref_repr = ref.repr\\n if ref_repr is None:\\n # skip this one this is not related to a Stalker Version\\n progress_controller.increment()\\n continue\\n\\n ref_reprs.append([ref, ref_repr])\\n if ref_repr != current_repr:\\n wrong_reprs.append(ref)\\n progress_controller.increment()\\n else:\\n progress_controller.complete()\\n return\\n\\n progress_controller.complete()\\n if len(wrong_reprs):\\n ref_repr_labels = []\\n for ref_repr in ref_reprs:\\n ref = ref_repr[0]\\n repr_name = ref_repr[1]\\n\\n color = \\\"red\\\" if current_repr != repr_name else \\\"green\\\"\\n\\n ref_repr_labels.append(\\n '<span style=\\\"color: %(color)s\\\">%(repr_name)s</span> -> '\\n \\\"%(ref)s\\\"\\n % {\\\"color\\\": color, \\\"repr_name\\\": repr_name, \\\"ref\\\": ref.refNode.name()}\\n )\\n\\n raise PublishError(\\n \\\"You are saving as the <b>%s</b> representation<br>\\\"\\n \\\"for the current scene, but the following references<br>\\\"\\n \\\"are not <b>%s</b> representations of their versions:<br><br>\\\"\\n \\\"%s\\\"\\n % (\\n current_repr,\\n current_repr,\\n \\\"<br>\\\".join(ref_repr_labels[:MAX_NODE_DISPLAY]),\\n )\\n )\",\n \"def run():\\n scene = lm.scene_object()\\n copy_latest_low()\\n copy_latest_high()\",\n \"def getVersions(self):\\n logger.debug(\\\"Func: getVersions\\\")\\n\\n try:\\n return self._currentSceneInfo[\\\"Versions\\\"]\\n except:\\n return []\",\n \"def test_list_versions(self):\\n self.metadata.create_or_update(data=self.create)\\n\\n # Find by name\\n res_name = self.metadata.get_by_name(\\n entity=Dashboard, fqn=self.entity.fullyQualifiedName\\n )\\n\\n res = self.metadata.get_list_entity_versions(\\n entity=Dashboard, entity_id=res_name.id.__root__\\n )\\n assert res\",\n \"def scene_is_modified():\\n\\n pass\",\n \"def test_item_revision_history(testapp, registry):\\n objv1 = {\\n 'title': \\\"Testing1\\\",\\n 'description': \\\"This is testing object 1\\\",\\n }\\n objv2 = {\\n 'title': \\\"Testing2\\\",\\n 'description': \\\"This is testing object 2\\\",\\n }\\n objv3 = {\\n 'title': \\\"Testing3\\\",\\n 'description': \\\"This is testing object 3\\\",\\n }\\n item_uuid = testapp.post_json('/embedding-tests', objv1, status=201).json['@graph'][0]['uuid']\\n testapp.patch_json('/' + item_uuid, objv2, status=200)\\n testapp.patch_json('/' + item_uuid, objv3, status=200)\\n\\n # now get revision history\\n revisions = testapp.get('/' + item_uuid + '/@@revision-history').json['revisions']\\n assert len(revisions) == 3 # we made 3 edits\\n\\n # lets make some more\\n testapp.patch_json('/' + item_uuid, objv2, status=200)\\n testapp.patch_json('/' + item_uuid, objv1, status=200)\\n revisions = testapp.get('/' + item_uuid + '/@@revision-history').json['revisions']\\n assert len(revisions) == 5 # now we made 5 edits\\n # they should be ordered by sid, recall the patch order above\\n for patched_metadata, revision in zip([objv1, objv2, objv3, objv2, objv1], revisions):\\n assert revision['title'] == patched_metadata['title']\",\n \"def revision(self):\\n self.r3.reuse = self.r2\\n self.r3.case = self.case\\n self.r3.revise()\\n self.r2.predictionGenre = self.case.playlist_genre\\n self.r2.predictionSubGenre =self.case.playlist_subgenre\\n print()\",\n \"def replace_old(self):\\n # Tell the client the index is ready to be used and to no longer show it as building.\\n self.db.indexes.find_one_and_update({\\\"_id\\\": self.params[\\\"index_id\\\"]}, {\\n \\\"$set\\\": {\\n \\\"ready\\\": True\\n }\\n })\\n\\n self.dispatch(\\\"indexes\\\", \\\"update\\\", [self.params[\\\"index_id\\\"]])\\n\\n active_indexes = virtool.db.sync.get_active_index_ids(self.db, self.params[\\\"ref_id\\\"])\\n\\n remove_unused_index_files(self.params[\\\"reference_path\\\"], active_indexes)\\n\\n query = {\\n \\\"_id\\\": {\\n \\\"$not\\\": {\\n \\\"$in\\\": active_indexes\\n }\\n }\\n }\\n\\n self.db.indexes.update_many(query, {\\n \\\"$set\\\": {\\n \\\"has_files\\\": False\\n }\\n })\\n\\n id_list = self.db.indexes.distinct(\\\"_id\\\", query)\\n\\n self.dispatch(\\\"indexes\\\", \\\"update\\\", id_list)\\n\\n # Find OTUs with changes.\\n pipeline = [\\n {\\\"$project\\\": {\\n \\\"reference\\\": True,\\n \\\"version\\\": True,\\n \\\"last_indexed_version\\\": True,\\n \\\"comp\\\": {\\\"$cmp\\\": [\\\"$version\\\", \\\"$last_indexed_version\\\"]}\\n }},\\n {\\\"$match\\\": {\\n \\\"reference.id\\\": self.params[\\\"ref_id\\\"],\\n \\\"comp\\\": {\\\"$ne\\\": 0}\\n }},\\n {\\\"$group\\\": {\\n \\\"_id\\\": \\\"$version\\\",\\n \\\"id_list\\\": {\\n \\\"$addToSet\\\": \\\"$_id\\\"\\n }\\n }}\\n ]\\n\\n id_version_key = {agg[\\\"_id\\\"]: agg[\\\"id_list\\\"] for agg in self.db.otus.aggregate(pipeline)}\\n\\n # For each version number\\n for version, id_list in id_version_key.items():\\n self.db.otus.update_many({\\\"_id\\\": {\\\"$in\\\": id_list}}, {\\n \\\"$set\\\": {\\n \\\"last_indexed_version\\\": version\\n }\\n })\",\n \"def _sceneChanged(self, oldScene, newScene):\\n pass\",\n \"def test_update_revision_from_history_with_diffsets(self):\\n repository = self.create_repository(tool_name='Test')\\n diffset_history = DiffSetHistory.objects.create()\\n diffset_history.diffsets.add(\\n self.create_diffset(repository=repository))\\n\\n diffset = DiffSet()\\n diffset.update_revision_from_history(diffset_history)\\n\\n self.assertEqual(diffset.revision, 2)\",\n \"def update_version_inputs(self, parent_ref=None):\\n logger.debug(\\\"parent_ref: %s\\\" % parent_ref)\\n\\n logger.debug(\\\"get a version\\\")\\n if not parent_ref:\\n logger.debug(\\\"got no parent_ref\\\")\\n version = self.get_current_version()\\n else:\\n logger.debug(\\\"have a parent_ref\\\")\\n version = self.get_version_from_full_path(parent_ref.path)\\n\\n if version:\\n logger.debug(\\\"got a version: %s\\\" % version.absolute_full_path)\\n # use the original version if it is a Repr version\\n from anima.representation import Representation\\n\\n if Representation.repr_separator in version.take_name and version.parent:\\n version = version.parent\\n logger.debug(\\n \\\"this is a representation switching to its parent: %s\\\" % version\\n )\\n\\n # update the reference list\\n referenced_versions = self.get_referenced_versions(parent_ref)\\n version.inputs = referenced_versions\\n\\n # commit data to the database\\n from stalker.db.session import DBSession\\n\\n DBSession.add(version)\\n DBSession.commit()\",\n \"def update(dl_url, force_update=False):\\n def compare_versions(vA, vB):\\n \\\"\\\"\\\"\\nCompares two version number strings\\n@param vA: first version string to compare\\n@param vB: second version string to compare\\n@author <a href=\\\"http_stream://sebthom.de/136-comparing-version-numbers-in-jython-pytho/\\\">Sebastian Thomschke</a>\\n@return negative if vA < vB, zero if vA == vB, positive if vA > vB.\\n\\\"\\\"\\\"\\n if vA == vB:\\n return 0\\n\\n def num(s):\\n if s.isdigit():\\n return int(s)\\n return s\\n\\n seqA = map(num, re.findall('\\\\d+|\\\\w+', vA.replace('-SNAPSHOT', '')))\\n seqB = map(num, re.findall('\\\\d+|\\\\w+', vB.replace('-SNAPSHOT', '')))\\n\\n # this is to ensure that 1.0 == 1.0.0 in cmp(..)\\n lenA, lenB = len(seqA), len(seqB)\\n for i in range(lenA, lenB):\\n seqA += (0,)\\n for i in range(lenB, lenA):\\n seqB += (0,)\\n\\n rc = cmp(seqA, seqB)\\n\\n if rc == 0:\\n if vA.endswith('-SNAPSHOT'):\\n return -1\\n if vB.endswith('-SNAPSHOT'):\\n return 1\\n return rc\\n\\n # dl the first 256 bytes and parse it for version number\\n try:\\n http_stream = urllib.urlopen(dl_url)\\n # update_file = http_stream.read(256)\\n update_file = http_stream.read(300)\\n http_stream.close()\\n\\n except IOError, (errno, strerror):\\n logging.info(\\\"Unable to retrieve version data\\\")\\n logging.info(\\\"Error %s: %s\\\" % (errno, strerror))\\n return\\n\\n match_regex = re.search(r'__version__ *= *\\\"(\\\\S+)\\\"', update_file)\\n if not match_regex:\\n logging.info(\\\"No version info could be found\\\")\\n return\\n update_version = match_regex.group(1)\\n\\n if not update_version:\\n logging.info(\\\"Unable to parse version data\\\")\\n return\\n\\n if force_update:\\n logging.info(\\\"Forcing update, downloading version %s...\\\" %\\n update_version)\\n\\n else:\\n cmp_result = compare_versions(__version__, update_version)\\n if cmp_result < 0:\\n logging.info(\\\"Newer version %s available, downloading...\\\" %\\n update_version)\\n elif cmp_result > 0:\\n logging.info(\\\"Local version %s newer then available %s, not updating.\\\" % (\\n __version__, update_version))\\n return\\n else:\\n logging.info(\\\"You already have the latest version.\\\")\\n return\\n\\n # dl, backup, and save the updated script\\n app_path = os.path.realpath(sys.argv[0])\\n # if __asModule__ == True:\\n # app_path = __file__\\n\\n if not os.access(app_path, os.W_OK):\\n logging.info(\\\"Cannot update -- unable to write to %s\\\" % app_path)\\n\\n dl_path = app_path + \\\".new\\\"\\n backup_path = app_path + \\\".old\\\"\\n try:\\n dl_file = open(dl_path, 'w')\\n http_stream = urllib.urlopen(dl_url)\\n total_size = None\\n bytes_so_far = 0\\n chunk_size = 8192\\n try:\\n total_size = int(http_stream.info().getheader(\\n 'Content-Length').strip())\\n except:\\n # The header is improper or missing Content-Length, just download\\n dl_file.write(http_stream.read())\\n\\n while total_size:\\n chunk = http_stream.read(chunk_size)\\n dl_file.write(chunk)\\n bytes_so_far += len(chunk)\\n\\n if not chunk:\\n break\\n\\n percent = float(bytes_so_far) / total_size\\n percent = round(percent*100, 2)\\n sys.stdout.write(\\\"Downloaded %d of %d bytes (%0.2f%%)\\\\r\\\" %\\n (bytes_so_far, total_size, percent))\\n\\n if bytes_so_far >= total_size:\\n sys.stdout.write('\\\\n')\\n\\n http_stream.close()\\n dl_file.close()\\n except IOError, (errno, strerror):\\n logging.info(\\\"Download failed\\\")\\n logging.info(\\\"Error %s: %s\\\" % (errno, strerror))\\n return\\n\\n try:\\n os.rename(app_path, backup_path)\\n except OSError, (errno, strerror):\\n logging.info(\\\"Unable to rename %s to %s: (%d) %s\\\" %\\n (app_path, backup_path, errno, strerror))\\n return\\n\\n try:\\n os.rename(dl_path, app_path)\\n except OSError, (errno, strerror):\\n logging.info(\\\"Unable to rename %s to %s: (%d) %s\\\" %\\n (dl_path, app_path, errno, strerror))\\n return\\n\\n try:\\n import shutil\\n shutil.copymode(backup_path, app_path)\\n except:\\n os.chmod(app_path, 0755)\\n\\n logging.info(\\\"New version installed as %s\\\" % app_path)\\n logging.info(\\\"(previous version backed up to %s)\\\" % (backup_path))\\n return True\",\n \"def test_updateTwistedVersionInformation(self):\\n baseDirectory = FilePath(self.mktemp())\\n baseDirectory.createDirectory()\\n now = date.today()\\n\\n projectName = 'foo'\\n oldVersion = Version(projectName, 2, 5, 0)\\n newVersion = getNextVersion(oldVersion, now=now)\\n\\n project = self.makeProject(oldVersion, baseDirectory)\\n\\n updateTwistedVersionInformation(baseDirectory, now=now)\\n\\n self.assertEqual(project.getVersion(), newVersion)\\n self.assertEqual(\\n project.directory.child('topfiles').child('README').getContent(),\\n newVersion.base())\",\n \"def refresh(self):\\n\\n # The timestamp role does not have signed metadata about it; otherwise we\\n # would need an infinite regress of metadata. Therefore, we use some\\n # default, sane metadata about it.\\n DEFAULT_TIMESTAMP_FILEINFO = {\\n 'hashes':None,\\n 'length': tuf.conf.DEFAULT_TIMESTAMP_REQUIRED_LENGTH\\n }\\n\\n # Update the top-level metadata. The _update_metadata_if_changed() and\\n # _update_metadata() calls below do NOT perform an update if there\\n # is insufficient trusted signatures for the specified metadata.\\n # Raise 'tuf.NoWorkingMirrorError' if an update fails.\\n\\n # Use default but sane information for timestamp metadata, and do not\\n # require strict checks on its required length.\\n self._update_metadata('timestamp', DEFAULT_TIMESTAMP_FILEINFO)\\n\\n self._update_metadata_if_changed('release', referenced_metadata='timestamp')\\n\\n self._update_metadata_if_changed('root')\\n\\n self._update_metadata_if_changed('targets')\\n\\n # Updated the top-level metadata (which all had valid signatures), however,\\n # have they expired? Raise 'tuf.ExpiredMetadataError' if any of the metadata\\n # has expired.\\n for metadata_role in ['timestamp', 'root', 'release', 'targets']:\\n self._ensure_not_expired(metadata_role)\",\n \"def compare_and_update(self):\\n\\n # first read in the old cites\\n try:\\n with open(\\\"cites.json\\\") as f:\\n cites_json = f.read()\\n old_cites = json.loads(cites_json)\\n except OSError:\\n old_cites = {}\\n\\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\\n print(\\\"number of citations = \\\", num_cites)\\n\\n h_index = 0\\n for n, p in enumerate(self.mypapers):\\n if p.citation_count >= n+1:\\n h_index = n + 1\\n print(\\\"h-index = \\\", h_index)\\n\\n # now create a dict of the cites and update the stored JSON\\n cites = {}\\n for p in self.mypapers:\\n cites[p.bibcode] = (p.title[0], p.citation_count)\\n\\n cite_json = json.dumps(cites, indent=4)\\n with open(\\\"cites.json\\\", \\\"w\\\") as f:\\n f.write(cite_json)\\n\\n # now look for differences in cites\\n for key, value in cites.items():\\n if key not in old_cites:\\n print(f\\\"new paper: {value[0]}\\\")\\n else:\\n if value[1] != old_cites[key][1]:\\n print(f\\\"change of {value[1] - old_cites[key][1]:+d} for paper: {value[0]}\\\")\",\n \"def update_previous_all_versions():\\n\\n # get all the ids\\n version_ids = m.meta.Session.query(distinct(tst.TestVersion.id)).filter_by(archived=False).\\\\\\n join('methods').filter_by(short_name='Online').\\\\\\n join('test','type').filter_by(short_name='RC').all()\\n\\n for version_id in version_ids:\\n update_previous(version_id)\",\n \"def compare_versions(vA, vB):\\n if vA == vB:\\n return 0\\n\\n def num(s):\\n if s.isdigit():\\n return int(s)\\n return s\\n\\n seqA = map(num, re.findall('\\\\d+|\\\\w+', vA.replace('-SNAPSHOT', '')))\\n seqB = map(num, re.findall('\\\\d+|\\\\w+', vB.replace('-SNAPSHOT', '')))\\n\\n # this is to ensure that 1.0 == 1.0.0 in cmp(..)\\n lenA, lenB = len(seqA), len(seqB)\\n for i in range(lenA, lenB):\\n seqA += (0,)\\n for i in range(lenB, lenA):\\n seqB += (0,)\\n\\n rc = cmp(seqA, seqB)\\n\\n if rc == 0:\\n if vA.endswith('-SNAPSHOT'):\\n return -1\\n if vB.endswith('-SNAPSHOT'):\\n return 1\\n return rc\",\n \"def compare_versions(vA, vB):\\n if vA == vB: return 0\\n\\n def num(s):\\n if s.isdigit(): return int(s)\\n return s\\n\\n seqA = map(num, re.findall('\\\\d+|\\\\w+', vA.replace('-SNAPSHOT', '')))\\n seqB = map(num, re.findall('\\\\d+|\\\\w+', vB.replace('-SNAPSHOT', '')))\\n\\n # this is to ensure that 1.0 == 1.0.0 in cmp(..)\\n lenA, lenB = len(seqA), len(seqB)\\n for i in range(lenA, lenB): seqA += (0,)\\n for i in range(lenB, lenA): seqB += (0,)\\n\\n rc = cmp(seqA, seqB)\\n\\n if rc == 0:\\n if vA.endswith('-SNAPSHOT'): return -1\\n if vB.endswith('-SNAPSHOT'): return 1\\n return rc\",\n \"def versions():\\n result = timeline.versions()\\n if result:\\n click.echo('\\\\n'.join(result))\",\n \"def compare_version(self, ref):\\n if not self.version:\\n print 'No version for package %s' % self.package\\n if not ref.version:\\n print 'No version for package %s' % ref.package\\n if not self.parsed_version:\\n self.parsed_version = parse_version(self.version)\\n if not ref.parsed_version:\\n ref.parsed_version = parse_version(ref.version)\\n return self.parsed_version.compare(ref.parsed_version)\",\n \"def _check_accessors_for_version_problems(self):\\n\\n accessors_needing_saving = []\\n for accessor in self._cache_accessors:\\n old_prov = accessor.load_provenance()\\n\\n if old_prov is None:\\n continue\\n\\n new_prov = accessor.query.provenance\\n\\n if old_prov.exactly_matches(new_prov):\\n continue\\n accessors_needing_saving.append(accessor)\\n\\n if old_prov.code_version_minor == new_prov.code_version_minor:\\n if old_prov.bytecode_hash != new_prov.bytecode_hash:\\n raise CodeVersioningError(\\n oneline(\\n f\\\"\\\"\\\"\\n Found a cached artifact with the same\\n descriptor ({accessor.query.dnode.to_descriptor()!r}) and\\n version (major={old_prov.code_version_major!r},\\n minor={old_prov.code_version_minor!r}),\\n But created by different code\\n (old hash {old_prov.bytecode_hash!r},\\n new hash {new_prov.bytecode_hash!r}).\\n Did you change your code but not update the\\n version number?\\n Change @version(major=) to indicate that your\\n function's behavior has changed, or @version(minor=)\\n to indicate that it has *not* changed.\\\"\\\"\\\"\\n )\\n )\\n\\n for accessor in accessors_needing_saving:\\n accessor.update_provenance()\",\n \"def testMultipleNewRefs(self):\\n self.mr2 = cdl_convert.MediaRef('goodbye')\\n\\n self.assertEqual(\\n {'hello': [self.mr], 'goodbye': [self.mr2]},\\n cdl_convert.MediaRef.members\\n )\\n\\n self.mr._filename = 'goodbye'\\n self.mr._change_membership(old_ref='hello')\\n\\n self.assertEqual(\\n {'goodbye': [self.mr2, self.mr]},\\n cdl_convert.MediaRef.members\\n )\",\n \"def _current_versions(self, job):\\n raise NotImplementedError\",\n \"def reference_scene(file_path, **kwargs):\\n\\n pass\",\n \"def main():\\n compare_versions(Reactome())\",\n \"def test_version():\\n with open(\\\"pyproject.toml\\\") as f:\\n tomllines = f.read().splitlines()\\n tomlversion = set([l for l in tomllines if \\\"version =\\\" in l])\\n initversion = set([f'version = \\\"{mei2volpiano.__version__}\\\"'])\\n # set is there to catch any duplicate/additional entries\\n assert initversion == tomlversion\",\n \"def upgrade_to_21():\\n\\n def update_project_template(template):\\n new_template = {'acquisitions': []}\\n for a in template.get('acquisitions', []):\\n new_a = {'minimum': a['minimum']}\\n properties = a['schema']['properties']\\n if 'measurement' in properties:\\n m_req = properties['measurement']['pattern']\\n m_req = re.sub('^\\\\(\\\\?i\\\\)', '', m_req)\\n new_a['files']=[{'measurement': m_req, 'minimum': 1}]\\n if 'label' in properties:\\n l_req = properties['label']['pattern']\\n l_req = re.sub('^\\\\(\\\\?i\\\\)', '', l_req)\\n new_a['label'] = l_req\\n new_template['acquisitions'].append(new_a)\\n\\n return new_template\\n\\n def dm_v2_updates(cont_list, cont_name):\\n for container in cont_list:\\n\\n query = {'_id': container['_id']}\\n update = {'$rename': {'metadata': 'info'}}\\n\\n if cont_name == 'projects' and container.get('template'):\\n new_template = update_project_template(json.loads(container.get('template')))\\n update['$set'] = {'template': new_template}\\n\\n\\n if cont_name == 'sessions':\\n update['$rename'].update({'subject.metadata': 'subject.info'})\\n\\n\\n measurement = None\\n modality = None\\n info = None\\n if cont_name == 'acquisitions':\\n update['$unset'] = {'instrument': '', 'measurement': ''}\\n measurement = container.get('measurement', None)\\n modality = container.get('instrument', None)\\n info = container.get('metadata', None)\\n if info:\\n config.db.acquisitions.update_one(query, {'$set': {'metadata': {}}})\\n\\n\\n # From mongo docs: '$rename does not work if these fields are in array elements.'\\n files = container.get('files')\\n if files is not None:\\n updated_files = []\\n for file_ in files:\\n file_['info'] = {}\\n if 'metadata' in file_:\\n file_['info'] = file_.pop('metadata', None)\\n if 'instrument' in file_:\\n file_['modality'] = file_.pop('instrument', None)\\n if measurement:\\n # Move the acquisition's measurement to all files\\n if file_.get('measurements'):\\n file_['measurements'].append(measurement)\\n else:\\n file_['measurements'] = [measurement]\\n if info and file_.get('type', '') == 'dicom':\\n # This is going to be the dicom header info\\n updated_info = info\\n updated_info.update(file_['info'])\\n file_['info'] = updated_info\\n if modality and not file_.get('modality'):\\n file_['modality'] = modality\\n\\n updated_files.append(file_)\\n if update.get('$set'):\\n update['$set']['files'] = updated_files\\n else:\\n update['$set'] = {'files': updated_files}\\n\\n result = config.db[cont_name].update_one(query, update)\\n\\n query = {'$or':[{'files.metadata': { '$exists': True}},\\n {'metadata': { '$exists': True}},\\n {'files.instrument': { '$exists': True}}]}\\n\\n dm_v2_updates(config.db.collections.find(query), 'collections')\\n\\n query['$or'].append({'template': { '$exists': True}})\\n dm_v2_updates(config.db.projects.find({}), 'projects')\\n\\n query['$or'].append({'subject': { '$exists': True}})\\n dm_v2_updates(config.db.sessions.find(query), 'sessions')\\n\\n query['$or'].append({'instrument': { '$exists': True}})\\n query['$or'].append({'measurement': { '$exists': True}})\\n dm_v2_updates(config.db.acquisitions.find(query), 'acquisitions')\",\n \"def compareTwoReco(reference, new, histos, debug=1):\\n\\n # Tracks with index False are the ones that have been matched to the reference track collection\\n new_valid = [True for i in new]\\n\\n # Tracks with index False are the ones that have been matched to the comparison track collection\\n original_valid = [True for i in reference]\\n print \\\" \\\".join(\\\"%10s\\\" % k for k in variables)\\n debug_verbose = checkDebug(debug, 'Verbose')\\n debug_ordinary = checkDebug(debug, 'Ordinary')\\n debug_recovery = checkDebug(debug, 'Recovery')\\n debug_lost = checkDebug(debug, 'Lost')\\n debug_fake = checkDebug(debug, 'Fake')\\n\\n for original_index, original in enumerate(reference):\\n # Fill in cumulative plots for the reference sample first\\n histos['reference_hits_vs_algo'].Fill(original.algo, original.hits)\\n histos['reference_hits_vs_orialgo'].Fill(original.orialgo, original.hits)\\n histos['reference_hits_vs_pt'].Fill(original.pt, original.hits)\\n histos['den'].Fill(original.pt)\\n histos['den_eta'].Fill(original.eta)\\n histos['den_phi'].Fill(original.phi)\\n histos['den_hits'].Fill(original.hits)\\n histos['den_algo'].Fill(original.algo)\\n histos['den_orialgo'].Fill(original.orialgo)\\n\\n # Now start to look for a matching track in the comparison track collection\\n window_depth = 400 # elements to span to look for best candidate\\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = -1, 100, 100\\n if debug_verbose:\\n print original\\n for i,j in enumerate(new):\\n if new_valid[i] == True:\\n if debug_verbose:\\n print \\\" \\\", i, j\\n if window_depth == 0:\\n break\\n dr_squared, dPt_over_pt = match(original, j)\\n if dr_squared < bestDeltaRMatch*bestDeltaRMatch and dPt_over_pt < DELTA_PT_OVER_PT_CUT:\\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = i, dr_squared, dPt_over_pt\\n if debug_verbose:\\n print \\\" \\\", window_depth, iBest, bestDeltaRMatch, dr_squared, bestDeltaPt_over_PtMatch, dPt_over_pt\\n if bestDeltaRMatch <= 0.0001 or bestDeltaPt_over_PtMatch == 0.0001:\\n break\\n window_depth -= 1\\n if iBest != -1 and bestDeltaRMatch < DELTA_R_CUT:\\n # These are the tracks in the reference track collection\\n # that have been matched to a track in the comparison\\n # track collection\\n new_valid[iBest] = False\\n original_valid[original_index] = False\\n assert original.run == new[iBest].run, \\\"run mismatch\\\"\\n assert original.ls == new[iBest].ls, \\\"ls mismatch\\\"\\n assert original.event == new[iBest].event, \\\"event mismatch\\\"\\n if debug_ordinary:\\n print original\\n print new[iBest]\\n print iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch, '\\\\n'\\n histos['num'].Fill(original.pt)\\n histos['num_eta'].Fill(original.eta)\\n histos['num_phi'].Fill(original.phi)\\n histos['num_hits'].Fill(original.hits)\\n histos['num_algo'].Fill(original.algo)\\n histos['num_orialgo'].Fill(original.orialgo)\\n histos['fake_num'].Fill(new[iBest].pt)\\n histos['fake_num_eta'].Fill(new[iBest].eta)\\n histos['fake_num_phi'].Fill(new[iBest].phi)\\n histos['fake_num_hits'].Fill(new[iBest].hits)\\n histos['fake_num_algo'].Fill(new[iBest].algo)\\n histos['fake_num_orialgo'].Fill(new[iBest].orialgo)\\n histos['comparison_algo_vs_reference_algo'].Fill(original.algo, new[iBest].algo)\\n histos['comparison_orialgo_vs_reference_orialgo'].Fill(original.orialgo, new[iBest].orialgo)\\n histos['comparison_hits_vs_reference_hits'].Fill(original.hits, new[iBest].hits)\\n\\n # Let's try a recovery loop with somewhat lesser stringent cuts\\n for original_index, original in enumerate(reference):\\n if original_valid[original_index]:\\n # Now start to look for a matching track in the comparison track collection\\n window_depth = 300 # elements to span to look for best candidate\\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = -1, 100, 100\\n if debug_verbose:\\n print \\\"Recovery \\\", original\\n for i,j in enumerate(new):\\n if new_valid[i] == True:\\n if debug_verbose:\\n print \\\"Recovery \\\", i, j\\n if window_depth == 0:\\n break\\n dr_squared, dPt_over_pt = match(original, j)\\n if dr_squared < bestDeltaRMatch*bestDeltaRMatch and dPt_over_pt < DELTA_PT_OVER_PT_CUT*6:\\n iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch = i, dr_squared, dPt_over_pt\\n if debug_verbose:\\n print \\\"Recovery \\\", window_depth, iBest, bestDeltaRMatch, dr_squared, bestDeltaPt_over_PtMatch, dPt_over_pt\\n if bestDeltaRMatch <= 0.0001 or bestDeltaPt_over_PtMatch == 0.0001:\\n break\\n window_depth -= 1\\n if iBest != -1 and bestDeltaRMatch < DELTA_R_CUT*10: # inflate cut on DeltaR to recover some good-medium matching\\n # These are the tracks in the reference track collection\\n # that have been matched to a track in the comparison\\n # track collection\\n new_valid[iBest] = False\\n original_valid[original_index] = False\\n if debug_recovery:\\n print \\\"Recovery \\\", original\\n print \\\"Recovery \\\", new[iBest]\\n print \\\"Recovery \\\", iBest, bestDeltaRMatch, bestDeltaPt_over_PtMatch\\n histos['num'].Fill(original.pt)\\n histos['num_eta'].Fill(original.eta)\\n histos['num_phi'].Fill(original.phi)\\n histos['num_hits'].Fill(original.hits)\\n histos['num_algo'].Fill(original.algo)\\n histos['num_orialgo'].Fill(original.orialgo)\\n histos['fake_num'].Fill(new[iBest].pt)\\n histos['fake_num_eta'].Fill(new[iBest].eta)\\n histos['fake_num_hits'].Fill(new[iBest].hits)\\n histos['fake_num_algo'].Fill(new[iBest].algo)\\n histos['fake_num_orialgo'].Fill(new[iBest].orialgo)\\n histos['comparison_algo_vs_reference_algo'].Fill(original.algo, new[iBest].algo)\\n histos['comparison_orialgo_vs_reference_orialgo'].Fill(original.orialgo, new[iBest].orialgo)\\n histos['comparison_hits_vs_reference_hits'].Fill(original.hits, new[iBest].hits)\\n\\n\\n # These are the tracks in the reference track collection\\n # that have *not* been associated to any track in the\\n # comparison collection == > LOST TRACKS\\n reference_not_assigned = [j for i,j in enumerate(reference) if original_valid[i]]\\n reference_not_assigned.sort(key=lambda tr: tr.algo)\\n if debug_lost:\\n print \\\"**** Lost tracks **** %d\\\" % len(reference_not_assigned)\\n for j in reference_not_assigned:\\n histos['lost_hits_vs_algo'].Fill(j.algo, j.hits)\\n histos['lost_hits_vs_orialgo'].Fill(j.orialgo, j.hits)\\n histos['lost_hits_vs_pt'].Fill(j.pt, j.hits)\\n histos['lost_eta'].Fill(j.eta)\\n if debug:\\n print j\\n if debug_lost:\\n print \\\"**** End of Lost tracks ****\\\"\\n\\n # Fake Tracks\\n for i, j in enumerate(new):\\n # Fill in the cumulative plots related to tracks in the comparison track collection\\n histos['comparison_hits_vs_algo'].Fill(j.algo, j.hits)\\n histos['comparison_hits_vs_orialgo'].Fill(j.orialgo, j.hits)\\n histos['comparison_hits_vs_pt'].Fill(j.pt, j.hits)\\n histos['fake_den'].Fill(j.pt)\\n histos['fake_den_eta'].Fill(j.eta)\\n histos['fake_den_phi'].Fill(j.phi)\\n histos['fake_den_hits'].Fill(j.hits)\\n histos['fake_den_algo'].Fill(j.algo)\\n histos['fake_den_orialgo'].Fill(j.orialgo)\\n\\n # These are the tracks in the comparison track collection\\n # that have *not* been associated to any track in the\\n # reference collection ==> FAKE TRACKS\\n new_not_assigned = [j for i,j in enumerate(new) if new_valid[i]]\\n new_not_assigned.sort(key=lambda tr: tr.algo)\\n if debug_fake:\\n print \\\"**** Fake tracks **** %d\\\" % len(new_not_assigned)\\n for j in new_not_assigned:\\n histos['fake_hits_vs_algo'].Fill(j.algo, j.hits)\\n histos['fake_hits_vs_orialgo'].Fill(j.orialgo, j.hits)\\n histos['fake_hits_vs_pt'].Fill(j.pt, j.hits)\\n if debug:\\n print j\\n if debug_fake:\\n print \\\"**** End of Fake tracks ****\\\"\",\n \"def open(self, version, force=False, representation=None,\\n reference_depth=0, skip_update_check=False):\\n version_full_path = os.path.normpath(version.absolute_full_path)\\n\\n # delete all the comps and open new one\\n #comps = self.fusion.GetCompList().values()\\n #for comp_ in comps:\\n # comp_.Close()\\n\\n self.fusion.LoadComp(version_full_path.encode())\\n\\n rfm = RecentFileManager()\\n rfm.add(self.name, version.absolute_full_path)\\n\\n # set the project_directory\\n #self.project_directory = os.path.dirname(version.absolute_path)\\n\\n # TODO: file paths in different OS'es should be replaced with the current one\\n # Check if the file paths are starting with a string matching one of\\n # the OS'es project_directory path and replace them with a relative one\\n # matching the current OS\\n\\n # replace paths\\n #self.replace_external_paths()\\n\\n # return True to specify everything was ok and an empty list\\n # for the versions those needs to be updated\\n return empty_reference_resolution()\",\n \"def isSceneModified(self):\\n logger.debug(\\\"Func: isSceneModified\\\")\\n return nuke.modified()\",\n \"def version(self):\",\n \"def updateEditedRefImages(self, recipe):\\n oldProperties = recipe.getProperties()\\n\\n show = oldProperties['show']\\n sequence = oldProperties['sequence']\\n oldMode = Mode(show, sequence)\\n updatedPoses = self.__getUpdatedPoses(recipe)\\n\\n newRecipeXML = CopySetup.localizeSetup(oldProperties, show, sequence,\\n renderCallback=self.__renderCallback,\\n setupCallback=self.feedReloadSetupsMultiTracks,\\n multiTrackCallback=self.feedReloadSetupsMultiTracks)\\n\\n newRecipe = Recipe.fromXMLElement(newRecipeXML)\\n\\n self.__storeUpdatedPoses(newRecipe, updatedPoses)\\n self.__updatePosesInSetup(newRecipe, updatedPoses)\\n\\n oldProperties = recipe.getProperties()\\n existingRecipeEditingPath = oldMode.get(\\\"[poseEditingFile]\\\", oldProperties).replace(\\\".psd\\\", \\\".xml\\\")\\n\\n # if a setup has been send to flix already we will now use the new recipe version\\n if self.fileServiceLocal.exists(existingRecipeEditingPath):\\n oldProperties = self.fileServiceLocal.loadXMLFile(existingRecipeEditingPath).find(\\\"Properties\\\").attrib\\n\\n\\n recipiesXML = ET.fromstring('<Recipies/>')\\n newSetupXML = newRecipe.getMasterXML()\\n\\n setupXML = ET.fromstring('<OldSetup show=\\\"%s\\\" sequence=\\\"%s\\\" beat=\\\"%s\\\" setup=\\\"%s\\\" version=\\\"%s\\\" />'\\\\\\n % (oldProperties[\\\"show\\\"],\\n oldProperties[\\\"sequence\\\"],\\n oldProperties[\\\"beat\\\"],\\n oldProperties[\\\"setup\\\"],\\n oldProperties[\\\"version\\\"]))\\n\\n\\n setupXML.append(newSetupXML)\\n recipiesXML.append(setupXML)\\n self.addFeedback(\\\"replaceSetupsMultiTracks\\\", recipiesXML)\\n\\n FlixNuke().fromRecipe(newRecipe)\\n FlixNuke().compRecipe(newRecipe, renderCallback=self.__renderCallback)\\n\\n newProperties = newRecipe.getProperties()\\n mode = Mode(newProperties.get('show', None), newProperties.get('sequence', None))\\n newMultitrackFile = mode.get('[recipeMultiTrackFile]', newProperties)\\n newMultitrack = self.fileServiceLocal.loadTextFile(newMultitrackFile)\\n\\n data = []\\n data.append('<Recipies>')\\n data.append(\\n \\\"\\\"\\\"<Setup\\n show=\\\"%(show)s\\\"\\n sequence=\\\"%(sequence)s\\\"\\n beat=\\\"%(beat)s\\\"\\n setup=\\\"%(setup)s\\\"\\n version=\\\"%(version)s\\\">'\\\"\\\"\\\" % newProperties)\\n data.append(newMultitrack + \\\"</Setup>\\\" + \\\"</Recipies>\\\")\\n dataString = \\\"\\\".join(data)\\n\\n self.feedReloadSetupsMultiTracks(dataString)\\n\\n# FlixNuke().compRecipe(newRecipe, fileOutNodes='fileOut_master_png')\\n\\n return newRecipe\",\n \"def notesChanged(self):\\n if self.controller:\\n self.versionProp.updateVersion(self.controller.current_version)\",\n \"def _update_aea_version(cls, agent_config: AgentConfig):\\n for item in agent_config.package_dependencies:\\n type_ = item.component_type.to_plural()\\n dotted_path = f\\\"vendor.{item.author}.{type_}.{item.name}.aea_version\\\"\\n path = os.path.join(\\\"vendor\\\", item.author, type_, item.name)\\n cls.nested_set_config(dotted_path, cls.AEA_VERSION_SPECIFIER)\\n cls.run_cli_command(\\\"fingerprint\\\", \\\"by-path\\\", path, cwd=cls._get_cwd())\",\n \"def getOpenSceneInfo(self):\\n logger.debug(\\\"Func: getOpenSceneInfo\\\")\\n\\n self._pathsDict[\\\"sceneFile\\\"] = self.getSceneFile()\\n if not self._pathsDict[\\\"sceneFile\\\"]:\\n return None\\n\\n # get name of the upper directory to find out base name\\n sceneDir = os.path.abspath(os.path.join(self._pathsDict[\\\"sceneFile\\\"], os.pardir))\\n baseSceneName = os.path.basename(sceneDir)\\n\\n upperSceneDir = os.path.abspath(os.path.join(sceneDir, os.pardir))\\n upperSceneDirName = os.path.basename(upperSceneDir)\\n\\n if upperSceneDirName in self._subProjectsList:\\n subProjectDir = upperSceneDir\\n subProject = upperSceneDirName\\n categoryDir = os.path.abspath(os.path.join(subProjectDir, os.pardir))\\n category = os.path.basename(categoryDir)\\n\\n dbCategoryPath = os.path.normpath(os.path.join(self._pathsDict[\\\"databaseDir\\\"], category))\\n dbPath = os.path.normpath(os.path.join(dbCategoryPath, subProject))\\n\\n pbCategoryPath = os.path.normpath(os.path.join(self._pathsDict[\\\"previewsDir\\\"], category))\\n pbSubPath = os.path.normpath(os.path.join(pbCategoryPath, subProject))\\n pbPath = os.path.normpath(os.path.join(pbSubPath, baseSceneName))\\n\\n else:\\n subProject = self._subProjectsList[0]\\n categoryDir = upperSceneDir\\n category = upperSceneDirName\\n dbPath = os.path.normpath(os.path.join(self._pathsDict[\\\"databaseDir\\\"], category))\\n pbCategoryPath = os.path.normpath(os.path.join(self._pathsDict[\\\"previewsDir\\\"], category))\\n pbPath = os.path.normpath(os.path.join(pbCategoryPath, baseSceneName))\\n\\n jsonFile = os.path.join(dbPath, \\\"{}.json\\\".format(baseSceneName))\\n if os.path.isfile(jsonFile):\\n version = (self.niceName(self._pathsDict[\\\"sceneFile\\\"])[-4:])\\n self._openSceneInfo = {\\n \\\"jsonFile\\\":jsonFile,\\n \\\"projectPath\\\":self._pathsDict[\\\"projectDir\\\"],\\n \\\"subProject\\\":subProject,\\n \\\"category\\\":category,\\n \\\"shotName\\\":baseSceneName,\\n \\\"version\\\":version,\\n \\\"previewPath\\\":pbPath\\n }\\n return self._openSceneInfo\\n else:\\n return None\",\n \"def update_versions(self, new_versions_list):\\n to_stop = [version for version in self if version not in new_versions_list]\\n for version_id in to_stop:\\n del self[version_id]\\n\\n for version_id in new_versions_list:\\n if version_id not in self:\\n self[version_id] = VersionManager(\\n self.zk_client, self.project_id, self.service_id, version_id,\\n self.callback)\\n\\n self[version_id].ensure_watch()\",\n \"def _update_version(self) -> None:\\n # Implement in child class.\\n raise NotImplementedError\",\n \"def referenceAsset(*args):\\n\\n #check that we have the correct tab open\\n path = \\\"\\\"\\n path = getSelectMasteredAsset()\\n\\n if path:\\n print \\\"shotWin.referenceAsset: referencing in - - -\\\", path\\n cFuncs.referenceInWithNamespace(path)\\n # now refresh. . .\\n populateMasteredAssets()\\n else:\\n cmds.warning(\\\"You must select an asset from the lists to reference in!\\\")\",\n \"def update(self):\\n self.content = self.get_content()\\n self.dependencies = self.content['requirements']['run']\\n self.pythonversion = self.content['extra']['pythonversion']\\n self.package_name = self.content['package']['name']\",\n \"def get_comp_versions (component):\\n vprint (\\\"Detecting current version for \\\" + component)\\n\\n regex = re.compile (r\\\"version \\\" + version_restr)\\n major = component + \\\"_major\\\"\\n minor = component + \\\"_minor\\\"\\n micro = component + \\\"_micro\\\"\\n\\n\\n version = (None, None, None)\\n with open (doc_root + \\\"/ACE_TAO/\\\" + component + \\\"/VERSION.txt\\\") as version_file:\\n for line in version_file:\\n match = regex.search (line)\\n if match is not None:\\n version = match.groups(default=0)\\n\\n vprint (\\\"Detected version %s.%s.%s\\\" % version)\\n\\n comp_versions[major] = int (version[0])\\n comp_versions[minor] = int (version[1])\\n comp_versions[micro] = int (version[2])\\n\\n break\\n\\n print (\\\"FATAL ERROR: Unable to locate current version for \\\" + component)\\n raise Exception\\n\\n # Also store the current release (old from now)\\n old_comp_versions[major] = comp_versions[major]\\n old_comp_versions[minor] = comp_versions[minor]\\n old_comp_versions[micro] = comp_versions[micro]\\n\\n if opts.update:\\n if opts.release_type == ReleaseType.major:\\n comp_versions[major] += 1\\n comp_versions[minor] = 0\\n comp_versions[micro] = 0\\n elif opts.release_type == ReleaseType.minor:\\n comp_versions[minor] += 1\\n comp_versions[micro] = 0\\n elif opts.release_type == ReleaseType.micro:\\n comp_versions[micro] += 1\\n\\n def make_version (versions, joiner):\\n return joiner.join ([\\n str (versions[component + '_' + x]) for x in ReleaseType.__members__.keys ()\\n ])\\n\\n comp_versions [component + \\\"_version\\\"] = make_version (comp_versions, '.')\\n comp_versions [component + \\\"_version_\\\"] = make_version (comp_versions, '_')\\n\\n comp_versions [component + \\\"_code\\\"] = \\\\\\n (comp_versions[major] << 16) + \\\\\\n (comp_versions[minor] << 8) + \\\\\\n comp_versions[micro]\\n\\n old_comp_versions [component + \\\"_version\\\"] = make_version (old_comp_versions, '.')\\n old_comp_versions [component + \\\"_version_\\\"] = make_version (old_comp_versions, '_')\\n\\n if opts.update:\\n vprint (\\\"Updating from version %s to version %s\\\" %\\n (old_comp_versions [component + \\\"_version\\\"], comp_versions [component + \\\"_version\\\"]))\\n else:\\n vprint (\\\"Found version %s\\\" %\\n (comp_versions [component + \\\"_version\\\"]))\\n\\n # else:\\n # comp_versions [component + \\\"_version\\\"] = \\\\\\n # str (comp_versions[major]) + '.' + \\\\\\n # str (comp_versions[minor])\",\n \"def _compare_variants(self, all_revisions):\\n previous = None\\n previous = self._compare(previous, '.xml', all_revisions)\\n previous = self._compare(previous, '-utf16.xml', all_revisions)\\n previous = self._compare(previous, '.xml.bz2', all_revisions)\\n self._compare(previous, '-utf16.xml.bz2', all_revisions)\",\n \"def compare():\\n body: t.Any = request.json\\n check_error({'input': {'old': {}, 'new': {}}}, body)\\n response_new = rpc_search({'input': body['input']['new']})\\n response_old = rpc_search({'input': body['input']['old']})\\n\\n modules_new = response_new['yang-catalog:modules']['module']\\n modules_old = response_old['yang-catalog:modules']['module']\\n\\n if len(modules_new) == 0 or len(modules_old) == 0:\\n abort(404, description='No hits found either in old or new input')\\n\\n new_mods = []\\n for mod_new in modules_new:\\n new_rev = mod_new['revision']\\n new_name = mod_new['name']\\n found = False\\n new_rev_found = False\\n for mod_old in modules_old:\\n old_rev = mod_old['revision']\\n old_name = mod_old['name']\\n if new_name == old_name and new_rev == old_rev:\\n found = True\\n break\\n if new_name == old_name and new_rev != old_rev:\\n new_rev_found = True\\n if not found:\\n mod_new['reason-to-show'] = 'New module'\\n new_mods.append(mod_new)\\n if new_rev_found:\\n mod_new['reason-to-show'] = 'Different revision'\\n new_mods.append(mod_new)\\n if len(new_mods) == 0:\\n abort(404, description='No new modules or modules with different revisions found')\\n output = {'output': new_mods}\\n return output\",\n \"def _compare(self, previous, variant, all_revisions):\\n entries = get_entries('article-pyrus' + variant,\\n allrevisions=all_revisions)\\n result = [entry.__dict__ for entry in entries]\\n if previous:\\n self.assertEqual(previous, result)\\n return result\",\n \"def test_check_version_release(self):\\n with self.assertLogs(\\\"dakara_feeder.version\\\", \\\"DEBUG\\\") as logger:\\n with patch.multiple(\\n \\\"dakara_feeder.version\\\", __version__=\\\"0.0.0\\\", __date__=\\\"1970-01-01\\\"\\n ):\\n check_version()\\n\\n # assert effect on logs\\n self.assertListEqual(\\n logger.output,\\n [\\\"INFO:dakara_feeder.version:\\\" \\\"Dakara feeder 0.0.0 (1970-01-01)\\\"],\\n )\",\n \"def _update_pyrex_file(self, lines, filename):\\n found_version_line = False\\n for lineno, line in enumerate(lines):\\n if line.startswith('__version__'):\\n found_version_line = True\\n break\\n if found_version_line:\\n if self.Verbose:\\n print 'Version string found on line %d' % lineno\\n lines[lineno] = '__version__ = \\\"%s\\\"\\\\n' % str(self.VersionTuple)\\n else:\\n print \\\"No version string found in %s\\\" % filename\\n return (lines, found_version_line)\",\n \"def test_update_refruns_info(self):\\n run_number = 321123\\n mixer.blend(\\n RunReconstruction,\\n reconstruction=RunReconstruction.EXPRESS,\\n run=mixer.blend(OmsRun, run_number=run_number),\\n is_reference=True,\\n )\\n req = RequestFactory().get(reverse(\\\"addrefrun:update_refruns_info\\\"))\\n req.user = mixer.blend(User, user_privilege=User.SHIFTLEADER)\\n resp = views.update_refruns_info(req)\\n\\n # Response is a JSON with a \\\"success\\\" key\\n data = json.loads(resp.content.decode(\\\"utf-8\\\"))\\n assert data[\\\"success\\\"] is True\",\n \"def compare_versions(version1, version2):\\n version1 = coerce_version(version1)\\n version2 = coerce_version(version2)\\n return compare_version_objects(version1, version2)\",\n \"def test_above_24_latest_version(self):\\n self.data['version'] = ''\\n self.data['appVersion'] = '28.0'\\n\\n up = self.get(self.data)\\n rdf = up.get_rdf()\\n assert rdf.find('20202020.01') > -1\",\n \"def _does_require_force_update(self):\\n\\n if self.current_version[0][0] > self.version_yaml[0]:\\n # The current version first index is greater than the one we have in the\\n # current version.yaml.\\n\\n # We return True.\\n return True\\n\\n # We return False, we do not need to force the update for\\n # the current version number.\\n return False\",\n \"def version_check(self):\\n # anchor_matcher --> matcher\\n if hasattr(self, \\\"anchor_matcher\\\"):\\n self.matcher = self.anchor_matcher\\n if hasattr(self, \\\"head_in_features\\\"):\\n self.in_features = self.head_in_features\\n if hasattr(self, \\\"test_topk_candidates\\\"):\\n self.topk_candidates = self.test_topk_candidates\\n if hasattr(self, \\\"test_score_thresh\\\"):\\n self.score_threshold = self.test_score_thresh\",\n \"def test_video_update(self):\\n updated = timezone.now()\\n v1 = make_video(media_id='1234', updated=updated)\\n v2 = make_video(media_id='5678', updated=updated)\\n set_resources_and_sync([v1, v2])\\n i1 = mpmodels.MediaItem.objects.get(jwp__key=v1.key)\\n i2 = mpmodels.MediaItem.objects.get(jwp__key=v2.key)\\n\\n # Update video 1 but not 2\\n v1['updated'] += 1\\n v1['title'] += 'new title'\\n set_resources_and_sync([v1, v2])\\n\\n new_i1 = mpmodels.MediaItem.objects.get(jwp__key=v1.key)\\n new_i2 = mpmodels.MediaItem.objects.get(jwp__key=v2.key)\\n\\n # Only video 1 was updated\\n self.assertGreater(new_i1.updated_at, i1.updated_at)\\n self.assertEqual(new_i1.title, v1['title'])\\n self.assertEqual(new_i2.updated_at, i2.updated_at)\",\n \"def test_dump_major_update_exists(self, tmpdir):\\n filename = tmpdir / 'exists.json'\\n with open(str(filename), 'w') as tmp_fh:\\n json.dump([self.dummy_metadata1], tmp_fh)\\n\\n schema_utils.dump_major_update(\\n self.dummy_metadata2,\\n filename,\\n )\\n\\n with open(str(filename), 'r') as tmp_fh:\\n saved_data = json.load(tmp_fh)\\n\\n assert saved_data[1] == self.dummy_metadata2\",\n \"def test_get_entity_version(self):\\n self.metadata.create_or_update(data=self.create)\\n\\n # Find by name\\n res_name = self.metadata.get_by_name(\\n entity=Dashboard, fqn=self.entity.fullyQualifiedName\\n )\\n res = self.metadata.get_entity_version(\\n entity=Dashboard, entity_id=res_name.id.__root__, version=0.1\\n )\\n\\n # check we get the correct version requested and the correct entity ID\\n assert res.version.__root__ == 0.1\\n assert res.id == res_name.id\",\n \"def next_available_version(self):\\n pattern = \\\"{descriptor}_{task}_v*{ext}\\\".format(\\n descriptor=self.descriptor, task=self.task, ext=self.extension)\\n matching_scenefiles = []\\n try:\\n for file_ in self.folder_path.files():\\n if file_.name.fnmatch(pattern):\\n matching_scenefiles.append(file_)\\n if not matching_scenefiles:\\n return 1\\n except OSError as err:\\n return 1\\n matching_scenefiles.sort()\\n latest_scenefile = matching_scenefiles[-1]\\n latest_version = latest_scenefile.name.stripext().split(\\\"_v\\\")[-1]\\n return int(latest_version) + 1\",\n \"def test_updates_static_version(self):\\n scripts.update_static_asset_version.main()\",\n \"def test_get_versions_cached(self):\\n versions = {\\\"foo-1.0.tar.gz\\\": \\\"../../packages/foo-1.0.tar.gz\\\"}\\n self.index._save_index(\\\"foo\\\", versions)\\n with patch(\\\"cheddar.index.remote.get\\\") as mocked:\\n result = self.index.get_versions(\\\"foo\\\")\\n eq_(result, versions)\\n eq_(mocked.call_count, 0)\",\n \"def update_metadata(cube_list, infile_history):\\n\\n equalise_attributes(cube_list)\\n\\n for cube in cube_list:\\n cube.attributes['history'] = gio.write_metadata(file_info=infile_history)\\n cube.data = numpy.array(cube.data) #removes _FillValue attribute\\n\\n return cube_list\",\n \"def upgrade(self):\",\n \"def upgrade(self):\",\n \"def _do_3dsmax_post_publish(self, work_template, progress_cb): \\n from Py3dsMax import mxs\\n \\n progress_cb(0, \\\"Versioning up the scene file\\\")\\n \\n # get the current scene path:\\n scene_path = os.path.abspath(os.path.join(mxs.maxFilePath, mxs.maxFileName))\\n \\n # increment version and construct new file name:\\n progress_cb(25, \\\"Finding next version number\\\")\\n fields = work_template.get_fields(scene_path)\\n next_version = self._get_next_work_file_version(work_template, fields)\\n fields[\\\"version\\\"] = next_version \\n new_scene_path = work_template.apply_fields(fields)\\n \\n # log info\\n self.parent.log_debug(\\\"Version up work file %s --> %s...\\\" % (scene_path, new_scene_path))\\n \\n # rename and save the file\\n progress_cb(50, \\\"Saving the scene file\\\")\\n mxs.saveMaxFile(new_scene_path)\\n \\n progress_cb(100)\",\n \"def test_changeAllProjectVersions(self):\\n root = FilePath(self.mktemp())\\n root.createDirectory()\\n structure = {\\n \\\"README\\\": \\\"Hi this is 1.0.0.\\\",\\n \\\"twisted\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.0\\\"},\\n \\\"_version.py\\\":\\n genVersion(\\\"twisted\\\", 1, 0, 0),\\n \\\"web\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.0\\\"},\\n \\\"_version.py\\\": genVersion(\\\"twisted.web\\\", 1, 0, 0)\\n }}}\\n self.createStructure(root, structure)\\n changeAllProjectVersions(root, Version(\\\"lol\\\", 1, 0, 2))\\n outStructure = {\\n \\\"README\\\": \\\"Hi this is 1.0.2.\\\",\\n \\\"twisted\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.2\\\"},\\n \\\"_version.py\\\":\\n genVersion(\\\"twisted\\\", 1, 0, 2),\\n \\\"web\\\": {\\n \\\"topfiles\\\": {\\n \\\"README\\\": \\\"Hi this is 1.0.2\\\"},\\n \\\"_version.py\\\": genVersion(\\\"twisted.web\\\", 1, 0, 2),\\n }}}\\n self.assertStructure(root, outStructure)\",\n \"def update_scenes(self) -> None:\\n self.scenes.update(\\n {\\n f\\\"{group.id}_{scene.id}\\\": scene\\n for group in self.groups.values() # type: ignore\\n for scene in group.scenes.values()\\n if f\\\"{group.id}_{scene.id}\\\" not in self.scenes\\n }\\n )\",\n \"def single_node_changed(self, old_version, new_version):\\n self.scene().update_scene_single_node_change(self.controller,\\n old_version,\\n new_version)\\n if self.controller and self.controller.reset_version_view:\\n self.scene().fitToAllViews()\\n if self.controller:\\n # self.versionProp.updateVersion(self.controller.current_version)\\n self.versionProp.updateVersion(self.controller.current_version)\\n self.emit(QtCore.SIGNAL(\\\"vistrailChanged()\\\"))\",\n \"async def check_new_version(now):\\n result = await get_newest_version(hass, huuid, include_components)\\n\\n if result is None:\\n return\\n\\n newest, releasenotes, android, apt = result\\n\\n # Load data from supervisor on hass.io\\n if hass.components.hassio.is_hassio():\\n newest = hass.components.hassio.get_homeassistant_version()\\n\\n # Validate version\\n if StrictVersion(newest) > StrictVersion(current_version):\\n _LOGGER.info(\\\"The latest available version is %s\\\", newest)\\n info = 'Dostępna jest nowa wersja ' + newest + '. ' + releasenotes\\n hass.states.async_set(\\n ENTITY_ID, info, {\\n ATTR_FRIENDLY_NAME: 'Aktualizacja',\\n \\\"icon\\\": \\\"mdi:update\\\",\\n \\\"reinstall_dom_app\\\": True,\\n \\\"reinstall_android_app\\\": android,\\n \\\"apt\\\": apt\\n }\\n )\\n # add all entities to keep the order\\n # hass.async_add_job(\\n # hass.services.async_call(\\n # 'group',\\n # 'set', {\\n # \\\"object_id\\\": \\\"dom_system_version\\\",\\n # \\\"entities\\\": [\\n # \\\"sensor.version_info\\\",\\n # \\\"script.ais_update_system\\\",\\n # \\\"camera.remote_access\\\",\\n # \\\"input_boolean.ais_remote_access\\\",\\n # \\\"sensor.ais_secure_android_id_dom\\\",\\n # \\\"script.ais_scan_network_devices\\\",\\n # \\\"script.ais_restart_system\\\",\\n # \\\"script.ais_stop_system\\\"]}))\\n\\n hass.states.async_set(\\n 'script.ais_update_system', 'off', {\\n ATTR_FRIENDLY_NAME: ' Zainstaluj aktualizację',\\n \\\"icon\\\": \\\"mdi:download\\\"\\n }\\n )\\n\\n else:\\n info = 'Twój system jest aktualny, wersja ' + newest + '. '\\n info += releasenotes\\n hass.states.async_set(\\n ENTITY_ID, info, {\\n ATTR_FRIENDLY_NAME: 'Wersja',\\n \\\"icon\\\": \\\"mdi:update\\\",\\n \\\"reinstall_dom_app\\\": False,\\n \\\"reinstall_android_app\\\": False,\\n \\\"apt\\\": apt\\n }\\n )\\n hass.states.async_set(\\n 'script.ais_update_system', 'off', {\\n ATTR_FRIENDLY_NAME: ' Sprawdź dostępność aktualizacji',\\n \\\"icon\\\": \\\"mdi:refresh\\\"\\n }\\n )\\n _LOGGER.info(\\n \\\"You are on the latest version (%s) of Assystent domowy\\\", newest)\",\n \"def test_update_metadata1(self):\\n pass\",\n \"def _update_metadata(self, container_name: Union[str, list] = None, multibeam_file_list: list = None, crs: str = None,\\n vertical_reference: str = None):\\n\\n if container_name:\\n if isinstance(container_name, str): # only one converted data instance provided\\n container_name = [container_name]\\n if isinstance(multibeam_file_list[0], str): # only one converted data instance provided\\n multibeam_file_list = [multibeam_file_list]\\n for contname, mfiles in zip(container_name, multibeam_file_list):\\n if mfiles:\\n if contname in self.sources and (set(mfiles) & set(self.sources[contname])):\\n raise ValueError('QuadManager: Found some of these new lines in the existing lines metadata for container: {}'.format(contname))\\n elif contname in self.sources:\\n self.sources[contname].extend(mfiles)\\n else:\\n self.sources[contname] = mfiles\\n else:\\n self.sources[contname] = ['unknown']\\n if self.crs and (self.crs != crs):\\n raise ValueError('QuadManager: Found existing coordinate system {}, new coordinate system {} must match'.format(self.crs,\\n crs))\\n if self.vertical_reference and (self.vertical_reference != vertical_reference):\\n raise ValueError('QuadManager: Found existing vertical reference {}, new vertical reference {} must match'.format(self.vertical_reference,\\n vertical_reference))\\n self.crs = crs\\n self.vertical_reference = vertical_reference\",\n \"def test_update_meta_file_meta_file_exists(self):\\n # Expected results\\n date_old = '2021-09-13'\\n date_new = '2021-09-14'\\n dates_expected = [date_old, date_new]\\n\\n meta_content = f\\\"{self.meta_date_col},{self.meta_timestamp_col}\\\\n{date_old},{datetime.today().strftime(self.meta_timestamp_format)}\\\"\\n self.bucket.put_object(Body=meta_content, Key=self.meta_key)\\n # method execution\\n MetaFile.update_meta_file(date_new, self.trg_bucket_connector)\\n # read meta file\\n df_result = self.trg_bucket_connector.read_meta_file()\\n dates_result = list(df_result[\\n self.meta_date_col])\\n self.assertEqual(dates_expected, dates_result)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6834863","0.6241142","0.6191124","0.6186932","0.6126859","0.5808705","0.57901436","0.5686815","0.5674683","0.56705105","0.5636066","0.5609275","0.55931807","0.553265","0.54316425","0.5389392","0.53655267","0.52925307","0.52915853","0.52761626","0.525988","0.52414685","0.52390707","0.52130103","0.52093095","0.5209143","0.5199475","0.5194603","0.5188073","0.518789","0.5162288","0.5145893","0.51250035","0.5107034","0.50821304","0.5080679","0.5047954","0.50455797","0.5037439","0.50348455","0.5028413","0.5026152","0.50120395","0.50112516","0.5010152","0.500348","0.50012976","0.49941537","0.4992418","0.49868062","0.49772117","0.49705935","0.4969132","0.496316","0.49631417","0.49500564","0.49243185","0.49090257","0.4879908","0.48745912","0.48724642","0.48717982","0.48699376","0.48654264","0.48635003","0.48541406","0.48522305","0.4849116","0.48462552","0.48457795","0.4835676","0.48280543","0.48242974","0.48237824","0.48231316","0.48209953","0.48186928","0.481793","0.48169884","0.4816908","0.48140877","0.48133916","0.48126563","0.48045552","0.47981226","0.47927183","0.4789297","0.4787659","0.47799137","0.47773045","0.47741565","0.47741565","0.4774089","0.475768","0.4752721","0.47519115","0.47479013","0.47466367","0.4743123","0.47416842"],"string":"[\n \"0.6834863\",\n \"0.6241142\",\n \"0.6191124\",\n \"0.6186932\",\n \"0.6126859\",\n \"0.5808705\",\n \"0.57901436\",\n \"0.5686815\",\n \"0.5674683\",\n \"0.56705105\",\n \"0.5636066\",\n \"0.5609275\",\n \"0.55931807\",\n \"0.553265\",\n \"0.54316425\",\n \"0.5389392\",\n \"0.53655267\",\n \"0.52925307\",\n \"0.52915853\",\n \"0.52761626\",\n \"0.525988\",\n \"0.52414685\",\n \"0.52390707\",\n \"0.52130103\",\n \"0.52093095\",\n \"0.5209143\",\n \"0.5199475\",\n \"0.5194603\",\n \"0.5188073\",\n \"0.518789\",\n \"0.5162288\",\n \"0.5145893\",\n \"0.51250035\",\n \"0.5107034\",\n \"0.50821304\",\n \"0.5080679\",\n \"0.5047954\",\n \"0.50455797\",\n \"0.5037439\",\n \"0.50348455\",\n \"0.5028413\",\n \"0.5026152\",\n \"0.50120395\",\n \"0.50112516\",\n \"0.5010152\",\n \"0.500348\",\n \"0.50012976\",\n \"0.49941537\",\n \"0.4992418\",\n \"0.49868062\",\n \"0.49772117\",\n \"0.49705935\",\n \"0.4969132\",\n \"0.496316\",\n \"0.49631417\",\n \"0.49500564\",\n \"0.49243185\",\n \"0.49090257\",\n \"0.4879908\",\n \"0.48745912\",\n \"0.48724642\",\n \"0.48717982\",\n \"0.48699376\",\n \"0.48654264\",\n \"0.48635003\",\n \"0.48541406\",\n \"0.48522305\",\n \"0.4849116\",\n \"0.48462552\",\n \"0.48457795\",\n \"0.4835676\",\n \"0.48280543\",\n \"0.48242974\",\n \"0.48237824\",\n \"0.48231316\",\n \"0.48209953\",\n \"0.48186928\",\n \"0.481793\",\n \"0.48169884\",\n \"0.4816908\",\n \"0.48140877\",\n \"0.48133916\",\n \"0.48126563\",\n \"0.48045552\",\n \"0.47981226\",\n \"0.47927183\",\n \"0.4789297\",\n \"0.4787659\",\n \"0.47799137\",\n \"0.47773045\",\n \"0.47741565\",\n \"0.47741565\",\n \"0.4774089\",\n \"0.475768\",\n \"0.4752721\",\n \"0.47519115\",\n \"0.47479013\",\n \"0.47466367\",\n \"0.4743123\",\n \"0.47416842\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6645423"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17448,"cells":{"query":{"kind":"string","value":"Renvoie la liste des noeuds du graph"},"document":{"kind":"string","value":"def getNoeuds(self) -> list:\n return self._noeuds"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def graph(self):\n ...","def _get_full_graph(self):","def populate_graph(self):","def print_list_of_nodes(self):\n\n for node in self.list_empty_nodes:\n print(\"--------------------------\")\n print(\"Node num : \"+str(node.num))\n print(\"Node distance from start point : \"+str(node.distance_from_start_point))\n if node.pere is None:\n print(\"Pas de père\")\n else:\n print(\"Num du père : \"+str(node.pere.num))","def nodes(self): \n return [n for n in self.iternodes()]","def list_all(self):\n if self.graph is None:\n self.logger.error(\"Graph value none cannot be iterated\")\n return list()\n\n nodes = list()\n for node in self.graph.nodes():\n if node == self.NONE_PACKAGE:\n continue\n nodes.append(node)\n return nodes","def get_neighbours(self):\n return []","def connected_components(self) -> List[list]:\n for n in self.dw_graph.get_all_v().values():\n n.distance=0.0\n mega_list = []\n for n in self.dw_graph.get_all_v().values():\n if n.distance!=-10:\n mega_list.append(self.connected_component(n.node_id))\n return mega_list","def pgcd_numerateurs(self):\n\t\tl = []\n\t\tif self.__valide:\n\t\t\tfor m in self.liste_decroissante():\n\t\t\t\te = abs(m.get_coefficient().get_num().valeur())\n\t\t\t\tif not (e in l):\n\t\t\t\t\tl.append(e)\n\t\treturn pgcd_liste(l)","def gen_graph(self):","def get_nodes(self):\n self.map_graph_id()\n self.nodes_list = [\n self.NX_GRAPHS[self.graph_id].nodes[idx]['label'] \n for idx in range(len(self.NX_GRAPHS[self.graph_id].nodes))]","def nodes(self, irc, msg, args, channel):\n pie = self.instances[irc.network]\n irc.reply([str(x) for x in pie.graphs[channel].nodes.values()])","def list(self):","def get_node_list(self):\n return []","def generateGraph(mids, chaptersField, labelsField):\n output = \"digraph G { \\n\"\n # On ne traite que les chapitres qui ont actives le graphe\n chapts = chapters.graphChapters()\n # le dico nodes contient une liste pour chaque chapitre. Chaque liste\n # contient tous les neuds (un par note) presents dans ce chapitre, et\n # representes par des tuples (noteId, label)\n nodes = {}\n for mid in mids:\n chapterField = chaptersField[mid]\n labelField = labelsField[mid]\n for id, flds in mw.col.db.execute(\"\"\"\n SELECT id, flds FROM notes WHERE mid=%d\n \"\"\" % mid):\n fields = splitFields(flds)\n chapter = fields[chapterField]\n if not chapter in chapts:\n continue\n label = fields[labelField]\n if(not chapter in nodes):\n nodes[chapter] = []\n nodes[chapter].append((id, label))\n # On genere les noeuds, dans des clusters (un par chapitre)\n notes = []\n for chap in nodes:\n output += \"\"\"subgraph cluster_%d {\n node [style=filled];\n label = \"%s\";\n color=blue;\n \"\"\" % (chapts[chap], chap)\n for n in nodes[chap]:\n output += \"\"\"n%d [label=\"%s\", URL=\"%d\"];\\n\"\"\" % (n[0], n[1], n[0])\n notes.append(n)\n output += \"\"\"\n }\\n\"\"\"\n # Puis on ajoute tous les liens ..\n for n in notes:\n for nid in mw.col.db.execute(\"\"\"SELECT N.noteId FROM `PATH.links` AS L\n JOIN `PATH.match` AS M ON M.id = L.matchId\n JOIN `PATH.nodes` AS N ON M.nodeId = N.id\n WHERE L.noteId = %d\"\"\" % (n[0])):\n output += \"\"\"n%d -> n%d;\\n\"\"\" % (nid[0], n[0])\n output += \"}\"\n generateGraphImage(output)","def get_nodes(self):\n pass","def unisolvent_nodes(self):\r\n return self.grid.unisolvent_nodes","def visualizar_fornecedores(self):\r\n for fornecedor in self.fornecedores:\r\n fornecedor.visualizar()","def knntsneplot(imdbid,topn):\n similarimdbids = getlistofimdbids(imdbid)[:topn] # get the imdbids\n mtitles = getsimilartitles(imdbid,topn)# get the titles to label them\n # get the indexes of elements\n indexessimilar = []\n for si in similarimdbids:\n currind = allimdbids.index(si)\n indexessimilar.append(currind)\n subtsneX = []\n subtsneY = [] \n subsvdX = []\n subsvdY = []\n for idxs in indexessimilar:\n subtsneX.append(tsne_Um[idxs, 0])\n subtsneY.append(tsne_Um[idxs, 1])\n \n subsvdX.append(USigmam[idxs, 0])\n subsvdY.append(USigmam[idxs, 1])\n \n return subtsneX, subtsneY, subsvdX, subsvdY, mtitles","def plot_graph(self) -> None:","def list_spectrographs(self) -> None:\n for key, item in self.spectrographs.items():\n item.summary()\n print(\"\\n\")","def graphs(self):\n return self.__graphs","def get_unvisited_nodes(self) -> List[Node]:\n\n db = mysql.connector.connect(\n host=self.db_uri, database=self.db_name, user=self.db_user, password=self.db_pass\n )\n\n cursor = db.cursor()\n\n query = \"select * from node where date_visited is null\"\n cursor.execute(query)\n\n nodes = cursor.fetchall()\n\n for node in nodes:\n yield Node(*node)","def nbr_tours(self):\n nbr_tours = 0\n for i in range(3):\n for j in range(3):\n if self.grille[i][j] != 0:\n nbr_tours += 1\n return nbr_tours","def __init__(self):\n self.grille = []\n for i in range(3):\n self.grille.append([0, 0, 0])","def heuristique_ACPM(self,draw=False):\n\n def getVertricesOfPath(edges):\n set_node = set()\n for e in edges:\n id1,id2 = self.getIdVerticesOfEdge(e)\n set_node.add(id1)\n set_node.add(id2)\n return set_node\n\n\n if draw:\n try:\n os.makedirs(self.dirname+\"/H_ACPM\")\n except:\n pass\n\n\n #Graphe de depart contenant tout les noeuds\n G = Graphe_Individu(self,self.wholeGraphDict)\n if draw:\n self.drawGraph(\"/H_ACPM/G0\",G.get_graphe().get_edges())\n\n\n nb_vertices = 1\n nb_tmp_vertices = 0\n i=0\n\n #Tant que le nombre de noeuds du graphe decroit, appliquer kruskal\n while nb_vertices > nb_tmp_vertices :\n g_acpm = G.get_MST()\n\n\n nb_vertices = len(G.get_dictSteinerNodes())\n set_Node = set(G.get_dictSteinerNodes().keys())\n dictSteinerNodez = self.eliminationFeuilles(g_acpm.get_edges(),set_Node)\n G = Graphe_Individu(self,dictSteinerNodez)\n nb_tmp_vertices = len(dictSteinerNodez)\n\n if draw:\n i+= 1\n self.drawGraph(\"/H_ACPM/G%d\"%i+\"_%d\"%g_acpm.get_total_weight(),g_acpm.get_edges())\n\n\n if draw:\n self.drawGraph(\"/H_ACPM/GFinal_%d\"%G.get_MST().get_total_weight(),G.get_MST().get_edges())\n\n for i in self.steinerNodes:\n if i not in dictSteinerNodez.keys():\n dictSteinerNodez[i] = 0\n\n # return Graphe_Individu(self,dictSteinerNodez)\n return dictSteinerNodez","def get_donnees(self):\r\n \r\n return self._donnees","def get_station_graph(start_station_id, end_station_list):\n start_station_graph = []\n for i in range(10):\n if end_station_list[i] is not None:\n start_station_graph.append((start_station_id, end_station_list[i]))\n return start_station_graph","def plot_sammen_vekst(urn, ordlister, window=5000, pr = 100):\n rammer = []\n for ordbag in ordlister:\n vekst = vekstdiagram(urn, params = {'words': ordbag, 'window':window, 'pr': pr} )\n vekst.columns = [ordbag[0]]\n rammer.append(vekst)\n return pd.concat(rammer)","def grAList() -> list:\n return [2, 5, 6, 9, 10, 11, 13, 17, 18, 30]","def buildGraph(self):\n return None","def allGraphs(date):\n g = getGraph()\n for uri, label, filename in subgraphs(date):\n if not label:\n label = \"(no label provided)\"\n g.parse(filename, format=SUBGRAPH_FORMAT)\n return g","def get_listu_analitickih_metoda(self):\n popis = sorted(list(self.analitickeMetode.keys()))\n return popis","def graphs_iso(n):\n assert n >= 0\n for g in unique_iso(graphs(n)):\n yield g","def lego_sets():\n # you must replace this line and return your own list\n return []","def get_puntos_articulacion(self):\n # Inicializo variables\n visitado = {}\n puntos_articulacion = set()\n for u in grafo.devolver_vertices():\n visitado[u] = False\n\n for v in grafo.devolver_vertices():\n if not visitado[v]:\n # Armo el arbol DFS desde v\n dfs = DFSIterativo(grafo, v, visitado)\n dfs.hacer_dfs()\n puntos_articulacion_v = dfs.get_puntos_articulacion()\n # Como v es raiz del arbol, lo saco para analizarlo por separado\n if v in puntos_articulacion_v: # Aunque deberia estar incluida siempre\n puntos_articulacion_v.remove(v)\n self.analizar_raiz(dfs.get_predecesor(), puntos_articulacion_v, v)\n puntos_articulacion.update(puntos_articulacion_v)\n return puntos_articulacion","def testNoeud(n, p, listAlgo):\r\n assert n.size == p.size\r\n res = np.zeros((len(listAlgo), n.size), dtype = int)\r\n progressBar = ProgressBar(maxValue = n.size * len(listAlgo))\r\n \r\n for ni in range(n.size):\r\n for i, (algoName, algoArgs) in enumerate(listAlgo):\r\n progressBar.update(ni * len(listAlgo) + i + 1)\r\n graphe = Graphe(nbSommets = n[ni], probaArete = p[ni])\r\n _, noeuds = getattr(graphe, algoName)(**algoArgs)\r\n res[i, ni] = noeuds\r\n print(\"\")\r\n \r\n return res","def X(self)->list:","def create_list() -> List[Optional[float]]:\n return [None] * num_stations","def getCaseDisp():\r\n liste = []\r\n for i in range(3):\r\n for j in range(3):\r\n if Grille[i][j] == 0:\r\n liste.append([i,j])\r\n return liste","def non_pol_neighbours_graph():\n data = pd.read_csv(\"/Users/emg/GitHub/thesis/output/2019_01/1000_residuals_output_utf8.csv\", index_col=0)\n\n labelled = label_subs(data)\n labelled['resid_rank'] = labelled.resid.rank(pct=True)\n top = subset_df(labelled, 'resid', q=0.95)\n\n edges = top.copy()[['source','target','resid']]\n edges_rev = edges.copy()\n edges_rev.columns = ['target','source','resid']\n directed_edges = pd.concat([edges,edges_rev], sort=True)\n directed_edges['resid_rank'] = directed_edges['resid'].rank(pct=True)\n\n df = label_subs(directed_edges)\n\n pol_subs = load_pol_subs()\n pol_names = pol_subs.subreddit.str.replace('\\\\','')\n pol_subs.subreddit=pol_subs.subreddit.str.replace('\\\\','')\n\n pol_neighbours = df[df['source'].isin(pol_names)].sort_values('resid', ascending=False)\n\n top_pol_neigh = pol_neighbours.groupby('source').head(10).sort_values(['source','resid'], ascending=[True,False])\n \n x = top_pol_neigh[~top_pol_neigh.target.isin(pol_names)][['source','target']]\n\n col_dict = pol_subs.set_index('subreddit').col.to_dict()\n for sub in x.target.unique():\n col_dict[sub] = 'gray'\n\n G = nx.from_pandas_edgelist(x)\n nx.set_node_attributes(G, col_dict, 'col')\n\n f = plt.figure(1)\n ax = f.add_subplot(1,1,1)\n\n colors = dict(G.nodes(data='col')).values()\n\n pos = nx.spring_layout(G, k=0.2)\n nx.draw_networkx(G, pos=pos, with_labels=False, node_color=colors, alpha=0.3)\n #nx.draw_networkx_labels(G, pos=pos, with_labels=True)\n\n plt.axis('off')\n f.set_facecolor('w')\n \n f.tight_layout()\n plt.savefig(figures_path(f\"{date}/non_pol_neighbours_graph.png\"))\n plt.close()","def build_graph(self):\n pass","def list():","def list():","def get_diagram(self):\n self_nodes=self.nodes.all()\n self_arrows=self.arrows.all()\n \n \n if len(self_nodes)==0:\n return False\n \n nodes = [n.get_icon_obj() for n in self_nodes]\n node_liens = [n.liens.all() for n in self_nodes]\n \n pairs = []\n for n,n_liens in zip(nodes,node_liens):\n if len(n_liens)==0:\n liens = Lien.objects.filter(cause__id=n.target_id).all()\n liens = [l.consequence.id for l in liens]\n temp = [(n,target) for target in nodes if target.target_id in liens]\n pairs.extend(temp)\n else:\n ids=set([(i.cause.id,i.consequence.id) for i in n_liens])\n for n in nodes:\n pairs.extend([(n,i) for i in nodes if i is not n and \n (n.target_id,i.target_id) in ids])\n ids = set([(i.cause.id,i.consequence.id) for i in self_arrows])\n pairs = [p for p in pairs if (p[0].target_id,p[1].target_id) not in ids]\n \n lines=[]\n arrows=[]\n for obj in self_arrows:\n \n n0=[i for i in nodes if i.node_id==obj.cause.id]\n n1=[i for i in nodes if i.node_id==obj.consequence.id]\n if len(n0)!=1 or len(n1)!=1:\n continue\n n0=n0[0]\n n1=n1[0]\n \n pt=[(obj.X0, obj.Y0), (obj.X1,obj.Y1)]\n pt=[np.array(i) for i in pt if None not in i]\n if len(pt)==0:\n pairs.append((n0,n1))\n continue\n pairs = [p for p in pairs if (p[0].node_id,p[1].node_id)!=(n0.node_id,n1.node_id)]\n vect = pt[0]-np.array(n0.pos)\n first_pt = np.array(n0.pos)+vect*n0.size/np.sqrt(sum(vect*vect))\n vect = np.array(n1.pos) - pt[-1]\n last_pt = np.array(n1.pos)-vect*n1.size/np.sqrt(sum(vect*vect))\n pt=[first_pt,*pt,last_pt]\n \n lines.extend([((*i,*j),n0.color) for i,j in zip(pt[:-1],pt[1:])])\n arrows.append(((*pt[-2],*pt[-1]),n0.color))\n \n \n margin=10\n line_width=2\n \n diagram=DiagramObj(self.id,nodes,pairs,margin,\n self.width,self.height,line_width)\n diagram.add_arrows(arrows,lines)\n print(diagram.lines)\n return diagram","def draw_graph(self):\n\t\tif None in self.graph:\n\t\t\tdel self.graph[None]\n\n\t\tfor vs in self.graph.itervalues():\n\t\t\tto_delete = []\n\t\t\tfor i in xrange(len(vs)):\n\t\t\t\tif vs[i] is None:\n\t\t\t\t\tto_delete.append(i)\n\n\t\t\tfor i in reversed(to_delete):\n\t\t\t\tdel vs[i]\n\n\t\tself.G=nx.Graph(self.graph)\n\n\t\tfor k,v in self.labels.iteritems():\n\t\t\tif v[:6] == 'Module':\n\t\t\t\troot = k\n\t\t\t\tbreak\n\n\t\treturn self.__dfs_plot(root)","def disconnected_nodes(self):\n return self._disconnected_nodes","def V(self) -> list:\n return list(self._graph.values())","def retireSommet(self, sommet):\r\n nouveauGraphe = copy.deepcopy(self) # on effectue une copie du graphe\r\n nouveauGraphe.n = self.n-1 # On a n-1 points\r\n # NB: il faut aussi changer m et listeArretes mais on va pas le faire tout de suite car pas urgent\r\n # 1. On suprrime la ligne d'indice sommet\r\n #* AUTRE MÉTHODE del nouveauGraphe.adjMatrix[sommet]\r\n # print(nouveauGraphe.adjMatrix)\r\n nouveauGraphe.adjMatrix.pop(sommet)\r\n # print(nouveauGraphe.adjMatrix)\r\n #2. On supprime la colonne d'indice sommet = on supprime l'index sommet de chaque sous liste\r\n # la liste comprehension ne marche pas bien :(\r\n for line in nouveauGraphe.adjMatrix:\r\n line.pop(sommet)\r\n # print(nouveauGraphe.adjMatrix)\r\n # nouveauGraphe.m = 0\r\n # 2ème méthode:\r\n # for ligne in nouveauGraphe.adjMatrix:\r\n # ligne.pop(sommet)\r\n return nouveauGraphe","def view_all_batters(self):\n conn = rs.create_connection(\"dailyfantasyscraper.db\")\n cur = conn.cursor()\n position = \"P\"\n cur.execute(\"SELECT * FROM rotowiredk where position != ?\", position)\n result = cur.fetchall()\n conn.commit()\n conn.close()\n\n for item in result:\n print(item)\n tree.insert('', 'end', values=item)","def depth_setsM(self):\n G = self.copy()\n depths = []\n while G.vertices() != []:\n outdegs = G.out_degree(labels=True)\n new_depth = [x for x in outdegs if outdegs[x]==0]\n depths.append(new_depth)\n G.delete_vertices(new_depth)\n return depths","def iter_nodes(self):","def get_ids(self):\n return self._graphs.keys()","def createGraph(self):\n \n for episode in self.episodes:\n listeSuccessors = [episode[episode[:,1] > episode[i,1]][:,0] # List of list of successors for each user\n for i in range(len(episode))] \n for i, successeur in enumerate(listeSuccessors): # for the list of successors of each user\n for v in successeur: # for every successor of a user\n u, proba = episode[i,0], np.random.random() # Generate a probability so within (0,1)\n self.successors[u][v] = proba # u ---(proba)---> v \n self.predecessors[v][u] = proba # v ---(proba)---> u","def _build_graph(self):\n pass","def init(self):\n self.padre = self.id\n self.sinVisitar = [] \n self.visited = False\n print (\"inicializo algoritmo\")\n for i in range (len(self.neighbors)): #De esta forma se pueden manipular las listas por aparte\n self.sinVisitar.append(self.neighbors[i])","def make_complete_graph(num_nodes):\r\n result = {}\r\n for idx in range(0,num_nodes):\r\n result[idx] = set([])\r\n for jdx in range(0,num_nodes):\r\n if (idx!=jdx):\r\n result[idx].add(jdx)\r\n return result","def cleanGraph2(self,graph):\n return [graph[i] for i in range(len(graph)-1) if graphp[i]!=graph[i+1]]","def gen_nodes(self):\n self.nodes = []\n for i in range(self.num_nodes):\n self.nodes.append(Node(self.fk))","def table_graph(self, i):\n return self.__graph_list[i]","def ngraphs(self):\n return len(self.__graph_list)","def make_undir_graph(V,E):\n\tUD = []\n\tfor (u,v) in E:\n\t\tif (v,u) not in E:\n\t\t\tUD.append((u,v))\n\t\t\tUD.append((v,u))\n\treturn UD","def get_end_nodes(graph):\n nodelist = [\n i\n for i in graph.nodes_iter()\n if len(graph.neighbors(i))==1\n ]\n return nodelist","def nodes( self, data = False ):\n return self._G.nodes(data = data)","def DorogovtsevGoltsevMendesGraph(n):\n import networkx\n return Graph(networkx.dorogovtsev_goltsev_mendes_graph(n),\\\n name=\"Dorogovtsev-Goltsev-Mendes Graph, %d-th generation\"%n)","def get_edges(self):\n try:\n temp = self.edges\n except:\n temp = []\n return temp","def plot_graph(self):\n plt.axis(\"off\")\n pos = nx.kamada_kawai_layout(self.graph)\n return nx.draw_networkx(self.graph, pos=pos, node_size=400)","def findZeroGraph(data, deviation, doFilter):\n first = data[0]\n print('frist', first)\n data = list(map(lambda x: (x[0]-first[0], x[1]), data))\n return findManualGraph(data, deviation, doFilter)","def get_unvisited_nodes(self, graph, solution):\n nodes = []\n for node in graph[solution.current]:\n if node not in solution:\n nodes.append(node)\n return nodes","def nodes(self):\n return self.sort_dict(self.trajectory_data)","def getCubes():","def get_labels(self):\n return []","def ordenar_vertices(grafo, distancia):\n return list(sorted(distancia.items(),key = lambda x:x[1] , reverse = True ))","def __len__(self):\n return len(self._graphs)","def fn(n):\n if n == len(graph)-1: return [[n]]\n ans = []\n for nn in graph[n]: \n ans.extend([[n] + x for x in fn(nn)])\n return ans","def ludnosc(lista):\n # wynik - lista zawierajaca wynik koncowy dzialania funkcji(lata i wartosci dla poszczegolnych panstw)\n wynik = []\n for panstwo in lista:\n # rok - lista zawierajaca lata\n # wartosc - lista zawierajaca wartosci dla lat\n rok = []\n wartosc = []\n for element in panstwo:\n # sprawdzenie czy klucz posiada odpowiednia wartosc\n if element[1].get('key') == \"EN.POP.DNST\":\n # dodanie roku do listy\n rok.append(int(element[2].text))\n # rozpatrywanie przypadku w ktorym wartosc jest None\n if element[3].text is None:\n wartosc.append(element[3].text)\n else:\n wartosc.append(float(element[3].text))\n # dodawanie list dla poszczegolnych panstw do listy wynikowej\n wynik.append(rok)\n wynik.append(wartosc)\n\n return wynik","def get_graphs(self):\n ids = self._graphs.keys()\n ids.sort()\n return [self._graphs[id] for id in ids]","def dump_graph(self):\n\n edges = []\n for vertex in self.__graph_dict:\n mylist = list(vertex)\n logging.debug(\"mylist : \", mylist)","def get_nodes(self, topogramId):\n return self.make_request(\"GET\", \"topograms/\"+topogramId+\"/nodes\", {})","def __filterEdges(self):","def get_listu_uredjaja(self):\n lista = sorted(list(self.uredjaji.keys()))\n return lista","def nonconnected():\n nonconnected = nx.MultiDiGraph()\n nonconnected.add_nodes_from(range(6))\n nonconnected.add_edges_from([\n (0, 1),\n (1, 2),\n (2, 3),\n (3, 0),\n (1, 0),\n (1, 0),\n (3, 2),\n (3, 3),\n (4, 4),\n (4, 4)\n ])\n return nonconnected","def terminals(self):\n unique_nodes, unique_counts = np.unique(self.edges, return_counts=True)\n return unique_nodes[ unique_counts == 1 ]","def empty(self):\n return _osgAnimation.VertexList_empty(self)","def neighbours(self):\n seen = set()\n return [l.other(self) for l in self.dovetails \\\n if id(l) not in seen and not seen.add(id(l))]","def preencherJogadores():\n global jogadores\n for x in participantes:\n if x['porta'] != lider['porta']:\n jogadores.append(x)","def get_list(self):\r\n return self.numbers","def edges(self, irc, msg, args, channel):\n pie = self.instances[irc.network]\n irc.reply([str(x) for x in pie.graphs[channel].edges.values()])","def __init__(self):\n self.numList=[]","def zyklus(self):\n neu = self.lebendig.copy() # Kopie erstellen\n for zelle in self.lebendig: # Alle lebendigen Zellen durchgehen\n anzahl = self.lebendige_nachbarn(zelle)\n if anzahl < 2 or anzahl > 3: # Vereinsamung oder Überbevölkerung\n neu.remove(zelle) # Stirbt - aus der Liste der Lebendigen entfernen\n # Prüfe alle Nachbarn einer lebendigen Zelle\n # wenn diese genau drei Nachbarn hat, muss sie evtl.\n # lebendig werden\n x, y = zelle\n if self.lebendige_nachbarn((x-1, y+1)) == 3:\n neu.add((x-1, y+1))\n if self.lebendige_nachbarn((x, y+1)) == 3:\n neu.add((x, y+1))\n if self.lebendige_nachbarn((x+1, y+1)) == 3:\n neu.add((x+1, y+1))\n if self.lebendige_nachbarn((x+1, y)) == 3:\n neu.add((x+1, y))\n if self.lebendige_nachbarn((x+1, y-1)) == 3:\n neu.add((x+1, y-1))\n if self.lebendige_nachbarn((x, y-1)) == 3:\n neu.add((x, y-1))\n if self.lebendige_nachbarn((x-1, y-1)) == 3:\n neu.add((x-1, y-1))\n if self.lebendige_nachbarn((x-1, y)) == 3:\n neu.add((x-1, y))\n self.lebendig = neu","def neighbours_L(self):\n seen = set()\n return [l.other(self) for l in self.dovetails_L \\\n if id(l) not in seen and not seen.add(id(l))]","def plotTime(self):\n plt.figure()\n t = [i for i in range(len(self.nodes_infected))]\n print(t)\n plt.title('Nodos infectados vs Tiempo')\n plt.xlabel('Instantes de tiempo')\n plt.ylabel('# de nodos infectados')\n plt.plot(t, self.nodes_infected)\n plt.grid(True)\n plt.show()","def plot_dendrogram_national():\n print 'Creating the national migration dendrogram'\n for method in ['NR']:\n for weight_fc in ['col', 'row']:\n mat = np.loadtxt('ultra_metric/%s_u%s.csv' % (weight_fc, method),\n dtype=float, delimiter=',')\n plot_dendrogram_by_mat(mat, weight_fc=weight_fc, method=method,\n state='USA', label=all_counties)\n #break\n #break","def fullgraphplot(time_lower,time_upper):\n\n edges_list,node_list,title_list = graphformation(time_lower,time_upper)\n node_size = []\n for i in range(len(node_list)):\n node_size.append(5)\n g = Network(\n height=\"750px\",\n width=\"100%\",\n bgcolor=\"#222222\",\n font_color=\"white\")\n g.add_nodes(node_list,label=node_list,title=title_list, size= node_size)\n g.add_edges(edges_list)\n g.show(\"nx.html\")\n return","def dekodeKromosom(self, kromosom):\n xMin, xMaks = self.batas[0]\n yMin, yMaks = self.batas[1]\n t, x, y = 0, 0, 0\n n = (self.panjangKromosom)//2\n for i in range(0, n):\n t += 2**(-(i+1))\n for i in range(0, n):\n x += kromosom[i] * 2**-(i+1)\n y += kromosom[n + i] * 2**-(i+1)\n x *= (xMaks - xMin / t)\n y *= (yMaks - yMin / t)\n x += xMin\n y += yMin\n return [x, y]","def __lista_krawedzi__(self):\n s = 0\n for cell in self.cells: # [ 1 0 12 11] = [kr1 kr2 kr3 kr4]\n s += len(cell) # zlicza ile w kolejnych komurkach pkt (krawedzi) (liczy ile jest w wierszu [1 2 12 11] czyli 4 krawedzie\n\n lista_kr = np.array([[0] * 4] * s, dtype=int)\n\n edgeNum = 0\n for i, cell in enumerate(self.cells):\n nNodes = len(cell)\n for l in range(nNodes):\n if l == nNodes-1:\n lista_kr[edgeNum] = [cell[l], cell[0], i, -1]\n else:\n lista_kr[edgeNum] = [cell[l], cell[l+1], i, -1]\n edgeNum += 1\n\n # [ 1 0 0 1] = [pkt1 pkt2 wl sasiad-jeszcze nie obecny]\n return lista_kr","def _CreateGraph(self):\n self.nodes = []\n self.edges = []\n for i, r in self.airports.set_index('airport_id').iterrows():\n self.nodes.append((i,r.to_dict()))\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\n self.edges.append((i[0],i[1],r.to_dict()))\n # print('node ex: {}'.format(self.nodes[0]))\n # print('edge ex: {}'.format(self.edges[0]))\n\n self.graph = self._CreateAdjacencyListGraph()","def _get_nodes(self):\n viewpoint = \"shiva_{}\".format(cherrypy.session[\"id\"])\n messages_db = self.mongo[viewpoint][\"messages\"]\n people_db = self.mongo[viewpoint][\"people\"]\n #\n senders = messages_db.distinct(\"sender\")\n owner_id = cherrypy.session[\"id\"]\n nodes = list()\n for sender in senders:\n person = people_db.find_one({\"id\": sender})\n if person is None:\n name = \"id{}\".format(sender)\n else:\n name = person[\"display_name\"]\n records = list(messages_db.aggregate([{\n \"$match\": {\n \"$or\": [\n {\"sender\": owner_id, \"receiver\": sender},\n {\"sender\": sender, \"receiver\": owner_id}\n ]\n }\n }, {\"$group\": {\"_id\": None, \"count\": {\"$sum\": 1}}}]))\n if not records:\n records = 0\n else:\n records = records[0][\"count\"]\n info = \"Total records: {}\".format(records)\n history_link = \"/vk/read?id={}\".format(sender)\n statistics_link = \"#\"\n if records > 0:\n nodes.append({\n \"id\": sender,\n \"name\": name,\n \"info\": info,\n \"records\": records,\n \"history_link\": history_link,\n \"statistics_link\": statistics_link\n })\n #\n return nodes","def get_subgraphs(graph):\n nodes_powerset = get_nodes_combinations(graph)\n #print(\"Doing\")\n #draw_graph(graph)\n subgraphs = []\n for nodes in nodes_powerset:\n subg = graph.subgraph(nodes)\n nodes = subg.nodes(data=True)\n if nx.is_weakly_connected(subg):\n subgraphs.append(subg)\n return subgraphs","def make_complete_graph(num_nodes):\n complete_digraph = {}\n if num_nodes > 0 and type(num_nodes) == int:\n neighbors = set([idx for idx in range(num_nodes)])\n for idx in range(num_nodes):\n complete_digraph[idx] = neighbors.copy() #creates adjacency set\n complete_digraph[idx].remove(idx) # pop out self-loop \n return complete_digraph"],"string":"[\n \"def graph(self):\\n ...\",\n \"def _get_full_graph(self):\",\n \"def populate_graph(self):\",\n \"def print_list_of_nodes(self):\\n\\n for node in self.list_empty_nodes:\\n print(\\\"--------------------------\\\")\\n print(\\\"Node num : \\\"+str(node.num))\\n print(\\\"Node distance from start point : \\\"+str(node.distance_from_start_point))\\n if node.pere is None:\\n print(\\\"Pas de père\\\")\\n else:\\n print(\\\"Num du père : \\\"+str(node.pere.num))\",\n \"def nodes(self): \\n return [n for n in self.iternodes()]\",\n \"def list_all(self):\\n if self.graph is None:\\n self.logger.error(\\\"Graph value none cannot be iterated\\\")\\n return list()\\n\\n nodes = list()\\n for node in self.graph.nodes():\\n if node == self.NONE_PACKAGE:\\n continue\\n nodes.append(node)\\n return nodes\",\n \"def get_neighbours(self):\\n return []\",\n \"def connected_components(self) -> List[list]:\\n for n in self.dw_graph.get_all_v().values():\\n n.distance=0.0\\n mega_list = []\\n for n in self.dw_graph.get_all_v().values():\\n if n.distance!=-10:\\n mega_list.append(self.connected_component(n.node_id))\\n return mega_list\",\n \"def pgcd_numerateurs(self):\\n\\t\\tl = []\\n\\t\\tif self.__valide:\\n\\t\\t\\tfor m in self.liste_decroissante():\\n\\t\\t\\t\\te = abs(m.get_coefficient().get_num().valeur())\\n\\t\\t\\t\\tif not (e in l):\\n\\t\\t\\t\\t\\tl.append(e)\\n\\t\\treturn pgcd_liste(l)\",\n \"def gen_graph(self):\",\n \"def get_nodes(self):\\n self.map_graph_id()\\n self.nodes_list = [\\n self.NX_GRAPHS[self.graph_id].nodes[idx]['label'] \\n for idx in range(len(self.NX_GRAPHS[self.graph_id].nodes))]\",\n \"def nodes(self, irc, msg, args, channel):\\n pie = self.instances[irc.network]\\n irc.reply([str(x) for x in pie.graphs[channel].nodes.values()])\",\n \"def list(self):\",\n \"def get_node_list(self):\\n return []\",\n \"def generateGraph(mids, chaptersField, labelsField):\\n output = \\\"digraph G { \\\\n\\\"\\n # On ne traite que les chapitres qui ont actives le graphe\\n chapts = chapters.graphChapters()\\n # le dico nodes contient une liste pour chaque chapitre. Chaque liste\\n # contient tous les neuds (un par note) presents dans ce chapitre, et\\n # representes par des tuples (noteId, label)\\n nodes = {}\\n for mid in mids:\\n chapterField = chaptersField[mid]\\n labelField = labelsField[mid]\\n for id, flds in mw.col.db.execute(\\\"\\\"\\\"\\n SELECT id, flds FROM notes WHERE mid=%d\\n \\\"\\\"\\\" % mid):\\n fields = splitFields(flds)\\n chapter = fields[chapterField]\\n if not chapter in chapts:\\n continue\\n label = fields[labelField]\\n if(not chapter in nodes):\\n nodes[chapter] = []\\n nodes[chapter].append((id, label))\\n # On genere les noeuds, dans des clusters (un par chapitre)\\n notes = []\\n for chap in nodes:\\n output += \\\"\\\"\\\"subgraph cluster_%d {\\n node [style=filled];\\n label = \\\"%s\\\";\\n color=blue;\\n \\\"\\\"\\\" % (chapts[chap], chap)\\n for n in nodes[chap]:\\n output += \\\"\\\"\\\"n%d [label=\\\"%s\\\", URL=\\\"%d\\\"];\\\\n\\\"\\\"\\\" % (n[0], n[1], n[0])\\n notes.append(n)\\n output += \\\"\\\"\\\"\\n }\\\\n\\\"\\\"\\\"\\n # Puis on ajoute tous les liens ..\\n for n in notes:\\n for nid in mw.col.db.execute(\\\"\\\"\\\"SELECT N.noteId FROM `PATH.links` AS L\\n JOIN `PATH.match` AS M ON M.id = L.matchId\\n JOIN `PATH.nodes` AS N ON M.nodeId = N.id\\n WHERE L.noteId = %d\\\"\\\"\\\" % (n[0])):\\n output += \\\"\\\"\\\"n%d -> n%d;\\\\n\\\"\\\"\\\" % (nid[0], n[0])\\n output += \\\"}\\\"\\n generateGraphImage(output)\",\n \"def get_nodes(self):\\n pass\",\n \"def unisolvent_nodes(self):\\r\\n return self.grid.unisolvent_nodes\",\n \"def visualizar_fornecedores(self):\\r\\n for fornecedor in self.fornecedores:\\r\\n fornecedor.visualizar()\",\n \"def knntsneplot(imdbid,topn):\\n similarimdbids = getlistofimdbids(imdbid)[:topn] # get the imdbids\\n mtitles = getsimilartitles(imdbid,topn)# get the titles to label them\\n # get the indexes of elements\\n indexessimilar = []\\n for si in similarimdbids:\\n currind = allimdbids.index(si)\\n indexessimilar.append(currind)\\n subtsneX = []\\n subtsneY = [] \\n subsvdX = []\\n subsvdY = []\\n for idxs in indexessimilar:\\n subtsneX.append(tsne_Um[idxs, 0])\\n subtsneY.append(tsne_Um[idxs, 1])\\n \\n subsvdX.append(USigmam[idxs, 0])\\n subsvdY.append(USigmam[idxs, 1])\\n \\n return subtsneX, subtsneY, subsvdX, subsvdY, mtitles\",\n \"def plot_graph(self) -> None:\",\n \"def list_spectrographs(self) -> None:\\n for key, item in self.spectrographs.items():\\n item.summary()\\n print(\\\"\\\\n\\\")\",\n \"def graphs(self):\\n return self.__graphs\",\n \"def get_unvisited_nodes(self) -> List[Node]:\\n\\n db = mysql.connector.connect(\\n host=self.db_uri, database=self.db_name, user=self.db_user, password=self.db_pass\\n )\\n\\n cursor = db.cursor()\\n\\n query = \\\"select * from node where date_visited is null\\\"\\n cursor.execute(query)\\n\\n nodes = cursor.fetchall()\\n\\n for node in nodes:\\n yield Node(*node)\",\n \"def nbr_tours(self):\\n nbr_tours = 0\\n for i in range(3):\\n for j in range(3):\\n if self.grille[i][j] != 0:\\n nbr_tours += 1\\n return nbr_tours\",\n \"def __init__(self):\\n self.grille = []\\n for i in range(3):\\n self.grille.append([0, 0, 0])\",\n \"def heuristique_ACPM(self,draw=False):\\n\\n def getVertricesOfPath(edges):\\n set_node = set()\\n for e in edges:\\n id1,id2 = self.getIdVerticesOfEdge(e)\\n set_node.add(id1)\\n set_node.add(id2)\\n return set_node\\n\\n\\n if draw:\\n try:\\n os.makedirs(self.dirname+\\\"/H_ACPM\\\")\\n except:\\n pass\\n\\n\\n #Graphe de depart contenant tout les noeuds\\n G = Graphe_Individu(self,self.wholeGraphDict)\\n if draw:\\n self.drawGraph(\\\"/H_ACPM/G0\\\",G.get_graphe().get_edges())\\n\\n\\n nb_vertices = 1\\n nb_tmp_vertices = 0\\n i=0\\n\\n #Tant que le nombre de noeuds du graphe decroit, appliquer kruskal\\n while nb_vertices > nb_tmp_vertices :\\n g_acpm = G.get_MST()\\n\\n\\n nb_vertices = len(G.get_dictSteinerNodes())\\n set_Node = set(G.get_dictSteinerNodes().keys())\\n dictSteinerNodez = self.eliminationFeuilles(g_acpm.get_edges(),set_Node)\\n G = Graphe_Individu(self,dictSteinerNodez)\\n nb_tmp_vertices = len(dictSteinerNodez)\\n\\n if draw:\\n i+= 1\\n self.drawGraph(\\\"/H_ACPM/G%d\\\"%i+\\\"_%d\\\"%g_acpm.get_total_weight(),g_acpm.get_edges())\\n\\n\\n if draw:\\n self.drawGraph(\\\"/H_ACPM/GFinal_%d\\\"%G.get_MST().get_total_weight(),G.get_MST().get_edges())\\n\\n for i in self.steinerNodes:\\n if i not in dictSteinerNodez.keys():\\n dictSteinerNodez[i] = 0\\n\\n # return Graphe_Individu(self,dictSteinerNodez)\\n return dictSteinerNodez\",\n \"def get_donnees(self):\\r\\n \\r\\n return self._donnees\",\n \"def get_station_graph(start_station_id, end_station_list):\\n start_station_graph = []\\n for i in range(10):\\n if end_station_list[i] is not None:\\n start_station_graph.append((start_station_id, end_station_list[i]))\\n return start_station_graph\",\n \"def plot_sammen_vekst(urn, ordlister, window=5000, pr = 100):\\n rammer = []\\n for ordbag in ordlister:\\n vekst = vekstdiagram(urn, params = {'words': ordbag, 'window':window, 'pr': pr} )\\n vekst.columns = [ordbag[0]]\\n rammer.append(vekst)\\n return pd.concat(rammer)\",\n \"def grAList() -> list:\\n return [2, 5, 6, 9, 10, 11, 13, 17, 18, 30]\",\n \"def buildGraph(self):\\n return None\",\n \"def allGraphs(date):\\n g = getGraph()\\n for uri, label, filename in subgraphs(date):\\n if not label:\\n label = \\\"(no label provided)\\\"\\n g.parse(filename, format=SUBGRAPH_FORMAT)\\n return g\",\n \"def get_listu_analitickih_metoda(self):\\n popis = sorted(list(self.analitickeMetode.keys()))\\n return popis\",\n \"def graphs_iso(n):\\n assert n >= 0\\n for g in unique_iso(graphs(n)):\\n yield g\",\n \"def lego_sets():\\n # you must replace this line and return your own list\\n return []\",\n \"def get_puntos_articulacion(self):\\n # Inicializo variables\\n visitado = {}\\n puntos_articulacion = set()\\n for u in grafo.devolver_vertices():\\n visitado[u] = False\\n\\n for v in grafo.devolver_vertices():\\n if not visitado[v]:\\n # Armo el arbol DFS desde v\\n dfs = DFSIterativo(grafo, v, visitado)\\n dfs.hacer_dfs()\\n puntos_articulacion_v = dfs.get_puntos_articulacion()\\n # Como v es raiz del arbol, lo saco para analizarlo por separado\\n if v in puntos_articulacion_v: # Aunque deberia estar incluida siempre\\n puntos_articulacion_v.remove(v)\\n self.analizar_raiz(dfs.get_predecesor(), puntos_articulacion_v, v)\\n puntos_articulacion.update(puntos_articulacion_v)\\n return puntos_articulacion\",\n \"def testNoeud(n, p, listAlgo):\\r\\n assert n.size == p.size\\r\\n res = np.zeros((len(listAlgo), n.size), dtype = int)\\r\\n progressBar = ProgressBar(maxValue = n.size * len(listAlgo))\\r\\n \\r\\n for ni in range(n.size):\\r\\n for i, (algoName, algoArgs) in enumerate(listAlgo):\\r\\n progressBar.update(ni * len(listAlgo) + i + 1)\\r\\n graphe = Graphe(nbSommets = n[ni], probaArete = p[ni])\\r\\n _, noeuds = getattr(graphe, algoName)(**algoArgs)\\r\\n res[i, ni] = noeuds\\r\\n print(\\\"\\\")\\r\\n \\r\\n return res\",\n \"def X(self)->list:\",\n \"def create_list() -> List[Optional[float]]:\\n return [None] * num_stations\",\n \"def getCaseDisp():\\r\\n liste = []\\r\\n for i in range(3):\\r\\n for j in range(3):\\r\\n if Grille[i][j] == 0:\\r\\n liste.append([i,j])\\r\\n return liste\",\n \"def non_pol_neighbours_graph():\\n data = pd.read_csv(\\\"/Users/emg/GitHub/thesis/output/2019_01/1000_residuals_output_utf8.csv\\\", index_col=0)\\n\\n labelled = label_subs(data)\\n labelled['resid_rank'] = labelled.resid.rank(pct=True)\\n top = subset_df(labelled, 'resid', q=0.95)\\n\\n edges = top.copy()[['source','target','resid']]\\n edges_rev = edges.copy()\\n edges_rev.columns = ['target','source','resid']\\n directed_edges = pd.concat([edges,edges_rev], sort=True)\\n directed_edges['resid_rank'] = directed_edges['resid'].rank(pct=True)\\n\\n df = label_subs(directed_edges)\\n\\n pol_subs = load_pol_subs()\\n pol_names = pol_subs.subreddit.str.replace('\\\\\\\\','')\\n pol_subs.subreddit=pol_subs.subreddit.str.replace('\\\\\\\\','')\\n\\n pol_neighbours = df[df['source'].isin(pol_names)].sort_values('resid', ascending=False)\\n\\n top_pol_neigh = pol_neighbours.groupby('source').head(10).sort_values(['source','resid'], ascending=[True,False])\\n \\n x = top_pol_neigh[~top_pol_neigh.target.isin(pol_names)][['source','target']]\\n\\n col_dict = pol_subs.set_index('subreddit').col.to_dict()\\n for sub in x.target.unique():\\n col_dict[sub] = 'gray'\\n\\n G = nx.from_pandas_edgelist(x)\\n nx.set_node_attributes(G, col_dict, 'col')\\n\\n f = plt.figure(1)\\n ax = f.add_subplot(1,1,1)\\n\\n colors = dict(G.nodes(data='col')).values()\\n\\n pos = nx.spring_layout(G, k=0.2)\\n nx.draw_networkx(G, pos=pos, with_labels=False, node_color=colors, alpha=0.3)\\n #nx.draw_networkx_labels(G, pos=pos, with_labels=True)\\n\\n plt.axis('off')\\n f.set_facecolor('w')\\n \\n f.tight_layout()\\n plt.savefig(figures_path(f\\\"{date}/non_pol_neighbours_graph.png\\\"))\\n plt.close()\",\n \"def build_graph(self):\\n pass\",\n \"def list():\",\n \"def list():\",\n \"def get_diagram(self):\\n self_nodes=self.nodes.all()\\n self_arrows=self.arrows.all()\\n \\n \\n if len(self_nodes)==0:\\n return False\\n \\n nodes = [n.get_icon_obj() for n in self_nodes]\\n node_liens = [n.liens.all() for n in self_nodes]\\n \\n pairs = []\\n for n,n_liens in zip(nodes,node_liens):\\n if len(n_liens)==0:\\n liens = Lien.objects.filter(cause__id=n.target_id).all()\\n liens = [l.consequence.id for l in liens]\\n temp = [(n,target) for target in nodes if target.target_id in liens]\\n pairs.extend(temp)\\n else:\\n ids=set([(i.cause.id,i.consequence.id) for i in n_liens])\\n for n in nodes:\\n pairs.extend([(n,i) for i in nodes if i is not n and \\n (n.target_id,i.target_id) in ids])\\n ids = set([(i.cause.id,i.consequence.id) for i in self_arrows])\\n pairs = [p for p in pairs if (p[0].target_id,p[1].target_id) not in ids]\\n \\n lines=[]\\n arrows=[]\\n for obj in self_arrows:\\n \\n n0=[i for i in nodes if i.node_id==obj.cause.id]\\n n1=[i for i in nodes if i.node_id==obj.consequence.id]\\n if len(n0)!=1 or len(n1)!=1:\\n continue\\n n0=n0[0]\\n n1=n1[0]\\n \\n pt=[(obj.X0, obj.Y0), (obj.X1,obj.Y1)]\\n pt=[np.array(i) for i in pt if None not in i]\\n if len(pt)==0:\\n pairs.append((n0,n1))\\n continue\\n pairs = [p for p in pairs if (p[0].node_id,p[1].node_id)!=(n0.node_id,n1.node_id)]\\n vect = pt[0]-np.array(n0.pos)\\n first_pt = np.array(n0.pos)+vect*n0.size/np.sqrt(sum(vect*vect))\\n vect = np.array(n1.pos) - pt[-1]\\n last_pt = np.array(n1.pos)-vect*n1.size/np.sqrt(sum(vect*vect))\\n pt=[first_pt,*pt,last_pt]\\n \\n lines.extend([((*i,*j),n0.color) for i,j in zip(pt[:-1],pt[1:])])\\n arrows.append(((*pt[-2],*pt[-1]),n0.color))\\n \\n \\n margin=10\\n line_width=2\\n \\n diagram=DiagramObj(self.id,nodes,pairs,margin,\\n self.width,self.height,line_width)\\n diagram.add_arrows(arrows,lines)\\n print(diagram.lines)\\n return diagram\",\n \"def draw_graph(self):\\n\\t\\tif None in self.graph:\\n\\t\\t\\tdel self.graph[None]\\n\\n\\t\\tfor vs in self.graph.itervalues():\\n\\t\\t\\tto_delete = []\\n\\t\\t\\tfor i in xrange(len(vs)):\\n\\t\\t\\t\\tif vs[i] is None:\\n\\t\\t\\t\\t\\tto_delete.append(i)\\n\\n\\t\\t\\tfor i in reversed(to_delete):\\n\\t\\t\\t\\tdel vs[i]\\n\\n\\t\\tself.G=nx.Graph(self.graph)\\n\\n\\t\\tfor k,v in self.labels.iteritems():\\n\\t\\t\\tif v[:6] == 'Module':\\n\\t\\t\\t\\troot = k\\n\\t\\t\\t\\tbreak\\n\\n\\t\\treturn self.__dfs_plot(root)\",\n \"def disconnected_nodes(self):\\n return self._disconnected_nodes\",\n \"def V(self) -> list:\\n return list(self._graph.values())\",\n \"def retireSommet(self, sommet):\\r\\n nouveauGraphe = copy.deepcopy(self) # on effectue une copie du graphe\\r\\n nouveauGraphe.n = self.n-1 # On a n-1 points\\r\\n # NB: il faut aussi changer m et listeArretes mais on va pas le faire tout de suite car pas urgent\\r\\n # 1. On suprrime la ligne d'indice sommet\\r\\n #* AUTRE MÉTHODE del nouveauGraphe.adjMatrix[sommet]\\r\\n # print(nouveauGraphe.adjMatrix)\\r\\n nouveauGraphe.adjMatrix.pop(sommet)\\r\\n # print(nouveauGraphe.adjMatrix)\\r\\n #2. On supprime la colonne d'indice sommet = on supprime l'index sommet de chaque sous liste\\r\\n # la liste comprehension ne marche pas bien :(\\r\\n for line in nouveauGraphe.adjMatrix:\\r\\n line.pop(sommet)\\r\\n # print(nouveauGraphe.adjMatrix)\\r\\n # nouveauGraphe.m = 0\\r\\n # 2ème méthode:\\r\\n # for ligne in nouveauGraphe.adjMatrix:\\r\\n # ligne.pop(sommet)\\r\\n return nouveauGraphe\",\n \"def view_all_batters(self):\\n conn = rs.create_connection(\\\"dailyfantasyscraper.db\\\")\\n cur = conn.cursor()\\n position = \\\"P\\\"\\n cur.execute(\\\"SELECT * FROM rotowiredk where position != ?\\\", position)\\n result = cur.fetchall()\\n conn.commit()\\n conn.close()\\n\\n for item in result:\\n print(item)\\n tree.insert('', 'end', values=item)\",\n \"def depth_setsM(self):\\n G = self.copy()\\n depths = []\\n while G.vertices() != []:\\n outdegs = G.out_degree(labels=True)\\n new_depth = [x for x in outdegs if outdegs[x]==0]\\n depths.append(new_depth)\\n G.delete_vertices(new_depth)\\n return depths\",\n \"def iter_nodes(self):\",\n \"def get_ids(self):\\n return self._graphs.keys()\",\n \"def createGraph(self):\\n \\n for episode in self.episodes:\\n listeSuccessors = [episode[episode[:,1] > episode[i,1]][:,0] # List of list of successors for each user\\n for i in range(len(episode))] \\n for i, successeur in enumerate(listeSuccessors): # for the list of successors of each user\\n for v in successeur: # for every successor of a user\\n u, proba = episode[i,0], np.random.random() # Generate a probability so within (0,1)\\n self.successors[u][v] = proba # u ---(proba)---> v \\n self.predecessors[v][u] = proba # v ---(proba)---> u\",\n \"def _build_graph(self):\\n pass\",\n \"def init(self):\\n self.padre = self.id\\n self.sinVisitar = [] \\n self.visited = False\\n print (\\\"inicializo algoritmo\\\")\\n for i in range (len(self.neighbors)): #De esta forma se pueden manipular las listas por aparte\\n self.sinVisitar.append(self.neighbors[i])\",\n \"def make_complete_graph(num_nodes):\\r\\n result = {}\\r\\n for idx in range(0,num_nodes):\\r\\n result[idx] = set([])\\r\\n for jdx in range(0,num_nodes):\\r\\n if (idx!=jdx):\\r\\n result[idx].add(jdx)\\r\\n return result\",\n \"def cleanGraph2(self,graph):\\n return [graph[i] for i in range(len(graph)-1) if graphp[i]!=graph[i+1]]\",\n \"def gen_nodes(self):\\n self.nodes = []\\n for i in range(self.num_nodes):\\n self.nodes.append(Node(self.fk))\",\n \"def table_graph(self, i):\\n return self.__graph_list[i]\",\n \"def ngraphs(self):\\n return len(self.__graph_list)\",\n \"def make_undir_graph(V,E):\\n\\tUD = []\\n\\tfor (u,v) in E:\\n\\t\\tif (v,u) not in E:\\n\\t\\t\\tUD.append((u,v))\\n\\t\\t\\tUD.append((v,u))\\n\\treturn UD\",\n \"def get_end_nodes(graph):\\n nodelist = [\\n i\\n for i in graph.nodes_iter()\\n if len(graph.neighbors(i))==1\\n ]\\n return nodelist\",\n \"def nodes( self, data = False ):\\n return self._G.nodes(data = data)\",\n \"def DorogovtsevGoltsevMendesGraph(n):\\n import networkx\\n return Graph(networkx.dorogovtsev_goltsev_mendes_graph(n),\\\\\\n name=\\\"Dorogovtsev-Goltsev-Mendes Graph, %d-th generation\\\"%n)\",\n \"def get_edges(self):\\n try:\\n temp = self.edges\\n except:\\n temp = []\\n return temp\",\n \"def plot_graph(self):\\n plt.axis(\\\"off\\\")\\n pos = nx.kamada_kawai_layout(self.graph)\\n return nx.draw_networkx(self.graph, pos=pos, node_size=400)\",\n \"def findZeroGraph(data, deviation, doFilter):\\n first = data[0]\\n print('frist', first)\\n data = list(map(lambda x: (x[0]-first[0], x[1]), data))\\n return findManualGraph(data, deviation, doFilter)\",\n \"def get_unvisited_nodes(self, graph, solution):\\n nodes = []\\n for node in graph[solution.current]:\\n if node not in solution:\\n nodes.append(node)\\n return nodes\",\n \"def nodes(self):\\n return self.sort_dict(self.trajectory_data)\",\n \"def getCubes():\",\n \"def get_labels(self):\\n return []\",\n \"def ordenar_vertices(grafo, distancia):\\n return list(sorted(distancia.items(),key = lambda x:x[1] , reverse = True ))\",\n \"def __len__(self):\\n return len(self._graphs)\",\n \"def fn(n):\\n if n == len(graph)-1: return [[n]]\\n ans = []\\n for nn in graph[n]: \\n ans.extend([[n] + x for x in fn(nn)])\\n return ans\",\n \"def ludnosc(lista):\\n # wynik - lista zawierajaca wynik koncowy dzialania funkcji(lata i wartosci dla poszczegolnych panstw)\\n wynik = []\\n for panstwo in lista:\\n # rok - lista zawierajaca lata\\n # wartosc - lista zawierajaca wartosci dla lat\\n rok = []\\n wartosc = []\\n for element in panstwo:\\n # sprawdzenie czy klucz posiada odpowiednia wartosc\\n if element[1].get('key') == \\\"EN.POP.DNST\\\":\\n # dodanie roku do listy\\n rok.append(int(element[2].text))\\n # rozpatrywanie przypadku w ktorym wartosc jest None\\n if element[3].text is None:\\n wartosc.append(element[3].text)\\n else:\\n wartosc.append(float(element[3].text))\\n # dodawanie list dla poszczegolnych panstw do listy wynikowej\\n wynik.append(rok)\\n wynik.append(wartosc)\\n\\n return wynik\",\n \"def get_graphs(self):\\n ids = self._graphs.keys()\\n ids.sort()\\n return [self._graphs[id] for id in ids]\",\n \"def dump_graph(self):\\n\\n edges = []\\n for vertex in self.__graph_dict:\\n mylist = list(vertex)\\n logging.debug(\\\"mylist : \\\", mylist)\",\n \"def get_nodes(self, topogramId):\\n return self.make_request(\\\"GET\\\", \\\"topograms/\\\"+topogramId+\\\"/nodes\\\", {})\",\n \"def __filterEdges(self):\",\n \"def get_listu_uredjaja(self):\\n lista = sorted(list(self.uredjaji.keys()))\\n return lista\",\n \"def nonconnected():\\n nonconnected = nx.MultiDiGraph()\\n nonconnected.add_nodes_from(range(6))\\n nonconnected.add_edges_from([\\n (0, 1),\\n (1, 2),\\n (2, 3),\\n (3, 0),\\n (1, 0),\\n (1, 0),\\n (3, 2),\\n (3, 3),\\n (4, 4),\\n (4, 4)\\n ])\\n return nonconnected\",\n \"def terminals(self):\\n unique_nodes, unique_counts = np.unique(self.edges, return_counts=True)\\n return unique_nodes[ unique_counts == 1 ]\",\n \"def empty(self):\\n return _osgAnimation.VertexList_empty(self)\",\n \"def neighbours(self):\\n seen = set()\\n return [l.other(self) for l in self.dovetails \\\\\\n if id(l) not in seen and not seen.add(id(l))]\",\n \"def preencherJogadores():\\n global jogadores\\n for x in participantes:\\n if x['porta'] != lider['porta']:\\n jogadores.append(x)\",\n \"def get_list(self):\\r\\n return self.numbers\",\n \"def edges(self, irc, msg, args, channel):\\n pie = self.instances[irc.network]\\n irc.reply([str(x) for x in pie.graphs[channel].edges.values()])\",\n \"def __init__(self):\\n self.numList=[]\",\n \"def zyklus(self):\\n neu = self.lebendig.copy() # Kopie erstellen\\n for zelle in self.lebendig: # Alle lebendigen Zellen durchgehen\\n anzahl = self.lebendige_nachbarn(zelle)\\n if anzahl < 2 or anzahl > 3: # Vereinsamung oder Überbevölkerung\\n neu.remove(zelle) # Stirbt - aus der Liste der Lebendigen entfernen\\n # Prüfe alle Nachbarn einer lebendigen Zelle\\n # wenn diese genau drei Nachbarn hat, muss sie evtl.\\n # lebendig werden\\n x, y = zelle\\n if self.lebendige_nachbarn((x-1, y+1)) == 3:\\n neu.add((x-1, y+1))\\n if self.lebendige_nachbarn((x, y+1)) == 3:\\n neu.add((x, y+1))\\n if self.lebendige_nachbarn((x+1, y+1)) == 3:\\n neu.add((x+1, y+1))\\n if self.lebendige_nachbarn((x+1, y)) == 3:\\n neu.add((x+1, y))\\n if self.lebendige_nachbarn((x+1, y-1)) == 3:\\n neu.add((x+1, y-1))\\n if self.lebendige_nachbarn((x, y-1)) == 3:\\n neu.add((x, y-1))\\n if self.lebendige_nachbarn((x-1, y-1)) == 3:\\n neu.add((x-1, y-1))\\n if self.lebendige_nachbarn((x-1, y)) == 3:\\n neu.add((x-1, y))\\n self.lebendig = neu\",\n \"def neighbours_L(self):\\n seen = set()\\n return [l.other(self) for l in self.dovetails_L \\\\\\n if id(l) not in seen and not seen.add(id(l))]\",\n \"def plotTime(self):\\n plt.figure()\\n t = [i for i in range(len(self.nodes_infected))]\\n print(t)\\n plt.title('Nodos infectados vs Tiempo')\\n plt.xlabel('Instantes de tiempo')\\n plt.ylabel('# de nodos infectados')\\n plt.plot(t, self.nodes_infected)\\n plt.grid(True)\\n plt.show()\",\n \"def plot_dendrogram_national():\\n print 'Creating the national migration dendrogram'\\n for method in ['NR']:\\n for weight_fc in ['col', 'row']:\\n mat = np.loadtxt('ultra_metric/%s_u%s.csv' % (weight_fc, method),\\n dtype=float, delimiter=',')\\n plot_dendrogram_by_mat(mat, weight_fc=weight_fc, method=method,\\n state='USA', label=all_counties)\\n #break\\n #break\",\n \"def fullgraphplot(time_lower,time_upper):\\n\\n edges_list,node_list,title_list = graphformation(time_lower,time_upper)\\n node_size = []\\n for i in range(len(node_list)):\\n node_size.append(5)\\n g = Network(\\n height=\\\"750px\\\",\\n width=\\\"100%\\\",\\n bgcolor=\\\"#222222\\\",\\n font_color=\\\"white\\\")\\n g.add_nodes(node_list,label=node_list,title=title_list, size= node_size)\\n g.add_edges(edges_list)\\n g.show(\\\"nx.html\\\")\\n return\",\n \"def dekodeKromosom(self, kromosom):\\n xMin, xMaks = self.batas[0]\\n yMin, yMaks = self.batas[1]\\n t, x, y = 0, 0, 0\\n n = (self.panjangKromosom)//2\\n for i in range(0, n):\\n t += 2**(-(i+1))\\n for i in range(0, n):\\n x += kromosom[i] * 2**-(i+1)\\n y += kromosom[n + i] * 2**-(i+1)\\n x *= (xMaks - xMin / t)\\n y *= (yMaks - yMin / t)\\n x += xMin\\n y += yMin\\n return [x, y]\",\n \"def __lista_krawedzi__(self):\\n s = 0\\n for cell in self.cells: # [ 1 0 12 11] = [kr1 kr2 kr3 kr4]\\n s += len(cell) # zlicza ile w kolejnych komurkach pkt (krawedzi) (liczy ile jest w wierszu [1 2 12 11] czyli 4 krawedzie\\n\\n lista_kr = np.array([[0] * 4] * s, dtype=int)\\n\\n edgeNum = 0\\n for i, cell in enumerate(self.cells):\\n nNodes = len(cell)\\n for l in range(nNodes):\\n if l == nNodes-1:\\n lista_kr[edgeNum] = [cell[l], cell[0], i, -1]\\n else:\\n lista_kr[edgeNum] = [cell[l], cell[l+1], i, -1]\\n edgeNum += 1\\n\\n # [ 1 0 0 1] = [pkt1 pkt2 wl sasiad-jeszcze nie obecny]\\n return lista_kr\",\n \"def _CreateGraph(self):\\n self.nodes = []\\n self.edges = []\\n for i, r in self.airports.set_index('airport_id').iterrows():\\n self.nodes.append((i,r.to_dict()))\\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\\n self.edges.append((i[0],i[1],r.to_dict()))\\n # print('node ex: {}'.format(self.nodes[0]))\\n # print('edge ex: {}'.format(self.edges[0]))\\n\\n self.graph = self._CreateAdjacencyListGraph()\",\n \"def _get_nodes(self):\\n viewpoint = \\\"shiva_{}\\\".format(cherrypy.session[\\\"id\\\"])\\n messages_db = self.mongo[viewpoint][\\\"messages\\\"]\\n people_db = self.mongo[viewpoint][\\\"people\\\"]\\n #\\n senders = messages_db.distinct(\\\"sender\\\")\\n owner_id = cherrypy.session[\\\"id\\\"]\\n nodes = list()\\n for sender in senders:\\n person = people_db.find_one({\\\"id\\\": sender})\\n if person is None:\\n name = \\\"id{}\\\".format(sender)\\n else:\\n name = person[\\\"display_name\\\"]\\n records = list(messages_db.aggregate([{\\n \\\"$match\\\": {\\n \\\"$or\\\": [\\n {\\\"sender\\\": owner_id, \\\"receiver\\\": sender},\\n {\\\"sender\\\": sender, \\\"receiver\\\": owner_id}\\n ]\\n }\\n }, {\\\"$group\\\": {\\\"_id\\\": None, \\\"count\\\": {\\\"$sum\\\": 1}}}]))\\n if not records:\\n records = 0\\n else:\\n records = records[0][\\\"count\\\"]\\n info = \\\"Total records: {}\\\".format(records)\\n history_link = \\\"/vk/read?id={}\\\".format(sender)\\n statistics_link = \\\"#\\\"\\n if records > 0:\\n nodes.append({\\n \\\"id\\\": sender,\\n \\\"name\\\": name,\\n \\\"info\\\": info,\\n \\\"records\\\": records,\\n \\\"history_link\\\": history_link,\\n \\\"statistics_link\\\": statistics_link\\n })\\n #\\n return nodes\",\n \"def get_subgraphs(graph):\\n nodes_powerset = get_nodes_combinations(graph)\\n #print(\\\"Doing\\\")\\n #draw_graph(graph)\\n subgraphs = []\\n for nodes in nodes_powerset:\\n subg = graph.subgraph(nodes)\\n nodes = subg.nodes(data=True)\\n if nx.is_weakly_connected(subg):\\n subgraphs.append(subg)\\n return subgraphs\",\n \"def make_complete_graph(num_nodes):\\n complete_digraph = {}\\n if num_nodes > 0 and type(num_nodes) == int:\\n neighbors = set([idx for idx in range(num_nodes)])\\n for idx in range(num_nodes):\\n complete_digraph[idx] = neighbors.copy() #creates adjacency set\\n complete_digraph[idx].remove(idx) # pop out self-loop \\n return complete_digraph\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61656684","0.6132704","0.5961668","0.5949655","0.5797799","0.5743925","0.57238185","0.57080424","0.5702499","0.56886774","0.5665844","0.5662841","0.55272645","0.5525712","0.55255455","0.5511591","0.5497783","0.5492539","0.54852766","0.54834795","0.5473209","0.5464706","0.5456676","0.54250747","0.5404155","0.5400207","0.5392925","0.5390274","0.5373806","0.5371759","0.53674173","0.53608817","0.53472763","0.5341333","0.53365","0.53349495","0.5322345","0.5322244","0.53120655","0.53054667","0.5301621","0.52850527","0.5278834","0.5278834","0.5272363","0.5261638","0.526069","0.52488345","0.5243966","0.5227717","0.5223513","0.52059484","0.5194288","0.51780164","0.5173398","0.51703477","0.51696205","0.516626","0.51561934","0.5154818","0.5153898","0.51445127","0.5143239","0.5139563","0.5136668","0.51275474","0.5121774","0.511451","0.51117355","0.5108957","0.51037693","0.510324","0.509967","0.50978875","0.5096919","0.50940424","0.50917494","0.5087962","0.5084377","0.5083616","0.5079235","0.5079008","0.50776976","0.5077462","0.50737864","0.5065711","0.5054266","0.50526047","0.5052343","0.50494707","0.5039975","0.50365204","0.5028296","0.50222176","0.5022105","0.5020846","0.50206906","0.5020651","0.50184196","0.5008314"],"string":"[\n \"0.61656684\",\n \"0.6132704\",\n \"0.5961668\",\n \"0.5949655\",\n \"0.5797799\",\n \"0.5743925\",\n \"0.57238185\",\n \"0.57080424\",\n \"0.5702499\",\n \"0.56886774\",\n \"0.5665844\",\n \"0.5662841\",\n \"0.55272645\",\n \"0.5525712\",\n \"0.55255455\",\n \"0.5511591\",\n \"0.5497783\",\n \"0.5492539\",\n \"0.54852766\",\n \"0.54834795\",\n \"0.5473209\",\n \"0.5464706\",\n \"0.5456676\",\n \"0.54250747\",\n \"0.5404155\",\n \"0.5400207\",\n \"0.5392925\",\n \"0.5390274\",\n \"0.5373806\",\n \"0.5371759\",\n \"0.53674173\",\n \"0.53608817\",\n \"0.53472763\",\n \"0.5341333\",\n \"0.53365\",\n \"0.53349495\",\n \"0.5322345\",\n \"0.5322244\",\n \"0.53120655\",\n \"0.53054667\",\n \"0.5301621\",\n \"0.52850527\",\n \"0.5278834\",\n \"0.5278834\",\n \"0.5272363\",\n \"0.5261638\",\n \"0.526069\",\n \"0.52488345\",\n \"0.5243966\",\n \"0.5227717\",\n \"0.5223513\",\n \"0.52059484\",\n \"0.5194288\",\n \"0.51780164\",\n \"0.5173398\",\n \"0.51703477\",\n \"0.51696205\",\n \"0.516626\",\n \"0.51561934\",\n \"0.5154818\",\n \"0.5153898\",\n \"0.51445127\",\n \"0.5143239\",\n \"0.5139563\",\n \"0.5136668\",\n \"0.51275474\",\n \"0.5121774\",\n \"0.511451\",\n \"0.51117355\",\n \"0.5108957\",\n \"0.51037693\",\n \"0.510324\",\n \"0.509967\",\n \"0.50978875\",\n \"0.5096919\",\n \"0.50940424\",\n \"0.50917494\",\n \"0.5087962\",\n \"0.5084377\",\n \"0.5083616\",\n \"0.5079235\",\n \"0.5079008\",\n \"0.50776976\",\n \"0.5077462\",\n \"0.50737864\",\n \"0.5065711\",\n \"0.5054266\",\n \"0.50526047\",\n \"0.5052343\",\n \"0.50494707\",\n \"0.5039975\",\n \"0.50365204\",\n \"0.5028296\",\n \"0.50222176\",\n \"0.5022105\",\n \"0.5020846\",\n \"0.50206906\",\n \"0.5020651\",\n \"0.50184196\",\n \"0.5008314\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59119767"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":17449,"cells":{"query":{"kind":"string","value":"Renvoie la liste des Arrettes du graph"},"document":{"kind":"string","value":"def getArrettes(self) -> list:\n return self._arrettes"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def graph(self):\n ...","def populate_graph(self):","def _get_full_graph(self):","def gen_graph(self):","def plot_graph(self) -> None:","def list(self):","def allGraphs(date):\n g = getGraph()\n for uri, label, filename in subgraphs(date):\n if not label:\n label = \"(no label provided)\"\n g.parse(filename, format=SUBGRAPH_FORMAT)\n return g","def nodes(self): \n return [n for n in self.iternodes()]","def get_edges(self):\n try:\n temp = self.edges\n except:\n temp = []\n return temp","def get_diagram(self):\n self_nodes=self.nodes.all()\n self_arrows=self.arrows.all()\n \n \n if len(self_nodes)==0:\n return False\n \n nodes = [n.get_icon_obj() for n in self_nodes]\n node_liens = [n.liens.all() for n in self_nodes]\n \n pairs = []\n for n,n_liens in zip(nodes,node_liens):\n if len(n_liens)==0:\n liens = Lien.objects.filter(cause__id=n.target_id).all()\n liens = [l.consequence.id for l in liens]\n temp = [(n,target) for target in nodes if target.target_id in liens]\n pairs.extend(temp)\n else:\n ids=set([(i.cause.id,i.consequence.id) for i in n_liens])\n for n in nodes:\n pairs.extend([(n,i) for i in nodes if i is not n and \n (n.target_id,i.target_id) in ids])\n ids = set([(i.cause.id,i.consequence.id) for i in self_arrows])\n pairs = [p for p in pairs if (p[0].target_id,p[1].target_id) not in ids]\n \n lines=[]\n arrows=[]\n for obj in self_arrows:\n \n n0=[i for i in nodes if i.node_id==obj.cause.id]\n n1=[i for i in nodes if i.node_id==obj.consequence.id]\n if len(n0)!=1 or len(n1)!=1:\n continue\n n0=n0[0]\n n1=n1[0]\n \n pt=[(obj.X0, obj.Y0), (obj.X1,obj.Y1)]\n pt=[np.array(i) for i in pt if None not in i]\n if len(pt)==0:\n pairs.append((n0,n1))\n continue\n pairs = [p for p in pairs if (p[0].node_id,p[1].node_id)!=(n0.node_id,n1.node_id)]\n vect = pt[0]-np.array(n0.pos)\n first_pt = np.array(n0.pos)+vect*n0.size/np.sqrt(sum(vect*vect))\n vect = np.array(n1.pos) - pt[-1]\n last_pt = np.array(n1.pos)-vect*n1.size/np.sqrt(sum(vect*vect))\n pt=[first_pt,*pt,last_pt]\n \n lines.extend([((*i,*j),n0.color) for i,j in zip(pt[:-1],pt[1:])])\n arrows.append(((*pt[-2],*pt[-1]),n0.color))\n \n \n margin=10\n line_width=2\n \n diagram=DiagramObj(self.id,nodes,pairs,margin,\n self.width,self.height,line_width)\n diagram.add_arrows(arrows,lines)\n print(diagram.lines)\n return diagram","def dump_graph(self):\n\n edges = []\n for vertex in self.__graph_dict:\n mylist = list(vertex)\n logging.debug(\"mylist : \", mylist)","def V(self) -> list:\n return list(self._graph.values())","def X(self)->list:","def graphs(self):\n return self.__graphs","def heuristique_ACPM(self,draw=False):\n\n def getVertricesOfPath(edges):\n set_node = set()\n for e in edges:\n id1,id2 = self.getIdVerticesOfEdge(e)\n set_node.add(id1)\n set_node.add(id2)\n return set_node\n\n\n if draw:\n try:\n os.makedirs(self.dirname+\"/H_ACPM\")\n except:\n pass\n\n\n #Graphe de depart contenant tout les noeuds\n G = Graphe_Individu(self,self.wholeGraphDict)\n if draw:\n self.drawGraph(\"/H_ACPM/G0\",G.get_graphe().get_edges())\n\n\n nb_vertices = 1\n nb_tmp_vertices = 0\n i=0\n\n #Tant que le nombre de noeuds du graphe decroit, appliquer kruskal\n while nb_vertices > nb_tmp_vertices :\n g_acpm = G.get_MST()\n\n\n nb_vertices = len(G.get_dictSteinerNodes())\n set_Node = set(G.get_dictSteinerNodes().keys())\n dictSteinerNodez = self.eliminationFeuilles(g_acpm.get_edges(),set_Node)\n G = Graphe_Individu(self,dictSteinerNodez)\n nb_tmp_vertices = len(dictSteinerNodez)\n\n if draw:\n i+= 1\n self.drawGraph(\"/H_ACPM/G%d\"%i+\"_%d\"%g_acpm.get_total_weight(),g_acpm.get_edges())\n\n\n if draw:\n self.drawGraph(\"/H_ACPM/GFinal_%d\"%G.get_MST().get_total_weight(),G.get_MST().get_edges())\n\n for i in self.steinerNodes:\n if i not in dictSteinerNodez.keys():\n dictSteinerNodez[i] = 0\n\n # return Graphe_Individu(self,dictSteinerNodez)\n return dictSteinerNodez","def buildRomaniagraph(Graph):\n \n g=Graph()\n for name in ('Oradea','Zerind','Arad','Timisoara', 'Lugoj','Mehadia', \n 'Dobreta','Sibiu','Fagaras','Rimnicu Vilcea','Pitesti','Craiova',\n 'Bucharest','Giurgiu','Urziceni','Hirsova','Eforie','Vaslui',\n 'Iasi','Nearnt'):\n g.addNode(Node(name))\n \n \n g.addEdge(Edge(g.getNode('Oradea'),g.getNode('Zerind')))\n g.addEdge(Edge(g.getNode('Oradea'),g.getNode('Sibiu')))\n g.addEdge(Edge(g.getNode('Zerind'),g.getNode('Arad')))\n g.addEdge(Edge(g.getNode('Arad'),g.getNode('Timisoara')))\n g.addEdge(Edge(g.getNode('Lugoj'),g.getNode('Timisoara')))\n g.addEdge(Edge(g.getNode('Lugoj'),g.getNode('Mehadia')))\n g.addEdge(Edge(g.getNode('Mehadia'),g.getNode('Dobreta')))\n g.addEdge(Edge(g.getNode('Dobreta'),g.getNode('Craiova')))\n g.addEdge(Edge(g.getNode('Craiova'),g.getNode('Pitesti')))\n g.addEdge(Edge(g.getNode('Craiova'),g.getNode('Rimnicu Vilcea')))\n g.addEdge(Edge(g.getNode('Rimnicu Vilcea'),g.getNode('Sibiu')))\n g.addEdge(Edge(g.getNode('Sibiu'),g.getNode('Fagaras')))\n g.addEdge(Edge(g.getNode('Pitesti'),g.getNode('Bucharest')))\n g.addEdge(Edge(g.getNode('Bucharest'),g.getNode('Giurgiu')))\n g.addEdge(Edge(g.getNode('Bucharest'),g.getNode('Urziceni')))\n g.addEdge(Edge(g.getNode('Arad'),g.getNode('Sibiu')))\n g.addEdge(Edge(g.getNode('Rimnicu Vilcea'),g.getNode('Pitesti')))\n g.addEdge(Edge(g.getNode('Bucharest'),g.getNode('Fagaras')))\n g.addEdge(Edge(g.getNode('Vaslui'),g.getNode('Urziceni')))\n g.addEdge(Edge(g.getNode('Hirsova'),g.getNode('Urziceni')))\n g.addEdge(Edge(g.getNode('Hirsova'),g.getNode('Eforie')))\n g.addEdge(Edge(g.getNode('Vaslui'),g.getNode('Iasi')))\n g.addEdge(Edge(g.getNode('Iasi'),g.getNode('Nearnt')))\n \n return g","def arrr_starrr_graph(self):\n\n plt.figure()\n total_cost = 0\n\n # plot batteries\n counter = 0\n for batt in self.grid.batteries:\n plt.plot(batt.x, batt.y, marker='x',\n color=colors[counter], markersize=10)\n counter += 1\n\n # iterate over houses and path\n for house in self.grid.houses:\n battery = self.grid.batteries[house.connection]\n\n # get path coordinates\n path_data = house.path\n\n # plot path and house\n plt.plot(path_data[0][0], path_data[0][1],\n color=colors[house.connection], linewidth=.3)\n plt.plot(house.x, house.y, marker='p',\n color=colors[house.connection])\n total_cost += path_data[1]\n plt.draw()\n plt.pause(0.000000001)\n\n plt.title(f\"total cost = {total_cost}\")","def arredondar(self):\n for y in range(self.altura):\n for x in range(self.largura):\n for i in range(3):\n self.lista[x][y][i] = int(self.lista[x][y][i])","def plot_sammen_vekst(urn, ordlister, window=5000, pr = 100):\n rammer = []\n for ordbag in ordlister:\n vekst = vekstdiagram(urn, params = {'words': ordbag, 'window':window, 'pr': pr} )\n vekst.columns = [ordbag[0]]\n rammer.append(vekst)\n return pd.concat(rammer)","def _CreateGraph(self):\n self.nodes = []\n self.edges = []\n for i, r in self.airports.set_index('airport_id').iterrows():\n self.nodes.append((i,r.to_dict()))\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\n self.edges.append((i[0],i[1],r.to_dict()))\n # print('node ex: {}'.format(self.nodes[0]))\n # print('edge ex: {}'.format(self.edges[0]))\n\n self.graph = self._CreateAdjacencyListGraph()","def iter_nodes(self):","def plot_edges(self, node_list):\n tree = MarkerArray()\n id = 1\n for node in self.node_list:\n if node.parent:\n # edge between nodes\n path = Marker()\n path.header.frame_id = \"map\"\n path.header.stamp = rospy.get_rostime()\n path.ns = \"markers\"\n path.id = id\n id += 1\n path.type = path.LINE_STRIP\n path.action = path.ADD\n path.scale.x = self.rviz_tuning_plt\n path.color.a = 1.0\n\n path.color.r = 1.0\n path.color.g = 0.7\n path.color.b = 0.0\n\n path.lifetime = rospy.Duration()\n path.pose.orientation.w = 1.0\n\n p1 = Point()\n p1.x = node.parent.x\n p1.y = node.parent.y\n p1.z = 0.02\n path.points.append(p1)\n\n p2 = Point()\n p2.x = node.x\n p2.y = node.y\n p2.z = 0.02\n path.points.append(p2)\n \n tree.markers.append(path)\n\n self.pub_edges.publish(tree)","def getSets():","def lego_sets():\n # you must replace this line and return your own list\n return []","def objects(self):","def build_graph(self):\n pass","def list():","def list():","def lego_sets():\n # you must replace this line and return your own list\n return lego_sets_list","def show_edges(self):\n for element in self.graph:\n print(element, self.graph[element])","def _build_graph(self):\n pass","def edges(self, irc, msg, args, channel):\n pie = self.instances[irc.network]\n irc.reply([str(x) for x in pie.graphs[channel].edges.values()])","def nodes(self, irc, msg, args, channel):\n pie = self.instances[irc.network]\n irc.reply([str(x) for x in pie.graphs[channel].nodes.values()])","def triples():","def plot_graph(self) -> None:\n\n nodes_on_graph = self.dw_graph.get_all_v()\n for k, v in nodes_on_graph.items():\n if v.position is None:\n x_rand = random.uniform(0.5, self.dw_graph.v_size())\n y_rand = random.uniform(0.5, self.dw_graph.v_size())\n v.position = (x_rand, y_rand)\n x_vals = []\n y_vals = []\n n = list(nodes_on_graph.keys())\n for k, v in nodes_on_graph.items(): # draw nodes\n x_vals.append(v.position[0])\n y_vals.append(v.position[1])\n\n fig, ax = plt.subplots()\n plt.plot(x_vals, y_vals, 'ro', markersize=5, data=\"d\")\n\n for p, txt in enumerate(n):\n ax.annotate(n[p], (x_vals[p]+0.00003, y_vals[p]), color='g')\n\n for n in nodes_on_graph:\n n1 = self.dw_graph.get_nodes(n)\n x = n1.position[0]\n y = n1.position[1]\n for r in self.dw_graph.all_out_edges_of_node(n):\n dx = self.dw_graph.get_nodes(r).position[0]\n dy = self.dw_graph.get_nodes(r).position[1]\n ax.quiver(x, y, dx-x, dy-y, angles='xy', scale_units='xy', scale=1)\n #plt.arrow(x, y, dx - x, dy - y, head_width=0.0009, width=0.00005, length_includes_head=True)\n\n\n plt.xlabel(\"x axis \")\n plt.ylabel(\"y axis \")\n plt.title(\"The title of the graph\")\n plt.show()","def __filterEdges(self):","def get_puntos_articulacion(self):\n # Inicializo variables\n visitado = {}\n puntos_articulacion = set()\n for u in grafo.devolver_vertices():\n visitado[u] = False\n\n for v in grafo.devolver_vertices():\n if not visitado[v]:\n # Armo el arbol DFS desde v\n dfs = DFSIterativo(grafo, v, visitado)\n dfs.hacer_dfs()\n puntos_articulacion_v = dfs.get_puntos_articulacion()\n # Como v es raiz del arbol, lo saco para analizarlo por separado\n if v in puntos_articulacion_v: # Aunque deberia estar incluida siempre\n puntos_articulacion_v.remove(v)\n self.analizar_raiz(dfs.get_predecesor(), puntos_articulacion_v, v)\n puntos_articulacion.update(puntos_articulacion_v)\n return puntos_articulacion","def items(self):","def af_list(self) -> List:\n ...","def list_all(self):\n if self.graph is None:\n self.logger.error(\"Graph value none cannot be iterated\")\n return list()\n\n nodes = list()\n for node in self.graph.nodes():\n if node == self.NONE_PACKAGE:\n continue\n nodes.append(node)\n return nodes","def colourMagnitudeDiagram(lens_r_list, lens_gr_list, lens_gi_list, source_r_list, source_gr_list,\n source_gi_list, positive_path):\n\n int_lens_r_list = []\n int_lens_gr_list = []\n int_lens_gi_list = []\n int_source_r_list = []\n int_source_gr_list = []\n int_source_gi_list = []\n\n # creating lists containing floats and integrs that can be used in the graph\n for i in range(0, len(lens_r_list)):\n int_lens_r = float(lens_r_list[i])\n int_lens_r_list.append(int_lens_r)\n int_lens_gr = float(lens_gr_list[i])\n int_lens_gr_list.append(int_lens_gr)\n int_lens_gi = float(lens_gi_list[i])\n int_lens_gi_list.append(int_lens_gi)\n int_source_r = float(source_r_list[i])\n int_source_r_list.append(int_source_r)\n int_source_gr = float(source_gr_list[i])\n int_source_gr_list.append(int_source_gr)\n int_source_gi = float(source_gi_list[i])\n int_source_gi_list.append(int_source_gi)\n\n # Getting the min and max fro the axees\n lens_r_min = min(int_lens_r_list)\n lens_r_max = max(int_lens_r_list)\n lens_gr_min = min(int_lens_gr_list)\n lens_gr_max = max(int_lens_gr_list)\n lens_gi_min = min(int_lens_gi_list)\n lens_gi_max = max(int_lens_gi_list)\n source_r_min = min(int_source_r_list)\n source_r_max = max(int_source_r_list)\n source_gr_min = min(int_source_gr_list)\n source_gr_max = max(int_source_gr_list)\n source_gi_min = min(int_source_gi_list)\n source_gi_max = max(int_source_gi_list)\n\n r_min = min(lens_r_min, source_r_min)\n r_max = max(lens_r_max, source_r_max)\n gr_min = min(lens_gr_min, source_gr_min)\n gr_max = max(lens_gr_max, source_gr_max)\n gi_min = min(lens_gi_min, source_gi_min)\n gi_max = max(lens_gi_max, source_gi_max)\n\n y_min = gr_min - 0.2\n y_max = gr_max + 0.2\n x_min = r_min - 0.2\n x_max = r_max + 0.2\n a_min = gi_min -0.2\n a_max = gi_max+0.2\n\n x = int_lens_r_list\n y = int_lens_gr_list\n x2 = int_source_r_list\n y2 = int_source_gr_list\n gi_source = int_source_gi_list\n gi_lens = int_lens_gi_list\n\n # plotting the colour magnitude graph\n fig1 = plt.figure()\n plt.scatter(x, y, color='b', marker='.', label='Lenses')\n plt.scatter(x2, y2, color='r', marker='.', label='Sources')\n plt.xlabel('r')\n plt.ylabel('g-r')\n\n axes = plt.axes()\n axes.set(xlim=(x_min, x_max), ylim=(y_min, y_max))\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\n\n if positive_path == 'Training/g_r_PositiveAll':\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs r) Diagram')\n elif positive_path == 'Testing/g_r_PositiveAll':\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs r) Diagram')\n elif positive_path == 'Training/g_r_AllData':\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs r) Diagram')\n plt.legend()\n plt.show()\n fig1.savefig('%s_g_r_colourMagnitudeDiagram.png' % positive_path)\n\n fig2 = plt.figure()\n plt.scatter(gi_lens, y, color = 'b', marker= '.', label = 'Lenses')\n plt.scatter(gi_source, y2, color ='r', marker='.', label = 'Sources')\n if positive_path == 'Training/g_r_PositiveAll':\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs g-i ) Diagram')\n elif positive_path == 'Testing/g_r_PositiveAll':\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs g-i) Diagram')\n elif positive_path == 'Training/g_r_AllData':\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs g-i) Diagram')\n\n axes = plt.axes()\n axes.set(xlim=(a_min, a_max), ylim=(y_min, y_max))\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\n\n plt.xlabel('g-i')\n plt.ylabel('g-r')\n plt.legend()\n plt.show()\n fig2.savefig('%s_g_i_colourMagnitudeDiagram.png' % positive_path)","def get_nodes(self):\n self.map_graph_id()\n self.nodes_list = [\n self.NX_GRAPHS[self.graph_id].nodes[idx]['label'] \n for idx in range(len(self.NX_GRAPHS[self.graph_id].nodes))]","def get_drawables(self):\n to_draw = []\n for k,v in self._to_draw.items():\n if isinstance(v,Iterable):\n for i in v:\n to_draw.append(i)\n else:\n to_draw.append(v)\n return to_draw","def edges(self):\n return self.show_edges()","def ytrain(self,)->list:","def plot(self):\n pass","def get_nodes(self):\n pass","def get_kritis(self, kriti_list):\n self.kritis = [[k.name, k.composer, k.link] for k in kriti_list if \n k.raga == self.name]","def get_node_list(self):\n return []","def fn(n):\n if n == len(graph)-1: return [[n]]\n ans = []\n for nn in graph[n]: \n ans.extend([[n] + x for x in fn(nn)])\n return ans","def get_vertices(self) -> []:\n return [i for i in self.adj_list]","def getBroaderPlotList(self, iCiv, bBorder = False):\n\t\t\n\t\treturn self.getRegionPlotList(self.getCoreRegions(iCiv) + self.getNormalRegions(iCiv) + self.getBroaderRegions(iCiv), bBorder)","def __init__(self):\n self.l = []\n self.r = []","def getList(self):","def getList(self):","def find_edges(self):\n self.edges = [deepcopy(self.grid[0]), [], deepcopy(self.grid[-1]), []]\n for g in self.grid:\n self.edges[3].append(g[0])\n self.edges[1].append(g[-1])\n self.edges[2]\n self.edges[3]","def visualizar_fornecedores(self):\r\n for fornecedor in self.fornecedores:\r\n fornecedor.visualizar()","def plot_data(self):","def gexf_graph():\n # you must replace these lines and supply your own graph\n my_gexf = Gexf(\"Jitendra Rathour\", \"title\")\n gexf.addGraph(\"undirected\", \"static\", \"Rebrickable Graph\")\n return gexf.graphs[0]","def getHierarchies():","def getHierarchies():","def get_node_list(self):\n return [[node] for node in self.graph.nodes]","def edges(self):\r\n return self.__generate_edges()","def ordenar_vertices(grafo, distancia):\n return list(sorted(distancia.items(),key = lambda x:x[1] , reverse = True ))","def trouver_navires(navire):\n # On recherche d'abord le personnage\n equipage = navire.equipage\n vigies = equipage.get_matelots_au_poste(\"vigie\")\n if vigies:\n personnage = vigies[0]\n else:\n personnage = navire.personnages[0]\n\n portee = get_portee(personnage.salle)\n points = Visible.observer(personnage, portee, 5,\n {\"\": navire})\n navires = [p[1] for p in points.points.values() if \\\n isinstance(p[1], type(navire))]\n return navires","def grAList() -> list:\n return [2, 5, 6, 9, 10, 11, 13, 17, 18, 30]","def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\n T = np.arange(rgt_alea.size)\n fig, ax = plt.subplots()\n ax.grid(True)\n plt.xlabel(\"T\")\n plt.ylabel(\"Regret\")\n \n ax.plot(T, rgt_alea, label = 'aléatoire') \n ax.plot(T, rgt_glou, label = 'glouton')\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\n ax.plot(T, rgt_ucb, label = 'UCB')\n ax.legend(loc = \"upper left\")\n plt.title(\"Regrets des 4 algorithmes par rapport à T\")","def subgraphs(date):\n dateDir = \"graph/%s\" % date\n if not os.path.isdir(dateDir):\n return\n for name in os.listdir(dateDir):\n filename = \"graph/%s/%s\" % (date, name)\n if filename.endswith('.uri'):\n continue\n uri, label = open(filename + \".uri\").read().split(\"\\0\")\n yield uri, label, filename","def get_graphs(self):\n ids = self._graphs.keys()\n ids.sort()\n return [self._graphs[id] for id in ids]","def setDrawing(self):\n self.graph_drawing=[]","def buildGraph(self):\n return None","def list(self):\n a0 = map(self._alphabet.unrank, self._labels[0])\n a1 = map(self._alphabet.unrank, self._labels[1])\n return [a0, a1]","def directions(self):\n return []","def create_edgelist(self):\n self.edgelist = []\n \n for i in range(len(self.Adjmatrix)):\n for j in range(len(self.Adjmatrix)):\n if(self.Adjmatrix[i, j] == 1):\n middlex = 0.5*(self.x[i] + self.x[j])\n middley = 0.5*(self.y[i] + self.y[j])\n self.edgelist.append({\"start node\": i, \"end node\": j, \"link length\": self.Dismatrix[i, j], \"edgediameter\": self.edgediameter, \"middlex\": middlex, \"middley\": middley})","def list_spectrographs(self) -> None:\n for key, item in self.spectrographs.items():\n item.summary()\n print(\"\\n\")","def items():","def getListOfAdditionalGraphicalObjects(self):\n return _libsbml.Layout_getListOfAdditionalGraphicalObjects(self)","def radial_graph(self):\n \n if self['M_RADIAL']['intens'] != None:\n name = self['name']\n id = self._getGraphId()\n figname = 'RADIAL_%s.eps' % id\n sxlabel = 'Pixel Radius' ; sylabel = 'Intens' \n title = 'Radial profile, %s' % (name,)\n y = self['M_RADIAL']['intens']\n x = self['M_RADIAL']['radii']\n xy = ((x,y),)\n Plot(xy,figname,sxlabel,sylabel,title)\n self['figures']['radial'] = figname\n else : pass","def make_recommendation_ga(playlist):\n tracklist = []\n\n # tracknames = list(playlist['name'])\n print(playlist.head())\n\n track_features = playlist[['danceability', 'energy']]\n # 'speechiness', 'acousticness',\n # 'instrumentalness', 'liveness', 'valence']]\n\n track_features_matrix = track_features.values\n\n path, fitness = ga.genetic_algorithm(track_features_matrix, plot=False)\n\n visualization.plot_path(\n track_features,\n path,\n fitness,\n mode=\"none\",\n keep=True\n )\n\n return tracklist","def to_list(self):\n t = ([],) * self.size\n for x in range(self.size):\n t[x].extend(self.get_links(x))\n\n return t1","def __call__(self, *args, **kwargs):\n return self._graph.all_edges(*args, **kwargs)","def __call__(self, *args, **kwargs):\n return self._graph.all_edges(*args, **kwargs)","def edges( self ):\n raise NotImplementedError(\"edges\");","def edges(self):\n return self.generate_edges()","def graph (self, next_nodes, query):\n programs = self.type_graph.get_transitions (query)\n result = []\n for program in programs:\n result += self.graph_inner (next_nodes, program)\n return result","def trajectoire(self):\n trajx = []\n trajy = []\n for i in range(0, len(self.pos)):\n trajx.append(self.pos[i].x)\n trajy.append(self.pos[i].y)\n plt.plot(trajx, trajy) # color=self.color)\n plt.show()","def getEntireASGList( self ):\r\n return self.__trackASG.keys()","def get_graph(**options):\r\n graph = bonobo.Graph()\r\n graph.add_chain(get_stock_list,extract, process, load)\r\n\r\n return graph","def relationships(self):","def vertices(self):\n return list(self.__graph.values())","def __lista_krawedzi__(self):\n s = 0\n for cell in self.cells: # [ 1 0 12 11] = [kr1 kr2 kr3 kr4]\n s += len(cell) # zlicza ile w kolejnych komurkach pkt (krawedzi) (liczy ile jest w wierszu [1 2 12 11] czyli 4 krawedzie\n\n lista_kr = np.array([[0] * 4] * s, dtype=int)\n\n edgeNum = 0\n for i, cell in enumerate(self.cells):\n nNodes = len(cell)\n for l in range(nNodes):\n if l == nNodes-1:\n lista_kr[edgeNum] = [cell[l], cell[0], i, -1]\n else:\n lista_kr[edgeNum] = [cell[l], cell[l+1], i, -1]\n edgeNum += 1\n\n # [ 1 0 0 1] = [pkt1 pkt2 wl sasiad-jeszcze nie obecny]\n return lista_kr","def __init__(self):\n self._list: List[Edge] = list()","def vertices(self):\n return list(self._graph)","def edges(self):\n edges = []\n for key in self._g:\n if self._g[key]:\n for value in self._g[key]:\n edges.append((key, value))\n return edges","def table_graph(self, i):\n return self.__graph_list[i]","def load_all_graphs():\n all_graphs = []\n for i in range(7):\n with open(f'Full_Network_Book_{i+1}.gml', 'rb') as graph_file:\n all_graphs.append(nx.read_gml(graph_file))\n\n return all_graphs","def draw_edges(self):\n pass","def reemplaza_tildes(palabra):","def build_graph(self):\n for each_list in self.lab.look():\n vertice = self._add_vertice(each_list)\n if vertice:\n self.unvisited.add(vertice)\n self.graph.addEdge((self.current, vertice))\n \n self.unvisited -= self.visited\n self._connect_neighbours()","def kAnonymit(g,k):\n rlist=list()\n degreeH=nx.degree_histogram(g)\n## print degreeH\n li=[[] for i in range(len(degreeH))]\n uNode=dict(zip(range(len(degreeH)),li))\n \n gd=g.degree()\n for e in gd:\n uNode[gd[e]].append(e)\n ad=list()\n mi=list()\n su=list() \n sel3(g,3,ad,mi,su)\n nodeAd=list()\n nodeMi=list()\n nodeSu=list() \n for i in ad:\n nodeAd=nodeAd+uNode[i]\n #nodeAd store the graph ID\n for i in mi:\n nodeMi=nodeMi+uNode[i]\n for i in su:\n nodeSu=nodeSu+uNode[i]\n\n print nodeAd,nodeMi,nodeSu\n\n\n i=0#Add edge \n while i<len(nodeAd)-1:\n j=i+1\n while j<len(nodeAd):\n if not g.has_edge(nodeAd[i],nodeAd[j]):\n g.add_edge(nodeAd[i],nodeAd[j])\n rlist.append(nodeAd[i])\n rlist.append(nodeAd[j])\n nodeAd.pop(j)\n nodeAd.pop(i)\n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeAd\n \n i=0#delet edge\n while i<len(nodeMi)-1:\n j=i+1\n while j<len(nodeMi):\n if g.has_edge(nodeMi[i],nodeMi[j]):\n g.remove_edge(nodeMi[i],nodeMi[j])\n rlist.append(nodeMi[i])\n rlist.append(nodeMi[j])\n nodeMi.pop(j)\n nodeMi.pop(i)\n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeMi\n \n i=0#Add edge \n while i<len(nodeSu):\n j=0\n while j<len(nodeAd):\n if not g.has_edge(nodeSu[i],nodeAd[j]):\n g.add_edge(nodeSu[i],nodeAd[j])\n rlist.append(nodeSu[i])\n rlist.append(nodeAd[j])\n nodeAd.pop(j)\n nodeSu.pop(i)\n \n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeSu\n \n i=0#delet edge\n while i<len(nodeSu):\n j=0\n while j<len(nodeMi):\n if g.has_edge(nodeSu[i],nodeMi[j]):\n g.remove_edge(nodeSu[i],nodeMi[j])\n rlist.append(nodeSu[i])\n rlist.append(nodeMi[j])\n nodeMi.pop(j)\n nodeSu.pop(i)\n i=i-1\n break\n else:\n j=j+1\n i=i+1\n print nodeSu\n return rlist"],"string":"[\n \"def graph(self):\\n ...\",\n \"def populate_graph(self):\",\n \"def _get_full_graph(self):\",\n \"def gen_graph(self):\",\n \"def plot_graph(self) -> None:\",\n \"def list(self):\",\n \"def allGraphs(date):\\n g = getGraph()\\n for uri, label, filename in subgraphs(date):\\n if not label:\\n label = \\\"(no label provided)\\\"\\n g.parse(filename, format=SUBGRAPH_FORMAT)\\n return g\",\n \"def nodes(self): \\n return [n for n in self.iternodes()]\",\n \"def get_edges(self):\\n try:\\n temp = self.edges\\n except:\\n temp = []\\n return temp\",\n \"def get_diagram(self):\\n self_nodes=self.nodes.all()\\n self_arrows=self.arrows.all()\\n \\n \\n if len(self_nodes)==0:\\n return False\\n \\n nodes = [n.get_icon_obj() for n in self_nodes]\\n node_liens = [n.liens.all() for n in self_nodes]\\n \\n pairs = []\\n for n,n_liens in zip(nodes,node_liens):\\n if len(n_liens)==0:\\n liens = Lien.objects.filter(cause__id=n.target_id).all()\\n liens = [l.consequence.id for l in liens]\\n temp = [(n,target) for target in nodes if target.target_id in liens]\\n pairs.extend(temp)\\n else:\\n ids=set([(i.cause.id,i.consequence.id) for i in n_liens])\\n for n in nodes:\\n pairs.extend([(n,i) for i in nodes if i is not n and \\n (n.target_id,i.target_id) in ids])\\n ids = set([(i.cause.id,i.consequence.id) for i in self_arrows])\\n pairs = [p for p in pairs if (p[0].target_id,p[1].target_id) not in ids]\\n \\n lines=[]\\n arrows=[]\\n for obj in self_arrows:\\n \\n n0=[i for i in nodes if i.node_id==obj.cause.id]\\n n1=[i for i in nodes if i.node_id==obj.consequence.id]\\n if len(n0)!=1 or len(n1)!=1:\\n continue\\n n0=n0[0]\\n n1=n1[0]\\n \\n pt=[(obj.X0, obj.Y0), (obj.X1,obj.Y1)]\\n pt=[np.array(i) for i in pt if None not in i]\\n if len(pt)==0:\\n pairs.append((n0,n1))\\n continue\\n pairs = [p for p in pairs if (p[0].node_id,p[1].node_id)!=(n0.node_id,n1.node_id)]\\n vect = pt[0]-np.array(n0.pos)\\n first_pt = np.array(n0.pos)+vect*n0.size/np.sqrt(sum(vect*vect))\\n vect = np.array(n1.pos) - pt[-1]\\n last_pt = np.array(n1.pos)-vect*n1.size/np.sqrt(sum(vect*vect))\\n pt=[first_pt,*pt,last_pt]\\n \\n lines.extend([((*i,*j),n0.color) for i,j in zip(pt[:-1],pt[1:])])\\n arrows.append(((*pt[-2],*pt[-1]),n0.color))\\n \\n \\n margin=10\\n line_width=2\\n \\n diagram=DiagramObj(self.id,nodes,pairs,margin,\\n self.width,self.height,line_width)\\n diagram.add_arrows(arrows,lines)\\n print(diagram.lines)\\n return diagram\",\n \"def dump_graph(self):\\n\\n edges = []\\n for vertex in self.__graph_dict:\\n mylist = list(vertex)\\n logging.debug(\\\"mylist : \\\", mylist)\",\n \"def V(self) -> list:\\n return list(self._graph.values())\",\n \"def X(self)->list:\",\n \"def graphs(self):\\n return self.__graphs\",\n \"def heuristique_ACPM(self,draw=False):\\n\\n def getVertricesOfPath(edges):\\n set_node = set()\\n for e in edges:\\n id1,id2 = self.getIdVerticesOfEdge(e)\\n set_node.add(id1)\\n set_node.add(id2)\\n return set_node\\n\\n\\n if draw:\\n try:\\n os.makedirs(self.dirname+\\\"/H_ACPM\\\")\\n except:\\n pass\\n\\n\\n #Graphe de depart contenant tout les noeuds\\n G = Graphe_Individu(self,self.wholeGraphDict)\\n if draw:\\n self.drawGraph(\\\"/H_ACPM/G0\\\",G.get_graphe().get_edges())\\n\\n\\n nb_vertices = 1\\n nb_tmp_vertices = 0\\n i=0\\n\\n #Tant que le nombre de noeuds du graphe decroit, appliquer kruskal\\n while nb_vertices > nb_tmp_vertices :\\n g_acpm = G.get_MST()\\n\\n\\n nb_vertices = len(G.get_dictSteinerNodes())\\n set_Node = set(G.get_dictSteinerNodes().keys())\\n dictSteinerNodez = self.eliminationFeuilles(g_acpm.get_edges(),set_Node)\\n G = Graphe_Individu(self,dictSteinerNodez)\\n nb_tmp_vertices = len(dictSteinerNodez)\\n\\n if draw:\\n i+= 1\\n self.drawGraph(\\\"/H_ACPM/G%d\\\"%i+\\\"_%d\\\"%g_acpm.get_total_weight(),g_acpm.get_edges())\\n\\n\\n if draw:\\n self.drawGraph(\\\"/H_ACPM/GFinal_%d\\\"%G.get_MST().get_total_weight(),G.get_MST().get_edges())\\n\\n for i in self.steinerNodes:\\n if i not in dictSteinerNodez.keys():\\n dictSteinerNodez[i] = 0\\n\\n # return Graphe_Individu(self,dictSteinerNodez)\\n return dictSteinerNodez\",\n \"def buildRomaniagraph(Graph):\\n \\n g=Graph()\\n for name in ('Oradea','Zerind','Arad','Timisoara', 'Lugoj','Mehadia', \\n 'Dobreta','Sibiu','Fagaras','Rimnicu Vilcea','Pitesti','Craiova',\\n 'Bucharest','Giurgiu','Urziceni','Hirsova','Eforie','Vaslui',\\n 'Iasi','Nearnt'):\\n g.addNode(Node(name))\\n \\n \\n g.addEdge(Edge(g.getNode('Oradea'),g.getNode('Zerind')))\\n g.addEdge(Edge(g.getNode('Oradea'),g.getNode('Sibiu')))\\n g.addEdge(Edge(g.getNode('Zerind'),g.getNode('Arad')))\\n g.addEdge(Edge(g.getNode('Arad'),g.getNode('Timisoara')))\\n g.addEdge(Edge(g.getNode('Lugoj'),g.getNode('Timisoara')))\\n g.addEdge(Edge(g.getNode('Lugoj'),g.getNode('Mehadia')))\\n g.addEdge(Edge(g.getNode('Mehadia'),g.getNode('Dobreta')))\\n g.addEdge(Edge(g.getNode('Dobreta'),g.getNode('Craiova')))\\n g.addEdge(Edge(g.getNode('Craiova'),g.getNode('Pitesti')))\\n g.addEdge(Edge(g.getNode('Craiova'),g.getNode('Rimnicu Vilcea')))\\n g.addEdge(Edge(g.getNode('Rimnicu Vilcea'),g.getNode('Sibiu')))\\n g.addEdge(Edge(g.getNode('Sibiu'),g.getNode('Fagaras')))\\n g.addEdge(Edge(g.getNode('Pitesti'),g.getNode('Bucharest')))\\n g.addEdge(Edge(g.getNode('Bucharest'),g.getNode('Giurgiu')))\\n g.addEdge(Edge(g.getNode('Bucharest'),g.getNode('Urziceni')))\\n g.addEdge(Edge(g.getNode('Arad'),g.getNode('Sibiu')))\\n g.addEdge(Edge(g.getNode('Rimnicu Vilcea'),g.getNode('Pitesti')))\\n g.addEdge(Edge(g.getNode('Bucharest'),g.getNode('Fagaras')))\\n g.addEdge(Edge(g.getNode('Vaslui'),g.getNode('Urziceni')))\\n g.addEdge(Edge(g.getNode('Hirsova'),g.getNode('Urziceni')))\\n g.addEdge(Edge(g.getNode('Hirsova'),g.getNode('Eforie')))\\n g.addEdge(Edge(g.getNode('Vaslui'),g.getNode('Iasi')))\\n g.addEdge(Edge(g.getNode('Iasi'),g.getNode('Nearnt')))\\n \\n return g\",\n \"def arrr_starrr_graph(self):\\n\\n plt.figure()\\n total_cost = 0\\n\\n # plot batteries\\n counter = 0\\n for batt in self.grid.batteries:\\n plt.plot(batt.x, batt.y, marker='x',\\n color=colors[counter], markersize=10)\\n counter += 1\\n\\n # iterate over houses and path\\n for house in self.grid.houses:\\n battery = self.grid.batteries[house.connection]\\n\\n # get path coordinates\\n path_data = house.path\\n\\n # plot path and house\\n plt.plot(path_data[0][0], path_data[0][1],\\n color=colors[house.connection], linewidth=.3)\\n plt.plot(house.x, house.y, marker='p',\\n color=colors[house.connection])\\n total_cost += path_data[1]\\n plt.draw()\\n plt.pause(0.000000001)\\n\\n plt.title(f\\\"total cost = {total_cost}\\\")\",\n \"def arredondar(self):\\n for y in range(self.altura):\\n for x in range(self.largura):\\n for i in range(3):\\n self.lista[x][y][i] = int(self.lista[x][y][i])\",\n \"def plot_sammen_vekst(urn, ordlister, window=5000, pr = 100):\\n rammer = []\\n for ordbag in ordlister:\\n vekst = vekstdiagram(urn, params = {'words': ordbag, 'window':window, 'pr': pr} )\\n vekst.columns = [ordbag[0]]\\n rammer.append(vekst)\\n return pd.concat(rammer)\",\n \"def _CreateGraph(self):\\n self.nodes = []\\n self.edges = []\\n for i, r in self.airports.set_index('airport_id').iterrows():\\n self.nodes.append((i,r.to_dict()))\\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\\n self.edges.append((i[0],i[1],r.to_dict()))\\n # print('node ex: {}'.format(self.nodes[0]))\\n # print('edge ex: {}'.format(self.edges[0]))\\n\\n self.graph = self._CreateAdjacencyListGraph()\",\n \"def iter_nodes(self):\",\n \"def plot_edges(self, node_list):\\n tree = MarkerArray()\\n id = 1\\n for node in self.node_list:\\n if node.parent:\\n # edge between nodes\\n path = Marker()\\n path.header.frame_id = \\\"map\\\"\\n path.header.stamp = rospy.get_rostime()\\n path.ns = \\\"markers\\\"\\n path.id = id\\n id += 1\\n path.type = path.LINE_STRIP\\n path.action = path.ADD\\n path.scale.x = self.rviz_tuning_plt\\n path.color.a = 1.0\\n\\n path.color.r = 1.0\\n path.color.g = 0.7\\n path.color.b = 0.0\\n\\n path.lifetime = rospy.Duration()\\n path.pose.orientation.w = 1.0\\n\\n p1 = Point()\\n p1.x = node.parent.x\\n p1.y = node.parent.y\\n p1.z = 0.02\\n path.points.append(p1)\\n\\n p2 = Point()\\n p2.x = node.x\\n p2.y = node.y\\n p2.z = 0.02\\n path.points.append(p2)\\n \\n tree.markers.append(path)\\n\\n self.pub_edges.publish(tree)\",\n \"def getSets():\",\n \"def lego_sets():\\n # you must replace this line and return your own list\\n return []\",\n \"def objects(self):\",\n \"def build_graph(self):\\n pass\",\n \"def list():\",\n \"def list():\",\n \"def lego_sets():\\n # you must replace this line and return your own list\\n return lego_sets_list\",\n \"def show_edges(self):\\n for element in self.graph:\\n print(element, self.graph[element])\",\n \"def _build_graph(self):\\n pass\",\n \"def edges(self, irc, msg, args, channel):\\n pie = self.instances[irc.network]\\n irc.reply([str(x) for x in pie.graphs[channel].edges.values()])\",\n \"def nodes(self, irc, msg, args, channel):\\n pie = self.instances[irc.network]\\n irc.reply([str(x) for x in pie.graphs[channel].nodes.values()])\",\n \"def triples():\",\n \"def plot_graph(self) -> None:\\n\\n nodes_on_graph = self.dw_graph.get_all_v()\\n for k, v in nodes_on_graph.items():\\n if v.position is None:\\n x_rand = random.uniform(0.5, self.dw_graph.v_size())\\n y_rand = random.uniform(0.5, self.dw_graph.v_size())\\n v.position = (x_rand, y_rand)\\n x_vals = []\\n y_vals = []\\n n = list(nodes_on_graph.keys())\\n for k, v in nodes_on_graph.items(): # draw nodes\\n x_vals.append(v.position[0])\\n y_vals.append(v.position[1])\\n\\n fig, ax = plt.subplots()\\n plt.plot(x_vals, y_vals, 'ro', markersize=5, data=\\\"d\\\")\\n\\n for p, txt in enumerate(n):\\n ax.annotate(n[p], (x_vals[p]+0.00003, y_vals[p]), color='g')\\n\\n for n in nodes_on_graph:\\n n1 = self.dw_graph.get_nodes(n)\\n x = n1.position[0]\\n y = n1.position[1]\\n for r in self.dw_graph.all_out_edges_of_node(n):\\n dx = self.dw_graph.get_nodes(r).position[0]\\n dy = self.dw_graph.get_nodes(r).position[1]\\n ax.quiver(x, y, dx-x, dy-y, angles='xy', scale_units='xy', scale=1)\\n #plt.arrow(x, y, dx - x, dy - y, head_width=0.0009, width=0.00005, length_includes_head=True)\\n\\n\\n plt.xlabel(\\\"x axis \\\")\\n plt.ylabel(\\\"y axis \\\")\\n plt.title(\\\"The title of the graph\\\")\\n plt.show()\",\n \"def __filterEdges(self):\",\n \"def get_puntos_articulacion(self):\\n # Inicializo variables\\n visitado = {}\\n puntos_articulacion = set()\\n for u in grafo.devolver_vertices():\\n visitado[u] = False\\n\\n for v in grafo.devolver_vertices():\\n if not visitado[v]:\\n # Armo el arbol DFS desde v\\n dfs = DFSIterativo(grafo, v, visitado)\\n dfs.hacer_dfs()\\n puntos_articulacion_v = dfs.get_puntos_articulacion()\\n # Como v es raiz del arbol, lo saco para analizarlo por separado\\n if v in puntos_articulacion_v: # Aunque deberia estar incluida siempre\\n puntos_articulacion_v.remove(v)\\n self.analizar_raiz(dfs.get_predecesor(), puntos_articulacion_v, v)\\n puntos_articulacion.update(puntos_articulacion_v)\\n return puntos_articulacion\",\n \"def items(self):\",\n \"def af_list(self) -> List:\\n ...\",\n \"def list_all(self):\\n if self.graph is None:\\n self.logger.error(\\\"Graph value none cannot be iterated\\\")\\n return list()\\n\\n nodes = list()\\n for node in self.graph.nodes():\\n if node == self.NONE_PACKAGE:\\n continue\\n nodes.append(node)\\n return nodes\",\n \"def colourMagnitudeDiagram(lens_r_list, lens_gr_list, lens_gi_list, source_r_list, source_gr_list,\\n source_gi_list, positive_path):\\n\\n int_lens_r_list = []\\n int_lens_gr_list = []\\n int_lens_gi_list = []\\n int_source_r_list = []\\n int_source_gr_list = []\\n int_source_gi_list = []\\n\\n # creating lists containing floats and integrs that can be used in the graph\\n for i in range(0, len(lens_r_list)):\\n int_lens_r = float(lens_r_list[i])\\n int_lens_r_list.append(int_lens_r)\\n int_lens_gr = float(lens_gr_list[i])\\n int_lens_gr_list.append(int_lens_gr)\\n int_lens_gi = float(lens_gi_list[i])\\n int_lens_gi_list.append(int_lens_gi)\\n int_source_r = float(source_r_list[i])\\n int_source_r_list.append(int_source_r)\\n int_source_gr = float(source_gr_list[i])\\n int_source_gr_list.append(int_source_gr)\\n int_source_gi = float(source_gi_list[i])\\n int_source_gi_list.append(int_source_gi)\\n\\n # Getting the min and max fro the axees\\n lens_r_min = min(int_lens_r_list)\\n lens_r_max = max(int_lens_r_list)\\n lens_gr_min = min(int_lens_gr_list)\\n lens_gr_max = max(int_lens_gr_list)\\n lens_gi_min = min(int_lens_gi_list)\\n lens_gi_max = max(int_lens_gi_list)\\n source_r_min = min(int_source_r_list)\\n source_r_max = max(int_source_r_list)\\n source_gr_min = min(int_source_gr_list)\\n source_gr_max = max(int_source_gr_list)\\n source_gi_min = min(int_source_gi_list)\\n source_gi_max = max(int_source_gi_list)\\n\\n r_min = min(lens_r_min, source_r_min)\\n r_max = max(lens_r_max, source_r_max)\\n gr_min = min(lens_gr_min, source_gr_min)\\n gr_max = max(lens_gr_max, source_gr_max)\\n gi_min = min(lens_gi_min, source_gi_min)\\n gi_max = max(lens_gi_max, source_gi_max)\\n\\n y_min = gr_min - 0.2\\n y_max = gr_max + 0.2\\n x_min = r_min - 0.2\\n x_max = r_max + 0.2\\n a_min = gi_min -0.2\\n a_max = gi_max+0.2\\n\\n x = int_lens_r_list\\n y = int_lens_gr_list\\n x2 = int_source_r_list\\n y2 = int_source_gr_list\\n gi_source = int_source_gi_list\\n gi_lens = int_lens_gi_list\\n\\n # plotting the colour magnitude graph\\n fig1 = plt.figure()\\n plt.scatter(x, y, color='b', marker='.', label='Lenses')\\n plt.scatter(x2, y2, color='r', marker='.', label='Sources')\\n plt.xlabel('r')\\n plt.ylabel('g-r')\\n\\n axes = plt.axes()\\n axes.set(xlim=(x_min, x_max), ylim=(y_min, y_max))\\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\\n\\n if positive_path == 'Training/g_r_PositiveAll':\\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs r) Diagram')\\n elif positive_path == 'Testing/g_r_PositiveAll':\\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs r) Diagram')\\n elif positive_path == 'Training/g_r_AllData':\\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs r) Diagram')\\n plt.legend()\\n plt.show()\\n fig1.savefig('%s_g_r_colourMagnitudeDiagram.png' % positive_path)\\n\\n fig2 = plt.figure()\\n plt.scatter(gi_lens, y, color = 'b', marker= '.', label = 'Lenses')\\n plt.scatter(gi_source, y2, color ='r', marker='.', label = 'Sources')\\n if positive_path == 'Training/g_r_PositiveAll':\\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs g-i ) Diagram')\\n elif positive_path == 'Testing/g_r_PositiveAll':\\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs g-i) Diagram')\\n elif positive_path == 'Training/g_r_AllData':\\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs g-i) Diagram')\\n\\n axes = plt.axes()\\n axes.set(xlim=(a_min, a_max), ylim=(y_min, y_max))\\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\\n\\n plt.xlabel('g-i')\\n plt.ylabel('g-r')\\n plt.legend()\\n plt.show()\\n fig2.savefig('%s_g_i_colourMagnitudeDiagram.png' % positive_path)\",\n \"def get_nodes(self):\\n self.map_graph_id()\\n self.nodes_list = [\\n self.NX_GRAPHS[self.graph_id].nodes[idx]['label'] \\n for idx in range(len(self.NX_GRAPHS[self.graph_id].nodes))]\",\n \"def get_drawables(self):\\n to_draw = []\\n for k,v in self._to_draw.items():\\n if isinstance(v,Iterable):\\n for i in v:\\n to_draw.append(i)\\n else:\\n to_draw.append(v)\\n return to_draw\",\n \"def edges(self):\\n return self.show_edges()\",\n \"def ytrain(self,)->list:\",\n \"def plot(self):\\n pass\",\n \"def get_nodes(self):\\n pass\",\n \"def get_kritis(self, kriti_list):\\n self.kritis = [[k.name, k.composer, k.link] for k in kriti_list if \\n k.raga == self.name]\",\n \"def get_node_list(self):\\n return []\",\n \"def fn(n):\\n if n == len(graph)-1: return [[n]]\\n ans = []\\n for nn in graph[n]: \\n ans.extend([[n] + x for x in fn(nn)])\\n return ans\",\n \"def get_vertices(self) -> []:\\n return [i for i in self.adj_list]\",\n \"def getBroaderPlotList(self, iCiv, bBorder = False):\\n\\t\\t\\n\\t\\treturn self.getRegionPlotList(self.getCoreRegions(iCiv) + self.getNormalRegions(iCiv) + self.getBroaderRegions(iCiv), bBorder)\",\n \"def __init__(self):\\n self.l = []\\n self.r = []\",\n \"def getList(self):\",\n \"def getList(self):\",\n \"def find_edges(self):\\n self.edges = [deepcopy(self.grid[0]), [], deepcopy(self.grid[-1]), []]\\n for g in self.grid:\\n self.edges[3].append(g[0])\\n self.edges[1].append(g[-1])\\n self.edges[2]\\n self.edges[3]\",\n \"def visualizar_fornecedores(self):\\r\\n for fornecedor in self.fornecedores:\\r\\n fornecedor.visualizar()\",\n \"def plot_data(self):\",\n \"def gexf_graph():\\n # you must replace these lines and supply your own graph\\n my_gexf = Gexf(\\\"Jitendra Rathour\\\", \\\"title\\\")\\n gexf.addGraph(\\\"undirected\\\", \\\"static\\\", \\\"Rebrickable Graph\\\")\\n return gexf.graphs[0]\",\n \"def getHierarchies():\",\n \"def getHierarchies():\",\n \"def get_node_list(self):\\n return [[node] for node in self.graph.nodes]\",\n \"def edges(self):\\r\\n return self.__generate_edges()\",\n \"def ordenar_vertices(grafo, distancia):\\n return list(sorted(distancia.items(),key = lambda x:x[1] , reverse = True ))\",\n \"def trouver_navires(navire):\\n # On recherche d'abord le personnage\\n equipage = navire.equipage\\n vigies = equipage.get_matelots_au_poste(\\\"vigie\\\")\\n if vigies:\\n personnage = vigies[0]\\n else:\\n personnage = navire.personnages[0]\\n\\n portee = get_portee(personnage.salle)\\n points = Visible.observer(personnage, portee, 5,\\n {\\\"\\\": navire})\\n navires = [p[1] for p in points.points.values() if \\\\\\n isinstance(p[1], type(navire))]\\n return navires\",\n \"def grAList() -> list:\\n return [2, 5, 6, 9, 10, 11, 13, 17, 18, 30]\",\n \"def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\\n T = np.arange(rgt_alea.size)\\n fig, ax = plt.subplots()\\n ax.grid(True)\\n plt.xlabel(\\\"T\\\")\\n plt.ylabel(\\\"Regret\\\")\\n \\n ax.plot(T, rgt_alea, label = 'aléatoire') \\n ax.plot(T, rgt_glou, label = 'glouton')\\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\\n ax.plot(T, rgt_ucb, label = 'UCB')\\n ax.legend(loc = \\\"upper left\\\")\\n plt.title(\\\"Regrets des 4 algorithmes par rapport à T\\\")\",\n \"def subgraphs(date):\\n dateDir = \\\"graph/%s\\\" % date\\n if not os.path.isdir(dateDir):\\n return\\n for name in os.listdir(dateDir):\\n filename = \\\"graph/%s/%s\\\" % (date, name)\\n if filename.endswith('.uri'):\\n continue\\n uri, label = open(filename + \\\".uri\\\").read().split(\\\"\\\\0\\\")\\n yield uri, label, filename\",\n \"def get_graphs(self):\\n ids = self._graphs.keys()\\n ids.sort()\\n return [self._graphs[id] for id in ids]\",\n \"def setDrawing(self):\\n self.graph_drawing=[]\",\n \"def buildGraph(self):\\n return None\",\n \"def list(self):\\n a0 = map(self._alphabet.unrank, self._labels[0])\\n a1 = map(self._alphabet.unrank, self._labels[1])\\n return [a0, a1]\",\n \"def directions(self):\\n return []\",\n \"def create_edgelist(self):\\n self.edgelist = []\\n \\n for i in range(len(self.Adjmatrix)):\\n for j in range(len(self.Adjmatrix)):\\n if(self.Adjmatrix[i, j] == 1):\\n middlex = 0.5*(self.x[i] + self.x[j])\\n middley = 0.5*(self.y[i] + self.y[j])\\n self.edgelist.append({\\\"start node\\\": i, \\\"end node\\\": j, \\\"link length\\\": self.Dismatrix[i, j], \\\"edgediameter\\\": self.edgediameter, \\\"middlex\\\": middlex, \\\"middley\\\": middley})\",\n \"def list_spectrographs(self) -> None:\\n for key, item in self.spectrographs.items():\\n item.summary()\\n print(\\\"\\\\n\\\")\",\n \"def items():\",\n \"def getListOfAdditionalGraphicalObjects(self):\\n return _libsbml.Layout_getListOfAdditionalGraphicalObjects(self)\",\n \"def radial_graph(self):\\n \\n if self['M_RADIAL']['intens'] != None:\\n name = self['name']\\n id = self._getGraphId()\\n figname = 'RADIAL_%s.eps' % id\\n sxlabel = 'Pixel Radius' ; sylabel = 'Intens' \\n title = 'Radial profile, %s' % (name,)\\n y = self['M_RADIAL']['intens']\\n x = self['M_RADIAL']['radii']\\n xy = ((x,y),)\\n Plot(xy,figname,sxlabel,sylabel,title)\\n self['figures']['radial'] = figname\\n else : pass\",\n \"def make_recommendation_ga(playlist):\\n tracklist = []\\n\\n # tracknames = list(playlist['name'])\\n print(playlist.head())\\n\\n track_features = playlist[['danceability', 'energy']]\\n # 'speechiness', 'acousticness',\\n # 'instrumentalness', 'liveness', 'valence']]\\n\\n track_features_matrix = track_features.values\\n\\n path, fitness = ga.genetic_algorithm(track_features_matrix, plot=False)\\n\\n visualization.plot_path(\\n track_features,\\n path,\\n fitness,\\n mode=\\\"none\\\",\\n keep=True\\n )\\n\\n return tracklist\",\n \"def to_list(self):\\n t = ([],) * self.size\\n for x in range(self.size):\\n t[x].extend(self.get_links(x))\\n\\n return t1\",\n \"def __call__(self, *args, **kwargs):\\n return self._graph.all_edges(*args, **kwargs)\",\n \"def __call__(self, *args, **kwargs):\\n return self._graph.all_edges(*args, **kwargs)\",\n \"def edges( self ):\\n raise NotImplementedError(\\\"edges\\\");\",\n \"def edges(self):\\n return self.generate_edges()\",\n \"def graph (self, next_nodes, query):\\n programs = self.type_graph.get_transitions (query)\\n result = []\\n for program in programs:\\n result += self.graph_inner (next_nodes, program)\\n return result\",\n \"def trajectoire(self):\\n trajx = []\\n trajy = []\\n for i in range(0, len(self.pos)):\\n trajx.append(self.pos[i].x)\\n trajy.append(self.pos[i].y)\\n plt.plot(trajx, trajy) # color=self.color)\\n plt.show()\",\n \"def getEntireASGList( self ):\\r\\n return self.__trackASG.keys()\",\n \"def get_graph(**options):\\r\\n graph = bonobo.Graph()\\r\\n graph.add_chain(get_stock_list,extract, process, load)\\r\\n\\r\\n return graph\",\n \"def relationships(self):\",\n \"def vertices(self):\\n return list(self.__graph.values())\",\n \"def __lista_krawedzi__(self):\\n s = 0\\n for cell in self.cells: # [ 1 0 12 11] = [kr1 kr2 kr3 kr4]\\n s += len(cell) # zlicza ile w kolejnych komurkach pkt (krawedzi) (liczy ile jest w wierszu [1 2 12 11] czyli 4 krawedzie\\n\\n lista_kr = np.array([[0] * 4] * s, dtype=int)\\n\\n edgeNum = 0\\n for i, cell in enumerate(self.cells):\\n nNodes = len(cell)\\n for l in range(nNodes):\\n if l == nNodes-1:\\n lista_kr[edgeNum] = [cell[l], cell[0], i, -1]\\n else:\\n lista_kr[edgeNum] = [cell[l], cell[l+1], i, -1]\\n edgeNum += 1\\n\\n # [ 1 0 0 1] = [pkt1 pkt2 wl sasiad-jeszcze nie obecny]\\n return lista_kr\",\n \"def __init__(self):\\n self._list: List[Edge] = list()\",\n \"def vertices(self):\\n return list(self._graph)\",\n \"def edges(self):\\n edges = []\\n for key in self._g:\\n if self._g[key]:\\n for value in self._g[key]:\\n edges.append((key, value))\\n return edges\",\n \"def table_graph(self, i):\\n return self.__graph_list[i]\",\n \"def load_all_graphs():\\n all_graphs = []\\n for i in range(7):\\n with open(f'Full_Network_Book_{i+1}.gml', 'rb') as graph_file:\\n all_graphs.append(nx.read_gml(graph_file))\\n\\n return all_graphs\",\n \"def draw_edges(self):\\n pass\",\n \"def reemplaza_tildes(palabra):\",\n \"def build_graph(self):\\n for each_list in self.lab.look():\\n vertice = self._add_vertice(each_list)\\n if vertice:\\n self.unvisited.add(vertice)\\n self.graph.addEdge((self.current, vertice))\\n \\n self.unvisited -= self.visited\\n self._connect_neighbours()\",\n \"def kAnonymit(g,k):\\n rlist=list()\\n degreeH=nx.degree_histogram(g)\\n## print degreeH\\n li=[[] for i in range(len(degreeH))]\\n uNode=dict(zip(range(len(degreeH)),li))\\n \\n gd=g.degree()\\n for e in gd:\\n uNode[gd[e]].append(e)\\n ad=list()\\n mi=list()\\n su=list() \\n sel3(g,3,ad,mi,su)\\n nodeAd=list()\\n nodeMi=list()\\n nodeSu=list() \\n for i in ad:\\n nodeAd=nodeAd+uNode[i]\\n #nodeAd store the graph ID\\n for i in mi:\\n nodeMi=nodeMi+uNode[i]\\n for i in su:\\n nodeSu=nodeSu+uNode[i]\\n\\n print nodeAd,nodeMi,nodeSu\\n\\n\\n i=0#Add edge \\n while i<len(nodeAd)-1:\\n j=i+1\\n while j<len(nodeAd):\\n if not g.has_edge(nodeAd[i],nodeAd[j]):\\n g.add_edge(nodeAd[i],nodeAd[j])\\n rlist.append(nodeAd[i])\\n rlist.append(nodeAd[j])\\n nodeAd.pop(j)\\n nodeAd.pop(i)\\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeAd\\n \\n i=0#delet edge\\n while i<len(nodeMi)-1:\\n j=i+1\\n while j<len(nodeMi):\\n if g.has_edge(nodeMi[i],nodeMi[j]):\\n g.remove_edge(nodeMi[i],nodeMi[j])\\n rlist.append(nodeMi[i])\\n rlist.append(nodeMi[j])\\n nodeMi.pop(j)\\n nodeMi.pop(i)\\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeMi\\n \\n i=0#Add edge \\n while i<len(nodeSu):\\n j=0\\n while j<len(nodeAd):\\n if not g.has_edge(nodeSu[i],nodeAd[j]):\\n g.add_edge(nodeSu[i],nodeAd[j])\\n rlist.append(nodeSu[i])\\n rlist.append(nodeAd[j])\\n nodeAd.pop(j)\\n nodeSu.pop(i)\\n \\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeSu\\n \\n i=0#delet edge\\n while i<len(nodeSu):\\n j=0\\n while j<len(nodeMi):\\n if g.has_edge(nodeSu[i],nodeMi[j]):\\n g.remove_edge(nodeSu[i],nodeMi[j])\\n rlist.append(nodeSu[i])\\n rlist.append(nodeMi[j])\\n nodeMi.pop(j)\\n nodeSu.pop(i)\\n i=i-1\\n break\\n else:\\n j=j+1\\n i=i+1\\n print nodeSu\\n return rlist\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6653496","0.6502852","0.61809665","0.5993094","0.59424686","0.57306266","0.5700172","0.5697255","0.5596236","0.5589515","0.55849475","0.55651873","0.5546893","0.5532899","0.55003935","0.5471013","0.54704064","0.5469078","0.5429392","0.54259014","0.5422125","0.54120696","0.54075927","0.53824794","0.5380849","0.5375487","0.53752476","0.53752476","0.53611207","0.53288084","0.5312337","0.53011554","0.52866673","0.5283773","0.52715015","0.5270854","0.526479","0.5263746","0.52577525","0.5254981","0.5251318","0.5245669","0.5229819","0.5221815","0.52153635","0.52136344","0.52093625","0.52070785","0.52057606","0.52041376","0.5199508","0.51983786","0.5198092","0.5189033","0.5189033","0.51835513","0.5178892","0.517851","0.5171804","0.5170235","0.5170235","0.516159","0.5158302","0.51477313","0.514373","0.51366204","0.51287615","0.5125696","0.51238096","0.5123504","0.512033","0.51191163","0.5116422","0.51109606","0.51096195","0.51088214","0.51077604","0.50995225","0.50953025","0.5088422","0.50838894","0.50838894","0.5083733","0.5083634","0.5080677","0.507962","0.5079039","0.50716597","0.50673956","0.5060057","0.505615","0.5053063","0.5036126","0.50358063","0.50335115","0.50282264","0.50266075","0.502609","0.5024912","0.50211364"],"string":"[\n \"0.6653496\",\n \"0.6502852\",\n \"0.61809665\",\n \"0.5993094\",\n \"0.59424686\",\n \"0.57306266\",\n \"0.5700172\",\n \"0.5697255\",\n \"0.5596236\",\n \"0.5589515\",\n \"0.55849475\",\n \"0.55651873\",\n \"0.5546893\",\n \"0.5532899\",\n \"0.55003935\",\n \"0.5471013\",\n \"0.54704064\",\n \"0.5469078\",\n \"0.5429392\",\n \"0.54259014\",\n \"0.5422125\",\n \"0.54120696\",\n \"0.54075927\",\n \"0.53824794\",\n \"0.5380849\",\n \"0.5375487\",\n \"0.53752476\",\n \"0.53752476\",\n \"0.53611207\",\n \"0.53288084\",\n \"0.5312337\",\n \"0.53011554\",\n \"0.52866673\",\n \"0.5283773\",\n \"0.52715015\",\n \"0.5270854\",\n \"0.526479\",\n \"0.5263746\",\n \"0.52577525\",\n \"0.5254981\",\n \"0.5251318\",\n \"0.5245669\",\n \"0.5229819\",\n \"0.5221815\",\n \"0.52153635\",\n \"0.52136344\",\n \"0.52093625\",\n \"0.52070785\",\n \"0.52057606\",\n \"0.52041376\",\n \"0.5199508\",\n \"0.51983786\",\n \"0.5198092\",\n \"0.5189033\",\n \"0.5189033\",\n \"0.51835513\",\n \"0.5178892\",\n \"0.517851\",\n \"0.5171804\",\n \"0.5170235\",\n \"0.5170235\",\n \"0.516159\",\n \"0.5158302\",\n \"0.51477313\",\n \"0.514373\",\n \"0.51366204\",\n \"0.51287615\",\n \"0.5125696\",\n \"0.51238096\",\n \"0.5123504\",\n \"0.512033\",\n \"0.51191163\",\n \"0.5116422\",\n \"0.51109606\",\n \"0.51096195\",\n \"0.51088214\",\n \"0.51077604\",\n \"0.50995225\",\n \"0.50953025\",\n \"0.5088422\",\n \"0.50838894\",\n \"0.50838894\",\n \"0.5083733\",\n \"0.5083634\",\n \"0.5080677\",\n \"0.507962\",\n \"0.5079039\",\n \"0.50716597\",\n \"0.50673956\",\n \"0.5060057\",\n \"0.505615\",\n \"0.5053063\",\n \"0.5036126\",\n \"0.50358063\",\n \"0.50335115\",\n \"0.50282264\",\n \"0.50266075\",\n \"0.502609\",\n \"0.5024912\",\n \"0.50211364\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.574315"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":17450,"cells":{"query":{"kind":"string","value":"Normalizes string, converts to lowercase, removes nonalpha characters, and converts spaces to hyphens. From Django's \"django/template/defaultfilters.py\"."},"document":{"kind":"string","value":"def _slugify(value):\n import unicodedata\n if not isinstance(value, unicode):\n value = unicode(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(_slugify_strip_re.sub('', value).strip().lower())\n return _slugify_hyphenate_re.sub('-', value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sanitize(str):\n res = str.lower()\n return res.replace(' ', '-')","def normalize(s):\n s = replace_whitespace(s)\n s = remove_dashes(s)\n s = s.lower()\n return s","def sanitize(string):\n retval = string.lower()\n retval = re.sub(r\"[^\\w\\s]\", '', retval)\n retval = re.sub(r\"\\s+\", '_', retval)\n return retval","def preprocess(self, s):\n stripped = re.sub(\"[^\\w\\s]\", \"\", s)\n stripped = re.sub(\"_\", \"\", stripped)\n\n stripped = re.sub(\"\\s+\", \" \", stripped)\n\n stripped = stripped.strip()\n\n return stripped.lower()","def normalize(name):\n name = name.lower()\n name = name.replace('-', '')\n name = name.replace(' ', '')\n return name","def slugify(s):\n s = reg_nonchar.sub('-', s).lower()\n s = reg_dashes.sub('-', s)\n s = reg_outer_dashes.sub('', s)\n return s","def normalise(string):\n return re.sub(' +', ' ', string).lower()","def normalize(self, string):\n\n # If the string is empty, just return it\n if len(string) is 0:\n return string\n\n # Setting all words to lowercase\n string = string.lower()\n\n # Removing punctuation\n if not string[-1].isalnum():\n string = string[:-1]\n\n # Removing words\n string = self.substitute_words(string)\n\n # Returning normalized text\n return string","def normalize(s):\n s = s.lower().replace(u' ', u'-')\n s = u''.join([c for c in s if c in string.letters + string.digits])\n return s","def slugify(value):\n import unicodedata\n import re\n from django.utils.safestring import mark_safe\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n return mark_safe(re.sub('[-\\s]+', '-', value))","def slugify(s):\n\treturn re.sub('[!@#$%^&*()\\\\\\\\/:.\"\"]+', '', s).replace(' ', '-').replace('--', '-').lower()","def normalize(s):\n def remove_articles(text):\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n\n def white_space_fix(text):\n return ' '.join(text.split())\n\n# def remove_punc(text):\n# exclude = set(string.punctuation)\n# return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(lower(s)))","def slugify(value):\n #import unicodedata\n #value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n value = re.sub('[-\\s]+', '-', value)\n return value","def normalize(s):\r\n def remove_articles(text):\r\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\r\n\r\n def white_space_fix(text):\r\n return ' '.join(text.split())\r\n\r\n def remove_punc(text):\r\n exclude = set(string.punctuation)\r\n return ''.join(ch for ch in text if ch not in exclude)\r\n\r\n def lower(text):\r\n return text.lower()\r\n\r\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize(s):\r\n def remove_articles(text):\r\n return re.sub(r'\\b(a|an|the)\\b', ' ', text)\r\n\r\n def white_space_fix(text):\r\n return ' '.join(text.split())\r\n\r\n def remove_punc(text):\r\n exclude = set(string.punctuation)\r\n return ''.join(ch for ch in text if ch not in exclude)\r\n\r\n def lower(text):\r\n return text.lower()\r\n\r\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def slugify(string):\n string = unicodedata.normalize('NFKC', string)\n # remove all non-word characters (except '-')\n string = re.sub(r'[^\\s\\w-]', '', string).strip().lower()\n # replace spaces (or groups of spaces and dashes) with dashes\n string = re.sub(r'[-\\s]+', '-', string)\n return string","def _slugify(value):\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = re.sub(r'[^\\w\\s-]', '', value.decode('utf-8', 'ignore'))\n value = value.strip().lower()\n value = re.sub(r'[-\\s]+', '-', value)\n return value","def _lowercase_despace_depunctuate(some_str=None):\n some_str = some_str.replace(\" \", \"\")\n some_str = some_str.replace(\"_\", \"\")\n some_str = some_str.replace(\"-\", \"\")\n some_str = some_str.lower()\n return some_str","def slugify(value):\n value = unicode(str(value))\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n return re.sub(r'[-\\s]+', '-', value)","def standardize(text):\n # FIXME regex restricts us to only ascii\n # FIXME move regex compilation outside\n p = re.compile('[^a-zA-Z]')\n retval = p.sub('', text)\n retval = retval.lower()\n return retval","def slugify(value):\n # import unicodedata\n # value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n # value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n # value = unicode(re.sub('[-\\s]+', '-', value))\n value = re.sub('[^\\w\\s-]', '', value).strip().lower()\n value = re.sub('[-\\s]+', '-', value)\n return value","def slugify(value):\n if type(value) == unicode:\n import unicodedata\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n return re.sub('[-\\s]+', '-', value)","def slugify2(value):\n try:\n value = unicodedata.normalize('NFC', value)\n value = downcode(value)\n value = unicodedata.normalize('NFD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n return safestring.mark_safe(re.sub('[-\\s]+', '-', value))\n except:\n if settings.TEMPLATE_DEBUG:\n raise\n else:\n return u''","def slugify(value):\n value = unicode(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub('[^\\w\\s-]', '', value).strip().lower()\n return re.sub('[-\\s]+', '-', value)","def slugify(self, value, allow_unicode=False):\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value.lower()).strip()\n return re.sub(r'[-\\s]+', '-', value)","def _slugify(value):\r\n try:\r\n import unicodedata\r\n value = unicodedata.normalize('NFKD', value)\r\n except ImportError:\r\n pass\r\n value = value.encode('ascii', 'ignore').decode()\r\n value = _slugify_strip_re.sub('', value).strip().lower()\r\n return _slugify_hyphenate_re.sub('-', value)","def super_flat(cls, s):\n if s is None:\n return ''\n\n return cls.sql_safe(cls.slugify(s).upper().replace('-', ''))","def slugify(value):\n import unicodedata\n value = unicode(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n value = unicode(re.sub('[-\\s]+', '-', value))\n return value","def slugify(value):\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = str(re.sub('[^\\w\\s-]', '', str(value)).strip().lower())\n return re.sub('[-\\s]+', '-', str(value))","def preprocess(string):\n cleaned = regex.sub(\" \", string)\n return cleaned.lower()","def sanitize(value):\n from re import sub\n from unicodedata import normalize\n value = normalize('NFKD', value).encode('ascii', 'ignore')\n value = sub('[^\\w\\s\\.-]', '', value.decode('utf-8')).strip().lower()\n return sub('[-_\\s]+', '_', value)","def slugify(value):\n return '_' + value.replace(' ', '-').lower()","def slugify(text):\n concatenated = re.sub('\\s+', '-', text.lower())\n return re.sub('[^A-Za-z0-9_-]', '', concatenated)","def slugify(text: str) -> str:\n return text.strip().replace(', ', '-').replace(' ', '_').lower()","def slugify(s):\n s = s.lower()\n\n for c in [' ', '-', '.', '/']:\n s = s.replace(c, '_')\n\n s = re.sub('\\W', '', s)\n s = s.replace('_', ' ')\n s = re.sub('\\s+', ' ', s)\n s = s.strip()\n s = s.replace(' ', '-')\n\n return s","def slugify(value):\n value = re.sub('[^\\w\\s-]', '', value).strip()\n return re.sub('[-\\s]+', '-', value)","def slugify(value: str) -> str:\n value = re.sub(r\"[^\\w\\s-]\", \"\", value.lower())\n return re.sub(r\"[-\\s]+\", \"-\", value).strip(\"-_\")","def normalize(string):\n\n normalized = []\n\n for ch in string:\n if ch.isalpha():\n normalized.append(ch.lower())\n\n target = \"\".join(normalized)\n return target","def normalize_word(word):\n\n return word.lower()","def _format(string):\n return str(filter(str.isalnum, string)).lower()","def _normalize_text(s: str) ->str:\n\n def remove_articles(text: str) ->str:\n return re.sub('\\\\b(a|an|the)\\\\b', ' ', text)\n\n def white_space_fix(text: str) ->str:\n return ' '.join(text.split())\n\n def remove_punc(text: str) ->str:\n exclude = set(string.punctuation)\n return ''.join(ch for ch in text if ch not in exclude)\n\n def lower(text: str) ->str:\n return text.lower()\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def clean_str(string):\n return string.strip().lower()","def slugify(s: str) -> str:\n\n return re.sub(re_forbidden, ' ', s.strip()).strip().replace(' ', '-')","def slugify(value, allow_unicode=False):\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n return re.sub(r'[-\\s]+', '-', value)","def slugify(value, allow_unicode=False):\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n return re.sub(r'[-\\s]+', '-', value)","def slugify(s):\n from future.builtins import str\n return django_slugify(unidecode(str(s)))","def slugify(value, allow_unicode=False):\n try: # PY2\n value = unicode(value)\n except NameError: # PY3\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize(\"NFKC\", value)\n else:\n value = (\n unicodedata.normalize(\"NFKD\", value)\n .encode(\"ascii\", \"ignore\")\n .decode(\"ascii\")\n )\n value = re.sub(r\"[^\\w\\s-]\", \"\", value).strip().lower()\n return re.sub(r\"[-\\s]+\", \"-\", value)","def slugify(name):\n return name.lower().strip().replace(' ', '-').replace('+', '_')","def slugify(value, allow_unicode=False):\n value\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n\n return re.sub(r'[-\\s]+', '-', value)","def normalize_text(s):\n import string, re\n\n def remove_articles(text):\n regex = re.compile(r\"\\b(a|an|the)\\b\", re.UNICODE)\n return re.sub(regex, \" \", text)\n\n def white_space_fix(text):\n return \" \".join(text.split())\n\n def remove_punc(text):\n exclude = set(string.punctuation)\n return \"\".join(ch for ch in text if ch not in exclude)\n\n def lower(text):\n return text.lower()\n\n return white_space_fix(remove_articles(remove_punc(lower(s))))","def normalize_text(w):\n return str(w, \"utf-8\").lower().replace(\"-\", \"\")","def slugify(value, allow_unicode=False):\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode(\n 'ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value.lower()).strip()\n return re.sub(r'[-\\s]+', '-', value)","def filter_lowercase(self, string):\n newstring = string.lower()\n return newstring","def string_to_onlyalpha(string):\n string.replace(\"-\", \" \")\n string.replace(\"'\", \"\")\n valids = re.sub(r\"[^A-Za-z ]+\", ' ', string)\n return valids.lower()","def to_clean_str(s: str) -> str:\n return re.sub(\"[^a-zA-Z0-9]\", \"\", s).lower()","def clean_data(s):\n s = s.strip()\n s = s.lower()\n return s","def normalize_string(self, my_string):\n return (\n unicodedata.normalize(\"NFKD\", my_string)\n .encode(\"ASCII\", \"ignore\")\n .decode()\n .replace(\" \", \"_\")\n .lower()\n )","def slugified(text):\n return re.sub(\"-+\", \"-\", re.sub(r\"\\W\", \"-\", text.lower()))","def sanitaze(field):\n return re.sub('[^0-9a-zA-Z]+', '-', str(field))","def normalize(item):\n item = item.lower().strip().rstrip('_')\n return item","def normalize(string: str) -> str:\n # Make lowercase, replace natural delimiters with spaces\n s = string.lower().replace('\\n', ' ').replace('\\t', ' ')\n\n # Remove characters other than the alphabet and spaces (filling with space)\n s = re.sub(pattern=r'[^a-z\\s]+', repl=' ', string=s, count=0)\n\n # Convert multiple spaces into single spaces\n s = re.sub(pattern=r'\\s+', repl=' ', string=s, count=0)\n return s.strip()","def _sanitizeName(name):\n\n name = name.lower() # lower.\n name = name.replace('.','') # remove periods.\n name = name.replace('-','') # remove dashes.\n name = name.replace(\"'\",'') # remove apostrophies.\n # return it.\n return name","def namify(text):\n return slugify(text).replace('-','_')","def sanitize_txt(x):\n return '_'.join(smart_split(x.lower()))","def normalize_case(text):\n text = str(text)\n return text.lower()","def normalize_hospital_name(name):\n normalized_name = name.lower()\n\n stopword_list = stopwords.words('english')\n filtered_words = [word for word in wordpunct_tokenize(normalized_name) if word not in stopword_list]\n\n slug = slugify(' '.join(filtered_words))\n\n return slug","def xslugify(value):\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub('[^\\w\\s/-]', '', value).strip().lower()\n return mark_safe(re.sub('[-\\s/]+', '-', value))","def cleanfieldlower(value):\n if not value:\n return None\n value = str(value)\n value = value.strip()\n value = value.lower()\n return value","def slugify(self, space='-'):\n #strtitle = unicode(self.title)\n strtitle = self.title\n import unicodedata\n import re\n strtitle = unicodedata.normalize('NFKD', strtitle).encode('ascii', 'ignore')\n strtitle = unicode(re.sub('[^\\w\\s-]', '', strtitle).strip().lower())\n return re.sub('[-\\s]+', space, strtitle)","def preprocess(text):\n return text.lower()","def normalize(input_str):\n filtered_input = []\n #check if string input\n if isinstance(input_str, basestring):\n input_str = input_str.strip()\n input_str = tokenize(input_str)\n for word in input_str:\n word = word.lower()\n filtered_input.append(word)\n return \" \".join(filtered_input)","def sluggify(cls, name: str) -> str:\n return \"-\".join([word for word in cls.split_on_non_word.split(name.strip().lower()) if word])","def _sanitize(self, name, camel_case=False):\n\n if camel_case:\n return self.NO_SPACES.sub('', name.title())\n return self.ALLOW_SPACES.sub('', name)","def normalise(str_val, casing='lower'):\n norm_val = None\n try:\n clean_val = clean(str_val)\n if casing == 'lower':\n norm_val = clean_val.lower()\n elif casing == 'upper':\n norm_val = clean_val.upper()\n else:\n norm_val = clean_val\n except Exception as e:\n pass\n return norm_val","def normalize(val):\n \n if val.find('-') != -1:\n val = val.replace('-','_')\n\n return val","def scrub_string(str):\n return \"\".join(ch for ch in str if ch.isalpha() or ch.isdigit()).lower()","def replace_bad_characters(self, str):\n\n str = unicode(BeautifulStoneSoup(str,\n convertEntities=BeautifulStoneSoup.HTML_ENTITIES))\n str = unicodedata.normalize('NFKD', str).encode('ascii', 'ignore')\n str = unicode(re.sub('[^\\w\\s-]', '', str).strip().lower())\n str = unicode(str.replace(' ', '-'))\n return str","def slugify(s):\n slug = s.lower() # force to lower case\n slug = re.sub('[^a-z0-9 -]', '', slug) # remove invalid chars\n slug = re.sub(r'\\s+', '-', slug) # collapse whitespace and replace by -\n slug = re.sub('-+', '-', slug) # collapse dashes\n if not slug:\n slug = 'untitled'\n return slug","def fixString(string):\n string = re.sub(r\"[^A-Z-]\", \"\", string)\n return string","def sanitize(mystr):\n retainlist = \"_-\"\n return re.sub(r'[^\\w' + retainlist + ']', '_', mystr)","def normalize(self, s):\n s = normalizing_regexp.sub('_', s)\n if s[0:1] in string.digits:\n s = '_' + s\n return s","def clean_name(s):\n return re.sub('[\\W_]+', '', s).lower()","def normalize_text(text):\n return normalize_case(normalize_punctuation(text))","def slugify(text):\n # Sorry: This is really goofy, but it was really easy to write and\n # \"good enough\".\n text = ''.join([c if c.isalnum() else '-' for c in text.lower()])\n\n while '--' in text:\n text = text.replace('--', '-')\n\n text = text.strip('-')\n\n return text","def normalize_name(name):\n return PUNCT_RE.sub('-', name.lower()).strip('-')","def get_normalised_phrase(self, sentence):\n return re.sub(r'[\\W_ ]+', ' ', sentence).lower()","def clean_word(self, word):\n return self.filter_pattern.sub(u'', word.lower())","def cleaning(string):\n\n if type(string) == float or type(string) == int:\n return string\n res = ''\n if string != string:\n return string\n string = string.replace(\"\\\\r\", \"\")\n string = string.replace(\"\\\\n\", \"\")\n string = string.replace(\"\\\\b\", \"\")\n string = string.replace(\"\\\\t\", \"\")\n for i in string:\n if i.isalpha():\n res = res + i\n return res.lower()","def slugify(value):\n import unicodedata\n try:\n unicode_type = unicode\n except NameError:\n unicode_type = str\n if not isinstance(value, unicode_type):\n value = unicode_type(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = unicode_type(_slugify_strip_re.sub('', value).strip())\n return _slugify_hyphenate_re.sub('_', value)","def mix_string (str):\n return str.replace(' ', '_').lower()","def normalize(string):\n string = string.replace('E', 'e')\n string = re.sub('(?<!e)-', ' -', string)\n string = re.sub('[ \\n\\r\\t,]+', ' ', string)\n string = re.sub(r'(\\.[0-9-]+)(?=\\.)', r'\\1 ', string)\n return string.strip()","def slugify(value, substitutions=()):\r\n # TODO Maybe steal again from current Django 1.5dev\r\n value = Markup(value).striptags()\r\n # value must be unicode per se\r\n import unicodedata\r\n from unidecode import unidecode\r\n # unidecode returns str in Py2 and 3, so in Py2 we have to make\r\n # it unicode again\r\n value = unidecode(value)\r\n if isinstance(value, six.binary_type):\r\n value = value.decode('ascii')\r\n # still unicode\r\n value = unicodedata.normalize('NFKD', value).lower()\r\n for src, dst in substitutions:\r\n value = value.replace(src.lower(), dst.lower())\r\n value = re.sub('[^\\w\\s-]', '', value).strip()\r\n value = re.sub('[-\\s]+', '-', value)\r\n # we want only ASCII chars\r\n value = value.encode('ascii', 'ignore')\r\n # but Pelican should generally use only unicode\r\n return value.decode('ascii')","def normalize_text(text):\n # mystring.replace('\\n', ' ').replace('\\r', '')\n return text.replace('\\n', ' ').replace('\\r', '').lower()","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()","def normalize_username(value):\n return value.lower()","def normalize(text):\n return text.lower().translate(TRANSLATION_TABLE)","def lower_replace(value):\n return value.lower().replace(\" \",\"_\")","def basic_cleaning2(string):\n\n string = string.lower()\n string = re.sub('[0-9\\(\\)\\!\\^\\%\\$\\'\\\"\\.;,-\\?\\{\\}\\[\\]\\\\/]', ' ', string)\n string = re.sub(' +', ' ', string)\n return string","def normalize(word):\n word = word.strip().lower()\n return ''.join(sorted(word))","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string) \n string = re.sub(r\"\\s{2,}\", \" \", string) \n return string.strip().lower()"],"string":"[\n \"def sanitize(str):\\n res = str.lower()\\n return res.replace(' ', '-')\",\n \"def normalize(s):\\n s = replace_whitespace(s)\\n s = remove_dashes(s)\\n s = s.lower()\\n return s\",\n \"def sanitize(string):\\n retval = string.lower()\\n retval = re.sub(r\\\"[^\\\\w\\\\s]\\\", '', retval)\\n retval = re.sub(r\\\"\\\\s+\\\", '_', retval)\\n return retval\",\n \"def preprocess(self, s):\\n stripped = re.sub(\\\"[^\\\\w\\\\s]\\\", \\\"\\\", s)\\n stripped = re.sub(\\\"_\\\", \\\"\\\", stripped)\\n\\n stripped = re.sub(\\\"\\\\s+\\\", \\\" \\\", stripped)\\n\\n stripped = stripped.strip()\\n\\n return stripped.lower()\",\n \"def normalize(name):\\n name = name.lower()\\n name = name.replace('-', '')\\n name = name.replace(' ', '')\\n return name\",\n \"def slugify(s):\\n s = reg_nonchar.sub('-', s).lower()\\n s = reg_dashes.sub('-', s)\\n s = reg_outer_dashes.sub('', s)\\n return s\",\n \"def normalise(string):\\n return re.sub(' +', ' ', string).lower()\",\n \"def normalize(self, string):\\n\\n # If the string is empty, just return it\\n if len(string) is 0:\\n return string\\n\\n # Setting all words to lowercase\\n string = string.lower()\\n\\n # Removing punctuation\\n if not string[-1].isalnum():\\n string = string[:-1]\\n\\n # Removing words\\n string = self.substitute_words(string)\\n\\n # Returning normalized text\\n return string\",\n \"def normalize(s):\\n s = s.lower().replace(u' ', u'-')\\n s = u''.join([c for c in s if c in string.letters + string.digits])\\n return s\",\n \"def slugify(value):\\n import unicodedata\\n import re\\n from django.utils.safestring import mark_safe\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n return mark_safe(re.sub('[-\\\\s]+', '-', value))\",\n \"def slugify(s):\\n\\treturn re.sub('[!@#$%^&*()\\\\\\\\\\\\\\\\/:.\\\"\\\"]+', '', s).replace(' ', '-').replace('--', '-').lower()\",\n \"def normalize(s):\\n def remove_articles(text):\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\n\\n def white_space_fix(text):\\n return ' '.join(text.split())\\n\\n# def remove_punc(text):\\n# exclude = set(string.punctuation)\\n# return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(lower(s)))\",\n \"def slugify(value):\\n #import unicodedata\\n #value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n value = re.sub('[-\\\\s]+', '-', value)\\n return value\",\n \"def normalize(s):\\r\\n def remove_articles(text):\\r\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\r\\n\\r\\n def white_space_fix(text):\\r\\n return ' '.join(text.split())\\r\\n\\r\\n def remove_punc(text):\\r\\n exclude = set(string.punctuation)\\r\\n return ''.join(ch for ch in text if ch not in exclude)\\r\\n\\r\\n def lower(text):\\r\\n return text.lower()\\r\\n\\r\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize(s):\\r\\n def remove_articles(text):\\r\\n return re.sub(r'\\\\b(a|an|the)\\\\b', ' ', text)\\r\\n\\r\\n def white_space_fix(text):\\r\\n return ' '.join(text.split())\\r\\n\\r\\n def remove_punc(text):\\r\\n exclude = set(string.punctuation)\\r\\n return ''.join(ch for ch in text if ch not in exclude)\\r\\n\\r\\n def lower(text):\\r\\n return text.lower()\\r\\n\\r\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def slugify(string):\\n string = unicodedata.normalize('NFKC', string)\\n # remove all non-word characters (except '-')\\n string = re.sub(r'[^\\\\s\\\\w-]', '', string).strip().lower()\\n # replace spaces (or groups of spaces and dashes) with dashes\\n string = re.sub(r'[-\\\\s]+', '-', string)\\n return string\",\n \"def _slugify(value):\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value.decode('utf-8', 'ignore'))\\n value = value.strip().lower()\\n value = re.sub(r'[-\\\\s]+', '-', value)\\n return value\",\n \"def _lowercase_despace_depunctuate(some_str=None):\\n some_str = some_str.replace(\\\" \\\", \\\"\\\")\\n some_str = some_str.replace(\\\"_\\\", \\\"\\\")\\n some_str = some_str.replace(\\\"-\\\", \\\"\\\")\\n some_str = some_str.lower()\\n return some_str\",\n \"def slugify(value):\\n value = unicode(str(value))\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def standardize(text):\\n # FIXME regex restricts us to only ascii\\n # FIXME move regex compilation outside\\n p = re.compile('[^a-zA-Z]')\\n retval = p.sub('', text)\\n retval = retval.lower()\\n return retval\",\n \"def slugify(value):\\n # import unicodedata\\n # value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n # value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n # value = unicode(re.sub('[-\\\\s]+', '-', value))\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip().lower()\\n value = re.sub('[-\\\\s]+', '-', value)\\n return value\",\n \"def slugify(value):\\n if type(value) == unicode:\\n import unicodedata\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n return re.sub('[-\\\\s]+', '-', value)\",\n \"def slugify2(value):\\n try:\\n value = unicodedata.normalize('NFC', value)\\n value = downcode(value)\\n value = unicodedata.normalize('NFD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n return safestring.mark_safe(re.sub('[-\\\\s]+', '-', value))\\n except:\\n if settings.TEMPLATE_DEBUG:\\n raise\\n else:\\n return u''\",\n \"def slugify(value):\\n value = unicode(value)\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub('[-\\\\s]+', '-', value)\",\n \"def slugify(self, value, allow_unicode=False):\\n value = str(value)\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value.lower()).strip()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def _slugify(value):\\r\\n try:\\r\\n import unicodedata\\r\\n value = unicodedata.normalize('NFKD', value)\\r\\n except ImportError:\\r\\n pass\\r\\n value = value.encode('ascii', 'ignore').decode()\\r\\n value = _slugify_strip_re.sub('', value).strip().lower()\\r\\n return _slugify_hyphenate_re.sub('-', value)\",\n \"def super_flat(cls, s):\\n if s is None:\\n return ''\\n\\n return cls.sql_safe(cls.slugify(s).upper().replace('-', ''))\",\n \"def slugify(value):\\n import unicodedata\\n value = unicode(value)\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n value = unicode(re.sub('[-\\\\s]+', '-', value))\\n return value\",\n \"def slugify(value):\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = str(re.sub('[^\\\\w\\\\s-]', '', str(value)).strip().lower())\\n return re.sub('[-\\\\s]+', '-', str(value))\",\n \"def preprocess(string):\\n cleaned = regex.sub(\\\" \\\", string)\\n return cleaned.lower()\",\n \"def sanitize(value):\\n from re import sub\\n from unicodedata import normalize\\n value = normalize('NFKD', value).encode('ascii', 'ignore')\\n value = sub('[^\\\\w\\\\s\\\\.-]', '', value.decode('utf-8')).strip().lower()\\n return sub('[-_\\\\s]+', '_', value)\",\n \"def slugify(value):\\n return '_' + value.replace(' ', '-').lower()\",\n \"def slugify(text):\\n concatenated = re.sub('\\\\s+', '-', text.lower())\\n return re.sub('[^A-Za-z0-9_-]', '', concatenated)\",\n \"def slugify(text: str) -> str:\\n return text.strip().replace(', ', '-').replace(' ', '_').lower()\",\n \"def slugify(s):\\n s = s.lower()\\n\\n for c in [' ', '-', '.', '/']:\\n s = s.replace(c, '_')\\n\\n s = re.sub('\\\\W', '', s)\\n s = s.replace('_', ' ')\\n s = re.sub('\\\\s+', ' ', s)\\n s = s.strip()\\n s = s.replace(' ', '-')\\n\\n return s\",\n \"def slugify(value):\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip()\\n return re.sub('[-\\\\s]+', '-', value)\",\n \"def slugify(value: str) -> str:\\n value = re.sub(r\\\"[^\\\\w\\\\s-]\\\", \\\"\\\", value.lower())\\n return re.sub(r\\\"[-\\\\s]+\\\", \\\"-\\\", value).strip(\\\"-_\\\")\",\n \"def normalize(string):\\n\\n normalized = []\\n\\n for ch in string:\\n if ch.isalpha():\\n normalized.append(ch.lower())\\n\\n target = \\\"\\\".join(normalized)\\n return target\",\n \"def normalize_word(word):\\n\\n return word.lower()\",\n \"def _format(string):\\n return str(filter(str.isalnum, string)).lower()\",\n \"def _normalize_text(s: str) ->str:\\n\\n def remove_articles(text: str) ->str:\\n return re.sub('\\\\\\\\b(a|an|the)\\\\\\\\b', ' ', text)\\n\\n def white_space_fix(text: str) ->str:\\n return ' '.join(text.split())\\n\\n def remove_punc(text: str) ->str:\\n exclude = set(string.punctuation)\\n return ''.join(ch for ch in text if ch not in exclude)\\n\\n def lower(text: str) ->str:\\n return text.lower()\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def clean_str(string):\\n return string.strip().lower()\",\n \"def slugify(s: str) -> str:\\n\\n return re.sub(re_forbidden, ' ', s.strip()).strip().replace(' ', '-')\",\n \"def slugify(value, allow_unicode=False):\\n value = str(value)\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def slugify(value, allow_unicode=False):\\n value = str(value)\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def slugify(s):\\n from future.builtins import str\\n return django_slugify(unidecode(str(s)))\",\n \"def slugify(value, allow_unicode=False):\\n try: # PY2\\n value = unicode(value)\\n except NameError: # PY3\\n value = str(value)\\n if allow_unicode:\\n value = unicodedata.normalize(\\\"NFKC\\\", value)\\n else:\\n value = (\\n unicodedata.normalize(\\\"NFKD\\\", value)\\n .encode(\\\"ascii\\\", \\\"ignore\\\")\\n .decode(\\\"ascii\\\")\\n )\\n value = re.sub(r\\\"[^\\\\w\\\\s-]\\\", \\\"\\\", value).strip().lower()\\n return re.sub(r\\\"[-\\\\s]+\\\", \\\"-\\\", value)\",\n \"def slugify(name):\\n return name.lower().strip().replace(' ', '-').replace('+', '_')\",\n \"def slugify(value, allow_unicode=False):\\n value\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def normalize_text(s):\\n import string, re\\n\\n def remove_articles(text):\\n regex = re.compile(r\\\"\\\\b(a|an|the)\\\\b\\\", re.UNICODE)\\n return re.sub(regex, \\\" \\\", text)\\n\\n def white_space_fix(text):\\n return \\\" \\\".join(text.split())\\n\\n def remove_punc(text):\\n exclude = set(string.punctuation)\\n return \\\"\\\".join(ch for ch in text if ch not in exclude)\\n\\n def lower(text):\\n return text.lower()\\n\\n return white_space_fix(remove_articles(remove_punc(lower(s))))\",\n \"def normalize_text(w):\\n return str(w, \\\"utf-8\\\").lower().replace(\\\"-\\\", \\\"\\\")\",\n \"def slugify(value, allow_unicode=False):\\n value = str(value)\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode(\\n 'ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value.lower()).strip()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def filter_lowercase(self, string):\\n newstring = string.lower()\\n return newstring\",\n \"def string_to_onlyalpha(string):\\n string.replace(\\\"-\\\", \\\" \\\")\\n string.replace(\\\"'\\\", \\\"\\\")\\n valids = re.sub(r\\\"[^A-Za-z ]+\\\", ' ', string)\\n return valids.lower()\",\n \"def to_clean_str(s: str) -> str:\\n return re.sub(\\\"[^a-zA-Z0-9]\\\", \\\"\\\", s).lower()\",\n \"def clean_data(s):\\n s = s.strip()\\n s = s.lower()\\n return s\",\n \"def normalize_string(self, my_string):\\n return (\\n unicodedata.normalize(\\\"NFKD\\\", my_string)\\n .encode(\\\"ASCII\\\", \\\"ignore\\\")\\n .decode()\\n .replace(\\\" \\\", \\\"_\\\")\\n .lower()\\n )\",\n \"def slugified(text):\\n return re.sub(\\\"-+\\\", \\\"-\\\", re.sub(r\\\"\\\\W\\\", \\\"-\\\", text.lower()))\",\n \"def sanitaze(field):\\n return re.sub('[^0-9a-zA-Z]+', '-', str(field))\",\n \"def normalize(item):\\n item = item.lower().strip().rstrip('_')\\n return item\",\n \"def normalize(string: str) -> str:\\n # Make lowercase, replace natural delimiters with spaces\\n s = string.lower().replace('\\\\n', ' ').replace('\\\\t', ' ')\\n\\n # Remove characters other than the alphabet and spaces (filling with space)\\n s = re.sub(pattern=r'[^a-z\\\\s]+', repl=' ', string=s, count=0)\\n\\n # Convert multiple spaces into single spaces\\n s = re.sub(pattern=r'\\\\s+', repl=' ', string=s, count=0)\\n return s.strip()\",\n \"def _sanitizeName(name):\\n\\n name = name.lower() # lower.\\n name = name.replace('.','') # remove periods.\\n name = name.replace('-','') # remove dashes.\\n name = name.replace(\\\"'\\\",'') # remove apostrophies.\\n # return it.\\n return name\",\n \"def namify(text):\\n return slugify(text).replace('-','_')\",\n \"def sanitize_txt(x):\\n return '_'.join(smart_split(x.lower()))\",\n \"def normalize_case(text):\\n text = str(text)\\n return text.lower()\",\n \"def normalize_hospital_name(name):\\n normalized_name = name.lower()\\n\\n stopword_list = stopwords.words('english')\\n filtered_words = [word for word in wordpunct_tokenize(normalized_name) if word not in stopword_list]\\n\\n slug = slugify(' '.join(filtered_words))\\n\\n return slug\",\n \"def xslugify(value):\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub('[^\\\\w\\\\s/-]', '', value).strip().lower()\\n return mark_safe(re.sub('[-\\\\s/]+', '-', value))\",\n \"def cleanfieldlower(value):\\n if not value:\\n return None\\n value = str(value)\\n value = value.strip()\\n value = value.lower()\\n return value\",\n \"def slugify(self, space='-'):\\n #strtitle = unicode(self.title)\\n strtitle = self.title\\n import unicodedata\\n import re\\n strtitle = unicodedata.normalize('NFKD', strtitle).encode('ascii', 'ignore')\\n strtitle = unicode(re.sub('[^\\\\w\\\\s-]', '', strtitle).strip().lower())\\n return re.sub('[-\\\\s]+', space, strtitle)\",\n \"def preprocess(text):\\n return text.lower()\",\n \"def normalize(input_str):\\n filtered_input = []\\n #check if string input\\n if isinstance(input_str, basestring):\\n input_str = input_str.strip()\\n input_str = tokenize(input_str)\\n for word in input_str:\\n word = word.lower()\\n filtered_input.append(word)\\n return \\\" \\\".join(filtered_input)\",\n \"def sluggify(cls, name: str) -> str:\\n return \\\"-\\\".join([word for word in cls.split_on_non_word.split(name.strip().lower()) if word])\",\n \"def _sanitize(self, name, camel_case=False):\\n\\n if camel_case:\\n return self.NO_SPACES.sub('', name.title())\\n return self.ALLOW_SPACES.sub('', name)\",\n \"def normalise(str_val, casing='lower'):\\n norm_val = None\\n try:\\n clean_val = clean(str_val)\\n if casing == 'lower':\\n norm_val = clean_val.lower()\\n elif casing == 'upper':\\n norm_val = clean_val.upper()\\n else:\\n norm_val = clean_val\\n except Exception as e:\\n pass\\n return norm_val\",\n \"def normalize(val):\\n \\n if val.find('-') != -1:\\n val = val.replace('-','_')\\n\\n return val\",\n \"def scrub_string(str):\\n return \\\"\\\".join(ch for ch in str if ch.isalpha() or ch.isdigit()).lower()\",\n \"def replace_bad_characters(self, str):\\n\\n str = unicode(BeautifulStoneSoup(str,\\n convertEntities=BeautifulStoneSoup.HTML_ENTITIES))\\n str = unicodedata.normalize('NFKD', str).encode('ascii', 'ignore')\\n str = unicode(re.sub('[^\\\\w\\\\s-]', '', str).strip().lower())\\n str = unicode(str.replace(' ', '-'))\\n return str\",\n \"def slugify(s):\\n slug = s.lower() # force to lower case\\n slug = re.sub('[^a-z0-9 -]', '', slug) # remove invalid chars\\n slug = re.sub(r'\\\\s+', '-', slug) # collapse whitespace and replace by -\\n slug = re.sub('-+', '-', slug) # collapse dashes\\n if not slug:\\n slug = 'untitled'\\n return slug\",\n \"def fixString(string):\\n string = re.sub(r\\\"[^A-Z-]\\\", \\\"\\\", string)\\n return string\",\n \"def sanitize(mystr):\\n retainlist = \\\"_-\\\"\\n return re.sub(r'[^\\\\w' + retainlist + ']', '_', mystr)\",\n \"def normalize(self, s):\\n s = normalizing_regexp.sub('_', s)\\n if s[0:1] in string.digits:\\n s = '_' + s\\n return s\",\n \"def clean_name(s):\\n return re.sub('[\\\\W_]+', '', s).lower()\",\n \"def normalize_text(text):\\n return normalize_case(normalize_punctuation(text))\",\n \"def slugify(text):\\n # Sorry: This is really goofy, but it was really easy to write and\\n # \\\"good enough\\\".\\n text = ''.join([c if c.isalnum() else '-' for c in text.lower()])\\n\\n while '--' in text:\\n text = text.replace('--', '-')\\n\\n text = text.strip('-')\\n\\n return text\",\n \"def normalize_name(name):\\n return PUNCT_RE.sub('-', name.lower()).strip('-')\",\n \"def get_normalised_phrase(self, sentence):\\n return re.sub(r'[\\\\W_ ]+', ' ', sentence).lower()\",\n \"def clean_word(self, word):\\n return self.filter_pattern.sub(u'', word.lower())\",\n \"def cleaning(string):\\n\\n if type(string) == float or type(string) == int:\\n return string\\n res = ''\\n if string != string:\\n return string\\n string = string.replace(\\\"\\\\\\\\r\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\n\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\b\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\t\\\", \\\"\\\")\\n for i in string:\\n if i.isalpha():\\n res = res + i\\n return res.lower()\",\n \"def slugify(value):\\n import unicodedata\\n try:\\n unicode_type = unicode\\n except NameError:\\n unicode_type = str\\n if not isinstance(value, unicode_type):\\n value = unicode_type(value)\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = unicode_type(_slugify_strip_re.sub('', value).strip())\\n return _slugify_hyphenate_re.sub('_', value)\",\n \"def mix_string (str):\\n return str.replace(' ', '_').lower()\",\n \"def normalize(string):\\n string = string.replace('E', 'e')\\n string = re.sub('(?<!e)-', ' -', string)\\n string = re.sub('[ \\\\n\\\\r\\\\t,]+', ' ', string)\\n string = re.sub(r'(\\\\.[0-9-]+)(?=\\\\.)', r'\\\\1 ', string)\\n return string.strip()\",\n \"def slugify(value, substitutions=()):\\r\\n # TODO Maybe steal again from current Django 1.5dev\\r\\n value = Markup(value).striptags()\\r\\n # value must be unicode per se\\r\\n import unicodedata\\r\\n from unidecode import unidecode\\r\\n # unidecode returns str in Py2 and 3, so in Py2 we have to make\\r\\n # it unicode again\\r\\n value = unidecode(value)\\r\\n if isinstance(value, six.binary_type):\\r\\n value = value.decode('ascii')\\r\\n # still unicode\\r\\n value = unicodedata.normalize('NFKD', value).lower()\\r\\n for src, dst in substitutions:\\r\\n value = value.replace(src.lower(), dst.lower())\\r\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip()\\r\\n value = re.sub('[-\\\\s]+', '-', value)\\r\\n # we want only ASCII chars\\r\\n value = value.encode('ascii', 'ignore')\\r\\n # but Pelican should generally use only unicode\\r\\n return value.decode('ascii')\",\n \"def normalize_text(text):\\n # mystring.replace('\\\\n', ' ').replace('\\\\r', '')\\n return text.replace('\\\\n', ' ').replace('\\\\r', '').lower()\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\",\n \"def normalize_username(value):\\n return value.lower()\",\n \"def normalize(text):\\n return text.lower().translate(TRANSLATION_TABLE)\",\n \"def lower_replace(value):\\n return value.lower().replace(\\\" \\\",\\\"_\\\")\",\n \"def basic_cleaning2(string):\\n\\n string = string.lower()\\n string = re.sub('[0-9\\\\(\\\\)\\\\!\\\\^\\\\%\\\\$\\\\'\\\\\\\"\\\\.;,-\\\\?\\\\{\\\\}\\\\[\\\\]\\\\\\\\/]', ' ', string)\\n string = re.sub(' +', ' ', string)\\n return string\",\n \"def normalize(word):\\n word = word.strip().lower()\\n return ''.join(sorted(word))\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string) \\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string) \\n return string.strip().lower()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8050005","0.7715769","0.7337892","0.7264596","0.7262135","0.7178926","0.7171534","0.714335","0.71391594","0.70830053","0.7070548","0.70457333","0.70426863","0.70101064","0.70101064","0.6989748","0.69828415","0.69823","0.69683266","0.69340587","0.69105923","0.69095606","0.69028836","0.68923014","0.68757033","0.6871821","0.6866692","0.6853498","0.68375236","0.6820818","0.6818862","0.6810878","0.67948574","0.6781245","0.6740451","0.67361176","0.6730431","0.6721456","0.6709893","0.6701637","0.6698458","0.669402","0.6684164","0.6677916","0.6677916","0.66597736","0.6659751","0.66571426","0.6651366","0.66154593","0.6607266","0.6594125","0.6590065","0.6578998","0.6572598","0.6566313","0.655975","0.6559497","0.64909726","0.648074","0.64709556","0.64678323","0.6465402","0.64599174","0.6449587","0.6440436","0.6431201","0.6429895","0.6427523","0.64225894","0.6421407","0.64207923","0.6408828","0.64007634","0.63622165","0.63496786","0.6348559","0.6347621","0.6344241","0.6342479","0.6339647","0.63361746","0.63349813","0.6332759","0.6332414","0.63274235","0.63163656","0.6306338","0.63052803","0.6297278","0.62876356","0.6284665","0.6284373","0.6275616","0.62746453","0.6266429","0.6262571","0.6260502","0.6244802","0.62445766"],"string":"[\n \"0.8050005\",\n \"0.7715769\",\n \"0.7337892\",\n \"0.7264596\",\n \"0.7262135\",\n \"0.7178926\",\n \"0.7171534\",\n \"0.714335\",\n \"0.71391594\",\n \"0.70830053\",\n \"0.7070548\",\n \"0.70457333\",\n \"0.70426863\",\n \"0.70101064\",\n \"0.70101064\",\n \"0.6989748\",\n \"0.69828415\",\n \"0.69823\",\n \"0.69683266\",\n \"0.69340587\",\n \"0.69105923\",\n \"0.69095606\",\n \"0.69028836\",\n \"0.68923014\",\n \"0.68757033\",\n \"0.6871821\",\n \"0.6866692\",\n \"0.6853498\",\n \"0.68375236\",\n \"0.6820818\",\n \"0.6818862\",\n \"0.6810878\",\n \"0.67948574\",\n \"0.6781245\",\n \"0.6740451\",\n \"0.67361176\",\n \"0.6730431\",\n \"0.6721456\",\n \"0.6709893\",\n \"0.6701637\",\n \"0.6698458\",\n \"0.669402\",\n \"0.6684164\",\n \"0.6677916\",\n \"0.6677916\",\n \"0.66597736\",\n \"0.6659751\",\n \"0.66571426\",\n \"0.6651366\",\n \"0.66154593\",\n \"0.6607266\",\n \"0.6594125\",\n \"0.6590065\",\n \"0.6578998\",\n \"0.6572598\",\n \"0.6566313\",\n \"0.655975\",\n \"0.6559497\",\n \"0.64909726\",\n \"0.648074\",\n \"0.64709556\",\n \"0.64678323\",\n \"0.6465402\",\n \"0.64599174\",\n \"0.6449587\",\n \"0.6440436\",\n \"0.6431201\",\n \"0.6429895\",\n \"0.6427523\",\n \"0.64225894\",\n \"0.6421407\",\n \"0.64207923\",\n \"0.6408828\",\n \"0.64007634\",\n \"0.63622165\",\n \"0.63496786\",\n \"0.6348559\",\n \"0.6347621\",\n \"0.6344241\",\n \"0.6342479\",\n \"0.6339647\",\n \"0.63361746\",\n \"0.63349813\",\n \"0.6332759\",\n \"0.6332414\",\n \"0.63274235\",\n \"0.63163656\",\n \"0.6306338\",\n \"0.63052803\",\n \"0.6297278\",\n \"0.62876356\",\n \"0.6284665\",\n \"0.6284373\",\n \"0.6275616\",\n \"0.62746453\",\n \"0.6266429\",\n \"0.6262571\",\n \"0.6260502\",\n \"0.6244802\",\n \"0.62445766\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6851728"},"document_rank":{"kind":"string","value":"28"}}},{"rowIdx":17451,"cells":{"query":{"kind":"string","value":"Read a .plist file from filepath. Return the unpacked root object (which usually is a dictionary)."},"document":{"kind":"string","value":"def readPlist(filepath):\n\tplistData = NSData.dataWithContentsOfFile_(filepath)\n\tdataObject, plistFormat, error = NSPropertyListSerialization.propertyListFromData_mutabilityOption_format_errorDescription_(plistData, NSPropertyListMutableContainers, None, None)\n\tif error:\n\t\terrmsg = \"%s in file %s\" % (error, filepath)\n\t\traise NSPropertyListSerializationException(errmsg)\n\telse:\n\t\treturn dataObject"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _read_plist(path: str, format: plistlib.PlistFormat) -> dict:\n if not os.path.isfile(path):\n raise ValueError(f'File {path} does not exist')\n\n with open(path, 'rb') as file:\n return plistlib.load(file, fmt=format, dict_type=dict)","def _read_plist(self, filename):\n file_path = self.get_file(filename)\n try:\n self._plist[filename] = readPlist(file_path)\n except:\n # Is binaryPlist?\n try:\n self._plist[filename] = readBinaryPlist(file_path)\n except:\n # What is it?\n pass","def load_plist(path):\n dt = _np.dtype({\"names\": ['ridx', 'azidx'],\n \"formats\": ['>u4', '>u4']})\n plist = _np.fromfile(path, dtype=dt)\n return plist","def read_plist(path: str) -> dict:\n return _read_plist(path, plistlib.FMT_XML)","def read_file(self, path):\n # pylint: disable=unused-variable\n info, pformat, error = (\n NSPropertyListSerialization.propertyListWithData_options_format_error_(\n NSData.dataWithContentsOfFile_(os.path.expanduser(path)),\n NSPropertyListMutableContainersAndLeaves,\n None,\n None\n ))\n # pylint: enable=unused-variable\n if info is None:\n if error is None:\n error = \"Invalid plist file.\"\n raise PlistParseError(\"Can't read %s: %s\" % (path, error))\n\n return info","def Read(self, file_object):\n try:\n self.root_key = plistlib.load(file_object)\n\n except plistlib.InvalidFileException as exception:\n raise IOError(exception)","def load_dictionary(filepath):\r\n # context manager read binary\r\n with open(filepath, 'rb') as file:\r\n # pickle load\r\n return pickle.load(file)","def parse_file(file_path):\n with open(file_path) as f:\n return XmlPropertyListParser().parse(f)","def loadPickle(filepath):\n\tf = open(filepath, 'rb')\n\tobj = pickle.load(f)\n\tf.close()\n\treturn obj","def load_file(self, filepath):\n filepath = self._yaml_extension(filepath)\n data = self._load_data_yaml(filepath)\n return data","def load_pkl_file(p):\n pkl_file = open(p, 'rb')\n obj = pickle.load(pkl_file)\n pkl_file.close()\n return obj","def _read_pkl(self, input_file):\n data = pickle.load(open(input_file, 'rb'))\n return data","def load(filepath):\n with open(filepath) as f:\n return Config(json.load(f))","def load_pkl(file_name):\n with open(file_name) as fp:\n data = pkl.load(fp)\n return data","def load(file_path):\n\t# assert type(file_path) == str, 'File_path must be a string'\n\t\n\twith open(file_path, 'rb') as f:\n\t\treturn pickle.load(f)","def pload(filename):\n return pickle.load(open(filename, 'rb'))","def read_dictionary(filepath):\n with open(filepath, 'r') as dict_file:\n return dict_file.read().splitlines()","def load_data(filepath):\n with open(filepath, 'r') as f:\n data = f.read()\n return data","def pickle_read(file_path):\n\n with open(file_path, 'rb') as file:\n return pickle.load(file)","def pklload(path:str):\n pkl = pickle.load(open(path, 'rb'))\n return pkl","def load(self):\n data = None\n try:\n with open(self.__filepath, 'r') as file:\n text = file.read()\n data = jsonpickle.decode(text)\n except FileNotFoundError:\n data = None\n except IOError as e:\n print(e)\n return data","def extract_from_pickle(filepath):\n\t\twith open(filepath, 'rb') as pfile:\n\t\t\tpy_obj = pickle.load(pfile)\n\t\t\treturn py_obj","def read_yaml_file(filepath: str) -> Dict:\n return yaml.safe_load(read_file(filepath))","def load_pkl(path):\r\n f = open(path, 'rb')\r\n try:\r\n rval = cPickle.load(f)\r\n finally:\r\n f.close()\r\n return rval","def read_pickle(file_path):\n with open(file_path, 'rb') as file:\n return pickle.load(file)","def fromFile(filename: unicode) -> ghidra.framework.ApplicationProperties:\n ...","def load(filename):\n import pickle\n return pickle.load(open(filename, 'r'))","def load_object(fpath):\r\n with open(fpath, 'rb') as i:\r\n return pickle.load(i)","def load_pkl_file(path):\n with open(path, 'rb') as pkl_file:\n return pickle.load(pkl_file)","def deserialize(file):\n global root_dir\n global wells_list\n global tops_list\n global project_file\n\n f = open(file, 'rb')\n\n current_project = pickle.load(f)\n root_dir = current_project.root_dir\n wells_list = current_project.wells_list\n tops_list = current_project.tops_list\n project_file = current_project.project_file","def read_input_plist(self):\n \n try:\n indata = self.infile.read()\n if indata:\n self.env = plistlib.readPlistFromString(indata)\n else:\n self.env = dict()\n except BaseException as e:\n raise ProcessorError(e)","def read_bplist(path: str) -> dict:\n return _read_plist(path, plistlib.FMT_BINARY)","def parse_pickle(filename):\n \n with open(filename) as file:\n return pickle.load(file)","def load_object(filepath):\n with open(filepath, 'rb') as f:\n obj = pickle.load(f)\n return obj","def load(fname):\r\n with open(fname, 'rb') as f:\r\n data = pickle.load(f)\r\n return data","def from_file(filename):\n # in order to complete this lab we are going to use the python lib json in which we have the function json.loads\n # which will automatically load a json from a string\n f = open(filename, 'r')\n string = f.read()\n return json.loads(string)","def load_info_from_file(filepath):\n with open(filepath, 'r') as f:\n tokens = json.load(f)\n\n return tokens","def from_plist(self, content):\r\n if biplist is None:\r\n raise UnsupportedDeserializationFormat(\"Usage of the plist aspects requires biplist.\")\r\n\r\n return biplist.readPlistFromString(content)","def read_data_from_file(file_path: str) -> Any:\n with open(file_path, 'rb') as file:\n data = pickle.load(file)\n return data","def load(filename):\n try:\n with open(filename, 'rb') as f:\n data = pickle.load(f)\n return data\n except FileNotFoundError:\n return None","def _parse_file(cls, filepath):\n hdus = sunpy.io.read_file(filepath)\n return cls._parse_hdus(hdus)","def read_config_file(filename):\n\n # Read the config file\n toml_data = open(filename).read()\n\n # Load the definitions in the config file\n data = toml.loads(toml_data)\n\n return data","def load_file(self, filepath):\n _, fileext = os.path.splitext(filepath)\n with open(filepath) as fhandler:\n return replace_paths({\n '.yml': lambda: yaml.load(fhandler, Loader=yamlloader.ordereddict.CLoader),\n '.json': lambda: json.load(fhandler, object_pairs_hook=OrderedDict)\n }.get(fileext, lambda: fhandler.read())())","def load_dictionary(filename):\n filename = os.path.join(FILE_DIR, 'assets/obj/' + filename)\n try:\n with open(filename, 'rb') as input:\n return pickle.load(input)\n except Exception as e:\n print(\"exception\", e)","def readobject(filename):\n # import cPickle as pickle\n with open(filename, 'rb') as input_file:\n return pickle.load(input_file)","def load_file(self, filename):\n with open(filename, \"rb\") as pickle_handle:\n return pickle.load(pickle_handle)","def load_pickle(filepath):\n logging.info('Loading object from pickle: {}'.format(filepath))\n with open(filepath, 'rb') as infile:\n return pickle.load(infile)","def reader(path: str) -> Tuple[List[str], str]:\n\n if not os.path.isfile(path):\n return None, \"No file %s found\", path\n\n with open(path, 'r') as f:\n try:\n return json.load(f), None\n\n except Exception as ex:\n return None, ex","def load(self, filename):\n import pickle\n return pickle.load(open(filename, 'rb'))","def load(filePath):\n\n stream = open(filePath, 'r')\n yamlDict = yaml.safe_load(stream)\n\n return yamlDict","def pickle_load(file_path):\n if not os.path.isfile(file_path):\n return None\n\n with open(file_path, 'rb') as f:\n return pickle.load(f)","def load_pkl_data(path):\n with open(path, 'rb') as fi:\n data = pickle.load(fi)\n return data","def load_pickle_data(filename):\n path = \"../tmp/{}.pckl\".format(filename)\n if os.path.exists(path):\n print(\"LOADING PCKL FILE FROM {}\".format(path))\n f = open(path, 'rb')\n obj = pickle.load(f)\n f.close()\n return obj","def loadMenuPreferences(path):\n with open(path, 'r') as f:\n return json.load(f)","def load_file(filename):\n with open(path.join(PATH_ROOT, filename), \"r\") as in_file:\n return in_file.readlines()","def __init__(self, filename=None):\n if filename:\n dict.__init__(self, self.read_file(filename))\n else:\n dict.__init__(self)\n self.new_plist()","def read_json(filepath):\n\n with open(filepath, 'r', encoding='utf-8') as file_obj:\n data = json.load(file_obj)\n\n return data","def read(self, filepath, dirpath=None):\n try:\n #filepath = os.path.normpath(filepath)\n with open(filepath) as f_p:\n try:\n self.json_dict = json.load(f_p)\n self.filepath = filepath\n return self.json_dict\n except ValueError as err:\n print('JSON content error in \"%s\"' % filepath)\n print(err)\n except (IOError, FileNotFoundError):\n print(\n 'Failed to open JSON file \"%s\" \"%s\"' %\n (os.path.abspath(''), filepath))\n raise NoSuchFileError(filepath)\n raise JsonContentError","def read_pickle(file_name):\n with open(file_name, 'rb') as f:\n obj = pickle.load(f)\n return obj","def readFile(self, fname):\n res = None\n with open(self.PATH + fname, 'rb') as handle:\n res = pickle.load(handle)\n return res","def get_data_from_file(filepath):\n with open(filepath) as f:\n return f.read()","def read_from_file(name):\n print 'reading structures from pickle'\n print '------------------------------'\n\n path = os.getcwd() + '/pickles/' + name + '.pkl'\n file = open(path, 'rb')\n new_obj = pickle.load(file)\n file.close()\n\n return new_obj","def read_pickle(path):\n with open(path, \"rb\") as f:\n data = pickle.load(f)\n\n return data","def load_json_data(filepath):\n with open(filepath,'r') as f:\n return json.load(f)","def loadFile(filename):\n\t\n\tf = open(filename, 'r')\n\t\n\ttry:\n\t\tcontent = f.read()\n\t\treturn content\n\tfinally:\n\t\tf.close()","def load_plyfile(filename):\n plydata = plyfile.PlyData.read(filename)\n return plydata","def load(filename):\n\n try:\n with open(filename) as data:\n return json.load(data)\n except:\n return None","def load_json(filepath: str):\n if not filepath:\n return None\n\n abs_path = _resolve_relative_path(filepath)\n with open(abs_path) as f:\n raw_json = f.read()\n\n return json.loads(raw_json)","def read(plist):\n length = len(sorted(plist, key=len, reverse=True)[0])\n array = np.array([l+[None]*(length-len(l)) for l in plist], dtype='<U60')\n return array","def read_pickle(path):\n with open(path, 'rb') as file:\n return pickle.load(file)","def load(filename):\n with open(filename, 'rb') as f:\n return pickle.load(f)","def loadStuff(path=None):\n\n if path == None:\n print(\"No path specified\")\n return\n\n try:\n pkl_file = open(path, 'rb')\n obj = cPickle.load(pkl_file)\n pkl_file.close()\n print('Data correctly loaded and returned')\n return obj\n\n except IOError as e:\n #print \"I/O error({0}):{1}\".format(e.errno, e.strerror)\n print('I/O error')\n except:\n print(\"Unexpected error\" % sys.exc_info()[0])\n raise","def read_test_file(path):\n #TODO Handle multiple test sets in a given doc\n teststruct = yaml.safe_load(os.path.expandvars(read_file(path)))\n return teststruct","def load_from(filename):\n from .io import load\n return load(filename)","def load_tree_from_file(fname: str = 'tree') -> dict:\r\n fid = open(fname + \".pkl\", \"rb\")\r\n return pickle.load(fid)","def open_data(filepath):\n\tdata = []\n\twith open(filepath) as f:\n\t data = f.readlines()\n\t data = list(map(json.loads, data)) \n\treturn data","def openFile(self, path):\n with open(path) as f:\n return json.load(f)","def read(filepath):\n with open(filepath, 'r') as f:\n el = PFSPWTIO.readline(f).split()\n n, m = int(el[0]), int(el[1])\n p = list()\n for i in range(n):\n el = PFSPWTIO.readline(f).split()\n p.append([int(el[i*2+1]) for i in range(m)])\n d, w = list(), list()\n if not f.readline()[:6] == 'Reldue':\n raise PFSPWTIO.PFSPException('Expected \"Reldue\" before deadlines.')\n for i in range(n):\n el = PFSPWTIO.readline(f).split()\n d.append(int(el[1]))\n w.append(int(el[3]))\n return Instance(p, d, w)","def load_pop(path):\n with open(path, 'rb') as input:\n pop = pickle.load(input)\n input.close()\n return pop","def file_reader(filePath):\n try:\n word_file = open(filePath, \"rt\")\n word_list = word_file.read().splitlines()\n word_file.close()\n return word_list\n except Exception:\n print(f\"An error has occured when reading the file.\")\n\n return","def parse_from_file(filepath: Path) -> \"PostMeta\":\n try:\n with open(filepath, \"r\", encoding=\"utf-8\", errors=\"strict\") as f:\n cfg_json = json.load(f)\n return PostMetaSchema().load(cfg_json)\n except IOError:\n raise ValueError(\n f'Could not open the config file at (\"{filepath.absolute()}\")'\n )\n except json.JSONDecodeError as e:\n raise ValueError(f\"Invalid JSON in the provided config file: {e}\")\n except msh.exceptions.ValidationError as e:\n raise ValueError(f\"Invalid post-meta.json file: {e}\")","def read_phonebook(phonebook):\n\n try:\n with open(phonebook) as infile:\n try:\n return cPickle.load(infile)\n except EOFError:\n return {}\n\n except IOError:\n print \"That phonebook does not exist!\"","def pickle_from_file(path):\n return pickle.load(open(path, 'rb'))","def read_file(filename):\n # Read in as nested dictionary\n # hipparcos_data = {'(star catalog number':\n # { 'parallax' : ... , 'apparent_magnitude' : ... , 'blue_minus_visual' : ... },\n # ... }\n\n return hipparcos_data","def read(file_or_filename):\n tree = ET.parse(file_or_filename)\n etElem = tree.getroot()\n mmElem = convert(etElem)\n if isinstance(mmElem, Elements.Map):\n return MindMap(mmElem)\n return mmElem","def read_json(filepath: str):\n if not filepath:\n raise ValueError\n if not os.path.isfile(filepath):\n raise FileNotFoundError\n with open(filepath) as f:\n return json.load(f)","def load_json(filepath: str):\n with open(filepath, encoding=\"utf-8\") as f:\n return json.load(f)","def load(path):\n \n with codecs.open(path, 'r', **rparams) as f:\n print ' > loading... {}'.format(path)\n if '.json' in path:\n obj = json.load(f, object_hook=json_numpy_obj_hook)\n elif '.pkl' in path:\n obj = pickle.load(file=f)\n else:\n # check the file referenced is sensible\n obj_id = [k for k in flocs.keys() if k in path]\n if obj_id is None or len(obj_id) != 1: raise ValueError(\n '{} not found in the path: \\n {}'.format(flocs.keys(), path))\n return obj","def read_json(filepath):\n return json.loads(open(filepath).read())","def parse():\n with open('src/wator/Properties.json') as data_file:\n data = json.load(data_file)\n return data","def read_object_from_file(file_name):\n if os.path.exists(file_name) is False:\n print (\"Error read path: [%s]\" % file_name)\n return None\n with open(file_name, 'r') as f:\n try:\n obj = json.load(f)\n except Exception:\n print (\"Error json: [%s]\" % f.read()[0:10])\n return None\n return obj","def load_json(filepath: str):\n with open(filepath, \"r\", encoding=\"utf8\") as f:\n return json.loads(f.read())","def ReadFileIntoString(filepath):\n with open(filepath, 'r') as file_handle:\n contents = file_handle.read()\n return contents","def load_pt_file(fname):\n fname = str(Path(fname).expanduser())\n data = torch.load(fname)\n if \"history\" not in data:\n data[\"history\"] = {}\n return data","def parse_config(path):\n with open(path, \"r\") as config_file:\n return toml.load(config_file)","def read_pk(filename):\n with open(filename, 'rb') as fd:\n ret = pickle.load(fd)\n return ret","def load(cls, filename):\n return cPickle.load(open(filename, \"rb\"))","def read_data_model(filename='data/data_model.pkl'):\n\n with open(filename, 'r') as pklfile:\n root = pkl.load(pklfile)\n\n return root","def loadPickle(pickle_file):\n print(\"Loading pickle data from file: \"+pickle_file)\n\n data = None\n try:\n with open(pickle_file, \"rb\") as fd:\n data = pickle.load(fd)\n except EOFError:\n pass\n except pickle.UnpicklingError as upe:\n print(\"Failed: Loading Pickle Data\")\n except IOError:\n data = {}\n\n return data","def open_inventorybook(filepath):\n path_exists = os.path.exists(filepath)\n inventorybook = None\n if path_exists:\n try: # safest way to open or close file.\n with open(filepath, 'r') as infile:\n inventorybook = json.load(infile)\n finally:\n infile.close()\n return inventorybook"],"string":"[\n \"def _read_plist(path: str, format: plistlib.PlistFormat) -> dict:\\n if not os.path.isfile(path):\\n raise ValueError(f'File {path} does not exist')\\n\\n with open(path, 'rb') as file:\\n return plistlib.load(file, fmt=format, dict_type=dict)\",\n \"def _read_plist(self, filename):\\n file_path = self.get_file(filename)\\n try:\\n self._plist[filename] = readPlist(file_path)\\n except:\\n # Is binaryPlist?\\n try:\\n self._plist[filename] = readBinaryPlist(file_path)\\n except:\\n # What is it?\\n pass\",\n \"def load_plist(path):\\n dt = _np.dtype({\\\"names\\\": ['ridx', 'azidx'],\\n \\\"formats\\\": ['>u4', '>u4']})\\n plist = _np.fromfile(path, dtype=dt)\\n return plist\",\n \"def read_plist(path: str) -> dict:\\n return _read_plist(path, plistlib.FMT_XML)\",\n \"def read_file(self, path):\\n # pylint: disable=unused-variable\\n info, pformat, error = (\\n NSPropertyListSerialization.propertyListWithData_options_format_error_(\\n NSData.dataWithContentsOfFile_(os.path.expanduser(path)),\\n NSPropertyListMutableContainersAndLeaves,\\n None,\\n None\\n ))\\n # pylint: enable=unused-variable\\n if info is None:\\n if error is None:\\n error = \\\"Invalid plist file.\\\"\\n raise PlistParseError(\\\"Can't read %s: %s\\\" % (path, error))\\n\\n return info\",\n \"def Read(self, file_object):\\n try:\\n self.root_key = plistlib.load(file_object)\\n\\n except plistlib.InvalidFileException as exception:\\n raise IOError(exception)\",\n \"def load_dictionary(filepath):\\r\\n # context manager read binary\\r\\n with open(filepath, 'rb') as file:\\r\\n # pickle load\\r\\n return pickle.load(file)\",\n \"def parse_file(file_path):\\n with open(file_path) as f:\\n return XmlPropertyListParser().parse(f)\",\n \"def loadPickle(filepath):\\n\\tf = open(filepath, 'rb')\\n\\tobj = pickle.load(f)\\n\\tf.close()\\n\\treturn obj\",\n \"def load_file(self, filepath):\\n filepath = self._yaml_extension(filepath)\\n data = self._load_data_yaml(filepath)\\n return data\",\n \"def load_pkl_file(p):\\n pkl_file = open(p, 'rb')\\n obj = pickle.load(pkl_file)\\n pkl_file.close()\\n return obj\",\n \"def _read_pkl(self, input_file):\\n data = pickle.load(open(input_file, 'rb'))\\n return data\",\n \"def load(filepath):\\n with open(filepath) as f:\\n return Config(json.load(f))\",\n \"def load_pkl(file_name):\\n with open(file_name) as fp:\\n data = pkl.load(fp)\\n return data\",\n \"def load(file_path):\\n\\t# assert type(file_path) == str, 'File_path must be a string'\\n\\t\\n\\twith open(file_path, 'rb') as f:\\n\\t\\treturn pickle.load(f)\",\n \"def pload(filename):\\n return pickle.load(open(filename, 'rb'))\",\n \"def read_dictionary(filepath):\\n with open(filepath, 'r') as dict_file:\\n return dict_file.read().splitlines()\",\n \"def load_data(filepath):\\n with open(filepath, 'r') as f:\\n data = f.read()\\n return data\",\n \"def pickle_read(file_path):\\n\\n with open(file_path, 'rb') as file:\\n return pickle.load(file)\",\n \"def pklload(path:str):\\n pkl = pickle.load(open(path, 'rb'))\\n return pkl\",\n \"def load(self):\\n data = None\\n try:\\n with open(self.__filepath, 'r') as file:\\n text = file.read()\\n data = jsonpickle.decode(text)\\n except FileNotFoundError:\\n data = None\\n except IOError as e:\\n print(e)\\n return data\",\n \"def extract_from_pickle(filepath):\\n\\t\\twith open(filepath, 'rb') as pfile:\\n\\t\\t\\tpy_obj = pickle.load(pfile)\\n\\t\\t\\treturn py_obj\",\n \"def read_yaml_file(filepath: str) -> Dict:\\n return yaml.safe_load(read_file(filepath))\",\n \"def load_pkl(path):\\r\\n f = open(path, 'rb')\\r\\n try:\\r\\n rval = cPickle.load(f)\\r\\n finally:\\r\\n f.close()\\r\\n return rval\",\n \"def read_pickle(file_path):\\n with open(file_path, 'rb') as file:\\n return pickle.load(file)\",\n \"def fromFile(filename: unicode) -> ghidra.framework.ApplicationProperties:\\n ...\",\n \"def load(filename):\\n import pickle\\n return pickle.load(open(filename, 'r'))\",\n \"def load_object(fpath):\\r\\n with open(fpath, 'rb') as i:\\r\\n return pickle.load(i)\",\n \"def load_pkl_file(path):\\n with open(path, 'rb') as pkl_file:\\n return pickle.load(pkl_file)\",\n \"def deserialize(file):\\n global root_dir\\n global wells_list\\n global tops_list\\n global project_file\\n\\n f = open(file, 'rb')\\n\\n current_project = pickle.load(f)\\n root_dir = current_project.root_dir\\n wells_list = current_project.wells_list\\n tops_list = current_project.tops_list\\n project_file = current_project.project_file\",\n \"def read_input_plist(self):\\n \\n try:\\n indata = self.infile.read()\\n if indata:\\n self.env = plistlib.readPlistFromString(indata)\\n else:\\n self.env = dict()\\n except BaseException as e:\\n raise ProcessorError(e)\",\n \"def read_bplist(path: str) -> dict:\\n return _read_plist(path, plistlib.FMT_BINARY)\",\n \"def parse_pickle(filename):\\n \\n with open(filename) as file:\\n return pickle.load(file)\",\n \"def load_object(filepath):\\n with open(filepath, 'rb') as f:\\n obj = pickle.load(f)\\n return obj\",\n \"def load(fname):\\r\\n with open(fname, 'rb') as f:\\r\\n data = pickle.load(f)\\r\\n return data\",\n \"def from_file(filename):\\n # in order to complete this lab we are going to use the python lib json in which we have the function json.loads\\n # which will automatically load a json from a string\\n f = open(filename, 'r')\\n string = f.read()\\n return json.loads(string)\",\n \"def load_info_from_file(filepath):\\n with open(filepath, 'r') as f:\\n tokens = json.load(f)\\n\\n return tokens\",\n \"def from_plist(self, content):\\r\\n if biplist is None:\\r\\n raise UnsupportedDeserializationFormat(\\\"Usage of the plist aspects requires biplist.\\\")\\r\\n\\r\\n return biplist.readPlistFromString(content)\",\n \"def read_data_from_file(file_path: str) -> Any:\\n with open(file_path, 'rb') as file:\\n data = pickle.load(file)\\n return data\",\n \"def load(filename):\\n try:\\n with open(filename, 'rb') as f:\\n data = pickle.load(f)\\n return data\\n except FileNotFoundError:\\n return None\",\n \"def _parse_file(cls, filepath):\\n hdus = sunpy.io.read_file(filepath)\\n return cls._parse_hdus(hdus)\",\n \"def read_config_file(filename):\\n\\n # Read the config file\\n toml_data = open(filename).read()\\n\\n # Load the definitions in the config file\\n data = toml.loads(toml_data)\\n\\n return data\",\n \"def load_file(self, filepath):\\n _, fileext = os.path.splitext(filepath)\\n with open(filepath) as fhandler:\\n return replace_paths({\\n '.yml': lambda: yaml.load(fhandler, Loader=yamlloader.ordereddict.CLoader),\\n '.json': lambda: json.load(fhandler, object_pairs_hook=OrderedDict)\\n }.get(fileext, lambda: fhandler.read())())\",\n \"def load_dictionary(filename):\\n filename = os.path.join(FILE_DIR, 'assets/obj/' + filename)\\n try:\\n with open(filename, 'rb') as input:\\n return pickle.load(input)\\n except Exception as e:\\n print(\\\"exception\\\", e)\",\n \"def readobject(filename):\\n # import cPickle as pickle\\n with open(filename, 'rb') as input_file:\\n return pickle.load(input_file)\",\n \"def load_file(self, filename):\\n with open(filename, \\\"rb\\\") as pickle_handle:\\n return pickle.load(pickle_handle)\",\n \"def load_pickle(filepath):\\n logging.info('Loading object from pickle: {}'.format(filepath))\\n with open(filepath, 'rb') as infile:\\n return pickle.load(infile)\",\n \"def reader(path: str) -> Tuple[List[str], str]:\\n\\n if not os.path.isfile(path):\\n return None, \\\"No file %s found\\\", path\\n\\n with open(path, 'r') as f:\\n try:\\n return json.load(f), None\\n\\n except Exception as ex:\\n return None, ex\",\n \"def load(self, filename):\\n import pickle\\n return pickle.load(open(filename, 'rb'))\",\n \"def load(filePath):\\n\\n stream = open(filePath, 'r')\\n yamlDict = yaml.safe_load(stream)\\n\\n return yamlDict\",\n \"def pickle_load(file_path):\\n if not os.path.isfile(file_path):\\n return None\\n\\n with open(file_path, 'rb') as f:\\n return pickle.load(f)\",\n \"def load_pkl_data(path):\\n with open(path, 'rb') as fi:\\n data = pickle.load(fi)\\n return data\",\n \"def load_pickle_data(filename):\\n path = \\\"../tmp/{}.pckl\\\".format(filename)\\n if os.path.exists(path):\\n print(\\\"LOADING PCKL FILE FROM {}\\\".format(path))\\n f = open(path, 'rb')\\n obj = pickle.load(f)\\n f.close()\\n return obj\",\n \"def loadMenuPreferences(path):\\n with open(path, 'r') as f:\\n return json.load(f)\",\n \"def load_file(filename):\\n with open(path.join(PATH_ROOT, filename), \\\"r\\\") as in_file:\\n return in_file.readlines()\",\n \"def __init__(self, filename=None):\\n if filename:\\n dict.__init__(self, self.read_file(filename))\\n else:\\n dict.__init__(self)\\n self.new_plist()\",\n \"def read_json(filepath):\\n\\n with open(filepath, 'r', encoding='utf-8') as file_obj:\\n data = json.load(file_obj)\\n\\n return data\",\n \"def read(self, filepath, dirpath=None):\\n try:\\n #filepath = os.path.normpath(filepath)\\n with open(filepath) as f_p:\\n try:\\n self.json_dict = json.load(f_p)\\n self.filepath = filepath\\n return self.json_dict\\n except ValueError as err:\\n print('JSON content error in \\\"%s\\\"' % filepath)\\n print(err)\\n except (IOError, FileNotFoundError):\\n print(\\n 'Failed to open JSON file \\\"%s\\\" \\\"%s\\\"' %\\n (os.path.abspath(''), filepath))\\n raise NoSuchFileError(filepath)\\n raise JsonContentError\",\n \"def read_pickle(file_name):\\n with open(file_name, 'rb') as f:\\n obj = pickle.load(f)\\n return obj\",\n \"def readFile(self, fname):\\n res = None\\n with open(self.PATH + fname, 'rb') as handle:\\n res = pickle.load(handle)\\n return res\",\n \"def get_data_from_file(filepath):\\n with open(filepath) as f:\\n return f.read()\",\n \"def read_from_file(name):\\n print 'reading structures from pickle'\\n print '------------------------------'\\n\\n path = os.getcwd() + '/pickles/' + name + '.pkl'\\n file = open(path, 'rb')\\n new_obj = pickle.load(file)\\n file.close()\\n\\n return new_obj\",\n \"def read_pickle(path):\\n with open(path, \\\"rb\\\") as f:\\n data = pickle.load(f)\\n\\n return data\",\n \"def load_json_data(filepath):\\n with open(filepath,'r') as f:\\n return json.load(f)\",\n \"def loadFile(filename):\\n\\t\\n\\tf = open(filename, 'r')\\n\\t\\n\\ttry:\\n\\t\\tcontent = f.read()\\n\\t\\treturn content\\n\\tfinally:\\n\\t\\tf.close()\",\n \"def load_plyfile(filename):\\n plydata = plyfile.PlyData.read(filename)\\n return plydata\",\n \"def load(filename):\\n\\n try:\\n with open(filename) as data:\\n return json.load(data)\\n except:\\n return None\",\n \"def load_json(filepath: str):\\n if not filepath:\\n return None\\n\\n abs_path = _resolve_relative_path(filepath)\\n with open(abs_path) as f:\\n raw_json = f.read()\\n\\n return json.loads(raw_json)\",\n \"def read(plist):\\n length = len(sorted(plist, key=len, reverse=True)[0])\\n array = np.array([l+[None]*(length-len(l)) for l in plist], dtype='<U60')\\n return array\",\n \"def read_pickle(path):\\n with open(path, 'rb') as file:\\n return pickle.load(file)\",\n \"def load(filename):\\n with open(filename, 'rb') as f:\\n return pickle.load(f)\",\n \"def loadStuff(path=None):\\n\\n if path == None:\\n print(\\\"No path specified\\\")\\n return\\n\\n try:\\n pkl_file = open(path, 'rb')\\n obj = cPickle.load(pkl_file)\\n pkl_file.close()\\n print('Data correctly loaded and returned')\\n return obj\\n\\n except IOError as e:\\n #print \\\"I/O error({0}):{1}\\\".format(e.errno, e.strerror)\\n print('I/O error')\\n except:\\n print(\\\"Unexpected error\\\" % sys.exc_info()[0])\\n raise\",\n \"def read_test_file(path):\\n #TODO Handle multiple test sets in a given doc\\n teststruct = yaml.safe_load(os.path.expandvars(read_file(path)))\\n return teststruct\",\n \"def load_from(filename):\\n from .io import load\\n return load(filename)\",\n \"def load_tree_from_file(fname: str = 'tree') -> dict:\\r\\n fid = open(fname + \\\".pkl\\\", \\\"rb\\\")\\r\\n return pickle.load(fid)\",\n \"def open_data(filepath):\\n\\tdata = []\\n\\twith open(filepath) as f:\\n\\t data = f.readlines()\\n\\t data = list(map(json.loads, data)) \\n\\treturn data\",\n \"def openFile(self, path):\\n with open(path) as f:\\n return json.load(f)\",\n \"def read(filepath):\\n with open(filepath, 'r') as f:\\n el = PFSPWTIO.readline(f).split()\\n n, m = int(el[0]), int(el[1])\\n p = list()\\n for i in range(n):\\n el = PFSPWTIO.readline(f).split()\\n p.append([int(el[i*2+1]) for i in range(m)])\\n d, w = list(), list()\\n if not f.readline()[:6] == 'Reldue':\\n raise PFSPWTIO.PFSPException('Expected \\\"Reldue\\\" before deadlines.')\\n for i in range(n):\\n el = PFSPWTIO.readline(f).split()\\n d.append(int(el[1]))\\n w.append(int(el[3]))\\n return Instance(p, d, w)\",\n \"def load_pop(path):\\n with open(path, 'rb') as input:\\n pop = pickle.load(input)\\n input.close()\\n return pop\",\n \"def file_reader(filePath):\\n try:\\n word_file = open(filePath, \\\"rt\\\")\\n word_list = word_file.read().splitlines()\\n word_file.close()\\n return word_list\\n except Exception:\\n print(f\\\"An error has occured when reading the file.\\\")\\n\\n return\",\n \"def parse_from_file(filepath: Path) -> \\\"PostMeta\\\":\\n try:\\n with open(filepath, \\\"r\\\", encoding=\\\"utf-8\\\", errors=\\\"strict\\\") as f:\\n cfg_json = json.load(f)\\n return PostMetaSchema().load(cfg_json)\\n except IOError:\\n raise ValueError(\\n f'Could not open the config file at (\\\"{filepath.absolute()}\\\")'\\n )\\n except json.JSONDecodeError as e:\\n raise ValueError(f\\\"Invalid JSON in the provided config file: {e}\\\")\\n except msh.exceptions.ValidationError as e:\\n raise ValueError(f\\\"Invalid post-meta.json file: {e}\\\")\",\n \"def read_phonebook(phonebook):\\n\\n try:\\n with open(phonebook) as infile:\\n try:\\n return cPickle.load(infile)\\n except EOFError:\\n return {}\\n\\n except IOError:\\n print \\\"That phonebook does not exist!\\\"\",\n \"def pickle_from_file(path):\\n return pickle.load(open(path, 'rb'))\",\n \"def read_file(filename):\\n # Read in as nested dictionary\\n # hipparcos_data = {'(star catalog number':\\n # { 'parallax' : ... , 'apparent_magnitude' : ... , 'blue_minus_visual' : ... },\\n # ... }\\n\\n return hipparcos_data\",\n \"def read(file_or_filename):\\n tree = ET.parse(file_or_filename)\\n etElem = tree.getroot()\\n mmElem = convert(etElem)\\n if isinstance(mmElem, Elements.Map):\\n return MindMap(mmElem)\\n return mmElem\",\n \"def read_json(filepath: str):\\n if not filepath:\\n raise ValueError\\n if not os.path.isfile(filepath):\\n raise FileNotFoundError\\n with open(filepath) as f:\\n return json.load(f)\",\n \"def load_json(filepath: str):\\n with open(filepath, encoding=\\\"utf-8\\\") as f:\\n return json.load(f)\",\n \"def load(path):\\n \\n with codecs.open(path, 'r', **rparams) as f:\\n print ' > loading... {}'.format(path)\\n if '.json' in path:\\n obj = json.load(f, object_hook=json_numpy_obj_hook)\\n elif '.pkl' in path:\\n obj = pickle.load(file=f)\\n else:\\n # check the file referenced is sensible\\n obj_id = [k for k in flocs.keys() if k in path]\\n if obj_id is None or len(obj_id) != 1: raise ValueError(\\n '{} not found in the path: \\\\n {}'.format(flocs.keys(), path))\\n return obj\",\n \"def read_json(filepath):\\n return json.loads(open(filepath).read())\",\n \"def parse():\\n with open('src/wator/Properties.json') as data_file:\\n data = json.load(data_file)\\n return data\",\n \"def read_object_from_file(file_name):\\n if os.path.exists(file_name) is False:\\n print (\\\"Error read path: [%s]\\\" % file_name)\\n return None\\n with open(file_name, 'r') as f:\\n try:\\n obj = json.load(f)\\n except Exception:\\n print (\\\"Error json: [%s]\\\" % f.read()[0:10])\\n return None\\n return obj\",\n \"def load_json(filepath: str):\\n with open(filepath, \\\"r\\\", encoding=\\\"utf8\\\") as f:\\n return json.loads(f.read())\",\n \"def ReadFileIntoString(filepath):\\n with open(filepath, 'r') as file_handle:\\n contents = file_handle.read()\\n return contents\",\n \"def load_pt_file(fname):\\n fname = str(Path(fname).expanduser())\\n data = torch.load(fname)\\n if \\\"history\\\" not in data:\\n data[\\\"history\\\"] = {}\\n return data\",\n \"def parse_config(path):\\n with open(path, \\\"r\\\") as config_file:\\n return toml.load(config_file)\",\n \"def read_pk(filename):\\n with open(filename, 'rb') as fd:\\n ret = pickle.load(fd)\\n return ret\",\n \"def load(cls, filename):\\n return cPickle.load(open(filename, \\\"rb\\\"))\",\n \"def read_data_model(filename='data/data_model.pkl'):\\n\\n with open(filename, 'r') as pklfile:\\n root = pkl.load(pklfile)\\n\\n return root\",\n \"def loadPickle(pickle_file):\\n print(\\\"Loading pickle data from file: \\\"+pickle_file)\\n\\n data = None\\n try:\\n with open(pickle_file, \\\"rb\\\") as fd:\\n data = pickle.load(fd)\\n except EOFError:\\n pass\\n except pickle.UnpicklingError as upe:\\n print(\\\"Failed: Loading Pickle Data\\\")\\n except IOError:\\n data = {}\\n\\n return data\",\n \"def open_inventorybook(filepath):\\n path_exists = os.path.exists(filepath)\\n inventorybook = None\\n if path_exists:\\n try: # safest way to open or close file.\\n with open(filepath, 'r') as infile:\\n inventorybook = json.load(infile)\\n finally:\\n infile.close()\\n return inventorybook\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7377023","0.7077552","0.7020647","0.70009124","0.69380563","0.6635612","0.66037655","0.6400661","0.6191319","0.60980314","0.59968185","0.5973855","0.5956443","0.5919474","0.5916405","0.59139293","0.58797735","0.58763933","0.58301824","0.5828497","0.58160996","0.5814658","0.57757777","0.5770311","0.57604474","0.57598037","0.5734003","0.57173073","0.56958675","0.5679054","0.5678769","0.56737715","0.56694335","0.566895","0.5661512","0.5648631","0.5645796","0.56387556","0.56372964","0.5633675","0.563134","0.5621808","0.5614496","0.56087595","0.5607474","0.5607241","0.5606868","0.56021494","0.5598796","0.559311","0.558406","0.5579482","0.5574264","0.5570301","0.556173","0.5557257","0.5550696","0.5550033","0.5548545","0.554844","0.55472213","0.5543072","0.55385906","0.55221134","0.5515126","0.55148864","0.5514301","0.5506324","0.5505501","0.55035496","0.54900306","0.5480384","0.5477097","0.5463586","0.54543024","0.54522455","0.544013","0.54394794","0.5439185","0.5439103","0.5430574","0.54286814","0.5428576","0.5427086","0.5423767","0.542265","0.5405343","0.5401729","0.54002744","0.5394828","0.53925776","0.5390756","0.53855306","0.5381971","0.53817064","0.5379133","0.53784317","0.53703344","0.53700435","0.53645694"],"string":"[\n \"0.7377023\",\n \"0.7077552\",\n \"0.7020647\",\n \"0.70009124\",\n \"0.69380563\",\n \"0.6635612\",\n \"0.66037655\",\n \"0.6400661\",\n \"0.6191319\",\n \"0.60980314\",\n \"0.59968185\",\n \"0.5973855\",\n \"0.5956443\",\n \"0.5919474\",\n \"0.5916405\",\n \"0.59139293\",\n \"0.58797735\",\n \"0.58763933\",\n \"0.58301824\",\n \"0.5828497\",\n \"0.58160996\",\n \"0.5814658\",\n \"0.57757777\",\n \"0.5770311\",\n \"0.57604474\",\n \"0.57598037\",\n \"0.5734003\",\n \"0.57173073\",\n \"0.56958675\",\n \"0.5679054\",\n \"0.5678769\",\n \"0.56737715\",\n \"0.56694335\",\n \"0.566895\",\n \"0.5661512\",\n \"0.5648631\",\n \"0.5645796\",\n \"0.56387556\",\n \"0.56372964\",\n \"0.5633675\",\n \"0.563134\",\n \"0.5621808\",\n \"0.5614496\",\n \"0.56087595\",\n \"0.5607474\",\n \"0.5607241\",\n \"0.5606868\",\n \"0.56021494\",\n \"0.5598796\",\n \"0.559311\",\n \"0.558406\",\n \"0.5579482\",\n \"0.5574264\",\n \"0.5570301\",\n \"0.556173\",\n \"0.5557257\",\n \"0.5550696\",\n \"0.5550033\",\n \"0.5548545\",\n \"0.554844\",\n \"0.55472213\",\n \"0.5543072\",\n \"0.55385906\",\n \"0.55221134\",\n \"0.5515126\",\n \"0.55148864\",\n \"0.5514301\",\n \"0.5506324\",\n \"0.5505501\",\n \"0.55035496\",\n \"0.54900306\",\n \"0.5480384\",\n \"0.5477097\",\n \"0.5463586\",\n \"0.54543024\",\n \"0.54522455\",\n \"0.544013\",\n \"0.54394794\",\n \"0.5439185\",\n \"0.5439103\",\n \"0.5430574\",\n \"0.54286814\",\n \"0.5428576\",\n \"0.5427086\",\n \"0.5423767\",\n \"0.542265\",\n \"0.5405343\",\n \"0.5401729\",\n \"0.54002744\",\n \"0.5394828\",\n \"0.53925776\",\n \"0.5390756\",\n \"0.53855306\",\n \"0.5381971\",\n \"0.53817064\",\n \"0.5379133\",\n \"0.53784317\",\n \"0.53703344\",\n \"0.53700435\",\n \"0.53645694\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76269853"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17452,"cells":{"query":{"kind":"string","value":"Print out the end status of the game when the server closes the connection."},"document":{"kind":"string","value":"def server_closed_connection(self):\n\n print(\"Game Over!\")\n if self._winner:\n print(\"Player {} wins!\".format(self._winner))\n else:\n print(\"Draw!\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def on_connection_end() -> None:\r\n print(\"Connection lost with G-Earth\")\r\n print()","def endSession(self):\n if(self.verb >= DLS_VERB_HIGH):\n print \"--Ending session with %s (no action)\" % (self.server)","def end(self):\n winners = mafia.str_player_list(self.game.winners())\n logging.info(\"Game over! Winners: %s\" % winners)\n\n subject = \"%s: The End\" % self.name\n body = \"Game over!\\n\\nCongratulations to %s for a well \" \\\n \"(or poorly; I can't tell) played game!\" % winners\n self.send_message(mafia.events.PUBLIC, subject, body)","def endGame(self, message):\n print(self.board)\n print(\"Game over! \" + message)\n self.gameOver = True","def endGame(self):\n pass","def handle_close(self):\r\n if self.log_fh:\r\n self.log_fh.write(\r\n \"Server closed connection at %s. Shutting down.\\n\" %\r\n time())\r\n self.close()","def handle_game_end(self, winner_color, win_reason): # possible GameHistory object...\n\n # TODO: implement this method\n print('\\--------------Game End--------------/')\n print(winner_color)\n print(win_reason)\n pass","def game_exit(self):\n self.set_state(GameState.EXITING)\n self.game_stop()\n self.game_log_statistics()","def on_client_exit(self, game) -> None:\n pass","def end(self):\n self.my_print(\"\\t[DONE]\", msg_types.INFO)\n self.in_progress = False","def api_end_game(self):\n pass","def server_exit():\n return","def endMessage(self):","def end(self):\n self.send_all(\"SHUTDOWN\") #On indique a tout le monde qu'on ferme\n self.socket.shutdown(socket.SHUT_RDWR)\n self.socket.close()","def end(self):\n # Update all the things.\n end_font = pygame.font.SysFont(*END_FONT)\n final_score = self.player.nest.acorn_count\n message = \"Game over! Final score: {0}\".format(final_score)\n text_surf = end_font.render(message, True, FONT_COLOUR)\n text_rect = text_surf.get_rect()\n text_rect.center = (SCREEN.width // 2, SCREEN.height // 2)\n\n # Draw all the things.\n self.screen_surf.fill(BKGD_COLOUR)\n self.screen_surf.blit(text_surf, text_rect)\n\n # Render the screen.\n pygame.display.update()\n\n # The main game loop.\n while self.mode is WorldMode.end:\n self.handle_events()","def handle_close(self):\r\n self.end_time = time.time()\r\n self.time_ran = self.end_time - self.start_time\r\n if self.status != 'PASS':\r\n server_log.info('Client {} aborted!'.format(self.client_id))\r\n self.status = 'ABORTED'\r\n self.close()","def end():\n logging.info(\"Execution Ended\")","def EndSession( self ):\r\n\r\n self._socket.write( 'X' ) \r\n # self._connection.write( 'X' ).flush() \r\n\r\n return self.GetServerResponse()","def shutdown():\n shutdown_server()\n return \"Shutting down server\"","def disconnect():\n logging.info('Client disconnected')","def outConnectionLost(self):\n self.logger('stdout closed by process %d' % self._pid)","def exit(self):\n self._status = \"\"\n self._sock.settimeout(1.0)\n self._sock.sendto(bytes(\"bla\", \"utf-8\"), (self._cfg.host, self._cfg.port))","def _on_surface_disconnected(self):\n\n # Close the socket\n self._client_socket.close()\n\n # Inform that the connection has been closed\n print(\"Video stream from {} address closed successfully\".format(self._client_address))","def handleClose(self):\n logging.info(\"%s %s\", self.address, \"closed\")\n self.logbook.clients_disconnected_count += 1","def end_of_game(self):\n end_game = pyip.inputYesNo(f'\\nDo you want to play again?: ')\n\n if end_game == 'no':\n print('\\n-- GAME OVER --')\n sys.exit()\n elif end_game == 'yes':\n self.game_counter += 1","def server_close(self):\n\t\tpass","def handle_close(self):\n print(self.addr, \"bye\")\n self.close()","def on_session_destroyed(session_context):\n if data.AUTO_SHUTDOWN:\n import sys\n\n sys.exit(\n \"\\033[1;31mThe session has ended - tab closed or timeout. \\n\\n --- Terminating the Forest progam and relinquishing control of port. ---\\033[1;00m\"\n )","def end(self, won, reason):\n pass\n # replace with your end logic","async def end(ctx):\n if ctx.message.channel.name.lower() not in tod_channels:\n return\n\n room = ctx.message.channel.name.lower()\n if room not in tod_games:\n await amor_manager.say(\"Truth Or Dare not in progress in {}\".format(room))\n else:\n host = tod_games[room]['host']\n await amor_manager.say(\"Game Over in {}! Thank you to {} for hosting this game!\".format(room, host))\n del tod_games[room]","def do_QUIT(self):\r\n self.send_response(200)\r\n self.end_headers()\r\n self.server.stop = True","def endGame(self, msg, win):\n elapsedTime = time.time() - self.startTime\n readableTime = str(int((elapsedTime / 60) / 60))\n readableTime += \":\" + str(int(elapsedTime / 60))\n readableTime += \":\" + str(elapsedTime % 60)[0:6]\n msg +=\"Time: \" + readableTime\n self.revealBombs(win)\n self.flagLabel.configure(text=\"Flags: \"+str(self.numFlags))\n messagebox.showinfo('Game Over', msg)","def received_CLOSING(self):\n\n\t\tself.player_frame.notify_rival_closing()\n\t\tself.player_frame.master.go_to_previous_screen(False)","def handle_close(self):\n LOGGER.debug(\"server closed\")\n self.clear()\n data = {}\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\n self.close()\n\n # close all and restart\n asyncore.close_all()","def end(self) -> None:\n unicurses.endwin()","def on_disconnect(self):\n print('Client disconnected!')","def _end(self):\n\n self.logger.msg1(\"Done\")","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))","def test_out_end_game(self):\n # Run test\n self.console.end_game(self.message)\n\n # Evaluate test\n calls = [call(str(self.console.hangman)), call(self.message),\n call(i18n.OUT_MSG_ANSWER.format(self.console.word.show()))]\n self.console.c_out.assert_has_calls(calls)","def screen_terminate(self):\n self._channel.send('\\x04')\n # make sure we didn't kill our connection\n if self._channel.closed:\n self.connected = False\n print(\"!! Connection closed by remote host\")\n return False\n else:\n rdata = '\\n'.join(self.recv_to_prompt())\n return bool(re.search(r'screen is terminating', rdata, re.I|re.M))","def endMyTurn(self):\n try:\n result = self.game.server.endEmpireTurn(self.game.authKey)\n if result == 0:\n if self.game.myEmpire['roundComplete'] == 1:\n self.modeMsgBox('You have now un-ended your turn')\n self.game.myEmpire['roundComplete'] = 0\n else:\n self.modeMsgBox('Your turn has been ended, thankyou')\n self.game.myEmpire['roundComplete'] = 1\n self.mainmenu.writeTextRoundEnds()\n elif type(result) == types.StringType:\n self.modeMsgBox(result)\n else:\n \"\"\"End Turn and wait for it to end\"\"\"\n result = self.game.server.endRound(self.game.authKey)\n self.game.server.logout(self.game.authKey)\n from anw.modes.modelogin import ModeLogin\n newMode = ModeLogin(self.game, 200)\n self.game.enterMode(newMode)\n except:\n self.modeMsgBox('endMyTurn->Connection to Server Lost')","def end(update, context) -> int:\n update.callback_query.edit_message_text(\n 'Bye! I hope we can talk again some day.')\n\n logger.info(\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\",\n update.callback_query.message.chat.first_name)\n return ConversationHandler.END","async def connection_lost(self):\n logging.info('connection dropped')","def print_connection_terminated(pkt):\n print_headers(pkt, overwrite_min=0)\n print(red(\"!!!! Attention: TCP/OpenFlow Connection Terminated!!\\n\"))","def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def endgame(winner):","def end_of_game(self, winner):\n pass","def end_game(self):\n self.end_writer.write(f\"You have won with {self.score} points! \" \\\n \"Do you want to play again? (y/n)\", font=(\"Arial\", 16, \"normal\"))\n turtle.Screen().onkey(None, \"Left\")\n turtle.Screen().onkey(None, \"Right\")\n turtle.Screen().onkey(None, \"Up\")\n turtle.Screen().onkey(None, \"Down\")","def playGameEnd(self, strVarName, value, message):\n self.WebviewResponse = value\n print \"Count of webview= \" + str(value)\n self.Naomi.StartUp()\n self.Naomi.printAndSay(\"Well, Can you say me how many red robots was displaying on the screen?\") # Print and say (if the robot is connected) the verbal response\n StartIteration()","def shutdown(self):\t\r\n\t\tself.is_running = False\r\n\t\tfor connection in self.established_connection_list:\r\n\t\t\tconnection.send('The server has been shutdown adruptly by the server owner.\\n')\r\n\t\t\tconnection.socket_send()","def report_end(self):\r\n print(f\"\\n\\n\\n{datetime.now()}\\tDone\\n\")","def server_close(self):\n\t\tself.socket.close()","def end_game(self, game_state: str):\n if game_state == \"win\":\n end_message = \"{0} wins! Would you like to play again?\".format(self.players[self.game.whose_turn])\n else:\n end_message = \"Cat's game! Would you like to play again?\"\n play_again = messagebox.askyesno(title='Game over', message=end_message)\n if play_again:\n self.game.reset_game_data()\n self.reset_game_ui()\n else:\n self.window.destroy()","def end(self):\n if self.conn:\n self.conn.close()\n self.conn = None\n #print(\"closed.\")","async def end_session(self):\n\t\t...","def end(self):\n self._log.debug('%s: doing ..', __class__.__name__)\n self._log.debug('%s: done.', __class__.__name__)","def close(self):\n # Report server that connection is closed\n self._socket.sendall(''.encode())\n self._socket.close()","def end():\n return say()","async def on_end(self, game_result: Result):","def on_close(ws):\n logging.info(\"### Web-socket Connection Closed ###\")","def bcp_game_end(self, **kwargs):\n self.player = None\n self.events.post('game_ended', **kwargs)","def _HandleShutdown(self):\n self.send_response(httplib.OK)\n self.send_header('Content-Type', 'text/plain')\n self.end_headers()\n self.wfile.write('API Server Quitting')\n self.server.shutdown()","def close(self):\n\t\tttotal = time.time() - self.stats['startloop'] - self.stats['waittime']\n\t\tapDisplay.printColor(\"COMPLETE LOOP:\\t\"+apDisplay.timeString(ttotal)+\n\t\t\t\" for \"+str(self.stats[\"count\"]-1)+\" series\",\"green\")\n\t\tappionScript.AppionScript.close(self)","def end_game(self):\n pygame.event.clear()\n self.screen.fill(BLACK)\n self.show_on_screen(\"GAME OVER\", (500, 600), font_size=50)\n self.show_on_screen(\"Press \\\"N\\\" to start a new game\", (500, 650), font_size=30)\n self.show_on_screen(\"Press \\\"ESC\\\" to exit\", (500, 710), font_size=30)\n self.show_on_screen(\"SCORE: \" + str(self.score), (500, 560), font_size=50)\n pygame.display.flip()\n\n # clears previously pressed key\n pygame.event.wait()\n while True:\n event = pygame.event.wait()\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n elif event.type == pygame.KEYDOWN:\n if event.key == pygame.K_ESCAPE:\n pygame.quit()\n sys.exit()\n elif event.key == pygame.K_n:\n self.reset_lander()\n self.play()","def end_turn(self):\n if self.turn == BLUE:\n self.turn = RED\n else:\n self.turn = BLUE\n\n self.hop = False\n if self.isTerminal():\n # if self.turn == BLUE:\n # print('RED WINS!')\n # else:\n # print('BLUE WINS!')\n if self.loop_mode:\n self.endit = True","def end(self):\n self.shutdown = True","def finish(self):\n global log_th\n log_th.log_info('{} disconnected from {} thread'.format(self.client_ip, threading.current_thread().name))","def stop(self):\n # This will result in a -1 being written to the stream, indicating the server is closing down.\n self._write_stream(-1, \"\")\n\n self.__channel.close()\n self.__channel = None\n self.__sys.stdout = self.__old_stdout\n self.__sys.stderr = self.__old_stderr","def close():\r\n logging.info(\"Program has Finished\")\r\n logging.shutdown()","def _exit_print(self):\n if self.cur_frame >= self.config.MAX_FRAMES:\n self.stopped = True","def gameEnd():\n result = cs411_game.gameEnd(request.form.get('Games_Game_ID'),\n\trequest.form.get('Contestant_Coontestant_ID'))\n if result == 1: return prepJSON({\"message\": \"success\"})\n else: raise InvalidUsage(result[\"message\"], 403)","def exit(self):\n self._channel.sendall(\"exit\\n\")\n time.sleep(0.2)\n if not self._channel.closed:\n if self._channel.recv_ready():\n # flush the recv buffer of the new prompt\n self._channel.recv(4096)\n retval = False\n else:\n self.close()\n self.connected = False\n retval = True\n\n return retval","def end_of_game(self):\n self.log.info('The game has ended')\n #\n end_callout = callout.FinishCallout(\n 'callout',\n 'finish_callout',\n ['exit_button'],\n S['end-game-callout'],\n self.deaths,\n )\n end_callout.show()\n #\n self.objects.append(end_callout)\n #\n while True:\n if end_callout.dismiss_button:\n music.fadeout(2)\n yield 2\n break\n yield 0\n #\n sys.exit(0)","def connection_closed(self):\n self.atv = None\n self._start_connect_loop()\n self._update_state(disconnected=True)","def display_end_game(self):\n game_view = self.get_view.get_game_view\n character = self.model.get_character\n\n if character.alive:\n game_view.game_win()\n else:\n game_view.game_over()\n\n game_view.update_display()","def END(self):\n log.debug(\"All packets successfully transmitted!\")","def end():\n curses.endwin()","def errConnectionLost(self):\n self.logger('stderr closed by process %d' % self._pid)","def check_game_end(self):\r\n\r\n if np.all(self.remaining == -1): # end of game\r\n self.show_results() # show the final results\r\n sys.exit() # exit the program\r","def tellIfEnded(self):\n self.congratulate()","def on_connection_closed(self):","def endCompetition(self):\n self.robot_exit = True","def handle_game_over(self, winner, end_state):\n #############################\n #\n #\n # YOUR CODE HERE\n #\n #\n ##############################\n print(\"Game over, these are the stats:\")\n print('Winner: ' + str(winner))\n print('End state: ' + str(end_state))","def event_game_over(self):\n print('Game over!')\n self._cmd_exit()","def endScreen(self, screen):\n\t\tmyfont = pygame.font.SysFont(\"Calibri\", 69)\n\t\tendLine1 = myfont.render(\"GAME OVER\", 1, (0, 0, 0))\n\t\tmyfont = pygame.font.SysFont(\"Arial\", 39)\n\t\tendLine2 = myfont.render(\"Final Score: \" + str(self.points), 1, (0, 0, 0))\n\t\tmyfont = pygame.font.SysFont(\"Arial\", 30)\n\t\tprintTime = myfont.render(\"TIME: \" +str(self.minutes) + \":\" + str(self.seconds), 1, (0,0,0))\n\t\tif self.seconds < 10:\n\t\t\tprintTime = myfont.render(\"TIME: \" +str(self.minutes) + \":0\" + str(self.seconds), 1, (0,0,0))\n\t\tscreen.fill((195,0,0))\n\t\tscreen.blit(endLine1, (150, 200))\n\t\tscreen.blit(endLine2, (175, 260))\n\t\tscreen.blit(printTime, (210, 320))\n\t\tpygame.display.flip()","def _close(self):\n\n\t\t# DEBUG message.\n\t\t_message(u'debug', u'images.ImageTracker.close', \\\n\t\t\tu\"Closed connection.\")","def eof_received(self):\n logger.debug(\"EOF from client, closing.\")\n self.connection_lost(None)","def closeConnection(self): \r\n \r\n try:\r\n ssh.close()\r\n feedback= '***Connection Closed***'\r\n return feedback\r\n except Exception as e:\r\n feedback= '***Connection failed to close :'+str(e)+'***'\r\n return feedback\r\n sys.exit(1)","def unload(self):\n self.logConnection(time.time(), self.host, self.port, 'server shutdown')\n Logger.unload(self)","def _end_game(self, winner_id=0):\r\n if winner_id == 0:\r\n # print(\"The game was a tie!\")\r\n pass\r\n else:\r\n # print(\"{0} has won the game!\".format(winner_id))\r\n pass\r\n self.winner = winner_id","def on_disconnect():\n print(\"User disconnected!\")","def goodbye(self, args):\n\t\tself.write_line(\"GOODBYE\")\n\t\tself.close();","def sendEnd(self):\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))","def end_game(self):\n self.game.stop_running()","def quit_game(self):\n self.done = True","def connection_lost(self, exc):\n logger.info('The server closed the connection')\n self.loop.stop()","def disconnect(self):\n print(\"<== Conexión cerrada ==>\")\n self.is_alive = False\n self._socket.close()","def end(self):\n print('\\nCompleted in {:.2f} seconds\\n'.format(time.time() - self.start_time))","def end_game(self):\n print(str(self.__players[0]._Player__name) + \" score is: \"\n + str(self.__fields[0].score))\n print(str(self.__players[1]._Player__name) + \" score is: \"\n + str(self.__fields[1].score))\n Game.play = False","def close(self):\n self.connection.close()\n print(\"Connection on port \" + str(self.port) + \" closed.\")"],"string":"[\n \"def on_connection_end() -> None:\\r\\n print(\\\"Connection lost with G-Earth\\\")\\r\\n print()\",\n \"def endSession(self):\\n if(self.verb >= DLS_VERB_HIGH):\\n print \\\"--Ending session with %s (no action)\\\" % (self.server)\",\n \"def end(self):\\n winners = mafia.str_player_list(self.game.winners())\\n logging.info(\\\"Game over! Winners: %s\\\" % winners)\\n\\n subject = \\\"%s: The End\\\" % self.name\\n body = \\\"Game over!\\\\n\\\\nCongratulations to %s for a well \\\" \\\\\\n \\\"(or poorly; I can't tell) played game!\\\" % winners\\n self.send_message(mafia.events.PUBLIC, subject, body)\",\n \"def endGame(self, message):\\n print(self.board)\\n print(\\\"Game over! \\\" + message)\\n self.gameOver = True\",\n \"def endGame(self):\\n pass\",\n \"def handle_close(self):\\r\\n if self.log_fh:\\r\\n self.log_fh.write(\\r\\n \\\"Server closed connection at %s. Shutting down.\\\\n\\\" %\\r\\n time())\\r\\n self.close()\",\n \"def handle_game_end(self, winner_color, win_reason): # possible GameHistory object...\\n\\n # TODO: implement this method\\n print('\\\\--------------Game End--------------/')\\n print(winner_color)\\n print(win_reason)\\n pass\",\n \"def game_exit(self):\\n self.set_state(GameState.EXITING)\\n self.game_stop()\\n self.game_log_statistics()\",\n \"def on_client_exit(self, game) -> None:\\n pass\",\n \"def end(self):\\n self.my_print(\\\"\\\\t[DONE]\\\", msg_types.INFO)\\n self.in_progress = False\",\n \"def api_end_game(self):\\n pass\",\n \"def server_exit():\\n return\",\n \"def endMessage(self):\",\n \"def end(self):\\n self.send_all(\\\"SHUTDOWN\\\") #On indique a tout le monde qu'on ferme\\n self.socket.shutdown(socket.SHUT_RDWR)\\n self.socket.close()\",\n \"def end(self):\\n # Update all the things.\\n end_font = pygame.font.SysFont(*END_FONT)\\n final_score = self.player.nest.acorn_count\\n message = \\\"Game over! Final score: {0}\\\".format(final_score)\\n text_surf = end_font.render(message, True, FONT_COLOUR)\\n text_rect = text_surf.get_rect()\\n text_rect.center = (SCREEN.width // 2, SCREEN.height // 2)\\n\\n # Draw all the things.\\n self.screen_surf.fill(BKGD_COLOUR)\\n self.screen_surf.blit(text_surf, text_rect)\\n\\n # Render the screen.\\n pygame.display.update()\\n\\n # The main game loop.\\n while self.mode is WorldMode.end:\\n self.handle_events()\",\n \"def handle_close(self):\\r\\n self.end_time = time.time()\\r\\n self.time_ran = self.end_time - self.start_time\\r\\n if self.status != 'PASS':\\r\\n server_log.info('Client {} aborted!'.format(self.client_id))\\r\\n self.status = 'ABORTED'\\r\\n self.close()\",\n \"def end():\\n logging.info(\\\"Execution Ended\\\")\",\n \"def EndSession( self ):\\r\\n\\r\\n self._socket.write( 'X' ) \\r\\n # self._connection.write( 'X' ).flush() \\r\\n\\r\\n return self.GetServerResponse()\",\n \"def shutdown():\\n shutdown_server()\\n return \\\"Shutting down server\\\"\",\n \"def disconnect():\\n logging.info('Client disconnected')\",\n \"def outConnectionLost(self):\\n self.logger('stdout closed by process %d' % self._pid)\",\n \"def exit(self):\\n self._status = \\\"\\\"\\n self._sock.settimeout(1.0)\\n self._sock.sendto(bytes(\\\"bla\\\", \\\"utf-8\\\"), (self._cfg.host, self._cfg.port))\",\n \"def _on_surface_disconnected(self):\\n\\n # Close the socket\\n self._client_socket.close()\\n\\n # Inform that the connection has been closed\\n print(\\\"Video stream from {} address closed successfully\\\".format(self._client_address))\",\n \"def handleClose(self):\\n logging.info(\\\"%s %s\\\", self.address, \\\"closed\\\")\\n self.logbook.clients_disconnected_count += 1\",\n \"def end_of_game(self):\\n end_game = pyip.inputYesNo(f'\\\\nDo you want to play again?: ')\\n\\n if end_game == 'no':\\n print('\\\\n-- GAME OVER --')\\n sys.exit()\\n elif end_game == 'yes':\\n self.game_counter += 1\",\n \"def server_close(self):\\n\\t\\tpass\",\n \"def handle_close(self):\\n print(self.addr, \\\"bye\\\")\\n self.close()\",\n \"def on_session_destroyed(session_context):\\n if data.AUTO_SHUTDOWN:\\n import sys\\n\\n sys.exit(\\n \\\"\\\\033[1;31mThe session has ended - tab closed or timeout. \\\\n\\\\n --- Terminating the Forest progam and relinquishing control of port. ---\\\\033[1;00m\\\"\\n )\",\n \"def end(self, won, reason):\\n pass\\n # replace with your end logic\",\n \"async def end(ctx):\\n if ctx.message.channel.name.lower() not in tod_channels:\\n return\\n\\n room = ctx.message.channel.name.lower()\\n if room not in tod_games:\\n await amor_manager.say(\\\"Truth Or Dare not in progress in {}\\\".format(room))\\n else:\\n host = tod_games[room]['host']\\n await amor_manager.say(\\\"Game Over in {}! Thank you to {} for hosting this game!\\\".format(room, host))\\n del tod_games[room]\",\n \"def do_QUIT(self):\\r\\n self.send_response(200)\\r\\n self.end_headers()\\r\\n self.server.stop = True\",\n \"def endGame(self, msg, win):\\n elapsedTime = time.time() - self.startTime\\n readableTime = str(int((elapsedTime / 60) / 60))\\n readableTime += \\\":\\\" + str(int(elapsedTime / 60))\\n readableTime += \\\":\\\" + str(elapsedTime % 60)[0:6]\\n msg +=\\\"Time: \\\" + readableTime\\n self.revealBombs(win)\\n self.flagLabel.configure(text=\\\"Flags: \\\"+str(self.numFlags))\\n messagebox.showinfo('Game Over', msg)\",\n \"def received_CLOSING(self):\\n\\n\\t\\tself.player_frame.notify_rival_closing()\\n\\t\\tself.player_frame.master.go_to_previous_screen(False)\",\n \"def handle_close(self):\\n LOGGER.debug(\\\"server closed\\\")\\n self.clear()\\n data = {}\\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\\n self.close()\\n\\n # close all and restart\\n asyncore.close_all()\",\n \"def end(self) -> None:\\n unicurses.endwin()\",\n \"def on_disconnect(self):\\n print('Client disconnected!')\",\n \"def _end(self):\\n\\n self.logger.msg1(\\\"Done\\\")\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\",\n \"def test_out_end_game(self):\\n # Run test\\n self.console.end_game(self.message)\\n\\n # Evaluate test\\n calls = [call(str(self.console.hangman)), call(self.message),\\n call(i18n.OUT_MSG_ANSWER.format(self.console.word.show()))]\\n self.console.c_out.assert_has_calls(calls)\",\n \"def screen_terminate(self):\\n self._channel.send('\\\\x04')\\n # make sure we didn't kill our connection\\n if self._channel.closed:\\n self.connected = False\\n print(\\\"!! Connection closed by remote host\\\")\\n return False\\n else:\\n rdata = '\\\\n'.join(self.recv_to_prompt())\\n return bool(re.search(r'screen is terminating', rdata, re.I|re.M))\",\n \"def endMyTurn(self):\\n try:\\n result = self.game.server.endEmpireTurn(self.game.authKey)\\n if result == 0:\\n if self.game.myEmpire['roundComplete'] == 1:\\n self.modeMsgBox('You have now un-ended your turn')\\n self.game.myEmpire['roundComplete'] = 0\\n else:\\n self.modeMsgBox('Your turn has been ended, thankyou')\\n self.game.myEmpire['roundComplete'] = 1\\n self.mainmenu.writeTextRoundEnds()\\n elif type(result) == types.StringType:\\n self.modeMsgBox(result)\\n else:\\n \\\"\\\"\\\"End Turn and wait for it to end\\\"\\\"\\\"\\n result = self.game.server.endRound(self.game.authKey)\\n self.game.server.logout(self.game.authKey)\\n from anw.modes.modelogin import ModeLogin\\n newMode = ModeLogin(self.game, 200)\\n self.game.enterMode(newMode)\\n except:\\n self.modeMsgBox('endMyTurn->Connection to Server Lost')\",\n \"def end(update, context) -> int:\\n update.callback_query.edit_message_text(\\n 'Bye! I hope we can talk again some day.')\\n\\n logger.info(\\\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\\\",\\n update.callback_query.message.chat.first_name)\\n return ConversationHandler.END\",\n \"async def connection_lost(self):\\n logging.info('connection dropped')\",\n \"def print_connection_terminated(pkt):\\n print_headers(pkt, overwrite_min=0)\\n print(red(\\\"!!!! Attention: TCP/OpenFlow Connection Terminated!!\\\\n\\\"))\",\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def endgame(winner):\",\n \"def end_of_game(self, winner):\\n pass\",\n \"def end_game(self):\\n self.end_writer.write(f\\\"You have won with {self.score} points! \\\" \\\\\\n \\\"Do you want to play again? (y/n)\\\", font=(\\\"Arial\\\", 16, \\\"normal\\\"))\\n turtle.Screen().onkey(None, \\\"Left\\\")\\n turtle.Screen().onkey(None, \\\"Right\\\")\\n turtle.Screen().onkey(None, \\\"Up\\\")\\n turtle.Screen().onkey(None, \\\"Down\\\")\",\n \"def playGameEnd(self, strVarName, value, message):\\n self.WebviewResponse = value\\n print \\\"Count of webview= \\\" + str(value)\\n self.Naomi.StartUp()\\n self.Naomi.printAndSay(\\\"Well, Can you say me how many red robots was displaying on the screen?\\\") # Print and say (if the robot is connected) the verbal response\\n StartIteration()\",\n \"def shutdown(self):\\t\\r\\n\\t\\tself.is_running = False\\r\\n\\t\\tfor connection in self.established_connection_list:\\r\\n\\t\\t\\tconnection.send('The server has been shutdown adruptly by the server owner.\\\\n')\\r\\n\\t\\t\\tconnection.socket_send()\",\n \"def report_end(self):\\r\\n print(f\\\"\\\\n\\\\n\\\\n{datetime.now()}\\\\tDone\\\\n\\\")\",\n \"def server_close(self):\\n\\t\\tself.socket.close()\",\n \"def end_game(self, game_state: str):\\n if game_state == \\\"win\\\":\\n end_message = \\\"{0} wins! Would you like to play again?\\\".format(self.players[self.game.whose_turn])\\n else:\\n end_message = \\\"Cat's game! Would you like to play again?\\\"\\n play_again = messagebox.askyesno(title='Game over', message=end_message)\\n if play_again:\\n self.game.reset_game_data()\\n self.reset_game_ui()\\n else:\\n self.window.destroy()\",\n \"def end(self):\\n if self.conn:\\n self.conn.close()\\n self.conn = None\\n #print(\\\"closed.\\\")\",\n \"async def end_session(self):\\n\\t\\t...\",\n \"def end(self):\\n self._log.debug('%s: doing ..', __class__.__name__)\\n self._log.debug('%s: done.', __class__.__name__)\",\n \"def close(self):\\n # Report server that connection is closed\\n self._socket.sendall(''.encode())\\n self._socket.close()\",\n \"def end():\\n return say()\",\n \"async def on_end(self, game_result: Result):\",\n \"def on_close(ws):\\n logging.info(\\\"### Web-socket Connection Closed ###\\\")\",\n \"def bcp_game_end(self, **kwargs):\\n self.player = None\\n self.events.post('game_ended', **kwargs)\",\n \"def _HandleShutdown(self):\\n self.send_response(httplib.OK)\\n self.send_header('Content-Type', 'text/plain')\\n self.end_headers()\\n self.wfile.write('API Server Quitting')\\n self.server.shutdown()\",\n \"def close(self):\\n\\t\\tttotal = time.time() - self.stats['startloop'] - self.stats['waittime']\\n\\t\\tapDisplay.printColor(\\\"COMPLETE LOOP:\\\\t\\\"+apDisplay.timeString(ttotal)+\\n\\t\\t\\t\\\" for \\\"+str(self.stats[\\\"count\\\"]-1)+\\\" series\\\",\\\"green\\\")\\n\\t\\tappionScript.AppionScript.close(self)\",\n \"def end_game(self):\\n pygame.event.clear()\\n self.screen.fill(BLACK)\\n self.show_on_screen(\\\"GAME OVER\\\", (500, 600), font_size=50)\\n self.show_on_screen(\\\"Press \\\\\\\"N\\\\\\\" to start a new game\\\", (500, 650), font_size=30)\\n self.show_on_screen(\\\"Press \\\\\\\"ESC\\\\\\\" to exit\\\", (500, 710), font_size=30)\\n self.show_on_screen(\\\"SCORE: \\\" + str(self.score), (500, 560), font_size=50)\\n pygame.display.flip()\\n\\n # clears previously pressed key\\n pygame.event.wait()\\n while True:\\n event = pygame.event.wait()\\n if event.type == pygame.QUIT:\\n pygame.quit()\\n sys.exit()\\n elif event.type == pygame.KEYDOWN:\\n if event.key == pygame.K_ESCAPE:\\n pygame.quit()\\n sys.exit()\\n elif event.key == pygame.K_n:\\n self.reset_lander()\\n self.play()\",\n \"def end_turn(self):\\n if self.turn == BLUE:\\n self.turn = RED\\n else:\\n self.turn = BLUE\\n\\n self.hop = False\\n if self.isTerminal():\\n # if self.turn == BLUE:\\n # print('RED WINS!')\\n # else:\\n # print('BLUE WINS!')\\n if self.loop_mode:\\n self.endit = True\",\n \"def end(self):\\n self.shutdown = True\",\n \"def finish(self):\\n global log_th\\n log_th.log_info('{} disconnected from {} thread'.format(self.client_ip, threading.current_thread().name))\",\n \"def stop(self):\\n # This will result in a -1 being written to the stream, indicating the server is closing down.\\n self._write_stream(-1, \\\"\\\")\\n\\n self.__channel.close()\\n self.__channel = None\\n self.__sys.stdout = self.__old_stdout\\n self.__sys.stderr = self.__old_stderr\",\n \"def close():\\r\\n logging.info(\\\"Program has Finished\\\")\\r\\n logging.shutdown()\",\n \"def _exit_print(self):\\n if self.cur_frame >= self.config.MAX_FRAMES:\\n self.stopped = True\",\n \"def gameEnd():\\n result = cs411_game.gameEnd(request.form.get('Games_Game_ID'),\\n\\trequest.form.get('Contestant_Coontestant_ID'))\\n if result == 1: return prepJSON({\\\"message\\\": \\\"success\\\"})\\n else: raise InvalidUsage(result[\\\"message\\\"], 403)\",\n \"def exit(self):\\n self._channel.sendall(\\\"exit\\\\n\\\")\\n time.sleep(0.2)\\n if not self._channel.closed:\\n if self._channel.recv_ready():\\n # flush the recv buffer of the new prompt\\n self._channel.recv(4096)\\n retval = False\\n else:\\n self.close()\\n self.connected = False\\n retval = True\\n\\n return retval\",\n \"def end_of_game(self):\\n self.log.info('The game has ended')\\n #\\n end_callout = callout.FinishCallout(\\n 'callout',\\n 'finish_callout',\\n ['exit_button'],\\n S['end-game-callout'],\\n self.deaths,\\n )\\n end_callout.show()\\n #\\n self.objects.append(end_callout)\\n #\\n while True:\\n if end_callout.dismiss_button:\\n music.fadeout(2)\\n yield 2\\n break\\n yield 0\\n #\\n sys.exit(0)\",\n \"def connection_closed(self):\\n self.atv = None\\n self._start_connect_loop()\\n self._update_state(disconnected=True)\",\n \"def display_end_game(self):\\n game_view = self.get_view.get_game_view\\n character = self.model.get_character\\n\\n if character.alive:\\n game_view.game_win()\\n else:\\n game_view.game_over()\\n\\n game_view.update_display()\",\n \"def END(self):\\n log.debug(\\\"All packets successfully transmitted!\\\")\",\n \"def end():\\n curses.endwin()\",\n \"def errConnectionLost(self):\\n self.logger('stderr closed by process %d' % self._pid)\",\n \"def check_game_end(self):\\r\\n\\r\\n if np.all(self.remaining == -1): # end of game\\r\\n self.show_results() # show the final results\\r\\n sys.exit() # exit the program\\r\",\n \"def tellIfEnded(self):\\n self.congratulate()\",\n \"def on_connection_closed(self):\",\n \"def endCompetition(self):\\n self.robot_exit = True\",\n \"def handle_game_over(self, winner, end_state):\\n #############################\\n #\\n #\\n # YOUR CODE HERE\\n #\\n #\\n ##############################\\n print(\\\"Game over, these are the stats:\\\")\\n print('Winner: ' + str(winner))\\n print('End state: ' + str(end_state))\",\n \"def event_game_over(self):\\n print('Game over!')\\n self._cmd_exit()\",\n \"def endScreen(self, screen):\\n\\t\\tmyfont = pygame.font.SysFont(\\\"Calibri\\\", 69)\\n\\t\\tendLine1 = myfont.render(\\\"GAME OVER\\\", 1, (0, 0, 0))\\n\\t\\tmyfont = pygame.font.SysFont(\\\"Arial\\\", 39)\\n\\t\\tendLine2 = myfont.render(\\\"Final Score: \\\" + str(self.points), 1, (0, 0, 0))\\n\\t\\tmyfont = pygame.font.SysFont(\\\"Arial\\\", 30)\\n\\t\\tprintTime = myfont.render(\\\"TIME: \\\" +str(self.minutes) + \\\":\\\" + str(self.seconds), 1, (0,0,0))\\n\\t\\tif self.seconds < 10:\\n\\t\\t\\tprintTime = myfont.render(\\\"TIME: \\\" +str(self.minutes) + \\\":0\\\" + str(self.seconds), 1, (0,0,0))\\n\\t\\tscreen.fill((195,0,0))\\n\\t\\tscreen.blit(endLine1, (150, 200))\\n\\t\\tscreen.blit(endLine2, (175, 260))\\n\\t\\tscreen.blit(printTime, (210, 320))\\n\\t\\tpygame.display.flip()\",\n \"def _close(self):\\n\\n\\t\\t# DEBUG message.\\n\\t\\t_message(u'debug', u'images.ImageTracker.close', \\\\\\n\\t\\t\\tu\\\"Closed connection.\\\")\",\n \"def eof_received(self):\\n logger.debug(\\\"EOF from client, closing.\\\")\\n self.connection_lost(None)\",\n \"def closeConnection(self): \\r\\n \\r\\n try:\\r\\n ssh.close()\\r\\n feedback= '***Connection Closed***'\\r\\n return feedback\\r\\n except Exception as e:\\r\\n feedback= '***Connection failed to close :'+str(e)+'***'\\r\\n return feedback\\r\\n sys.exit(1)\",\n \"def unload(self):\\n self.logConnection(time.time(), self.host, self.port, 'server shutdown')\\n Logger.unload(self)\",\n \"def _end_game(self, winner_id=0):\\r\\n if winner_id == 0:\\r\\n # print(\\\"The game was a tie!\\\")\\r\\n pass\\r\\n else:\\r\\n # print(\\\"{0} has won the game!\\\".format(winner_id))\\r\\n pass\\r\\n self.winner = winner_id\",\n \"def on_disconnect():\\n print(\\\"User disconnected!\\\")\",\n \"def goodbye(self, args):\\n\\t\\tself.write_line(\\\"GOODBYE\\\")\\n\\t\\tself.close();\",\n \"def sendEnd(self):\\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))\",\n \"def end_game(self):\\n self.game.stop_running()\",\n \"def quit_game(self):\\n self.done = True\",\n \"def connection_lost(self, exc):\\n logger.info('The server closed the connection')\\n self.loop.stop()\",\n \"def disconnect(self):\\n print(\\\"<== Conexión cerrada ==>\\\")\\n self.is_alive = False\\n self._socket.close()\",\n \"def end(self):\\n print('\\\\nCompleted in {:.2f} seconds\\\\n'.format(time.time() - self.start_time))\",\n \"def end_game(self):\\n print(str(self.__players[0]._Player__name) + \\\" score is: \\\"\\n + str(self.__fields[0].score))\\n print(str(self.__players[1]._Player__name) + \\\" score is: \\\"\\n + str(self.__fields[1].score))\\n Game.play = False\",\n \"def close(self):\\n self.connection.close()\\n print(\\\"Connection on port \\\" + str(self.port) + \\\" closed.\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7442786","0.7357788","0.70812464","0.7068161","0.6758912","0.67495376","0.6671296","0.6662621","0.66588444","0.6629597","0.6612989","0.6597301","0.658959","0.6576882","0.657107","0.65640134","0.65556294","0.64894456","0.6479372","0.64617205","0.64595443","0.64184713","0.6407799","0.64009345","0.6375154","0.6372171","0.63383776","0.6331625","0.63315636","0.6331007","0.63273096","0.63191587","0.63174284","0.6307823","0.6301279","0.6293671","0.6284317","0.62737125","0.6265939","0.6264826","0.62530136","0.62517995","0.6247512","0.6226207","0.6225419","0.62130445","0.6212272","0.6211774","0.62109494","0.6208226","0.62011665","0.6188803","0.61881775","0.6187788","0.61823493","0.6181095","0.61761194","0.61748224","0.6163598","0.61624616","0.6160494","0.61594135","0.6158299","0.61552936","0.6151105","0.6149988","0.6144937","0.614104","0.61376834","0.61324364","0.61211914","0.6114749","0.61122614","0.61098963","0.61046994","0.60939115","0.6081973","0.6081384","0.6078904","0.6070956","0.60700315","0.60679305","0.60667914","0.60610753","0.6060096","0.6050384","0.6043991","0.6038073","0.6033922","0.6029948","0.60181624","0.6015568","0.601505","0.6014502","0.60128295","0.6005372","0.6001055","0.59985423","0.59974295","0.5997425"],"string":"[\n \"0.7442786\",\n \"0.7357788\",\n \"0.70812464\",\n \"0.7068161\",\n \"0.6758912\",\n \"0.67495376\",\n \"0.6671296\",\n \"0.6662621\",\n \"0.66588444\",\n \"0.6629597\",\n \"0.6612989\",\n \"0.6597301\",\n \"0.658959\",\n \"0.6576882\",\n \"0.657107\",\n \"0.65640134\",\n \"0.65556294\",\n \"0.64894456\",\n \"0.6479372\",\n \"0.64617205\",\n \"0.64595443\",\n \"0.64184713\",\n \"0.6407799\",\n \"0.64009345\",\n \"0.6375154\",\n \"0.6372171\",\n \"0.63383776\",\n \"0.6331625\",\n \"0.63315636\",\n \"0.6331007\",\n \"0.63273096\",\n \"0.63191587\",\n \"0.63174284\",\n \"0.6307823\",\n \"0.6301279\",\n \"0.6293671\",\n \"0.6284317\",\n \"0.62737125\",\n \"0.6265939\",\n \"0.6264826\",\n \"0.62530136\",\n \"0.62517995\",\n \"0.6247512\",\n \"0.6226207\",\n \"0.6225419\",\n \"0.62130445\",\n \"0.6212272\",\n \"0.6211774\",\n \"0.62109494\",\n \"0.6208226\",\n \"0.62011665\",\n \"0.6188803\",\n \"0.61881775\",\n \"0.6187788\",\n \"0.61823493\",\n \"0.6181095\",\n \"0.61761194\",\n \"0.61748224\",\n \"0.6163598\",\n \"0.61624616\",\n \"0.6160494\",\n \"0.61594135\",\n \"0.6158299\",\n \"0.61552936\",\n \"0.6151105\",\n \"0.6149988\",\n \"0.6144937\",\n \"0.614104\",\n \"0.61376834\",\n \"0.61324364\",\n \"0.61211914\",\n \"0.6114749\",\n \"0.61122614\",\n \"0.61098963\",\n \"0.61046994\",\n \"0.60939115\",\n \"0.6081973\",\n \"0.6081384\",\n \"0.6078904\",\n \"0.6070956\",\n \"0.60700315\",\n \"0.60679305\",\n \"0.60667914\",\n \"0.60610753\",\n \"0.6060096\",\n \"0.6050384\",\n \"0.6043991\",\n \"0.6038073\",\n \"0.6033922\",\n \"0.6029948\",\n \"0.60181624\",\n \"0.6015568\",\n \"0.601505\",\n \"0.6014502\",\n \"0.60128295\",\n \"0.6005372\",\n \"0.6001055\",\n \"0.59985423\",\n \"0.59974295\",\n \"0.5997425\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78706425"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17453,"cells":{"query":{"kind":"string","value":"Find the next empty square in the board."},"document":{"kind":"string","value":"def _find_empty_cell(self):\n\n for r, row in enumerate(self._board):\n for c, cell in enumerate(row):\n if cell is None:\n return r, c"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_next_empty_cell(self):\n for row in range(len(self.grid)):\n for col in range(len(self.grid[0])):\n if self.grid[row][col] == 0:\n return (row, col)\n return None","def find_empty_squares(board):\n for i in range(len(board)):\n for j in range(len(board[0])):\n if board[i][j] == 0:\n return (i,j) #row , column\n\n #if there are no blank squres\n return None","def find_next_empty_cell(grid):\n for i, row in enumerate(grid):\n for j, col in enumerate(row):\n if col == 0:\n return (i, j)\n return None","def find_blank_square(self, state):\n\n return state.index(0)","def firstEmptyCell(board):\r\n for i in range(9):\r\n for j in range(9):\r\n if board[i][j] == 0:\r\n return (i, j) # row, col\r\n return None","def find_empty(self):\n num_rows = len(self.board)\n num_cols = len(self.board[0])\n\n for i in range(num_rows):\n for j in range(num_cols):\n if self.board[i][j] == 0:\n return (i, j)","def find_empty(board):\n for ii in range(len(board)):\n for jj in range(len(board[ii])):\n if board[ii][jj] == 0:\n print('Empty: ', (jj , ii)) # column, row\n return jj, ii # column, row\n return None","def playable_square(self, col) -> Square:\n for row in reversed(range(len(self.state[0]))):\n square = Square(row, col)\n if self.is_empty(square):\n return square","def find_empty_space(board: list) -> tuple:\n board_length = len(board)\n for i in range(board_length):\n for j in range(board_length):\n if board[i][j] == 0:\n return (i,j)","def find_empty(game_board):\n for row in range(len(game_board)):\n for col in range(len(game_board[row])):\n if len(game_board[row][col]) == 2:\n return row, col\n for row in range(len(game_board)):\n for col in range(len(game_board[row])):\n if len(game_board[row][col]) >= 3:\n return row, col\n\n return None","def find_empty(grid):\n for i in range(LEN_GRID):\n for j in range(LEN_GRID):\n if grid[i][j] == 0:\n return (i, j) # row, col\n return None","def findEmpty(grid):\n for x in range(len(grid.board)):\n for y in range(len(grid.board[0])):\n if grid.board[x][y] == 0:\n return [x,y]","def is_valid_move(board, col):\n\n for row in range(len(board)-1, -1, -1):\n if board[row][col] == Utilities.EMPTY:\n return row\n\n return -1","def find_bracket(self, square, player, board, direction):\r\n curr = square+ direction\r\n opp = self.opponent(player)\r\n if(board[curr]!=opp):\r\n return None\r\n while(self.is_valid(curr) and board[curr]==opp):\r\n curr+=direction\r\n if(self.is_valid(curr) and board[curr] == player):\r\n return curr\r\n return None","def player(board):\n if not terminal(board):\n cnt_x = 0\n cnt_o = 0\n for i in range(3):\n for j in range(3):\n if board[i][j] == EMPTY:\n continue\n elif board[i][j] == X:\n cnt_x += 1\n else:\n cnt_o += 1\n\n if cnt_x <= cnt_o:\n return X\n else:\n return O\n else:\n return None","def get_next_board(Board):\n paddedBoard = pad_board(Board)\n\n dims = paddedBoard.shape\n rows = dims[0] - 2\n cols = dims[1] - 2\n\n nextBoard = np.zeros((rows, cols), dtype=int)\n\n for r in range(1, dims[0] - 1):\n for c in range(1, dims[1] - 1):\n numNeighbours = check_neighbours(r, c, paddedBoard)\n # ... ... ... ... ...\n # ... Game Logic ...\n # ... ... ... ... ...\n\n return nextBoard","def find_blank_cell(self, board: list):\n cells = {}\n for i in range(9): # Iterate over rows\n for j in range(9): # Iterate over columns\n if board[i][j] == 0:\n cells[str(i) + ' ' + str(j)] = self.count_numbers(board, j, i)\n m = max(cells.values())\n for k in cells:\n if cells[k] == m:\n s = k.split()\n x, y = int(s[1]), int(s[0])\n return x, y","def player(board):\n moves = 0\n\n for row in range(len(board)):\n for column in range(len(board[row])):\n if board[row][column] != EMPTY:\n moves+=1\n\n if moves % 2 == 0:\n return X\n return O\n\n # raise NotImplementedError","def terminal(board):\n emptyCounter = 0\n for row in board:\n emptyCounter += row.count(EMPTY)\n if emptyCounter == 0:\n return True\n elif winner(board) is not None:\n return True\n else:\n return False","def find_empty_space(self, state):\r\n for i in range(3):\r\n for j in range(3):\r\n if state[i][j] == 0:\r\n return (i, j)","def player(board):\n plays = 0\n\n # Count non-empty squares\n for i in range(3):\n for j in range(3):\n if board[i][j] != EMPTY:\n plays += 1\n\n # Even number of plays -> X's turn\n if plays % 2 == 0:\n return X\n else:\n return O","def solve(self):\n\n # Create the square the represents the beginning of the game when no-one is on the board\n # and add it to the queue\n #\n zero_square = _BoardSquare(square_number=0,\n die_rolls=0,\n previous_square=None,\n previous_roll=0)\n self._board_square_queue.put(zero_square)\n\n while not self._board_square_queue.empty():\n next_node = self._board_square_queue.get()\n square_number = next_node.square_number\n for die_value in range(0, DIE_FACES):\n roll = die_value + 1\n next_square_number = square_number + roll\n\n # when we are near the end, we will get\n # rolls that put us past the last square\n # we can skip those\n if next_square_number > 100:\n continue\n\n # if the board square we land on is a chute or ladder\n # we jump to the target board square instead\n if next_square_number in self._shortcuts:\n next_square_number = self._shortcuts[next_square_number]\n\n # Look up the square in the board list\n # which acts as a hash lookup on board square number\n try:\n existing_square = self._board[next_square_number - 1]\n except Exception:\n # this would happen if we tried to access the\n # hash list oustside of its bounds\n print(\"next_square_number: %d\" % next_square_number)\n raise\n\n if existing_square == None:\n # The square is not in the list yet, so this is\n # the first time we have landed on it\n # create a BoardSquare to represent it\n # and add it to the hash-list\n # and add it to the queue\n square = _BoardSquare(square_number=next_square_number,\n die_rolls=next_node.die_rolls + 1,\n previous_square=next_node,\n previous_roll=roll)\n\n self._board[next_square_number - 1] = square\n self._board_square_queue.put(square)\n elif existing_square.die_rolls > next_node.die_rolls + 1:\n existing_square.die_rolls = next_node.die_rolls + 1\n existing_square.previous_square = next_node\n existing_square.previous_roll = roll\n self._board_square_queue.put(existing_square)\n\n #print(self)\n #self.print_solution()","def terminal(board):\n if winner(board) == X or winner(board) == O:\n return True\n \n for row in range(len(board)):\n for col in range(len(board[0])):\n if board[row][col] == EMPTY:\n return False\n\n return True","def walk_down(self):\n if self.col_num == len(self.master_grid.matrix[0])-1 and self.row_num == len(self.master_grid.matrix)-1:\n return None\n if self.row_num == len(self.master_grid.matrix)-1:\n return Square(0, self.col_num+1, self.master_grid)\n else:\n return Square(self.row_num + 1, self.col_num, self.master_grid)","def get_square_index(self, cell):\n return next(s for s, square in enumerate(self.squares) if cell in square)","def terminal(board):\n if winner(board) == X or winner(board) == O:\n return True\n ended = True\n for i in range(3):\n for j in range(3):\n if board[i][j] == EMPTY:\n ended = False\n return ended","def terminal(board) -> bool:\n if winner(board) is not None:\n return True\n\n for row in board:\n for column in row:\n if column == EMPTY:\n return False\n return True","def find_first_free_cell(board, picked_column):\n for row in reversed(range(len(board))):\n if board[row][picked_column] == 0:\n return row","def terminal(board):\n if winner(board) is None:\n for row in board:\n for cell in row:\n if cell == EMPTY:\n return False\n \n # Returns true if there's a winner or no empty space\n return True","def get_empty_squares(self):\n empty = []\n for row in range(self._dim):\n for col in range(self._dim):\n if self._board[row][col] == EMPTY:\n empty.append((row, col))\n return empty","def get_next_available_cell(self, starting_cell, board):\n row = starting_cell.row\n column = starting_cell.column + self.total_column_req + 1\n if column >= board.columns:\n row = starting_cell.row + self.total_rows_req + 1\n column = 0\n\n if row >= board.rows:\n print('Cannot place ship outside board.')\n return None\n return board.grid[row][column]","def terminal(board):\n noVal = False\n for i in board:\n for j in i:\n if j == EMPTY:\n noVal = True\n if not noVal:\n return True\n \n if winner(board) == X or winner(board) == O:\n return True\n \n\n return False","def player(board):\n turn = 0\n for i in range(3):\n for j in range(3):\n if board[i][j] != EMPTY:\n turn+=1\n if turn % 2 != 0:\n return O\n else:\n return X","def terminal(board):\n if winner(board) == X or winner(board) == O:\n return True\n\n for i in range(len(board)):\n for j in range(len(board)):\n if board[i][j] == EMPTY:\n return False\n\n return True","def board_is_empty():\n if STATE[-1].strip() == '-' * 7:\n return True\n else:\n return False","def solve(board):\n find = find_blank(board)\n \n if not find:\n return True\n #will loop through untill the blanks are filled\n\n\n\n else:\n row, col = find\n\n for i in range(1, 10):\n if valid(board, i, (row, col)):\n board[row][col] = i\n\n if solve(board):\n return True\n board[row][col] = 0\n return False","def winner(board):\n for i in range(len(board)):\n\n # Check rows\n if board[i][0] == board[i][1] == board[i][2] and not board[i][1] == EMPTY:\n return board[i][1]\n\n # Check columns\n elif board[0][i] == board[1][i] == board[2][i] and not board[1][i] == EMPTY:\n return board[1][i]\n\n # Check diagonals\n if board[0][0] == board[1][1] == board[2][2] and not board[1][1] == EMPTY:\n return board[1][1]\n\n if board[2][0] == board[1][1] == board[0][2] and not board[1][1] == EMPTY:\n return board[1][1]\n\n # No winner if get to this point\n return None","def terminal(board):\n if sum(row.count(EMPTY) for row in board) == 0 or winner(board) == X or winner(board) == O:\n return True\n else:\n return False","def find_bracket(square, player, board, direction):\n bracket = square + direction\n if board[bracket] == player:\n return None\n opp = Othello.opponent(player)\n while board[bracket] == opp:\n bracket += direction\n return None if board[bracket] in (OUTER, EMPTY) else bracket","def terminal(board):\n if not winner(board):\n for i in range(3):\n for j in range(3):\n if board[i][j] == EMPTY:\n return False\n return True","def find_unsettled_spot(self):\n\t\tfor i in range(9):\n\t\t\tfor j in range(9):\n\t\t\t\tif self.grid[i][j] == 0:\n\t\t\t\t\treturn i, j\n\t\treturn","def firstMove(board):\r\n x = board.size / 2\r\n return (x, x)","def winner(board):\n for i in range(3):\n firstnumber=board[i][0]\n if firstnumber!=EMPTY:\n secondnumber=board[i][1]\n if secondnumber==firstnumber:\n if board[i][2]==secondnumber:\n return secondnumber\n else:\n continue\n else:\n continue\n else:\n continue\n for i in range(3):\n firstnumber=board[0][i]\n if firstnumber!=EMPTY:\n secondnumber=board[1][i]\n if secondnumber==firstnumber:\n if board[2][i]==secondnumber:\n return secondnumber\n else:\n continue\n else:\n continue\n else:\n continue\n firstnumber=board[0][0]\n if firstnumber!= EMPTY:\n if board[1][1]==firstnumber:\n if board[2][2]==firstnumber:\n return firstnumber\n firstnumber=board[2][0]\n if firstnumber!= EMPTY:\n if board[1][1]==firstnumber:\n if board[0][2]==firstnumber:\n return firstnumber\n return None\n raise NotImplementedError","def make_random_move(self):\n # get copy of the empty board\n board = set([(i, j) for i in range(self.height) for j in range(self.width)])\n\n for move in board:\n if not move in self.moves_made and not move in self.mines:\n return move\n\n return None","def find_empty_cell(d):\n for i in range(5):\n if d[i][x-1] == '.':\n y = i\n return y","def findEmptyCell(board: str, threeInRow: Tuple[int, int, int]) -> int:\n emptyCells = [\n index for index in threeInRow if isAvailable(board, index)]\n return choice(emptyCells)","def is_white_square(square):\n file = chess.square_file(square)\n rank = chess.square_rank(square)\n if (file + rank) % 2 == 0:\n return 1\n else:\n return 0","def terminal(board):\n var = winner(board)\n count = 0\n rows = 3\n columns = 3\n for i in range(rows):\n for j in range(columns):\n if board[i][j] != EMPTY:\n count += 1\n if count == 9 or var is not None:\n return True\n else:\n return False\n\n #raise NotImplementedError","def check_board(board):\n\tgrid = board.grid\n\tboard_size = board.size\n\t# Get diagonals\n\tleft_diag = [grid[i][i] for i in range (0, board_size)]\n\tright_diag = [grid[i][board_size - 1 - i] for i in range (0, board_size)]\n\n\t# Check columns and rows\n\tfor i in range(0, board_size):\n\t\tprev_character_col = grid[0][i]\n\t\tfor j in range(0, board_size):\n\t\t\tcurrent_character_col = grid[j][i]\n\t\t\tif(current_character_col == board.terminal or current_character_col != prev_character_col):\n\t\t\t\tprev_character_col = None\n\t\t\t\tbreak\n\t\tif(prev_character_col):\n\t\t\treturn prev_character_col\n\n\tfor i in range(0, board_size):\n\t\tprev_character_row = grid[i][0]\n\t\tfor j in range(0, board_size):\n\t\t\tcurrent_character_row = grid[i][j]\n\t\t\tif(current_character_row == board.terminal or current_character_row != prev_character_row):\n\t\t\t\tprev_character_row = None\n\t\t\t\tbreak\n\t\tif(prev_character_row):\n\t\t\treturn prev_character_row\n\n\t# Check diagonals\n\tleft_diag = list(set(left_diag))\n\tright_diag = list(set(right_diag))\n\n\tif(len(left_diag) == 1 and left_diag[0] != board.terminal):\n\t\treturn left_diag[0]\n\n\tif(len(right_diag) == 1 and right_diag[0] != board.terminal):\n\t\treturn right_diag[0]\n\n\tavail_positions = board.empty_spots()\n\tif(len(avail_positions) == 0):\n\t\treturn cnsts.DRAW\n\telse:\n\t\treturn cnsts.IN_PROGRESS","def terminal(board):\r\n if winner(board) == \"X\" or winner(board) == \"O\":\r\n return True\r\n \r\n for row in board:\r\n for col in row:\r\n if col == EMPTY:\r\n return False\r\n else:\r\n return True\r\n\r\n raise NotImplementedError","def terminal(board):\n if winner(board) is not None:\n return True\n non_empty_count=0\n for i in range(3):\n for j in range(3):\n if board[i][j]==EMPTY:\n return False\n else:\n non_empty_count+=1\n if(non_empty_count==9):\n return True\n return False\n raise NotImplementedError","def square_empty(column, row):\n if np.flipud(STATE)[row][column] == '-':\n return True\n else:\n return False","def test_initial_board():\n board = Board()\n\n for i in range(board.get_grid_size()[0]):\n for j in range(board.get_grid_size()[1]):\n assert board.get_token(i, j) == Board.EMPTY","def get_empty_square(self) -> list:\n empty_square = []\n for line_index in range(len(self.grid)):\n for col_index in range(len(self.grid[line_index])):\n if self.grid[line_index][col_index].color is None:\n empty_square.append((line_index, col_index))\n\n return empty_square","def winner(board):\n\n # Check none empty horizontals\n for i in range(3):\n if board[i][0] and board[i][0] == board[i][1] == board[i][2]:\n return board[i][0]\n\n # Check none empty verticals\n for j in range(3):\n if board[0][j] and board[0][j] == board[1][j] == board[2][j]:\n return board[0][j]\n\n # Check none empty L-R diagonal\n if board[0][0] and board[0][0] == board[1][1] == board[2][2]:\n return board[0][0]\n\n # Check none empty R-L diagonal\n if board[0][2] and board[0][2] == board[1][1] == board[2][0]:\n return board[0][2]","def winner(board):\n # check columns\n for j in range(3):\n if board[1][j] == board[0][j] and board[0][j] == board[2][j] and board[1][j] != EMPTY:\n return board[1][j]\n # check rows\n for i in range(3):\n if board[i][0] == board[i][1] and board[i][1] == board[i][2] and board[i][0] != EMPTY:\n return board[i][0]\n # check diagnols\n if board[0][0] == board[1][1] and board[1][1] == board[2][2] and board[0][0] != EMPTY:\n return board[1][1]\n if board[0][2] == board[1][1] and board[1][1] == board[2][0] and board[0][2] != EMPTY:\n return board[1][1]\n return None","def player(board):\n\n # Game is over\n if terminal(board):\n return None\n\n # Count number of occurences of X and O\n x_count = 0\n o_count = 0\n for row in board:\n for box in row:\n if box == X:\n x_count = x_count + 1\n elif box == O:\n o_count = o_count + 1\n # When move count is tied, X is next\n if x_count <= o_count:\n return X\n # When X has moved once more than O, next move is O\n else:\n return O","def find_empty(bo):\n for i in range(len(bo)):\n for j in range(len(bo[0])):\n if bo[i][j] == 0:\n return (i, j)\n\n return None","def winner(board):\n # Checking for 3 in a row\n for row in board:\n if row[0] is not EMPTY and row[0] == row[1] == row[2]:\n return row[0]\n\n # Checking for 3 in a col\n for col in range(len(board)):\n if board[0][col] is not EMPTY and board[0][col] == board[1][col] == board[2][col]:\n return board[0][col]\n\n # Checking for Diagonals\n if board[0][0] is not EMPTY and board[0][0] == board[1][1] == board[2][2]:\n return board[0][0]\n \n if board[0][2] is not EMPTY and board[0][2] == board[2][0] == board[1][1]:\n return board[0][2]\n\n return None","def terminal(board):\n if winner(board) == None:\n for i in range(3):\n if EMPTY in board[i]:\n return False\n\n return True\n\n return True","def find_empty(puzzle):\r\n empty_squares = []\r\n for y in range(len(puzzle.squares)):\r\n for x in range(len(puzzle.squares[0])):\r\n if puzzle.squares[y][x].is_editable() is True:\r\n empty_squares.append((x, y))\r\n return empty_squares","def terminal(board):\n # If winner game is over\n if winner(board) is not None:\n return True\n else:\n # Check number of blanks remaining, if any left and no winner game is not over\n for i in range(len(board)):\n for j in range(len(board)):\n if board[i][j] == EMPTY:\n return False\n return True","def get_next_cell(self):\r\n if len(self.queue) == 0:\r\n return\r\n val = self.peek_queue()\r\n self.queue.remove(val)\r\n return val[0]","def get_empty_cells(board):\n empty_cells = [idx for idx, e in enumerate(board) if e == ' ']\n return empty_cells","def winner(board):\n if board[0][0] != EMPTY and (board[0][0] == board[0][1] == board[0][2] \n or board[0][0] == board[1][1] == board[2][2] \n or board[0][0] == board[1][0] == board[2][0]):\n return board[0][0]\n\n elif board[1][1] != EMPTY and (board[1][0] == board[1][1] == board[1][2]\n or board[0][1] == board[1][1] == board[2][1]):\n return board[1][1]\n \n elif board[2][2] != EMPTY and (board[0][2] == board[1][2] == board[2][2]\n or board[2][0] == board[2][1] == board[2][2]):\n return board[2][2]\n \n elif board[2][0] != EMPTY and (board[2][0] == board[1][1] == board[0][2]):\n return board[2][0]\n \n else:\n None","def get_checker(self, square):\n\n logger.debug(u'get_checker({})'.format(square))\n\n row, column = square\n if row < 0 or row > 7 or column < 0 or column > 7:\n return None\n else:\n return self.squares[row][column]","def solve(board):\r\n cell = firstEmptyCell(board)\r\n # Base case for recursion\r\n # no empty cells, also all other cells filled validly, so board solved\r\n if cell is None:\r\n return True\r\n \r\n x, y = cell\r\n for i in range(1,10):\r\n # if i is valid at cell in board, go to next empty cell (recursively)\r\n if isvalid(board, i, cell):\r\n board[x][y] = i\r\n\r\n # try filling next empty cell till successful\r\n if solve(board):\r\n return True\r\n\r\n # i is invalid in cell, try i+1\r\n board[x][y] = 0\r\n \r\n # board can't be solved\r\n return False","def is_empty(self, square: Square):\n return self.state[0][square.row][square.col] == 0 and self.state[1][square.row][square.col] == 0","def winner(board):\n # return 0[[0EMPTY, 1EMPTY, 2EMPTY],\n # 1[EMPTY, EMPTY, EMPTY],\n # 2[EMPTY, EMPTY, EMPTY]]\n # Check columns\n if board[0][0] == board[1][0] and board[1][0] == board[2][0]:\n return board[0][0]\n elif board[0][1] == board[1][1] and board[1][1] == board[2][1]:\n return board[0][1]\n elif board[0][2] == board[1][2] and board[1][2] == board[2][2]:\n return board[0][2]\n # Check rows\n elif all(i == board[0][0] for i in board[0]):\n return board[0][0]\n elif all(i == board[1][0] for i in board[1]):\n return board[1][0]\n elif all(i == board[2][0] for i in board[2]):\n return board[2][0]\n # Check diagonals\n elif board[0][0] == board[1][1] and board[1][1] == board[2][2]:\n return board[0][0]\n elif board[0][2] == board[1][1] and board[1][1] == board[2][0]:\n return board [0][2]\n else:\n return None","def find_solution(size: int) -> None:\n board = Board(size)\n if not solve_one(col=0, board=board):\n print(f\"No valid arrangement for board of size {size}\")\n return\n board.print_state()","def GetMove(self, board):\n move = None\n while True:\n move = input(\"Enter coordinates as XY (e.g. 21): \")\n if board[Game.GetIndexFromCoords(*move)] == \" \":\n return Game.GetIndexFromCoords(*move)\n else:\n print(\"Space occupied.\")","def winner(board):\n if board[0][0] == board[0][1] == board[0][2] != EMPTY:\n if board[0][0] == X:\n return X\n else:\n return O\n\n elif board[1][0] == board[1][1] == board[1][2] != EMPTY:\n if board[1][0] == X:\n return X\n else:\n return O\n\n elif board[2][0] == board[2][1] == board[2][2] != EMPTY:\n if board[2][0] == X:\n return X\n else:\n return O\n\n elif board[0][0] == board[1][0] == board[2][0] != EMPTY:\n if board[0][0] == X:\n return X\n else:\n return O\n\n elif board[0][1] == board[1][1] == board[2][1] != EMPTY:\n if board[0][1] == X:\n return X\n else:\n return O\n\n elif board[0][2] == board[1][2] == board[2][2] != EMPTY:\n if board[0][2] == X:\n return X\n else:\n return O\n\n elif board[0][0] == board[1][1] == board[2][2] != EMPTY:\n if board[0][0] == X:\n return X\n else:\n return O\n\n elif board[0][2] == board[1][1] == board[2][0] != EMPTY:\n if board[0][2] == X:\n return X\n else:\n return O\n else:\n return None\n\n #raise NotImplementedError","def take_corner():\n if board[0] == '_':\n return 0\n elif board[2] == '_':\n return 2\n elif board[6] == '_':\n return 6\n elif board[8] == '_':\n return 8\n else:\n return -1","def test_get_empty_squares(self):\n self.assertEqual(self.game.get_empty_squares(), [\n (0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)])\n\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = PLAYERX\n self.assertEqual(self.game.get_empty_squares(), [])\n\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = PLAYERO\n self.assertEqual(self.game.get_empty_squares(), [])","def board_full(board):\n\n for row in board:\n for piece in row:\n if piece == '*': return False;\n\n return True;","def player(board):\n count = 0\n rows = 3\n columns = 3\n for i in range(rows):\n for j in range(columns):\n if board[i][j] != EMPTY:\n count += 1\n if count % 2 == 0:\n player = X\n else:\n player = O\n return player\n\n #raise NotImplementedError","def maximum_sub_square(square_matrix):\n if not square_matrix:\n return (0, 0, 0)\n n = len(square_matrix)\n start_r, start_c, size = 0, 0, 0\n for i in range(n):\n # if there is no hope to find larger one, then break\n if i + size >= n:\n break\n # O(n^n)\n new_c, new_size = get_max_black_square(square_matrix, i, size)\n if new_size > size:\n start_r = i\n start_c = new_c\n size = new_size\n return (start_r, start_c, size)","def walk_right(self):\n if self.col_num == len(self.master_grid.matrix[0])-1 and self.row_num == len(self.master_grid.matrix)-1:\n return None\n if self.col_num == len(self.master_grid.matrix[0])-1:\n return Square(self.row_num+1, 0, self.master_grid)\n else:\n return Square(self.row_num, self.col_num + 1, self.master_grid)","def winner(board):\n # Check Rows\n for row in board:\n if row[0] != EMPTY and row[0] == row[1] and row[0] == row[2]:\n return row[0]\n \n # Check Columns\n for j in range(3):\n if board[0][j] != EMPTY and board[0][j] == board[1][j]:\n if board[0][j] == board[2][j]:\n return board[0][j]\n \n # Check Diagonals\n if board[1][1] != EMPTY:\n if board[0][0] == board[1][1] and board[0][0] == board[2][2]:\n return board[0][0]\n if board[0][2] == board[1][1] and board[0][2] == board[2][0]:\n return board[0][2]\n\n return None","def check_empty_space(gridcopy):\n for i in range(len(gridcopy)):\n if \"*\" in gridcopy[i]:\n return i, gridcopy[i].index(\"*\")\n # Raise Error if there is no empty space in the puzzle.\n return AssertionError, \"No empty space in the puzzle.\"","def terminal(board):\n if winner(board) in [X,O] or sum([list.count(EMPTY) for list in board]) == 0:\n return True\n else:\n return False","def legal_moves(self, player, board):\r\n #go through the whole board and check whether the piece is on the board or not\r\n #num/row size - num%col == num2/row size - num@%col\r\n #num/row size + num%col\r\n moves = list()\r\n opp = self.opponent(player)\r\n #print(board)\r\n for i in self.squares():\r\n if board[i] == core.EMPTY:\r\n for d in core.DIRECTIONS:\r\n endPt = self.find_bracket(i, player, board, d)\r\n if endPt!= None:\r\n moves.append(i)\r\n break\r\n\r\n return moves","def find_piece(self, piece_to_find):\n for row in range(BOARD_SIZE):\n for col in range(BOARD_SIZE):\n if self.board[row][col] is piece_to_find:\n return Square.at(row, col)\n raise Exception('The supplied piece is not on the board')","def find_piece(self, piece_to_find):\n for row in range(BOARD_SIZE):\n for col in range(BOARD_SIZE):\n if self.board[row][col] is piece_to_find:\n return Square.at(row, col)\n raise Exception('The supplied piece is not on the board')","def check_tie(board):\n return 0 not in board[0]","def get_piece(self, square):\n return self.board[square.row][square.col]","def get_piece(self, square):\n return self.board[square.row][square.col]","def is_board_full(board):\n for i in range(1, 10):\n if check_empty_space(board, i):\n return False\n return True","def advance(board):\n new_board = set()\n for cell in board:\n if ru1(cell):\n continue\n\n else:\n new_board.add(cell) # your code below\n pass\n\n return new_board","def terminal(board):\n for i in (O, X):\n for j in range(3):\n if (board[j][0] == i and board[j][1] == i and board[j][2] == i):\n return True\n if (board[0][j] == i and board[1][j] == i and board[2][j] == i):\n return True\n if (board[0][0] == i and board[1][1] == i and board[2][2] == i):\n return True\n if (board[2][0] == i and board[1][1] == i and board[0][2] == i):\n return True\n for i in board:\n for j in i:\n if (j == EMPTY):\n return False\n return True","def test_minimax(self):\n \n # empty board\n board = \"EEEEEEEEE\"\n res = MinimaxApiView.get_next_move(board, 'O')\n self.assertEqual(res, 0)\n board = \"OEEEEEEEE\"\n res = MinimaxApiView.get_next_move(board, 'X')\n self.assertEqual(res, 4)","def next_move(self, board):\n \n return self.best_move(self.score_columns(board))","def chessboardGame(x, y):\n xin = x\n yin = y\n\n # These squares have no possible move, therefore, are losing;\n # we chose these squares by sight; while loop below expands these sets\n # until we encompass whole board\n # it was not clear to me in the beginning that every square has a unique\n # determinant ending under optimal play\n losing_start = set([(1, 1), (2, 1), (1, 2), (2, 2)])\n\n # These squares can jump to losing_start in one move, so are winning\n winning_start = set([(1, 3), (1, 4), (2, 3), (2, 4),\n (3, 1), (3, 2), (3, 3), (3, 4),\n (4, 1), (4, 2), (4, 3)])\n\n def nextset(x, y):\n def isvalid(coord):\n return True if coord[0] >= 1 and coord[1] >= 1 \\\n and coord[0] <= 15 and coord[1] <= 15 else False\n\n nextsquares = [(x - 2, y + 1), (x - 2, y - 1), (x + 1, y - 2),\n (x - 1, y - 2)]\n nextsquares = set([*filter(isvalid, nextsquares)])\n # print(nextsquares)\n return nextsquares\n\n # run a few times through whole board;\n # it takes 5 times to find a definitive win path for all 225 squares\n # 161 squares are winning for first player\n # 64 squares are losing starting for first player\n test_set = [(i, j) for i in range(1, 16) for j in range(1, 16)]\n times = 1\n while (len(winning_start) + len(losing_start)) < 225:\n for coords in test_set:\n x_ = coords[0]\n y_ = coords[1]\n thenextset = nextset(x_, y_)\n # print('testing', x_, y_, thenextset)\n\n if (x_, y_) in losing_start:\n # print('No Path, Second wins')\n pass\n elif (x_, y_) in winning_start:\n # print('One jump to terminal square, First wins')\n pass\n elif (len(winning_start.intersection(thenextset))\n == len(thenextset)):\n # if next set ONLY includes winning_starts, First loses because\n # he has no choice but give win to opponent\n # need to add x,y to losing_start\n losing_start.add((x_, y_))\n # print('we lose, Second wins')\n elif len(losing_start.intersection(thenextset)) > 0:\n # if next set includes ANY losing_start, we win by choosing it\n # need to add x,y to winning_start\n winning_start.add((x_, y_))\n # print('First wins')\n else:\n # print('do not know')\n pass\n\n print('Run', times, len(winning_start) + len(losing_start))\n times += 1\n\n print(len(winning_start))\n print(len(losing_start))\n\n # prints schematic of Winor Loss of each of 15x15 squares\n\n print(' '.join(map(str, [i for i in range(1, 16)])))\n for i in range(15):\n row = ''\n for j in range(15):\n if test_set[i * 15 + j] in winning_start:\n row = row + 'W '\n else:\n row = row + 'L '\n print(row + str(i))\n\n if (xin, yin) in winning_start:\n print('First wins with', xin, yin)\n return 'First'\n else:\n print('Second wins with', xin, yin)\n return 'Second'","def get_move(self, board):\n while True:\n col = random.randint(0, board.width)\n row = board.try_move(col)\n\n if row >= 0:\n break\n\n return row, col","def terminal(board):\n\n # No winner case\n if winner(board) is None:\n for row in board:\n for box in row:\n # The game is not over\n if box is None:\n return False\n # Scratch game\n return True\n else:\n # There was a winner\n return True","def get_empty_squares(self):\n return self.empty_squares","def generate_solution(self, grid):\n number_list = [1,2,3,4,5,6,7,8,9]\n for i in range(0,81):\n row=i//9\n col=i%9\n #find next empty cell\n if grid[row][col]==0:\n shuffle(number_list)\n for number in number_list:\n if self.valid_location(grid,row,col,number):\n self.path.append((number,row,col))\n grid[row][col]=number\n if not self.find_empty_square(grid):\n return True\n else:\n if self.generate_solution(grid):\n #if the grid is full\n return True\n break\n grid[row][col]=0\n return False","def compute_best_move(board):\n best_node = alphabeta([0, []], depth_tree, float(\"-inf\"), float(\"inf\"), True, board)\n\n if (best_node[0] == float(\"inf\")) and (len(best_node[1]) == 1):\n return best_node[1][0], True\n return best_node[1][0], False","def __find_start(puzzle):\n for i in range(len(puzzle)):\n for j in range(len(puzzle[0])):\n if puzzle[i][j] == 0:\n return i\n return 0","def check_if_board_full(self, board):\n for i in range(self.height // 80):\n for j in range(self.width // 80):\n if board[(j, i)] == 0:\n return False\n elif j == self.width // 80:\n break\n else:\n pass\n print(\"Board full! :(\")\n return True"],"string":"[\n \"def get_next_empty_cell(self):\\n for row in range(len(self.grid)):\\n for col in range(len(self.grid[0])):\\n if self.grid[row][col] == 0:\\n return (row, col)\\n return None\",\n \"def find_empty_squares(board):\\n for i in range(len(board)):\\n for j in range(len(board[0])):\\n if board[i][j] == 0:\\n return (i,j) #row , column\\n\\n #if there are no blank squres\\n return None\",\n \"def find_next_empty_cell(grid):\\n for i, row in enumerate(grid):\\n for j, col in enumerate(row):\\n if col == 0:\\n return (i, j)\\n return None\",\n \"def find_blank_square(self, state):\\n\\n return state.index(0)\",\n \"def firstEmptyCell(board):\\r\\n for i in range(9):\\r\\n for j in range(9):\\r\\n if board[i][j] == 0:\\r\\n return (i, j) # row, col\\r\\n return None\",\n \"def find_empty(self):\\n num_rows = len(self.board)\\n num_cols = len(self.board[0])\\n\\n for i in range(num_rows):\\n for j in range(num_cols):\\n if self.board[i][j] == 0:\\n return (i, j)\",\n \"def find_empty(board):\\n for ii in range(len(board)):\\n for jj in range(len(board[ii])):\\n if board[ii][jj] == 0:\\n print('Empty: ', (jj , ii)) # column, row\\n return jj, ii # column, row\\n return None\",\n \"def playable_square(self, col) -> Square:\\n for row in reversed(range(len(self.state[0]))):\\n square = Square(row, col)\\n if self.is_empty(square):\\n return square\",\n \"def find_empty_space(board: list) -> tuple:\\n board_length = len(board)\\n for i in range(board_length):\\n for j in range(board_length):\\n if board[i][j] == 0:\\n return (i,j)\",\n \"def find_empty(game_board):\\n for row in range(len(game_board)):\\n for col in range(len(game_board[row])):\\n if len(game_board[row][col]) == 2:\\n return row, col\\n for row in range(len(game_board)):\\n for col in range(len(game_board[row])):\\n if len(game_board[row][col]) >= 3:\\n return row, col\\n\\n return None\",\n \"def find_empty(grid):\\n for i in range(LEN_GRID):\\n for j in range(LEN_GRID):\\n if grid[i][j] == 0:\\n return (i, j) # row, col\\n return None\",\n \"def findEmpty(grid):\\n for x in range(len(grid.board)):\\n for y in range(len(grid.board[0])):\\n if grid.board[x][y] == 0:\\n return [x,y]\",\n \"def is_valid_move(board, col):\\n\\n for row in range(len(board)-1, -1, -1):\\n if board[row][col] == Utilities.EMPTY:\\n return row\\n\\n return -1\",\n \"def find_bracket(self, square, player, board, direction):\\r\\n curr = square+ direction\\r\\n opp = self.opponent(player)\\r\\n if(board[curr]!=opp):\\r\\n return None\\r\\n while(self.is_valid(curr) and board[curr]==opp):\\r\\n curr+=direction\\r\\n if(self.is_valid(curr) and board[curr] == player):\\r\\n return curr\\r\\n return None\",\n \"def player(board):\\n if not terminal(board):\\n cnt_x = 0\\n cnt_o = 0\\n for i in range(3):\\n for j in range(3):\\n if board[i][j] == EMPTY:\\n continue\\n elif board[i][j] == X:\\n cnt_x += 1\\n else:\\n cnt_o += 1\\n\\n if cnt_x <= cnt_o:\\n return X\\n else:\\n return O\\n else:\\n return None\",\n \"def get_next_board(Board):\\n paddedBoard = pad_board(Board)\\n\\n dims = paddedBoard.shape\\n rows = dims[0] - 2\\n cols = dims[1] - 2\\n\\n nextBoard = np.zeros((rows, cols), dtype=int)\\n\\n for r in range(1, dims[0] - 1):\\n for c in range(1, dims[1] - 1):\\n numNeighbours = check_neighbours(r, c, paddedBoard)\\n # ... ... ... ... ...\\n # ... Game Logic ...\\n # ... ... ... ... ...\\n\\n return nextBoard\",\n \"def find_blank_cell(self, board: list):\\n cells = {}\\n for i in range(9): # Iterate over rows\\n for j in range(9): # Iterate over columns\\n if board[i][j] == 0:\\n cells[str(i) + ' ' + str(j)] = self.count_numbers(board, j, i)\\n m = max(cells.values())\\n for k in cells:\\n if cells[k] == m:\\n s = k.split()\\n x, y = int(s[1]), int(s[0])\\n return x, y\",\n \"def player(board):\\n moves = 0\\n\\n for row in range(len(board)):\\n for column in range(len(board[row])):\\n if board[row][column] != EMPTY:\\n moves+=1\\n\\n if moves % 2 == 0:\\n return X\\n return O\\n\\n # raise NotImplementedError\",\n \"def terminal(board):\\n emptyCounter = 0\\n for row in board:\\n emptyCounter += row.count(EMPTY)\\n if emptyCounter == 0:\\n return True\\n elif winner(board) is not None:\\n return True\\n else:\\n return False\",\n \"def find_empty_space(self, state):\\r\\n for i in range(3):\\r\\n for j in range(3):\\r\\n if state[i][j] == 0:\\r\\n return (i, j)\",\n \"def player(board):\\n plays = 0\\n\\n # Count non-empty squares\\n for i in range(3):\\n for j in range(3):\\n if board[i][j] != EMPTY:\\n plays += 1\\n\\n # Even number of plays -> X's turn\\n if plays % 2 == 0:\\n return X\\n else:\\n return O\",\n \"def solve(self):\\n\\n # Create the square the represents the beginning of the game when no-one is on the board\\n # and add it to the queue\\n #\\n zero_square = _BoardSquare(square_number=0,\\n die_rolls=0,\\n previous_square=None,\\n previous_roll=0)\\n self._board_square_queue.put(zero_square)\\n\\n while not self._board_square_queue.empty():\\n next_node = self._board_square_queue.get()\\n square_number = next_node.square_number\\n for die_value in range(0, DIE_FACES):\\n roll = die_value + 1\\n next_square_number = square_number + roll\\n\\n # when we are near the end, we will get\\n # rolls that put us past the last square\\n # we can skip those\\n if next_square_number > 100:\\n continue\\n\\n # if the board square we land on is a chute or ladder\\n # we jump to the target board square instead\\n if next_square_number in self._shortcuts:\\n next_square_number = self._shortcuts[next_square_number]\\n\\n # Look up the square in the board list\\n # which acts as a hash lookup on board square number\\n try:\\n existing_square = self._board[next_square_number - 1]\\n except Exception:\\n # this would happen if we tried to access the\\n # hash list oustside of its bounds\\n print(\\\"next_square_number: %d\\\" % next_square_number)\\n raise\\n\\n if existing_square == None:\\n # The square is not in the list yet, so this is\\n # the first time we have landed on it\\n # create a BoardSquare to represent it\\n # and add it to the hash-list\\n # and add it to the queue\\n square = _BoardSquare(square_number=next_square_number,\\n die_rolls=next_node.die_rolls + 1,\\n previous_square=next_node,\\n previous_roll=roll)\\n\\n self._board[next_square_number - 1] = square\\n self._board_square_queue.put(square)\\n elif existing_square.die_rolls > next_node.die_rolls + 1:\\n existing_square.die_rolls = next_node.die_rolls + 1\\n existing_square.previous_square = next_node\\n existing_square.previous_roll = roll\\n self._board_square_queue.put(existing_square)\\n\\n #print(self)\\n #self.print_solution()\",\n \"def terminal(board):\\n if winner(board) == X or winner(board) == O:\\n return True\\n \\n for row in range(len(board)):\\n for col in range(len(board[0])):\\n if board[row][col] == EMPTY:\\n return False\\n\\n return True\",\n \"def walk_down(self):\\n if self.col_num == len(self.master_grid.matrix[0])-1 and self.row_num == len(self.master_grid.matrix)-1:\\n return None\\n if self.row_num == len(self.master_grid.matrix)-1:\\n return Square(0, self.col_num+1, self.master_grid)\\n else:\\n return Square(self.row_num + 1, self.col_num, self.master_grid)\",\n \"def get_square_index(self, cell):\\n return next(s for s, square in enumerate(self.squares) if cell in square)\",\n \"def terminal(board):\\n if winner(board) == X or winner(board) == O:\\n return True\\n ended = True\\n for i in range(3):\\n for j in range(3):\\n if board[i][j] == EMPTY:\\n ended = False\\n return ended\",\n \"def terminal(board) -> bool:\\n if winner(board) is not None:\\n return True\\n\\n for row in board:\\n for column in row:\\n if column == EMPTY:\\n return False\\n return True\",\n \"def find_first_free_cell(board, picked_column):\\n for row in reversed(range(len(board))):\\n if board[row][picked_column] == 0:\\n return row\",\n \"def terminal(board):\\n if winner(board) is None:\\n for row in board:\\n for cell in row:\\n if cell == EMPTY:\\n return False\\n \\n # Returns true if there's a winner or no empty space\\n return True\",\n \"def get_empty_squares(self):\\n empty = []\\n for row in range(self._dim):\\n for col in range(self._dim):\\n if self._board[row][col] == EMPTY:\\n empty.append((row, col))\\n return empty\",\n \"def get_next_available_cell(self, starting_cell, board):\\n row = starting_cell.row\\n column = starting_cell.column + self.total_column_req + 1\\n if column >= board.columns:\\n row = starting_cell.row + self.total_rows_req + 1\\n column = 0\\n\\n if row >= board.rows:\\n print('Cannot place ship outside board.')\\n return None\\n return board.grid[row][column]\",\n \"def terminal(board):\\n noVal = False\\n for i in board:\\n for j in i:\\n if j == EMPTY:\\n noVal = True\\n if not noVal:\\n return True\\n \\n if winner(board) == X or winner(board) == O:\\n return True\\n \\n\\n return False\",\n \"def player(board):\\n turn = 0\\n for i in range(3):\\n for j in range(3):\\n if board[i][j] != EMPTY:\\n turn+=1\\n if turn % 2 != 0:\\n return O\\n else:\\n return X\",\n \"def terminal(board):\\n if winner(board) == X or winner(board) == O:\\n return True\\n\\n for i in range(len(board)):\\n for j in range(len(board)):\\n if board[i][j] == EMPTY:\\n return False\\n\\n return True\",\n \"def board_is_empty():\\n if STATE[-1].strip() == '-' * 7:\\n return True\\n else:\\n return False\",\n \"def solve(board):\\n find = find_blank(board)\\n \\n if not find:\\n return True\\n #will loop through untill the blanks are filled\\n\\n\\n\\n else:\\n row, col = find\\n\\n for i in range(1, 10):\\n if valid(board, i, (row, col)):\\n board[row][col] = i\\n\\n if solve(board):\\n return True\\n board[row][col] = 0\\n return False\",\n \"def winner(board):\\n for i in range(len(board)):\\n\\n # Check rows\\n if board[i][0] == board[i][1] == board[i][2] and not board[i][1] == EMPTY:\\n return board[i][1]\\n\\n # Check columns\\n elif board[0][i] == board[1][i] == board[2][i] and not board[1][i] == EMPTY:\\n return board[1][i]\\n\\n # Check diagonals\\n if board[0][0] == board[1][1] == board[2][2] and not board[1][1] == EMPTY:\\n return board[1][1]\\n\\n if board[2][0] == board[1][1] == board[0][2] and not board[1][1] == EMPTY:\\n return board[1][1]\\n\\n # No winner if get to this point\\n return None\",\n \"def terminal(board):\\n if sum(row.count(EMPTY) for row in board) == 0 or winner(board) == X or winner(board) == O:\\n return True\\n else:\\n return False\",\n \"def find_bracket(square, player, board, direction):\\n bracket = square + direction\\n if board[bracket] == player:\\n return None\\n opp = Othello.opponent(player)\\n while board[bracket] == opp:\\n bracket += direction\\n return None if board[bracket] in (OUTER, EMPTY) else bracket\",\n \"def terminal(board):\\n if not winner(board):\\n for i in range(3):\\n for j in range(3):\\n if board[i][j] == EMPTY:\\n return False\\n return True\",\n \"def find_unsettled_spot(self):\\n\\t\\tfor i in range(9):\\n\\t\\t\\tfor j in range(9):\\n\\t\\t\\t\\tif self.grid[i][j] == 0:\\n\\t\\t\\t\\t\\treturn i, j\\n\\t\\treturn\",\n \"def firstMove(board):\\r\\n x = board.size / 2\\r\\n return (x, x)\",\n \"def winner(board):\\n for i in range(3):\\n firstnumber=board[i][0]\\n if firstnumber!=EMPTY:\\n secondnumber=board[i][1]\\n if secondnumber==firstnumber:\\n if board[i][2]==secondnumber:\\n return secondnumber\\n else:\\n continue\\n else:\\n continue\\n else:\\n continue\\n for i in range(3):\\n firstnumber=board[0][i]\\n if firstnumber!=EMPTY:\\n secondnumber=board[1][i]\\n if secondnumber==firstnumber:\\n if board[2][i]==secondnumber:\\n return secondnumber\\n else:\\n continue\\n else:\\n continue\\n else:\\n continue\\n firstnumber=board[0][0]\\n if firstnumber!= EMPTY:\\n if board[1][1]==firstnumber:\\n if board[2][2]==firstnumber:\\n return firstnumber\\n firstnumber=board[2][0]\\n if firstnumber!= EMPTY:\\n if board[1][1]==firstnumber:\\n if board[0][2]==firstnumber:\\n return firstnumber\\n return None\\n raise NotImplementedError\",\n \"def make_random_move(self):\\n # get copy of the empty board\\n board = set([(i, j) for i in range(self.height) for j in range(self.width)])\\n\\n for move in board:\\n if not move in self.moves_made and not move in self.mines:\\n return move\\n\\n return None\",\n \"def find_empty_cell(d):\\n for i in range(5):\\n if d[i][x-1] == '.':\\n y = i\\n return y\",\n \"def findEmptyCell(board: str, threeInRow: Tuple[int, int, int]) -> int:\\n emptyCells = [\\n index for index in threeInRow if isAvailable(board, index)]\\n return choice(emptyCells)\",\n \"def is_white_square(square):\\n file = chess.square_file(square)\\n rank = chess.square_rank(square)\\n if (file + rank) % 2 == 0:\\n return 1\\n else:\\n return 0\",\n \"def terminal(board):\\n var = winner(board)\\n count = 0\\n rows = 3\\n columns = 3\\n for i in range(rows):\\n for j in range(columns):\\n if board[i][j] != EMPTY:\\n count += 1\\n if count == 9 or var is not None:\\n return True\\n else:\\n return False\\n\\n #raise NotImplementedError\",\n \"def check_board(board):\\n\\tgrid = board.grid\\n\\tboard_size = board.size\\n\\t# Get diagonals\\n\\tleft_diag = [grid[i][i] for i in range (0, board_size)]\\n\\tright_diag = [grid[i][board_size - 1 - i] for i in range (0, board_size)]\\n\\n\\t# Check columns and rows\\n\\tfor i in range(0, board_size):\\n\\t\\tprev_character_col = grid[0][i]\\n\\t\\tfor j in range(0, board_size):\\n\\t\\t\\tcurrent_character_col = grid[j][i]\\n\\t\\t\\tif(current_character_col == board.terminal or current_character_col != prev_character_col):\\n\\t\\t\\t\\tprev_character_col = None\\n\\t\\t\\t\\tbreak\\n\\t\\tif(prev_character_col):\\n\\t\\t\\treturn prev_character_col\\n\\n\\tfor i in range(0, board_size):\\n\\t\\tprev_character_row = grid[i][0]\\n\\t\\tfor j in range(0, board_size):\\n\\t\\t\\tcurrent_character_row = grid[i][j]\\n\\t\\t\\tif(current_character_row == board.terminal or current_character_row != prev_character_row):\\n\\t\\t\\t\\tprev_character_row = None\\n\\t\\t\\t\\tbreak\\n\\t\\tif(prev_character_row):\\n\\t\\t\\treturn prev_character_row\\n\\n\\t# Check diagonals\\n\\tleft_diag = list(set(left_diag))\\n\\tright_diag = list(set(right_diag))\\n\\n\\tif(len(left_diag) == 1 and left_diag[0] != board.terminal):\\n\\t\\treturn left_diag[0]\\n\\n\\tif(len(right_diag) == 1 and right_diag[0] != board.terminal):\\n\\t\\treturn right_diag[0]\\n\\n\\tavail_positions = board.empty_spots()\\n\\tif(len(avail_positions) == 0):\\n\\t\\treturn cnsts.DRAW\\n\\telse:\\n\\t\\treturn cnsts.IN_PROGRESS\",\n \"def terminal(board):\\r\\n if winner(board) == \\\"X\\\" or winner(board) == \\\"O\\\":\\r\\n return True\\r\\n \\r\\n for row in board:\\r\\n for col in row:\\r\\n if col == EMPTY:\\r\\n return False\\r\\n else:\\r\\n return True\\r\\n\\r\\n raise NotImplementedError\",\n \"def terminal(board):\\n if winner(board) is not None:\\n return True\\n non_empty_count=0\\n for i in range(3):\\n for j in range(3):\\n if board[i][j]==EMPTY:\\n return False\\n else:\\n non_empty_count+=1\\n if(non_empty_count==9):\\n return True\\n return False\\n raise NotImplementedError\",\n \"def square_empty(column, row):\\n if np.flipud(STATE)[row][column] == '-':\\n return True\\n else:\\n return False\",\n \"def test_initial_board():\\n board = Board()\\n\\n for i in range(board.get_grid_size()[0]):\\n for j in range(board.get_grid_size()[1]):\\n assert board.get_token(i, j) == Board.EMPTY\",\n \"def get_empty_square(self) -> list:\\n empty_square = []\\n for line_index in range(len(self.grid)):\\n for col_index in range(len(self.grid[line_index])):\\n if self.grid[line_index][col_index].color is None:\\n empty_square.append((line_index, col_index))\\n\\n return empty_square\",\n \"def winner(board):\\n\\n # Check none empty horizontals\\n for i in range(3):\\n if board[i][0] and board[i][0] == board[i][1] == board[i][2]:\\n return board[i][0]\\n\\n # Check none empty verticals\\n for j in range(3):\\n if board[0][j] and board[0][j] == board[1][j] == board[2][j]:\\n return board[0][j]\\n\\n # Check none empty L-R diagonal\\n if board[0][0] and board[0][0] == board[1][1] == board[2][2]:\\n return board[0][0]\\n\\n # Check none empty R-L diagonal\\n if board[0][2] and board[0][2] == board[1][1] == board[2][0]:\\n return board[0][2]\",\n \"def winner(board):\\n # check columns\\n for j in range(3):\\n if board[1][j] == board[0][j] and board[0][j] == board[2][j] and board[1][j] != EMPTY:\\n return board[1][j]\\n # check rows\\n for i in range(3):\\n if board[i][0] == board[i][1] and board[i][1] == board[i][2] and board[i][0] != EMPTY:\\n return board[i][0]\\n # check diagnols\\n if board[0][0] == board[1][1] and board[1][1] == board[2][2] and board[0][0] != EMPTY:\\n return board[1][1]\\n if board[0][2] == board[1][1] and board[1][1] == board[2][0] and board[0][2] != EMPTY:\\n return board[1][1]\\n return None\",\n \"def player(board):\\n\\n # Game is over\\n if terminal(board):\\n return None\\n\\n # Count number of occurences of X and O\\n x_count = 0\\n o_count = 0\\n for row in board:\\n for box in row:\\n if box == X:\\n x_count = x_count + 1\\n elif box == O:\\n o_count = o_count + 1\\n # When move count is tied, X is next\\n if x_count <= o_count:\\n return X\\n # When X has moved once more than O, next move is O\\n else:\\n return O\",\n \"def find_empty(bo):\\n for i in range(len(bo)):\\n for j in range(len(bo[0])):\\n if bo[i][j] == 0:\\n return (i, j)\\n\\n return None\",\n \"def winner(board):\\n # Checking for 3 in a row\\n for row in board:\\n if row[0] is not EMPTY and row[0] == row[1] == row[2]:\\n return row[0]\\n\\n # Checking for 3 in a col\\n for col in range(len(board)):\\n if board[0][col] is not EMPTY and board[0][col] == board[1][col] == board[2][col]:\\n return board[0][col]\\n\\n # Checking for Diagonals\\n if board[0][0] is not EMPTY and board[0][0] == board[1][1] == board[2][2]:\\n return board[0][0]\\n \\n if board[0][2] is not EMPTY and board[0][2] == board[2][0] == board[1][1]:\\n return board[0][2]\\n\\n return None\",\n \"def terminal(board):\\n if winner(board) == None:\\n for i in range(3):\\n if EMPTY in board[i]:\\n return False\\n\\n return True\\n\\n return True\",\n \"def find_empty(puzzle):\\r\\n empty_squares = []\\r\\n for y in range(len(puzzle.squares)):\\r\\n for x in range(len(puzzle.squares[0])):\\r\\n if puzzle.squares[y][x].is_editable() is True:\\r\\n empty_squares.append((x, y))\\r\\n return empty_squares\",\n \"def terminal(board):\\n # If winner game is over\\n if winner(board) is not None:\\n return True\\n else:\\n # Check number of blanks remaining, if any left and no winner game is not over\\n for i in range(len(board)):\\n for j in range(len(board)):\\n if board[i][j] == EMPTY:\\n return False\\n return True\",\n \"def get_next_cell(self):\\r\\n if len(self.queue) == 0:\\r\\n return\\r\\n val = self.peek_queue()\\r\\n self.queue.remove(val)\\r\\n return val[0]\",\n \"def get_empty_cells(board):\\n empty_cells = [idx for idx, e in enumerate(board) if e == ' ']\\n return empty_cells\",\n \"def winner(board):\\n if board[0][0] != EMPTY and (board[0][0] == board[0][1] == board[0][2] \\n or board[0][0] == board[1][1] == board[2][2] \\n or board[0][0] == board[1][0] == board[2][0]):\\n return board[0][0]\\n\\n elif board[1][1] != EMPTY and (board[1][0] == board[1][1] == board[1][2]\\n or board[0][1] == board[1][1] == board[2][1]):\\n return board[1][1]\\n \\n elif board[2][2] != EMPTY and (board[0][2] == board[1][2] == board[2][2]\\n or board[2][0] == board[2][1] == board[2][2]):\\n return board[2][2]\\n \\n elif board[2][0] != EMPTY and (board[2][0] == board[1][1] == board[0][2]):\\n return board[2][0]\\n \\n else:\\n None\",\n \"def get_checker(self, square):\\n\\n logger.debug(u'get_checker({})'.format(square))\\n\\n row, column = square\\n if row < 0 or row > 7 or column < 0 or column > 7:\\n return None\\n else:\\n return self.squares[row][column]\",\n \"def solve(board):\\r\\n cell = firstEmptyCell(board)\\r\\n # Base case for recursion\\r\\n # no empty cells, also all other cells filled validly, so board solved\\r\\n if cell is None:\\r\\n return True\\r\\n \\r\\n x, y = cell\\r\\n for i in range(1,10):\\r\\n # if i is valid at cell in board, go to next empty cell (recursively)\\r\\n if isvalid(board, i, cell):\\r\\n board[x][y] = i\\r\\n\\r\\n # try filling next empty cell till successful\\r\\n if solve(board):\\r\\n return True\\r\\n\\r\\n # i is invalid in cell, try i+1\\r\\n board[x][y] = 0\\r\\n \\r\\n # board can't be solved\\r\\n return False\",\n \"def is_empty(self, square: Square):\\n return self.state[0][square.row][square.col] == 0 and self.state[1][square.row][square.col] == 0\",\n \"def winner(board):\\n # return 0[[0EMPTY, 1EMPTY, 2EMPTY],\\n # 1[EMPTY, EMPTY, EMPTY],\\n # 2[EMPTY, EMPTY, EMPTY]]\\n # Check columns\\n if board[0][0] == board[1][0] and board[1][0] == board[2][0]:\\n return board[0][0]\\n elif board[0][1] == board[1][1] and board[1][1] == board[2][1]:\\n return board[0][1]\\n elif board[0][2] == board[1][2] and board[1][2] == board[2][2]:\\n return board[0][2]\\n # Check rows\\n elif all(i == board[0][0] for i in board[0]):\\n return board[0][0]\\n elif all(i == board[1][0] for i in board[1]):\\n return board[1][0]\\n elif all(i == board[2][0] for i in board[2]):\\n return board[2][0]\\n # Check diagonals\\n elif board[0][0] == board[1][1] and board[1][1] == board[2][2]:\\n return board[0][0]\\n elif board[0][2] == board[1][1] and board[1][1] == board[2][0]:\\n return board [0][2]\\n else:\\n return None\",\n \"def find_solution(size: int) -> None:\\n board = Board(size)\\n if not solve_one(col=0, board=board):\\n print(f\\\"No valid arrangement for board of size {size}\\\")\\n return\\n board.print_state()\",\n \"def GetMove(self, board):\\n move = None\\n while True:\\n move = input(\\\"Enter coordinates as XY (e.g. 21): \\\")\\n if board[Game.GetIndexFromCoords(*move)] == \\\" \\\":\\n return Game.GetIndexFromCoords(*move)\\n else:\\n print(\\\"Space occupied.\\\")\",\n \"def winner(board):\\n if board[0][0] == board[0][1] == board[0][2] != EMPTY:\\n if board[0][0] == X:\\n return X\\n else:\\n return O\\n\\n elif board[1][0] == board[1][1] == board[1][2] != EMPTY:\\n if board[1][0] == X:\\n return X\\n else:\\n return O\\n\\n elif board[2][0] == board[2][1] == board[2][2] != EMPTY:\\n if board[2][0] == X:\\n return X\\n else:\\n return O\\n\\n elif board[0][0] == board[1][0] == board[2][0] != EMPTY:\\n if board[0][0] == X:\\n return X\\n else:\\n return O\\n\\n elif board[0][1] == board[1][1] == board[2][1] != EMPTY:\\n if board[0][1] == X:\\n return X\\n else:\\n return O\\n\\n elif board[0][2] == board[1][2] == board[2][2] != EMPTY:\\n if board[0][2] == X:\\n return X\\n else:\\n return O\\n\\n elif board[0][0] == board[1][1] == board[2][2] != EMPTY:\\n if board[0][0] == X:\\n return X\\n else:\\n return O\\n\\n elif board[0][2] == board[1][1] == board[2][0] != EMPTY:\\n if board[0][2] == X:\\n return X\\n else:\\n return O\\n else:\\n return None\\n\\n #raise NotImplementedError\",\n \"def take_corner():\\n if board[0] == '_':\\n return 0\\n elif board[2] == '_':\\n return 2\\n elif board[6] == '_':\\n return 6\\n elif board[8] == '_':\\n return 8\\n else:\\n return -1\",\n \"def test_get_empty_squares(self):\\n self.assertEqual(self.game.get_empty_squares(), [\\n (0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)])\\n\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = PLAYERX\\n self.assertEqual(self.game.get_empty_squares(), [])\\n\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = PLAYERO\\n self.assertEqual(self.game.get_empty_squares(), [])\",\n \"def board_full(board):\\n\\n for row in board:\\n for piece in row:\\n if piece == '*': return False;\\n\\n return True;\",\n \"def player(board):\\n count = 0\\n rows = 3\\n columns = 3\\n for i in range(rows):\\n for j in range(columns):\\n if board[i][j] != EMPTY:\\n count += 1\\n if count % 2 == 0:\\n player = X\\n else:\\n player = O\\n return player\\n\\n #raise NotImplementedError\",\n \"def maximum_sub_square(square_matrix):\\n if not square_matrix:\\n return (0, 0, 0)\\n n = len(square_matrix)\\n start_r, start_c, size = 0, 0, 0\\n for i in range(n):\\n # if there is no hope to find larger one, then break\\n if i + size >= n:\\n break\\n # O(n^n)\\n new_c, new_size = get_max_black_square(square_matrix, i, size)\\n if new_size > size:\\n start_r = i\\n start_c = new_c\\n size = new_size\\n return (start_r, start_c, size)\",\n \"def walk_right(self):\\n if self.col_num == len(self.master_grid.matrix[0])-1 and self.row_num == len(self.master_grid.matrix)-1:\\n return None\\n if self.col_num == len(self.master_grid.matrix[0])-1:\\n return Square(self.row_num+1, 0, self.master_grid)\\n else:\\n return Square(self.row_num, self.col_num + 1, self.master_grid)\",\n \"def winner(board):\\n # Check Rows\\n for row in board:\\n if row[0] != EMPTY and row[0] == row[1] and row[0] == row[2]:\\n return row[0]\\n \\n # Check Columns\\n for j in range(3):\\n if board[0][j] != EMPTY and board[0][j] == board[1][j]:\\n if board[0][j] == board[2][j]:\\n return board[0][j]\\n \\n # Check Diagonals\\n if board[1][1] != EMPTY:\\n if board[0][0] == board[1][1] and board[0][0] == board[2][2]:\\n return board[0][0]\\n if board[0][2] == board[1][1] and board[0][2] == board[2][0]:\\n return board[0][2]\\n\\n return None\",\n \"def check_empty_space(gridcopy):\\n for i in range(len(gridcopy)):\\n if \\\"*\\\" in gridcopy[i]:\\n return i, gridcopy[i].index(\\\"*\\\")\\n # Raise Error if there is no empty space in the puzzle.\\n return AssertionError, \\\"No empty space in the puzzle.\\\"\",\n \"def terminal(board):\\n if winner(board) in [X,O] or sum([list.count(EMPTY) for list in board]) == 0:\\n return True\\n else:\\n return False\",\n \"def legal_moves(self, player, board):\\r\\n #go through the whole board and check whether the piece is on the board or not\\r\\n #num/row size - num%col == num2/row size - num@%col\\r\\n #num/row size + num%col\\r\\n moves = list()\\r\\n opp = self.opponent(player)\\r\\n #print(board)\\r\\n for i in self.squares():\\r\\n if board[i] == core.EMPTY:\\r\\n for d in core.DIRECTIONS:\\r\\n endPt = self.find_bracket(i, player, board, d)\\r\\n if endPt!= None:\\r\\n moves.append(i)\\r\\n break\\r\\n\\r\\n return moves\",\n \"def find_piece(self, piece_to_find):\\n for row in range(BOARD_SIZE):\\n for col in range(BOARD_SIZE):\\n if self.board[row][col] is piece_to_find:\\n return Square.at(row, col)\\n raise Exception('The supplied piece is not on the board')\",\n \"def find_piece(self, piece_to_find):\\n for row in range(BOARD_SIZE):\\n for col in range(BOARD_SIZE):\\n if self.board[row][col] is piece_to_find:\\n return Square.at(row, col)\\n raise Exception('The supplied piece is not on the board')\",\n \"def check_tie(board):\\n return 0 not in board[0]\",\n \"def get_piece(self, square):\\n return self.board[square.row][square.col]\",\n \"def get_piece(self, square):\\n return self.board[square.row][square.col]\",\n \"def is_board_full(board):\\n for i in range(1, 10):\\n if check_empty_space(board, i):\\n return False\\n return True\",\n \"def advance(board):\\n new_board = set()\\n for cell in board:\\n if ru1(cell):\\n continue\\n\\n else:\\n new_board.add(cell) # your code below\\n pass\\n\\n return new_board\",\n \"def terminal(board):\\n for i in (O, X):\\n for j in range(3):\\n if (board[j][0] == i and board[j][1] == i and board[j][2] == i):\\n return True\\n if (board[0][j] == i and board[1][j] == i and board[2][j] == i):\\n return True\\n if (board[0][0] == i and board[1][1] == i and board[2][2] == i):\\n return True\\n if (board[2][0] == i and board[1][1] == i and board[0][2] == i):\\n return True\\n for i in board:\\n for j in i:\\n if (j == EMPTY):\\n return False\\n return True\",\n \"def test_minimax(self):\\n \\n # empty board\\n board = \\\"EEEEEEEEE\\\"\\n res = MinimaxApiView.get_next_move(board, 'O')\\n self.assertEqual(res, 0)\\n board = \\\"OEEEEEEEE\\\"\\n res = MinimaxApiView.get_next_move(board, 'X')\\n self.assertEqual(res, 4)\",\n \"def next_move(self, board):\\n \\n return self.best_move(self.score_columns(board))\",\n \"def chessboardGame(x, y):\\n xin = x\\n yin = y\\n\\n # These squares have no possible move, therefore, are losing;\\n # we chose these squares by sight; while loop below expands these sets\\n # until we encompass whole board\\n # it was not clear to me in the beginning that every square has a unique\\n # determinant ending under optimal play\\n losing_start = set([(1, 1), (2, 1), (1, 2), (2, 2)])\\n\\n # These squares can jump to losing_start in one move, so are winning\\n winning_start = set([(1, 3), (1, 4), (2, 3), (2, 4),\\n (3, 1), (3, 2), (3, 3), (3, 4),\\n (4, 1), (4, 2), (4, 3)])\\n\\n def nextset(x, y):\\n def isvalid(coord):\\n return True if coord[0] >= 1 and coord[1] >= 1 \\\\\\n and coord[0] <= 15 and coord[1] <= 15 else False\\n\\n nextsquares = [(x - 2, y + 1), (x - 2, y - 1), (x + 1, y - 2),\\n (x - 1, y - 2)]\\n nextsquares = set([*filter(isvalid, nextsquares)])\\n # print(nextsquares)\\n return nextsquares\\n\\n # run a few times through whole board;\\n # it takes 5 times to find a definitive win path for all 225 squares\\n # 161 squares are winning for first player\\n # 64 squares are losing starting for first player\\n test_set = [(i, j) for i in range(1, 16) for j in range(1, 16)]\\n times = 1\\n while (len(winning_start) + len(losing_start)) < 225:\\n for coords in test_set:\\n x_ = coords[0]\\n y_ = coords[1]\\n thenextset = nextset(x_, y_)\\n # print('testing', x_, y_, thenextset)\\n\\n if (x_, y_) in losing_start:\\n # print('No Path, Second wins')\\n pass\\n elif (x_, y_) in winning_start:\\n # print('One jump to terminal square, First wins')\\n pass\\n elif (len(winning_start.intersection(thenextset))\\n == len(thenextset)):\\n # if next set ONLY includes winning_starts, First loses because\\n # he has no choice but give win to opponent\\n # need to add x,y to losing_start\\n losing_start.add((x_, y_))\\n # print('we lose, Second wins')\\n elif len(losing_start.intersection(thenextset)) > 0:\\n # if next set includes ANY losing_start, we win by choosing it\\n # need to add x,y to winning_start\\n winning_start.add((x_, y_))\\n # print('First wins')\\n else:\\n # print('do not know')\\n pass\\n\\n print('Run', times, len(winning_start) + len(losing_start))\\n times += 1\\n\\n print(len(winning_start))\\n print(len(losing_start))\\n\\n # prints schematic of Winor Loss of each of 15x15 squares\\n\\n print(' '.join(map(str, [i for i in range(1, 16)])))\\n for i in range(15):\\n row = ''\\n for j in range(15):\\n if test_set[i * 15 + j] in winning_start:\\n row = row + 'W '\\n else:\\n row = row + 'L '\\n print(row + str(i))\\n\\n if (xin, yin) in winning_start:\\n print('First wins with', xin, yin)\\n return 'First'\\n else:\\n print('Second wins with', xin, yin)\\n return 'Second'\",\n \"def get_move(self, board):\\n while True:\\n col = random.randint(0, board.width)\\n row = board.try_move(col)\\n\\n if row >= 0:\\n break\\n\\n return row, col\",\n \"def terminal(board):\\n\\n # No winner case\\n if winner(board) is None:\\n for row in board:\\n for box in row:\\n # The game is not over\\n if box is None:\\n return False\\n # Scratch game\\n return True\\n else:\\n # There was a winner\\n return True\",\n \"def get_empty_squares(self):\\n return self.empty_squares\",\n \"def generate_solution(self, grid):\\n number_list = [1,2,3,4,5,6,7,8,9]\\n for i in range(0,81):\\n row=i//9\\n col=i%9\\n #find next empty cell\\n if grid[row][col]==0:\\n shuffle(number_list)\\n for number in number_list:\\n if self.valid_location(grid,row,col,number):\\n self.path.append((number,row,col))\\n grid[row][col]=number\\n if not self.find_empty_square(grid):\\n return True\\n else:\\n if self.generate_solution(grid):\\n #if the grid is full\\n return True\\n break\\n grid[row][col]=0\\n return False\",\n \"def compute_best_move(board):\\n best_node = alphabeta([0, []], depth_tree, float(\\\"-inf\\\"), float(\\\"inf\\\"), True, board)\\n\\n if (best_node[0] == float(\\\"inf\\\")) and (len(best_node[1]) == 1):\\n return best_node[1][0], True\\n return best_node[1][0], False\",\n \"def __find_start(puzzle):\\n for i in range(len(puzzle)):\\n for j in range(len(puzzle[0])):\\n if puzzle[i][j] == 0:\\n return i\\n return 0\",\n \"def check_if_board_full(self, board):\\n for i in range(self.height // 80):\\n for j in range(self.width // 80):\\n if board[(j, i)] == 0:\\n return False\\n elif j == self.width // 80:\\n break\\n else:\\n pass\\n print(\\\"Board full! :(\\\")\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.771726","0.76045936","0.759847","0.74498457","0.72153336","0.70994276","0.6846962","0.67881215","0.67627925","0.67542005","0.6752573","0.6666043","0.6641799","0.66194654","0.6548566","0.6522966","0.6478559","0.64705473","0.6379805","0.63471836","0.63375825","0.63269955","0.63252753","0.6310146","0.63079506","0.6307062","0.6295373","0.6269423","0.6255785","0.6253784","0.6248047","0.6226629","0.62264645","0.6217337","0.6188782","0.61831695","0.6171842","0.6166506","0.6160901","0.6153405","0.6143997","0.6143038","0.6132449","0.6129465","0.61094165","0.60969526","0.6080385","0.6069049","0.6063418","0.6057096","0.6055324","0.60512304","0.60340255","0.6031023","0.60289764","0.602448","0.60151035","0.5994376","0.5991258","0.5984412","0.59689176","0.59681726","0.5963558","0.59634113","0.5954615","0.59471506","0.5945383","0.59420544","0.5930702","0.59168905","0.59102875","0.59055746","0.5901507","0.5893717","0.58820546","0.58811635","0.5879724","0.5874974","0.5868665","0.586478","0.58632135","0.5850954","0.5848808","0.5848808","0.5848144","0.5846448","0.5846448","0.58440095","0.5841234","0.5828964","0.58248365","0.5817707","0.58116996","0.5809112","0.58001995","0.5798561","0.5798463","0.5787461","0.57841974","0.57817066"],"string":"[\n \"0.771726\",\n \"0.76045936\",\n \"0.759847\",\n \"0.74498457\",\n \"0.72153336\",\n \"0.70994276\",\n \"0.6846962\",\n \"0.67881215\",\n \"0.67627925\",\n \"0.67542005\",\n \"0.6752573\",\n \"0.6666043\",\n \"0.6641799\",\n \"0.66194654\",\n \"0.6548566\",\n \"0.6522966\",\n \"0.6478559\",\n \"0.64705473\",\n \"0.6379805\",\n \"0.63471836\",\n \"0.63375825\",\n \"0.63269955\",\n \"0.63252753\",\n \"0.6310146\",\n \"0.63079506\",\n \"0.6307062\",\n \"0.6295373\",\n \"0.6269423\",\n \"0.6255785\",\n \"0.6253784\",\n \"0.6248047\",\n \"0.6226629\",\n \"0.62264645\",\n \"0.6217337\",\n \"0.6188782\",\n \"0.61831695\",\n \"0.6171842\",\n \"0.6166506\",\n \"0.6160901\",\n \"0.6153405\",\n \"0.6143997\",\n \"0.6143038\",\n \"0.6132449\",\n \"0.6129465\",\n \"0.61094165\",\n \"0.60969526\",\n \"0.6080385\",\n \"0.6069049\",\n \"0.6063418\",\n \"0.6057096\",\n \"0.6055324\",\n \"0.60512304\",\n \"0.60340255\",\n \"0.6031023\",\n \"0.60289764\",\n \"0.602448\",\n \"0.60151035\",\n \"0.5994376\",\n \"0.5991258\",\n \"0.5984412\",\n \"0.59689176\",\n \"0.59681726\",\n \"0.5963558\",\n \"0.59634113\",\n \"0.5954615\",\n \"0.59471506\",\n \"0.5945383\",\n \"0.59420544\",\n \"0.5930702\",\n \"0.59168905\",\n \"0.59102875\",\n \"0.59055746\",\n \"0.5901507\",\n \"0.5893717\",\n \"0.58820546\",\n \"0.58811635\",\n \"0.5879724\",\n \"0.5874974\",\n \"0.5868665\",\n \"0.586478\",\n \"0.58632135\",\n \"0.5850954\",\n \"0.5848808\",\n \"0.5848808\",\n \"0.5848144\",\n \"0.5846448\",\n \"0.5846448\",\n \"0.58440095\",\n \"0.5841234\",\n \"0.5828964\",\n \"0.58248365\",\n \"0.5817707\",\n \"0.58116996\",\n \"0.5809112\",\n \"0.58001995\",\n \"0.5798561\",\n \"0.5798463\",\n \"0.5787461\",\n \"0.57841974\",\n \"0.57817066\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7049689"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":17454,"cells":{"query":{"kind":"string","value":"Handle a received message from the server."},"document":{"kind":"string","value":"def message_received_from_server(self, message):\n\n if message[\"type\"] == \"state\":\n self._board = message[\"board\"]\n self._winner = message[\"winner\"]\n elif message[\"type\"] == \"turn\":\n row, col = self._find_empty_cell()\n self.send_message_to_server({\n \"type\": \"move\",\n \"row\": row,\n \"column\": col\n })"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def handle(self, message):\n print(\"You received a message:\")\n print(message)\n # Overwrite this function to do something with the message!","def received_message(self, m):\n self.receiver.handle_message(m)","def handle(self):\n try:\n # Wait for data\n data = json.loads(self.request.recv(1024).decode('UTF-8').strip())\n\n # Process data\n self.process_data(data)\n\n except Exception as e:\n print(\"Exception wile receiving message: \", e)\n self.request.sendall(\n bytes(json.dumps({'return': 'error'}), 'UTF-8'))","def handle_message(self, message):\n print(f\"Got message {message}\")\n if message >> 7 == 1:\n # String\n self.receive_char_array(message)\n elif message >> 3 == 0b00000:\n # Servo position\n self.receive_servo_position(message)\n elif message == 0b00001000:\n # All servo positions\n self.receive_all_servo_positions()\n elif message == 0b00001001:\n # All servo limits\n self.receive_all_servo_limits()\n elif message == 0b00001010:\n # Raw force reading\n self.receive_raw_force()\n print(f\"Handled message {message}\")","def _handle_message(self, msg):\n self.event('message', msg)","def process(self, msg):\n print \"HANDLER: received a msg: %s\" % msg","def handle(self, message):","def handle_message(self, msg):\n pass","def handle_message(self, mxmsg):\n if self._handler is None:\n raise NotImplementedError()\n\n self.notify_started()\n response = self._handler(mxmsg)\n if response == ():\n self.no_response()\n elif isinstance(response, str):\n self.send_message(message=response, type=MessageTypes.PING)\n elif isinstance(response, dict):\n self.send_message(**response)\n else:\n raise ValueError(\"Unsupported handler return type %r\" %\n type(response))","def _handle_msg(self, msg):\n data = msg['content']['data']\n method = data['method']\n\n if method == 'update':\n if 'state' in data:\n state = data['state']\n if 'buffer_paths' in data:\n _put_buffers(state, data['buffer_paths'], msg['buffers'])\n self.set_state(state)\n\n # Handle a state request.\n elif method == 'request_state':\n self.send_state()\n\n # Handle a custom msg from the front-end.\n elif method == 'custom':\n if 'content' in data:\n self._handle_custom_msg(data['content'], msg['buffers'])\n\n # Catch remainder.\n else:\n self.log.error('Unknown front-end to back-end widget msg with method \"%s\"' % method)","def handleMessage(msg):","def handle_message(self, msg):\n Logger.debug(\"Slave: Trying to parse\")\n if MessageKeys.command_key in msg.fields:\n Logger.info(\"Slave: Message command: %s\", str(msg.get_command()))\n return self.messagehandler[msg.get_command()](self, msg)\n return self.handle_invalid_command(msg)","def handle_received(self) -> None:\n self.buffer: bytes\n while self.buffer:\n try:\n request, self.buffer = parse_request(self.buffer)\n if request is None:\n _LOGGER.debug(\"Not enough data to parse request on event channel\")\n break\n\n _LOGGER.debug(\"Got message on event channel: %s\", request)\n\n # Send a positive response to satisfy the other end of the channel\n # TODO: Add public method to pyatv.http to format a message\n headers = {\n \"Content-Length\": 0,\n \"Audio-Latency\": 0,\n \"Server\": request.headers.get(\"Server\"),\n \"CSeq\": request.headers.get(\"CSeq\"),\n }\n response = (\n f\"{request.protocol}/{request.version} 200 OK\\r\\n\"\n + \"\\r\\n\".join(f\"{key}: {value}\" for key, value in headers.items())\n + \"\\r\\n\\r\\n\"\n )\n self.send(response.encode(\"utf-8\"))\n except Exception:\n _LOGGER.exception(\"Failed to handle message on event channel\")","def on_message(self, message):\n log.debug(\"Protocol got message {message}\", message=message)\n if message['type'] == \"change\":\n self.handler.process_packet(message['packet'])\n self.send_packet()\n elif message['type'] == \"chat\":\n self.on_chat_message(message)\n elif message['type'] == \"action\":\n self.on_action(message)\n else:\n log.warn(\"Unrecognized message type {type}\", type=message['type'])","def handle_msg(self, msg):\n self.logger.debug(\"Received: {}\".format(msg))\n\n try:\n msg_type = msg[\"type\"]\n except KeyError as e:\n return msgs.error(e)\n\n if msg_type == \"ping_req\":\n reply = msgs.ping_reply()\n elif msg_type == \"list_req\":\n reply = self.list_callables()\n elif msg_type == \"call_req\":\n try:\n obj_name = msg[\"obj_name\"]\n method = msg[\"method\"]\n params = msg[\"params\"]\n reply = self.call_method(obj_name, method, params)\n except KeyError as e:\n return msgs.error(e)\n elif msg_type == \"exit_req\":\n self.logger.info(\"Received message to die. Bye!\")\n reply = msgs.exit_reply()\n # Need to actually send reply here as we're about to exit\n self.logger.debug(\"Sending: {}\".format(reply))\n self.ctrl_sock.send_json(reply)\n self.clean_up()\n sys.exit(0)\n else:\n err_msg = \"Unrecognized message: {}\".format(msg)\n self.logger.warning(err_msg)\n reply = msgs.error(err_msg)\n return reply","def handle(self, message: Message) -> None:\n self.handled_message = message","def handle(self):\n self.ip = self.client_address[0]\n self.port = self.client_address[1]\n self.connection = self.request\n\n state.addConnection(self.connection)\n\n # Loop that listens for messages from the client\n while True:\n received_string = self.connection.recv(4096)\n\n if len(received_string) == 0:\n continue\n\n # TODO: Add handling of received payload from client\n\n # Convert payload from JSON to object\n payloadToData = json.loads(received_string)\n\n # determine what request is being made\n request_handler = RequestHandler(payloadToData,\n state,\n self.connection)\n\n # execute and generate response (JSON formatted)\n jsonResponse = request_handler.callHandler()\n\n if not jsonResponse == 'BROADCAST':\n # send response\n self.connection.send(bytes(jsonResponse, \"ascii\"))","def handle_message(self, data, channel):\n pass","def handle(self):\n self.ip = self.client_address[0]\n self.port = self.client_address[1]\n self.connection = self.request\n #self.logged_in = False\n\n # Loop that listens for messages from the client\n while True:\n received_string = self.connection.recv(4096).strip()\n if received_string:\n jsonObject = json.loads(received_string)\n request = jsonObject.get('request')\n #print(received_string)\n #self.handle_data(received_string)\n if request == 'login':\n print 'logging in'\n self.login(jsonObject)\n elif request == 'logout':\n self.logout()\n elif request == 'msg':\n self.send(jsonObject)\n elif request == 'names':\n self.getNames()\n elif request == 'help':\n return \"geiegohruuhiegr\"\n else:\n return \"you suck\"\n\n else:\n print('The client is disconnected.')\n break \n # TODO: Add handling of received payload from client","def message_received(self, message):\n \n # Routing\n if self.route_message(message) == True:\n return\n \n # Handlers?\n if len(self._handlers) > 0:\n for handler in self._handlers:\n handler(message)\n \n # Storage?\n else:\n timestamp = 0\n t = datetime.datetime.today()\n timestamp = t.microsecond/1000 + t.second*1000 + \\\n t.minute*60*1000 + t.hour*60*60*1000 + t.day*24*60*60*1000\n while timestamp > 4294967295: timestamp -= 4294967295\n self._messages.put(tuple([timestamp,message]))","def handle_msg(self, state_id, msg):\n pass","def handle_message(self, message):","def handle_message(self, validated_message: dict):\n self.logger.debug(f'Sensor received message {validated_message}')\n if (validated_message['messageType'] !=\n model.MessageTypes.Control.value):\n self.logger.debug(\n 'Sensor ignoring because messageType was not control'\n )\n return\n if validated_message['messageBody']['target'] != self.component_id:\n self.logger.debug(\n 'Sensor ignoring because not targeted at me'\n )\n return\n\n subtype = validated_message['messageSubtype']\n try:\n self.logger.debug(f'Dispatching message with subtype {subtype}')\n self.message_handler_table[subtype](validated_message)\n except KeyError:\n self.logger.warning(f'No handler for with subtype {subtype}')\n pass","def handleMessage(self):\n try:\n # This whole block is wrapped in a try/except because the default\n # behaviour of the SimpleWebSocketServer library is to silently\n # discard any exceptions raised by these handlers. This is very\n # unhelpful. A workaround is to catch and log any exceptions\n # explicitly here.\n logging.debug(\"%s %s\", self.address, \"incoming message\")\n is_binary = not isinstance(self.data, str)\n if is_binary:\n message = self.data\n else:\n message = json.loads(self.data)\n\n self.logbook.messages_received.append(message)\n for response in self.get_responses(message, is_binary=is_binary):\n self.logbook.messages_sent.append(response)\n self.sendMessage(json.dumps(response).encode(\"utf-8\"))\n except Exception as exc: # pylint: disable=broad-except\n logging.exception(str(exc))\n self.close(status=1011, reason=\"Internal server error\")","def received(self, message):\n raise NotImplementedError()","def receive_message(self, message):\r\n return","def run(self):\n alogger.info(\"Recieved message from %s, Message: (%d) %s\" % (self.client.getaddress(), self.action_type, self.message))\n \n #Try to call th function associated with this message type.\n #format = \"handle_<type>\" (eg: handle_100)\n fn = globals().get(\"handle_\" + str(self.action_type))\n if fn and callable(fn):\n fn(self.message, self.address, self.client)\n else:\n alogger.info(\"Received unknown message from %d, type: %d\" % (self.client.getaddress(), self.action_type))","def receive_message(self, message):","def receive(self, msg):\n pass","def handle_message(self, data):\n message = Message.from_text(data)\n if message is not None:\n print(message.username, message.action, message.channel, message.content)\n self._callback(\"message\", message) # TODO: add additional callbacks","def handle_read(self):\n packet = self.recv(8192)\n if packet == \"\":\n #print \"[WARNING] Socket closed by remote host %s:%s\" % (\n # self.address,self.port)\n self.close()\n return\n packet_list = messages.separate_messages(packet)\n #received_types = \" + \".join(\n # messages.get_message_type(messages.parse(packet))\n # for packet in packet_list)\n #print \"From %s:%s received: \" % (self.address, self.port), received_types\n # Process a single message at a time\n for packet in packet_list:\n message = messages.parse(packet)\n if messages.get_message_type(message) == \"OFPT_ECHO_REQUEST\":\n self.buffer.append(messages.of_echo_reply)\n else:\n self.handle_message(message)","def on_message(self, event):\n self.response = event.message\n self.connection.container.yield_() # Wake up the wait() loop to handle the message.","def _r_handle_message_contents(self, msg, protocol):\n if isinstance(msg, ResponseMessage):\n d = self._waiting_messages.pop(msg.response_to, None)\n if d is not None:\n d.callback(msg)\n elif isinstance(msg, ServerMotdMessage):\n print(\"Connected: %s\" % msg.motd)\n self._r_successful_connection()\n elif isinstance(msg, EventMessage):\n callback = self._event_callbacks.get((msg.service_name, msg.event_name))\n if callback is not None:\n threads.deferToThread(callback, *msg.pargs, **msg.kwargs)","def handle_message(**payload):\n handler_instance = message.MessageHandler(payload)\n handler_instance.handle()","def receive(self, message):","def handle_msg(msg):\n if comm._msg_callback:\n comm._msg_callback(msg)","def handle_message(self, message):\n\n\t\tself.log.debug(\"%s handle_message %s\", self.name, message)\n\n\t\tif message[\"Type\"] == \"command\":\n\t\t\ttry:\n\t\t\t\tcommand_callable = \"command_%s\" % message[\"Message\"][\"command\"]\n\t\t\t\tif hasattr(self, command_callable) and callable(getattr(self, command_callable)):\n\t\t\t\t\tcall = getattr(self, command_callable)\n\t\t\t\t\tcall(message[\"Message\"][\"arguments\"])\n\t\t\texcept Exception as e:\n\t\t\t\tself.log.error(\"%s invalid command %s %s\", self.name, message, e)","def reply_handler(msg):\n print(\"Server Response: %s, %s\" % (msg.typeName, msg))\n pass","def handle(self, msg, peer_protocol):\n msg_id = msg[0]\n if msg_id == 0:\n self._handle_handshake(msg, peer_protocol)\n elif msg_id == 1: #update\n print(msg, len(msg))\n self._handle_update(msg)","def _message(self, msg):\n\n self.log('Message received:', msg['body'], pretty=True)\n\n if msg['type'] in ('chat', 'normal'):\n body = str(msg['body'])\n if body.startswith('/'):\n cmd, arg_string = body.split(' ', maxsplit=1)\n cmd = cmd.lstrip('/')\n\n if arg_string:\n args = arg_string.split(' ')\n else:\n args = None\n\n self.log('IRC remote command received:', cmd, args)\n return\n else:\n if True:\n msg.reply(\"Sorry, I did not understand that:\\n%s\" % body).send()","def on_message(self, data):\n req = json.loads(data)\n self.serve(req)","def on_message(self, data):\n req = json.loads(data)\n self.serve(req)","def handle_message(self, msg: mqtt.MQTTMessage) -> None:\n payload = json.loads(msg.payload.decode(\"utf-8\"))\n logging.info(f\"Received a new message: {payload}\")\n if \"volume\" in payload:\n validate(payload, schema=self.volume_schema)\n self.volume = payload[\"volume\"]\n elif \"volumeCtrl\" in payload:\n validate(payload, schema=self.volume_ctrl_schema)\n self.volume_up() if payload[\"volumeCtrl\"] == \"+\" else self.volume_down()\n elif \"mute\" in payload:\n validate(payload, schema=self.mute_schema)\n self.mute = payload[\"mute\"]\n elif \"toggle\" in payload:\n validate(payload, schema=self.toggle_schema)\n self.toggle_mute() if payload[\"toggle\"] == \"mute\" else self.toggle_pause()\n elif \"ctrl\" in payload:\n validate(payload, schema=self.ctrl_schema)\n self.skip_forward() if payload[\"ctrl\"] == \">>\" else self.skip_backward()\n else:\n raise ValueError(f\"Cannot handle message: {payload}, not a valid command\")","def handleMessage(self, channels, sender, code, datagram):\n self.stateServer.handle(channels, sender, code, datagram)\n self.clientAgent.handle(channels, sender, code, datagram)\n self.databaseServer.handle(channels, sender, code, datagram)","def reply_handler(msg):\n print \"Server Response: %s, %s\" % (msg.typeName, msg)","def reply_handler(msg):\n print \"Server Response: %s, %s\" % (msg.typeName, msg)","def _on_message(self, raw_msg):\n strmsg = raw_msg.decode()\n msg = json.loads(strmsg)\n\n print(msg)\n\n if self._handlers.get(msg['msgid']):\n for handler in self._handlers[msg['msgid']]:\n handler.handle(msg)","def handle_message(self, message):\n\n\t\tself.console.handle_message(message)","def handle(self):\r\n # self.request is the TCP socket connected to the client\r\n # read the incoming command\r\n request = self.request.recv(1024).strip()\r\n # write to the queue waiting to be processed by the server\r\n INPUT_QUEUE.put(request)\r\n # wait for the server answer in the output queue\r\n response = OUTPUT_QUEUE.get(timeout=5.0)\r\n # send back the answer\r\n self.request.send(response)","def on_receive(self, msg):\n raise NotImplementedError","async def handle_message(self, peer_name):\n try:\n reader = self.peers[peer_name][\"reader\"]\n buffer = self.peers[peer_name][\"buffer\"]\n except KeyError:\n print(f\"Error: Connection to {peer_name} doesn't exist.\")\n return\n\n data = await reader.read(1024*8)\n\n if not data:\n raise NodeDisconnectException(f\"Node {peer_name} disconnected.\")\n\n buffer.write(data)\n try:\n message_header, message = buffer.receive_message()\n except InvalidMessageChecksum as ex:\n print(f\"Warning: {ex} (node {peer_name}).\")\n return\n\n if message_header is not None:\n await self.handle_message_header(peer_name, message_header, data)\n\n if not message:\n return\n\n # Executes proper message handler.\n handle_func_name = \"handle_\" + message_header.command\n handle_func = getattr(self, handle_func_name, None)\n if handle_func and callable(handle_func):\n await handle_func(peer_name, message_header, message)","def messageReceived(self, message):\n raise NotImplementedError(self)","def processReceivedMessage(iTag, clsName, msgID, msg): #@NoSelf","def handle_message(self, message):\n\n try:\n controller_func = get_controller_func(message.code)\n\n if controller_func:\n response = get_controller_func(message.code)(message.payload)\n self.send_message(response)\n else:\n self.send_bad_request()\n except Exception as e:\n Logger.log_error(e)\n self.send_server_error()","def receiveMessage(self, user, message):\n pass","def handle_message(self, message):\n print \"[WARNING] No message handling implemented!\"","def message_received(self, message_header, message):\n\t\t# reset the ping counter\n\t\tself.running = time.time()\n\t\tself.logger.receive(\"{0} - {1} - {2}\".format(self.peerip, message_header.command, str(message)))\n\t\thandle_func_name = \"handle_\" + message_header.command\n\t\thandle_func = getattr(self, handle_func_name, None)\n\t\tif handle_func:\n\t\t\thandle_func(message_header, message)","def process_received_message(self, message):\n self.log.debug('Received \"%s\"', message)\n self.receive_queue.put(message)","def on_message(self, message):\n print \"Client %s received a message : %s\" % (self.id, message)","def onMessage(self, msg, binary):\r\n# print('WebSocket: Received new message from client. '\r\n# '(binary={0})'.format(binary))\r\n\r\n try:\r\n self._assembler.processMessage(msg, binary)\r\n except InvalidRequest as e:\r\n self.sendErrorMessage('Invalid Request: {0}'.format(e))\r\n except DeadConnection:\r\n self.sendErrorMessage('Dead Connection')\r\n self.dropConnection()\r\n except:\r\n import traceback\r\n traceback.print_exc()\r\n self.sendErrorMessage('Fatal Error')","def handle(self):\n global latest_status\n data = self.request[0]\n socket = self.request[1]\n logging.info(\"Received {} bytes from {}\".format(len(data), self.client_address[0]))\n jss = interface.joystick_status_pb2.JoystickStatus()\n jss.ParseFromString(data)\n sent = jss.sent.ToDatetime()\n if not latest_status:\n latest_status = jss\n else:\n if latest_status.sent.ToDatetime() < sent:\n latest_status = jss\n else:\n logging.warning(\"Discarded stray package.\")\n ack = interface.joystick_status_pb2.JoystickAck()\n ack.sent.CopyFrom(jss.sent)\n ack.received.GetCurrentTime()\n response = ack.SerializeToString()\n socket.sendto(response, self.client_address)","def execute_message_received(self, message_received):\n pass","def receive_message(self, context, message):\r\n pass","def handle(self, m):\n\n\t\tline = m.split(\" \")\n\n\t\tif line[0] == \"PING\":\n\t\t\tself(\"PONG\", line[1])\n\t\telif len(line) > 1 and line[1] == \"001\":\n\t\t\tself.callbacks[\"loggedin\"](self, *line)\n\t\telif len(line) > 1 and line[1] == \"JOIN\":\n\t\t\tself.callbacks[\"joined\"](self, *line)\n\t\telif len(line) > 1 and line[1] == \"PRIVMSG\":\n\t\t\tself.callbacks[\"messaged\"](self, *line)","def incoming(self, msg):\n hdr = msg.header\n\n # Signals:\n if hdr.message_type is MessageType.signal:\n key = (hdr.fields.get(HeaderFields.path, None),\n hdr.fields.get(HeaderFields.interface, None),\n hdr.fields.get(HeaderFields.member, None)\n )\n cb = self.signal_callbacks.get(key, None)\n if cb is not None:\n cb(msg.body)\n return\n\n # Method returns & errors\n reply_serial = hdr.fields.get(HeaderFields.reply_serial, -1)\n reply_handle = self.awaiting_reply.pop(reply_serial, None)\n if reply_handle is not None:\n if hdr.message_type is MessageType.method_return:\n reply_handle.set_result(msg.body)\n return\n elif hdr.message_type is MessageType.error:\n reply_handle.set_exception(DBusErrorResponse(msg))\n return\n\n if self.on_unhandled:\n self.on_unhandled(msg)","def handle(self):\n data = self.request.recv(1024)\n self.request.send(data)","def handle_recv(self,stream,msgs):\n pass","def handle(self):\n socket = self.request[1]\n data = self.request[0].strip()\n logger.info(\"Address {} at {} wrote: '{}'\".format(self.client_address[1], self.client_address[0], data))\n cmd_strn, ret = self.command_service(data)\n print(ret)\n self.command_response(cmd_strn, ret, self.request[1], self.client_address[0],\n self.mapInterface.router[cmd_strn])","def _messageReceived(self, msg):\r\n if len(msg) < 17:\r\n self.transport.loseConnection()\r\n\r\n flag = msg[:1]\r\n\r\n if flag == self._TRUE:\r\n destID = UUID(bytes=msg[1:17])\r\n offset = 17\r\n elif flag == self._FALSE:\r\n destID = None\r\n offset = 1\r\n else:\r\n log.msg('Protocol Error: Could not identify flag.')\r\n self.transport.loseConnection()\r\n return\r\n\r\n remoteID = UUID(bytes=msg[offset:offset + 16])\r\n offset += 16\r\n\r\n idLen, = self._MSG_ID_STRUCT.unpack(msg[offset:offset + 1])\r\n offset += 1\r\n\r\n msgID = msg[offset:offset + idLen]\r\n offset += idLen\r\n\r\n self.messageReceived(remoteID, buffer(msg, offset), msgID, destID)","def handle(msg):\n\n # glance to get some meta on the message\n content_type, chat_type, chat_id = telepot.glance(msg)\n chat_id = str(chat_id)\n\n # we only want to process text messages from our specified chat\n if (content_type == 'text') and (chat_id in allowed_chat_ids):\n command = msg['text']\n try:\n _cmd = get_command(command)\n except UserWarning as ex:\n logger.error(ex)\n raise\n _cmd.execute(chat_id)","def receive_message(self, message):\r\n self.state.receive_message(message)\r\n return","def on_message(self, handler: Callable[[Request], Coroutine[Any, Any, Any]]):\n self.on_message_handler = handler","async def handle(self, message: discord.Message):\n raise NotImplementedError()","def async_handle_message(self, msg: dict) -> None:\n if msg[\"type\"] == \"result\":\n future = self._result_futures.get(msg[\"messageId\"])\n\n if future is None:\n self._logger.warning(\n \"Received result for unknown message: %s\", msg[\"messageId\"]\n )\n return\n\n if msg[\"success\"]:\n future.set_result(msg[\"result\"])\n return\n\n future.set_exception(FailedCommand(msg[\"messageId\"], msg[\"errorCode\"]))\n return\n\n if self.driver is None:\n raise InvalidState(\"Did not receive state as first message\")\n\n if msg[\"type\"] != \"event\":\n # Can't handle\n return\n\n event = Event(type=msg[\"event\"][\"event\"], data=msg[\"event\"])\n self.driver.receive_event(event)","def _on_message(self, message):\n print(\"RECEIVED on \" + self.session_name + \":\")\n message_json = json.loads(message)\n print(json.dumps(message_json, sort_keys=True, indent=2, separators=(',', ':')))\n\n for singleMsg in message_json:\n self._process_message(singleMsg)","def handle_message(self, from_channel, to_channel, message_type, *args):\n logger.debug(\"{} received message: {} -> {} ({})\".format(\n self, from_channel, to_channel, message_type,\n ))\n # TODO: Validation\n if message_type == msgtypes.AI_CHANNEL_ASSIGNED:\n channel = args[0]\n self.handle_channel_assigned(channel)\n elif message_type == msgtypes.AI_CONNECTED:\n channel = args[0]\n self.handle_ai_connected(channel)\n elif message_type == msgtypes.CLIENT_CONNECTED:\n client_id = args[0]\n self.handle_client_connected(client_id)\n elif message_type == msgtypes.CLIENT_DISCONNECTED:\n client_id = args[0]\n self.handle_client_disconnected(client_id)\n elif message_type == msgtypes.DOBJECT_CREATED:\n dobject_id = args[0]\n token = args[1]\n self.handle_dobject_created(dobject_id, token)\n elif message_type == msgtypes.CREATE_DOBJECT_VIEW:\n dobject_id = args[0]\n dclass = args[1]\n fields = args[2]\n self.handle_create_dobject_view(dobject_id, dclass, fields)\n elif message_type == msgtypes.CREATE_AI_VIEW:\n dobject_id = args[0]\n dclass = args[1]\n fields = args[2]\n self.handle_create_ai_view(dobject_id, dclass, fields)\n elif message_type == msgtypes.FIELD_UPDATE:\n source = from_channel\n dobject_id = args[0]\n field_id = args[1]\n values = args[2]\n self.handle_field_update(source, dobject_id, field_id, values)\n else:\n # FIXME: Better to log it and drop it on the floor?\n raise NotImplementedError","def message_received_handler(pdu, **kwargs):\n\n logging.warning('Message received handler (Override me)')","def handle_message(self, sender, message):\n self.logger.debug('handle_message(%r, %r)', sender, message.handler)\n\n message_handler = self.message_handlers.get(message.handler)\n if message_handler is None:\n self.logger.warning(\"sender=%r, No handler found: '%s'\",\n sender, message.handler)\n return\n\n message_handler(sender, message)","def handle_comm_msg(self, message):\n msg = self._unwrap(message)\n\n try:\n self.geonotebook._recv_msg(msg)\n\n except jsonrpc.JSONRPCError as e:\n self.geonotebook._send_msg(\n json_rpc_result(None, e.tojson(), msg['id'])\n )\n self.log.error(u\"JSONRPCError (%s): %s\" % (e.code, e.message))\n\n except Exception as e:\n self.log.error(u\"Error processing msg: {}\".format(str(e)))","def handle_message(self, message_type, *args):\n logger.debug(\"ClientRepository got message to handle: {}\".format(\n message_type\n ))\n if message_type == msgtypes.CONNECTED:\n self.handle_connected()\n elif message_type == msgtypes.DISCONNECTED:\n reason = args[0]\n self.handle_disconnected(reason)\n elif message_type == msgtypes.CREATE_DOBJECT_VIEW:\n dobject_id = args[0]\n dclass = args[1]\n fields = args[2]\n self.handle_create_dobject_view(dobject_id, dclass, fields)\n elif message_type == msgtypes.BECOME_OWNER:\n dobject_id = args[0]\n self.handle_become_owner(dobject_id)\n elif message_type == msgtypes.FIELD_UPDATE:\n dobject_id = args[0]\n field_id = args[1]\n values = args[2]\n self.handle_field_update(dobject_id, field_id, values)\n else:\n # FIXME: Better to log it and drop it on the floor?\n raise NotImplementedError","def handle_message(self, message: object) -> None:\n logging.debug('Client message: {}', message)\n if isinstance(message, ArduinoConnectionMessage):\n pass # TODO: Update self.window\n elif isinstance(message, SystemInfoMessage):\n pass # TODO: Update self.window","def _on_host_msg_received(self, data):\n self._log.info(\"Received a message from the host: {}\".format(data))\n data = json.loads(data)","def _handle_message(self, bus, message):\n if message.type == Gst.MessageType.EOS:\n logger.info(\"End-Of-Stream reached.\\n\")\n # file finished playing\n self.pipeline.set_state(Gst.State.NULL)\n #self.playing = False\n # if self.finished_callback:\n # self.finished_callback()\n \n elif message.type == Gst.MessageType.ERROR:\n # error\n self.pipeline.set_state(Gst.State.NULL)\n err, debug_info = message.parse_error()\n logger.error(f\"Error received from element {message.src.get_name()}: {err.message}\\n\")\n logger.error(f\"Debugging information: {debug_info if debug_info else 'none'}\\n\")\n #self.playing = False \n elif message.type == Gst.MessageType.STATE_CHANGED:\n # We are only interested in state-changed messages from the pipeline\n if message.src == self.pipeline:\n old_state, new_state, pending_state = message.parse_state_changed()\n logger.info(f\"Pipeline state changed from {Gst.Element.state_get_name(old_state)} to {Gst.Element.state_get_name(new_state)}:\\n\")","def onMessage(self, message):\n raise NotImplementedError","def messageHandler(self):\n\n while len(self.ReceiveMessageBuffer) > 0: # if message handler is called all received messages will be processed\n #print 'entered message handler of ID {0}'.format(self.CommID)\n msg = self.ReceiveMessageBuffer.popleft()\n self.MsgReceiveCount += 1\n self.MsgReceiveCount_interval += 1\n type = msg.getType()\n # for communication test:\n if type == 0: #System message\n print 'ID {0} has received msg {1} from ID {2}'.format(self.CommID, msg.getData(), msg.getIDSender())\n # send reply\n data = msg.getData()\n if data == 'ping':\n retval = self.sendMessage(msg.getIDSender(), 0, 'pong')\n return retval\n elif data == 'pong':\n retval = self.sendMessage(msg.getIDSender(), 0, 'ping')\n return retval\n # elif data[0] == 'system':\n # if(data[1] == 'startRONOPT'):\n # #save fluctuation curve of cluster\n # self.EFluctuationCurve = data[4]\n # #begin with local optimization (data[2] = fromTime, data[3]=toTime)\n # self.stateRONOPT = 0\n # for n in range(len(self.Neighbors)):\n # self.NeighborMessageRec[n] = 0\n # self.RemainderOfNeighborsOpt(data[2],data[3],1)\n #########################################################################################################\n\n elif type == 20: # pseudo tree generation message\n ret = self.messageHandler_PseudoTree(msg)\n if ret == -1:\n break\n\n elif type == 40: # load propagation message\n self.messageHandler_LoadProp(msg)\n\n elif type == 70:\n self.messageHandler_RemainderMulticast(msg) #remainder multicast optimization\n\n return 0","async def on_socket_receive(self, msg: \"Msg | MsgProto\") -> None:","def on_message(\n self, client: mqtt.Client, userdata: Any, msg: mqtt.MQTTMessage\n ) -> None:\n try:\n self.handle_message(msg)\n except UnicodeDecodeError as e:\n logging.error(f\"Cannot decode message: {msg.payload}, {e}\")\n except JSONDecodeError:\n logging.error(\n f\"Cannot deserialize message: {msg.payload.decode('utf-8')}, not a valid JSON\"\n )\n except ValidationError as e:\n logging.error(f\"Invalid JSON Schema: {e.message}\")\n except ValueError as e:\n logging.error(str(e))\n except Exception as e:\n logging.error(f\"Unexpected error while receiving a new message: {e}\")","def handleRecvData(self, data):\n\n\t\t#Look for commands\n\t\tif data == 'Hello':\n\t\t\t#Inform client it is 'connected'\n\t\t\tself.transmit(\"Welcome\")\n\n\t\telif data == 'kill':\t\n\t\t\t#Stop the server running\n\t\t\tself.running = False\n\n\t\telif data == 'control':\n\t\t\t#Print out if in control of car\n\t\t\tprint(\"Control query\")\n\t\t\tif self.arduino:\n\t\t\t\t#print(self.address)\n\t\t\t\tself.transmit(\"Control: True\")\n\t\t\telse:\n\t\t\t\t#print(self.address)\n\t\t\t\tself.transmit(\"Control: False\")\n\n\t\telif data == 'Hello?':\n\t\t\t#The client is still alive therefore set missing count to 0\n\t\t\tself.missingCount = 0\n\n\t\t#Look for Arduino Data\n\t\telif self.arduino:\n\t\t\t#Check if controlling the car and it's a valid car command\n\t\t\tif self.arduino.readPack(data): \n\t\t\t\tprint(self.address)\n\t\t\t\tprint(\"Sent to arduino: %s\" % data)\n\t\t\telse:\n\t\t\t\t#Print out other data\n\t\t\t\tprint(\"Not valid Arduino data\")\n\t\t\t\tprint(self.address)\n\t\t\t\tprint(data)\n\n\t\telse:\n\t\t\t#All other data print out\n\t\t\tprint(self.address)\n\t\t\tprint(data)","def messageHandler(self, source, message, messageId):\n try:\n type, params, data = message.split(':',2)\n except:\n # Not a real message\n return\n \n try:\n getattr(self, \"thive_%s\" % type)(messageId, params.split(), data)\n except exceptions.AttributeError, c:\n raise c\n print \"[HIVE] No method bound for command '%s'\" % type","def on_message(data):\n pass","def processMessage(self, msg):\r\n LOG(\"Received message: \" + msg.getId())\r\n \r\n # Process messages incoming from child executor, if any\r\n procId = msg[FIELD_PROC_ID]\r\n if procId != self.procId:\r\n if self.childManager.hasChild():\r\n self.childManager.processChildMessage(msg)\r\n else:\r\n LOG(\"Unexpected child message: \" + msg.getId(), LOG_ERROR)\r\n elif msg.getType() == MSG_TYPE_COMMAND:\r\n if msg.getId() == Messages.MSG_ADD_CLIENT:\r\n self.addClient(msg)\r\n elif msg.getId() == Messages.MSG_REMOVE_CLIENT:\r\n self.removeClient(msg)\r\n elif msg.getId() == Messages.CMD_CLOSE:\r\n self.cleanup()\r\n elif msg.getId() == Messages.CMD_RELOAD:\r\n REGISTRY['CIF'].clearAsRun()\r\n self.cleanup( executionOnly = True )\r\n self.setupResources()\r\n self.prepareExecution()\r\n else:\r\n cmdId = msg[\"Id\"]\r\n if cmdId in [ Messages.CMD_ABORT, Messages.CMD_PAUSE ]:\r\n self.mailbox.push( msg, high_priority = True )\r\n else:\r\n self.mailbox.push( msg )\r\n else:\r\n LOG(\"Unexpected message: \" + msg.getId() + \"/\" + msg.getType(), LOG_ERROR)","def handle_request(self):\n try:\n data = self.sock.recv(1024)\n except socket.error as e: # ...,e:\n if e == 10040:\n print('Message too long, ignoring.')\n return\n raise\n self.append_to_seq(parse_packet(data))","def message_handler(msg):\n logging.info(\"Message Text: %s\" % msg['msg'])\n\n message_entry = Message(request.sid, msg['room'], msg['msg'], msg['time'])\n if msg['msg'] != \"User has connected!\":\n logging.info(\"About to add to DB\")\n db.session.add(message_entry)\n db.session.commit()\n logging.info(\"Added to DB\")\n send(msg['msg'], room=msg['room'])","def on_receive(self, message):\n\n if message.get('command') == 'dispatch':\n self.dispatch(message['filename'])\n\n else:\n log.error('Dispatcher received unexpected message type: {}'.format(\n message))","def handle(self, message):\n if not message['successful']:\n raise BayeuxError(\n 'Unsuccessful disconnect response: {}'\n .format(message.get('error')))","def handle(self):\n # Since we only expect small packets, we don't need to read a buffer of\n # size, say, 4096.\n data = self.request.recv(64)\n\n logging.debug(\"Received '%s' from '%s'\", repr(data), self.client_address[0])\n\n if data[0:2] == \"\\x01\\x00\":\n # Authentication attempt. Since this address is whitelisted (as it\n # was successfully verified by the server in `verify_request`), we\n # can safely acknowledge the authentication.\n client = None\n\n try:\n client = self.server.authenticator.add_new_client(\n self.client_address[0],\n data.split(\"\\x00\")[1]\n )\n\n except:\n logging.exception(\"An exception occurred creating a new client\")\n return\n\n\n self.server.storage_manager.add_client(client)\n\n self.request.send(\"\\x01\\x00%s\\x00%s\\x00%s\\x00\" % (\n client.token,\n self.server.receiver_address[0],\n self.server.receiver_address[1]\n )\n )\n\n else:\n logging.info(\"Unknown request '%s' from '%s'\", \n data, self.client_address[0])\n\n self.request.send(\"\")","def process_chatter(self, msg):\n # note, nothing in here is ROS specific, it's just python code that\n # runs when new info appears\n\n print msg.data # print the recieved message\n\n self.msgs_recieved += 1 # increase msg count\n self.msgs_recieved %= 500 # mod 500 so we don't get enormous numbers\n self.msg = \"%d messages recieved\" % self.msgs_recieved # set message","def handle_received(self) -> None:\n self.buffer: bytes\n while len(self.buffer) >= DataHeader.length:\n header = DataHeader.decode(self.buffer, allow_excessive=True)\n if len(self.buffer) < header.size:\n _LOGGER.debug(\n \"Not enough data on data channel (has %d, expects %d)\",\n len(self.buffer),\n header.size,\n )\n break\n\n try:\n self._process_message_from_buffer(header)\n except Exception:\n _LOGGER.exception(\"failed to process data frame\")\n\n self.buffer = self.buffer[header.size :]","def gateway_receive(self, msg):\n\n if self.debug_api_messages:\n self.dump_input_message(msg)\n\n try:\n o = json.loads(msg)\n except Exception as e:\n return self.receive_exception(sys.exc_info()[0], e, msg)\n\n msg_type = o['type']\n msg_id = o['id']\n msg_data = o['data']\n\n if self.log_api_messages:\n self.output(f\"--> {msg_type} {msg_id} {msg_data}\")\n\n if msg_type == 'system':\n self.handle_system_message(msg_id, msg_data)\n else:\n if msg_id in self.active_cxn:\n c = self.active_cxn[msg_id].receive(msg_type, msg_data)\n else:\n self.error_handler(self.id, f\"Message Received on Unknown connection: {repr(msg)}\")\n\n return True","async def websocket_receive(self, event):\n data_received = event.get('text')\n if not data_received:\n return\n\n data = json.loads(data_received)\n message = data['message']\n\n await self.new_message(message)"],"string":"[\n \"def handle(self, message):\\n print(\\\"You received a message:\\\")\\n print(message)\\n # Overwrite this function to do something with the message!\",\n \"def received_message(self, m):\\n self.receiver.handle_message(m)\",\n \"def handle(self):\\n try:\\n # Wait for data\\n data = json.loads(self.request.recv(1024).decode('UTF-8').strip())\\n\\n # Process data\\n self.process_data(data)\\n\\n except Exception as e:\\n print(\\\"Exception wile receiving message: \\\", e)\\n self.request.sendall(\\n bytes(json.dumps({'return': 'error'}), 'UTF-8'))\",\n \"def handle_message(self, message):\\n print(f\\\"Got message {message}\\\")\\n if message >> 7 == 1:\\n # String\\n self.receive_char_array(message)\\n elif message >> 3 == 0b00000:\\n # Servo position\\n self.receive_servo_position(message)\\n elif message == 0b00001000:\\n # All servo positions\\n self.receive_all_servo_positions()\\n elif message == 0b00001001:\\n # All servo limits\\n self.receive_all_servo_limits()\\n elif message == 0b00001010:\\n # Raw force reading\\n self.receive_raw_force()\\n print(f\\\"Handled message {message}\\\")\",\n \"def _handle_message(self, msg):\\n self.event('message', msg)\",\n \"def process(self, msg):\\n print \\\"HANDLER: received a msg: %s\\\" % msg\",\n \"def handle(self, message):\",\n \"def handle_message(self, msg):\\n pass\",\n \"def handle_message(self, mxmsg):\\n if self._handler is None:\\n raise NotImplementedError()\\n\\n self.notify_started()\\n response = self._handler(mxmsg)\\n if response == ():\\n self.no_response()\\n elif isinstance(response, str):\\n self.send_message(message=response, type=MessageTypes.PING)\\n elif isinstance(response, dict):\\n self.send_message(**response)\\n else:\\n raise ValueError(\\\"Unsupported handler return type %r\\\" %\\n type(response))\",\n \"def _handle_msg(self, msg):\\n data = msg['content']['data']\\n method = data['method']\\n\\n if method == 'update':\\n if 'state' in data:\\n state = data['state']\\n if 'buffer_paths' in data:\\n _put_buffers(state, data['buffer_paths'], msg['buffers'])\\n self.set_state(state)\\n\\n # Handle a state request.\\n elif method == 'request_state':\\n self.send_state()\\n\\n # Handle a custom msg from the front-end.\\n elif method == 'custom':\\n if 'content' in data:\\n self._handle_custom_msg(data['content'], msg['buffers'])\\n\\n # Catch remainder.\\n else:\\n self.log.error('Unknown front-end to back-end widget msg with method \\\"%s\\\"' % method)\",\n \"def handleMessage(msg):\",\n \"def handle_message(self, msg):\\n Logger.debug(\\\"Slave: Trying to parse\\\")\\n if MessageKeys.command_key in msg.fields:\\n Logger.info(\\\"Slave: Message command: %s\\\", str(msg.get_command()))\\n return self.messagehandler[msg.get_command()](self, msg)\\n return self.handle_invalid_command(msg)\",\n \"def handle_received(self) -> None:\\n self.buffer: bytes\\n while self.buffer:\\n try:\\n request, self.buffer = parse_request(self.buffer)\\n if request is None:\\n _LOGGER.debug(\\\"Not enough data to parse request on event channel\\\")\\n break\\n\\n _LOGGER.debug(\\\"Got message on event channel: %s\\\", request)\\n\\n # Send a positive response to satisfy the other end of the channel\\n # TODO: Add public method to pyatv.http to format a message\\n headers = {\\n \\\"Content-Length\\\": 0,\\n \\\"Audio-Latency\\\": 0,\\n \\\"Server\\\": request.headers.get(\\\"Server\\\"),\\n \\\"CSeq\\\": request.headers.get(\\\"CSeq\\\"),\\n }\\n response = (\\n f\\\"{request.protocol}/{request.version} 200 OK\\\\r\\\\n\\\"\\n + \\\"\\\\r\\\\n\\\".join(f\\\"{key}: {value}\\\" for key, value in headers.items())\\n + \\\"\\\\r\\\\n\\\\r\\\\n\\\"\\n )\\n self.send(response.encode(\\\"utf-8\\\"))\\n except Exception:\\n _LOGGER.exception(\\\"Failed to handle message on event channel\\\")\",\n \"def on_message(self, message):\\n log.debug(\\\"Protocol got message {message}\\\", message=message)\\n if message['type'] == \\\"change\\\":\\n self.handler.process_packet(message['packet'])\\n self.send_packet()\\n elif message['type'] == \\\"chat\\\":\\n self.on_chat_message(message)\\n elif message['type'] == \\\"action\\\":\\n self.on_action(message)\\n else:\\n log.warn(\\\"Unrecognized message type {type}\\\", type=message['type'])\",\n \"def handle_msg(self, msg):\\n self.logger.debug(\\\"Received: {}\\\".format(msg))\\n\\n try:\\n msg_type = msg[\\\"type\\\"]\\n except KeyError as e:\\n return msgs.error(e)\\n\\n if msg_type == \\\"ping_req\\\":\\n reply = msgs.ping_reply()\\n elif msg_type == \\\"list_req\\\":\\n reply = self.list_callables()\\n elif msg_type == \\\"call_req\\\":\\n try:\\n obj_name = msg[\\\"obj_name\\\"]\\n method = msg[\\\"method\\\"]\\n params = msg[\\\"params\\\"]\\n reply = self.call_method(obj_name, method, params)\\n except KeyError as e:\\n return msgs.error(e)\\n elif msg_type == \\\"exit_req\\\":\\n self.logger.info(\\\"Received message to die. Bye!\\\")\\n reply = msgs.exit_reply()\\n # Need to actually send reply here as we're about to exit\\n self.logger.debug(\\\"Sending: {}\\\".format(reply))\\n self.ctrl_sock.send_json(reply)\\n self.clean_up()\\n sys.exit(0)\\n else:\\n err_msg = \\\"Unrecognized message: {}\\\".format(msg)\\n self.logger.warning(err_msg)\\n reply = msgs.error(err_msg)\\n return reply\",\n \"def handle(self, message: Message) -> None:\\n self.handled_message = message\",\n \"def handle(self):\\n self.ip = self.client_address[0]\\n self.port = self.client_address[1]\\n self.connection = self.request\\n\\n state.addConnection(self.connection)\\n\\n # Loop that listens for messages from the client\\n while True:\\n received_string = self.connection.recv(4096)\\n\\n if len(received_string) == 0:\\n continue\\n\\n # TODO: Add handling of received payload from client\\n\\n # Convert payload from JSON to object\\n payloadToData = json.loads(received_string)\\n\\n # determine what request is being made\\n request_handler = RequestHandler(payloadToData,\\n state,\\n self.connection)\\n\\n # execute and generate response (JSON formatted)\\n jsonResponse = request_handler.callHandler()\\n\\n if not jsonResponse == 'BROADCAST':\\n # send response\\n self.connection.send(bytes(jsonResponse, \\\"ascii\\\"))\",\n \"def handle_message(self, data, channel):\\n pass\",\n \"def handle(self):\\n self.ip = self.client_address[0]\\n self.port = self.client_address[1]\\n self.connection = self.request\\n #self.logged_in = False\\n\\n # Loop that listens for messages from the client\\n while True:\\n received_string = self.connection.recv(4096).strip()\\n if received_string:\\n jsonObject = json.loads(received_string)\\n request = jsonObject.get('request')\\n #print(received_string)\\n #self.handle_data(received_string)\\n if request == 'login':\\n print 'logging in'\\n self.login(jsonObject)\\n elif request == 'logout':\\n self.logout()\\n elif request == 'msg':\\n self.send(jsonObject)\\n elif request == 'names':\\n self.getNames()\\n elif request == 'help':\\n return \\\"geiegohruuhiegr\\\"\\n else:\\n return \\\"you suck\\\"\\n\\n else:\\n print('The client is disconnected.')\\n break \\n # TODO: Add handling of received payload from client\",\n \"def message_received(self, message):\\n \\n # Routing\\n if self.route_message(message) == True:\\n return\\n \\n # Handlers?\\n if len(self._handlers) > 0:\\n for handler in self._handlers:\\n handler(message)\\n \\n # Storage?\\n else:\\n timestamp = 0\\n t = datetime.datetime.today()\\n timestamp = t.microsecond/1000 + t.second*1000 + \\\\\\n t.minute*60*1000 + t.hour*60*60*1000 + t.day*24*60*60*1000\\n while timestamp > 4294967295: timestamp -= 4294967295\\n self._messages.put(tuple([timestamp,message]))\",\n \"def handle_msg(self, state_id, msg):\\n pass\",\n \"def handle_message(self, message):\",\n \"def handle_message(self, validated_message: dict):\\n self.logger.debug(f'Sensor received message {validated_message}')\\n if (validated_message['messageType'] !=\\n model.MessageTypes.Control.value):\\n self.logger.debug(\\n 'Sensor ignoring because messageType was not control'\\n )\\n return\\n if validated_message['messageBody']['target'] != self.component_id:\\n self.logger.debug(\\n 'Sensor ignoring because not targeted at me'\\n )\\n return\\n\\n subtype = validated_message['messageSubtype']\\n try:\\n self.logger.debug(f'Dispatching message with subtype {subtype}')\\n self.message_handler_table[subtype](validated_message)\\n except KeyError:\\n self.logger.warning(f'No handler for with subtype {subtype}')\\n pass\",\n \"def handleMessage(self):\\n try:\\n # This whole block is wrapped in a try/except because the default\\n # behaviour of the SimpleWebSocketServer library is to silently\\n # discard any exceptions raised by these handlers. This is very\\n # unhelpful. A workaround is to catch and log any exceptions\\n # explicitly here.\\n logging.debug(\\\"%s %s\\\", self.address, \\\"incoming message\\\")\\n is_binary = not isinstance(self.data, str)\\n if is_binary:\\n message = self.data\\n else:\\n message = json.loads(self.data)\\n\\n self.logbook.messages_received.append(message)\\n for response in self.get_responses(message, is_binary=is_binary):\\n self.logbook.messages_sent.append(response)\\n self.sendMessage(json.dumps(response).encode(\\\"utf-8\\\"))\\n except Exception as exc: # pylint: disable=broad-except\\n logging.exception(str(exc))\\n self.close(status=1011, reason=\\\"Internal server error\\\")\",\n \"def received(self, message):\\n raise NotImplementedError()\",\n \"def receive_message(self, message):\\r\\n return\",\n \"def run(self):\\n alogger.info(\\\"Recieved message from %s, Message: (%d) %s\\\" % (self.client.getaddress(), self.action_type, self.message))\\n \\n #Try to call th function associated with this message type.\\n #format = \\\"handle_<type>\\\" (eg: handle_100)\\n fn = globals().get(\\\"handle_\\\" + str(self.action_type))\\n if fn and callable(fn):\\n fn(self.message, self.address, self.client)\\n else:\\n alogger.info(\\\"Received unknown message from %d, type: %d\\\" % (self.client.getaddress(), self.action_type))\",\n \"def receive_message(self, message):\",\n \"def receive(self, msg):\\n pass\",\n \"def handle_message(self, data):\\n message = Message.from_text(data)\\n if message is not None:\\n print(message.username, message.action, message.channel, message.content)\\n self._callback(\\\"message\\\", message) # TODO: add additional callbacks\",\n \"def handle_read(self):\\n packet = self.recv(8192)\\n if packet == \\\"\\\":\\n #print \\\"[WARNING] Socket closed by remote host %s:%s\\\" % (\\n # self.address,self.port)\\n self.close()\\n return\\n packet_list = messages.separate_messages(packet)\\n #received_types = \\\" + \\\".join(\\n # messages.get_message_type(messages.parse(packet))\\n # for packet in packet_list)\\n #print \\\"From %s:%s received: \\\" % (self.address, self.port), received_types\\n # Process a single message at a time\\n for packet in packet_list:\\n message = messages.parse(packet)\\n if messages.get_message_type(message) == \\\"OFPT_ECHO_REQUEST\\\":\\n self.buffer.append(messages.of_echo_reply)\\n else:\\n self.handle_message(message)\",\n \"def on_message(self, event):\\n self.response = event.message\\n self.connection.container.yield_() # Wake up the wait() loop to handle the message.\",\n \"def _r_handle_message_contents(self, msg, protocol):\\n if isinstance(msg, ResponseMessage):\\n d = self._waiting_messages.pop(msg.response_to, None)\\n if d is not None:\\n d.callback(msg)\\n elif isinstance(msg, ServerMotdMessage):\\n print(\\\"Connected: %s\\\" % msg.motd)\\n self._r_successful_connection()\\n elif isinstance(msg, EventMessage):\\n callback = self._event_callbacks.get((msg.service_name, msg.event_name))\\n if callback is not None:\\n threads.deferToThread(callback, *msg.pargs, **msg.kwargs)\",\n \"def handle_message(**payload):\\n handler_instance = message.MessageHandler(payload)\\n handler_instance.handle()\",\n \"def receive(self, message):\",\n \"def handle_msg(msg):\\n if comm._msg_callback:\\n comm._msg_callback(msg)\",\n \"def handle_message(self, message):\\n\\n\\t\\tself.log.debug(\\\"%s handle_message %s\\\", self.name, message)\\n\\n\\t\\tif message[\\\"Type\\\"] == \\\"command\\\":\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tcommand_callable = \\\"command_%s\\\" % message[\\\"Message\\\"][\\\"command\\\"]\\n\\t\\t\\t\\tif hasattr(self, command_callable) and callable(getattr(self, command_callable)):\\n\\t\\t\\t\\t\\tcall = getattr(self, command_callable)\\n\\t\\t\\t\\t\\tcall(message[\\\"Message\\\"][\\\"arguments\\\"])\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tself.log.error(\\\"%s invalid command %s %s\\\", self.name, message, e)\",\n \"def reply_handler(msg):\\n print(\\\"Server Response: %s, %s\\\" % (msg.typeName, msg))\\n pass\",\n \"def handle(self, msg, peer_protocol):\\n msg_id = msg[0]\\n if msg_id == 0:\\n self._handle_handshake(msg, peer_protocol)\\n elif msg_id == 1: #update\\n print(msg, len(msg))\\n self._handle_update(msg)\",\n \"def _message(self, msg):\\n\\n self.log('Message received:', msg['body'], pretty=True)\\n\\n if msg['type'] in ('chat', 'normal'):\\n body = str(msg['body'])\\n if body.startswith('/'):\\n cmd, arg_string = body.split(' ', maxsplit=1)\\n cmd = cmd.lstrip('/')\\n\\n if arg_string:\\n args = arg_string.split(' ')\\n else:\\n args = None\\n\\n self.log('IRC remote command received:', cmd, args)\\n return\\n else:\\n if True:\\n msg.reply(\\\"Sorry, I did not understand that:\\\\n%s\\\" % body).send()\",\n \"def on_message(self, data):\\n req = json.loads(data)\\n self.serve(req)\",\n \"def on_message(self, data):\\n req = json.loads(data)\\n self.serve(req)\",\n \"def handle_message(self, msg: mqtt.MQTTMessage) -> None:\\n payload = json.loads(msg.payload.decode(\\\"utf-8\\\"))\\n logging.info(f\\\"Received a new message: {payload}\\\")\\n if \\\"volume\\\" in payload:\\n validate(payload, schema=self.volume_schema)\\n self.volume = payload[\\\"volume\\\"]\\n elif \\\"volumeCtrl\\\" in payload:\\n validate(payload, schema=self.volume_ctrl_schema)\\n self.volume_up() if payload[\\\"volumeCtrl\\\"] == \\\"+\\\" else self.volume_down()\\n elif \\\"mute\\\" in payload:\\n validate(payload, schema=self.mute_schema)\\n self.mute = payload[\\\"mute\\\"]\\n elif \\\"toggle\\\" in payload:\\n validate(payload, schema=self.toggle_schema)\\n self.toggle_mute() if payload[\\\"toggle\\\"] == \\\"mute\\\" else self.toggle_pause()\\n elif \\\"ctrl\\\" in payload:\\n validate(payload, schema=self.ctrl_schema)\\n self.skip_forward() if payload[\\\"ctrl\\\"] == \\\">>\\\" else self.skip_backward()\\n else:\\n raise ValueError(f\\\"Cannot handle message: {payload}, not a valid command\\\")\",\n \"def handleMessage(self, channels, sender, code, datagram):\\n self.stateServer.handle(channels, sender, code, datagram)\\n self.clientAgent.handle(channels, sender, code, datagram)\\n self.databaseServer.handle(channels, sender, code, datagram)\",\n \"def reply_handler(msg):\\n print \\\"Server Response: %s, %s\\\" % (msg.typeName, msg)\",\n \"def reply_handler(msg):\\n print \\\"Server Response: %s, %s\\\" % (msg.typeName, msg)\",\n \"def _on_message(self, raw_msg):\\n strmsg = raw_msg.decode()\\n msg = json.loads(strmsg)\\n\\n print(msg)\\n\\n if self._handlers.get(msg['msgid']):\\n for handler in self._handlers[msg['msgid']]:\\n handler.handle(msg)\",\n \"def handle_message(self, message):\\n\\n\\t\\tself.console.handle_message(message)\",\n \"def handle(self):\\r\\n # self.request is the TCP socket connected to the client\\r\\n # read the incoming command\\r\\n request = self.request.recv(1024).strip()\\r\\n # write to the queue waiting to be processed by the server\\r\\n INPUT_QUEUE.put(request)\\r\\n # wait for the server answer in the output queue\\r\\n response = OUTPUT_QUEUE.get(timeout=5.0)\\r\\n # send back the answer\\r\\n self.request.send(response)\",\n \"def on_receive(self, msg):\\n raise NotImplementedError\",\n \"async def handle_message(self, peer_name):\\n try:\\n reader = self.peers[peer_name][\\\"reader\\\"]\\n buffer = self.peers[peer_name][\\\"buffer\\\"]\\n except KeyError:\\n print(f\\\"Error: Connection to {peer_name} doesn't exist.\\\")\\n return\\n\\n data = await reader.read(1024*8)\\n\\n if not data:\\n raise NodeDisconnectException(f\\\"Node {peer_name} disconnected.\\\")\\n\\n buffer.write(data)\\n try:\\n message_header, message = buffer.receive_message()\\n except InvalidMessageChecksum as ex:\\n print(f\\\"Warning: {ex} (node {peer_name}).\\\")\\n return\\n\\n if message_header is not None:\\n await self.handle_message_header(peer_name, message_header, data)\\n\\n if not message:\\n return\\n\\n # Executes proper message handler.\\n handle_func_name = \\\"handle_\\\" + message_header.command\\n handle_func = getattr(self, handle_func_name, None)\\n if handle_func and callable(handle_func):\\n await handle_func(peer_name, message_header, message)\",\n \"def messageReceived(self, message):\\n raise NotImplementedError(self)\",\n \"def processReceivedMessage(iTag, clsName, msgID, msg): #@NoSelf\",\n \"def handle_message(self, message):\\n\\n try:\\n controller_func = get_controller_func(message.code)\\n\\n if controller_func:\\n response = get_controller_func(message.code)(message.payload)\\n self.send_message(response)\\n else:\\n self.send_bad_request()\\n except Exception as e:\\n Logger.log_error(e)\\n self.send_server_error()\",\n \"def receiveMessage(self, user, message):\\n pass\",\n \"def handle_message(self, message):\\n print \\\"[WARNING] No message handling implemented!\\\"\",\n \"def message_received(self, message_header, message):\\n\\t\\t# reset the ping counter\\n\\t\\tself.running = time.time()\\n\\t\\tself.logger.receive(\\\"{0} - {1} - {2}\\\".format(self.peerip, message_header.command, str(message)))\\n\\t\\thandle_func_name = \\\"handle_\\\" + message_header.command\\n\\t\\thandle_func = getattr(self, handle_func_name, None)\\n\\t\\tif handle_func:\\n\\t\\t\\thandle_func(message_header, message)\",\n \"def process_received_message(self, message):\\n self.log.debug('Received \\\"%s\\\"', message)\\n self.receive_queue.put(message)\",\n \"def on_message(self, message):\\n print \\\"Client %s received a message : %s\\\" % (self.id, message)\",\n \"def onMessage(self, msg, binary):\\r\\n# print('WebSocket: Received new message from client. '\\r\\n# '(binary={0})'.format(binary))\\r\\n\\r\\n try:\\r\\n self._assembler.processMessage(msg, binary)\\r\\n except InvalidRequest as e:\\r\\n self.sendErrorMessage('Invalid Request: {0}'.format(e))\\r\\n except DeadConnection:\\r\\n self.sendErrorMessage('Dead Connection')\\r\\n self.dropConnection()\\r\\n except:\\r\\n import traceback\\r\\n traceback.print_exc()\\r\\n self.sendErrorMessage('Fatal Error')\",\n \"def handle(self):\\n global latest_status\\n data = self.request[0]\\n socket = self.request[1]\\n logging.info(\\\"Received {} bytes from {}\\\".format(len(data), self.client_address[0]))\\n jss = interface.joystick_status_pb2.JoystickStatus()\\n jss.ParseFromString(data)\\n sent = jss.sent.ToDatetime()\\n if not latest_status:\\n latest_status = jss\\n else:\\n if latest_status.sent.ToDatetime() < sent:\\n latest_status = jss\\n else:\\n logging.warning(\\\"Discarded stray package.\\\")\\n ack = interface.joystick_status_pb2.JoystickAck()\\n ack.sent.CopyFrom(jss.sent)\\n ack.received.GetCurrentTime()\\n response = ack.SerializeToString()\\n socket.sendto(response, self.client_address)\",\n \"def execute_message_received(self, message_received):\\n pass\",\n \"def receive_message(self, context, message):\\r\\n pass\",\n \"def handle(self, m):\\n\\n\\t\\tline = m.split(\\\" \\\")\\n\\n\\t\\tif line[0] == \\\"PING\\\":\\n\\t\\t\\tself(\\\"PONG\\\", line[1])\\n\\t\\telif len(line) > 1 and line[1] == \\\"001\\\":\\n\\t\\t\\tself.callbacks[\\\"loggedin\\\"](self, *line)\\n\\t\\telif len(line) > 1 and line[1] == \\\"JOIN\\\":\\n\\t\\t\\tself.callbacks[\\\"joined\\\"](self, *line)\\n\\t\\telif len(line) > 1 and line[1] == \\\"PRIVMSG\\\":\\n\\t\\t\\tself.callbacks[\\\"messaged\\\"](self, *line)\",\n \"def incoming(self, msg):\\n hdr = msg.header\\n\\n # Signals:\\n if hdr.message_type is MessageType.signal:\\n key = (hdr.fields.get(HeaderFields.path, None),\\n hdr.fields.get(HeaderFields.interface, None),\\n hdr.fields.get(HeaderFields.member, None)\\n )\\n cb = self.signal_callbacks.get(key, None)\\n if cb is not None:\\n cb(msg.body)\\n return\\n\\n # Method returns & errors\\n reply_serial = hdr.fields.get(HeaderFields.reply_serial, -1)\\n reply_handle = self.awaiting_reply.pop(reply_serial, None)\\n if reply_handle is not None:\\n if hdr.message_type is MessageType.method_return:\\n reply_handle.set_result(msg.body)\\n return\\n elif hdr.message_type is MessageType.error:\\n reply_handle.set_exception(DBusErrorResponse(msg))\\n return\\n\\n if self.on_unhandled:\\n self.on_unhandled(msg)\",\n \"def handle(self):\\n data = self.request.recv(1024)\\n self.request.send(data)\",\n \"def handle_recv(self,stream,msgs):\\n pass\",\n \"def handle(self):\\n socket = self.request[1]\\n data = self.request[0].strip()\\n logger.info(\\\"Address {} at {} wrote: '{}'\\\".format(self.client_address[1], self.client_address[0], data))\\n cmd_strn, ret = self.command_service(data)\\n print(ret)\\n self.command_response(cmd_strn, ret, self.request[1], self.client_address[0],\\n self.mapInterface.router[cmd_strn])\",\n \"def _messageReceived(self, msg):\\r\\n if len(msg) < 17:\\r\\n self.transport.loseConnection()\\r\\n\\r\\n flag = msg[:1]\\r\\n\\r\\n if flag == self._TRUE:\\r\\n destID = UUID(bytes=msg[1:17])\\r\\n offset = 17\\r\\n elif flag == self._FALSE:\\r\\n destID = None\\r\\n offset = 1\\r\\n else:\\r\\n log.msg('Protocol Error: Could not identify flag.')\\r\\n self.transport.loseConnection()\\r\\n return\\r\\n\\r\\n remoteID = UUID(bytes=msg[offset:offset + 16])\\r\\n offset += 16\\r\\n\\r\\n idLen, = self._MSG_ID_STRUCT.unpack(msg[offset:offset + 1])\\r\\n offset += 1\\r\\n\\r\\n msgID = msg[offset:offset + idLen]\\r\\n offset += idLen\\r\\n\\r\\n self.messageReceived(remoteID, buffer(msg, offset), msgID, destID)\",\n \"def handle(msg):\\n\\n # glance to get some meta on the message\\n content_type, chat_type, chat_id = telepot.glance(msg)\\n chat_id = str(chat_id)\\n\\n # we only want to process text messages from our specified chat\\n if (content_type == 'text') and (chat_id in allowed_chat_ids):\\n command = msg['text']\\n try:\\n _cmd = get_command(command)\\n except UserWarning as ex:\\n logger.error(ex)\\n raise\\n _cmd.execute(chat_id)\",\n \"def receive_message(self, message):\\r\\n self.state.receive_message(message)\\r\\n return\",\n \"def on_message(self, handler: Callable[[Request], Coroutine[Any, Any, Any]]):\\n self.on_message_handler = handler\",\n \"async def handle(self, message: discord.Message):\\n raise NotImplementedError()\",\n \"def async_handle_message(self, msg: dict) -> None:\\n if msg[\\\"type\\\"] == \\\"result\\\":\\n future = self._result_futures.get(msg[\\\"messageId\\\"])\\n\\n if future is None:\\n self._logger.warning(\\n \\\"Received result for unknown message: %s\\\", msg[\\\"messageId\\\"]\\n )\\n return\\n\\n if msg[\\\"success\\\"]:\\n future.set_result(msg[\\\"result\\\"])\\n return\\n\\n future.set_exception(FailedCommand(msg[\\\"messageId\\\"], msg[\\\"errorCode\\\"]))\\n return\\n\\n if self.driver is None:\\n raise InvalidState(\\\"Did not receive state as first message\\\")\\n\\n if msg[\\\"type\\\"] != \\\"event\\\":\\n # Can't handle\\n return\\n\\n event = Event(type=msg[\\\"event\\\"][\\\"event\\\"], data=msg[\\\"event\\\"])\\n self.driver.receive_event(event)\",\n \"def _on_message(self, message):\\n print(\\\"RECEIVED on \\\" + self.session_name + \\\":\\\")\\n message_json = json.loads(message)\\n print(json.dumps(message_json, sort_keys=True, indent=2, separators=(',', ':')))\\n\\n for singleMsg in message_json:\\n self._process_message(singleMsg)\",\n \"def handle_message(self, from_channel, to_channel, message_type, *args):\\n logger.debug(\\\"{} received message: {} -> {} ({})\\\".format(\\n self, from_channel, to_channel, message_type,\\n ))\\n # TODO: Validation\\n if message_type == msgtypes.AI_CHANNEL_ASSIGNED:\\n channel = args[0]\\n self.handle_channel_assigned(channel)\\n elif message_type == msgtypes.AI_CONNECTED:\\n channel = args[0]\\n self.handle_ai_connected(channel)\\n elif message_type == msgtypes.CLIENT_CONNECTED:\\n client_id = args[0]\\n self.handle_client_connected(client_id)\\n elif message_type == msgtypes.CLIENT_DISCONNECTED:\\n client_id = args[0]\\n self.handle_client_disconnected(client_id)\\n elif message_type == msgtypes.DOBJECT_CREATED:\\n dobject_id = args[0]\\n token = args[1]\\n self.handle_dobject_created(dobject_id, token)\\n elif message_type == msgtypes.CREATE_DOBJECT_VIEW:\\n dobject_id = args[0]\\n dclass = args[1]\\n fields = args[2]\\n self.handle_create_dobject_view(dobject_id, dclass, fields)\\n elif message_type == msgtypes.CREATE_AI_VIEW:\\n dobject_id = args[0]\\n dclass = args[1]\\n fields = args[2]\\n self.handle_create_ai_view(dobject_id, dclass, fields)\\n elif message_type == msgtypes.FIELD_UPDATE:\\n source = from_channel\\n dobject_id = args[0]\\n field_id = args[1]\\n values = args[2]\\n self.handle_field_update(source, dobject_id, field_id, values)\\n else:\\n # FIXME: Better to log it and drop it on the floor?\\n raise NotImplementedError\",\n \"def message_received_handler(pdu, **kwargs):\\n\\n logging.warning('Message received handler (Override me)')\",\n \"def handle_message(self, sender, message):\\n self.logger.debug('handle_message(%r, %r)', sender, message.handler)\\n\\n message_handler = self.message_handlers.get(message.handler)\\n if message_handler is None:\\n self.logger.warning(\\\"sender=%r, No handler found: '%s'\\\",\\n sender, message.handler)\\n return\\n\\n message_handler(sender, message)\",\n \"def handle_comm_msg(self, message):\\n msg = self._unwrap(message)\\n\\n try:\\n self.geonotebook._recv_msg(msg)\\n\\n except jsonrpc.JSONRPCError as e:\\n self.geonotebook._send_msg(\\n json_rpc_result(None, e.tojson(), msg['id'])\\n )\\n self.log.error(u\\\"JSONRPCError (%s): %s\\\" % (e.code, e.message))\\n\\n except Exception as e:\\n self.log.error(u\\\"Error processing msg: {}\\\".format(str(e)))\",\n \"def handle_message(self, message_type, *args):\\n logger.debug(\\\"ClientRepository got message to handle: {}\\\".format(\\n message_type\\n ))\\n if message_type == msgtypes.CONNECTED:\\n self.handle_connected()\\n elif message_type == msgtypes.DISCONNECTED:\\n reason = args[0]\\n self.handle_disconnected(reason)\\n elif message_type == msgtypes.CREATE_DOBJECT_VIEW:\\n dobject_id = args[0]\\n dclass = args[1]\\n fields = args[2]\\n self.handle_create_dobject_view(dobject_id, dclass, fields)\\n elif message_type == msgtypes.BECOME_OWNER:\\n dobject_id = args[0]\\n self.handle_become_owner(dobject_id)\\n elif message_type == msgtypes.FIELD_UPDATE:\\n dobject_id = args[0]\\n field_id = args[1]\\n values = args[2]\\n self.handle_field_update(dobject_id, field_id, values)\\n else:\\n # FIXME: Better to log it and drop it on the floor?\\n raise NotImplementedError\",\n \"def handle_message(self, message: object) -> None:\\n logging.debug('Client message: {}', message)\\n if isinstance(message, ArduinoConnectionMessage):\\n pass # TODO: Update self.window\\n elif isinstance(message, SystemInfoMessage):\\n pass # TODO: Update self.window\",\n \"def _on_host_msg_received(self, data):\\n self._log.info(\\\"Received a message from the host: {}\\\".format(data))\\n data = json.loads(data)\",\n \"def _handle_message(self, bus, message):\\n if message.type == Gst.MessageType.EOS:\\n logger.info(\\\"End-Of-Stream reached.\\\\n\\\")\\n # file finished playing\\n self.pipeline.set_state(Gst.State.NULL)\\n #self.playing = False\\n # if self.finished_callback:\\n # self.finished_callback()\\n \\n elif message.type == Gst.MessageType.ERROR:\\n # error\\n self.pipeline.set_state(Gst.State.NULL)\\n err, debug_info = message.parse_error()\\n logger.error(f\\\"Error received from element {message.src.get_name()}: {err.message}\\\\n\\\")\\n logger.error(f\\\"Debugging information: {debug_info if debug_info else 'none'}\\\\n\\\")\\n #self.playing = False \\n elif message.type == Gst.MessageType.STATE_CHANGED:\\n # We are only interested in state-changed messages from the pipeline\\n if message.src == self.pipeline:\\n old_state, new_state, pending_state = message.parse_state_changed()\\n logger.info(f\\\"Pipeline state changed from {Gst.Element.state_get_name(old_state)} to {Gst.Element.state_get_name(new_state)}:\\\\n\\\")\",\n \"def onMessage(self, message):\\n raise NotImplementedError\",\n \"def messageHandler(self):\\n\\n while len(self.ReceiveMessageBuffer) > 0: # if message handler is called all received messages will be processed\\n #print 'entered message handler of ID {0}'.format(self.CommID)\\n msg = self.ReceiveMessageBuffer.popleft()\\n self.MsgReceiveCount += 1\\n self.MsgReceiveCount_interval += 1\\n type = msg.getType()\\n # for communication test:\\n if type == 0: #System message\\n print 'ID {0} has received msg {1} from ID {2}'.format(self.CommID, msg.getData(), msg.getIDSender())\\n # send reply\\n data = msg.getData()\\n if data == 'ping':\\n retval = self.sendMessage(msg.getIDSender(), 0, 'pong')\\n return retval\\n elif data == 'pong':\\n retval = self.sendMessage(msg.getIDSender(), 0, 'ping')\\n return retval\\n # elif data[0] == 'system':\\n # if(data[1] == 'startRONOPT'):\\n # #save fluctuation curve of cluster\\n # self.EFluctuationCurve = data[4]\\n # #begin with local optimization (data[2] = fromTime, data[3]=toTime)\\n # self.stateRONOPT = 0\\n # for n in range(len(self.Neighbors)):\\n # self.NeighborMessageRec[n] = 0\\n # self.RemainderOfNeighborsOpt(data[2],data[3],1)\\n #########################################################################################################\\n\\n elif type == 20: # pseudo tree generation message\\n ret = self.messageHandler_PseudoTree(msg)\\n if ret == -1:\\n break\\n\\n elif type == 40: # load propagation message\\n self.messageHandler_LoadProp(msg)\\n\\n elif type == 70:\\n self.messageHandler_RemainderMulticast(msg) #remainder multicast optimization\\n\\n return 0\",\n \"async def on_socket_receive(self, msg: \\\"Msg | MsgProto\\\") -> None:\",\n \"def on_message(\\n self, client: mqtt.Client, userdata: Any, msg: mqtt.MQTTMessage\\n ) -> None:\\n try:\\n self.handle_message(msg)\\n except UnicodeDecodeError as e:\\n logging.error(f\\\"Cannot decode message: {msg.payload}, {e}\\\")\\n except JSONDecodeError:\\n logging.error(\\n f\\\"Cannot deserialize message: {msg.payload.decode('utf-8')}, not a valid JSON\\\"\\n )\\n except ValidationError as e:\\n logging.error(f\\\"Invalid JSON Schema: {e.message}\\\")\\n except ValueError as e:\\n logging.error(str(e))\\n except Exception as e:\\n logging.error(f\\\"Unexpected error while receiving a new message: {e}\\\")\",\n \"def handleRecvData(self, data):\\n\\n\\t\\t#Look for commands\\n\\t\\tif data == 'Hello':\\n\\t\\t\\t#Inform client it is 'connected'\\n\\t\\t\\tself.transmit(\\\"Welcome\\\")\\n\\n\\t\\telif data == 'kill':\\t\\n\\t\\t\\t#Stop the server running\\n\\t\\t\\tself.running = False\\n\\n\\t\\telif data == 'control':\\n\\t\\t\\t#Print out if in control of car\\n\\t\\t\\tprint(\\\"Control query\\\")\\n\\t\\t\\tif self.arduino:\\n\\t\\t\\t\\t#print(self.address)\\n\\t\\t\\t\\tself.transmit(\\\"Control: True\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\t#print(self.address)\\n\\t\\t\\t\\tself.transmit(\\\"Control: False\\\")\\n\\n\\t\\telif data == 'Hello?':\\n\\t\\t\\t#The client is still alive therefore set missing count to 0\\n\\t\\t\\tself.missingCount = 0\\n\\n\\t\\t#Look for Arduino Data\\n\\t\\telif self.arduino:\\n\\t\\t\\t#Check if controlling the car and it's a valid car command\\n\\t\\t\\tif self.arduino.readPack(data): \\n\\t\\t\\t\\tprint(self.address)\\n\\t\\t\\t\\tprint(\\\"Sent to arduino: %s\\\" % data)\\n\\t\\t\\telse:\\n\\t\\t\\t\\t#Print out other data\\n\\t\\t\\t\\tprint(\\\"Not valid Arduino data\\\")\\n\\t\\t\\t\\tprint(self.address)\\n\\t\\t\\t\\tprint(data)\\n\\n\\t\\telse:\\n\\t\\t\\t#All other data print out\\n\\t\\t\\tprint(self.address)\\n\\t\\t\\tprint(data)\",\n \"def messageHandler(self, source, message, messageId):\\n try:\\n type, params, data = message.split(':',2)\\n except:\\n # Not a real message\\n return\\n \\n try:\\n getattr(self, \\\"thive_%s\\\" % type)(messageId, params.split(), data)\\n except exceptions.AttributeError, c:\\n raise c\\n print \\\"[HIVE] No method bound for command '%s'\\\" % type\",\n \"def on_message(data):\\n pass\",\n \"def processMessage(self, msg):\\r\\n LOG(\\\"Received message: \\\" + msg.getId())\\r\\n \\r\\n # Process messages incoming from child executor, if any\\r\\n procId = msg[FIELD_PROC_ID]\\r\\n if procId != self.procId:\\r\\n if self.childManager.hasChild():\\r\\n self.childManager.processChildMessage(msg)\\r\\n else:\\r\\n LOG(\\\"Unexpected child message: \\\" + msg.getId(), LOG_ERROR)\\r\\n elif msg.getType() == MSG_TYPE_COMMAND:\\r\\n if msg.getId() == Messages.MSG_ADD_CLIENT:\\r\\n self.addClient(msg)\\r\\n elif msg.getId() == Messages.MSG_REMOVE_CLIENT:\\r\\n self.removeClient(msg)\\r\\n elif msg.getId() == Messages.CMD_CLOSE:\\r\\n self.cleanup()\\r\\n elif msg.getId() == Messages.CMD_RELOAD:\\r\\n REGISTRY['CIF'].clearAsRun()\\r\\n self.cleanup( executionOnly = True )\\r\\n self.setupResources()\\r\\n self.prepareExecution()\\r\\n else:\\r\\n cmdId = msg[\\\"Id\\\"]\\r\\n if cmdId in [ Messages.CMD_ABORT, Messages.CMD_PAUSE ]:\\r\\n self.mailbox.push( msg, high_priority = True )\\r\\n else:\\r\\n self.mailbox.push( msg )\\r\\n else:\\r\\n LOG(\\\"Unexpected message: \\\" + msg.getId() + \\\"/\\\" + msg.getType(), LOG_ERROR)\",\n \"def handle_request(self):\\n try:\\n data = self.sock.recv(1024)\\n except socket.error as e: # ...,e:\\n if e == 10040:\\n print('Message too long, ignoring.')\\n return\\n raise\\n self.append_to_seq(parse_packet(data))\",\n \"def message_handler(msg):\\n logging.info(\\\"Message Text: %s\\\" % msg['msg'])\\n\\n message_entry = Message(request.sid, msg['room'], msg['msg'], msg['time'])\\n if msg['msg'] != \\\"User has connected!\\\":\\n logging.info(\\\"About to add to DB\\\")\\n db.session.add(message_entry)\\n db.session.commit()\\n logging.info(\\\"Added to DB\\\")\\n send(msg['msg'], room=msg['room'])\",\n \"def on_receive(self, message):\\n\\n if message.get('command') == 'dispatch':\\n self.dispatch(message['filename'])\\n\\n else:\\n log.error('Dispatcher received unexpected message type: {}'.format(\\n message))\",\n \"def handle(self, message):\\n if not message['successful']:\\n raise BayeuxError(\\n 'Unsuccessful disconnect response: {}'\\n .format(message.get('error')))\",\n \"def handle(self):\\n # Since we only expect small packets, we don't need to read a buffer of\\n # size, say, 4096.\\n data = self.request.recv(64)\\n\\n logging.debug(\\\"Received '%s' from '%s'\\\", repr(data), self.client_address[0])\\n\\n if data[0:2] == \\\"\\\\x01\\\\x00\\\":\\n # Authentication attempt. Since this address is whitelisted (as it\\n # was successfully verified by the server in `verify_request`), we\\n # can safely acknowledge the authentication.\\n client = None\\n\\n try:\\n client = self.server.authenticator.add_new_client(\\n self.client_address[0],\\n data.split(\\\"\\\\x00\\\")[1]\\n )\\n\\n except:\\n logging.exception(\\\"An exception occurred creating a new client\\\")\\n return\\n\\n\\n self.server.storage_manager.add_client(client)\\n\\n self.request.send(\\\"\\\\x01\\\\x00%s\\\\x00%s\\\\x00%s\\\\x00\\\" % (\\n client.token,\\n self.server.receiver_address[0],\\n self.server.receiver_address[1]\\n )\\n )\\n\\n else:\\n logging.info(\\\"Unknown request '%s' from '%s'\\\", \\n data, self.client_address[0])\\n\\n self.request.send(\\\"\\\")\",\n \"def process_chatter(self, msg):\\n # note, nothing in here is ROS specific, it's just python code that\\n # runs when new info appears\\n\\n print msg.data # print the recieved message\\n\\n self.msgs_recieved += 1 # increase msg count\\n self.msgs_recieved %= 500 # mod 500 so we don't get enormous numbers\\n self.msg = \\\"%d messages recieved\\\" % self.msgs_recieved # set message\",\n \"def handle_received(self) -> None:\\n self.buffer: bytes\\n while len(self.buffer) >= DataHeader.length:\\n header = DataHeader.decode(self.buffer, allow_excessive=True)\\n if len(self.buffer) < header.size:\\n _LOGGER.debug(\\n \\\"Not enough data on data channel (has %d, expects %d)\\\",\\n len(self.buffer),\\n header.size,\\n )\\n break\\n\\n try:\\n self._process_message_from_buffer(header)\\n except Exception:\\n _LOGGER.exception(\\\"failed to process data frame\\\")\\n\\n self.buffer = self.buffer[header.size :]\",\n \"def gateway_receive(self, msg):\\n\\n if self.debug_api_messages:\\n self.dump_input_message(msg)\\n\\n try:\\n o = json.loads(msg)\\n except Exception as e:\\n return self.receive_exception(sys.exc_info()[0], e, msg)\\n\\n msg_type = o['type']\\n msg_id = o['id']\\n msg_data = o['data']\\n\\n if self.log_api_messages:\\n self.output(f\\\"--> {msg_type} {msg_id} {msg_data}\\\")\\n\\n if msg_type == 'system':\\n self.handle_system_message(msg_id, msg_data)\\n else:\\n if msg_id in self.active_cxn:\\n c = self.active_cxn[msg_id].receive(msg_type, msg_data)\\n else:\\n self.error_handler(self.id, f\\\"Message Received on Unknown connection: {repr(msg)}\\\")\\n\\n return True\",\n \"async def websocket_receive(self, event):\\n data_received = event.get('text')\\n if not data_received:\\n return\\n\\n data = json.loads(data_received)\\n message = data['message']\\n\\n await self.new_message(message)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81080824","0.7629044","0.7614227","0.759747","0.7582353","0.7567339","0.738713","0.7327663","0.72030395","0.7197576","0.71963334","0.7182009","0.7154054","0.7151546","0.7145494","0.7141526","0.71328634","0.7116805","0.7099164","0.70977116","0.7085751","0.7084311","0.7066889","0.7064054","0.70099896","0.69724286","0.6966045","0.6944866","0.6941899","0.6937269","0.6929161","0.6927838","0.6919572","0.69186205","0.69156957","0.69074947","0.6901418","0.68918514","0.6885582","0.6879068","0.68673486","0.68673486","0.6865417","0.68616724","0.6807202","0.6807202","0.68049294","0.6757885","0.6735066","0.6733857","0.6720056","0.6674185","0.66663355","0.6646294","0.66413254","0.6627546","0.662054","0.66171414","0.65851605","0.6584926","0.65826964","0.65713286","0.6568319","0.6567723","0.65651184","0.65631616","0.65564847","0.65522337","0.65470034","0.65421","0.65263176","0.65204334","0.6515881","0.65114564","0.65085006","0.65014714","0.6497725","0.6488891","0.647787","0.64737576","0.64685535","0.6457723","0.6455475","0.64536744","0.64490336","0.6443553","0.64428866","0.6441046","0.64250493","0.6421266","0.64063233","0.6392624","0.6389208","0.638466","0.63723975","0.6363818","0.6358043","0.63570714","0.6353284","0.6336439"],"string":"[\n \"0.81080824\",\n \"0.7629044\",\n \"0.7614227\",\n \"0.759747\",\n \"0.7582353\",\n \"0.7567339\",\n \"0.738713\",\n \"0.7327663\",\n \"0.72030395\",\n \"0.7197576\",\n \"0.71963334\",\n \"0.7182009\",\n \"0.7154054\",\n \"0.7151546\",\n \"0.7145494\",\n \"0.7141526\",\n \"0.71328634\",\n \"0.7116805\",\n \"0.7099164\",\n \"0.70977116\",\n \"0.7085751\",\n \"0.7084311\",\n \"0.7066889\",\n \"0.7064054\",\n \"0.70099896\",\n \"0.69724286\",\n \"0.6966045\",\n \"0.6944866\",\n \"0.6941899\",\n \"0.6937269\",\n \"0.6929161\",\n \"0.6927838\",\n \"0.6919572\",\n \"0.69186205\",\n \"0.69156957\",\n \"0.69074947\",\n \"0.6901418\",\n \"0.68918514\",\n \"0.6885582\",\n \"0.6879068\",\n \"0.68673486\",\n \"0.68673486\",\n \"0.6865417\",\n \"0.68616724\",\n \"0.6807202\",\n \"0.6807202\",\n \"0.68049294\",\n \"0.6757885\",\n \"0.6735066\",\n \"0.6733857\",\n \"0.6720056\",\n \"0.6674185\",\n \"0.66663355\",\n \"0.6646294\",\n \"0.66413254\",\n \"0.6627546\",\n \"0.662054\",\n \"0.66171414\",\n \"0.65851605\",\n \"0.6584926\",\n \"0.65826964\",\n \"0.65713286\",\n \"0.6568319\",\n \"0.6567723\",\n \"0.65651184\",\n \"0.65631616\",\n \"0.65564847\",\n \"0.65522337\",\n \"0.65470034\",\n \"0.65421\",\n \"0.65263176\",\n \"0.65204334\",\n \"0.6515881\",\n \"0.65114564\",\n \"0.65085006\",\n \"0.65014714\",\n \"0.6497725\",\n \"0.6488891\",\n \"0.647787\",\n \"0.64737576\",\n \"0.64685535\",\n \"0.6457723\",\n \"0.6455475\",\n \"0.64536744\",\n \"0.64490336\",\n \"0.6443553\",\n \"0.64428866\",\n \"0.6441046\",\n \"0.64250493\",\n \"0.6421266\",\n \"0.64063233\",\n \"0.6392624\",\n \"0.6389208\",\n \"0.638466\",\n \"0.63723975\",\n \"0.6363818\",\n \"0.6358043\",\n \"0.63570714\",\n \"0.6353284\",\n \"0.6336439\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6640116"},"document_rank":{"kind":"string","value":"55"}}},{"rowIdx":17455,"cells":{"query":{"kind":"string","value":"Clean AFOS and friends"},"document":{"kind":"string","value":"def main():\n pgconn = get_dbconn(\"afos\")\n acursor = pgconn.cursor()\n\n # reflect changes to docs/datasets/afos.md\n # RRM removed due to request\n acursor.execute(\n \"delete from products WHERE \"\n \"entered < ('YESTERDAY'::date - '7 days'::interval) and \"\n \"entered > ('YESTERDAY'::date - '31 days'::interval) and \"\n \"(pil ~* '^(RR[1-9RSAZ]|ECM|ECS|ECX|LAV|LEV|MAV|MET|MTR|MEX|NBE|\"\n \"NBH|NBP|NBS|NBX|RWR|STO|HML|WRK|OSO|SCV|LLL)' \"\n \"or pil in ('HPTNCF', 'WTSNCF', 'TSTNCF', 'HD3RSA', \"\n \"'XF03DY', 'XOBUS', 'ECMNC1', 'SYNBOU', 'MISWTM', 'MISWTX', \"\n \"'MISMA1', 'MISAM1'))\"\n )\n if acursor.rowcount == 0:\n LOG.warning(\"Found no products to delete between 7-31 days\")\n acursor.close()\n pgconn.commit()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cleaning (data):","def clean(_context):","def clean(c):","def cleanup(self):\n for residue in self.debumper.biomolecule.residues:\n if not isinstance(residue, aa.Amino):\n continue\n if residue.name == \"GLH\" or \"GLH\" in residue.patches:\n if residue.has_atom(\"HE1\") and residue.has_atom(\"HE2\"):\n residue.remove_atom(\"HE1\")\n elif residue.name == \"ASH\" or \"ASH\" in residue.patches:\n if residue.has_atom(\"HD1\") and residue.has_atom(\"HD2\"):\n residue.remove_atom(\"HD1\")","def unfriend(self, remove):\n remover_friends_list = self # person terminating the friendship \n \n # remove friend from remover friend list\n remover_friends_list.remove_friend(removee)\n\n #remove friend from removee friend list\n friends_list = FriendList.objects.get(user=removee)\n friend_list.remove_friend(self.user)","def clean(self):\n pass","def clean(self):","def _clean_up(self):","def clean(self):\n\n pass","def clean_up(self):\n\t\tpass","def clean_up(self):\n pass","def clean_up(self):\n pass","def clean():\n try:\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\n except:\n pass","def clean_all(args):\n\n teapot.party.clean_all(\n attendees=args.attendees,\n )","def clean_mentions(self, tweet):\n self.mentions = [tag.strip('@') for tag in tweet.split() if tag.startswith('@')]\n\n for mention in self.mentions:\n tweet = tweet.replace('@'+mention, '')\n\n tweet = self.clean_unnecessary_whitespaces(tweet)\n\n return tweet","def clean_up_data(self):\n pass","def cleanup():\n for s in [missiles, explosions, bonus]:\n\n set_to_remove = set([])\n for m in s:\n if m.isDead:\n set_to_remove.add(m)\n\n s.difference_update(set_to_remove)","def clean(self, context):\n pass","def unfriend(self, removee):\n remover_friends_list = self # person terminating the friendship\n # Remove friend from remover friend list\n remover_friends_list.remove_friend(removee)\n # Remove friend from removee's friend list\n friends_list = FriendList.objects.get(user=removee)\n friends_list.remove_friend(self.user)","async def clean(self, ctx):\n pass","def clean(self):\n for i in range(len(self.asteroid_type) - 1, -1, -1):\n x, y = self.get_coords(self.asteroid_type[i])\n if x < -self.gap:\n self.del_asteroid(i)","def clean_all(self, tweet):\n tweet = self.clean_urls(tweet)\n tweet = self.clean_hashtags(tweet)\n tweet = self.clean_mentions(tweet)\n tweet = self.clean_emojis_and_smileys(tweet)\n tweet = self.clean_unnecessary_characters(tweet)\n tweet = self.clean_reserved_words(tweet)\n\n return tweet","def _clean_actors(self):\n for actor in self.actor_list:\n actor.destroy()","def cleanAll(self):\n for i in range(len(self.asteroid_type) - 1, -1, -1):\n x, y = self.get_coords(self.asteroid_type[i])\n self.del_asteroid(i)\n\n for i in range(len(self.asteroid_id_e) - 1, -1, -1):\n x, y = self.get_coords(self.asteroid_id_e[i])\n self.del_asteroid_e(i)","def clean_role():","def horde_cleanup(self):","def clean_perms(self) -> None:\n self.log.debug(\"Cleaning faulty perms\")\n sesh = self.appbuilder.get_session\n perms = sesh.query(Permission).filter(\n or_(\n Permission.action == None, # noqa\n Permission.resource == None, # noqa\n )\n )\n # Since FAB doesn't define ON DELETE CASCADE on these tables, we need\n # to delete the _object_ so that SQLA knows to delete the many-to-many\n # relationship object too. :(\n\n deleted_count = 0\n for perm in perms:\n sesh.delete(perm)\n deleted_count += 1\n sesh.commit()\n if deleted_count:\n self.log.info(\"Deleted %s faulty permissions\", deleted_count)","def ReorganizeFriendList(self):\n with sqlite3.connect(self.DBname) as conn:\n c = conn.cursor()\n c.execute(\"select ZID, FRIENDS from user_info\")\n user_list = c.fetchall()\n for user, friends in user_list:\n out = set()\n friends = [x.strip() for x in friends.split(\",\")]\n for friend in friends:\n c.execute(\"select FRIENDS from user_info where ZID = (?)\",[friend])\n TFL = c.fetchone()[0]\n TFL = [x.strip() for x in TFL.split(\",\")]\n if user not in TFL:\n out.add(friend)\n NFL = list(set(friends) - out)\n self.UpdateFriendList(user,NFL)","def clearScrubber(self):\r\n self.c.delete(\"scrubber\")\r\n if self.scrubber != []:\r\n for cid in self.scrubber:\r\n self.c.delete(cid)","def cleanup(self):\n return self.cleanupNonces(), self.cleanupAssociations()","def clear(self):\n self.name = ''\n self.public_email = False\n self.avatar = None\n self.bio = ''\n self.website = ''\n self.twitter = ''\n self.facebook = ''\n self.mozillians = ''\n self.irc_handle = ''\n self.city = ''\n self.is_fxa_migrated = False\n self.fxa_uid = ''","def cleanUp(self):\r\n # All intermediates should be removed by app controller\r\n pass","def cleanUp(self):\r\n pass","def remove_ats(self):\n\t\tfor key in self.keys():\n\t\t\tif key[:1] == '@':\n\t\t\t\ttry: del self[key]\n\t\t\t\texcept: pass","def cleanUpExisting(filters = []):\n\tif cmds.objExists('BOAT_OceanLocators_hrc'):\n\t\tgetLocs = cmds.listRelatives('BOAT_OceanLocators_hrc', children = True)\n\t\tgetLocs = [loc for loc in getLocs for fil in filters if fil in loc] if getLocs else None\n\n\t\tif getLocs:\n\t\t\tfor loc in getLocs:\n\t\t\t\thooked_expression = cmds.listConnections(loc, type = 'expression')\n\t\t\t\thooked_expression = list( set( hooked_expression ) ) if hooked_expression else []\n\t\t\t\t[cmds.delete(exp) for exp in hooked_expression if exp.endswith('_boatLockToOcean')]\n\t\t\t\tcmds.delete(loc)\n\n\t## Now clean up all the LEGACY expressions...\n\t[cmds.delete(exp) for exp in cmds.ls(type = 'expression') if exp.endswith('_loc_lockToOcean')]","def clean(self):\n self.clean_rally_conf()\n self.clean_rally_logs()\n if self.flavor_alt:\n self.orig_cloud.delete_flavor(self.flavor_alt.id)\n super().clean()","def clean(self):\n pass\n #TODO check whether short name is really clean and short!","def clean(self):\n cursor = self.cnx.cursor()\n cursor.execute(\"DROP TABLE IF EXISTS FileNameFilter_scores\")\n cursor.execute(\"DROP TABLE IF EXISTS FileNameFilter_unique_name\")\n self.cnx.commit()\n cursor.close()","def clean_up(self):\n # TODO: Implement if needed\n pass","def clean(self):\n raise NotImplementedError","def clean(obj):\n clean_up_generated_files(obj)","def clean_user_tokens() -> None:\n asyncio.run(clean_old_user_tokens())","def clean_object(metadata, analysistype):\n for sample in metadata:\n try:\n delattr(sample[analysistype], \"targetnames\")\n except AttributeError:\n pass\n try:\n delattr(sample[analysistype], \"targets\")\n except AttributeError:\n pass\n try:\n delattr(sample[analysistype], \"dnaseq\")\n except AttributeError:\n pass\n try:\n delattr(sample[analysistype], \"protseq\")\n except AttributeError:\n pass","def cleanup(adata, del_prediction=False, del_2nd_moments=False):\n\n if \"pca_fit\" in adata.uns_keys():\n adata.uns[\"pca_fit\"] = None\n if \"velocyto_SVR\" in adata.uns_keys():\n adata.uns[\"velocyto_SVR\"][\"SVR\"] = None\n if \"umap_fit\" in adata.uns_keys():\n adata.uns[\"umap_fit\"][\"fit\"] = None\n if \"velocity_pca_fit\" in adata.uns_keys():\n adata.uns[\"velocity_pca_fit\"] = None\n if \"kmc\" in adata.uns_keys():\n adata.uns[\"kmc\"] = None\n if \"kinetics_heatmap\" in adata.uns_keys():\n adata.uns.pop(\"kinetics_heatmap\")\n if \"hdbscan\" in adata.uns_keys():\n adata.uns.pop(\"hdbscan\")\n\n VF_keys = [i if i.startswith(\"VecFld\") else None for i in adata.uns_keys()]\n for i in VF_keys:\n if i is not None and \"VecFld2D\" in adata.uns[i].keys():\n del adata.uns[i][\"VecFld2D\"]\n\n fate_keys = [i if i.startswith(\"fate\") else None for i in adata.uns_keys()]\n for i in fate_keys:\n if i is not None:\n if adata.uns[i][\"init_cells\"] is not None:\n adata.uns[i][\"init_cells\"] = list(adata.uns[i][\"init_cells\"])\n if \"prediction\" in adata.uns[i].keys():\n if del_prediction:\n del adata.uns[i][\"prediction\"]\n if \"VecFld_true\" in adata.uns[i].keys():\n if adata.uns[i][\"VecFld_true\"] is not None:\n del adata.uns[i][\"VecFld_true\"]\n\n if del_2nd_moments:\n from .tools.utils import remove_2nd_moments\n\n remove_2nd_moments(adata)\n\n return adata","def clean_up(self, graph):\n # Delete albums associated with place\n if len(self.albums) != 0:\n for album in self.albums:\n album.clean_up()\n album.delete(graph)","def cleanup_aai(cls):\n logger.info(\"####################### Start to clean up AAI settings\")\n aai = Customer.get_by_global_customer_id(\"5GCustomer\")\n aai.delete()","def clean_metadata(metaobj):\n if len(metaobj) == 1 and 'href' in metaobj[0] and '/api/programs/' in metaobj[0]['href']:\n metaobj = metaobj # Keep lastUpdated for program\n else:\n metaobj = remove_subset_from_set(metaobj, 'lastUpdated')\n metaobj = remove_subset_from_set(metaobj, 'lastUpdatedBy')\n metaobj = remove_subset_from_set(metaobj, 'created')\n metaobj = remove_subset_from_set(metaobj, 'createdBy')\n metaobj = remove_subset_from_set(metaobj, 'href')\n metaobj = remove_subset_from_set(metaobj, 'access')\n metaobj = remove_subset_from_set(metaobj, 'favorites')\n metaobj = remove_subset_from_set(metaobj, 'allItems')\n metaobj = remove_subset_from_set(metaobj, 'displayName')\n metaobj = remove_subset_from_set(metaobj, 'displayFormName')\n metaobj = remove_subset_from_set(metaobj, 'displayShortName')\n metaobj = remove_subset_from_set(metaobj, 'displayDenominatorDescription')\n metaobj = remove_subset_from_set(metaobj, 'displayNumeratorDescription')\n metaobj = remove_subset_from_set(metaobj, 'displayDescription')\n metaobj = remove_subset_from_set(metaobj, 'interpretations')\n if len(metaobj) > 0:\n for subtag in ['dashboardItems', 'analyticsPeriodBoundaries', 'mapViews', 'user', 'userGroupAccesses',\n 'programStageDataElements', 'programTrackedEntityAttributes',\n 'trackedEntityTypeAttributes', 'userCredentials', 'legends', 'greyedFields']:\n for i in range(0, len(metaobj)):\n if subtag in metaobj[i]:\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdated')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdatedBy')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'created')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'createdBy')\n # There is access : { read: true, delete: false ... } dictionary\n # and there is access : \"rw----\"... Make sure we only delete the dictionary version\n if subtag not in ['user', 'userGroupAccesses']:\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'access')\n\n if subtag == 'programTrackedEntityAttributes':\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'name')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayName')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayFormName')\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayShortName')\n\n return metaobj","def removeAutoSaveFilter(filter):","def clean_user_functs(sv):\r\n markvirtual(sv) # identify virtual objects \r\n finalize_user_functions(sv) # transform user functions into dict\r\n for nam in list(sv.Object_list): # remove virtual objects\r\n nod=sv.Object[nam]\r\n if nod.isvirtual: eliminate(sv, nam)","def cleanup() -> None:\n\n for fname in glob(os.path.join(tdir, 'alexandria.*')):\n if os.path.splitext(fname)[1] not in {'.c', '.h'}:\n os.unlink(fname)","def _cleanupAddress(self, address):\n clean = []\n \n # This is sort of a desultory effort but I'm not convinced \n # that these cleanups will actually result in cleaner searches\n for word in address.split(None):\n lower = word.lower()\n \n # Some things we just nuke\n if lower == 'at': continue\n elif lower == 'btw': continue\n elif lower == 'btwn': continue\n elif word.isdigit(): continue\n \n # Or we make substitiutions\n elif lower == 'st' or lower == 'st.':\n word = 'Street'\n elif lower == 'ave' or lower == 'ave.':\n word = 'Avenue'\n elif lower == 'pl' or lower == 'pl.':\n word = 'Place'\n elif lower == 'n': word = 'North'\n elif lower == 'e': word = 'East'\n elif lower == 's': word = 'South'\n elif lower == 'w': word = 'West'\n \n clean.append(word)\n return ' '.join(clean)","def remove_mentions(text):\r\n text = re.sub(r'@\\S+', '', text)\r\n text = re.sub(r'@', \"at\", text)\r\n return text","def cleandaughterlists(lst):\n for sent in lst:\n for token in sent:\n if 'daughters' in token:\n del token['daughters']","def cleanup():\n cat = CSVCatalog.CSVCatalog()\n cat.drop_table(\"people\")\n cat.drop_table(\"batting\")\n cat.drop_table(\"teams\")","def cleanup():","def clean():\n\n tracks = []\n removed_playlists = 0\n for playlist in PlaylistManager.find():\n\n if len(playlist.tracks) == 0:\n PlaylistManager.remove(playlist.id)\n removed_playlists += 1\n else:\n tracks += playlist.tracks\n\n tracks = list(set(tracks))\n removed_tracks = 0\n for track in TrackManager.find():\n if track.id not in tracks:\n TrackManager.remove(track.id)\n removed_tracks += 1\n\n click.secho(\"Cleanup removed:\", bold=True)\n click.secho(\n tabulate( # type: ignore\n [\n (magenta(\"Tracks:\"), removed_tracks),\n (magenta(\"Playlists:\"), removed_playlists),\n ],\n tablefmt=\"plain\",\n colalign=(\"right\", \"left\"),\n )\n )","def clean(self):\n filtered_items = {}\n for name, ls in self.items.items():\n filtered_ls = []\n for i in ls:\n if i.alive():\n filtered_ls.append(i)\n else:\n self.del_item(i)\n filtered_items[name] = filtered_ls\n self.items = filtered_items","async def _wipe_casino(self, ctx):\n await self.db.clear_all()\n msg = \"{0.name} ({0.id}) wiped all casino data.\".format(ctx.author)\n await ctx.send(msg)","def cleanup(self):\n for attribute in self._all_db_field_names:\n delattr(self, attribute)","def clean(self):\n super().clean()\n if self.user2:\n self.orig_cloud.delete_user(self.user2.id)","def clean(self):\n self.clean_rally_conf()\n rally.RallyBase.clean_rally_logs()\n if self.image_alt:\n self.cloud.delete_image(self.image_alt)\n if self.flavor_alt:\n self.orig_cloud.delete_flavor(self.flavor_alt.id)\n super().clean()","def cleanup():\n cat = CSVCatalog.CSVCatalog()\n cat.drop_table(\"people\", force_drop=True)\n cat.drop_table(\"batting\", force_drop=True)\n cat.drop_table(\"teams\", force_drop=True)","def remove_user_mentions(text):\n text = re.sub('\\B@\\w*[a-zA-Z]+\\w*', '', text)\n return text","def remove_mentions(self,text):\n return re.sub(r'@\\w+', '', text)","def removeBadAA(mer,badaa=None):\n if badaa is None:\n badaa = BADAA\n if not mer is None:\n return re.sub('[%s]' % badaa, '', mer)\n else:\n return mer","def clean_fasta(fasta_in=FASTA_OLD, fasta_out=FASTA):\n with open(fasta_in, 'r') as file_in, open(fasta_out, 'a') as file_out:\n file_out.write(file_in.readline())\n for line in file_in:\n if line.startswith(\">\"):\n file_out.write(\"\\n\" + line)\n else:\n file_out.write(line.strip())","def verbose_clean_up_f(f):\r\n deletion_list = [l.strip() for l in f]\r\n remove_all(deletion_list)\r\n print \"Post-run clean-up complete.\"\r\n return True","def _remove_data(things, lst_remove=None):\n\n for data in things:\n data.pop(\"_sa_instance_state\", None)\n data.pop(\"user_id\", None)\n\n if lst_remove is not None:\n for str_remove in lst_remove:\n if str_remove in data:\n data.pop(str_remove, None)\n\n return things","def clean_data(self, path, exclude_msgtypes=None):","def clean(self):\n return","def test_remove_friends_symmetrical(self):\n u = AppUser(id = 1)\n u.django_user = User.objects.create(username='Testuser')\n u.save()\n f = AppUser(id = 2)\n f.django_user = User.objects.create(username='Testuser2')\n f.save()\n\n u.friends.add(f)\n f.friends.remove(u)\n self.assertIs(u in f.friends.all(), False)\n self.assertIs(f in u.friends.all(), False)","def clean(self, x): # should not contain any other arguments (use fields set in constructor instead).\n\n def repl(m):\n return chr(int('0x' + m.group(1), 16))\n\n # replace double escaped \"\\\\\" unicode strings with their unicode characters\n x = [re.sub(r'\\\\n', '\\n', message) for message in x]\n x = [re.sub(r'\\\\x([a-f0-9]{2})', repl, message) for message in x]\n x = [re.sub(r'\\\\u([a-f0-9]{4})', repl, message) for message in x]\n if self.ignore_urls:\n x = [re.sub(self.re_url, '', message) for message in x]\n\n if self.fix_contractions:\n import contractions\n x = [contractions.fix(message) for message in x]\n\n if self.remove_foreign_characters:\n # replace accented characters with unaccented\n x = [unidecode.unidecode(message) for message in x]\n\n # replace nonascii characters with space\n x = [''.join(character if ord(character) < 128 else ' ' for character in message) for message in x]\n\n # Create sentence structure like nltk gutenberg.sents()\n # list of sentences for each message:\n x = [self.sent_detector.tokenize(message.strip()) for message in x]\n # list of list of words for each message/sentence:\n x = [[self.word_tokenizer.tokenize(sentence) for sentence in message] for message in x]\n\n if self.lower:\n # lower_sents: lowercase words ignoring punctuation\n x = [[[\n word.lower() for word in sentence] for sentence in message\n ] for message in x]\n\n if self.remove_punctuation:\n x = [[[\n word for word in sentence if word not in list(string.punctuation)] for sentence in message\n ] for message in x]\n\n if self.stem:\n x = [[[self.stemmer.stem(word) for word in sentence] for sentence in message] for message in x]\n\n if self.lower and self.bigrams:\n # clean_sents: replace common adjacent words with bigrams\n x = [[self.bigram[sentence] for sentence in message] for message in x]\n\n if self.omit_stopwords:\n x = [[[word for word in sentence if word not in stopwords.words('english')] for sentence in message] for\n message in x]\n\n # convert back to one string per message (join words into sentences and sentences into messages)\n x = ['\\n'.join(' '.join(sentence) for sentence in message) for message in x]\n return x","def cleanup(self):","def cleanup(self):","def cleanup(self):","def clean_edges(self):","def clean():\n clean_flatbuffer_binaries()\n clean_webp_textures()","def clean(tweet):\n #Separates the contractions and the punctuation\n\n\n tweet = re.sub(\"[!#.,\\\"]\", \"\", tweet).replace(\"<user>\", \"\")\n tweet = re.sub(\"[!#.,\\\"]\", \"\", tweet).replace(\"<url>\", \"\")\n tweet = correct_spell(tweet)\n return tweet.strip().lower()","def clean(self):\r\n return self.clean()","def delete_all(self):\n for tag in self._segments['APP1'].get_tag_list():\n try:\n self.__delattr__(tag)\n except AttributeError:\n warnings.warn(\"could not delete tag \" + tag, RuntimeWarning)","def clean(self):\n\t\tfor v in self:\n\t\t\tv.reset_distance()\n\t\t\tv.reset_predecessor()\n\t\t\tv.reset_visited()","def backwards(apps, schema_editor):\n Referral = apps.get_model(\"core\", \"Referral\")\n\n for referral in Referral.objects.all():\n referral.users.clear()\n referral.save()","def clean(self):\n # Delete vertices / faces / colors / normals :\n self._vert_buffer.delete()\n self._index_buffer.delete()\n self._normals_buffer.delete()\n self._xrange_buffer.delete()\n self._math_buffer.delete()","def handleCleanMetadataRecon(self):\n logging.debug(\"Removing compromising personal info and remaking the file...\")\n filePath = self.filesList.selectedItems()[0].text(2)\n fileType = self.filesList.getFileObj(filePath).type\n self.printPdfPersonalData(filePath, \n fileType,\n AddedFile.changeBase(filePath, self.outputPath))\n self.tabArea.setCurrentIndex(1)\n self.changeCursor()\n self.filesList.getFileObj(filePath).reconMetaCleaned = True","def _clean_objects(*args):\n\n for arg in args:\n attrs = dir(arg)\n # QuadContourSet\n if 'collections' in attrs:\n for item in arg.collections: item.remove()\n # Wind barbs and contour labels\n elif '__len__' in attrs:\n for item in arg:\n item.remove()\n # Text strings and everything else\n elif 'get_text' in attrs: arg.remove()","def removeAutoSaveDeleteFilter(filter):","def clean():\n user_init.clean_setup()","def basic_clean_up_f(f):\r\n deletion_list = [l.strip() for l in f]\r\n remove_all(deletion_list)\r\n return True","def unfriend(self, removee):\n\t\tremover_friend_list = self # person terminating the friendship\n\n\t\t# Remove friend from friend request\n\t\tremover_friend_list.remove_friend(removee)\n\n\t\t# Remove friend from the removeee friend list\n\t\tfriends_list = FriendList.objects.get(user=removee)\n\t\tfriends_list.remove_friend(self.user)","def clean():\n filter_phase_data()\n combine_phase_data()\n remove_duplicates_phase_data()","def remove():","def cleanup(self):\n\n pass","def createFriendlyEmpireList(self):\n friends = []\n for shipID, myShip in self.myGalaxy.ships.iteritems():\n if shipID not in self.targets and myShip.empireID not in friends:\n friends.append(myShip.empireID)\n return friends","def clear(self):\n for tag in self.meta.findall(CN('meta:user-defined')):\n self.meta.remove(tag)","def migrate_fix_invalid_bot_owner_values(\n apps: StateApps, schema_editor: BaseDatabaseSchemaEditor\n) -> None:\n UserProfile = apps.get_model(\"zerver\", \"UserProfile\")\n UserProfile.objects.filter(is_bot=False).exclude(bot_owner=None).update(bot_owner=None)","def cleanup(self):\n for key in list(self.__dict__.keys()):\n delattr(self, key)","def clean(self):\n for nodeId in list(self.nodes.keys()):\n if not self.nodes[nodeId].safe:\n del self.nodes[nodeId]","def cleanholdercandidates(lst):\n for sent in lst:\n for token in sent:\n if 'holder_candidate' in token:\n del token['holder_candidate']","def recompute_friends(self):\r\n \r\n return True","def foaf_ids_bad(user):\n return [foaf_id \n for friend_id in friendships[user[\"id\"]]\n for foaf_id in friendships[friend_id]]","def clean(self):\n files = ['CHG', 'CHGCAR', 'POSCAR', 'INCAR', 'CONTCAR',\n 'DOSCAR', 'EIGENVAL', 'IBZKPT', 'KPOINTS', 'OSZICAR',\n 'OUTCAR', 'PCDAT', 'POTCAR', 'vasprun.xml',\n 'WAVECAR', 'XDATCAR', 'PROCAR', 'ase-sort.dat',\n 'LOCPOT', 'AECCAR0', 'AECCAR1', 'AECCAR2',\n 'WAVECAR.GTO', 'vasp.out', 'vasp.err']\n for f in files:\n try:\n os.remove(f)\n except OSError:\n pass"],"string":"[\n \"def cleaning (data):\",\n \"def clean(_context):\",\n \"def clean(c):\",\n \"def cleanup(self):\\n for residue in self.debumper.biomolecule.residues:\\n if not isinstance(residue, aa.Amino):\\n continue\\n if residue.name == \\\"GLH\\\" or \\\"GLH\\\" in residue.patches:\\n if residue.has_atom(\\\"HE1\\\") and residue.has_atom(\\\"HE2\\\"):\\n residue.remove_atom(\\\"HE1\\\")\\n elif residue.name == \\\"ASH\\\" or \\\"ASH\\\" in residue.patches:\\n if residue.has_atom(\\\"HD1\\\") and residue.has_atom(\\\"HD2\\\"):\\n residue.remove_atom(\\\"HD1\\\")\",\n \"def unfriend(self, remove):\\n remover_friends_list = self # person terminating the friendship \\n \\n # remove friend from remover friend list\\n remover_friends_list.remove_friend(removee)\\n\\n #remove friend from removee friend list\\n friends_list = FriendList.objects.get(user=removee)\\n friend_list.remove_friend(self.user)\",\n \"def clean(self):\\n pass\",\n \"def clean(self):\",\n \"def _clean_up(self):\",\n \"def clean(self):\\n\\n pass\",\n \"def clean_up(self):\\n\\t\\tpass\",\n \"def clean_up(self):\\n pass\",\n \"def clean_up(self):\\n pass\",\n \"def clean():\\n try:\\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\\n except:\\n pass\",\n \"def clean_all(args):\\n\\n teapot.party.clean_all(\\n attendees=args.attendees,\\n )\",\n \"def clean_mentions(self, tweet):\\n self.mentions = [tag.strip('@') for tag in tweet.split() if tag.startswith('@')]\\n\\n for mention in self.mentions:\\n tweet = tweet.replace('@'+mention, '')\\n\\n tweet = self.clean_unnecessary_whitespaces(tweet)\\n\\n return tweet\",\n \"def clean_up_data(self):\\n pass\",\n \"def cleanup():\\n for s in [missiles, explosions, bonus]:\\n\\n set_to_remove = set([])\\n for m in s:\\n if m.isDead:\\n set_to_remove.add(m)\\n\\n s.difference_update(set_to_remove)\",\n \"def clean(self, context):\\n pass\",\n \"def unfriend(self, removee):\\n remover_friends_list = self # person terminating the friendship\\n # Remove friend from remover friend list\\n remover_friends_list.remove_friend(removee)\\n # Remove friend from removee's friend list\\n friends_list = FriendList.objects.get(user=removee)\\n friends_list.remove_friend(self.user)\",\n \"async def clean(self, ctx):\\n pass\",\n \"def clean(self):\\n for i in range(len(self.asteroid_type) - 1, -1, -1):\\n x, y = self.get_coords(self.asteroid_type[i])\\n if x < -self.gap:\\n self.del_asteroid(i)\",\n \"def clean_all(self, tweet):\\n tweet = self.clean_urls(tweet)\\n tweet = self.clean_hashtags(tweet)\\n tweet = self.clean_mentions(tweet)\\n tweet = self.clean_emojis_and_smileys(tweet)\\n tweet = self.clean_unnecessary_characters(tweet)\\n tweet = self.clean_reserved_words(tweet)\\n\\n return tweet\",\n \"def _clean_actors(self):\\n for actor in self.actor_list:\\n actor.destroy()\",\n \"def cleanAll(self):\\n for i in range(len(self.asteroid_type) - 1, -1, -1):\\n x, y = self.get_coords(self.asteroid_type[i])\\n self.del_asteroid(i)\\n\\n for i in range(len(self.asteroid_id_e) - 1, -1, -1):\\n x, y = self.get_coords(self.asteroid_id_e[i])\\n self.del_asteroid_e(i)\",\n \"def clean_role():\",\n \"def horde_cleanup(self):\",\n \"def clean_perms(self) -> None:\\n self.log.debug(\\\"Cleaning faulty perms\\\")\\n sesh = self.appbuilder.get_session\\n perms = sesh.query(Permission).filter(\\n or_(\\n Permission.action == None, # noqa\\n Permission.resource == None, # noqa\\n )\\n )\\n # Since FAB doesn't define ON DELETE CASCADE on these tables, we need\\n # to delete the _object_ so that SQLA knows to delete the many-to-many\\n # relationship object too. :(\\n\\n deleted_count = 0\\n for perm in perms:\\n sesh.delete(perm)\\n deleted_count += 1\\n sesh.commit()\\n if deleted_count:\\n self.log.info(\\\"Deleted %s faulty permissions\\\", deleted_count)\",\n \"def ReorganizeFriendList(self):\\n with sqlite3.connect(self.DBname) as conn:\\n c = conn.cursor()\\n c.execute(\\\"select ZID, FRIENDS from user_info\\\")\\n user_list = c.fetchall()\\n for user, friends in user_list:\\n out = set()\\n friends = [x.strip() for x in friends.split(\\\",\\\")]\\n for friend in friends:\\n c.execute(\\\"select FRIENDS from user_info where ZID = (?)\\\",[friend])\\n TFL = c.fetchone()[0]\\n TFL = [x.strip() for x in TFL.split(\\\",\\\")]\\n if user not in TFL:\\n out.add(friend)\\n NFL = list(set(friends) - out)\\n self.UpdateFriendList(user,NFL)\",\n \"def clearScrubber(self):\\r\\n self.c.delete(\\\"scrubber\\\")\\r\\n if self.scrubber != []:\\r\\n for cid in self.scrubber:\\r\\n self.c.delete(cid)\",\n \"def cleanup(self):\\n return self.cleanupNonces(), self.cleanupAssociations()\",\n \"def clear(self):\\n self.name = ''\\n self.public_email = False\\n self.avatar = None\\n self.bio = ''\\n self.website = ''\\n self.twitter = ''\\n self.facebook = ''\\n self.mozillians = ''\\n self.irc_handle = ''\\n self.city = ''\\n self.is_fxa_migrated = False\\n self.fxa_uid = ''\",\n \"def cleanUp(self):\\r\\n # All intermediates should be removed by app controller\\r\\n pass\",\n \"def cleanUp(self):\\r\\n pass\",\n \"def remove_ats(self):\\n\\t\\tfor key in self.keys():\\n\\t\\t\\tif key[:1] == '@':\\n\\t\\t\\t\\ttry: del self[key]\\n\\t\\t\\t\\texcept: pass\",\n \"def cleanUpExisting(filters = []):\\n\\tif cmds.objExists('BOAT_OceanLocators_hrc'):\\n\\t\\tgetLocs = cmds.listRelatives('BOAT_OceanLocators_hrc', children = True)\\n\\t\\tgetLocs = [loc for loc in getLocs for fil in filters if fil in loc] if getLocs else None\\n\\n\\t\\tif getLocs:\\n\\t\\t\\tfor loc in getLocs:\\n\\t\\t\\t\\thooked_expression = cmds.listConnections(loc, type = 'expression')\\n\\t\\t\\t\\thooked_expression = list( set( hooked_expression ) ) if hooked_expression else []\\n\\t\\t\\t\\t[cmds.delete(exp) for exp in hooked_expression if exp.endswith('_boatLockToOcean')]\\n\\t\\t\\t\\tcmds.delete(loc)\\n\\n\\t## Now clean up all the LEGACY expressions...\\n\\t[cmds.delete(exp) for exp in cmds.ls(type = 'expression') if exp.endswith('_loc_lockToOcean')]\",\n \"def clean(self):\\n self.clean_rally_conf()\\n self.clean_rally_logs()\\n if self.flavor_alt:\\n self.orig_cloud.delete_flavor(self.flavor_alt.id)\\n super().clean()\",\n \"def clean(self):\\n pass\\n #TODO check whether short name is really clean and short!\",\n \"def clean(self):\\n cursor = self.cnx.cursor()\\n cursor.execute(\\\"DROP TABLE IF EXISTS FileNameFilter_scores\\\")\\n cursor.execute(\\\"DROP TABLE IF EXISTS FileNameFilter_unique_name\\\")\\n self.cnx.commit()\\n cursor.close()\",\n \"def clean_up(self):\\n # TODO: Implement if needed\\n pass\",\n \"def clean(self):\\n raise NotImplementedError\",\n \"def clean(obj):\\n clean_up_generated_files(obj)\",\n \"def clean_user_tokens() -> None:\\n asyncio.run(clean_old_user_tokens())\",\n \"def clean_object(metadata, analysistype):\\n for sample in metadata:\\n try:\\n delattr(sample[analysistype], \\\"targetnames\\\")\\n except AttributeError:\\n pass\\n try:\\n delattr(sample[analysistype], \\\"targets\\\")\\n except AttributeError:\\n pass\\n try:\\n delattr(sample[analysistype], \\\"dnaseq\\\")\\n except AttributeError:\\n pass\\n try:\\n delattr(sample[analysistype], \\\"protseq\\\")\\n except AttributeError:\\n pass\",\n \"def cleanup(adata, del_prediction=False, del_2nd_moments=False):\\n\\n if \\\"pca_fit\\\" in adata.uns_keys():\\n adata.uns[\\\"pca_fit\\\"] = None\\n if \\\"velocyto_SVR\\\" in adata.uns_keys():\\n adata.uns[\\\"velocyto_SVR\\\"][\\\"SVR\\\"] = None\\n if \\\"umap_fit\\\" in adata.uns_keys():\\n adata.uns[\\\"umap_fit\\\"][\\\"fit\\\"] = None\\n if \\\"velocity_pca_fit\\\" in adata.uns_keys():\\n adata.uns[\\\"velocity_pca_fit\\\"] = None\\n if \\\"kmc\\\" in adata.uns_keys():\\n adata.uns[\\\"kmc\\\"] = None\\n if \\\"kinetics_heatmap\\\" in adata.uns_keys():\\n adata.uns.pop(\\\"kinetics_heatmap\\\")\\n if \\\"hdbscan\\\" in adata.uns_keys():\\n adata.uns.pop(\\\"hdbscan\\\")\\n\\n VF_keys = [i if i.startswith(\\\"VecFld\\\") else None for i in adata.uns_keys()]\\n for i in VF_keys:\\n if i is not None and \\\"VecFld2D\\\" in adata.uns[i].keys():\\n del adata.uns[i][\\\"VecFld2D\\\"]\\n\\n fate_keys = [i if i.startswith(\\\"fate\\\") else None for i in adata.uns_keys()]\\n for i in fate_keys:\\n if i is not None:\\n if adata.uns[i][\\\"init_cells\\\"] is not None:\\n adata.uns[i][\\\"init_cells\\\"] = list(adata.uns[i][\\\"init_cells\\\"])\\n if \\\"prediction\\\" in adata.uns[i].keys():\\n if del_prediction:\\n del adata.uns[i][\\\"prediction\\\"]\\n if \\\"VecFld_true\\\" in adata.uns[i].keys():\\n if adata.uns[i][\\\"VecFld_true\\\"] is not None:\\n del adata.uns[i][\\\"VecFld_true\\\"]\\n\\n if del_2nd_moments:\\n from .tools.utils import remove_2nd_moments\\n\\n remove_2nd_moments(adata)\\n\\n return adata\",\n \"def clean_up(self, graph):\\n # Delete albums associated with place\\n if len(self.albums) != 0:\\n for album in self.albums:\\n album.clean_up()\\n album.delete(graph)\",\n \"def cleanup_aai(cls):\\n logger.info(\\\"####################### Start to clean up AAI settings\\\")\\n aai = Customer.get_by_global_customer_id(\\\"5GCustomer\\\")\\n aai.delete()\",\n \"def clean_metadata(metaobj):\\n if len(metaobj) == 1 and 'href' in metaobj[0] and '/api/programs/' in metaobj[0]['href']:\\n metaobj = metaobj # Keep lastUpdated for program\\n else:\\n metaobj = remove_subset_from_set(metaobj, 'lastUpdated')\\n metaobj = remove_subset_from_set(metaobj, 'lastUpdatedBy')\\n metaobj = remove_subset_from_set(metaobj, 'created')\\n metaobj = remove_subset_from_set(metaobj, 'createdBy')\\n metaobj = remove_subset_from_set(metaobj, 'href')\\n metaobj = remove_subset_from_set(metaobj, 'access')\\n metaobj = remove_subset_from_set(metaobj, 'favorites')\\n metaobj = remove_subset_from_set(metaobj, 'allItems')\\n metaobj = remove_subset_from_set(metaobj, 'displayName')\\n metaobj = remove_subset_from_set(metaobj, 'displayFormName')\\n metaobj = remove_subset_from_set(metaobj, 'displayShortName')\\n metaobj = remove_subset_from_set(metaobj, 'displayDenominatorDescription')\\n metaobj = remove_subset_from_set(metaobj, 'displayNumeratorDescription')\\n metaobj = remove_subset_from_set(metaobj, 'displayDescription')\\n metaobj = remove_subset_from_set(metaobj, 'interpretations')\\n if len(metaobj) > 0:\\n for subtag in ['dashboardItems', 'analyticsPeriodBoundaries', 'mapViews', 'user', 'userGroupAccesses',\\n 'programStageDataElements', 'programTrackedEntityAttributes',\\n 'trackedEntityTypeAttributes', 'userCredentials', 'legends', 'greyedFields']:\\n for i in range(0, len(metaobj)):\\n if subtag in metaobj[i]:\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdated')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'lastUpdatedBy')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'created')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'createdBy')\\n # There is access : { read: true, delete: false ... } dictionary\\n # and there is access : \\\"rw----\\\"... Make sure we only delete the dictionary version\\n if subtag not in ['user', 'userGroupAccesses']:\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'access')\\n\\n if subtag == 'programTrackedEntityAttributes':\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'name')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayName')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayFormName')\\n metaobj[i][subtag] = remove_subset_from_set(metaobj[i][subtag], 'displayShortName')\\n\\n return metaobj\",\n \"def removeAutoSaveFilter(filter):\",\n \"def clean_user_functs(sv):\\r\\n markvirtual(sv) # identify virtual objects \\r\\n finalize_user_functions(sv) # transform user functions into dict\\r\\n for nam in list(sv.Object_list): # remove virtual objects\\r\\n nod=sv.Object[nam]\\r\\n if nod.isvirtual: eliminate(sv, nam)\",\n \"def cleanup() -> None:\\n\\n for fname in glob(os.path.join(tdir, 'alexandria.*')):\\n if os.path.splitext(fname)[1] not in {'.c', '.h'}:\\n os.unlink(fname)\",\n \"def _cleanupAddress(self, address):\\n clean = []\\n \\n # This is sort of a desultory effort but I'm not convinced \\n # that these cleanups will actually result in cleaner searches\\n for word in address.split(None):\\n lower = word.lower()\\n \\n # Some things we just nuke\\n if lower == 'at': continue\\n elif lower == 'btw': continue\\n elif lower == 'btwn': continue\\n elif word.isdigit(): continue\\n \\n # Or we make substitiutions\\n elif lower == 'st' or lower == 'st.':\\n word = 'Street'\\n elif lower == 'ave' or lower == 'ave.':\\n word = 'Avenue'\\n elif lower == 'pl' or lower == 'pl.':\\n word = 'Place'\\n elif lower == 'n': word = 'North'\\n elif lower == 'e': word = 'East'\\n elif lower == 's': word = 'South'\\n elif lower == 'w': word = 'West'\\n \\n clean.append(word)\\n return ' '.join(clean)\",\n \"def remove_mentions(text):\\r\\n text = re.sub(r'@\\\\S+', '', text)\\r\\n text = re.sub(r'@', \\\"at\\\", text)\\r\\n return text\",\n \"def cleandaughterlists(lst):\\n for sent in lst:\\n for token in sent:\\n if 'daughters' in token:\\n del token['daughters']\",\n \"def cleanup():\\n cat = CSVCatalog.CSVCatalog()\\n cat.drop_table(\\\"people\\\")\\n cat.drop_table(\\\"batting\\\")\\n cat.drop_table(\\\"teams\\\")\",\n \"def cleanup():\",\n \"def clean():\\n\\n tracks = []\\n removed_playlists = 0\\n for playlist in PlaylistManager.find():\\n\\n if len(playlist.tracks) == 0:\\n PlaylistManager.remove(playlist.id)\\n removed_playlists += 1\\n else:\\n tracks += playlist.tracks\\n\\n tracks = list(set(tracks))\\n removed_tracks = 0\\n for track in TrackManager.find():\\n if track.id not in tracks:\\n TrackManager.remove(track.id)\\n removed_tracks += 1\\n\\n click.secho(\\\"Cleanup removed:\\\", bold=True)\\n click.secho(\\n tabulate( # type: ignore\\n [\\n (magenta(\\\"Tracks:\\\"), removed_tracks),\\n (magenta(\\\"Playlists:\\\"), removed_playlists),\\n ],\\n tablefmt=\\\"plain\\\",\\n colalign=(\\\"right\\\", \\\"left\\\"),\\n )\\n )\",\n \"def clean(self):\\n filtered_items = {}\\n for name, ls in self.items.items():\\n filtered_ls = []\\n for i in ls:\\n if i.alive():\\n filtered_ls.append(i)\\n else:\\n self.del_item(i)\\n filtered_items[name] = filtered_ls\\n self.items = filtered_items\",\n \"async def _wipe_casino(self, ctx):\\n await self.db.clear_all()\\n msg = \\\"{0.name} ({0.id}) wiped all casino data.\\\".format(ctx.author)\\n await ctx.send(msg)\",\n \"def cleanup(self):\\n for attribute in self._all_db_field_names:\\n delattr(self, attribute)\",\n \"def clean(self):\\n super().clean()\\n if self.user2:\\n self.orig_cloud.delete_user(self.user2.id)\",\n \"def clean(self):\\n self.clean_rally_conf()\\n rally.RallyBase.clean_rally_logs()\\n if self.image_alt:\\n self.cloud.delete_image(self.image_alt)\\n if self.flavor_alt:\\n self.orig_cloud.delete_flavor(self.flavor_alt.id)\\n super().clean()\",\n \"def cleanup():\\n cat = CSVCatalog.CSVCatalog()\\n cat.drop_table(\\\"people\\\", force_drop=True)\\n cat.drop_table(\\\"batting\\\", force_drop=True)\\n cat.drop_table(\\\"teams\\\", force_drop=True)\",\n \"def remove_user_mentions(text):\\n text = re.sub('\\\\B@\\\\w*[a-zA-Z]+\\\\w*', '', text)\\n return text\",\n \"def remove_mentions(self,text):\\n return re.sub(r'@\\\\w+', '', text)\",\n \"def removeBadAA(mer,badaa=None):\\n if badaa is None:\\n badaa = BADAA\\n if not mer is None:\\n return re.sub('[%s]' % badaa, '', mer)\\n else:\\n return mer\",\n \"def clean_fasta(fasta_in=FASTA_OLD, fasta_out=FASTA):\\n with open(fasta_in, 'r') as file_in, open(fasta_out, 'a') as file_out:\\n file_out.write(file_in.readline())\\n for line in file_in:\\n if line.startswith(\\\">\\\"):\\n file_out.write(\\\"\\\\n\\\" + line)\\n else:\\n file_out.write(line.strip())\",\n \"def verbose_clean_up_f(f):\\r\\n deletion_list = [l.strip() for l in f]\\r\\n remove_all(deletion_list)\\r\\n print \\\"Post-run clean-up complete.\\\"\\r\\n return True\",\n \"def _remove_data(things, lst_remove=None):\\n\\n for data in things:\\n data.pop(\\\"_sa_instance_state\\\", None)\\n data.pop(\\\"user_id\\\", None)\\n\\n if lst_remove is not None:\\n for str_remove in lst_remove:\\n if str_remove in data:\\n data.pop(str_remove, None)\\n\\n return things\",\n \"def clean_data(self, path, exclude_msgtypes=None):\",\n \"def clean(self):\\n return\",\n \"def test_remove_friends_symmetrical(self):\\n u = AppUser(id = 1)\\n u.django_user = User.objects.create(username='Testuser')\\n u.save()\\n f = AppUser(id = 2)\\n f.django_user = User.objects.create(username='Testuser2')\\n f.save()\\n\\n u.friends.add(f)\\n f.friends.remove(u)\\n self.assertIs(u in f.friends.all(), False)\\n self.assertIs(f in u.friends.all(), False)\",\n \"def clean(self, x): # should not contain any other arguments (use fields set in constructor instead).\\n\\n def repl(m):\\n return chr(int('0x' + m.group(1), 16))\\n\\n # replace double escaped \\\"\\\\\\\\\\\" unicode strings with their unicode characters\\n x = [re.sub(r'\\\\\\\\n', '\\\\n', message) for message in x]\\n x = [re.sub(r'\\\\\\\\x([a-f0-9]{2})', repl, message) for message in x]\\n x = [re.sub(r'\\\\\\\\u([a-f0-9]{4})', repl, message) for message in x]\\n if self.ignore_urls:\\n x = [re.sub(self.re_url, '', message) for message in x]\\n\\n if self.fix_contractions:\\n import contractions\\n x = [contractions.fix(message) for message in x]\\n\\n if self.remove_foreign_characters:\\n # replace accented characters with unaccented\\n x = [unidecode.unidecode(message) for message in x]\\n\\n # replace nonascii characters with space\\n x = [''.join(character if ord(character) < 128 else ' ' for character in message) for message in x]\\n\\n # Create sentence structure like nltk gutenberg.sents()\\n # list of sentences for each message:\\n x = [self.sent_detector.tokenize(message.strip()) for message in x]\\n # list of list of words for each message/sentence:\\n x = [[self.word_tokenizer.tokenize(sentence) for sentence in message] for message in x]\\n\\n if self.lower:\\n # lower_sents: lowercase words ignoring punctuation\\n x = [[[\\n word.lower() for word in sentence] for sentence in message\\n ] for message in x]\\n\\n if self.remove_punctuation:\\n x = [[[\\n word for word in sentence if word not in list(string.punctuation)] for sentence in message\\n ] for message in x]\\n\\n if self.stem:\\n x = [[[self.stemmer.stem(word) for word in sentence] for sentence in message] for message in x]\\n\\n if self.lower and self.bigrams:\\n # clean_sents: replace common adjacent words with bigrams\\n x = [[self.bigram[sentence] for sentence in message] for message in x]\\n\\n if self.omit_stopwords:\\n x = [[[word for word in sentence if word not in stopwords.words('english')] for sentence in message] for\\n message in x]\\n\\n # convert back to one string per message (join words into sentences and sentences into messages)\\n x = ['\\\\n'.join(' '.join(sentence) for sentence in message) for message in x]\\n return x\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def clean_edges(self):\",\n \"def clean():\\n clean_flatbuffer_binaries()\\n clean_webp_textures()\",\n \"def clean(tweet):\\n #Separates the contractions and the punctuation\\n\\n\\n tweet = re.sub(\\\"[!#.,\\\\\\\"]\\\", \\\"\\\", tweet).replace(\\\"<user>\\\", \\\"\\\")\\n tweet = re.sub(\\\"[!#.,\\\\\\\"]\\\", \\\"\\\", tweet).replace(\\\"<url>\\\", \\\"\\\")\\n tweet = correct_spell(tweet)\\n return tweet.strip().lower()\",\n \"def clean(self):\\r\\n return self.clean()\",\n \"def delete_all(self):\\n for tag in self._segments['APP1'].get_tag_list():\\n try:\\n self.__delattr__(tag)\\n except AttributeError:\\n warnings.warn(\\\"could not delete tag \\\" + tag, RuntimeWarning)\",\n \"def clean(self):\\n\\t\\tfor v in self:\\n\\t\\t\\tv.reset_distance()\\n\\t\\t\\tv.reset_predecessor()\\n\\t\\t\\tv.reset_visited()\",\n \"def backwards(apps, schema_editor):\\n Referral = apps.get_model(\\\"core\\\", \\\"Referral\\\")\\n\\n for referral in Referral.objects.all():\\n referral.users.clear()\\n referral.save()\",\n \"def clean(self):\\n # Delete vertices / faces / colors / normals :\\n self._vert_buffer.delete()\\n self._index_buffer.delete()\\n self._normals_buffer.delete()\\n self._xrange_buffer.delete()\\n self._math_buffer.delete()\",\n \"def handleCleanMetadataRecon(self):\\n logging.debug(\\\"Removing compromising personal info and remaking the file...\\\")\\n filePath = self.filesList.selectedItems()[0].text(2)\\n fileType = self.filesList.getFileObj(filePath).type\\n self.printPdfPersonalData(filePath, \\n fileType,\\n AddedFile.changeBase(filePath, self.outputPath))\\n self.tabArea.setCurrentIndex(1)\\n self.changeCursor()\\n self.filesList.getFileObj(filePath).reconMetaCleaned = True\",\n \"def _clean_objects(*args):\\n\\n for arg in args:\\n attrs = dir(arg)\\n # QuadContourSet\\n if 'collections' in attrs:\\n for item in arg.collections: item.remove()\\n # Wind barbs and contour labels\\n elif '__len__' in attrs:\\n for item in arg:\\n item.remove()\\n # Text strings and everything else\\n elif 'get_text' in attrs: arg.remove()\",\n \"def removeAutoSaveDeleteFilter(filter):\",\n \"def clean():\\n user_init.clean_setup()\",\n \"def basic_clean_up_f(f):\\r\\n deletion_list = [l.strip() for l in f]\\r\\n remove_all(deletion_list)\\r\\n return True\",\n \"def unfriend(self, removee):\\n\\t\\tremover_friend_list = self # person terminating the friendship\\n\\n\\t\\t# Remove friend from friend request\\n\\t\\tremover_friend_list.remove_friend(removee)\\n\\n\\t\\t# Remove friend from the removeee friend list\\n\\t\\tfriends_list = FriendList.objects.get(user=removee)\\n\\t\\tfriends_list.remove_friend(self.user)\",\n \"def clean():\\n filter_phase_data()\\n combine_phase_data()\\n remove_duplicates_phase_data()\",\n \"def remove():\",\n \"def cleanup(self):\\n\\n pass\",\n \"def createFriendlyEmpireList(self):\\n friends = []\\n for shipID, myShip in self.myGalaxy.ships.iteritems():\\n if shipID not in self.targets and myShip.empireID not in friends:\\n friends.append(myShip.empireID)\\n return friends\",\n \"def clear(self):\\n for tag in self.meta.findall(CN('meta:user-defined')):\\n self.meta.remove(tag)\",\n \"def migrate_fix_invalid_bot_owner_values(\\n apps: StateApps, schema_editor: BaseDatabaseSchemaEditor\\n) -> None:\\n UserProfile = apps.get_model(\\\"zerver\\\", \\\"UserProfile\\\")\\n UserProfile.objects.filter(is_bot=False).exclude(bot_owner=None).update(bot_owner=None)\",\n \"def cleanup(self):\\n for key in list(self.__dict__.keys()):\\n delattr(self, key)\",\n \"def clean(self):\\n for nodeId in list(self.nodes.keys()):\\n if not self.nodes[nodeId].safe:\\n del self.nodes[nodeId]\",\n \"def cleanholdercandidates(lst):\\n for sent in lst:\\n for token in sent:\\n if 'holder_candidate' in token:\\n del token['holder_candidate']\",\n \"def recompute_friends(self):\\r\\n \\r\\n return True\",\n \"def foaf_ids_bad(user):\\n return [foaf_id \\n for friend_id in friendships[user[\\\"id\\\"]]\\n for foaf_id in friendships[friend_id]]\",\n \"def clean(self):\\n files = ['CHG', 'CHGCAR', 'POSCAR', 'INCAR', 'CONTCAR',\\n 'DOSCAR', 'EIGENVAL', 'IBZKPT', 'KPOINTS', 'OSZICAR',\\n 'OUTCAR', 'PCDAT', 'POTCAR', 'vasprun.xml',\\n 'WAVECAR', 'XDATCAR', 'PROCAR', 'ase-sort.dat',\\n 'LOCPOT', 'AECCAR0', 'AECCAR1', 'AECCAR2',\\n 'WAVECAR.GTO', 'vasp.out', 'vasp.err']\\n for f in files:\\n try:\\n os.remove(f)\\n except OSError:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6322474","0.63047546","0.6143992","0.6081225","0.6070681","0.5969005","0.5901452","0.5889662","0.584422","0.58323973","0.5830357","0.5830357","0.58122694","0.5788065","0.57749754","0.57663184","0.5693208","0.5692589","0.56887585","0.56775504","0.5664054","0.5654475","0.5643052","0.5629475","0.56286937","0.56276035","0.5621128","0.55967265","0.55818474","0.55584985","0.5557708","0.555395","0.55299145","0.55266273","0.5523494","0.55173135","0.5515789","0.54994017","0.5490472","0.5456466","0.5446726","0.5443319","0.5419467","0.5410722","0.54034954","0.53920203","0.5389128","0.53799784","0.53716","0.5358835","0.53523093","0.5351071","0.53357816","0.5333914","0.5322931","0.53204995","0.5309938","0.5302589","0.5293565","0.5284722","0.5275691","0.5272375","0.5257921","0.52559626","0.5252046","0.52444917","0.5231487","0.5229417","0.52269185","0.52208585","0.5212553","0.5204899","0.5200722","0.5200722","0.5200722","0.519502","0.51880026","0.5182617","0.51806104","0.5179514","0.5178418","0.51731926","0.51730996","0.516987","0.51692253","0.5167698","0.51612324","0.5155637","0.51428413","0.5138599","0.5133808","0.51311165","0.51288474","0.5127296","0.5124907","0.51231015","0.5121801","0.5118162","0.5115131","0.5112061","0.5111094"],"string":"[\n \"0.6322474\",\n \"0.63047546\",\n \"0.6143992\",\n \"0.6081225\",\n \"0.6070681\",\n \"0.5969005\",\n \"0.5901452\",\n \"0.5889662\",\n \"0.584422\",\n \"0.58323973\",\n \"0.5830357\",\n \"0.5830357\",\n \"0.58122694\",\n \"0.5788065\",\n \"0.57749754\",\n \"0.57663184\",\n \"0.5693208\",\n \"0.5692589\",\n \"0.56887585\",\n \"0.56775504\",\n \"0.5664054\",\n \"0.5654475\",\n \"0.5643052\",\n \"0.5629475\",\n \"0.56286937\",\n \"0.56276035\",\n \"0.5621128\",\n \"0.55967265\",\n \"0.55818474\",\n \"0.55584985\",\n \"0.5557708\",\n \"0.555395\",\n \"0.55299145\",\n \"0.55266273\",\n \"0.5523494\",\n \"0.55173135\",\n \"0.5515789\",\n \"0.54994017\",\n \"0.5490472\",\n \"0.5456466\",\n \"0.5446726\",\n \"0.5443319\",\n \"0.5419467\",\n \"0.5410722\",\n \"0.54034954\",\n \"0.53920203\",\n \"0.5389128\",\n \"0.53799784\",\n \"0.53716\",\n \"0.5358835\",\n \"0.53523093\",\n \"0.5351071\",\n \"0.53357816\",\n \"0.5333914\",\n \"0.5322931\",\n \"0.53204995\",\n \"0.5309938\",\n \"0.5302589\",\n \"0.5293565\",\n \"0.5284722\",\n \"0.5275691\",\n \"0.5272375\",\n \"0.5257921\",\n \"0.52559626\",\n \"0.5252046\",\n \"0.52444917\",\n \"0.5231487\",\n \"0.5229417\",\n \"0.52269185\",\n \"0.52208585\",\n \"0.5212553\",\n \"0.5204899\",\n \"0.5200722\",\n \"0.5200722\",\n \"0.5200722\",\n \"0.519502\",\n \"0.51880026\",\n \"0.5182617\",\n \"0.51806104\",\n \"0.5179514\",\n \"0.5178418\",\n \"0.51731926\",\n \"0.51730996\",\n \"0.516987\",\n \"0.51692253\",\n \"0.5167698\",\n \"0.51612324\",\n \"0.5155637\",\n \"0.51428413\",\n \"0.5138599\",\n \"0.5133808\",\n \"0.51311165\",\n \"0.51288474\",\n \"0.5127296\",\n \"0.5124907\",\n \"0.51231015\",\n \"0.5121801\",\n \"0.5118162\",\n \"0.5115131\",\n \"0.5112061\",\n \"0.5111094\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17456,"cells":{"query":{"kind":"string","value":"Track a download in Piwik"},"document":{"kind":"string","value":"def track_download_request(download_url, download_title):\n from indico_piwik.plugin import PiwikPlugin\n\n if not download_url:\n raise ValueError(\"download_url can't be empty\")\n if not download_title:\n raise ValueError(\"download_title can't be empty\")\n\n request = PiwikRequest(server_url=PiwikPlugin.settings.get('server_api_url'),\n site_id=PiwikPlugin.settings.get('site_id_events'),\n api_token=PiwikPlugin.settings.get('server_token'),\n query_script=PiwikPlugin.track_script)\n\n action_url = quote(download_url)\n dt = datetime.now()\n request.call(idsite=request.site_id,\n rec=1,\n action_name=quote(download_title),\n url=action_url,\n download=action_url,\n h=dt.hour, m=dt.minute, s=dt.second)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def download(self):\n pass","def download(self):\n pass","def download(self, download_request):\n raise NotImplementedError","def download_progress(self, cloud_file, size, downloaded):","def dowload_vt():\n print get_date_time_now() + \" ==> Download VT Samples started!\"\n print get_date_time_now() + \" ==> Nothing downloaded\"","def download():\n raise NotImplementedError","def download(self, url_match):\n pass","def log_download(self, download):\n with self._conn.begin():\n self._conn.execute(\n \"VALUES (log_download(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s))\",\n (\n sanitize(download.filename),\n download.host,\n download.timestamp.astimezone(UTC).replace(tzinfo=None),\n sanitize(download.arch),\n sanitize(download.distro_name),\n sanitize(download.distro_version),\n sanitize(download.os_name),\n sanitize(download.os_version),\n sanitize(download.py_name),\n sanitize(download.py_version),\n sanitize(download.installer_name),\n sanitize(download.installer_version),\n sanitize(download.setuptools_version),\n ))","def download(self, download_path):\n return","def download():\n if auth.has_membership(1):\n user = \"Admin\"\n elif auth.has_membership(2):\n user = \"Examiner\"\n elif auth.has_membership(3):\n user = \"student\"\n elif auth.has_membership(5):\n user = \"Managment\"\n\n db.activity_log.insert( Title_entry=\"Download assignment\", \n referance_id=auth.user.id,\n remarks=\"content downloaded by {}\".format(user))\n db.commit()\n return response.download(request, db)","def _download_single(url, to, id):\n if os.path.exists(to):\n error_flags[id] = 1\n return\n\n try:\n request = rq.Request(url=url, headers=forge_agent_header)\n info = rq.urlopen(request).read()\n\n except urllib.error.URLError as e:\n print(url, 'urllib error')\n error_flags[id] = 2\n return\n\n except Exception as e:\n print(url, e)\n error_flags[id] = 2\n return\n\n with open(to, \"wb\") as file:\n print(url, 'writing')\n file.write(info)\n\n error_flags[id] = 1","def report_download(self, data, token):\n requestcontent = json.loads(data)\n if len(requestcontent) > 2:\n request_context = requestcontent[2]\n if 'progress_code' in request_context:\n context = request.env.context.copy()\n context.update(request_context)\n request._env = request.env(context=context)\n request._context = context\n web_progress_obj = request.env['web.progress']\n web_progress_obj.web_progress_percent(0, 'Report')\n ret = super(WPReportController, self).report_download(data, token)\n web_progress_obj.web_progress_percent(100, 'Report done')\n return ret","def download_files(self):","def onContentDownload(self, fetcher, numBytes): #$NON-NLS-1$\r","def download_track(trackpath):\n\n if trackpath.startswith('gs://'):\n localpath = path.basename(trackpath)\n if not any([path.exists(localpath), path.isfile(localpath)]):\n subprocess.run(['gsutil', '-m', 'cp', trackpath, './'], check=True)\n\n elif any(trackpath.startswith(prefix) for prefix in 'http:// https:// ftp://'.split()):\n localpath = path.basename(trackpath)\n if not any([path.exists(localpath), path.isfile(localpath)]):\n subprocess.run(['wget', '--no-check-certificate', trackpath], check=True)\n\n else:\n localpath = trackpath\n\n # Correct for URL convention of replacing spaces with \"%20\"\n localpath = localpath.replace('%20', ' ')\n\n return localpath","def download(self, release: CrossrefEventsRelease, **kwargs):\n release.download()","def onContentDownloadStart(self, fetcher, contentLength): #$NON-NLS-1$\r","def download(self, release: DoabRelease, **kwargs):\n # Download release\n release.download()","def test_download(self):\n pass","def report(self, url):\n\n print(self.get(url))","def download_track(self, track = None, url = None):\n # check that track doesn't exist\n if url == None or track == None:\n return\n\n print \"Retrieving the name of the track.\"\n filename = self.get_track_filename(url)\n\n print \"Filename found: \" + filename\n \n if (filename, track.user[\"username\"]) in self.past_songs_db_data or \\\n (filename, \"\") in self.past_songs_db_data or \\\n os.path.isfile(filename): \n print \"File exists\"\n else:\n print \"Downloading\"\n filename = wget.download(url)\n self.set_track_metadata(track, filename, url)\n mp3_name = filename[:-4] + \".mp3\"\n\n # Save filename for future reference\n self.past_songs_db.write(filename + \"\\n\")\n self.past_songs_db_data.append((filename, track.user[\"username\"]))\n \n if not filename.endswith(\".mp3\"):\n self.past_songs_db.write(mp3_name + \"\\n\")\n self.past_songs_db_data.append((mp3_name, track.user[\"username\"]))\n \n print","def _download(item):\n\n filename = item.filename()\n filename = os.path.join(item.vdir(), filename)\n logger.info(\"Downloading '%s' to %s\" % (item.show, filename))\n\n f = open(filename, \"wb\")\n\n buf = net.tivoget(item.show.url)\n for chunk in buf:\n f.write(chunk)\n\n f.close()\n\n item.downloaded = True\n item.save()","def tracking_url(self) -> str:\n return pulumi.get(self, \"tracking_url\")","def download_report(self, response):\n \n if self.is_visited(response.url) == True:\n return None\n \n def get_filename_from_url(url):\n #http://www.gtja.com/f//lotus/201510/20151023%20Company%20Report%2001816%20HK_addStamper_addEncrypt.pdf\n import re\n pattern = re.compile(\"http://www.gtja.com/f//lotus/(\\d+)/(.*)\")\n result = pattern.match(url)\n if result is None:\n return str(datetime.date.today()), hashlib.md5(url).hexdigest() + \".pdf\"\n else:\n #return str(datetime.date.today()), hashlib.md5(url).hexdigest() + \".pdf\"\n return result.group(1), unquote(result.group(2))\n \n date, name = get_filename_from_url(response.url) #TODO Create date directory.\n\n file_path = settings[\"FILES_STORE_PATH\"] + date + \"/\"\n if os.path.exists(file_path) != True:\n os.mkdir(file_path)\n\n filename = file_path + name\n with open(filename.decode(\"utf-8\"), \"wb\") as f: #TODO what is the diffenrence between \"w+\" and \"wb\"\n f.write(response.body)\n \n item = ReportFileItem()\n item[\"url\"] = unquote(response.url)\n item[\"date\"] = date\n item[\"path\"] = \"/\" + date + \"/\" + name #Relative path\n item[\"link\"] = response.meta[\"link_url\"]\n item[\"create_date\"] = datetime.datetime.now()\n \n self.visit(response.url)\n \n return item","def onContentDownloadComplete(self, fetcher, connectionResp): #$NON-NLS-1$\r","def post_download(self, remote_files):\n pass","def download():\n env_banner()\n\n download_data = Download()\n download_data()\n click.echo('Download done.')","def start_download(url):\n return _add_download_to_deluge(url)","def downloadFile()-> None:\n logging.info(f\"Downloading current data set {getTime()}\")\n with open(DATA_FILE,\"wb\") as f:\n f.write(get(\"https://covid.ourworldindata.org/data/owid-covid-data.csv\").text.encode())\n logging.info(f\"Finished Downloading current data set {getTime()}\")","def download_report(request):\n params = request.query_params\n provider_uuid = params.get(\"provider_uuid\")\n bill_date = params.get(\"bill_date\")\n async_download_result = check_report_updates.delay(provider_uuid=provider_uuid, bill_date=bill_date)\n return Response({\"Download Request Task ID\": str(async_download_result)})","def download_monitoring_bulletin(release_id):\n ret = BROWSER_DRIVER.render_bulletin(release_id)\n\n var_checker(\"ret\", ret, True)\n\n if ret[\"success\"]:\n return send_file(ret[\"pdf_path\"], as_attachment=True,\n attachment_filename=APP_CONFIG[\"bulletin_save_path\"])\n\n return ret[\"error\"]","def __download(self, downloadType):\n\n downloadAudit = {}\n downloadAudit['download_type'] = downloadType\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\n #first make an entry into the db stating that a download operation is to be attempted\n downloadAudit['comment'] = 'Starting download operation'\n newRecordId = self.__auditFileRequest(downloadAudit)\n\n downloadAudit = {}\n try:\n req = urllib2.Request(self.remoteUrl)\n r = urllib2.urlopen(req)\n except URLError, urle:\n if hasattr(urle, 'reason'):\n downloadAudit['comment'] = urle.reason\n else:\n downloadAudit['comment'] = urle.__str__()\n \n self.__auditFileRequest(downloadAudit, newRecordId)\n \n return None #just return since there has been an error in connecting with the remote server\n\n try:\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\n\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\n file.write(r.read())\n\n #headerInfo = r.info()\n\n isFileToBeProcessed = False #default is not to process file\n\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\n #information is in the db\n latestFetch = self.__recentDownload(downloadType)\n\n if latestFetch:\n downloadAudit['file_size'] = latestFetch[0]\n file.flush()#make sure all the content is written to file \n os.fsync(file.fileno())\n if latestFetch[0] != os.path.getsize(file.name):\n isFileToBeProcessed = True\n else:\n isFileToBeProcessed = True\n\n if isFileToBeProcessed:\n downloadAudit['file_size'] = os.path.getsize(file.name)\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n downloadAudit['download_completion_time'] = datetime.now()\n #now make an audit entry into the database\n downloadAudit['comment'] = 'New file to be processed.'\n else:\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\n #now make an audit entry into the database\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\n\n self.__auditFileRequest(downloadAudit, newRecordId)\n file.close()\n except Exception as e:\n self.logger.exception('\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\n\n return newRecordId","def get_download_url():\n return _DOWNLOAD","def single_download(self, url, meta_mode=False):\n self.println(DL_HEAD)\n try:\n if self.djs_core is None or self.analyzer is None:\n print(\"Download failed, enter `help` for help.\")\n else:\n if meta_mode:\n self._meta_download([url, ])\n else:\n self._download([url, ])\n os.chdir(self.home)\n except Exception as e:\n self.println(\"Download failed and stopped.\")\n print(str(e))\n self.println(DL_TAIL)","def TrackerURL(issue):\n # make the server/project customizable?\n return 'http://code.google.com/p/lilypond/issues/detail?id=%s' % issue","def run(self):\n download(self.attempt)","def download_finish(self, cloud_file):","def download(self, account, code):\n\n url = Spider.BASE_URL + \"/p/%s/?taken-by=%s\" % (code, account)\n r = self.session.get(url)\n content_match = re.search(r\"<script.*?>\\s*?window._sharedData\\s*?=\\s*?({.*}).*?</script>\", r.text,\n re.MULTILINE)\n data = json.loads(content_match.group(1))\n media = data['entry_data']['PostPage'][0]['graphql']['shortcode_media']\n download_urls = []\n if media['__typename'] == 'GraphVideo': # video\n download_urls.append(media[\"video_url\"])\n if media['__typename'] == 'GraphImage': # image\n download_urls.append(media[\"display_url\"])\n if media['__typename'] == 'GraphSidecar': # slide\n nodes = media['edge_sidecar_to_children']['edges']\n for node in nodes:\n node = node['node']\n if node['is_video']:\n download_urls.append(node['video_url'])\n else:\n download_urls.append(node['display_url'])\n\n actual_download_dir = os.path.join(download_dir, account)\n if not os.path.isdir(actual_download_dir):\n os.mkdir(actual_download_dir)\n for url in download_urls:\n filename = os.path.join(actual_download_dir, url.split('/')[-1].split('?')[0])\n temp_name = filename + '.tmp'\n if os.path.isfile(filename):\n if self.spider.auto_stop:\n print('file', filename, \"already exists, exiting......\")\n sys.exit()\n print('file', filename, \"already exists, skipping\")\n else:\n print('downloading %s:' % filename)\n r = self.session.get(url, stream=True)\n content_length = int(r.headers['content-length'])\n curr = 0\n with open(temp_name, 'wb') as f:\n for chunk in r.iter_content(chunk_size=1024):\n f.write(chunk)\n curr += 1024\n progress(curr, content_length)\n os.rename(temp_name, filename)\n self.spider.item_count += 1","def download_release(self):\n if self.cache_dir is not None:\n download = Download(cache=self.cache_dir)\n else:\n self.log.warning(\"not using a download cache for uwsgi\")\n download = Download()\n\n download_url = self.options.get(\"download-url\", DOWNLOAD_URL)\n download_path, is_temp = download(\n download_url.format(self.uwsgi_version), md5sum=self.md5sum)\n return download_path","def pre_download(self, remote_files):\n pass","def get_track_filename(self, url = None):\n track_file = urllib.urlopen(url)\n headers = track_file.info()\n track_file.close()\n return wget.filename_from_headers(headers)","def log_export(request, xform, export_type):\n # log download as well\n audit = {\"xform\": xform.id_string, \"export_type\": export_type}\n log.audit_log(\n log.Actions.EXPORT_DOWNLOADED,\n request.user,\n xform.user,\n _(f\"Downloaded {export_type.upper()} export on '{xform.id_string}'.\"),\n audit,\n request,\n )","def download_link(self, handle):\n return None","def download_link(self): # pragma: no cover\n\n if PyFunceble.Check(self.file).is_url():\n # We get the destination.\n destination = self.file.split(\"/\")[-1]\n\n if self.file and self.autocontinue.is_empty():\n # The given file is an URL.\n\n if (\n not PyFunceble.path.isfile(destination)\n or PyFunceble.INTERN[\"counter\"][\"number\"][\"tested\"] == 0\n ):\n # The filename does not exist in the current directory\n # or the currently number of tested is equal to 0.\n\n # We download the content of the link.\n Download(self.file, destination).text()\n\n # We update the global file with the destination.\n self.file = destination","def sendTrack(self, interpreter, title, link=None):\n\t\treturn urllib2.urlopen(\n\t\t\t\t\turllib2.Request(\n\t\t\t\t\t\tself.api_base_url+self.api_key,\n\t\t\t\t\t\turllib.urlencode({\n\t\t\t\t\t\t\t'interpreter': interpreter,\n\t\t\t\t\t\t\t'title': title,\n\t\t\t\t\t\t\t'link': link\n\t\t\t\t\t\t}))\n\t\t\t\t\t).read()","def test_download_simfile(self):\n scrape_category.download_simfile(self.simfile, self.dest,\n tidy=False,\n use_logfile=True,\n extract=True,\n link=self.link)\n\n # There should now be three files - a download log, a zip, and\n # an unzipped simfile.\n self.check_saved_files(log=True, unzipped=True, zipped=True)\n\n records = {\"100\": self.simfile}\n updated_records = scrape_category.update_records_from_log(records, self.dest)\n assert len(updated_records) == 1\n assert \"100\" in updated_records\n # The records should be updated to reflect where the simfile\n # was actually saved\n assert updated_records[\"100\"].name == \"foo\"","def fetch(self, url, listener, useCache = True): #$NON-NLS-1$\r","def download(attempt):\n file = jobtracker.query(\"SELECT * FROM files \" \\\n \"WHERE id=%d\" % attempt['file_id'], \\\n fetchone=True)\n request = jobtracker.query(\"SELECT * FROM requests \" \\\n \"WHERE id=%d\" % file['request_id'], \\\n fetchone=True)\n\n queries = []\n\n # Download using bbftp\n res = DownloaderSPAN512.exec_download(request, file)\n\n\n # bbftp should report 'get filename OK' if the transfer is successfull\n if res == 'OK': \n queries.append(\"UPDATE files \" \\\n \"SET status='unverified', \" \\\n \"updated_at='%s', \" \\\n \"details='Download is complete - File is unverified' \" \\\n \"WHERE id=%d\" % \\\n (jobtracker.nowstr(), file['id']))\n queries.append(\"UPDATE download_attempts \" \\\n \"SET status='complete', \" \\\n \"details='Download is complete', \" \\\n \"updated_at='%s' \" \\\n \"WHERE id=%d\" % \\\n (jobtracker.nowstr(), attempt['id']))\n else:\t\t \n\tqueries.append(\"UPDATE files \" \\\n \"SET status='failed', \" \\\n \"updated_at='%s', \" \\\n \"details='Download failed - %s' \" \\\n \"WHERE id=%d\" % \\\n (jobtracker.nowstr(), str(res), file['id']))\n\tqueries.append(\"UPDATE download_attempts \" \\\n \"SET status='download_failed', \" \\\n \"details='Download failed - %s', \" \\\n \"updated_at='%s' \" \\\n \"WHERE id=%d\" % \\\n (str(res), jobtracker.nowstr(), attempt['id']))\n\n jobtracker.query(queries)","def download_report_hook(count, block_size, total_size):\n percent = int(count * block_size * 100 / total_size)\n print(\"\\r%d%%\" % percent + \" completed\", end=\"\\r\")","def test_download1(self):\n pass","def download():\n \n browser.find_element_by_xpath('//*[@id=\"ctl00_contentPlaceHolder_divAllVariablesPerYear2012\"]/div[2]/div[2]/div[1]/a').click()","def download(all):\n print(\"Downloading\")","def download1():\n #t=request.vars.arg(0)\n response.flash=request\n #print request.wsgi.environ['HTTP_REFERER']\n #print 'yghklo=',request.args[0]\n a=db(db.Project.Project_File==request.args[0]).select(db.Project.ALL)\n #a=db(db.Project.id==38).select(db.Project.ALL)\n #if a == None:\n#\t print 'silent'\n # print 'a= aabhas download',a[0].no_of_download, a[0].Project_File\n # if a[0].no_of_download==None:\n#\t a[0].no_download=0\n db(db.Project.Project_File==a[0].Project_File).update(no_of_download=(a[0].no_of_download or 0)+1)\n print 'a.id=',a[0].id\n # print len(a),'\\n'\n #print \"\\n\\n\\n\\n\"\n return response.download(request, db)","async def process_download_request(self, request: DownloadRequest):\n await self.emit(DiscoveryRequest(publication=request.publication))","def _download_file(self, report_date):\n fdate = report_date.strftime('%Y-%m-%d')\n ddate = '/'.join(fdate.split('-')[:-1])\n link = FILE_URL % (ddate, fdate)\n name = os.path.basename(urlparse(link).path)\n \n try:\n print ' Accessing %s.' % name\n r = requests.get(link, stream=True)\n r.raise_for_status()\n except RequestException as e:\n status = r.status_code\n \n if status == 404:\n pass\n if status >= 500:\n print ' - Unable to download %s: %s\\n' % (name, e)\n self.failed += 1\n else:\n print ' - Downloading %s.' % name\n fpath = os.path.join(self.path, name)\n \n with open(fpath, 'wb') as f:\n for chunk in r.iter_content(1024):\n f.write(chunk)\n \n self.counts += 1\n print ' - Saved %s.' % name","def download_file(self, parsed_event, input_dir_path):","def download_report_hook(count, block_size, total_size):\n percent = int(count * block_size * 100 / total_size)\n print(\"\\r%d%%\" % percent + \" completed\", end=\"\\r\")","def upload_downloaded(self, release: ThothRelease, **kwargs) -> None:\n success = gcs_upload_files(bucket_name=self.cloud_workspace.download_bucket, file_paths=[release.download_path])\n set_task_state(success, kwargs[\"ti\"].task_id, release=release)","def uci_gotrack(redownload: bool = False) -> Dataset:\n return Dataset.get(\"uci_gotrack\", redownload=redownload)","def _process_logs_download(self, logfile):\r\n\r\n print 'Downloading PCU logs'\r\n command = 'robot --outputdir \"C:\\Robot Framework\\Output\\PCU_logs\" {}.robot'.format(self.name)\r\n\r\n return self._run_command(command, logfile)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download():\n return response.download(request, db)","def download_report(self):\n self.ensure_one()\n if self.attachment_id:\n return {\n 'type': 'ir.actions.act_url',\n 'url': '/web/content/%s?download=true' % self.attachment_id.id,\n 'target': 'download',\n }\n return True","def download(bot, update):\n update.message.reply_text(''' ''')","def anon_download(url: str):\n if verify(url):\n location = download(url)\n return location\n return 6","def track(self,name):\n self.sendCommand(\"global /track\",name)","def download_coverage(self, name, dst_directory):\n logging.info('Not downloading coverage because no Filestore.')","def reportProgress(self):\n \n pass"],"string":"[\n \"def download(self):\\n pass\",\n \"def download(self):\\n pass\",\n \"def download(self, download_request):\\n raise NotImplementedError\",\n \"def download_progress(self, cloud_file, size, downloaded):\",\n \"def dowload_vt():\\n print get_date_time_now() + \\\" ==> Download VT Samples started!\\\"\\n print get_date_time_now() + \\\" ==> Nothing downloaded\\\"\",\n \"def download():\\n raise NotImplementedError\",\n \"def download(self, url_match):\\n pass\",\n \"def log_download(self, download):\\n with self._conn.begin():\\n self._conn.execute(\\n \\\"VALUES (log_download(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s))\\\",\\n (\\n sanitize(download.filename),\\n download.host,\\n download.timestamp.astimezone(UTC).replace(tzinfo=None),\\n sanitize(download.arch),\\n sanitize(download.distro_name),\\n sanitize(download.distro_version),\\n sanitize(download.os_name),\\n sanitize(download.os_version),\\n sanitize(download.py_name),\\n sanitize(download.py_version),\\n sanitize(download.installer_name),\\n sanitize(download.installer_version),\\n sanitize(download.setuptools_version),\\n ))\",\n \"def download(self, download_path):\\n return\",\n \"def download():\\n if auth.has_membership(1):\\n user = \\\"Admin\\\"\\n elif auth.has_membership(2):\\n user = \\\"Examiner\\\"\\n elif auth.has_membership(3):\\n user = \\\"student\\\"\\n elif auth.has_membership(5):\\n user = \\\"Managment\\\"\\n\\n db.activity_log.insert( Title_entry=\\\"Download assignment\\\", \\n referance_id=auth.user.id,\\n remarks=\\\"content downloaded by {}\\\".format(user))\\n db.commit()\\n return response.download(request, db)\",\n \"def _download_single(url, to, id):\\n if os.path.exists(to):\\n error_flags[id] = 1\\n return\\n\\n try:\\n request = rq.Request(url=url, headers=forge_agent_header)\\n info = rq.urlopen(request).read()\\n\\n except urllib.error.URLError as e:\\n print(url, 'urllib error')\\n error_flags[id] = 2\\n return\\n\\n except Exception as e:\\n print(url, e)\\n error_flags[id] = 2\\n return\\n\\n with open(to, \\\"wb\\\") as file:\\n print(url, 'writing')\\n file.write(info)\\n\\n error_flags[id] = 1\",\n \"def report_download(self, data, token):\\n requestcontent = json.loads(data)\\n if len(requestcontent) > 2:\\n request_context = requestcontent[2]\\n if 'progress_code' in request_context:\\n context = request.env.context.copy()\\n context.update(request_context)\\n request._env = request.env(context=context)\\n request._context = context\\n web_progress_obj = request.env['web.progress']\\n web_progress_obj.web_progress_percent(0, 'Report')\\n ret = super(WPReportController, self).report_download(data, token)\\n web_progress_obj.web_progress_percent(100, 'Report done')\\n return ret\",\n \"def download_files(self):\",\n \"def onContentDownload(self, fetcher, numBytes): #$NON-NLS-1$\\r\",\n \"def download_track(trackpath):\\n\\n if trackpath.startswith('gs://'):\\n localpath = path.basename(trackpath)\\n if not any([path.exists(localpath), path.isfile(localpath)]):\\n subprocess.run(['gsutil', '-m', 'cp', trackpath, './'], check=True)\\n\\n elif any(trackpath.startswith(prefix) for prefix in 'http:// https:// ftp://'.split()):\\n localpath = path.basename(trackpath)\\n if not any([path.exists(localpath), path.isfile(localpath)]):\\n subprocess.run(['wget', '--no-check-certificate', trackpath], check=True)\\n\\n else:\\n localpath = trackpath\\n\\n # Correct for URL convention of replacing spaces with \\\"%20\\\"\\n localpath = localpath.replace('%20', ' ')\\n\\n return localpath\",\n \"def download(self, release: CrossrefEventsRelease, **kwargs):\\n release.download()\",\n \"def onContentDownloadStart(self, fetcher, contentLength): #$NON-NLS-1$\\r\",\n \"def download(self, release: DoabRelease, **kwargs):\\n # Download release\\n release.download()\",\n \"def test_download(self):\\n pass\",\n \"def report(self, url):\\n\\n print(self.get(url))\",\n \"def download_track(self, track = None, url = None):\\n # check that track doesn't exist\\n if url == None or track == None:\\n return\\n\\n print \\\"Retrieving the name of the track.\\\"\\n filename = self.get_track_filename(url)\\n\\n print \\\"Filename found: \\\" + filename\\n \\n if (filename, track.user[\\\"username\\\"]) in self.past_songs_db_data or \\\\\\n (filename, \\\"\\\") in self.past_songs_db_data or \\\\\\n os.path.isfile(filename): \\n print \\\"File exists\\\"\\n else:\\n print \\\"Downloading\\\"\\n filename = wget.download(url)\\n self.set_track_metadata(track, filename, url)\\n mp3_name = filename[:-4] + \\\".mp3\\\"\\n\\n # Save filename for future reference\\n self.past_songs_db.write(filename + \\\"\\\\n\\\")\\n self.past_songs_db_data.append((filename, track.user[\\\"username\\\"]))\\n \\n if not filename.endswith(\\\".mp3\\\"):\\n self.past_songs_db.write(mp3_name + \\\"\\\\n\\\")\\n self.past_songs_db_data.append((mp3_name, track.user[\\\"username\\\"]))\\n \\n print\",\n \"def _download(item):\\n\\n filename = item.filename()\\n filename = os.path.join(item.vdir(), filename)\\n logger.info(\\\"Downloading '%s' to %s\\\" % (item.show, filename))\\n\\n f = open(filename, \\\"wb\\\")\\n\\n buf = net.tivoget(item.show.url)\\n for chunk in buf:\\n f.write(chunk)\\n\\n f.close()\\n\\n item.downloaded = True\\n item.save()\",\n \"def tracking_url(self) -> str:\\n return pulumi.get(self, \\\"tracking_url\\\")\",\n \"def download_report(self, response):\\n \\n if self.is_visited(response.url) == True:\\n return None\\n \\n def get_filename_from_url(url):\\n #http://www.gtja.com/f//lotus/201510/20151023%20Company%20Report%2001816%20HK_addStamper_addEncrypt.pdf\\n import re\\n pattern = re.compile(\\\"http://www.gtja.com/f//lotus/(\\\\d+)/(.*)\\\")\\n result = pattern.match(url)\\n if result is None:\\n return str(datetime.date.today()), hashlib.md5(url).hexdigest() + \\\".pdf\\\"\\n else:\\n #return str(datetime.date.today()), hashlib.md5(url).hexdigest() + \\\".pdf\\\"\\n return result.group(1), unquote(result.group(2))\\n \\n date, name = get_filename_from_url(response.url) #TODO Create date directory.\\n\\n file_path = settings[\\\"FILES_STORE_PATH\\\"] + date + \\\"/\\\"\\n if os.path.exists(file_path) != True:\\n os.mkdir(file_path)\\n\\n filename = file_path + name\\n with open(filename.decode(\\\"utf-8\\\"), \\\"wb\\\") as f: #TODO what is the diffenrence between \\\"w+\\\" and \\\"wb\\\"\\n f.write(response.body)\\n \\n item = ReportFileItem()\\n item[\\\"url\\\"] = unquote(response.url)\\n item[\\\"date\\\"] = date\\n item[\\\"path\\\"] = \\\"/\\\" + date + \\\"/\\\" + name #Relative path\\n item[\\\"link\\\"] = response.meta[\\\"link_url\\\"]\\n item[\\\"create_date\\\"] = datetime.datetime.now()\\n \\n self.visit(response.url)\\n \\n return item\",\n \"def onContentDownloadComplete(self, fetcher, connectionResp): #$NON-NLS-1$\\r\",\n \"def post_download(self, remote_files):\\n pass\",\n \"def download():\\n env_banner()\\n\\n download_data = Download()\\n download_data()\\n click.echo('Download done.')\",\n \"def start_download(url):\\n return _add_download_to_deluge(url)\",\n \"def downloadFile()-> None:\\n logging.info(f\\\"Downloading current data set {getTime()}\\\")\\n with open(DATA_FILE,\\\"wb\\\") as f:\\n f.write(get(\\\"https://covid.ourworldindata.org/data/owid-covid-data.csv\\\").text.encode())\\n logging.info(f\\\"Finished Downloading current data set {getTime()}\\\")\",\n \"def download_report(request):\\n params = request.query_params\\n provider_uuid = params.get(\\\"provider_uuid\\\")\\n bill_date = params.get(\\\"bill_date\\\")\\n async_download_result = check_report_updates.delay(provider_uuid=provider_uuid, bill_date=bill_date)\\n return Response({\\\"Download Request Task ID\\\": str(async_download_result)})\",\n \"def download_monitoring_bulletin(release_id):\\n ret = BROWSER_DRIVER.render_bulletin(release_id)\\n\\n var_checker(\\\"ret\\\", ret, True)\\n\\n if ret[\\\"success\\\"]:\\n return send_file(ret[\\\"pdf_path\\\"], as_attachment=True,\\n attachment_filename=APP_CONFIG[\\\"bulletin_save_path\\\"])\\n\\n return ret[\\\"error\\\"]\",\n \"def __download(self, downloadType):\\n\\n downloadAudit = {}\\n downloadAudit['download_type'] = downloadType\\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\\n #first make an entry into the db stating that a download operation is to be attempted\\n downloadAudit['comment'] = 'Starting download operation'\\n newRecordId = self.__auditFileRequest(downloadAudit)\\n\\n downloadAudit = {}\\n try:\\n req = urllib2.Request(self.remoteUrl)\\n r = urllib2.urlopen(req)\\n except URLError, urle:\\n if hasattr(urle, 'reason'):\\n downloadAudit['comment'] = urle.reason\\n else:\\n downloadAudit['comment'] = urle.__str__()\\n \\n self.__auditFileRequest(downloadAudit, newRecordId)\\n \\n return None #just return since there has been an error in connecting with the remote server\\n\\n try:\\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\\n\\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\\n file.write(r.read())\\n\\n #headerInfo = r.info()\\n\\n isFileToBeProcessed = False #default is not to process file\\n\\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\\n #information is in the db\\n latestFetch = self.__recentDownload(downloadType)\\n\\n if latestFetch:\\n downloadAudit['file_size'] = latestFetch[0]\\n file.flush()#make sure all the content is written to file \\n os.fsync(file.fileno())\\n if latestFetch[0] != os.path.getsize(file.name):\\n isFileToBeProcessed = True\\n else:\\n isFileToBeProcessed = True\\n\\n if isFileToBeProcessed:\\n downloadAudit['file_size'] = os.path.getsize(file.name)\\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n downloadAudit['download_completion_time'] = datetime.now()\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'New file to be processed.'\\n else:\\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\\n\\n self.__auditFileRequest(downloadAudit, newRecordId)\\n file.close()\\n except Exception as e:\\n self.logger.exception('\\\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\\n\\n return newRecordId\",\n \"def get_download_url():\\n return _DOWNLOAD\",\n \"def single_download(self, url, meta_mode=False):\\n self.println(DL_HEAD)\\n try:\\n if self.djs_core is None or self.analyzer is None:\\n print(\\\"Download failed, enter `help` for help.\\\")\\n else:\\n if meta_mode:\\n self._meta_download([url, ])\\n else:\\n self._download([url, ])\\n os.chdir(self.home)\\n except Exception as e:\\n self.println(\\\"Download failed and stopped.\\\")\\n print(str(e))\\n self.println(DL_TAIL)\",\n \"def TrackerURL(issue):\\n # make the server/project customizable?\\n return 'http://code.google.com/p/lilypond/issues/detail?id=%s' % issue\",\n \"def run(self):\\n download(self.attempt)\",\n \"def download_finish(self, cloud_file):\",\n \"def download(self, account, code):\\n\\n url = Spider.BASE_URL + \\\"/p/%s/?taken-by=%s\\\" % (code, account)\\n r = self.session.get(url)\\n content_match = re.search(r\\\"<script.*?>\\\\s*?window._sharedData\\\\s*?=\\\\s*?({.*}).*?</script>\\\", r.text,\\n re.MULTILINE)\\n data = json.loads(content_match.group(1))\\n media = data['entry_data']['PostPage'][0]['graphql']['shortcode_media']\\n download_urls = []\\n if media['__typename'] == 'GraphVideo': # video\\n download_urls.append(media[\\\"video_url\\\"])\\n if media['__typename'] == 'GraphImage': # image\\n download_urls.append(media[\\\"display_url\\\"])\\n if media['__typename'] == 'GraphSidecar': # slide\\n nodes = media['edge_sidecar_to_children']['edges']\\n for node in nodes:\\n node = node['node']\\n if node['is_video']:\\n download_urls.append(node['video_url'])\\n else:\\n download_urls.append(node['display_url'])\\n\\n actual_download_dir = os.path.join(download_dir, account)\\n if not os.path.isdir(actual_download_dir):\\n os.mkdir(actual_download_dir)\\n for url in download_urls:\\n filename = os.path.join(actual_download_dir, url.split('/')[-1].split('?')[0])\\n temp_name = filename + '.tmp'\\n if os.path.isfile(filename):\\n if self.spider.auto_stop:\\n print('file', filename, \\\"already exists, exiting......\\\")\\n sys.exit()\\n print('file', filename, \\\"already exists, skipping\\\")\\n else:\\n print('downloading %s:' % filename)\\n r = self.session.get(url, stream=True)\\n content_length = int(r.headers['content-length'])\\n curr = 0\\n with open(temp_name, 'wb') as f:\\n for chunk in r.iter_content(chunk_size=1024):\\n f.write(chunk)\\n curr += 1024\\n progress(curr, content_length)\\n os.rename(temp_name, filename)\\n self.spider.item_count += 1\",\n \"def download_release(self):\\n if self.cache_dir is not None:\\n download = Download(cache=self.cache_dir)\\n else:\\n self.log.warning(\\\"not using a download cache for uwsgi\\\")\\n download = Download()\\n\\n download_url = self.options.get(\\\"download-url\\\", DOWNLOAD_URL)\\n download_path, is_temp = download(\\n download_url.format(self.uwsgi_version), md5sum=self.md5sum)\\n return download_path\",\n \"def pre_download(self, remote_files):\\n pass\",\n \"def get_track_filename(self, url = None):\\n track_file = urllib.urlopen(url)\\n headers = track_file.info()\\n track_file.close()\\n return wget.filename_from_headers(headers)\",\n \"def log_export(request, xform, export_type):\\n # log download as well\\n audit = {\\\"xform\\\": xform.id_string, \\\"export_type\\\": export_type}\\n log.audit_log(\\n log.Actions.EXPORT_DOWNLOADED,\\n request.user,\\n xform.user,\\n _(f\\\"Downloaded {export_type.upper()} export on '{xform.id_string}'.\\\"),\\n audit,\\n request,\\n )\",\n \"def download_link(self, handle):\\n return None\",\n \"def download_link(self): # pragma: no cover\\n\\n if PyFunceble.Check(self.file).is_url():\\n # We get the destination.\\n destination = self.file.split(\\\"/\\\")[-1]\\n\\n if self.file and self.autocontinue.is_empty():\\n # The given file is an URL.\\n\\n if (\\n not PyFunceble.path.isfile(destination)\\n or PyFunceble.INTERN[\\\"counter\\\"][\\\"number\\\"][\\\"tested\\\"] == 0\\n ):\\n # The filename does not exist in the current directory\\n # or the currently number of tested is equal to 0.\\n\\n # We download the content of the link.\\n Download(self.file, destination).text()\\n\\n # We update the global file with the destination.\\n self.file = destination\",\n \"def sendTrack(self, interpreter, title, link=None):\\n\\t\\treturn urllib2.urlopen(\\n\\t\\t\\t\\t\\turllib2.Request(\\n\\t\\t\\t\\t\\t\\tself.api_base_url+self.api_key,\\n\\t\\t\\t\\t\\t\\turllib.urlencode({\\n\\t\\t\\t\\t\\t\\t\\t'interpreter': interpreter,\\n\\t\\t\\t\\t\\t\\t\\t'title': title,\\n\\t\\t\\t\\t\\t\\t\\t'link': link\\n\\t\\t\\t\\t\\t\\t}))\\n\\t\\t\\t\\t\\t).read()\",\n \"def test_download_simfile(self):\\n scrape_category.download_simfile(self.simfile, self.dest,\\n tidy=False,\\n use_logfile=True,\\n extract=True,\\n link=self.link)\\n\\n # There should now be three files - a download log, a zip, and\\n # an unzipped simfile.\\n self.check_saved_files(log=True, unzipped=True, zipped=True)\\n\\n records = {\\\"100\\\": self.simfile}\\n updated_records = scrape_category.update_records_from_log(records, self.dest)\\n assert len(updated_records) == 1\\n assert \\\"100\\\" in updated_records\\n # The records should be updated to reflect where the simfile\\n # was actually saved\\n assert updated_records[\\\"100\\\"].name == \\\"foo\\\"\",\n \"def fetch(self, url, listener, useCache = True): #$NON-NLS-1$\\r\",\n \"def download(attempt):\\n file = jobtracker.query(\\\"SELECT * FROM files \\\" \\\\\\n \\\"WHERE id=%d\\\" % attempt['file_id'], \\\\\\n fetchone=True)\\n request = jobtracker.query(\\\"SELECT * FROM requests \\\" \\\\\\n \\\"WHERE id=%d\\\" % file['request_id'], \\\\\\n fetchone=True)\\n\\n queries = []\\n\\n # Download using bbftp\\n res = DownloaderSPAN512.exec_download(request, file)\\n\\n\\n # bbftp should report 'get filename OK' if the transfer is successfull\\n if res == 'OK': \\n queries.append(\\\"UPDATE files \\\" \\\\\\n \\\"SET status='unverified', \\\" \\\\\\n \\\"updated_at='%s', \\\" \\\\\\n \\\"details='Download is complete - File is unverified' \\\" \\\\\\n \\\"WHERE id=%d\\\" % \\\\\\n (jobtracker.nowstr(), file['id']))\\n queries.append(\\\"UPDATE download_attempts \\\" \\\\\\n \\\"SET status='complete', \\\" \\\\\\n \\\"details='Download is complete', \\\" \\\\\\n \\\"updated_at='%s' \\\" \\\\\\n \\\"WHERE id=%d\\\" % \\\\\\n (jobtracker.nowstr(), attempt['id']))\\n else:\\t\\t \\n\\tqueries.append(\\\"UPDATE files \\\" \\\\\\n \\\"SET status='failed', \\\" \\\\\\n \\\"updated_at='%s', \\\" \\\\\\n \\\"details='Download failed - %s' \\\" \\\\\\n \\\"WHERE id=%d\\\" % \\\\\\n (jobtracker.nowstr(), str(res), file['id']))\\n\\tqueries.append(\\\"UPDATE download_attempts \\\" \\\\\\n \\\"SET status='download_failed', \\\" \\\\\\n \\\"details='Download failed - %s', \\\" \\\\\\n \\\"updated_at='%s' \\\" \\\\\\n \\\"WHERE id=%d\\\" % \\\\\\n (str(res), jobtracker.nowstr(), attempt['id']))\\n\\n jobtracker.query(queries)\",\n \"def download_report_hook(count, block_size, total_size):\\n percent = int(count * block_size * 100 / total_size)\\n print(\\\"\\\\r%d%%\\\" % percent + \\\" completed\\\", end=\\\"\\\\r\\\")\",\n \"def test_download1(self):\\n pass\",\n \"def download():\\n \\n browser.find_element_by_xpath('//*[@id=\\\"ctl00_contentPlaceHolder_divAllVariablesPerYear2012\\\"]/div[2]/div[2]/div[1]/a').click()\",\n \"def download(all):\\n print(\\\"Downloading\\\")\",\n \"def download1():\\n #t=request.vars.arg(0)\\n response.flash=request\\n #print request.wsgi.environ['HTTP_REFERER']\\n #print 'yghklo=',request.args[0]\\n a=db(db.Project.Project_File==request.args[0]).select(db.Project.ALL)\\n #a=db(db.Project.id==38).select(db.Project.ALL)\\n #if a == None:\\n#\\t print 'silent'\\n # print 'a= aabhas download',a[0].no_of_download, a[0].Project_File\\n # if a[0].no_of_download==None:\\n#\\t a[0].no_download=0\\n db(db.Project.Project_File==a[0].Project_File).update(no_of_download=(a[0].no_of_download or 0)+1)\\n print 'a.id=',a[0].id\\n # print len(a),'\\\\n'\\n #print \\\"\\\\n\\\\n\\\\n\\\\n\\\"\\n return response.download(request, db)\",\n \"async def process_download_request(self, request: DownloadRequest):\\n await self.emit(DiscoveryRequest(publication=request.publication))\",\n \"def _download_file(self, report_date):\\n fdate = report_date.strftime('%Y-%m-%d')\\n ddate = '/'.join(fdate.split('-')[:-1])\\n link = FILE_URL % (ddate, fdate)\\n name = os.path.basename(urlparse(link).path)\\n \\n try:\\n print ' Accessing %s.' % name\\n r = requests.get(link, stream=True)\\n r.raise_for_status()\\n except RequestException as e:\\n status = r.status_code\\n \\n if status == 404:\\n pass\\n if status >= 500:\\n print ' - Unable to download %s: %s\\\\n' % (name, e)\\n self.failed += 1\\n else:\\n print ' - Downloading %s.' % name\\n fpath = os.path.join(self.path, name)\\n \\n with open(fpath, 'wb') as f:\\n for chunk in r.iter_content(1024):\\n f.write(chunk)\\n \\n self.counts += 1\\n print ' - Saved %s.' % name\",\n \"def download_file(self, parsed_event, input_dir_path):\",\n \"def download_report_hook(count, block_size, total_size):\\n percent = int(count * block_size * 100 / total_size)\\n print(\\\"\\\\r%d%%\\\" % percent + \\\" completed\\\", end=\\\"\\\\r\\\")\",\n \"def upload_downloaded(self, release: ThothRelease, **kwargs) -> None:\\n success = gcs_upload_files(bucket_name=self.cloud_workspace.download_bucket, file_paths=[release.download_path])\\n set_task_state(success, kwargs[\\\"ti\\\"].task_id, release=release)\",\n \"def uci_gotrack(redownload: bool = False) -> Dataset:\\n return Dataset.get(\\\"uci_gotrack\\\", redownload=redownload)\",\n \"def _process_logs_download(self, logfile):\\r\\n\\r\\n print 'Downloading PCU logs'\\r\\n command = 'robot --outputdir \\\"C:\\\\Robot Framework\\\\Output\\\\PCU_logs\\\" {}.robot'.format(self.name)\\r\\n\\r\\n return self._run_command(command, logfile)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download():\\n return response.download(request, db)\",\n \"def download_report(self):\\n self.ensure_one()\\n if self.attachment_id:\\n return {\\n 'type': 'ir.actions.act_url',\\n 'url': '/web/content/%s?download=true' % self.attachment_id.id,\\n 'target': 'download',\\n }\\n return True\",\n \"def download(bot, update):\\n update.message.reply_text(''' ''')\",\n \"def anon_download(url: str):\\n if verify(url):\\n location = download(url)\\n return location\\n return 6\",\n \"def track(self,name):\\n self.sendCommand(\\\"global /track\\\",name)\",\n \"def download_coverage(self, name, dst_directory):\\n logging.info('Not downloading coverage because no Filestore.')\",\n \"def reportProgress(self):\\n \\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62857395","0.62857395","0.6190119","0.61862576","0.60990036","0.60830104","0.59435177","0.5895922","0.58797216","0.58775985","0.58351547","0.5832015","0.5803117","0.579149","0.5750479","0.5704647","0.5701332","0.5676492","0.5647217","0.5612867","0.5608241","0.5603173","0.55964583","0.5589744","0.5561809","0.55588883","0.5555596","0.5519754","0.5517934","0.55163425","0.55105776","0.54966944","0.5471067","0.54640615","0.5462311","0.5442475","0.54304314","0.54047483","0.5402031","0.53822136","0.53762555","0.5374055","0.5367329","0.5357724","0.5347038","0.5339609","0.5337415","0.5337007","0.5336345","0.5326347","0.5320796","0.5320291","0.53198546","0.5311222","0.5292978","0.5285057","0.52733254","0.5270783","0.5257755","0.5254076","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5239365","0.5232758","0.5231386","0.52288544","0.5221406","0.52169937","0.5194193"],"string":"[\n \"0.62857395\",\n \"0.62857395\",\n \"0.6190119\",\n \"0.61862576\",\n \"0.60990036\",\n \"0.60830104\",\n \"0.59435177\",\n \"0.5895922\",\n \"0.58797216\",\n \"0.58775985\",\n \"0.58351547\",\n \"0.5832015\",\n \"0.5803117\",\n \"0.579149\",\n \"0.5750479\",\n \"0.5704647\",\n \"0.5701332\",\n \"0.5676492\",\n \"0.5647217\",\n \"0.5612867\",\n \"0.5608241\",\n \"0.5603173\",\n \"0.55964583\",\n \"0.5589744\",\n \"0.5561809\",\n \"0.55588883\",\n \"0.5555596\",\n \"0.5519754\",\n \"0.5517934\",\n \"0.55163425\",\n \"0.55105776\",\n \"0.54966944\",\n \"0.5471067\",\n \"0.54640615\",\n \"0.5462311\",\n \"0.5442475\",\n \"0.54304314\",\n \"0.54047483\",\n \"0.5402031\",\n \"0.53822136\",\n \"0.53762555\",\n \"0.5374055\",\n \"0.5367329\",\n \"0.5357724\",\n \"0.5347038\",\n \"0.5339609\",\n \"0.5337415\",\n \"0.5337007\",\n \"0.5336345\",\n \"0.5326347\",\n \"0.5320796\",\n \"0.5320291\",\n \"0.53198546\",\n \"0.5311222\",\n \"0.5292978\",\n \"0.5285057\",\n \"0.52733254\",\n \"0.5270783\",\n \"0.5257755\",\n \"0.5254076\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5239365\",\n \"0.5232758\",\n \"0.5231386\",\n \"0.52288544\",\n \"0.5221406\",\n \"0.52169937\",\n \"0.5194193\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7677783"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17457,"cells":{"query":{"kind":"string","value":"T.__new__(S, ...) > a new object with type S, a subtype of T"},"document":{"kind":"string","value":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __new__(cls):\n return object.__new__(cls)","def __new__(cls):\n return object.__new__(cls)","def __newobj__(cls, *args):\n return cls.__new__(cls, *args)","def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","def __new__(self, ???):","def __new__(S, *more): # real signature unknown; restored from __doc__\r\n pass","def __new__(S, *more): # real signature unknown; restored from __doc__\r\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\r\n pass","def create(cls, **dictionary):\n new_inst = cls.__new__(cls)\n if cls.__name__ == \"Rectangle\":\n new_inst.__init__(42, 98)\n elif cls.__name__ == \"Square\":\n new_inst.__init__(42)\n new_inst.update(**dictionary)\n return new_inst","def __new__(cls, name):\n for sub_cls in cls.__subclasses__():\n if sub_cls.__name__ == name:\n return super().__new__(sub_cls)","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass","def __new__(*args, **kwargs): # real signature unknown\n pass"],"string":"[\n \"def __new__(cls):\\n return object.__new__(cls)\",\n \"def __new__(cls):\\n return object.__new__(cls)\",\n \"def __newobj__(cls, *args):\\n return cls.__new__(cls, *args)\",\n \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"def __new__(self, ???):\",\n \"def __new__(S, *more): # real signature unknown; restored from __doc__\\r\\n pass\",\n \"def __new__(S, *more): # real signature unknown; restored from __doc__\\r\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\r\\n pass\",\n \"def create(cls, **dictionary):\\n new_inst = cls.__new__(cls)\\n if cls.__name__ == \\\"Rectangle\\\":\\n new_inst.__init__(42, 98)\\n elif cls.__name__ == \\\"Square\\\":\\n new_inst.__init__(42)\\n new_inst.update(**dictionary)\\n return new_inst\",\n \"def __new__(cls, name):\\n for sub_cls in cls.__subclasses__():\\n if sub_cls.__name__ == name:\\n return super().__new__(sub_cls)\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"def __new__(*args, **kwargs): # real signature unknown\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72864395","0.72864395","0.7262999","0.7261351","0.7261351","0.7261351","0.7261351","0.7228152","0.72100246","0.72100246","0.66210115","0.66210115","0.66210115","0.66210115","0.66210115","0.66210115","0.6530774","0.6427577","0.6424623","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465","0.64146465"],"string":"[\n \"0.72864395\",\n \"0.72864395\",\n \"0.7262999\",\n \"0.7261351\",\n \"0.7261351\",\n \"0.7261351\",\n \"0.7261351\",\n \"0.7228152\",\n \"0.72100246\",\n \"0.72100246\",\n \"0.66210115\",\n \"0.66210115\",\n \"0.66210115\",\n \"0.66210115\",\n \"0.66210115\",\n \"0.66210115\",\n \"0.6530774\",\n \"0.6427577\",\n \"0.6424623\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\",\n \"0.64146465\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73813504"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17458,"cells":{"query":{"kind":"string","value":"Create the test server application instance."},"document":{"kind":"string","value":"def create_app(self):\n coverage.process_startup()\n app = create_test_app()\n selenium_server_url = \"http://{}:{}/wd/hub\".format(\n os.environ.get(\"{{ cookiecutter.project_slug|upper }}_SELENIUM_HOST\", \"chrome\"),\n os.environ.get(\"{{ cookiecutter.project_slug|upper }}_SELENIUM_PORT\", \"4444\"),\n )\n self.browser = Remote(\n command_executor=selenium_server_url,\n desired_capabilities=DesiredCapabilities.CHROME.copy(),\n )\n self.browser.implicitly_wait(3)\n return app"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_app(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n app.config.from_mapping(\n SECRET_KEY=os.environ.get('FLASK_SECRET_KEY', 'dev'),\n # SQLALCHEMY_DATABASE_URI='sqlite:////' + os.path.join(app.instance_path, 'app.sqlite'),\n SQLALCHEMY_DATABASE_URI=os.environ.get('FLASK_SQLALCHEMY_DATABASE_URI'),\n SQLALCHEMY_TRACK_MODIFICATIONS=False,\n )\n\n if test_config is None:\n app.config.from_pyfile('config.py', silent=True)\n else:\n app.config.from_mapping(test_config)\n\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n # Set custom json encoder\n app.json_encoder = JSONEncoder\n\n # SQLAlchemy\n from tuinbouwer_server_api.models import db, migrate\n db.init_app(app)\n migrate.init_app(app, db)\n\n # Apscheduler\n from tuinbouwer_server_api.scheduler import scheduler, start_jobs\n scheduler.init_app(app)\n scheduler.start()\n start_jobs()\n \n # CORS\n CORS(app, resources={r'/*': {'origins': '*'}})\n\n # Website\n app.register_blueprint(website.frontend.blueprint)\n \n # API\n app.register_blueprint(api.sensor.blueprint)\n app.register_blueprint(api.frontend.blueprint)\n\n\n return app","def create_embedded():\n from .server import create_application\n return create_application()","def create_and_run():\n\n app = App()\n app.run()","def create_app(test_config=None) -> Flask:\n app = Flask(\"front-end-web-server\", instance_relative_config=True)\n\n if test_config is None:\n # Get path to configuration file and load that configuration into Flask's app.config object.\n curr_dir = os.path.abspath(os.path.dirname(__file__))\n config_dir = os.path.dirname(curr_dir)\n config_filename = os.path.join(config_dir, \"config.py\")\n\n # load the instance config, if it exists, when not testing\n app.config.from_pyfile(config_filename)\n\n print(app.config.get(\"SECRET_KEY\"))\n else:\n # load the test config if passed in\n app.config.from_mapping(test_config)\n\n # ensure the instance folder exists\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n # a simple page that says hell\n @app.route(\"/\")\n def index():\n return \"Index page!\"\n\n from .views import hello\n\n app.register_blueprint(hello)\n\n return app","def create_app(test_config=\"test_config.py\"):\n app = Flask(__name__, instance_relative_config=True)\n\n # set common config values\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n # if not testing, config is loaded from config.py in the instance folder\n if test_config is None:\n app.config.from_pyfile(\"config.py\")\n else:\n # whichever config file name you pass in also has to be in the instance folder\n app.config.from_pyfile(test_config)\n\n db.init_app(app)\n login_manager.init_app(app)\n bootstrap.init_app(app)\n migrate.init_app(app, db)\n mail.init_app(app)\n app.redis = Redis.from_url(REDIS_URL)\n app.task_queue = rq.Queue(\"yamz-tasks\", connection=app.redis)\n app.elasticsearch = Elasticsearch(ELASTICSEARCH_URL)\n\n # apply the blueprints to the app\n from .main import main as main_blueprint\n\n app.register_blueprint(main_blueprint)\n\n from .auth import auth as auth_blueprint\n\n app.register_blueprint(auth_blueprint)\n\n from .term import term as term_blueprint\n\n app.register_blueprint(term_blueprint, url_prefix=\"/term\")\n\n from .graph import graph as graph_blueprint\n\n app.register_blueprint(graph_blueprint, url_prefix=\"/graph\")\n\n # register command line functions\n @app.cli.command()\n def test():\n \"\"\"Run the unit tests.\"\"\"\n import unittest\n\n tests = unittest.TestLoader().discover(\"tests\")\n unittest.TextTestRunner(verbosity=2).run(tests)\n\n return app","def test_create(self):\n self.app\n pass","def CreateApp(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n\n if test_config:\n pass\n\n @app.route('/validate', methods=['POST'])\n def Validate(): # pylint: disable=unused-variable\n # TODO(dberris): Implement this!\n return ''\n\n @app.route('/service-metadata')\n def ServiceMetadata(): # pylint: disable=unused-variable\n # TODO(dberris): Implement this!\n return ''\n\n return app","def create_app(test_config: dict = None) -> Flask:\n\n # create and configure the app\n app = Flask(__name__, instance_relative_config=True)\n\n # Load in the configuration\n if test_config is None:\n # load the instance config, if it exists, when not testing\n app.config.from_pyfile(os.path.join(os.path.dirname(__file__), 'config.py'), silent=True)\n print(app.config)\n else:\n # load the test config if passed in\n app.config.from_mapping(test_config)\n\n # Make the initial status page\n start_time = datetime.now().isoformat()\n\n @app.route('/')\n def home():\n return render_template('home.html',\n message=f'Running on {platform.node()} since {start_time}')\n\n # Load the blueprints\n from .views import ingest\n app.register_blueprint(ingest.bp)\n\n return app","def setUp(self):\n _, instance_path, shared_inputs = sys.argv\n app = lnt.server.ui.app.App.create_standalone(instance_path)\n app.testing = True\n self.client = app.test_client()\n self.shared_inputs = shared_inputs","def create_app(test_config=None):\n app = Flask(__name__,\n instance_relative_config=True,\n static_folder=\"resources\")\n app.config.from_mapping(\n SECRET_KEY=\"dev\",\n SQLALCHEMY_DATABASE_URI=\"sqlite:///\" + os.path.join(\n app.instance_path, \"database.sqlite\"),\n SQLALCHEMY_TRACK_MODIFICATIONS=False,\n API_DOMAIN=\"http://localhost:5000\"\n )\n if test_config is None:\n # load the instance config, if it exists, when not testing\n app.config.from_pyfile(\"config.py\", silent=True)\n else:\n # load the test config if passed in\n app.config.from_mapping(test_config)\n\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n # Initialize DB\n db.init_app(app)\n db.create_all(app=app)\n\n # Initialize Marshmallow Serialization\n ma.init_app(app)\n\n # Initialize Blueprints\n from . import local\n app.register_blueprint(local.bp)\n\n @app.route(\"/\", defaults={\"path\": \"\"})\n @app.route(\"/<path:path>\")\n def index(path):\n return render_template(\n \"index.html\", api_domain=app.config.get(\"API_DOMAIN\")\n )\n\n return app","def create_local_app():\n app = create_app()\n app.test_request_context().push()\n return app","def create_app(test_config=None):\n\n app = Flask(__name__, instance_relative_config=True)\n\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n if test_config is None:\n app.config.from_pyfile('config.py', silent=False)\n else:\n app.config.from_mapping(test_config)\n\n # Instantiate the Database.\n db.init_app(app=app)\n migrate = Migrate(app=app, db=db)\n\n # Initialize WTForms to handle JSON data.\n wtforms_json.init()\n\n # Routing starts from here.\n app.add_url_rule(\"/\", view_func=Home.as_view(\"home\"))\n app.register_blueprint(api)\n\n return app","def create_app(self):\n\n app = create_app()\n app.config.from_object('project.config.TestingConfig')\n return app","def create(cls, application, **kwargs):\n host, port = get_free_port()\n if 'port' not in kwargs:\n kwargs['port'] = port\n if 'host' not in kwargs:\n kwargs['host'] = host\n if 'expose_tracebacks' not in kwargs:\n kwargs['expose_tracebacks'] = True\n server = cls(application, **kwargs)\n server.runner = threading.Thread(target=server.run)\n server.runner.daemon = True\n server.runner.start()\n return server","def testapp():\n from space_rocks import main\n app = main({})\n from webtest import TestApp\n return TestApp(app)","def create_server(self, test_server):\n debug.virtual('wxMediator.create_server')","def create_app(self):\n app.config.from_object('config.TestingConfig')\n return app","def app():\n print('creating app with test vars')\n\n app = create_app('test')\n app.testing = True\n\n ctx = app.app_context()\n ctx.push()\n yield app\n\n ctx.pop()","def create_app():\n app = FastAPI()\n configure_rest_server(app=app, router_configs=WEB_SERVICES_ROUTER_CONFIGS, db_configs=DB_CONFIGS)\n return app","def create_app(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n app.config.from_mapping(\n # a default secret that should be overridden by instance config\n SECRET_KEY=\"dev\",\n # store the database in the instance folder\n DATABASE=os.path.join(app.instance_path, \"sqlite\"),\n )\n\n if test_config is None:\n # load the instance config, if it exists, when not testing\n app.config.from_pyfile(\"config.py\", silent=True)\n else:\n # load the test config if passed in\n app.config.update(test_config)\n\n # ensure the instance folder exists\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n @app.route(\"/hello\")\n def hello():\n return \"Hello, World!\"\n\n # register the database commands\n from music_recommender import db\n\n db.init_app(app)\n\n # apply the blueprints to the app\n from music_recommender import auth\n\n app.register_blueprint(auth.bp)\n\n # make url_for('index') == url_for('body.index')\n # in another app, you might define a separate main index here with\n # app.route, while giving the body blueprint a url_prefix, but for\n # the tutorial the body will be the main index\n app.add_url_rule(\"/\", endpoint=\"index\")\n\n return app","def application():\n yield create_test_application()","def app():\n return create_app()","def test_create_app():\n assert not create_app().testing\n assert create_app({'TESTING': True}).testing","def make_server() -> Flask:\n app: Flask = Flask(__name__)\n return app","def test_create_app():\n app = web.YumWeb(__name__)\n assert app.name == 'web_tests'","def CreateApp(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n _ = Talisman(app)\n\n environ = os.environ if test_config is None else test_config.get(\n 'environ', {})\n\n # We can set up a preconfigured HTTP instance from the test_config, otherwise\n # we'll use the default auth for the production environment.\n client_config = {}\n if test_config:\n client_config['http'] = test_config.get('http')\n else:\n client_config['credentials'], _ = google.auth.default()\n\n # In the python37 environment, we need to synthesize the URL from the\n # various parts in the environment variable, because we do not have access\n # to the appengine APIs in the python37 standard environment.\n domain_parts = {\n 'app_id': environ.get('GOOGLE_CLOUD_PROJECT', ''),\n 'service': environ.get('GAE_SERVICE', ''),\n }\n empty_keys = [k for k, v in domain_parts.items() if len(v) == 0]\n if len(empty_keys):\n raise MissingEnvironmentVars(empty_keys)\n domain = '{parts[service]}-dot-{parts[app_id]}.appspot.com'.format(\n parts=domain_parts)\n\n # We create an instance of the luci-config and Auth client, which we'll use\n # in all requests handled in this application.\n config_client = service_client.CreateServiceClient(\n 'https://luci-config.appspot.com/_ah/api', 'config', 'v1',\n **client_config)\n auth_client = service_client.CreateServiceClient(\n 'https://chrome-infra-auth.appspot.com/_ah/api', 'auth', 'v1',\n **client_config)\n\n # First we check whether the test_config already has a predefined\n # datastore_client.\n if test_config:\n datastore_client = test_config.get('datastore_client')\n else:\n datastore_client = datastore.Client()\n\n @app.route('/service-metadata')\n def ServiceMetadata(): # pylint: disable=unused-variable\n return jsonify({\n 'version': '1.0',\n 'validation': {\n 'patterns': [{\n 'config_set': 'regex:projects/.+',\n 'path': 'regex:chromeperf-sheriffs.cfg'\n }],\n 'url': 'https://%s/configs/validate' % (domain)\n }\n })\n\n @app.route('/warmup')\n def Warmup(): # pylint: disable=unused-variable\n \"\"\"Caching configs and compiled patterns for warmup.\"\"\"\n configs = luci_config.ListAllConfigs(\n datastore_client, _cache_timestamp=time.time() + 10)\n for _, revision, subscription in configs:\n luci_config.GetMatcher(revision, subscription)\n return jsonify({})\n\n @app.route('/configs/validate', methods=['POST'])\n def Validate(): # pylint: disable=unused-variable\n validation_request = request.get_json()\n if validation_request is None:\n return u'Invalid request.', 400\n for member in ('config_set', 'path', 'content'):\n if member not in validation_request:\n return u'Missing \\'%s\\' member in request.' % (member), 400\n try:\n _ = validator.Validate(\n base64.standard_b64decode(validation_request['content']))\n except validator.Error as error:\n return jsonify({\n 'messages': [{\n 'path': validation_request['path'],\n 'severity': 'ERROR',\n 'text': '%s' % (error)\n }]\n })\n return jsonify({})\n\n @app.route('/configs/update')\n def UpdateConfigs(): # pylint: disable=unused-variable\n \"\"\"Poll the luci-config service.\"\"\"\n try:\n configs = luci_config.FindAllSheriffConfigs(config_client)\n luci_config.StoreConfigs(datastore_client, configs.get('configs', []))\n return jsonify({})\n except (luci_config.InvalidConfigError,\n luci_config.InvalidContentError) as error:\n logging.warning('loading configs from luci-config failed: %s', error)\n return jsonify({}), 500\n\n @app.route('/subscriptions/match', methods=['POST'])\n def MatchSubscriptions(): # pylint: disable=unused-variable\n \"\"\"Match the subscriptions given the request.\n\n This is an API handler, which requires that we have an authenticated user\n making the request. We'll require that the user be a service account\n (from the main dashboard service).\n\n TODO(fancl): Allow access from an authenticated user. We'll enforce\n that we're only serving \"public\" Subscriptions to non-privileged users.\n \"\"\"\n\n try:\n match_request = json_format.Parse(request.get_data(),\n sheriff_config_pb2.MatchRequest())\n except json_format.ParseError as error:\n return jsonify(\n {'messages': [{\n 'severity': 'ERROR',\n 'text': '%s' % (error)\n }]}), 400\n match_response = sheriff_config_pb2.MatchResponse()\n configs = list(\n luci_config.FindMatchingConfigs(datastore_client, match_request))\n configs = match_policy.FilterSubscriptionsByPolicy(match_request, configs)\n for config_set, revision, subscription in configs:\n subscription_metadata = match_response.subscriptions.add()\n subscription_metadata.config_set = config_set\n subscription_metadata.revision = revision\n luci_config.CopyNormalizedSubscription(\n subscription,\n subscription_metadata.subscription,\n )\n\n # Then we find one anomaly config that matches.\n matcher = luci_config.GetMatcher(revision, subscription)\n anomaly_config = matcher.GetAnomalyConfig(match_request.path)\n if anomaly_config:\n subscription_metadata.subscription.anomaly_configs.append(\n anomaly_config)\n if not match_response.subscriptions:\n return jsonify({}), 404\n return (json_format.MessageToJson(\n match_response, preserving_proto_field_name=True), 200, {\n 'Content-Type': 'application/json'\n })\n\n @app.route('/subscriptions/list', methods=['POST'])\n def ListSubscriptions(): # pylint: disable=unused-variable\n \"\"\"List all visible subscriptions based on identity.\n\n This is an API handler, which requires that we have an authenticated user\n making the request. We'll require that the user be a service account\n (from the main dashboard service).\n\n \"\"\"\n\n try:\n list_request = json_format.Parse(request.get_data(),\n sheriff_config_pb2.ListRequest())\n except json_format.ParseError as error:\n return jsonify(\n {'messages': [{\n 'severity': 'ERROR',\n 'text': '%s' % (error)\n }]}), 400\n list_response = sheriff_config_pb2.ListResponse()\n configs = list(luci_config.ListAllConfigs(datastore_client))\n configs = match_policy.FilterSubscriptionsByIdentity(\n auth_client, list_request, configs)\n for config_set, revision, subscription in configs:\n subscription_metadata = list_response.subscriptions.add()\n subscription_metadata.config_set = config_set\n subscription_metadata.revision = revision\n luci_config.CopyNormalizedSubscription(subscription,\n subscription_metadata.subscription)\n return (json_format.MessageToJson(\n list_response, preserving_proto_field_name=True), 200, {\n 'Content-Type': 'application/json'\n })\n\n return app","def create_app(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n CORS(app)\n app.config.from_mapping(\n SQLALCHEMY_ECHO=False,\n SQLALCHEMY_TRACK_MODIFICATIONS=False,\n PROPAGATE_EXCEPTIONS=True,\n SQLALCHEMY_DATABASE_URI=os.environ['DATABASE'],\n JWT_SECRET_KEY='sad32du834gfjуо21?sdfja<<dfsj3ha!@Dsak',#os.urandom(24),\n RESTFUL_JSON={'ensure_ascii': False},\n JSON_AS_ASCII=False\n )\n\n db.init_app(app)\n\n with app.app_context():\n jwt = JWTManager(app)\n for res, endpoint in admin_resources:\n app.route('/admin' + endpoint)(res)\n moment = Moment(app)\n app.jinja_env.globals.update(offer_format=offer_format)\n\n api = Api(app, default_mediatype='application/json; charset=utf-8')\n\n for res, endpoint in resources:\n api.add_resource(res, '/api' + endpoint)\n return app","def setUp(self) -> None:\n self.workdir = tempfile.mkdtemp()\n self.server_name = 'fooserver.localdomain'\n self.app = create_web_app()\n self.app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'\n self.app.config['SERVER_NAME'] = self.server_name\n self.app.config['STORAGE_BASE_PATH'] = self.workdir\n\n # There is a bug in arxiv.base where it doesn't pick up app config\n # parameters. Until then, we pass it to os.environ.\n os.environ['JWT_SECRET'] = self.app.config.get('JWT_SECRET')\n self.client = self.app.test_client()\n # self.app.app_context().push()\n with self.app.app_context():\n database.db.create_all()\n\n with open('schema/resources/Workspace.json') as f:\n self.schema = json.load(f)","def app():\n app = create_app()\n return app","def app() -> Generator:\n app = create_app({\"TESTING\": True})\n\n yield app","def create_app(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n\n if app.config[\"ENV\"] == \"production\":\n app.config.from_object(\"config.ProductionConfig\")\n else:\n app.config.from_object(\"config.DevelopmentConfig\")\n\n if app.config['SECRET_KEY'] is None:\n raise Exception(\n \"SECRET_KEY can't be None. Try to generate one by command: python -c 'import os; print(os.urandom(16))', and copy the result into configs.py.\")\n\n if app.config['OWNER_USER_ID'] is None:\n raise Exception(\n \"OWNER_USER_ID can't be None. It is an integer user_id of your Clubhouse account, you can get it from token json file generated by OpenClubhouse-worker\")\n\n @app.route(\"/alive\")\n def alive():\n return {\"alive\": True}\n\n db = MongoEngine(app)\n channelsCache.init_cache(app.logger)\n\n # apply the blueprints to the app\n from handlers import clubhouse\n\n app.register_blueprint(clubhouse.bp)\n # in another app, you might define a separate main index here with\n # app.route, while giving the blog blueprint a url_prefix, but for\n # the tutorial the blog will be the main index\n app.add_url_rule(\"/\", endpoint=\"index\")\n\n return app","def pytest_configure() -> None: # pragma: no cover\n print(\"Starting server app\")\n PROC.start()\n time.sleep(1)\n if PROC.exitcode is not None:\n pytest.exit(\"Failed to start the server, exit code {}\\nLogs are in logs/server.log\".format(PROC.exitcode))\n return\n\n create_generated_client()","def create():\n\n return App()","def create_app():\n from server.web import create_app\n # If we do a static javascript app via flask, add it here\n # from server.web import create_app as create_web_app\n return create_app()","def _create_app_instance(script_info):\n return create_app()","def create_app(self):\r\n self.app = Flask(__name__, instance_relative_config=True)\r\n\r\n # Init the secret key of the app -it is a must for flask to run\r\n self.app.config.from_mapping(\r\n SECRET_KEY='!ZNeverSayNever116Z!',\r\n MONGODB_SETTINGS= {'host': 'mongodb://localhost/opc_integrity'}\r\n )\r\n initialize_db(self.app)\r\n\r\n\r\n # Init the app with core routes\r\n routes.init_app(self.app)","def create_app(test_config=None):\n app = Flask(__name__)\n\n # apply the blueprints to the app\n from app import common\n\n app.register_blueprint(common.bp)\n\n # default url for site\n app.add_url_rule(\"/\", endpoint=\"index\")\n\n return app","def create_server(self, test_server):\n factory = tcp_server.AppStateFactorySimple()\n return tcp_server.ServerNewMediator(data_events = self,\n test_server = test_server,\n editor_factory = factory)","def make_app(controller_klass=None, environ=None):\n if controller_klass is None:\n controller_klass = TGController\n\n tg.config['renderers'] = default_config['renderers']\n app = TGApp(config=default_config)\n app.controller_classes['root'] = ControllerWrap(controller_klass)\n\n app = FakeRoutes(app)\n\n app = RegistryManager(app)\n app = beaker.middleware.SessionMiddleware(app, {}, data_dir=session_dir)\n app = CacheMiddleware(app, {}, data_dir=os.path.join(data_dir, 'cache'))\n return TestApp(app)","def create_app(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n app.config.from_mapping(\n # a default secret that should be overridden by instance config\n SECRET_KEY=\"dev\",\n # store the database in the instance folder\n # DATABASE=os.path.join(app.instance_path, \"flaskr.sqlite\"),\n )\n\n # if test_config is None:\n # # load the instance config, if it exists, when not testing\n # app.config.from_pyfile(\"config.py\", silent=True)\n # else:\n # # load the test config if passed in\n # app.config.update(test_config)\n\n # ensure the instance folder exists\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n @app.route(\"/\", methods=[\"GET\"])\n def test_route():\n return \"<h1>This is a test route!<h1><p>Use /account or /auth</p>\"\n\n # register the database commands\n # from flaskr import db\n\n # db.init_app(app)\n\n # apply the blueprints to the app\n from dashr import auth, dash\n\n app.register_blueprint(auth.bp)\n app.register_blueprint(dash.bp)\n\n # make url_for('index') == url_for('blog.index')\n # in another app, you might define a separate main index here with\n # app.route, while giving the blog blueprint a url_prefix, but for\n # the tutorial the blog will be the main index\n app.add_url_rule(\"/\", endpoint=\"auth.login\")\n\n return app","def test_app():\n param = {\n 'DEBUG': True,\n 'TESTING': True\n }\n _app = create_app(settings_override=param)\n\n ctx = _app.app_context()\n ctx.push()\n yield _app\n ctx.pop()","def run_server():\n app = init_app()\n app.run(host=app.config['HOST'], port=app.config['PORT'])","def create_app():\n app = Flask(__name__, instance_relative_config=False)\n app.config.from_object('config.Config')\n db.init_app(app)\n ma.init_app(app)\n migrate = Migrate(app, db)\n\n with app.app_context():\n from . import routes\n\n # Create tables for our models\n db.create_all()\n app.logger.info(\"application started\")\n\n return app","def create_app() -> Flask:\r\n app = Flask(__name__.split('.')[0])\r\n init_config(app)\r\n app.register_blueprint(observer)\r\n app.teardown_appcontext(close_db)\r\n app.cli.add_command(init_db)\r\n\r\n return app","def create_app(instance_path=None, test_config=None):\n\n # create and configure the bit-wallet\n app = Flask(__name__, instance_path=instance_path)\n app.jinja_options[\"autoescape\"] = None\n app.config.from_mapping(\n SECRET_KEY='dev',\n UPLOAD_FOLDER='upload',\n MYSQL_HOST=os.getenv('MYSQL_HOST'),\n MYSQL_USER=os.getenv('MYSQL_USER'),\n MYSQL_PASSWORD=os.getenv('MYSQL_PASSWORD'),\n MYSQL_DATABASE=os.getenv('MYSQL_DATABASE'),\n )\n\n if test_config is not None:\n # load the test config if passed in\n app.config.from_mapping(test_config)\n\n # ensure the instance folder and the UPLOAD_FOLDER folder exist\n try:\n os.makedirs(os.path.join(app.instance_path, app.config['UPLOAD_FOLDER']))\n except OSError:\n pass\n\n db.init_app(app)\n\n app.register_blueprint(auth.bp)\n app.register_blueprint(blog.bp)\n app.register_blueprint(account.bp)\n app.add_url_rule('/', endpoint='index')\n\n return app","def __create_test_environment(self):\n os.chdir(self.wd)\n temp_dir = tempfile.gettempdir()\n self.test_root = os.path.join(temp_dir, \"test-grpc\")\n print(\"Creating testing environment in {}\".format(self.test_root))\n if os.path.exists(self.test_root):\n # delete any previous environment\n shutil.rmtree(self.test_root)\n # create root directory\n os.makedirs(self.test_root)\n def copy_app(name):\n app_root = os.path.join(self.test_root, name)\n os.makedirs(app_root)\n filename = \"grpc-{}\".format(name)\n src = os.path.join(self.args.bin, filename)\n dst = os.path.join(app_root, filename)\n shutil.copy(src, dst)\n return dst\n # copy client and server into the new test environment\n self.server_path = copy_app(\"server\")\n self.client_path = copy_app(\"client\")","def test_server(loop):\n\n servers = []\n\n async def create_server(app, **kwargs):\n server = TestServer(app, **kwargs)\n await server.start_server()\n servers.append(server)\n return server\n\n yield create_server\n\n # Close Server\n if servers:\n for server in servers:\n loop.run_until_complete(server.close())","def app():\n _app = create_app(TestConfig)\n ctx = _app.test_request_context()\n ctx.push()\n\n yield _app\n\n ctx.pop()","def app():\n _app = create_app(TestConfig)\n ctx = _app.test_request_context()\n ctx.push()\n\n yield _app\n\n ctx.pop()","def app():\n _app = create_app(TestConfig)\n ctx = _app.test_request_context()\n ctx.push()\n\n yield _app\n\n ctx.pop()","def testing_app() -> Flask:\n app = create_app(\"app.config.TestingConfig\")\n with app.app_context():\n db.drop_all()\n db.create_all()\n return app","def create_app(self):\n self.setUpPyfakefs()\n self.fake_os = fake_filesystem.FakeOsModule(self.fs)\n\n populate_fakefs(self)\n\n app = MDFakeFSTestSite(\n \"MDWeb\",\n app_options={}\n )\n\n # Add the partials directory so we have access in the FakeFS\n self.fs.add_real_directory(app.config['PARTIALS_TEMPLATE_PATH'])\n \n app.start()\n\n return app","def app():\n app = create_app(schema=create_schema())\n with app.test_request_context():\n yield app","def test_stage_create_app(self, mock_stage_create_app):\n app = MDFakeFSTestSite(\n \"MDWeb\",\n app_options={}\n )\n app.start()\n\n self.assertTrue(mock_stage_create_app.called)","def test_app():\n # setup\n app = main.create_application()\n app.dependency_overrides[get_settings] = get_settings_override\n with TestClient(app) as test_client:\n yield test_client\n # teardown","def app(request):\n _app = create_app(TestConfig)\n request.instance.app = _app\n\n ctx = _app.test_request_context()\n ctx.push()\n\n request.addfinalizer(ctx.pop)\n return _app","def get_test_app(relpath=None):\n from nucleon.config import settings\n\n app = get_app(relpath)\n settings._set_environment('test')\n on_initialise.fire()\n on_start.fire()\n return TestApp(app)","def create_app():\n logging.basicConfig(\n level=REANA_LOG_LEVEL,\n format=REANA_LOG_FORMAT\n )\n app = Flask(__name__)\n app.config.from_object('reana_server.config')\n app.secret_key = \"hyper secret key\"\n\n # Register API routes\n from .rest import ping, secrets, users, workflows # noqa\n app.register_blueprint(ping.blueprint, url_prefix='/api')\n app.register_blueprint(workflows.blueprint, url_prefix='/api')\n app.register_blueprint(users.blueprint, url_prefix='/api')\n app.register_blueprint(secrets.blueprint, url_prefix='/api')\n\n app.session = Session\n CORS(app)\n return app","def setUp(self):\n self.app = Flask(__name__)\n self.app_context = self.app.app_context()\n self.app_context.push()\n self.client = self.app.test_client()","def setUp(self):\n self.app = create_app('testing')\n self.client = self.app.test_client()","def create_app():\n app = Flask(__name__, instance_relative_config=False)\n # app.config.from_object('config.Config')\n file_path = os.path.abspath(os.getcwd())+\"/mpulse.db\"\n app.config.from_mapping(\n SECRET_KEY='dev',\n SQLALCHEMY_DATABASE_URI = 'sqlite:///'+file_path,\n SCHEMA=os.path.join(os.path.dirname(__file__), 'schema.sql'),\n SQLALCHEMY_TRACK_MODIFICATIONS = False,\n JSON_SORT_KEYS=False\n )\n\n # ensure the instance folder exists\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n \n # init database\n db.init_app(app)\n \n with app.app_context():\n \n # Create tables if they don't exist\n db.create_all() \n \n # Include our api Routes for members\n from . import members\n # Register Blueprints\n app.register_blueprint(members.bp)\n\n return app","def create_app(self):\n raise NotImplementedError","def test_client_factory():\n\tapp = make_app(blueprints.api_server, settings.TestingConfig)\n\treturn app.test_client()","def app():\n db_uri = '{0}_test'.format(settings.SQLALCHEMY_DATABASE_URI)\n params = {\n 'DEBUG': False,\n 'TESTING': True,\n 'WTF_CSRF_ENABLED': False,\n 'SQLALCHEMY_DATABASE_URI': db_uri\n }\n\n _app = create_app(settings_override=params)\n\n # Establish an application context before running the tests.\n ctx = _app.app_context()\n ctx.push()\n\n yield _app\n\n ctx.pop()","def create_app(testing=False, cli=False):\n app = Flask(__name__)\n app.config.from_object(\"flask_cli.config\")\n\n if testing is True:\n app.config[\"TESTING\"] = True\n\n configure_extensions(app, cli)\n configure_apispec(app)\n register_blueprints(app)\n return app","def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client","def create_app():\n app = Flask(__name__)\n app.config.from_object('app.configs.config')\n app.config.from_object('app.configs.settings')\n return app","def app():\n os.environ[\"FLASK_ENV\"] = \"test\"\n return create_app()","def create_app():\n\n # Create app\n app = Flask(__name__)\n app.config.from_object(\"nextbus.config.Config\")\n\n app.logger = logger.app_logger\n # Load logging configuration and log initial configuration\n logger.load_config(app)\n\n # Initialise SQLAlchemy and Migrate in app\n db.init_app(app)\n migrate.init_app(app, db)\n\n # Adding app, db and model objects to flask shell\n from nextbus import models\n app.shell_context_processor(\n lambda: {\"app\": app, \"db\": db, \"models\": models}\n )\n\n from nextbus.converters import add_converters\n add_converters(app)\n\n from nextbus.views import page\n from nextbus.resources import api\n app.register_blueprint(page)\n app.register_blueprint(api)\n\n return app","def create_app():\n app = Flask(__name__, instance_relative_config=False)\n app.register_blueprint(auth_bp, url_prefix='/auth')\n app.register_blueprint(errors_bp, url_prefix='/error')\n app.config.from_object('config.Config')\n\n db.init_app(app)\n store.bind(db)\n login_manager.init_app(app)\n Session(app)\n captcha = FlaskSessionCaptcha(app)\n captcha.init_app(app)\n\n\n with app.app_context():\n from . import routes # Import routes\n db.create_all() # Create sql tables for our data models\n\n return app","def application(self):\n if not ApplicationFixture._test_app:\n app = self.APP_CLASS()\n app.run_tests()\n ApplicationFixture._test_app = app\n return ApplicationFixture._test_app","def setup_app():\n\n # 1 Create Flask application\n app = Flask(\n import_name=__name__,\n template_folder=\"templates\",\n static_folder=\"static\"\n )\n\n # 2 Update the apps configuration\n app = config_selector(app)\n register_error_handlers(app)\n\n cache.init_app(app)\n\n # 3 Set up logger\n setup_logger(app.config)\n LOGGER.info(\"Set up app & logger.\")\n\n # 4 Init clients\n init_clients(app.config)\n\n # 5 Init Daemon\n start_daemon(app.config)\n\n # 6 Register blueprints\n register_blueprints(app)\n Bootstrap(app)\n\n return app","def test_11_create_application(self, mock):\r\n # Create an app as an anonymous user\r\n with self.flask_app.app_context():\r\n res = self.new_application(method=\"GET\")\r\n assert self.html_title(\"Sign in\") in res.data, res\r\n assert \"Please sign in to access this page\" in res.data, res\r\n\r\n res = self.new_application()\r\n assert self.html_title(\"Sign in\") in res.data, res.data\r\n assert \"Please sign in to access this page.\" in res.data, res.data\r\n\r\n # Sign in and create an application\r\n res = self.register()\r\n\r\n res = self.new_application(method=\"GET\")\r\n assert self.html_title(\"Create an Application\") in res.data, res\r\n assert \"Create the application\" in res.data, res\r\n\r\n res = self.new_application(long_description='My Description')\r\n assert \"<strong>Sample App</strong>: Update the application\" in res.data\r\n assert \"Application created!\" in res.data, res\r\n\r\n app = db.session.query(App).first()\r\n assert app.name == 'Sample App', 'Different names %s' % app.name\r\n assert app.short_name == 'sampleapp', \\\r\n 'Different names %s' % app.short_name\r\n\r\n assert app.long_description == 'My Description', \\\r\n \"Long desc should be the same: %s\" % app.long_description","def testapp():\n from webtest import TestApp\n from learning_journal import main\n\n app = main({}, **{'sqlalchemy.url': 'postgres:///amosboldor'})\n testapp = TestApp(app)\n\n session_factory = app.registry[\"dbsession_factory\"]\n engine = session_factory().bind\n Base.metadata.drop_all(bind=engine)\n Base.metadata.create_all(bind=engine)\n\n return testapp","def setUp(self):\n #app['TESTING'] = True\n self.test_app = app.test_client()","def create_app():\n app = Flask(__name__)\n\n\n @app.route('/')\n def barebones():\n return 'the barebones'\n\n return app","def app(config_path, dbtransaction, test_url):\n settings = get_appsettings(config_path)\n settings['sqlalchemy.url'] = test_url\n app = main({}, **settings)\n return TestApp(app)","def testapp(request):\n from webtest import TestApp\n from pyramid.config import Configurator\n\n def main(global_config, **settings):\n config = Configurator(settings=settings)\n config.include('pyramid_jinja2')\n config.include('.models')\n config.include('.routes')\n config.include('.security')\n config.scan()\n return config.make_wsgi_app()\n\n app = main({}, **{'sqlalchemy.url': TEST_DB})\n testapp = TestApp(app)\n\n SessionFactory = app.registry[\"dbsession_factory\"]\n engine = SessionFactory().bind\n Base.metadata.drop_all(engine)\n Base.metadata.create_all(bind=engine)\n\n return testapp","def create_app():\n\n app = FastAPI()\n add_root_route(app)\n\n return app","def init():\n server = Flask(__name__)\n \n return server","def setUpClass(cls):\n\n cls.client = TestWorkflow.start()\n\n cls.httpd = HTTPServer((\"127.0.0.1\", 8001), RequestHandler)\n\n server = Thread(target=cls.httpd.serve_forever)\n server.setDaemon(True)\n server.start()","def app():\n # create a temporary file to isolate the database for each test\n # create the app with common test config\n app = create_app({\"TESTING\": True, \"DATABASE_NAME\": \"AAPI_DB_Test\"})\n\n # create the database and load test data\n with app.app_context():\n init_db()\n\n yield app","def app(loop, initialize_test_env):\n async def dispose_test_env():\n async for key in EmaNews().redis.iscan(match=\"emanews:session:*\"):\n await EmaNews().redis.delete(key)\n\n singletons.destroy_all()\n c = Config()\n server = EmaNews(\n db_host=c[\"DB_HOST\"],\n db_port=c[\"DB_PORT\"],\n db_username=c[\"DB_USERNAME\"],\n db_password=c[\"DB_PASSWORD\"],\n db_database=c[\"TEST_DB_NAME\"],\n db_pool_minsize=c[\"DB_POOL_MIN_SIZE\"],\n db_pool_maxsize=c[\"DB_POOL_MAX_SIZE\"],\n redis_host=c[\"REDIS_HOST\"],\n redis_port=c[\"REDIS_PORT\"],\n redis_password=c[\"REDIS_PASSWORD\"],\n redis_database=c[\"REDIS_TEST_DATABASE\"],\n redis_pool_size=c[\"REDIS_POOL_SIZE\"],\n telegram_token=c[\"TELEGRAM_TOKEN\"],\n # mailgun_client=MailgunClient(\n # domain=c[\"MAILGUN_DOMAIN\"],\n # key=c[\"MAILGUN_KEY\"],\n # default_from=c[\"MAILGUN_DEFAULT_SENDER\"]\n # ) if c[\"MAILGUN_KEY\"] is not None else DummyMailgunClient(),\n mailgun_client=DummyMailgunClient(),\n debug=False\n )\n server.initialize()\n yield server\n loop.run_until_complete(dispose_test_env())\n loop.run_until_complete(server.dispose())","def setUp(self):\n self.api = \"http://localhost:4031/\"\n self.version = \"0.2\"\n self.app = init_api()","def init_app():\r\n LOG.info('Initialising web server.')\r\n app = web.Application(middlewares=[api_key()])\r\n app.router.add_routes(routes)\r\n set_cors(app)\r\n app.on_startup.append(init_db)\r\n app.on_cleanup.append(close_db)\r\n return app","def test_app_is_created(app):\n assert app.name == \"myapp.app\"","def prepare_app():\n application = service.Application('buildbot-worker')\n master = (GATEWAY\n if conf.Washer.FORCE_GATEWAY\n else conf.Buildbot.BUILDMASTER)\n worker = Worker(master,\n conf.Buildbot.BUILDMASTER_PORT,\n conf.Buildbot.WORKERNAME,\n conf.Buildbot.WORKERPASS,\n conf.Buildbot.BASEDIR,\n conf.Buildbot.KEEPALIVE,\n umask=None,\n maxdelay=conf.Buildbot.MAXDELAY,\n numcpus=None,\n allow_shutdown=None,\n maxRetries=None)\n worker.setServiceParent(application)\n\n class InlineApplication(UnixApplicationRunner):\n def createOrGetApplication(self):\n nonlocal application\n return application\n\n options = ServerOptions()\n options[\"nodaemon\"] = not conf.Washer.DAEMON\n options[\"logfile\"] = conf.Washer.LOG_FILE\n\n commands.register()\n\n return InlineApplication(options)","def standalone():\n parser = argparse.ArgumentParser(\n description=\"Start Argo Workflow API Dispatch Server\"\n )\n parser.add_argument(\n \"-p\", \"--port\", help=\"Server listening port\", type=int, default=8080\n )\n parser.add_argument(\n \"-b\", \"--bind-address\", help=\"Server bind address\", default=\"127.0.0.1\"\n )\n parser.add_argument(\n \"-m\",\n \"--mock\",\n \"--mock-authentication\",\n action=\"store_true\",\n help=\"Do not require a JWT; mock out authentication\",\n )\n parser.add_argument(\n \"--no-verify-signature\",\n action=\"store_true\",\n help=\"Do not verify JWT signature\",\n )\n parser.add_argument(\n \"--no-verify-audience\",\n action=\"store_true\",\n help=\"Do not verify JWT audience\",\n )\n args = parser.parse_args()\n mock = args.mock\n v_s = True\n v_a = True\n if args.no_verify_signature:\n v_s = False\n if args.no_verify_audience:\n v_a = False\n server = Server(_mock=mock, verify_signature=v_s, verify_audience=v_a)\n httpd = WSGIServer(server.app, host=args.bind_address, port=args.port)\n httpd.start()","def app(self):\n app = Flask('testapp')\n app.config.update({\n 'HADES_CELERY_APP_NAME': 'test',\n 'HADES_BROKER_URI': 'rpc://broker/',\n 'HADES_RESULT_BACKEND_URI': 'rpc://backend/',\n })\n return app","def create_app():\n\n config = config_by_name[os.getenv('APP_SETTINGS', 'dev')]\n flask_app = Flask(__name__, static_folder=None, instance_relative_config=True)\n flask_app.config.from_object(config)\n with flask_app.app_context():\n app_manager = Manager(flask_app)\n\n from app.models import db, ma\n db.init_app(flask_app)\n Migrate(flask_app, db)\n app_manager.add_command('db', MigrateCommand)\n ma.init_app(flask_app)\n\n from app.service import mail\n mail.init_app(flask_app)\n\n from app.api import blueprint_api\n flask_app.register_blueprint(blueprint_api)\n\n json_logging.ENABLE_JSON_LOGGING = True\n json_logging.COMPONENT_NAME = 'MS-Auth'\n json_logging.COMPONENT_ID = 1\n json_logging.init(framework_name='flask')\n json_logging.init_request_instrument(flask_app)\n\n return flask_app, app_manager","def app(request): \n\n # app instance\n app = create_app(\"testing_app\", {\n 'API_BASE_PATH': \"/api/\",\n 'TESTING': True, \n 'SECRET_KEY': 'test', \n })\n\n # add to the scope\n ctx = app.app_context()\n ctx.push()\n\n def teardown(): \n init_db()\n ctx.pop() \n\n init_db()\n\n request.addfinalizer(teardown)\n yield app \n \n drop_db()","def make_server(host, port, app, server_class=WSGIServer, handler_class=WSGIRequestHandler):\n server = server_class((host, port), handler_class)\n server.set_app(app)\n return server","async def create_app(dev: bool = True) -> web.Application:\n app = web.Application()\n setup_jinja2(app)\n routes_setup(app, dev)\n if not dev:\n # not a development server - we need to run directly\n parser = argparse.ArgumentParser()\n parser.add_argument('--port', default=4000)\n parser.add_argument('--host', default='0.0.0.0')\n parser.add_argument('--path', default='/tmp/navigation_editor')\n server_cfg = vars(parser.parse_args())\n web.run_app(\n app,\n host=server_cfg['host'],\n port=int(server_cfg['port']),\n path=server_cfg['path'])\n return app","def setup():\n global server, app\n \n galaxy_test_host = os.environ.get( 'GALAXY_TEST_HOST', default_galaxy_test_host )\n galaxy_test_port = os.environ.get( 'GALAXY_TEST_PORT', default_galaxy_test_port )\n \n start_server = 'GALAXY_TEST_EXTERNAL' not in os.environ \n \n if start_server:\n \n tempdir = tempfile.mkdtemp()\n file_path = os.path.join( tempdir, 'database', 'files' )\n os.makedirs( file_path )\n if 'GALAXY_TEST_DBURI' in os.environ:\n database_connection = os.environ['GALAXY_TEST_DBURI']\n else:\n database_connection = 'sqlite:///' + os.path.join( tempdir, 'database', 'universe.sqlite' )\n \n app = UniverseApplication( job_queue_workers = 5,\n template_path = \"templates\",\n database_connection = database_connection,\n file_path = file_path,\n tool_config_file = \"tool_conf.xml\",\n tool_path = \"tools\",\n test_conf = \"test.conf\",\n log_destination = \"stdout\",\n use_heartbeat=True )\n \n log.info( \"Embedded Universe application started\" )\n\n webapp = universe_wsgi.app_factory( dict(),\n use_translogger = False,\n app=app )\n\n server = galaxy.web.server.serve( webapp, dict(), \n host=galaxy_test_host, \n port=galaxy_test_port, \n start_loop=False )\n \n atexit.register( teardown )\n \n import threading\n t = threading.Thread( target=server.serve_forever )\n t.start()\n\n time.sleep( 2 )\n \n log.info( \"Embedded web server started\" )\n \n if app:\n # TODO: provisions for loading toolbox from file when using external server\n import test_toolbox\n test_toolbox.toolbox = app.toolbox\n else:\n from galaxy import tools\n import test_toolbox\n test_toolbox.toolbox = tools.ToolBox( 'tool_conf.xml', 'tools' )\n \n # Test if the server is up\n import httplib\n conn = httplib.HTTPConnection( galaxy_test_host, galaxy_test_port )\n conn.request( \"GET\", \"/\" )\n assert conn.getresponse().status == 200, \"Test HTTP server did not return '200 OK'\"\n \n os.environ['GALAXY_TEST_HOST'] = galaxy_test_host\n os.environ['GALAXY_TEST_PORT'] = galaxy_test_port\n os.environ['GALAXY_TEST_FILE_DIR'] = galaxy_test_file_dir","def app_instance(scope=\"session\"):\n app = create_app(\"test\")\n with app.app_context():\n db.create_all()\n yield app\n db.drop_all()","def client(application):\n\n return app.test_client()","def client(app): # pylint: disable=redefined-outer-name\n yield TestApp(app)","def create_app():\n # Creates flask object with directory for to serve static files\n app = Flask(__name__, static_url_path=C.STATIC_FILE_PATH)\n\n # Enabling CORS for the application\n CORS(app)\n\n app.debug = True\n # Registering books controller\n from app.controllers.books import mod\n app.register_blueprint(mod)\n\n # Test Route\n @app.route('/hello')\n def hello_world():\n return 'Hello World!'\n\n # Index route - serves index.html\n @mod.route('/')\n def main():\n return mod.send_static_file(\"index.html\")\n\n # serve routes from index by default\n app.add_url_rule('/', endpoint='index')\n\n return app","def setUp(self):\n config.ENV = \"testing\"\n self.app = init_app()\n self.client = self.app.test_client\n with self.app.app_context():\n db.drop_all()\n db.create_all()\n for actor in test.populate_db.sample_actors:\n Actor(**actor).insert()\n\n for movie in test.populate_db.sample_movies:\n Movie(**movie).insert()\n return self.app\n # binds the app to the current context","def setUp(self):\r\n self.app = app.test_client()\r\n self.app.testing = True"],"string":"[\n \"def create_app(test_config=None):\\n app = Flask(__name__, instance_relative_config=True)\\n app.config.from_mapping(\\n SECRET_KEY=os.environ.get('FLASK_SECRET_KEY', 'dev'),\\n # SQLALCHEMY_DATABASE_URI='sqlite:////' + os.path.join(app.instance_path, 'app.sqlite'),\\n SQLALCHEMY_DATABASE_URI=os.environ.get('FLASK_SQLALCHEMY_DATABASE_URI'),\\n SQLALCHEMY_TRACK_MODIFICATIONS=False,\\n )\\n\\n if test_config is None:\\n app.config.from_pyfile('config.py', silent=True)\\n else:\\n app.config.from_mapping(test_config)\\n\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n\\n # Set custom json encoder\\n app.json_encoder = JSONEncoder\\n\\n # SQLAlchemy\\n from tuinbouwer_server_api.models import db, migrate\\n db.init_app(app)\\n migrate.init_app(app, db)\\n\\n # Apscheduler\\n from tuinbouwer_server_api.scheduler import scheduler, start_jobs\\n scheduler.init_app(app)\\n scheduler.start()\\n start_jobs()\\n \\n # CORS\\n CORS(app, resources={r'/*': {'origins': '*'}})\\n\\n # Website\\n app.register_blueprint(website.frontend.blueprint)\\n \\n # API\\n app.register_blueprint(api.sensor.blueprint)\\n app.register_blueprint(api.frontend.blueprint)\\n\\n\\n return app\",\n \"def create_embedded():\\n from .server import create_application\\n return create_application()\",\n \"def create_and_run():\\n\\n app = App()\\n app.run()\",\n \"def create_app(test_config=None) -> Flask:\\n app = Flask(\\\"front-end-web-server\\\", instance_relative_config=True)\\n\\n if test_config is None:\\n # Get path to configuration file and load that configuration into Flask's app.config object.\\n curr_dir = os.path.abspath(os.path.dirname(__file__))\\n config_dir = os.path.dirname(curr_dir)\\n config_filename = os.path.join(config_dir, \\\"config.py\\\")\\n\\n # load the instance config, if it exists, when not testing\\n app.config.from_pyfile(config_filename)\\n\\n print(app.config.get(\\\"SECRET_KEY\\\"))\\n else:\\n # load the test config if passed in\\n app.config.from_mapping(test_config)\\n\\n # ensure the instance folder exists\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n\\n # a simple page that says hell\\n @app.route(\\\"/\\\")\\n def index():\\n return \\\"Index page!\\\"\\n\\n from .views import hello\\n\\n app.register_blueprint(hello)\\n\\n return app\",\n \"def create_app(test_config=\\\"test_config.py\\\"):\\n app = Flask(__name__, instance_relative_config=True)\\n\\n # set common config values\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n\\n # if not testing, config is loaded from config.py in the instance folder\\n if test_config is None:\\n app.config.from_pyfile(\\\"config.py\\\")\\n else:\\n # whichever config file name you pass in also has to be in the instance folder\\n app.config.from_pyfile(test_config)\\n\\n db.init_app(app)\\n login_manager.init_app(app)\\n bootstrap.init_app(app)\\n migrate.init_app(app, db)\\n mail.init_app(app)\\n app.redis = Redis.from_url(REDIS_URL)\\n app.task_queue = rq.Queue(\\\"yamz-tasks\\\", connection=app.redis)\\n app.elasticsearch = Elasticsearch(ELASTICSEARCH_URL)\\n\\n # apply the blueprints to the app\\n from .main import main as main_blueprint\\n\\n app.register_blueprint(main_blueprint)\\n\\n from .auth import auth as auth_blueprint\\n\\n app.register_blueprint(auth_blueprint)\\n\\n from .term import term as term_blueprint\\n\\n app.register_blueprint(term_blueprint, url_prefix=\\\"/term\\\")\\n\\n from .graph import graph as graph_blueprint\\n\\n app.register_blueprint(graph_blueprint, url_prefix=\\\"/graph\\\")\\n\\n # register command line functions\\n @app.cli.command()\\n def test():\\n \\\"\\\"\\\"Run the unit tests.\\\"\\\"\\\"\\n import unittest\\n\\n tests = unittest.TestLoader().discover(\\\"tests\\\")\\n unittest.TextTestRunner(verbosity=2).run(tests)\\n\\n return app\",\n \"def test_create(self):\\n self.app\\n pass\",\n \"def CreateApp(test_config=None):\\n app = Flask(__name__, instance_relative_config=True)\\n\\n if test_config:\\n pass\\n\\n @app.route('/validate', methods=['POST'])\\n def Validate(): # pylint: disable=unused-variable\\n # TODO(dberris): Implement this!\\n return ''\\n\\n @app.route('/service-metadata')\\n def ServiceMetadata(): # pylint: disable=unused-variable\\n # TODO(dberris): Implement this!\\n return ''\\n\\n return app\",\n \"def create_app(test_config: dict = None) -> Flask:\\n\\n # create and configure the app\\n app = Flask(__name__, instance_relative_config=True)\\n\\n # Load in the configuration\\n if test_config is None:\\n # load the instance config, if it exists, when not testing\\n app.config.from_pyfile(os.path.join(os.path.dirname(__file__), 'config.py'), silent=True)\\n print(app.config)\\n else:\\n # load the test config if passed in\\n app.config.from_mapping(test_config)\\n\\n # Make the initial status page\\n start_time = datetime.now().isoformat()\\n\\n @app.route('/')\\n def home():\\n return render_template('home.html',\\n message=f'Running on {platform.node()} since {start_time}')\\n\\n # Load the blueprints\\n from .views import ingest\\n app.register_blueprint(ingest.bp)\\n\\n return app\",\n \"def setUp(self):\\n _, instance_path, shared_inputs = sys.argv\\n app = lnt.server.ui.app.App.create_standalone(instance_path)\\n app.testing = True\\n self.client = app.test_client()\\n self.shared_inputs = shared_inputs\",\n \"def create_app(test_config=None):\\n app = Flask(__name__,\\n instance_relative_config=True,\\n static_folder=\\\"resources\\\")\\n app.config.from_mapping(\\n SECRET_KEY=\\\"dev\\\",\\n SQLALCHEMY_DATABASE_URI=\\\"sqlite:///\\\" + os.path.join(\\n app.instance_path, \\\"database.sqlite\\\"),\\n SQLALCHEMY_TRACK_MODIFICATIONS=False,\\n API_DOMAIN=\\\"http://localhost:5000\\\"\\n )\\n if test_config is None:\\n # load the instance config, if it exists, when not testing\\n app.config.from_pyfile(\\\"config.py\\\", silent=True)\\n else:\\n # load the test config if passed in\\n app.config.from_mapping(test_config)\\n\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n\\n # Initialize DB\\n db.init_app(app)\\n db.create_all(app=app)\\n\\n # Initialize Marshmallow Serialization\\n ma.init_app(app)\\n\\n # Initialize Blueprints\\n from . import local\\n app.register_blueprint(local.bp)\\n\\n @app.route(\\\"/\\\", defaults={\\\"path\\\": \\\"\\\"})\\n @app.route(\\\"/<path:path>\\\")\\n def index(path):\\n return render_template(\\n \\\"index.html\\\", api_domain=app.config.get(\\\"API_DOMAIN\\\")\\n )\\n\\n return app\",\n \"def create_local_app():\\n app = create_app()\\n app.test_request_context().push()\\n return app\",\n \"def create_app(test_config=None):\\n\\n app = Flask(__name__, instance_relative_config=True)\\n\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n\\n if test_config is None:\\n app.config.from_pyfile('config.py', silent=False)\\n else:\\n app.config.from_mapping(test_config)\\n\\n # Instantiate the Database.\\n db.init_app(app=app)\\n migrate = Migrate(app=app, db=db)\\n\\n # Initialize WTForms to handle JSON data.\\n wtforms_json.init()\\n\\n # Routing starts from here.\\n app.add_url_rule(\\\"/\\\", view_func=Home.as_view(\\\"home\\\"))\\n app.register_blueprint(api)\\n\\n return app\",\n \"def create_app(self):\\n\\n app = create_app()\\n app.config.from_object('project.config.TestingConfig')\\n return app\",\n \"def create(cls, application, **kwargs):\\n host, port = get_free_port()\\n if 'port' not in kwargs:\\n kwargs['port'] = port\\n if 'host' not in kwargs:\\n kwargs['host'] = host\\n if 'expose_tracebacks' not in kwargs:\\n kwargs['expose_tracebacks'] = True\\n server = cls(application, **kwargs)\\n server.runner = threading.Thread(target=server.run)\\n server.runner.daemon = True\\n server.runner.start()\\n return server\",\n \"def testapp():\\n from space_rocks import main\\n app = main({})\\n from webtest import TestApp\\n return TestApp(app)\",\n \"def create_server(self, test_server):\\n debug.virtual('wxMediator.create_server')\",\n \"def create_app(self):\\n app.config.from_object('config.TestingConfig')\\n return app\",\n \"def app():\\n print('creating app with test vars')\\n\\n app = create_app('test')\\n app.testing = True\\n\\n ctx = app.app_context()\\n ctx.push()\\n yield app\\n\\n ctx.pop()\",\n \"def create_app():\\n app = FastAPI()\\n configure_rest_server(app=app, router_configs=WEB_SERVICES_ROUTER_CONFIGS, db_configs=DB_CONFIGS)\\n return app\",\n \"def create_app(test_config=None):\\n app = Flask(__name__, instance_relative_config=True)\\n app.config.from_mapping(\\n # a default secret that should be overridden by instance config\\n SECRET_KEY=\\\"dev\\\",\\n # store the database in the instance folder\\n DATABASE=os.path.join(app.instance_path, \\\"sqlite\\\"),\\n )\\n\\n if test_config is None:\\n # load the instance config, if it exists, when not testing\\n app.config.from_pyfile(\\\"config.py\\\", silent=True)\\n else:\\n # load the test config if passed in\\n app.config.update(test_config)\\n\\n # ensure the instance folder exists\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n\\n @app.route(\\\"/hello\\\")\\n def hello():\\n return \\\"Hello, World!\\\"\\n\\n # register the database commands\\n from music_recommender import db\\n\\n db.init_app(app)\\n\\n # apply the blueprints to the app\\n from music_recommender import auth\\n\\n app.register_blueprint(auth.bp)\\n\\n # make url_for('index') == url_for('body.index')\\n # in another app, you might define a separate main index here with\\n # app.route, while giving the body blueprint a url_prefix, but for\\n # the tutorial the body will be the main index\\n app.add_url_rule(\\\"/\\\", endpoint=\\\"index\\\")\\n\\n return app\",\n \"def application():\\n yield create_test_application()\",\n \"def app():\\n return create_app()\",\n \"def test_create_app():\\n assert not create_app().testing\\n assert create_app({'TESTING': True}).testing\",\n \"def make_server() -> Flask:\\n app: Flask = Flask(__name__)\\n return app\",\n \"def test_create_app():\\n app = web.YumWeb(__name__)\\n assert app.name == 'web_tests'\",\n \"def CreateApp(test_config=None):\\n app = Flask(__name__, instance_relative_config=True)\\n _ = Talisman(app)\\n\\n environ = os.environ if test_config is None else test_config.get(\\n 'environ', {})\\n\\n # We can set up a preconfigured HTTP instance from the test_config, otherwise\\n # we'll use the default auth for the production environment.\\n client_config = {}\\n if test_config:\\n client_config['http'] = test_config.get('http')\\n else:\\n client_config['credentials'], _ = google.auth.default()\\n\\n # In the python37 environment, we need to synthesize the URL from the\\n # various parts in the environment variable, because we do not have access\\n # to the appengine APIs in the python37 standard environment.\\n domain_parts = {\\n 'app_id': environ.get('GOOGLE_CLOUD_PROJECT', ''),\\n 'service': environ.get('GAE_SERVICE', ''),\\n }\\n empty_keys = [k for k, v in domain_parts.items() if len(v) == 0]\\n if len(empty_keys):\\n raise MissingEnvironmentVars(empty_keys)\\n domain = '{parts[service]}-dot-{parts[app_id]}.appspot.com'.format(\\n parts=domain_parts)\\n\\n # We create an instance of the luci-config and Auth client, which we'll use\\n # in all requests handled in this application.\\n config_client = service_client.CreateServiceClient(\\n 'https://luci-config.appspot.com/_ah/api', 'config', 'v1',\\n **client_config)\\n auth_client = service_client.CreateServiceClient(\\n 'https://chrome-infra-auth.appspot.com/_ah/api', 'auth', 'v1',\\n **client_config)\\n\\n # First we check whether the test_config already has a predefined\\n # datastore_client.\\n if test_config:\\n datastore_client = test_config.get('datastore_client')\\n else:\\n datastore_client = datastore.Client()\\n\\n @app.route('/service-metadata')\\n def ServiceMetadata(): # pylint: disable=unused-variable\\n return jsonify({\\n 'version': '1.0',\\n 'validation': {\\n 'patterns': [{\\n 'config_set': 'regex:projects/.+',\\n 'path': 'regex:chromeperf-sheriffs.cfg'\\n }],\\n 'url': 'https://%s/configs/validate' % (domain)\\n }\\n })\\n\\n @app.route('/warmup')\\n def Warmup(): # pylint: disable=unused-variable\\n \\\"\\\"\\\"Caching configs and compiled patterns for warmup.\\\"\\\"\\\"\\n configs = luci_config.ListAllConfigs(\\n datastore_client, _cache_timestamp=time.time() + 10)\\n for _, revision, subscription in configs:\\n luci_config.GetMatcher(revision, subscription)\\n return jsonify({})\\n\\n @app.route('/configs/validate', methods=['POST'])\\n def Validate(): # pylint: disable=unused-variable\\n validation_request = request.get_json()\\n if validation_request is None:\\n return u'Invalid request.', 400\\n for member in ('config_set', 'path', 'content'):\\n if member not in validation_request:\\n return u'Missing \\\\'%s\\\\' member in request.' % (member), 400\\n try:\\n _ = validator.Validate(\\n base64.standard_b64decode(validation_request['content']))\\n except validator.Error as error:\\n return jsonify({\\n 'messages': [{\\n 'path': validation_request['path'],\\n 'severity': 'ERROR',\\n 'text': '%s' % (error)\\n }]\\n })\\n return jsonify({})\\n\\n @app.route('/configs/update')\\n def UpdateConfigs(): # pylint: disable=unused-variable\\n \\\"\\\"\\\"Poll the luci-config service.\\\"\\\"\\\"\\n try:\\n configs = luci_config.FindAllSheriffConfigs(config_client)\\n luci_config.StoreConfigs(datastore_client, configs.get('configs', []))\\n return jsonify({})\\n except (luci_config.InvalidConfigError,\\n luci_config.InvalidContentError) as error:\\n logging.warning('loading configs from luci-config failed: %s', error)\\n return jsonify({}), 500\\n\\n @app.route('/subscriptions/match', methods=['POST'])\\n def MatchSubscriptions(): # pylint: disable=unused-variable\\n \\\"\\\"\\\"Match the subscriptions given the request.\\n\\n This is an API handler, which requires that we have an authenticated user\\n making the request. We'll require that the user be a service account\\n (from the main dashboard service).\\n\\n TODO(fancl): Allow access from an authenticated user. We'll enforce\\n that we're only serving \\\"public\\\" Subscriptions to non-privileged users.\\n \\\"\\\"\\\"\\n\\n try:\\n match_request = json_format.Parse(request.get_data(),\\n sheriff_config_pb2.MatchRequest())\\n except json_format.ParseError as error:\\n return jsonify(\\n {'messages': [{\\n 'severity': 'ERROR',\\n 'text': '%s' % (error)\\n }]}), 400\\n match_response = sheriff_config_pb2.MatchResponse()\\n configs = list(\\n luci_config.FindMatchingConfigs(datastore_client, match_request))\\n configs = match_policy.FilterSubscriptionsByPolicy(match_request, configs)\\n for config_set, revision, subscription in configs:\\n subscription_metadata = match_response.subscriptions.add()\\n subscription_metadata.config_set = config_set\\n subscription_metadata.revision = revision\\n luci_config.CopyNormalizedSubscription(\\n subscription,\\n subscription_metadata.subscription,\\n )\\n\\n # Then we find one anomaly config that matches.\\n matcher = luci_config.GetMatcher(revision, subscription)\\n anomaly_config = matcher.GetAnomalyConfig(match_request.path)\\n if anomaly_config:\\n subscription_metadata.subscription.anomaly_configs.append(\\n anomaly_config)\\n if not match_response.subscriptions:\\n return jsonify({}), 404\\n return (json_format.MessageToJson(\\n match_response, preserving_proto_field_name=True), 200, {\\n 'Content-Type': 'application/json'\\n })\\n\\n @app.route('/subscriptions/list', methods=['POST'])\\n def ListSubscriptions(): # pylint: disable=unused-variable\\n \\\"\\\"\\\"List all visible subscriptions based on identity.\\n\\n This is an API handler, which requires that we have an authenticated user\\n making the request. We'll require that the user be a service account\\n (from the main dashboard service).\\n\\n \\\"\\\"\\\"\\n\\n try:\\n list_request = json_format.Parse(request.get_data(),\\n sheriff_config_pb2.ListRequest())\\n except json_format.ParseError as error:\\n return jsonify(\\n {'messages': [{\\n 'severity': 'ERROR',\\n 'text': '%s' % (error)\\n }]}), 400\\n list_response = sheriff_config_pb2.ListResponse()\\n configs = list(luci_config.ListAllConfigs(datastore_client))\\n configs = match_policy.FilterSubscriptionsByIdentity(\\n auth_client, list_request, configs)\\n for config_set, revision, subscription in configs:\\n subscription_metadata = list_response.subscriptions.add()\\n subscription_metadata.config_set = config_set\\n subscription_metadata.revision = revision\\n luci_config.CopyNormalizedSubscription(subscription,\\n subscription_metadata.subscription)\\n return (json_format.MessageToJson(\\n list_response, preserving_proto_field_name=True), 200, {\\n 'Content-Type': 'application/json'\\n })\\n\\n return app\",\n \"def create_app(test_config=None):\\n app = Flask(__name__, instance_relative_config=True)\\n CORS(app)\\n app.config.from_mapping(\\n SQLALCHEMY_ECHO=False,\\n SQLALCHEMY_TRACK_MODIFICATIONS=False,\\n PROPAGATE_EXCEPTIONS=True,\\n SQLALCHEMY_DATABASE_URI=os.environ['DATABASE'],\\n JWT_SECRET_KEY='sad32du834gfjуо21?sdfja<<dfsj3ha!@Dsak',#os.urandom(24),\\n RESTFUL_JSON={'ensure_ascii': False},\\n JSON_AS_ASCII=False\\n )\\n\\n db.init_app(app)\\n\\n with app.app_context():\\n jwt = JWTManager(app)\\n for res, endpoint in admin_resources:\\n app.route('/admin' + endpoint)(res)\\n moment = Moment(app)\\n app.jinja_env.globals.update(offer_format=offer_format)\\n\\n api = Api(app, default_mediatype='application/json; charset=utf-8')\\n\\n for res, endpoint in resources:\\n api.add_resource(res, '/api' + endpoint)\\n return app\",\n \"def setUp(self) -> None:\\n self.workdir = tempfile.mkdtemp()\\n self.server_name = 'fooserver.localdomain'\\n self.app = create_web_app()\\n self.app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'\\n self.app.config['SERVER_NAME'] = self.server_name\\n self.app.config['STORAGE_BASE_PATH'] = self.workdir\\n\\n # There is a bug in arxiv.base where it doesn't pick up app config\\n # parameters. Until then, we pass it to os.environ.\\n os.environ['JWT_SECRET'] = self.app.config.get('JWT_SECRET')\\n self.client = self.app.test_client()\\n # self.app.app_context().push()\\n with self.app.app_context():\\n database.db.create_all()\\n\\n with open('schema/resources/Workspace.json') as f:\\n self.schema = json.load(f)\",\n \"def app():\\n app = create_app()\\n return app\",\n \"def app() -> Generator:\\n app = create_app({\\\"TESTING\\\": True})\\n\\n yield app\",\n \"def create_app(test_config=None):\\n app = Flask(__name__, instance_relative_config=True)\\n\\n if app.config[\\\"ENV\\\"] == \\\"production\\\":\\n app.config.from_object(\\\"config.ProductionConfig\\\")\\n else:\\n app.config.from_object(\\\"config.DevelopmentConfig\\\")\\n\\n if app.config['SECRET_KEY'] is None:\\n raise Exception(\\n \\\"SECRET_KEY can't be None. Try to generate one by command: python -c 'import os; print(os.urandom(16))', and copy the result into configs.py.\\\")\\n\\n if app.config['OWNER_USER_ID'] is None:\\n raise Exception(\\n \\\"OWNER_USER_ID can't be None. It is an integer user_id of your Clubhouse account, you can get it from token json file generated by OpenClubhouse-worker\\\")\\n\\n @app.route(\\\"/alive\\\")\\n def alive():\\n return {\\\"alive\\\": True}\\n\\n db = MongoEngine(app)\\n channelsCache.init_cache(app.logger)\\n\\n # apply the blueprints to the app\\n from handlers import clubhouse\\n\\n app.register_blueprint(clubhouse.bp)\\n # in another app, you might define a separate main index here with\\n # app.route, while giving the blog blueprint a url_prefix, but for\\n # the tutorial the blog will be the main index\\n app.add_url_rule(\\\"/\\\", endpoint=\\\"index\\\")\\n\\n return app\",\n \"def pytest_configure() -> None: # pragma: no cover\\n print(\\\"Starting server app\\\")\\n PROC.start()\\n time.sleep(1)\\n if PROC.exitcode is not None:\\n pytest.exit(\\\"Failed to start the server, exit code {}\\\\nLogs are in logs/server.log\\\".format(PROC.exitcode))\\n return\\n\\n create_generated_client()\",\n \"def create():\\n\\n return App()\",\n \"def create_app():\\n from server.web import create_app\\n # If we do a static javascript app via flask, add it here\\n # from server.web import create_app as create_web_app\\n return create_app()\",\n \"def _create_app_instance(script_info):\\n return create_app()\",\n \"def create_app(self):\\r\\n self.app = Flask(__name__, instance_relative_config=True)\\r\\n\\r\\n # Init the secret key of the app -it is a must for flask to run\\r\\n self.app.config.from_mapping(\\r\\n SECRET_KEY='!ZNeverSayNever116Z!',\\r\\n MONGODB_SETTINGS= {'host': 'mongodb://localhost/opc_integrity'}\\r\\n )\\r\\n initialize_db(self.app)\\r\\n\\r\\n\\r\\n # Init the app with core routes\\r\\n routes.init_app(self.app)\",\n \"def create_app(test_config=None):\\n app = Flask(__name__)\\n\\n # apply the blueprints to the app\\n from app import common\\n\\n app.register_blueprint(common.bp)\\n\\n # default url for site\\n app.add_url_rule(\\\"/\\\", endpoint=\\\"index\\\")\\n\\n return app\",\n \"def create_server(self, test_server):\\n factory = tcp_server.AppStateFactorySimple()\\n return tcp_server.ServerNewMediator(data_events = self,\\n test_server = test_server,\\n editor_factory = factory)\",\n \"def make_app(controller_klass=None, environ=None):\\n if controller_klass is None:\\n controller_klass = TGController\\n\\n tg.config['renderers'] = default_config['renderers']\\n app = TGApp(config=default_config)\\n app.controller_classes['root'] = ControllerWrap(controller_klass)\\n\\n app = FakeRoutes(app)\\n\\n app = RegistryManager(app)\\n app = beaker.middleware.SessionMiddleware(app, {}, data_dir=session_dir)\\n app = CacheMiddleware(app, {}, data_dir=os.path.join(data_dir, 'cache'))\\n return TestApp(app)\",\n \"def create_app(test_config=None):\\n app = Flask(__name__, instance_relative_config=True)\\n app.config.from_mapping(\\n # a default secret that should be overridden by instance config\\n SECRET_KEY=\\\"dev\\\",\\n # store the database in the instance folder\\n # DATABASE=os.path.join(app.instance_path, \\\"flaskr.sqlite\\\"),\\n )\\n\\n # if test_config is None:\\n # # load the instance config, if it exists, when not testing\\n # app.config.from_pyfile(\\\"config.py\\\", silent=True)\\n # else:\\n # # load the test config if passed in\\n # app.config.update(test_config)\\n\\n # ensure the instance folder exists\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n\\n @app.route(\\\"/\\\", methods=[\\\"GET\\\"])\\n def test_route():\\n return \\\"<h1>This is a test route!<h1><p>Use /account or /auth</p>\\\"\\n\\n # register the database commands\\n # from flaskr import db\\n\\n # db.init_app(app)\\n\\n # apply the blueprints to the app\\n from dashr import auth, dash\\n\\n app.register_blueprint(auth.bp)\\n app.register_blueprint(dash.bp)\\n\\n # make url_for('index') == url_for('blog.index')\\n # in another app, you might define a separate main index here with\\n # app.route, while giving the blog blueprint a url_prefix, but for\\n # the tutorial the blog will be the main index\\n app.add_url_rule(\\\"/\\\", endpoint=\\\"auth.login\\\")\\n\\n return app\",\n \"def test_app():\\n param = {\\n 'DEBUG': True,\\n 'TESTING': True\\n }\\n _app = create_app(settings_override=param)\\n\\n ctx = _app.app_context()\\n ctx.push()\\n yield _app\\n ctx.pop()\",\n \"def run_server():\\n app = init_app()\\n app.run(host=app.config['HOST'], port=app.config['PORT'])\",\n \"def create_app():\\n app = Flask(__name__, instance_relative_config=False)\\n app.config.from_object('config.Config')\\n db.init_app(app)\\n ma.init_app(app)\\n migrate = Migrate(app, db)\\n\\n with app.app_context():\\n from . import routes\\n\\n # Create tables for our models\\n db.create_all()\\n app.logger.info(\\\"application started\\\")\\n\\n return app\",\n \"def create_app() -> Flask:\\r\\n app = Flask(__name__.split('.')[0])\\r\\n init_config(app)\\r\\n app.register_blueprint(observer)\\r\\n app.teardown_appcontext(close_db)\\r\\n app.cli.add_command(init_db)\\r\\n\\r\\n return app\",\n \"def create_app(instance_path=None, test_config=None):\\n\\n # create and configure the bit-wallet\\n app = Flask(__name__, instance_path=instance_path)\\n app.jinja_options[\\\"autoescape\\\"] = None\\n app.config.from_mapping(\\n SECRET_KEY='dev',\\n UPLOAD_FOLDER='upload',\\n MYSQL_HOST=os.getenv('MYSQL_HOST'),\\n MYSQL_USER=os.getenv('MYSQL_USER'),\\n MYSQL_PASSWORD=os.getenv('MYSQL_PASSWORD'),\\n MYSQL_DATABASE=os.getenv('MYSQL_DATABASE'),\\n )\\n\\n if test_config is not None:\\n # load the test config if passed in\\n app.config.from_mapping(test_config)\\n\\n # ensure the instance folder and the UPLOAD_FOLDER folder exist\\n try:\\n os.makedirs(os.path.join(app.instance_path, app.config['UPLOAD_FOLDER']))\\n except OSError:\\n pass\\n\\n db.init_app(app)\\n\\n app.register_blueprint(auth.bp)\\n app.register_blueprint(blog.bp)\\n app.register_blueprint(account.bp)\\n app.add_url_rule('/', endpoint='index')\\n\\n return app\",\n \"def __create_test_environment(self):\\n os.chdir(self.wd)\\n temp_dir = tempfile.gettempdir()\\n self.test_root = os.path.join(temp_dir, \\\"test-grpc\\\")\\n print(\\\"Creating testing environment in {}\\\".format(self.test_root))\\n if os.path.exists(self.test_root):\\n # delete any previous environment\\n shutil.rmtree(self.test_root)\\n # create root directory\\n os.makedirs(self.test_root)\\n def copy_app(name):\\n app_root = os.path.join(self.test_root, name)\\n os.makedirs(app_root)\\n filename = \\\"grpc-{}\\\".format(name)\\n src = os.path.join(self.args.bin, filename)\\n dst = os.path.join(app_root, filename)\\n shutil.copy(src, dst)\\n return dst\\n # copy client and server into the new test environment\\n self.server_path = copy_app(\\\"server\\\")\\n self.client_path = copy_app(\\\"client\\\")\",\n \"def test_server(loop):\\n\\n servers = []\\n\\n async def create_server(app, **kwargs):\\n server = TestServer(app, **kwargs)\\n await server.start_server()\\n servers.append(server)\\n return server\\n\\n yield create_server\\n\\n # Close Server\\n if servers:\\n for server in servers:\\n loop.run_until_complete(server.close())\",\n \"def app():\\n _app = create_app(TestConfig)\\n ctx = _app.test_request_context()\\n ctx.push()\\n\\n yield _app\\n\\n ctx.pop()\",\n \"def app():\\n _app = create_app(TestConfig)\\n ctx = _app.test_request_context()\\n ctx.push()\\n\\n yield _app\\n\\n ctx.pop()\",\n \"def app():\\n _app = create_app(TestConfig)\\n ctx = _app.test_request_context()\\n ctx.push()\\n\\n yield _app\\n\\n ctx.pop()\",\n \"def testing_app() -> Flask:\\n app = create_app(\\\"app.config.TestingConfig\\\")\\n with app.app_context():\\n db.drop_all()\\n db.create_all()\\n return app\",\n \"def create_app(self):\\n self.setUpPyfakefs()\\n self.fake_os = fake_filesystem.FakeOsModule(self.fs)\\n\\n populate_fakefs(self)\\n\\n app = MDFakeFSTestSite(\\n \\\"MDWeb\\\",\\n app_options={}\\n )\\n\\n # Add the partials directory so we have access in the FakeFS\\n self.fs.add_real_directory(app.config['PARTIALS_TEMPLATE_PATH'])\\n \\n app.start()\\n\\n return app\",\n \"def app():\\n app = create_app(schema=create_schema())\\n with app.test_request_context():\\n yield app\",\n \"def test_stage_create_app(self, mock_stage_create_app):\\n app = MDFakeFSTestSite(\\n \\\"MDWeb\\\",\\n app_options={}\\n )\\n app.start()\\n\\n self.assertTrue(mock_stage_create_app.called)\",\n \"def test_app():\\n # setup\\n app = main.create_application()\\n app.dependency_overrides[get_settings] = get_settings_override\\n with TestClient(app) as test_client:\\n yield test_client\\n # teardown\",\n \"def app(request):\\n _app = create_app(TestConfig)\\n request.instance.app = _app\\n\\n ctx = _app.test_request_context()\\n ctx.push()\\n\\n request.addfinalizer(ctx.pop)\\n return _app\",\n \"def get_test_app(relpath=None):\\n from nucleon.config import settings\\n\\n app = get_app(relpath)\\n settings._set_environment('test')\\n on_initialise.fire()\\n on_start.fire()\\n return TestApp(app)\",\n \"def create_app():\\n logging.basicConfig(\\n level=REANA_LOG_LEVEL,\\n format=REANA_LOG_FORMAT\\n )\\n app = Flask(__name__)\\n app.config.from_object('reana_server.config')\\n app.secret_key = \\\"hyper secret key\\\"\\n\\n # Register API routes\\n from .rest import ping, secrets, users, workflows # noqa\\n app.register_blueprint(ping.blueprint, url_prefix='/api')\\n app.register_blueprint(workflows.blueprint, url_prefix='/api')\\n app.register_blueprint(users.blueprint, url_prefix='/api')\\n app.register_blueprint(secrets.blueprint, url_prefix='/api')\\n\\n app.session = Session\\n CORS(app)\\n return app\",\n \"def setUp(self):\\n self.app = Flask(__name__)\\n self.app_context = self.app.app_context()\\n self.app_context.push()\\n self.client = self.app.test_client()\",\n \"def setUp(self):\\n self.app = create_app('testing')\\n self.client = self.app.test_client()\",\n \"def create_app():\\n app = Flask(__name__, instance_relative_config=False)\\n # app.config.from_object('config.Config')\\n file_path = os.path.abspath(os.getcwd())+\\\"/mpulse.db\\\"\\n app.config.from_mapping(\\n SECRET_KEY='dev',\\n SQLALCHEMY_DATABASE_URI = 'sqlite:///'+file_path,\\n SCHEMA=os.path.join(os.path.dirname(__file__), 'schema.sql'),\\n SQLALCHEMY_TRACK_MODIFICATIONS = False,\\n JSON_SORT_KEYS=False\\n )\\n\\n # ensure the instance folder exists\\n try:\\n os.makedirs(app.instance_path)\\n except OSError:\\n pass\\n \\n # init database\\n db.init_app(app)\\n \\n with app.app_context():\\n \\n # Create tables if they don't exist\\n db.create_all() \\n \\n # Include our api Routes for members\\n from . import members\\n # Register Blueprints\\n app.register_blueprint(members.bp)\\n\\n return app\",\n \"def create_app(self):\\n raise NotImplementedError\",\n \"def test_client_factory():\\n\\tapp = make_app(blueprints.api_server, settings.TestingConfig)\\n\\treturn app.test_client()\",\n \"def app():\\n db_uri = '{0}_test'.format(settings.SQLALCHEMY_DATABASE_URI)\\n params = {\\n 'DEBUG': False,\\n 'TESTING': True,\\n 'WTF_CSRF_ENABLED': False,\\n 'SQLALCHEMY_DATABASE_URI': db_uri\\n }\\n\\n _app = create_app(settings_override=params)\\n\\n # Establish an application context before running the tests.\\n ctx = _app.app_context()\\n ctx.push()\\n\\n yield _app\\n\\n ctx.pop()\",\n \"def create_app(testing=False, cli=False):\\n app = Flask(__name__)\\n app.config.from_object(\\\"flask_cli.config\\\")\\n\\n if testing is True:\\n app.config[\\\"TESTING\\\"] = True\\n\\n configure_extensions(app, cli)\\n configure_apispec(app)\\n register_blueprints(app)\\n return app\",\n \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\",\n \"def create_app():\\n app = Flask(__name__)\\n app.config.from_object('app.configs.config')\\n app.config.from_object('app.configs.settings')\\n return app\",\n \"def app():\\n os.environ[\\\"FLASK_ENV\\\"] = \\\"test\\\"\\n return create_app()\",\n \"def create_app():\\n\\n # Create app\\n app = Flask(__name__)\\n app.config.from_object(\\\"nextbus.config.Config\\\")\\n\\n app.logger = logger.app_logger\\n # Load logging configuration and log initial configuration\\n logger.load_config(app)\\n\\n # Initialise SQLAlchemy and Migrate in app\\n db.init_app(app)\\n migrate.init_app(app, db)\\n\\n # Adding app, db and model objects to flask shell\\n from nextbus import models\\n app.shell_context_processor(\\n lambda: {\\\"app\\\": app, \\\"db\\\": db, \\\"models\\\": models}\\n )\\n\\n from nextbus.converters import add_converters\\n add_converters(app)\\n\\n from nextbus.views import page\\n from nextbus.resources import api\\n app.register_blueprint(page)\\n app.register_blueprint(api)\\n\\n return app\",\n \"def create_app():\\n app = Flask(__name__, instance_relative_config=False)\\n app.register_blueprint(auth_bp, url_prefix='/auth')\\n app.register_blueprint(errors_bp, url_prefix='/error')\\n app.config.from_object('config.Config')\\n\\n db.init_app(app)\\n store.bind(db)\\n login_manager.init_app(app)\\n Session(app)\\n captcha = FlaskSessionCaptcha(app)\\n captcha.init_app(app)\\n\\n\\n with app.app_context():\\n from . import routes # Import routes\\n db.create_all() # Create sql tables for our data models\\n\\n return app\",\n \"def application(self):\\n if not ApplicationFixture._test_app:\\n app = self.APP_CLASS()\\n app.run_tests()\\n ApplicationFixture._test_app = app\\n return ApplicationFixture._test_app\",\n \"def setup_app():\\n\\n # 1 Create Flask application\\n app = Flask(\\n import_name=__name__,\\n template_folder=\\\"templates\\\",\\n static_folder=\\\"static\\\"\\n )\\n\\n # 2 Update the apps configuration\\n app = config_selector(app)\\n register_error_handlers(app)\\n\\n cache.init_app(app)\\n\\n # 3 Set up logger\\n setup_logger(app.config)\\n LOGGER.info(\\\"Set up app & logger.\\\")\\n\\n # 4 Init clients\\n init_clients(app.config)\\n\\n # 5 Init Daemon\\n start_daemon(app.config)\\n\\n # 6 Register blueprints\\n register_blueprints(app)\\n Bootstrap(app)\\n\\n return app\",\n \"def test_11_create_application(self, mock):\\r\\n # Create an app as an anonymous user\\r\\n with self.flask_app.app_context():\\r\\n res = self.new_application(method=\\\"GET\\\")\\r\\n assert self.html_title(\\\"Sign in\\\") in res.data, res\\r\\n assert \\\"Please sign in to access this page\\\" in res.data, res\\r\\n\\r\\n res = self.new_application()\\r\\n assert self.html_title(\\\"Sign in\\\") in res.data, res.data\\r\\n assert \\\"Please sign in to access this page.\\\" in res.data, res.data\\r\\n\\r\\n # Sign in and create an application\\r\\n res = self.register()\\r\\n\\r\\n res = self.new_application(method=\\\"GET\\\")\\r\\n assert self.html_title(\\\"Create an Application\\\") in res.data, res\\r\\n assert \\\"Create the application\\\" in res.data, res\\r\\n\\r\\n res = self.new_application(long_description='My Description')\\r\\n assert \\\"<strong>Sample App</strong>: Update the application\\\" in res.data\\r\\n assert \\\"Application created!\\\" in res.data, res\\r\\n\\r\\n app = db.session.query(App).first()\\r\\n assert app.name == 'Sample App', 'Different names %s' % app.name\\r\\n assert app.short_name == 'sampleapp', \\\\\\r\\n 'Different names %s' % app.short_name\\r\\n\\r\\n assert app.long_description == 'My Description', \\\\\\r\\n \\\"Long desc should be the same: %s\\\" % app.long_description\",\n \"def testapp():\\n from webtest import TestApp\\n from learning_journal import main\\n\\n app = main({}, **{'sqlalchemy.url': 'postgres:///amosboldor'})\\n testapp = TestApp(app)\\n\\n session_factory = app.registry[\\\"dbsession_factory\\\"]\\n engine = session_factory().bind\\n Base.metadata.drop_all(bind=engine)\\n Base.metadata.create_all(bind=engine)\\n\\n return testapp\",\n \"def setUp(self):\\n #app['TESTING'] = True\\n self.test_app = app.test_client()\",\n \"def create_app():\\n app = Flask(__name__)\\n\\n\\n @app.route('/')\\n def barebones():\\n return 'the barebones'\\n\\n return app\",\n \"def app(config_path, dbtransaction, test_url):\\n settings = get_appsettings(config_path)\\n settings['sqlalchemy.url'] = test_url\\n app = main({}, **settings)\\n return TestApp(app)\",\n \"def testapp(request):\\n from webtest import TestApp\\n from pyramid.config import Configurator\\n\\n def main(global_config, **settings):\\n config = Configurator(settings=settings)\\n config.include('pyramid_jinja2')\\n config.include('.models')\\n config.include('.routes')\\n config.include('.security')\\n config.scan()\\n return config.make_wsgi_app()\\n\\n app = main({}, **{'sqlalchemy.url': TEST_DB})\\n testapp = TestApp(app)\\n\\n SessionFactory = app.registry[\\\"dbsession_factory\\\"]\\n engine = SessionFactory().bind\\n Base.metadata.drop_all(engine)\\n Base.metadata.create_all(bind=engine)\\n\\n return testapp\",\n \"def create_app():\\n\\n app = FastAPI()\\n add_root_route(app)\\n\\n return app\",\n \"def init():\\n server = Flask(__name__)\\n \\n return server\",\n \"def setUpClass(cls):\\n\\n cls.client = TestWorkflow.start()\\n\\n cls.httpd = HTTPServer((\\\"127.0.0.1\\\", 8001), RequestHandler)\\n\\n server = Thread(target=cls.httpd.serve_forever)\\n server.setDaemon(True)\\n server.start()\",\n \"def app():\\n # create a temporary file to isolate the database for each test\\n # create the app with common test config\\n app = create_app({\\\"TESTING\\\": True, \\\"DATABASE_NAME\\\": \\\"AAPI_DB_Test\\\"})\\n\\n # create the database and load test data\\n with app.app_context():\\n init_db()\\n\\n yield app\",\n \"def app(loop, initialize_test_env):\\n async def dispose_test_env():\\n async for key in EmaNews().redis.iscan(match=\\\"emanews:session:*\\\"):\\n await EmaNews().redis.delete(key)\\n\\n singletons.destroy_all()\\n c = Config()\\n server = EmaNews(\\n db_host=c[\\\"DB_HOST\\\"],\\n db_port=c[\\\"DB_PORT\\\"],\\n db_username=c[\\\"DB_USERNAME\\\"],\\n db_password=c[\\\"DB_PASSWORD\\\"],\\n db_database=c[\\\"TEST_DB_NAME\\\"],\\n db_pool_minsize=c[\\\"DB_POOL_MIN_SIZE\\\"],\\n db_pool_maxsize=c[\\\"DB_POOL_MAX_SIZE\\\"],\\n redis_host=c[\\\"REDIS_HOST\\\"],\\n redis_port=c[\\\"REDIS_PORT\\\"],\\n redis_password=c[\\\"REDIS_PASSWORD\\\"],\\n redis_database=c[\\\"REDIS_TEST_DATABASE\\\"],\\n redis_pool_size=c[\\\"REDIS_POOL_SIZE\\\"],\\n telegram_token=c[\\\"TELEGRAM_TOKEN\\\"],\\n # mailgun_client=MailgunClient(\\n # domain=c[\\\"MAILGUN_DOMAIN\\\"],\\n # key=c[\\\"MAILGUN_KEY\\\"],\\n # default_from=c[\\\"MAILGUN_DEFAULT_SENDER\\\"]\\n # ) if c[\\\"MAILGUN_KEY\\\"] is not None else DummyMailgunClient(),\\n mailgun_client=DummyMailgunClient(),\\n debug=False\\n )\\n server.initialize()\\n yield server\\n loop.run_until_complete(dispose_test_env())\\n loop.run_until_complete(server.dispose())\",\n \"def setUp(self):\\n self.api = \\\"http://localhost:4031/\\\"\\n self.version = \\\"0.2\\\"\\n self.app = init_api()\",\n \"def init_app():\\r\\n LOG.info('Initialising web server.')\\r\\n app = web.Application(middlewares=[api_key()])\\r\\n app.router.add_routes(routes)\\r\\n set_cors(app)\\r\\n app.on_startup.append(init_db)\\r\\n app.on_cleanup.append(close_db)\\r\\n return app\",\n \"def test_app_is_created(app):\\n assert app.name == \\\"myapp.app\\\"\",\n \"def prepare_app():\\n application = service.Application('buildbot-worker')\\n master = (GATEWAY\\n if conf.Washer.FORCE_GATEWAY\\n else conf.Buildbot.BUILDMASTER)\\n worker = Worker(master,\\n conf.Buildbot.BUILDMASTER_PORT,\\n conf.Buildbot.WORKERNAME,\\n conf.Buildbot.WORKERPASS,\\n conf.Buildbot.BASEDIR,\\n conf.Buildbot.KEEPALIVE,\\n umask=None,\\n maxdelay=conf.Buildbot.MAXDELAY,\\n numcpus=None,\\n allow_shutdown=None,\\n maxRetries=None)\\n worker.setServiceParent(application)\\n\\n class InlineApplication(UnixApplicationRunner):\\n def createOrGetApplication(self):\\n nonlocal application\\n return application\\n\\n options = ServerOptions()\\n options[\\\"nodaemon\\\"] = not conf.Washer.DAEMON\\n options[\\\"logfile\\\"] = conf.Washer.LOG_FILE\\n\\n commands.register()\\n\\n return InlineApplication(options)\",\n \"def standalone():\\n parser = argparse.ArgumentParser(\\n description=\\\"Start Argo Workflow API Dispatch Server\\\"\\n )\\n parser.add_argument(\\n \\\"-p\\\", \\\"--port\\\", help=\\\"Server listening port\\\", type=int, default=8080\\n )\\n parser.add_argument(\\n \\\"-b\\\", \\\"--bind-address\\\", help=\\\"Server bind address\\\", default=\\\"127.0.0.1\\\"\\n )\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--mock\\\",\\n \\\"--mock-authentication\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Do not require a JWT; mock out authentication\\\",\\n )\\n parser.add_argument(\\n \\\"--no-verify-signature\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Do not verify JWT signature\\\",\\n )\\n parser.add_argument(\\n \\\"--no-verify-audience\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Do not verify JWT audience\\\",\\n )\\n args = parser.parse_args()\\n mock = args.mock\\n v_s = True\\n v_a = True\\n if args.no_verify_signature:\\n v_s = False\\n if args.no_verify_audience:\\n v_a = False\\n server = Server(_mock=mock, verify_signature=v_s, verify_audience=v_a)\\n httpd = WSGIServer(server.app, host=args.bind_address, port=args.port)\\n httpd.start()\",\n \"def app(self):\\n app = Flask('testapp')\\n app.config.update({\\n 'HADES_CELERY_APP_NAME': 'test',\\n 'HADES_BROKER_URI': 'rpc://broker/',\\n 'HADES_RESULT_BACKEND_URI': 'rpc://backend/',\\n })\\n return app\",\n \"def create_app():\\n\\n config = config_by_name[os.getenv('APP_SETTINGS', 'dev')]\\n flask_app = Flask(__name__, static_folder=None, instance_relative_config=True)\\n flask_app.config.from_object(config)\\n with flask_app.app_context():\\n app_manager = Manager(flask_app)\\n\\n from app.models import db, ma\\n db.init_app(flask_app)\\n Migrate(flask_app, db)\\n app_manager.add_command('db', MigrateCommand)\\n ma.init_app(flask_app)\\n\\n from app.service import mail\\n mail.init_app(flask_app)\\n\\n from app.api import blueprint_api\\n flask_app.register_blueprint(blueprint_api)\\n\\n json_logging.ENABLE_JSON_LOGGING = True\\n json_logging.COMPONENT_NAME = 'MS-Auth'\\n json_logging.COMPONENT_ID = 1\\n json_logging.init(framework_name='flask')\\n json_logging.init_request_instrument(flask_app)\\n\\n return flask_app, app_manager\",\n \"def app(request): \\n\\n # app instance\\n app = create_app(\\\"testing_app\\\", {\\n 'API_BASE_PATH': \\\"/api/\\\",\\n 'TESTING': True, \\n 'SECRET_KEY': 'test', \\n })\\n\\n # add to the scope\\n ctx = app.app_context()\\n ctx.push()\\n\\n def teardown(): \\n init_db()\\n ctx.pop() \\n\\n init_db()\\n\\n request.addfinalizer(teardown)\\n yield app \\n \\n drop_db()\",\n \"def make_server(host, port, app, server_class=WSGIServer, handler_class=WSGIRequestHandler):\\n server = server_class((host, port), handler_class)\\n server.set_app(app)\\n return server\",\n \"async def create_app(dev: bool = True) -> web.Application:\\n app = web.Application()\\n setup_jinja2(app)\\n routes_setup(app, dev)\\n if not dev:\\n # not a development server - we need to run directly\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--port', default=4000)\\n parser.add_argument('--host', default='0.0.0.0')\\n parser.add_argument('--path', default='/tmp/navigation_editor')\\n server_cfg = vars(parser.parse_args())\\n web.run_app(\\n app,\\n host=server_cfg['host'],\\n port=int(server_cfg['port']),\\n path=server_cfg['path'])\\n return app\",\n \"def setup():\\n global server, app\\n \\n galaxy_test_host = os.environ.get( 'GALAXY_TEST_HOST', default_galaxy_test_host )\\n galaxy_test_port = os.environ.get( 'GALAXY_TEST_PORT', default_galaxy_test_port )\\n \\n start_server = 'GALAXY_TEST_EXTERNAL' not in os.environ \\n \\n if start_server:\\n \\n tempdir = tempfile.mkdtemp()\\n file_path = os.path.join( tempdir, 'database', 'files' )\\n os.makedirs( file_path )\\n if 'GALAXY_TEST_DBURI' in os.environ:\\n database_connection = os.environ['GALAXY_TEST_DBURI']\\n else:\\n database_connection = 'sqlite:///' + os.path.join( tempdir, 'database', 'universe.sqlite' )\\n \\n app = UniverseApplication( job_queue_workers = 5,\\n template_path = \\\"templates\\\",\\n database_connection = database_connection,\\n file_path = file_path,\\n tool_config_file = \\\"tool_conf.xml\\\",\\n tool_path = \\\"tools\\\",\\n test_conf = \\\"test.conf\\\",\\n log_destination = \\\"stdout\\\",\\n use_heartbeat=True )\\n \\n log.info( \\\"Embedded Universe application started\\\" )\\n\\n webapp = universe_wsgi.app_factory( dict(),\\n use_translogger = False,\\n app=app )\\n\\n server = galaxy.web.server.serve( webapp, dict(), \\n host=galaxy_test_host, \\n port=galaxy_test_port, \\n start_loop=False )\\n \\n atexit.register( teardown )\\n \\n import threading\\n t = threading.Thread( target=server.serve_forever )\\n t.start()\\n\\n time.sleep( 2 )\\n \\n log.info( \\\"Embedded web server started\\\" )\\n \\n if app:\\n # TODO: provisions for loading toolbox from file when using external server\\n import test_toolbox\\n test_toolbox.toolbox = app.toolbox\\n else:\\n from galaxy import tools\\n import test_toolbox\\n test_toolbox.toolbox = tools.ToolBox( 'tool_conf.xml', 'tools' )\\n \\n # Test if the server is up\\n import httplib\\n conn = httplib.HTTPConnection( galaxy_test_host, galaxy_test_port )\\n conn.request( \\\"GET\\\", \\\"/\\\" )\\n assert conn.getresponse().status == 200, \\\"Test HTTP server did not return '200 OK'\\\"\\n \\n os.environ['GALAXY_TEST_HOST'] = galaxy_test_host\\n os.environ['GALAXY_TEST_PORT'] = galaxy_test_port\\n os.environ['GALAXY_TEST_FILE_DIR'] = galaxy_test_file_dir\",\n \"def app_instance(scope=\\\"session\\\"):\\n app = create_app(\\\"test\\\")\\n with app.app_context():\\n db.create_all()\\n yield app\\n db.drop_all()\",\n \"def client(application):\\n\\n return app.test_client()\",\n \"def client(app): # pylint: disable=redefined-outer-name\\n yield TestApp(app)\",\n \"def create_app():\\n # Creates flask object with directory for to serve static files\\n app = Flask(__name__, static_url_path=C.STATIC_FILE_PATH)\\n\\n # Enabling CORS for the application\\n CORS(app)\\n\\n app.debug = True\\n # Registering books controller\\n from app.controllers.books import mod\\n app.register_blueprint(mod)\\n\\n # Test Route\\n @app.route('/hello')\\n def hello_world():\\n return 'Hello World!'\\n\\n # Index route - serves index.html\\n @mod.route('/')\\n def main():\\n return mod.send_static_file(\\\"index.html\\\")\\n\\n # serve routes from index by default\\n app.add_url_rule('/', endpoint='index')\\n\\n return app\",\n \"def setUp(self):\\n config.ENV = \\\"testing\\\"\\n self.app = init_app()\\n self.client = self.app.test_client\\n with self.app.app_context():\\n db.drop_all()\\n db.create_all()\\n for actor in test.populate_db.sample_actors:\\n Actor(**actor).insert()\\n\\n for movie in test.populate_db.sample_movies:\\n Movie(**movie).insert()\\n return self.app\\n # binds the app to the current context\",\n \"def setUp(self):\\r\\n self.app = app.test_client()\\r\\n self.app.testing = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7417531","0.73058623","0.7296637","0.7289872","0.72620034","0.72145504","0.71641845","0.7141066","0.7107825","0.71020865","0.7096506","0.70654595","0.70639825","0.704083","0.70407116","0.7004533","0.6990313","0.6979766","0.69694114","0.6966402","0.69436395","0.6885268","0.687958","0.6863076","0.6856578","0.6845318","0.6845004","0.68401647","0.68250376","0.6822378","0.68196124","0.6817133","0.68075204","0.67793334","0.67792815","0.6778062","0.67673886","0.6763279","0.6756038","0.67470086","0.67361814","0.6697783","0.66952443","0.6694875","0.66801023","0.6676963","0.66680413","0.6664379","0.6664379","0.6664379","0.66604364","0.66573095","0.6630893","0.66205364","0.6616356","0.6612105","0.66095763","0.66069466","0.6594857","0.65946406","0.658885","0.6586453","0.6579101","0.65774876","0.65622807","0.65354633","0.6524754","0.65079355","0.6507885","0.6493849","0.6483886","0.64749813","0.645126","0.64469445","0.64441836","0.64441746","0.6434645","0.6432905","0.6421996","0.6419286","0.6414165","0.6412791","0.63986355","0.6396728","0.6395987","0.63946986","0.6389205","0.6377917","0.6375975","0.6364912","0.63625103","0.6358668","0.63567144","0.6351848","0.63499963","0.63445264","0.6338952","0.63360983","0.6333819","0.6333046"],"string":"[\n \"0.7417531\",\n \"0.73058623\",\n \"0.7296637\",\n \"0.7289872\",\n \"0.72620034\",\n \"0.72145504\",\n \"0.71641845\",\n \"0.7141066\",\n \"0.7107825\",\n \"0.71020865\",\n \"0.7096506\",\n \"0.70654595\",\n \"0.70639825\",\n \"0.704083\",\n \"0.70407116\",\n \"0.7004533\",\n \"0.6990313\",\n \"0.6979766\",\n \"0.69694114\",\n \"0.6966402\",\n \"0.69436395\",\n \"0.6885268\",\n \"0.687958\",\n \"0.6863076\",\n \"0.6856578\",\n \"0.6845318\",\n \"0.6845004\",\n \"0.68401647\",\n \"0.68250376\",\n \"0.6822378\",\n \"0.68196124\",\n \"0.6817133\",\n \"0.68075204\",\n \"0.67793334\",\n \"0.67792815\",\n \"0.6778062\",\n \"0.67673886\",\n \"0.6763279\",\n \"0.6756038\",\n \"0.67470086\",\n \"0.67361814\",\n \"0.6697783\",\n \"0.66952443\",\n \"0.6694875\",\n \"0.66801023\",\n \"0.6676963\",\n \"0.66680413\",\n \"0.6664379\",\n \"0.6664379\",\n \"0.6664379\",\n \"0.66604364\",\n \"0.66573095\",\n \"0.6630893\",\n \"0.66205364\",\n \"0.6616356\",\n \"0.6612105\",\n \"0.66095763\",\n \"0.66069466\",\n \"0.6594857\",\n \"0.65946406\",\n \"0.658885\",\n \"0.6586453\",\n \"0.6579101\",\n \"0.65774876\",\n \"0.65622807\",\n \"0.65354633\",\n \"0.6524754\",\n \"0.65079355\",\n \"0.6507885\",\n \"0.6493849\",\n \"0.6483886\",\n \"0.64749813\",\n \"0.645126\",\n \"0.64469445\",\n \"0.64441836\",\n \"0.64441746\",\n \"0.6434645\",\n \"0.6432905\",\n \"0.6421996\",\n \"0.6419286\",\n \"0.6414165\",\n \"0.6412791\",\n \"0.63986355\",\n \"0.6396728\",\n \"0.6395987\",\n \"0.63946986\",\n \"0.6389205\",\n \"0.6377917\",\n \"0.6375975\",\n \"0.6364912\",\n \"0.63625103\",\n \"0.6358668\",\n \"0.63567144\",\n \"0.6351848\",\n \"0.63499963\",\n \"0.63445264\",\n \"0.6338952\",\n \"0.63360983\",\n \"0.6333819\",\n \"0.6333046\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7206358"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":17459,"cells":{"query":{"kind":"string","value":"Close the browser, and clean up the database."},"document":{"kind":"string","value":"def tearDown(self):\n self.browser.quit()\n clean_db()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def close(self):\n\t\tself.db.close()","def close(self):\n self.db.close()","def close_db():\n if ( not g.get( 'db' ) is None ):\n g.db.dispose()\n\n g.db = None","def disconnect(self):\n self.db.close()","def close(self):\n self.db.remove()\n self.engine.dispose()","def cleanup(_):\n self.db.close()","def close(self):\n if getattr(self, \"_db\", None):\n self._db.close()\n self._db = None","def save_before_close(self):\n if self.db_window:\n self.db_window.destroy()\n self.destroy()","def close_db_connection(cls):\n db.close()","def exit(self):\n self.__mongo_db.close_connection()\n self.__postgre_db.close_connection()","def close(self):\n self.cursor.close()\n self.db.close()","def close(self):\n# self.cursor.close()\n\tself.db.close()","def close(self):\n if getattr(self, \"_db\", None) is not None:\n self._db.close()\n self._db = None","def close(self):\n if getattr(self, \"_db\", None) is not None:\n self._db.close()\n self._db = None","def close(self):\n if getattr(self, \"_db\", None) is not None:\n self._db.close()\n self._db = None","def close(self):\n self.__database__.close()","def close(db):\n storage.close()","def disconnect(self):\n self._save_database()\n self.dbh.close()\n self._remove_temporary_file()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n storage.close();","def close_db(error):\n storage.close()","def after_request():\n db.close()","def close_db(error):\n\tif hasattr(g, 'sqlite_db'):\n\t\tg.sqlite_db.close()","def close_db(error):\n\tif hasattr(g, 'sqlite_db'):\n\t\tg.sqlite_db.close()","def close_db(error):\n if hasattr(g, \"sqlite_db\"):\n g.sqlite_db.close()","def disconnect(self):\n if self.db:\n try:\n self.db.close()\n finally:\n self.db = None","def close_db(error):\r\n if hasattr(g, 'sqlite_db'):\r\n g.sqlite_db.close()","def close_db(error):\n\n if hasattr(g, \"sqlite_db\"):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close(self):\n\t\tif self.connected:\n#\t\t\t\t self.__db.close()\n\t\t\tself.__engine.dispose()\n\t\t\tself.connected = False","def close_db(error):\n if hasattr(flask.g, 'client'):\n flask.g.client.close()","def close(self):\n self.db.commit()\n self.db.close()","def Close(self):\n l_logger.debug(\"Closing database connection\")\n self.db.close()","def close_db(error):\n if hasattr(g, 'database'):\n g.database.close()","def close_db(nothing):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()","def close_db(self):\n with nostderr():\n tb.file._open_files.close_all()","def close_db(error):\n if \"db_engine_main\" in g:\n g.db_engine_main.dispose()","def close(self):\n self._flush()\n self.database.close()","def i_close_the_browser():\n driver.close()\n driver.quit()","async def close(self):\n print('Close {}'.format(str(self.__hash__)))\n await self.db.close()","def close(error):\n if hasattr(flask.g, 'sqlite_db'):\n flask.g.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'mysql_db'):\n g.mysql_db.close()","def close_db(error):\n if hasattr(g, 'sqlite_db'):\n g.sqlite_db.close()\n if error is not None:\n print( \"An error has been produced.\")","def close_db(error):\n if hasattr(g, \"sql_db\"):\n g.sql_db.close()","def close_db():\n if \"db\" in context:\n context[\"db\"].close()","def close(self):\n if self.authenticated:\n self.db.logout()\n if self.connection is not None:\n self.connection.close()","def disconnect_from_db(self):\n self.db_cur.close()\n self.db_conn.close()","def db_close(e=None):\n database = g.pop('db', None)\n\n if database:\n database.close()","def teardown_session(e):\n my_db.close()\n OT_spider.close()","def clean_up(self):\n self.session.close()","def disconnect_db(self) -> None:","def close_db(connection: sqlite3.Connection):\n connection.commit()\n connection.close()","def db_disconnect(self):\n \n if(self.connection):\n self.connection.close()\n return","def close_database(self) -> None:\n self.api = None\n self.base = None\n self.table = None","def close_database(self):\n if self._conn is not None:\n self._conn.close()\n self._conn = None","def close(self):\n self.browser.quit()\n\n return None","def close(self):\n if self._dbopen:\n self._dbcon.close()","def _close_database(self):\n assert self._conn is not None\n logging.info(\"Closing file {!r}.\".format(self._filename))\n self._cursor.close()\n self._cursor = None\n self._conn.close()\n self._conn = None\n self._filename = None\n self._sessionUID = None","def shutdown_db():\n print('Exit.')\n global conn\n conn.close()","def close(self):\n self.conn.close()\n self.destroy()","def close_browser():\n driver.close()","def quit_browser(self):\n if self.browser is not None:\n self.browser.quit()\n self.browser = None","def close_db(error):\n conn = g.get('sqlite_db', None)\n if conn is not None:\n conn.close()","def close_database(exception):\n # top = _app_ctx_stack.top\n # if hasattr(top, 'sqlite_db'):\n # top.sqlite_db.close()\n client.close()","def after_all(context):\n context.browser.quit()\n app = context.server.get_app()\n with app.app_context():\n app.extensions[\"sqlalchemy\"].db.drop_all()\n context.server.shutdown()\n context.thread.join()","def close_db(error):\n if hasattr(g, 'pg_cur'):\n g.pg_cur.close()\n g.pg_conn.close()","def _shutdown_browser(self):\n if self.browser:\n self.browser.close()","def _destroy(self):\n self.cursor.close()","def close(self):\n self.conn.commit()\n self.cursor().close()\n self.conn.close()\n self._db = None","def teardown(self):\n self.log.info('Close browser')\n self.driver.quit()","def closedb(e=None):\n db = g.pop('db', None)\n\n if db is not None:\n db.close()","def close(self):\n self.clean()\n\n for conn in self.conn__.values():\n try:\n conn.commit()\n conn.close()\n except sqlite3.ProgrammingError:\n pass","def close(self):\n self._backend.disconnect()","def tteardown_class(self):\n os.close(self.db_fd)\n os.unlink(app.config['DATABASE'])","async def on_shutdown(self):\n await self.controller.database.close()","def close(self):\n self.session.close()","def teardown_db(e):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()","def close(self):\n Log.debug('DB -> close')\n # 关闭数据库连接\n self.conn.close()","def close_db_session(self, session):\r\n session.close()","def tearDown(self):\n with self.app.app_context():\n destroy()\n self.db.close()","def close_database(db):\n global DB_CONNECTIONS\n con = DB_CONNECTIONS.get(db, None)\n if con is not None:\n db_name = Path(db).name\n logging.info(f\">>> Closing database `{db_name}` <<<\")\n # Closing will delete the connection. An in-memory DB will lose all data permanently.\n # See https://stackoverflow.com/questions/48732439/deleting-a-database-file-in-memory\n con.close()\n DB_CONNECTIONS.pop(db, None)","def close_db(e=None):\n db = g.pop(\"db\", None)\n\n if db is not None:\n db.close()","def close_db(e=None):\n db = g.pop(\"db\", None)\n\n if db is not None:\n db.close()","def close(self):\n\n self._driver.quit()"],"string":"[\n \"def close(self):\\n\\t\\tself.db.close()\",\n \"def close(self):\\n self.db.close()\",\n \"def close_db():\\n if ( not g.get( 'db' ) is None ):\\n g.db.dispose()\\n\\n g.db = None\",\n \"def disconnect(self):\\n self.db.close()\",\n \"def close(self):\\n self.db.remove()\\n self.engine.dispose()\",\n \"def cleanup(_):\\n self.db.close()\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None):\\n self._db.close()\\n self._db = None\",\n \"def save_before_close(self):\\n if self.db_window:\\n self.db_window.destroy()\\n self.destroy()\",\n \"def close_db_connection(cls):\\n db.close()\",\n \"def exit(self):\\n self.__mongo_db.close_connection()\\n self.__postgre_db.close_connection()\",\n \"def close(self):\\n self.cursor.close()\\n self.db.close()\",\n \"def close(self):\\n# self.cursor.close()\\n\\tself.db.close()\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None) is not None:\\n self._db.close()\\n self._db = None\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None) is not None:\\n self._db.close()\\n self._db = None\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None) is not None:\\n self._db.close()\\n self._db = None\",\n \"def close(self):\\n self.__database__.close()\",\n \"def close(db):\\n storage.close()\",\n \"def disconnect(self):\\n self._save_database()\\n self.dbh.close()\\n self._remove_temporary_file()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n storage.close();\",\n \"def close_db(error):\\n storage.close()\",\n \"def after_request():\\n db.close()\",\n \"def close_db(error):\\n\\tif hasattr(g, 'sqlite_db'):\\n\\t\\tg.sqlite_db.close()\",\n \"def close_db(error):\\n\\tif hasattr(g, 'sqlite_db'):\\n\\t\\tg.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, \\\"sqlite_db\\\"):\\n g.sqlite_db.close()\",\n \"def disconnect(self):\\n if self.db:\\n try:\\n self.db.close()\\n finally:\\n self.db = None\",\n \"def close_db(error):\\r\\n if hasattr(g, 'sqlite_db'):\\r\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n\\n if hasattr(g, \\\"sqlite_db\\\"):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close(self):\\n\\t\\tif self.connected:\\n#\\t\\t\\t\\t self.__db.close()\\n\\t\\t\\tself.__engine.dispose()\\n\\t\\t\\tself.connected = False\",\n \"def close_db(error):\\n if hasattr(flask.g, 'client'):\\n flask.g.client.close()\",\n \"def close(self):\\n self.db.commit()\\n self.db.close()\",\n \"def Close(self):\\n l_logger.debug(\\\"Closing database connection\\\")\\n self.db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'database'):\\n g.database.close()\",\n \"def close_db(nothing):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\",\n \"def close_db(self):\\n with nostderr():\\n tb.file._open_files.close_all()\",\n \"def close_db(error):\\n if \\\"db_engine_main\\\" in g:\\n g.db_engine_main.dispose()\",\n \"def close(self):\\n self._flush()\\n self.database.close()\",\n \"def i_close_the_browser():\\n driver.close()\\n driver.quit()\",\n \"async def close(self):\\n print('Close {}'.format(str(self.__hash__)))\\n await self.db.close()\",\n \"def close(error):\\n if hasattr(flask.g, 'sqlite_db'):\\n flask.g.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'mysql_db'):\\n g.mysql_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'sqlite_db'):\\n g.sqlite_db.close()\\n if error is not None:\\n print( \\\"An error has been produced.\\\")\",\n \"def close_db(error):\\n if hasattr(g, \\\"sql_db\\\"):\\n g.sql_db.close()\",\n \"def close_db():\\n if \\\"db\\\" in context:\\n context[\\\"db\\\"].close()\",\n \"def close(self):\\n if self.authenticated:\\n self.db.logout()\\n if self.connection is not None:\\n self.connection.close()\",\n \"def disconnect_from_db(self):\\n self.db_cur.close()\\n self.db_conn.close()\",\n \"def db_close(e=None):\\n database = g.pop('db', None)\\n\\n if database:\\n database.close()\",\n \"def teardown_session(e):\\n my_db.close()\\n OT_spider.close()\",\n \"def clean_up(self):\\n self.session.close()\",\n \"def disconnect_db(self) -> None:\",\n \"def close_db(connection: sqlite3.Connection):\\n connection.commit()\\n connection.close()\",\n \"def db_disconnect(self):\\n \\n if(self.connection):\\n self.connection.close()\\n return\",\n \"def close_database(self) -> None:\\n self.api = None\\n self.base = None\\n self.table = None\",\n \"def close_database(self):\\n if self._conn is not None:\\n self._conn.close()\\n self._conn = None\",\n \"def close(self):\\n self.browser.quit()\\n\\n return None\",\n \"def close(self):\\n if self._dbopen:\\n self._dbcon.close()\",\n \"def _close_database(self):\\n assert self._conn is not None\\n logging.info(\\\"Closing file {!r}.\\\".format(self._filename))\\n self._cursor.close()\\n self._cursor = None\\n self._conn.close()\\n self._conn = None\\n self._filename = None\\n self._sessionUID = None\",\n \"def shutdown_db():\\n print('Exit.')\\n global conn\\n conn.close()\",\n \"def close(self):\\n self.conn.close()\\n self.destroy()\",\n \"def close_browser():\\n driver.close()\",\n \"def quit_browser(self):\\n if self.browser is not None:\\n self.browser.quit()\\n self.browser = None\",\n \"def close_db(error):\\n conn = g.get('sqlite_db', None)\\n if conn is not None:\\n conn.close()\",\n \"def close_database(exception):\\n # top = _app_ctx_stack.top\\n # if hasattr(top, 'sqlite_db'):\\n # top.sqlite_db.close()\\n client.close()\",\n \"def after_all(context):\\n context.browser.quit()\\n app = context.server.get_app()\\n with app.app_context():\\n app.extensions[\\\"sqlalchemy\\\"].db.drop_all()\\n context.server.shutdown()\\n context.thread.join()\",\n \"def close_db(error):\\n if hasattr(g, 'pg_cur'):\\n g.pg_cur.close()\\n g.pg_conn.close()\",\n \"def _shutdown_browser(self):\\n if self.browser:\\n self.browser.close()\",\n \"def _destroy(self):\\n self.cursor.close()\",\n \"def close(self):\\n self.conn.commit()\\n self.cursor().close()\\n self.conn.close()\\n self._db = None\",\n \"def teardown(self):\\n self.log.info('Close browser')\\n self.driver.quit()\",\n \"def closedb(e=None):\\n db = g.pop('db', None)\\n\\n if db is not None:\\n db.close()\",\n \"def close(self):\\n self.clean()\\n\\n for conn in self.conn__.values():\\n try:\\n conn.commit()\\n conn.close()\\n except sqlite3.ProgrammingError:\\n pass\",\n \"def close(self):\\n self._backend.disconnect()\",\n \"def tteardown_class(self):\\n os.close(self.db_fd)\\n os.unlink(app.config['DATABASE'])\",\n \"async def on_shutdown(self):\\n await self.controller.database.close()\",\n \"def close(self):\\n self.session.close()\",\n \"def teardown_db(e):\\n db = getattr(g, '_database', None)\\n if db is not None:\\n db.close()\",\n \"def close(self):\\n Log.debug('DB -> close')\\n # 关闭数据库连接\\n self.conn.close()\",\n \"def close_db_session(self, session):\\r\\n session.close()\",\n \"def tearDown(self):\\n with self.app.app_context():\\n destroy()\\n self.db.close()\",\n \"def close_database(db):\\n global DB_CONNECTIONS\\n con = DB_CONNECTIONS.get(db, None)\\n if con is not None:\\n db_name = Path(db).name\\n logging.info(f\\\">>> Closing database `{db_name}` <<<\\\")\\n # Closing will delete the connection. An in-memory DB will lose all data permanently.\\n # See https://stackoverflow.com/questions/48732439/deleting-a-database-file-in-memory\\n con.close()\\n DB_CONNECTIONS.pop(db, None)\",\n \"def close_db(e=None):\\n db = g.pop(\\\"db\\\", None)\\n\\n if db is not None:\\n db.close()\",\n \"def close_db(e=None):\\n db = g.pop(\\\"db\\\", None)\\n\\n if db is not None:\\n db.close()\",\n \"def close(self):\\n\\n self._driver.quit()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77067506","0.7608747","0.7564536","0.748835","0.7487941","0.74589837","0.7395597","0.73916876","0.73889124","0.73546255","0.7339338","0.73319304","0.7327354","0.7327354","0.7327354","0.73114276","0.7295338","0.72949415","0.7287185","0.72680247","0.7231159","0.72304285","0.72189486","0.72189486","0.7212779","0.7206875","0.7203071","0.7187281","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71797055","0.71774083","0.7175594","0.71746457","0.7167604","0.7166291","0.7161968","0.7146437","0.71371","0.71285135","0.71271706","0.7122922","0.711099","0.70872974","0.70733315","0.7064457","0.7060261","0.70476127","0.7045375","0.70397097","0.7022796","0.70131534","0.699597","0.6984114","0.69671065","0.69649523","0.69524455","0.6950879","0.6948949","0.6945247","0.6940546","0.6935688","0.6922106","0.69121146","0.69104344","0.6902952","0.6897348","0.68917","0.6890173","0.68807036","0.68761885","0.6862441","0.6858316","0.68548864","0.6829815","0.6828161","0.6826954","0.68236357","0.68145883","0.68134856","0.6809656","0.6807294","0.6804262","0.68035007","0.68035007","0.6799398"],"string":"[\n \"0.77067506\",\n \"0.7608747\",\n \"0.7564536\",\n \"0.748835\",\n \"0.7487941\",\n \"0.74589837\",\n \"0.7395597\",\n \"0.73916876\",\n \"0.73889124\",\n \"0.73546255\",\n \"0.7339338\",\n \"0.73319304\",\n \"0.7327354\",\n \"0.7327354\",\n \"0.7327354\",\n \"0.73114276\",\n \"0.7295338\",\n \"0.72949415\",\n \"0.7287185\",\n \"0.72680247\",\n \"0.7231159\",\n \"0.72304285\",\n \"0.72189486\",\n \"0.72189486\",\n \"0.7212779\",\n \"0.7206875\",\n \"0.7203071\",\n \"0.7187281\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71797055\",\n \"0.71774083\",\n \"0.7175594\",\n \"0.71746457\",\n \"0.7167604\",\n \"0.7166291\",\n \"0.7161968\",\n \"0.7146437\",\n \"0.71371\",\n \"0.71285135\",\n \"0.71271706\",\n \"0.7122922\",\n \"0.711099\",\n \"0.70872974\",\n \"0.70733315\",\n \"0.7064457\",\n \"0.7060261\",\n \"0.70476127\",\n \"0.7045375\",\n \"0.70397097\",\n \"0.7022796\",\n \"0.70131534\",\n \"0.699597\",\n \"0.6984114\",\n \"0.69671065\",\n \"0.69649523\",\n \"0.69524455\",\n \"0.6950879\",\n \"0.6948949\",\n \"0.6945247\",\n \"0.6940546\",\n \"0.6935688\",\n \"0.6922106\",\n \"0.69121146\",\n \"0.69104344\",\n \"0.6902952\",\n \"0.6897348\",\n \"0.68917\",\n \"0.6890173\",\n \"0.68807036\",\n \"0.68761885\",\n \"0.6862441\",\n \"0.6858316\",\n \"0.68548864\",\n \"0.6829815\",\n \"0.6828161\",\n \"0.6826954\",\n \"0.68236357\",\n \"0.68145883\",\n \"0.68134856\",\n \"0.6809656\",\n \"0.6807294\",\n \"0.6804262\",\n \"0.68035007\",\n \"0.68035007\",\n \"0.6799398\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7449147"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":17460,"cells":{"query":{"kind":"string","value":"Returns the names of all qualities with exitsting expert knowledge."},"document":{"kind":"string","value":"def get_qualities_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([q for _, q in self.expert_knowledge])))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\n expert_knowledge = reduce(\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\n qualities,\n set()\n )\n return sorted(list(expert_knowledge))","def get_qualification_list(self):\n try:\n qualifications = self.db_handler.get_qualifications_list()\n self.logger.write_to_log('qualifications got', 'model')\n return qualifications\n except Exception as err:\n method_name = sys._getframe().f_code.co_name\n\n self.logger.write_to_log('exception', 'model')\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')","def list_qualifications() -> Dict[str, Any]:\n qualifications_schema = QualificationSchema(many=True)\n qualifications = Qualification.query.all()\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))","def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))","def get_assessment_terms(self):\n return # osid.assessment.AssessmentQueryInspector","def get_exercise_recording_full_names(self):\n full_names = set()\n for er in self.exercise_recordings:\n full_names.add(er.full_name)\n return full_names","def ieqs(self):\n return self.inequalities()","def qps(self):\n from admin.models.qualification_pack import QualificationPack\n return QualificationPack.objects.filter(\n occupation=self, is_draft=False\n )","def earned_hw_scores(self):\r\n return [s.earned for s in self.get_grade_summary()['totaled_scores']['Homework']]","def get_knowledge_category_terms(self):\n return # osid.grading.GradeQueryInspector","def end_effectors(self) -> list:\n S = self.parents\n return [[x, f\"q{x[1:]}\"] for x in S if S.out_degree(x) == 0]","def get_well_aliases(self):\n return self.info_wells['well'].unique()","def all_present_experiments(self):\n return _yield_subdir_names(self.exp_configs)","def qalist(self):\n return self._palist.qalist","def display_injury_type(self):\n return ', '.join(type_of_injury.name for genre in self.type_of_injury.all()[:3])","def find_gene_name(qualifiers):\n if not isinstance(qualifiers, dict):\n raise TypeError(\"Expected qualifier dictionary\")\n for tag in [\"protein_id\", \"locus_tag\", \"id\", \"gene\", \"name\", \"label\"]:\n if tag in qualifiers:\n return qualifiers[tag][0]\n return \"N.A.\"","def inconsistent_entityName(self):\n a = [s for s in self.subjects if len([sa for sa in s.samples if sa.inconsistent_entityName]) > 0]\n if len(a) == 0:\n return None\n return a","def get_assessment_terms(self):\n return # osid.repository.AssetQueryInspector","def get_level_terms(self):\n return # osid.grading.GradeQueryInspector","def endog_names(self):\n return self.data.ynames","def exog_names(self):\n return self.data.xnames","def pyranose_names(self):\n output = set()\n for item in self.pyranoses():\n if item in self.pyranose_fac:\n output.add(self.pyranose_fac[item][\"name\"])\n return list(output)","def get_cognitive_process_terms(self):\n return # osid.grading.GradeQueryInspector","def all_tone_qualities():\n\n tone_qualities = crud.get_tone_qualities()\n\n return render_template('all_tone_qualities.html', tone_qualities=tone_qualities)","def getStudyNames(self):\n try:\n con = self.getMetadataDatabaseConnection()\n results = con.cursor()\n con.cursor().callproc('qiime_assets.get_study_names', [results])\n study_name_list = []\n for row in results:\n if row[0] is None:\n continue\n else:\n study_name_list.append(row)\n return study_name_list\n except Exception, e:\n print 'Exception caught: %s.\\nThe error is: %s' % (type(e), e)\n return False","def find_qualifications(\n self, qualification_name: Optional[str] = None\n ) -> List[Qualification]:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n \"\"\"\n SELECT * from qualifications\n WHERE (?1 IS NULL OR qualification_name = ?1)\n \"\"\",\n (qualification_name,),\n )\n rows = c.fetchall()\n return [\n Qualification(\n self, str(r[\"qualification_id\"]), row=r, _used_new_call=True\n )\n for r in rows\n ]","def getQualitativeSpecies(self):\n return _libsbml.Output_getQualitativeSpecies(self)","def IEs(self):\n return self._ies","def getEMPStudyList(self):\n try:\n studies = []\n con = self.getMetadataDatabaseConnection()\n results = con.cursor()\n con.cursor().callproc('qiime_assets.get_emp_study_list', [results])\n for row in results:\n # study_id, sample_id, sample_name, project_name, study_title, email, sample_count, metadata_complete,\n # study_score, sample_score, s.number_samples_promised, s.number_samples_in_freezer, \n # s.principal_investigator\n studies.append((row[0], row[1], row[2], row[3], row[4], row[5],\n row[6], row[7], row[8], row[9], row[10], row[11], row[12]))\n return studies\n except Exception, e:\n print 'Exception caught: %s.\\nThe error is: %s' % (type(e), e)","def getOqiNames( self ):\n\n if self.oqiNames:\n return self.oqiNames.keys()\n\n n = self.adb.get( \"nSrss\" )\n for indx in xrange( n ):\n name = self.adb.get( \"srsName\", indx )\n self.oqiNames[ name ] = indx\n\n return self.oqiNames.keys()","def measurements(self):\n exp_type = 'Q_MS_MEASUREMENT'\n path = \"/%s/%s\" % (space, project)\n search = self.transaction.getSearchService()\n exps = search.listExperiments(path)\n return [exp for exp in exps if exp.getExperimentType() == exp_type]","def get_requisite_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_parameters_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([p for p, _ in self.expert_knowledge])))","def furanose_names(self):\n output = set()\n for item in self.monomers():\n if item in self.furanose_fac:\n output.add(self.furanose_fac[item][\"name\"])\n return list(output)","def get_equivalent_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_resource_terms(self):\n return # osid.resource.ResourceQueryInspector","def get_resource_terms(self):\n return # osid.resource.ResourceQueryInspector","def fqns(self):\n return [fqn for fqn in self.runinfos]","def getOthNames( self ):\n\n if self.othNames:\n return self.othNames.keys()\n \n nSets = self.adb.get( \"nOths\" )\n for id1 in range( nSets ):\n name = self.adb.get( \"othName\", id1 )\n self.othNames[ name ] = id1\n\n return self.othNames.keys()","def xff_alternative_names(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"xff_alternative_names\")","def get_pro_independence_parties():\n return ['JxSí', 'CUP', 'JUNTSxCAT', 'ERC-CatSí', 'Govern']","def display_skill(self):\n return ', '.join([skill.name for skill in self.skill.all()[:3]])","def examplars(self):\n return np.array(\n [\n examplar_idx for class_examplars in self._examplars.values()\n for examplar_idx in class_examplars\n ]\n )","def get_ancestor_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def unique_detector_names() :\n return experiment_info.unique_detector_names()","def list_available_strains(self):\n return [strain for strain in self.sample_dict]","def get_parameters_affecting_quality(self, quality: str) -> List[str]:\n return [p for p, q in self.correlating_pq_tuples if q == quality]","def all_experiments():\n elo_explain_experiments()\n alpha_beta_experiments()\n mtcs_experiments()","def getName(self):\n return _libsbml.QualExtension_getName(self)","def eigs(self):\n return np.concatenate(self.operator.eigenvalues)","def get_names(self):\n return [doc['name'] for doc in self.vocab]","def pyranoses(self):\n return sorted(set([self[x.split(\"_\")[-1]][\"name\"] for x in self.pyranose_fac.keys()]))","def anexos(self):\n return self._anexos","def qcs(self):\n return self.aggregation","def getListOfQualitativeSpecies(self, *args):\n return _libsbml.QualModelPlugin_getListOfQualitativeSpecies(self, *args)","def get_names(self):\n\n return self.mod_suites.keys()","def getQualitativeSpecies(self):\n return _libsbml.Input_getQualitativeSpecies(self)","def getEssentialList(self):\n return self.essentials","def get_a_list_of_testset_names() -> str:\n message = 'The available test sets are:'\n for testset in sorted(DATASETS.keys(), reverse=True):\n message += '\\n%20s: %s' % (testset, DATASETS[testset].get('description', ''))\n return message","def df_sample_names(self):\n return self.abundance_mat_mult(True)","def getName(self):\n return _libsbml.QualitativeSpecies_getName(self)","def get_all_qids(self):\n return self._entity_symbols.get_all_qids()","def termnames(self):\n\n names = []\n for term in self.terms:\n names += [term.termname]\n return names","def termnames(self):\n\n names = []\n for term in self.terms:\n names += [term.termname]\n return names","def monomer_names(self):\n output = set()\n for item in self.monomers():\n if item in self.pyranose_fac:\n output.add(self.pyranose_fac[item][\"name\"])\n return list(output)","def display_allergens(self):\n return ', '.join(allergens.name for allergens in self.allergens.all()[:3])","def get_exercises(self):\n exercises = set()\n for er in self.exercise_recordings:\n if er.exercise not in exercises:\n exercises.add(er.exercise)\n return list(exercises)","def names(self):\n return list(item.name for item in self.mechanisms)","def names(self):\n\t\treturn","def energies(self, num_levels=-1):\n if not self.solved: self.solve()\n return self.en[:num_levels]","def print_experiments(self):\n for env, agents in self.experiment_structure.items():\n for agent, _ in agents.items():\n self.logger.info('Environment: {}\\tAgent: {}'.format(env, agent))","def getQualitativeSpecies(self, *args):\n return _libsbml.QualModelPlugin_getQualitativeSpecies(self, *args)","def get_supported_instruments():\n manuf_instruments = {\n agilent.name: agilent.instruments,\n horiba.name: horiba.instruments,\n tecan.name: tecan.instruments,\n MIT.name: MIT.instruments,\n }\n # instruments = [Aqualog, Fluorolog, Cary]\n df = pd.DataFrame()\n for manuf, instruments in manuf_instruments.items():\n for i in instruments:\n for j in i.supported_models:\n d = {\n \"manufacturer\": manuf,\n \"name\": i.name,\n \"supported_models\": j,\n \"object\": i,\n }\n df = df.append(d, ignore_index=True)\n\n df.set_index([\"manufacturer\", \"supported_models\"], inplace=True)\n df_display = df.drop(columns=[\"object\"])\n return df_display, df","def get_unaccepted_qualification_requests(self):\n try:\n role_requests = self.db_handler.get_unaccepted_qualification_requests()\n\n self.logger.write_to_log('qualification requests got', 'model')\n\n return role_requests\n\n except Exception as err:\n method_name = sys._getframe().f_code.co_name\n\n self.logger.write_to_log('exception', 'model')\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')","def instruments(self):\r\n return self.get_field('instrument')","def get_maturities(self):\r\n return sorted(self.instruments.keys())","def get_providers(self):\n return [\"Rainfall\", \"Average Rainfall Sea\", \"Average Rainfall Land\"]","def obs_names(self):\n return self._obs_names","def queryYM( self, geneName, level ):\n result = [] \n \n # Get a new query on the table\n query = service.new_query(\"Gene\")\n query.add_constraint(\"interactions.participant2\", \"Gene\") \n \n query.add_view( \"primaryIdentifier\", \"symbol\", \"secondaryIdentifier\", \"sgdAlias\", \"name\",\n \"organism.shortName\", \"interactions.details.annotationType\",\n \"interactions.details.phenotype\", \"interactions.details.role1\",\n \"interactions.details.experimentType\", \"interactions.participant2.symbol\",\n \"interactions.participant2.secondaryIdentifier\",\n \"interactions.details.experiment.name\",\n \"interactions.details.relationshipType\" )\n \n query.add_constraint(\"organism.shortName\", \"=\", \"S. cerevisiae\", code = \"B\")\n query.add_constraint(\"Gene\", \"LOOKUP\", geneName, code = \"A\")\n \n for row in query.rows():\n data = level, row[\"primaryIdentifier\"], row[\"symbol\"], row[\"secondaryIdentifier\"], row[\"sgdAlias\"], \\\n row[\"name\"], row[\"organism.shortName\"], row[\"interactions.details.annotationType\"], \\\n row[\"interactions.details.phenotype\"], row[\"interactions.details.role1\"], \\\n row[\"interactions.details.experimentType\"], row[\"interactions.participant2.symbol\"], \\\n row[\"interactions.participant2.secondaryIdentifier\"], \\\n row[\"interactions.details.experiment.name\"], row[\"interactions.details.relationshipType\"]\n item = list(data)\n result.append(item) \n \n return result","def getAllSheetNames(self):\n\n\t\t\treturn self.thing.get_sheet_names()","def getElementName(self):\n return _libsbml.ListOfQualitativeSpecies_getElementName(self)","def iguales(self):\n return self._iguales","def getAllAuthorQueries(self):\n return self.queries[\"au\"]","def get_all_explorations():\n return [exp_domain.Exploration(e) for e in\n exp_models.ExplorationModel.get_all()]","def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]","def available_characterizations(self):\n return self.characterization_results.keys()","def get_absence_information(\n self, *qas: List[QualifiedAgent]\n ) -> List[ExtendedAbsenceInformation]:\n wum: WorklistUpdateManagerApi = self._service_provider.get_service(WorklistUpdateManagerApi)\n pol_res: PolicyResolutionApi = self._service_provider.get_service(PolicyResolutionApi)\n if qas is None or len(qas) == 0:\n qas = self.get_possible_absentees()\n\n res: List[ExtendedAbsenceInformation] = []\n for absentee in qas:\n ai: AbsenceInformation = wum.get_absence_information(body=absentee)\n is_absent_now = False\n substitute_summary = \"\"\n substitute_agents = []\n if ai is not None:\n unix_millis_now = TO_BPM_DATE(datetime.utcnow())\n # from date not in the future\n absence_started = ai.from_date < unix_millis_now\n absence_lasting = ai.to_date == 0 or ai.to_date > unix_millis_now\n is_absent_now = absence_started and absence_lasting\n if ai.substitution_rule:\n substitute_agents = self._rem_iter_handler.consume(\n pol_res.resolve_policy(org_policy=ai.substitution_rule),\n \"agents\",\n QaRemoteIteratorApi,\n )\n substitute_summary = OrgUtils.summarize_qa_list(*substitute_agents)\n else:\n ai = {}\n res.append(\n ExtendedAbsenceInformation(\n absentee, is_absent_now, substitute_summary, substitute_agents, ai\n )\n )\n return res","def collect_quantum_energies(quantum_outputs):\n #here we will cycle throught the outputs in order to detect SCF enery\n input_files = glob.glob(quantum_outputs)\n dict_energy = {}\n #now cycle through all the output gaussian files\n for f in input_files:\n #to be sure we take the last indexes\n phi =int( f.split(\"/\")[-2]) # to be more consistent, we know that in -2 there's phi\n psi =int( f.split(\"/\")[-1].split(\".out\")[0].split(\"structure_\")[1])\n #first fix phi and psi values:\n #plot from -180 to 180 so we can compare with Ramachandran\n if phi > 180.0:\n phi = phi - 360.0\n if psi > 180.0 :\n psi = psi - 360.0\n #open the output file\n gout = open(f,\"r\").readlines()\n #Extract energies\n scf = []\n for line in gout:\n if \"SCF Done\" in line:\n scf.append(line.split()[4])\n dict_energy[phi,psi] = float(scf[-1])*627.50\n print(\"Apparently quantum energies were correctly extracted\")\n\n return dict_energy","def get_all_terms_for_export(self):\n from apps.schedule.models.term import Term\n return Term.objects.filter(event__group=self)","def get_all_of_experiment(self, experiment_name: str):\n query = (\n f\"SELECT * FROM {self.table_name} WHERE experiment_name='{experiment_name}'\"\n )\n c = self.db.cursor()\n c.execute(query)\n queries = c.fetchall()\n return queries","def get_examiners(self):\n return u', '.join([u.username for u in self.examiners.all()])","def filter_cobra_model_genes():\n model_genes = [str(x.id) for x in self.base_cobra_model.genes]\n keep_alleles = []\n for col in strain_allele_df.columns:\n if col.split(allele_gene_sep)[0] in model_genes:\n keep_alleles.append(col)\n # X_model = strain_allele_df.drop(drop_alleles, axis=1)\n print(\"# alleles:\", len(strain_allele_df.columns), \"-> removing alleles not in GEM -> # alleles after:\", len(keep_alleles))\n return keep_alleles","def get_agency_terms(self):\n return # osid.authentication.AgencyQueryInspector","def getResRatioVarNames( self ):\n\n self.updateAdb( )\n\n return self.resNames.keys()","def inequalities(self):\n try:\n return self._inequalities\n except AttributeError:\n self._ieqs = [list(x) for x in self.inequality_generator()]\n return self._ieqs"],"string":"[\n \"def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\\n expert_knowledge = reduce(\\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\\n qualities,\\n set()\\n )\\n return sorted(list(expert_knowledge))\",\n \"def get_qualification_list(self):\\n try:\\n qualifications = self.db_handler.get_qualifications_list()\\n self.logger.write_to_log('qualifications got', 'model')\\n return qualifications\\n except Exception as err:\\n method_name = sys._getframe().f_code.co_name\\n\\n self.logger.write_to_log('exception', 'model')\\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')\",\n \"def list_qualifications() -> Dict[str, Any]:\\n qualifications_schema = QualificationSchema(many=True)\\n qualifications = Qualification.query.all()\\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))\",\n \"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\\n parameters = reduce(\\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\\n qualities,\\n set()\\n )\\n return sorted(list(parameters))\",\n \"def get_assessment_terms(self):\\n return # osid.assessment.AssessmentQueryInspector\",\n \"def get_exercise_recording_full_names(self):\\n full_names = set()\\n for er in self.exercise_recordings:\\n full_names.add(er.full_name)\\n return full_names\",\n \"def ieqs(self):\\n return self.inequalities()\",\n \"def qps(self):\\n from admin.models.qualification_pack import QualificationPack\\n return QualificationPack.objects.filter(\\n occupation=self, is_draft=False\\n )\",\n \"def earned_hw_scores(self):\\r\\n return [s.earned for s in self.get_grade_summary()['totaled_scores']['Homework']]\",\n \"def get_knowledge_category_terms(self):\\n return # osid.grading.GradeQueryInspector\",\n \"def end_effectors(self) -> list:\\n S = self.parents\\n return [[x, f\\\"q{x[1:]}\\\"] for x in S if S.out_degree(x) == 0]\",\n \"def get_well_aliases(self):\\n return self.info_wells['well'].unique()\",\n \"def all_present_experiments(self):\\n return _yield_subdir_names(self.exp_configs)\",\n \"def qalist(self):\\n return self._palist.qalist\",\n \"def display_injury_type(self):\\n return ', '.join(type_of_injury.name for genre in self.type_of_injury.all()[:3])\",\n \"def find_gene_name(qualifiers):\\n if not isinstance(qualifiers, dict):\\n raise TypeError(\\\"Expected qualifier dictionary\\\")\\n for tag in [\\\"protein_id\\\", \\\"locus_tag\\\", \\\"id\\\", \\\"gene\\\", \\\"name\\\", \\\"label\\\"]:\\n if tag in qualifiers:\\n return qualifiers[tag][0]\\n return \\\"N.A.\\\"\",\n \"def inconsistent_entityName(self):\\n a = [s for s in self.subjects if len([sa for sa in s.samples if sa.inconsistent_entityName]) > 0]\\n if len(a) == 0:\\n return None\\n return a\",\n \"def get_assessment_terms(self):\\n return # osid.repository.AssetQueryInspector\",\n \"def get_level_terms(self):\\n return # osid.grading.GradeQueryInspector\",\n \"def endog_names(self):\\n return self.data.ynames\",\n \"def exog_names(self):\\n return self.data.xnames\",\n \"def pyranose_names(self):\\n output = set()\\n for item in self.pyranoses():\\n if item in self.pyranose_fac:\\n output.add(self.pyranose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def get_cognitive_process_terms(self):\\n return # osid.grading.GradeQueryInspector\",\n \"def all_tone_qualities():\\n\\n tone_qualities = crud.get_tone_qualities()\\n\\n return render_template('all_tone_qualities.html', tone_qualities=tone_qualities)\",\n \"def getStudyNames(self):\\n try:\\n con = self.getMetadataDatabaseConnection()\\n results = con.cursor()\\n con.cursor().callproc('qiime_assets.get_study_names', [results])\\n study_name_list = []\\n for row in results:\\n if row[0] is None:\\n continue\\n else:\\n study_name_list.append(row)\\n return study_name_list\\n except Exception, e:\\n print 'Exception caught: %s.\\\\nThe error is: %s' % (type(e), e)\\n return False\",\n \"def find_qualifications(\\n self, qualification_name: Optional[str] = None\\n ) -> List[Qualification]:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n SELECT * from qualifications\\n WHERE (?1 IS NULL OR qualification_name = ?1)\\n \\\"\\\"\\\",\\n (qualification_name,),\\n )\\n rows = c.fetchall()\\n return [\\n Qualification(\\n self, str(r[\\\"qualification_id\\\"]), row=r, _used_new_call=True\\n )\\n for r in rows\\n ]\",\n \"def getQualitativeSpecies(self):\\n return _libsbml.Output_getQualitativeSpecies(self)\",\n \"def IEs(self):\\n return self._ies\",\n \"def getEMPStudyList(self):\\n try:\\n studies = []\\n con = self.getMetadataDatabaseConnection()\\n results = con.cursor()\\n con.cursor().callproc('qiime_assets.get_emp_study_list', [results])\\n for row in results:\\n # study_id, sample_id, sample_name, project_name, study_title, email, sample_count, metadata_complete,\\n # study_score, sample_score, s.number_samples_promised, s.number_samples_in_freezer, \\n # s.principal_investigator\\n studies.append((row[0], row[1], row[2], row[3], row[4], row[5],\\n row[6], row[7], row[8], row[9], row[10], row[11], row[12]))\\n return studies\\n except Exception, e:\\n print 'Exception caught: %s.\\\\nThe error is: %s' % (type(e), e)\",\n \"def getOqiNames( self ):\\n\\n if self.oqiNames:\\n return self.oqiNames.keys()\\n\\n n = self.adb.get( \\\"nSrss\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"srsName\\\", indx )\\n self.oqiNames[ name ] = indx\\n\\n return self.oqiNames.keys()\",\n \"def measurements(self):\\n exp_type = 'Q_MS_MEASUREMENT'\\n path = \\\"/%s/%s\\\" % (space, project)\\n search = self.transaction.getSearchService()\\n exps = search.listExperiments(path)\\n return [exp for exp in exps if exp.getExperimentType() == exp_type]\",\n \"def get_requisite_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_parameters_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([p for p, _ in self.expert_knowledge])))\",\n \"def furanose_names(self):\\n output = set()\\n for item in self.monomers():\\n if item in self.furanose_fac:\\n output.add(self.furanose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def get_equivalent_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_resource_terms(self):\\n return # osid.resource.ResourceQueryInspector\",\n \"def get_resource_terms(self):\\n return # osid.resource.ResourceQueryInspector\",\n \"def fqns(self):\\n return [fqn for fqn in self.runinfos]\",\n \"def getOthNames( self ):\\n\\n if self.othNames:\\n return self.othNames.keys()\\n \\n nSets = self.adb.get( \\\"nOths\\\" )\\n for id1 in range( nSets ):\\n name = self.adb.get( \\\"othName\\\", id1 )\\n self.othNames[ name ] = id1\\n\\n return self.othNames.keys()\",\n \"def xff_alternative_names(self) -> pulumi.Output[Sequence[str]]:\\n return pulumi.get(self, \\\"xff_alternative_names\\\")\",\n \"def get_pro_independence_parties():\\n return ['JxSí', 'CUP', 'JUNTSxCAT', 'ERC-CatSí', 'Govern']\",\n \"def display_skill(self):\\n return ', '.join([skill.name for skill in self.skill.all()[:3]])\",\n \"def examplars(self):\\n return np.array(\\n [\\n examplar_idx for class_examplars in self._examplars.values()\\n for examplar_idx in class_examplars\\n ]\\n )\",\n \"def get_ancestor_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def unique_detector_names() :\\n return experiment_info.unique_detector_names()\",\n \"def list_available_strains(self):\\n return [strain for strain in self.sample_dict]\",\n \"def get_parameters_affecting_quality(self, quality: str) -> List[str]:\\n return [p for p, q in self.correlating_pq_tuples if q == quality]\",\n \"def all_experiments():\\n elo_explain_experiments()\\n alpha_beta_experiments()\\n mtcs_experiments()\",\n \"def getName(self):\\n return _libsbml.QualExtension_getName(self)\",\n \"def eigs(self):\\n return np.concatenate(self.operator.eigenvalues)\",\n \"def get_names(self):\\n return [doc['name'] for doc in self.vocab]\",\n \"def pyranoses(self):\\n return sorted(set([self[x.split(\\\"_\\\")[-1]][\\\"name\\\"] for x in self.pyranose_fac.keys()]))\",\n \"def anexos(self):\\n return self._anexos\",\n \"def qcs(self):\\n return self.aggregation\",\n \"def getListOfQualitativeSpecies(self, *args):\\n return _libsbml.QualModelPlugin_getListOfQualitativeSpecies(self, *args)\",\n \"def get_names(self):\\n\\n return self.mod_suites.keys()\",\n \"def getQualitativeSpecies(self):\\n return _libsbml.Input_getQualitativeSpecies(self)\",\n \"def getEssentialList(self):\\n return self.essentials\",\n \"def get_a_list_of_testset_names() -> str:\\n message = 'The available test sets are:'\\n for testset in sorted(DATASETS.keys(), reverse=True):\\n message += '\\\\n%20s: %s' % (testset, DATASETS[testset].get('description', ''))\\n return message\",\n \"def df_sample_names(self):\\n return self.abundance_mat_mult(True)\",\n \"def getName(self):\\n return _libsbml.QualitativeSpecies_getName(self)\",\n \"def get_all_qids(self):\\n return self._entity_symbols.get_all_qids()\",\n \"def termnames(self):\\n\\n names = []\\n for term in self.terms:\\n names += [term.termname]\\n return names\",\n \"def termnames(self):\\n\\n names = []\\n for term in self.terms:\\n names += [term.termname]\\n return names\",\n \"def monomer_names(self):\\n output = set()\\n for item in self.monomers():\\n if item in self.pyranose_fac:\\n output.add(self.pyranose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def display_allergens(self):\\n return ', '.join(allergens.name for allergens in self.allergens.all()[:3])\",\n \"def get_exercises(self):\\n exercises = set()\\n for er in self.exercise_recordings:\\n if er.exercise not in exercises:\\n exercises.add(er.exercise)\\n return list(exercises)\",\n \"def names(self):\\n return list(item.name for item in self.mechanisms)\",\n \"def names(self):\\n\\t\\treturn\",\n \"def energies(self, num_levels=-1):\\n if not self.solved: self.solve()\\n return self.en[:num_levels]\",\n \"def print_experiments(self):\\n for env, agents in self.experiment_structure.items():\\n for agent, _ in agents.items():\\n self.logger.info('Environment: {}\\\\tAgent: {}'.format(env, agent))\",\n \"def getQualitativeSpecies(self, *args):\\n return _libsbml.QualModelPlugin_getQualitativeSpecies(self, *args)\",\n \"def get_supported_instruments():\\n manuf_instruments = {\\n agilent.name: agilent.instruments,\\n horiba.name: horiba.instruments,\\n tecan.name: tecan.instruments,\\n MIT.name: MIT.instruments,\\n }\\n # instruments = [Aqualog, Fluorolog, Cary]\\n df = pd.DataFrame()\\n for manuf, instruments in manuf_instruments.items():\\n for i in instruments:\\n for j in i.supported_models:\\n d = {\\n \\\"manufacturer\\\": manuf,\\n \\\"name\\\": i.name,\\n \\\"supported_models\\\": j,\\n \\\"object\\\": i,\\n }\\n df = df.append(d, ignore_index=True)\\n\\n df.set_index([\\\"manufacturer\\\", \\\"supported_models\\\"], inplace=True)\\n df_display = df.drop(columns=[\\\"object\\\"])\\n return df_display, df\",\n \"def get_unaccepted_qualification_requests(self):\\n try:\\n role_requests = self.db_handler.get_unaccepted_qualification_requests()\\n\\n self.logger.write_to_log('qualification requests got', 'model')\\n\\n return role_requests\\n\\n except Exception as err:\\n method_name = sys._getframe().f_code.co_name\\n\\n self.logger.write_to_log('exception', 'model')\\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')\",\n \"def instruments(self):\\r\\n return self.get_field('instrument')\",\n \"def get_maturities(self):\\r\\n return sorted(self.instruments.keys())\",\n \"def get_providers(self):\\n return [\\\"Rainfall\\\", \\\"Average Rainfall Sea\\\", \\\"Average Rainfall Land\\\"]\",\n \"def obs_names(self):\\n return self._obs_names\",\n \"def queryYM( self, geneName, level ):\\n result = [] \\n \\n # Get a new query on the table\\n query = service.new_query(\\\"Gene\\\")\\n query.add_constraint(\\\"interactions.participant2\\\", \\\"Gene\\\") \\n \\n query.add_view( \\\"primaryIdentifier\\\", \\\"symbol\\\", \\\"secondaryIdentifier\\\", \\\"sgdAlias\\\", \\\"name\\\",\\n \\\"organism.shortName\\\", \\\"interactions.details.annotationType\\\",\\n \\\"interactions.details.phenotype\\\", \\\"interactions.details.role1\\\",\\n \\\"interactions.details.experimentType\\\", \\\"interactions.participant2.symbol\\\",\\n \\\"interactions.participant2.secondaryIdentifier\\\",\\n \\\"interactions.details.experiment.name\\\",\\n \\\"interactions.details.relationshipType\\\" )\\n \\n query.add_constraint(\\\"organism.shortName\\\", \\\"=\\\", \\\"S. cerevisiae\\\", code = \\\"B\\\")\\n query.add_constraint(\\\"Gene\\\", \\\"LOOKUP\\\", geneName, code = \\\"A\\\")\\n \\n for row in query.rows():\\n data = level, row[\\\"primaryIdentifier\\\"], row[\\\"symbol\\\"], row[\\\"secondaryIdentifier\\\"], row[\\\"sgdAlias\\\"], \\\\\\n row[\\\"name\\\"], row[\\\"organism.shortName\\\"], row[\\\"interactions.details.annotationType\\\"], \\\\\\n row[\\\"interactions.details.phenotype\\\"], row[\\\"interactions.details.role1\\\"], \\\\\\n row[\\\"interactions.details.experimentType\\\"], row[\\\"interactions.participant2.symbol\\\"], \\\\\\n row[\\\"interactions.participant2.secondaryIdentifier\\\"], \\\\\\n row[\\\"interactions.details.experiment.name\\\"], row[\\\"interactions.details.relationshipType\\\"]\\n item = list(data)\\n result.append(item) \\n \\n return result\",\n \"def getAllSheetNames(self):\\n\\n\\t\\t\\treturn self.thing.get_sheet_names()\",\n \"def getElementName(self):\\n return _libsbml.ListOfQualitativeSpecies_getElementName(self)\",\n \"def iguales(self):\\n return self._iguales\",\n \"def getAllAuthorQueries(self):\\n return self.queries[\\\"au\\\"]\",\n \"def get_all_explorations():\\n return [exp_domain.Exploration(e) for e in\\n exp_models.ExplorationModel.get_all()]\",\n \"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\\n return [q for p, q in self.correlating_pq_tuples if p == parameter]\",\n \"def available_characterizations(self):\\n return self.characterization_results.keys()\",\n \"def get_absence_information(\\n self, *qas: List[QualifiedAgent]\\n ) -> List[ExtendedAbsenceInformation]:\\n wum: WorklistUpdateManagerApi = self._service_provider.get_service(WorklistUpdateManagerApi)\\n pol_res: PolicyResolutionApi = self._service_provider.get_service(PolicyResolutionApi)\\n if qas is None or len(qas) == 0:\\n qas = self.get_possible_absentees()\\n\\n res: List[ExtendedAbsenceInformation] = []\\n for absentee in qas:\\n ai: AbsenceInformation = wum.get_absence_information(body=absentee)\\n is_absent_now = False\\n substitute_summary = \\\"\\\"\\n substitute_agents = []\\n if ai is not None:\\n unix_millis_now = TO_BPM_DATE(datetime.utcnow())\\n # from date not in the future\\n absence_started = ai.from_date < unix_millis_now\\n absence_lasting = ai.to_date == 0 or ai.to_date > unix_millis_now\\n is_absent_now = absence_started and absence_lasting\\n if ai.substitution_rule:\\n substitute_agents = self._rem_iter_handler.consume(\\n pol_res.resolve_policy(org_policy=ai.substitution_rule),\\n \\\"agents\\\",\\n QaRemoteIteratorApi,\\n )\\n substitute_summary = OrgUtils.summarize_qa_list(*substitute_agents)\\n else:\\n ai = {}\\n res.append(\\n ExtendedAbsenceInformation(\\n absentee, is_absent_now, substitute_summary, substitute_agents, ai\\n )\\n )\\n return res\",\n \"def collect_quantum_energies(quantum_outputs):\\n #here we will cycle throught the outputs in order to detect SCF enery\\n input_files = glob.glob(quantum_outputs)\\n dict_energy = {}\\n #now cycle through all the output gaussian files\\n for f in input_files:\\n #to be sure we take the last indexes\\n phi =int( f.split(\\\"/\\\")[-2]) # to be more consistent, we know that in -2 there's phi\\n psi =int( f.split(\\\"/\\\")[-1].split(\\\".out\\\")[0].split(\\\"structure_\\\")[1])\\n #first fix phi and psi values:\\n #plot from -180 to 180 so we can compare with Ramachandran\\n if phi > 180.0:\\n phi = phi - 360.0\\n if psi > 180.0 :\\n psi = psi - 360.0\\n #open the output file\\n gout = open(f,\\\"r\\\").readlines()\\n #Extract energies\\n scf = []\\n for line in gout:\\n if \\\"SCF Done\\\" in line:\\n scf.append(line.split()[4])\\n dict_energy[phi,psi] = float(scf[-1])*627.50\\n print(\\\"Apparently quantum energies were correctly extracted\\\")\\n\\n return dict_energy\",\n \"def get_all_terms_for_export(self):\\n from apps.schedule.models.term import Term\\n return Term.objects.filter(event__group=self)\",\n \"def get_all_of_experiment(self, experiment_name: str):\\n query = (\\n f\\\"SELECT * FROM {self.table_name} WHERE experiment_name='{experiment_name}'\\\"\\n )\\n c = self.db.cursor()\\n c.execute(query)\\n queries = c.fetchall()\\n return queries\",\n \"def get_examiners(self):\\n return u', '.join([u.username for u in self.examiners.all()])\",\n \"def filter_cobra_model_genes():\\n model_genes = [str(x.id) for x in self.base_cobra_model.genes]\\n keep_alleles = []\\n for col in strain_allele_df.columns:\\n if col.split(allele_gene_sep)[0] in model_genes:\\n keep_alleles.append(col)\\n # X_model = strain_allele_df.drop(drop_alleles, axis=1)\\n print(\\\"# alleles:\\\", len(strain_allele_df.columns), \\\"-> removing alleles not in GEM -> # alleles after:\\\", len(keep_alleles))\\n return keep_alleles\",\n \"def get_agency_terms(self):\\n return # osid.authentication.AgencyQueryInspector\",\n \"def getResRatioVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.resNames.keys()\",\n \"def inequalities(self):\\n try:\\n return self._inequalities\\n except AttributeError:\\n self._ieqs = [list(x) for x in self.inequality_generator()]\\n return self._ieqs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6796296","0.6399958","0.5745922","0.570581","0.56431264","0.5579781","0.5524196","0.5486619","0.5414175","0.5371931","0.5336818","0.5334099","0.5322436","0.53192633","0.5316867","0.5283304","0.5277264","0.527555","0.52340245","0.5215532","0.5200703","0.5166302","0.5160945","0.51546645","0.5123504","0.51158535","0.5115845","0.51147926","0.5109323","0.50758994","0.507025","0.5067665","0.50517756","0.50425583","0.50382686","0.503731","0.503731","0.5019872","0.5019872","0.5019872","0.5004851","0.5004851","0.49992588","0.49982157","0.49935472","0.49913025","0.4983468","0.4982841","0.4982288","0.49779952","0.49552256","0.49468613","0.49397513","0.4937397","0.49274415","0.4917682","0.49073195","0.49072877","0.49071485","0.48974538","0.4890717","0.48713148","0.48707634","0.48660398","0.48573858","0.48471487","0.48435813","0.48088107","0.48088107","0.4806889","0.47993872","0.47692665","0.4768064","0.47632542","0.4762745","0.47612494","0.47597453","0.47594243","0.4759013","0.47582117","0.47419423","0.47343293","0.4732898","0.47306636","0.47297972","0.47283158","0.4728048","0.47243553","0.47240865","0.47237977","0.47234705","0.47195986","0.4719327","0.47071734","0.47041523","0.47021532","0.4692973","0.4688208","0.46832332","0.46830297"],"string":"[\n \"0.6796296\",\n \"0.6399958\",\n \"0.5745922\",\n \"0.570581\",\n \"0.56431264\",\n \"0.5579781\",\n \"0.5524196\",\n \"0.5486619\",\n \"0.5414175\",\n \"0.5371931\",\n \"0.5336818\",\n \"0.5334099\",\n \"0.5322436\",\n \"0.53192633\",\n \"0.5316867\",\n \"0.5283304\",\n \"0.5277264\",\n \"0.527555\",\n \"0.52340245\",\n \"0.5215532\",\n \"0.5200703\",\n \"0.5166302\",\n \"0.5160945\",\n \"0.51546645\",\n \"0.5123504\",\n \"0.51158535\",\n \"0.5115845\",\n \"0.51147926\",\n \"0.5109323\",\n \"0.50758994\",\n \"0.507025\",\n \"0.5067665\",\n \"0.50517756\",\n \"0.50425583\",\n \"0.50382686\",\n \"0.503731\",\n \"0.503731\",\n \"0.5019872\",\n \"0.5019872\",\n \"0.5019872\",\n \"0.5004851\",\n \"0.5004851\",\n \"0.49992588\",\n \"0.49982157\",\n \"0.49935472\",\n \"0.49913025\",\n \"0.4983468\",\n \"0.4982841\",\n \"0.4982288\",\n \"0.49779952\",\n \"0.49552256\",\n \"0.49468613\",\n \"0.49397513\",\n \"0.4937397\",\n \"0.49274415\",\n \"0.4917682\",\n \"0.49073195\",\n \"0.49072877\",\n \"0.49071485\",\n \"0.48974538\",\n \"0.4890717\",\n \"0.48713148\",\n \"0.48707634\",\n \"0.48660398\",\n \"0.48573858\",\n \"0.48471487\",\n \"0.48435813\",\n \"0.48088107\",\n \"0.48088107\",\n \"0.4806889\",\n \"0.47993872\",\n \"0.47692665\",\n \"0.4768064\",\n \"0.47632542\",\n \"0.4762745\",\n \"0.47612494\",\n \"0.47597453\",\n \"0.47594243\",\n \"0.4759013\",\n \"0.47582117\",\n \"0.47419423\",\n \"0.47343293\",\n \"0.4732898\",\n \"0.47306636\",\n \"0.47297972\",\n \"0.47283158\",\n \"0.4728048\",\n \"0.47243553\",\n \"0.47240865\",\n \"0.47237977\",\n \"0.47234705\",\n \"0.47195986\",\n \"0.4719327\",\n \"0.47071734\",\n \"0.47041523\",\n \"0.47021532\",\n \"0.4692973\",\n \"0.4688208\",\n \"0.46832332\",\n \"0.46830297\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68818367"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17461,"cells":{"query":{"kind":"string","value":"Returns the names of all parameters with exitsting expert knowledge."},"document":{"kind":"string","value":"def get_parameters_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([p for p, _ in self.expert_knowledge])))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def parameter_names(self) -> List[str]:","def _get_fitted_param_names(self):\n return self._fitted_param_names","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def parameters(self):\n return []","def get_ext_param_names(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\n ret.append(tmp.decode(\"UTF-8\"))\n\n return ret","def get_param_names(self):\n return list(self.params.keys())","def return_all_parameter_names():\n a = list(titles)\n a.append(r\"$\\chi^{2}$ per degree of freedom\")\n b = list(labels)\n b.append(\"chi2_per_dof\")\n return a, b","def _get_param_names(self):\r\n return sorted([p\r\n for p in self.__dict__\r\n if p != 'additional_args'])","def parameters(self):\n return [term.parameter for term in self.terms]","def parameters_names(cls):\n return cls._Parameters._fields","def get_ext_param_descriptions(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_description(self.xc_func_info, p)\n ret.append(tmp.decode(\"UTF-8\"))\n\n return ret","def parameter_names(self):\n raise NotImplementedError(\"the parameter_names property should \"\n \"be defined in the Estimator sub-class\")","def get_param_names(hf):\n parameters = get_params(hf)\n return [p.name for p in parameters]","def potential_parameters(cls):\n return [\"k\", \"length\"]","def getParameters(self): #$NON-NLS-1$\r","def paramDetails(cls):\n return {\n 'dim': (10, 20, 2, 20),\n 'nIter': (1, 10, 2, 5),\n 'lamb': (.1, 1., .1, .05),\n 'alph': (30, 50, 5, 40)\n }","def parameter_names(self):\n return [x for x in self.transformations.values() if isinstance(x, str)]","def get_param_texts(self):\n return self.param_texts","def parameterNames(self, p_int): # real signature unknown; restored from __doc__\n return []","def parameters(self):\n ps = super().parameters()\n exclude = set(self.estimator.parameters())\n ps = (p for p in ps if not p in exclude)\n return ps","def get_params(self):\n return []","def get_layer_var_names(self):\n return(self.params)","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def param_unc_names(self) -> List[str]:\n return self._param_unc_names(self.model).decode(\"utf-8\").split(\",\")","def param(self):\r\n\r\n return []","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def param(self):\n return []","def param(self):\n return []","def return_parameter_names():\n return list(titles), list(labels)","def get_parameter_names(self, exclude_pop_model=False):\n if (self._population_model is None) or exclude_pop_model:\n names = self._mechanistic_model.parameters()\n for error_model in self._error_models:\n names += error_model.get_parameter_names()\n return names\n\n return self._population_model.get_parameter_names()","def param(self):\r\n return []","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def parameters(self):\n return [p for _, a in vars(self).items() for p in self._params(a)]","def get_mandatory_param_names(self):\n all_names = self.params.keys()\n return [name for name in all_names \n if not self.params[name].is_optional]","def parameters(self):\n pass","def parameter_names(self) -> list:\n parameters = []\n parameters.extend(self.properties.parameter_names)\n return parameters","def keys(self):\n return self.params.keys()","def parameters(self):\n return self.vars","def get_str_param_names(self):\n # Exclude self.api and self.names from the command string\n return self.get_attribute_names(FormattedParameter)","def get_params_list():\n return common.QOL_PARAMS","def get_parameters_list(self):\n return self.description[\"config\"][\"values\"].keys()","def parameters(self):\n return self.pars","def print_param_names(model):\n for (param_name, param) in model.get_parameters().items():\n print(param_name, param.shape)","def parameters(self):","def get_all_component_parameters(self) -> Dict[str, Any]:\n return self._node[\"app_data\"][\"component_parameters\"]","def _get_param_names(self):\n temp_params = {'function': self.function, 'target': self.target}\n\n temp_params.update(self.kwargs)\n\n return temp_params","def get_params(self):","def parameters(self):\n return [i for i in self.variables if has_roles(i, Parameter)]","def print_params():\n\n help_out = convert_phil_to_text(master_phil, att_level=1)\n txt_out = convert_phil_to_text(master_phil)\n\n return help_out, txt_out","def names(self) -> List[str]:\n return sorted(self.hyperparams)","def summary_parameters(self):\n return {'filters': ', '.join(self.getOption('filters'))}","def potential_parameters(cls):\n return [\"k\", \"angle\"]","def parameters(self):\n return self._params","def _get_params(self):\r\n return self.k._get_params()","def params(self) -> dict:\n\n if not self.exp_metadata.parameters:\n self.exp_metadata.parameters = {}\n return self.exp_metadata.parameters","def potential_parameters(cls):\n return [\"length\", \"distance\"]","def names(self):\n result = []\n result.extend(self.positional_arguments)\n if self.arbitary_positional_arguments is not None:\n result.append(self.arbitary_positional_arguments)\n if self.arbitary_keyword_arguments is not None:\n result.append(self.arbitary_keyword_arguments)\n result.extend(self.keyword_arguments)\n return result","def get_params(self):\n pass","def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\n return sorted(list(set(expert_knowledge)))","def help(cls):\n print(cls._LIST_PARAMETERS)","def _get_parameters(self) -> list:\n return self.parameters","def parameters(self):\n return self.model.parameters()","def accepted_params(self):\n return self.ptm.current_luiti_visualiser_env[\"additional_task_parameters\"]","def getListOfParameters(self):\n return self.model.getListOfParameters()","def _create_parameter_names(self):\n self._parameter_names = self.parameter_schema[\"problem\"][\"names\"]","def show_parameters(self):\n with np.printoptions(precision=3, suppress=True):\n print('number of wind phase = {}'.format(self.ncomp))\n print('galactic parameter = {}'.format(self.scaling_field))\n print('reference height = {}'.format(self.z0))\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\n params = getattr(self,p)\n print(p)\n for k,v in params.items():\n print(' {} = {}'.format(k,v))","def params(self):\n return self._pars","def log_parameters(self, all_vars):\n del all_vars['self']\n del all_vars['adj_matrix']\n del all_vars['features']\n del all_vars['labels']\n del all_vars['tvt_nids']\n self.logger.info(f'Parameters: {all_vars}')","def getInfoVariableNames(self, product):\r\n return []","def display_parameters(self):\n l = []\n for param in self.parameters.all():\n if len(param.value) > 16:\n l.append(u\"{}={}...\".format(param.name, param.value[:16]))\n else:\n l.append(u\"{}={}\".format(param.name, param.value))\n return \"; \".join(l)","def get_hyperparams(self):","def params():\n raise NotImplementedError","def parameters(self):\n return self._default_params","def get_params(self):\n\n return None","def get_params(self):\n\n return None","def get_params(self):\n\n return None","def parameters(self):\n return self.trainer_parameters","def parameters(self):\n return self.trainer_parameters","def _get_param_names(cls):\n # fetch the constructor or the original constructor before\n # deprecation wrapping if any\n init = getattr(cls.__init__, 'deprecated_original', cls.__init__)\n if init is object.__init__:\n # No explicit constructor to introspect\n return []\n\n # introspect the constructor arguments to find the model parameters\n # to represent\n init_signature = signature(init)\n # Consider the constructor parameters excluding 'self'\n parameters = [\n p for p in init_signature.parameters.values()\n if p.name != 'self' and p.kind != p.VAR_KEYWORD\n ]\n for p in parameters:\n if p.kind == p.VAR_POSITIONAL:\n raise EstimatorParameterError(\n 'scikit-learn estimators should always '\n 'specify their parameters in the signature'\n ' of their __init__ (no varargs).'\n \" %s with constructor %s doesn't \"\n ' follow this convention.' % (cls, init_signature))\n # Extract and sort argument names excluding 'self'\n return sorted([p.name for p in parameters])","def get_all_variables_names(self):\n return self.project.get_variable_names() + self.design.get_variable_names()","def parameter_list(self):\n return [\n [encut, kpoint_mesh]\n for encut, kpoint_mesh in zip(\n self._job.iteration_frame.ENCUT, self._job.iteration_frame.KPOINT_MESH\n )\n ]","def parameters(self) -> Dict[str, Any]:\n return self.trainer_parameters","def _get_parameters(self):\n return None","def parameters(self):\n return [i.parameter for i in self.joints.values()]","def extract_parameters(self) -> Dict[str, Set[str]]:\n regex = \"\\{([A-Za-z0-9_]+)\\}\"\n reserved_parameters = [\n \"output\",\n \"input\",\n \"output_vec\",\n \"input_vec\",\n \"df\",\n \"vec_open\",\n \"vec_close\",\n ]\n parameters = {}\n for scope in self.scopes:\n parameters[scope] = set(\n [\n x\n for x in re.findall(regex, self.call)\n if x not in reserved_parameters\n ]\n )\n return parameters","def getParams(self):\n return self.trainError, self.trainAcc, self.w","def parameters(self):\n return self._params","def get_explanatory_variables(self):\n return list(self._explanatory_variables)","def get_paramnames_list(self):\n # TODO include syselem?\n\n query = \"SELECT NAME FROM %s\" % self.__schema\n with self.__connection.cursor() as cursor:\n cursor.execute(query)\n result = cursor.fetchall()\n return [val['NAME'] for val in result]","def inspect_state(self):\n for name in self._param_store.get_all_param_names():\n self._logger.info(\"Param [%s]: %r\", name,\n pyro.param(name).data.numpy())","def get_ext_param_default_values(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_default_value(self.xc_func_info, p)\n ret.append(tmp)\n\n return ret","def _create_parameter_names(self):\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']","def get_resource_params(self):\n return SBE37Parameter.list()","def display_parameters(self):\n\n self.logging.debug(\"============\")\n for attr in self.parm_list:\n self.logging.debug(attr.label + \" (\" + attr.when + \")\" + \" = \" + str(attr.value))\n self.logging.debug(\"============\")","def get_resource_params():\n return Parameter.list()","def parameters(self):\n return {\"P\": self.P,\n \"T\": self.T}","def getparameters(filter,title = \"\"):\n vardic = filter.__dict__\n for i in list(vardic.keys()):\n if vardic[i] is not None:\n title += \" \"+i+\": \"\n title += str(vardic[i])+\",\"\n return title[:-1]","def get_params(self):\n return {\n \"nspecies\": self.nspecies,\n \"lmax\": self.lmax,\n \"nmax\": self.nmax,\n \"rcut\": self.rcut,\n \"sigma\": self.sigma,\n \"trans_width\": self.trans_width\n }"],"string":"[\n \"def parameter_names(self) -> List[str]:\",\n \"def _get_fitted_param_names(self):\\n return self._fitted_param_names\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def parameters(self):\\n return []\",\n \"def get_ext_param_names(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\\n ret.append(tmp.decode(\\\"UTF-8\\\"))\\n\\n return ret\",\n \"def get_param_names(self):\\n return list(self.params.keys())\",\n \"def return_all_parameter_names():\\n a = list(titles)\\n a.append(r\\\"$\\\\chi^{2}$ per degree of freedom\\\")\\n b = list(labels)\\n b.append(\\\"chi2_per_dof\\\")\\n return a, b\",\n \"def _get_param_names(self):\\r\\n return sorted([p\\r\\n for p in self.__dict__\\r\\n if p != 'additional_args'])\",\n \"def parameters(self):\\n return [term.parameter for term in self.terms]\",\n \"def parameters_names(cls):\\n return cls._Parameters._fields\",\n \"def get_ext_param_descriptions(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_description(self.xc_func_info, p)\\n ret.append(tmp.decode(\\\"UTF-8\\\"))\\n\\n return ret\",\n \"def parameter_names(self):\\n raise NotImplementedError(\\\"the parameter_names property should \\\"\\n \\\"be defined in the Estimator sub-class\\\")\",\n \"def get_param_names(hf):\\n parameters = get_params(hf)\\n return [p.name for p in parameters]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"length\\\"]\",\n \"def getParameters(self): #$NON-NLS-1$\\r\",\n \"def paramDetails(cls):\\n return {\\n 'dim': (10, 20, 2, 20),\\n 'nIter': (1, 10, 2, 5),\\n 'lamb': (.1, 1., .1, .05),\\n 'alph': (30, 50, 5, 40)\\n }\",\n \"def parameter_names(self):\\n return [x for x in self.transformations.values() if isinstance(x, str)]\",\n \"def get_param_texts(self):\\n return self.param_texts\",\n \"def parameterNames(self, p_int): # real signature unknown; restored from __doc__\\n return []\",\n \"def parameters(self):\\n ps = super().parameters()\\n exclude = set(self.estimator.parameters())\\n ps = (p for p in ps if not p in exclude)\\n return ps\",\n \"def get_params(self):\\n return []\",\n \"def get_layer_var_names(self):\\n return(self.params)\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def param_unc_names(self) -> List[str]:\\n return self._param_unc_names(self.model).decode(\\\"utf-8\\\").split(\\\",\\\")\",\n \"def param(self):\\r\\n\\r\\n return []\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def param(self):\\n return []\",\n \"def param(self):\\n return []\",\n \"def return_parameter_names():\\n return list(titles), list(labels)\",\n \"def get_parameter_names(self, exclude_pop_model=False):\\n if (self._population_model is None) or exclude_pop_model:\\n names = self._mechanistic_model.parameters()\\n for error_model in self._error_models:\\n names += error_model.get_parameter_names()\\n return names\\n\\n return self._population_model.get_parameter_names()\",\n \"def param(self):\\r\\n return []\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def parameters(self):\\n return [p for _, a in vars(self).items() for p in self._params(a)]\",\n \"def get_mandatory_param_names(self):\\n all_names = self.params.keys()\\n return [name for name in all_names \\n if not self.params[name].is_optional]\",\n \"def parameters(self):\\n pass\",\n \"def parameter_names(self) -> list:\\n parameters = []\\n parameters.extend(self.properties.parameter_names)\\n return parameters\",\n \"def keys(self):\\n return self.params.keys()\",\n \"def parameters(self):\\n return self.vars\",\n \"def get_str_param_names(self):\\n # Exclude self.api and self.names from the command string\\n return self.get_attribute_names(FormattedParameter)\",\n \"def get_params_list():\\n return common.QOL_PARAMS\",\n \"def get_parameters_list(self):\\n return self.description[\\\"config\\\"][\\\"values\\\"].keys()\",\n \"def parameters(self):\\n return self.pars\",\n \"def print_param_names(model):\\n for (param_name, param) in model.get_parameters().items():\\n print(param_name, param.shape)\",\n \"def parameters(self):\",\n \"def get_all_component_parameters(self) -> Dict[str, Any]:\\n return self._node[\\\"app_data\\\"][\\\"component_parameters\\\"]\",\n \"def _get_param_names(self):\\n temp_params = {'function': self.function, 'target': self.target}\\n\\n temp_params.update(self.kwargs)\\n\\n return temp_params\",\n \"def get_params(self):\",\n \"def parameters(self):\\n return [i for i in self.variables if has_roles(i, Parameter)]\",\n \"def print_params():\\n\\n help_out = convert_phil_to_text(master_phil, att_level=1)\\n txt_out = convert_phil_to_text(master_phil)\\n\\n return help_out, txt_out\",\n \"def names(self) -> List[str]:\\n return sorted(self.hyperparams)\",\n \"def summary_parameters(self):\\n return {'filters': ', '.join(self.getOption('filters'))}\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"angle\\\"]\",\n \"def parameters(self):\\n return self._params\",\n \"def _get_params(self):\\r\\n return self.k._get_params()\",\n \"def params(self) -> dict:\\n\\n if not self.exp_metadata.parameters:\\n self.exp_metadata.parameters = {}\\n return self.exp_metadata.parameters\",\n \"def potential_parameters(cls):\\n return [\\\"length\\\", \\\"distance\\\"]\",\n \"def names(self):\\n result = []\\n result.extend(self.positional_arguments)\\n if self.arbitary_positional_arguments is not None:\\n result.append(self.arbitary_positional_arguments)\\n if self.arbitary_keyword_arguments is not None:\\n result.append(self.arbitary_keyword_arguments)\\n result.extend(self.keyword_arguments)\\n return result\",\n \"def get_params(self):\\n pass\",\n \"def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\\n return sorted(list(set(expert_knowledge)))\",\n \"def help(cls):\\n print(cls._LIST_PARAMETERS)\",\n \"def _get_parameters(self) -> list:\\n return self.parameters\",\n \"def parameters(self):\\n return self.model.parameters()\",\n \"def accepted_params(self):\\n return self.ptm.current_luiti_visualiser_env[\\\"additional_task_parameters\\\"]\",\n \"def getListOfParameters(self):\\n return self.model.getListOfParameters()\",\n \"def _create_parameter_names(self):\\n self._parameter_names = self.parameter_schema[\\\"problem\\\"][\\\"names\\\"]\",\n \"def show_parameters(self):\\n with np.printoptions(precision=3, suppress=True):\\n print('number of wind phase = {}'.format(self.ncomp))\\n print('galactic parameter = {}'.format(self.scaling_field))\\n print('reference height = {}'.format(self.z0))\\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\\n params = getattr(self,p)\\n print(p)\\n for k,v in params.items():\\n print(' {} = {}'.format(k,v))\",\n \"def params(self):\\n return self._pars\",\n \"def log_parameters(self, all_vars):\\n del all_vars['self']\\n del all_vars['adj_matrix']\\n del all_vars['features']\\n del all_vars['labels']\\n del all_vars['tvt_nids']\\n self.logger.info(f'Parameters: {all_vars}')\",\n \"def getInfoVariableNames(self, product):\\r\\n return []\",\n \"def display_parameters(self):\\n l = []\\n for param in self.parameters.all():\\n if len(param.value) > 16:\\n l.append(u\\\"{}={}...\\\".format(param.name, param.value[:16]))\\n else:\\n l.append(u\\\"{}={}\\\".format(param.name, param.value))\\n return \\\"; \\\".join(l)\",\n \"def get_hyperparams(self):\",\n \"def params():\\n raise NotImplementedError\",\n \"def parameters(self):\\n return self._default_params\",\n \"def get_params(self):\\n\\n return None\",\n \"def get_params(self):\\n\\n return None\",\n \"def get_params(self):\\n\\n return None\",\n \"def parameters(self):\\n return self.trainer_parameters\",\n \"def parameters(self):\\n return self.trainer_parameters\",\n \"def _get_param_names(cls):\\n # fetch the constructor or the original constructor before\\n # deprecation wrapping if any\\n init = getattr(cls.__init__, 'deprecated_original', cls.__init__)\\n if init is object.__init__:\\n # No explicit constructor to introspect\\n return []\\n\\n # introspect the constructor arguments to find the model parameters\\n # to represent\\n init_signature = signature(init)\\n # Consider the constructor parameters excluding 'self'\\n parameters = [\\n p for p in init_signature.parameters.values()\\n if p.name != 'self' and p.kind != p.VAR_KEYWORD\\n ]\\n for p in parameters:\\n if p.kind == p.VAR_POSITIONAL:\\n raise EstimatorParameterError(\\n 'scikit-learn estimators should always '\\n 'specify their parameters in the signature'\\n ' of their __init__ (no varargs).'\\n \\\" %s with constructor %s doesn't \\\"\\n ' follow this convention.' % (cls, init_signature))\\n # Extract and sort argument names excluding 'self'\\n return sorted([p.name for p in parameters])\",\n \"def get_all_variables_names(self):\\n return self.project.get_variable_names() + self.design.get_variable_names()\",\n \"def parameter_list(self):\\n return [\\n [encut, kpoint_mesh]\\n for encut, kpoint_mesh in zip(\\n self._job.iteration_frame.ENCUT, self._job.iteration_frame.KPOINT_MESH\\n )\\n ]\",\n \"def parameters(self) -> Dict[str, Any]:\\n return self.trainer_parameters\",\n \"def _get_parameters(self):\\n return None\",\n \"def parameters(self):\\n return [i.parameter for i in self.joints.values()]\",\n \"def extract_parameters(self) -> Dict[str, Set[str]]:\\n regex = \\\"\\\\{([A-Za-z0-9_]+)\\\\}\\\"\\n reserved_parameters = [\\n \\\"output\\\",\\n \\\"input\\\",\\n \\\"output_vec\\\",\\n \\\"input_vec\\\",\\n \\\"df\\\",\\n \\\"vec_open\\\",\\n \\\"vec_close\\\",\\n ]\\n parameters = {}\\n for scope in self.scopes:\\n parameters[scope] = set(\\n [\\n x\\n for x in re.findall(regex, self.call)\\n if x not in reserved_parameters\\n ]\\n )\\n return parameters\",\n \"def getParams(self):\\n return self.trainError, self.trainAcc, self.w\",\n \"def parameters(self):\\n return self._params\",\n \"def get_explanatory_variables(self):\\n return list(self._explanatory_variables)\",\n \"def get_paramnames_list(self):\\n # TODO include syselem?\\n\\n query = \\\"SELECT NAME FROM %s\\\" % self.__schema\\n with self.__connection.cursor() as cursor:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return [val['NAME'] for val in result]\",\n \"def inspect_state(self):\\n for name in self._param_store.get_all_param_names():\\n self._logger.info(\\\"Param [%s]: %r\\\", name,\\n pyro.param(name).data.numpy())\",\n \"def get_ext_param_default_values(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_default_value(self.xc_func_info, p)\\n ret.append(tmp)\\n\\n return ret\",\n \"def _create_parameter_names(self):\\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']\",\n \"def get_resource_params(self):\\n return SBE37Parameter.list()\",\n \"def display_parameters(self):\\n\\n self.logging.debug(\\\"============\\\")\\n for attr in self.parm_list:\\n self.logging.debug(attr.label + \\\" (\\\" + attr.when + \\\")\\\" + \\\" = \\\" + str(attr.value))\\n self.logging.debug(\\\"============\\\")\",\n \"def get_resource_params():\\n return Parameter.list()\",\n \"def parameters(self):\\n return {\\\"P\\\": self.P,\\n \\\"T\\\": self.T}\",\n \"def getparameters(filter,title = \\\"\\\"):\\n vardic = filter.__dict__\\n for i in list(vardic.keys()):\\n if vardic[i] is not None:\\n title += \\\" \\\"+i+\\\": \\\"\\n title += str(vardic[i])+\\\",\\\"\\n return title[:-1]\",\n \"def get_params(self):\\n return {\\n \\\"nspecies\\\": self.nspecies,\\n \\\"lmax\\\": self.lmax,\\n \\\"nmax\\\": self.nmax,\\n \\\"rcut\\\": self.rcut,\\n \\\"sigma\\\": self.sigma,\\n \\\"trans_width\\\": self.trans_width\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6681219","0.65302336","0.6523821","0.6485351","0.64587325","0.64377654","0.63435304","0.63268703","0.6247378","0.62124157","0.6184946","0.61841184","0.6173412","0.6156799","0.61424565","0.6138973","0.6092514","0.6074142","0.6068428","0.60556334","0.6051626","0.6050011","0.6045184","0.6039994","0.60339165","0.60132223","0.60107785","0.601017","0.601017","0.60091674","0.59972245","0.5978218","0.5940225","0.5940225","0.5908332","0.5904098","0.5901429","0.58977073","0.5896153","0.5890066","0.5870233","0.5864973","0.5846721","0.5846597","0.5844435","0.5839493","0.5823365","0.5807861","0.5794078","0.5791786","0.57726437","0.57589275","0.57524216","0.57194114","0.56898475","0.5686706","0.5671468","0.56519854","0.5635233","0.56322193","0.5628191","0.5618302","0.5614707","0.5607305","0.5591271","0.5588382","0.55848736","0.55820245","0.55792195","0.5568757","0.5555558","0.55554485","0.55543625","0.5551982","0.5550106","0.5541891","0.5541891","0.5541891","0.5541478","0.5541478","0.5534491","0.5530818","0.5530205","0.5525826","0.5524756","0.5522612","0.55188525","0.55057025","0.5494233","0.5492151","0.5488744","0.5471646","0.5465832","0.5463677","0.5458118","0.54518163","0.5450242","0.54476815","0.54460835","0.54453915"],"string":"[\n \"0.6681219\",\n \"0.65302336\",\n \"0.6523821\",\n \"0.6485351\",\n \"0.64587325\",\n \"0.64377654\",\n \"0.63435304\",\n \"0.63268703\",\n \"0.6247378\",\n \"0.62124157\",\n \"0.6184946\",\n \"0.61841184\",\n \"0.6173412\",\n \"0.6156799\",\n \"0.61424565\",\n \"0.6138973\",\n \"0.6092514\",\n \"0.6074142\",\n \"0.6068428\",\n \"0.60556334\",\n \"0.6051626\",\n \"0.6050011\",\n \"0.6045184\",\n \"0.6039994\",\n \"0.60339165\",\n \"0.60132223\",\n \"0.60107785\",\n \"0.601017\",\n \"0.601017\",\n \"0.60091674\",\n \"0.59972245\",\n \"0.5978218\",\n \"0.5940225\",\n \"0.5940225\",\n \"0.5908332\",\n \"0.5904098\",\n \"0.5901429\",\n \"0.58977073\",\n \"0.5896153\",\n \"0.5890066\",\n \"0.5870233\",\n \"0.5864973\",\n \"0.5846721\",\n \"0.5846597\",\n \"0.5844435\",\n \"0.5839493\",\n \"0.5823365\",\n \"0.5807861\",\n \"0.5794078\",\n \"0.5791786\",\n \"0.57726437\",\n \"0.57589275\",\n \"0.57524216\",\n \"0.57194114\",\n \"0.56898475\",\n \"0.5686706\",\n \"0.5671468\",\n \"0.56519854\",\n \"0.5635233\",\n \"0.56322193\",\n \"0.5628191\",\n \"0.5618302\",\n \"0.5614707\",\n \"0.5607305\",\n \"0.5591271\",\n \"0.5588382\",\n \"0.55848736\",\n \"0.55820245\",\n \"0.55792195\",\n \"0.5568757\",\n \"0.5555558\",\n \"0.55554485\",\n \"0.55543625\",\n \"0.5551982\",\n \"0.5550106\",\n \"0.5541891\",\n \"0.5541891\",\n \"0.5541891\",\n \"0.5541478\",\n \"0.5541478\",\n \"0.5534491\",\n \"0.5530818\",\n \"0.5530205\",\n \"0.5525826\",\n \"0.5524756\",\n \"0.5522612\",\n \"0.55188525\",\n \"0.55057025\",\n \"0.5494233\",\n \"0.5492151\",\n \"0.5488744\",\n \"0.5471646\",\n \"0.5465832\",\n \"0.5463677\",\n \"0.5458118\",\n \"0.54518163\",\n \"0.5450242\",\n \"0.54476815\",\n \"0.54460835\",\n \"0.54453915\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7096718"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17462,"cells":{"query":{"kind":"string","value":"Returns the names of all qualities that are affected by the given parameter."},"document":{"kind":"string","value":"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))","def get_parameters_affecting_quality(self, quality: str) -> List[str]:\n return [p for p, q in self.correlating_pq_tuples if q == quality]","def get_qualification_list(self):\n try:\n qualifications = self.db_handler.get_qualifications_list()\n self.logger.write_to_log('qualifications got', 'model')\n return qualifications\n except Exception as err:\n method_name = sys._getframe().f_code.co_name\n\n self.logger.write_to_log('exception', 'model')\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')","def parameter_names(self) -> List[str]:","def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\n else: adjust = lambda x: x\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\n else: names = [adjust(x.name) for x in self.parameters]\n if add_self: names = map(lambda x: adjust(self.name) + \".\" + x, names)\n return names","def parameter_names(self) -> list:\n parameters = []\n parameters.extend(self.properties.parameter_names)\n return parameters","def param_names(\n self, *, include_tp: bool = False, include_gq: bool = False\n ) -> List[str]:\n return (\n self._param_names(self.model, int(include_tp), int(include_gq))\n .decode(\"utf-8\")\n .split(\",\")\n )","def parameters_names(cls):\n return cls._Parameters._fields","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def parameters(self):\n return [i.parameter for i in self.joints.values()]","def parameters(self):\n return [term.parameter for term in self.terms]","def parameter_names(self):\n raise NotImplementedError(\"the parameter_names property should \"\n \"be defined in the Estimator sub-class\")","def getResRatioVarNames( self ):\n\n self.updateAdb( )\n\n return self.resNames.keys()","def list_qualifications() -> Dict[str, Any]:\n qualifications_schema = QualificationSchema(many=True)\n qualifications = Qualification.query.all()\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))","def getListOfQualitativeSpecies(self, *args):\n return _libsbml.QualModelPlugin_getListOfQualitativeSpecies(self, *args)","def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\n return sorted(list(set(expert_knowledge)))","def get_param(self, obs_name):\n return self.datasets[obs_name].keys()","def get_param_names(hf):\n parameters = get_params(hf)\n return [p.name for p in parameters]","def parameterNames(self, p_int): # real signature unknown; restored from __doc__\n return []","def get_paramnames_list(self):\n # TODO include syselem?\n\n query = \"SELECT NAME FROM %s\" % self.__schema\n with self.__connection.cursor() as cursor:\n cursor.execute(query)\n result = cursor.fetchall()\n return [val['NAME'] for val in result]","def get_str_param_names(self):\n # Exclude self.api and self.names from the command string\n return self.get_attribute_names(FormattedParameter)","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def getName(self):\n return _libsbml.QualitativeSpecies_getName(self)","def _get_fitted_param_names(self):\n return self._fitted_param_names","def get_qualities_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([q for _, q in self.expert_knowledge])))","def GetResourceNames(self):\r\n return [x.name for x in self.resources]","def getName(self):\n return _libsbml.QualExtension_getName(self)","def parameters(self):\n return [o.parameters for o in self.obs]","def find_qualifications(\n self, qualification_name: Optional[str] = None\n ) -> List[Qualification]:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n \"\"\"\n SELECT * from qualifications\n WHERE (?1 IS NULL OR qualification_name = ?1)\n \"\"\",\n (qualification_name,),\n )\n rows = c.fetchall()\n return [\n Qualification(\n self, str(r[\"qualification_id\"]), row=r, _used_new_call=True\n )\n for r in rows\n ]","def return_all_parameter_names():\n a = list(titles)\n a.append(r\"$\\chi^{2}$ per degree of freedom\")\n b = list(labels)\n b.append(\"chi2_per_dof\")\n return a, b","def get_param_names(self):\n return list(self.params.keys())","def find_gene_name(qualifiers):\n if not isinstance(qualifiers, dict):\n raise TypeError(\"Expected qualifier dictionary\")\n for tag in [\"protein_id\", \"locus_tag\", \"id\", \"gene\", \"name\", \"label\"]:\n if tag in qualifiers:\n return qualifiers[tag][0]\n return \"N.A.\"","def name(self):\n return [o.name for o in self.obs]","def _get_param_names_transformed(self):\r\n n = self._get_param_names()\r\n\r\n # remove/concatenate the tied parameter names\r\n if len(self.tied_indices):\r\n for t in self.tied_indices:\r\n n[t[0]] = \"<tie>\".join([n[tt] for tt in t])\r\n remove = np.hstack([t[1:] for t in self.tied_indices])\r\n else:\r\n remove = np.empty(shape=(0,), dtype=np.int)\r\n\r\n # also remove the fixed params\r\n if len(self.fixed_indices):\r\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\r\n\r\n # add markers to show that some variables are constrained\r\n for i, t in zip(self.constrained_indices, self.constraints):\r\n for ii in i:\r\n n[ii] = n[ii] + t.__str__()\r\n\r\n n = [nn for i, nn in enumerate(n) if not i in remove]\r\n return n","def names(self) -> list[str]:","def getNames(self) -> List[unicode]:\n ...","def paramset_to_rootnames(paramset):\n\n if paramset.name == 'lumi':\n return 'Lumi'\n if paramset.is_scalar:\n if paramset.constrained:\n return f'alpha_{paramset.name}'\n return f'{paramset.name}'\n return [f'gamma_{paramset.name}_{index}' for index in range(paramset.n_parameters)]","def return_parameter_names():\n return list(titles), list(labels)","def param_result_as_string_list(self, param_name):\n p_mean = self.param_dict[param_name][0]\n p_se = self.param_dict[param_name][1]\n return [self.activator, self.nbd_site, str(self.rep_index),\n self.measurement, param_name, str(p_mean),\n str(p_se)]","def params(self):\n return [p for sublist in [o.params for o in self.obs] for p in sublist]","def get_resource_params():\n return Parameter.list()","def params(self) -> Iterable[sympy.Symbol]:\n for i in range(self.iterations):\n for p in range(len(self.qubits)):\n if (self.include_all_z or not\n numpy.isclose(self.orbital_energies[p], 0)):\n yield LetterWithSubscripts('U', p, i)\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\n if (self.include_all_cz or not\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\n yield LetterWithSubscripts('V', p, q, i)","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def getSolRatioVarNames( self ):\n\n self.updateAdb( )\n\n return self.solNames.keys()","def parameters(self):\n return []","def science_parameter_list(cls):\n result = []\n for key in cls.list():\n if key not in GliderParticleKey.list():\n result.append(key)\n\n return result","def parameters_list(self):\n return [getattr(self.parameters, p) for p in self.parameters_names()]","def qparams(self):\n return self._qparams","def parameter_names(self):\n return [x for x in self.transformations.values() if isinstance(x, str)]","def get_param_name(self, param_id, syselem):\n\n with self.__connection.cursor() as cursor:\n query = \"SELECT NAME FROM %s WHERE PID= '%s' AND SYSTEM_ELEMENT= '%s'\" % (self.__schema, param_id, syselem)\n cursor.execute(query)\n result = cursor.fetchone()\n return result['NAME']","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def names(self) -> List:\n ...","def parameters(self):\n return [i for i in self.variables if has_roles(i, Parameter)]","def getElementName(self):\n return _libsbml.ListOfQualitativeSpecies_getElementName(self)","def parameters(self):\n #print \"in instrument.parameter()\"\n return self._params","def getSensorVariableNames(self, product):\r\n\r\n return []","def get(self, *args):\n return _libsbml.ListOfQualitativeSpecies_get(self, *args)","def qualname_candidates(identifier: str, context_qualname: str) -> list[str]:\n end = len(context_qualname)\n ret = []\n while end > 0:\n ret.append(f\"{context_qualname[:end]}.{identifier}\")\n end = context_qualname.rfind(\".\", 0, end)\n ret.append(identifier)\n return ret","def getInfoVariableNames(self, product):\r\n return []","def scopes(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"scopes\")","def _get_param_row_names(\n self, env_id: str, env: Environment, env_params: EnvParams\n ) -> List[str]:\n if env_id == \"HendrixPerishableSubstitutionTwoProduct\":\n return [\"a\", \"b\"]\n else:\n return []","def scopes(self) -> Sequence[str]:\n return pulumi.get(self, \"scopes\")","def scopes(self) -> Sequence[str]:\n return pulumi.get(self, \"scopes\")","def find_resonator_names(measurement):\n res_names = []\n for k in measurement.keys():\n if 'R' in k:\n res_names.append(k)\n return res_names","def get_resource_params(self):\n return Parameter.list()","def get_resource_params(self):\n return Parameter.list()","def getSensorVariableNames(self, product):\r\n return []","def affecteds_names(self):\n return [m.name for m in self.affecteds]","def getQualitativeSpecies(self):\n return _libsbml.Output_getQualitativeSpecies(self)","def get_strains(names, q_dof):\n strain_functions = []\n for n, this_dof in zip(names, q_dof):\n check_qdof(n, this_dof)\n if n == 'linear_helix':\n strain_functions.append(linear_helix_strain)\n elif n == 'pure_helix':\n strain_functions.append(pure_helix_strain)\n elif n == 'torsion_helix':\n strain_functions.append(torsion_helix_strain)\n elif n == 'torsion_linear_helix':\n strain_functions.append(torsion_linear_helix_strain)\n elif n == 'quadratic':\n strain_functions.append(quadratic_strain)\n elif n == 'linear':\n strain_functions.append(linear_strain)\n elif n == 'constant':\n strain_functions.append(constant_strain)\n elif n == 'full':\n strain_functions.append(full_strain)\n else:\n print(f'{n} is not a defined strain base.')\n return strain_functions","def param(self):\n return []","def param(self):\n return []","def _get_parameters(self) -> list:\n return self.parameters","def sensitivity(self, component = '', parameter = -1, reactor = ''):\n \n n = 0\n if reactor <> '':\n for reac in self._reactors:\n if reac.name() == reactor:\n break\n else:\n n = n+1\n np = self.nSensParams()\n if parameter >= 0 and parameter < np:\n return _cantera.reactornet_sensitivity(self.__reactornet_id,\n component, parameter, n)\n elif parameter == -1:\n s = []\n for m in range(np):\n s.append(_cantera.reactornet_sensitivity(self.__reactornet_id,\n component, m, n))\n return s\n else:\n raise CanteraError(\"sensitivity requested for illegal parameter number:\"+`parameter`)","def drug_names():\n results = set()\n if 'qry' in request.args and len(request.args['qry']) >= 3:\n look_for = f\"{request.args['qry'].lower()}%\"\n drug_list = FTA.find_by_name(look_for, False )\n results = set([f\"{d.PROPRIETARY_NAME} - {d.NONPROPRIETARY_NAME}\" for d in drug_list if d.ACTIVE])\n\n results = sorted(list(results))\n return jsonify(results)","def __str__(self):\r\n res = [self.Name + ' parameters:']\r\n for t in self._tracked_properties:\r\n res.append(t + ':' + str(getattr(self, t)))\r\n for k, v in sorted(self.Params.items()):\r\n res.append(str(k) + ':' + str(v))\r\n return '\\n'.join(res)","def qps(self):\n from admin.models.qualification_pack import QualificationPack\n return QualificationPack.objects.filter(\n occupation=self, is_draft=False\n )","def identifiers(self):\n identifiers = []\n\n for item in self._definition.get('identifiers', []):\n identifiers.append(Parameter(**item))\n\n return identifiers","def names(self) -> Sequence[str]:\n return pulumi.get(self, \"names\")","def names(self) -> Sequence[str]:\n return pulumi.get(self, \"names\")","def get_suffstat_names():\n params = ['sum_x', 'sum_x_squared']\n return params","def param_quantizers(self):\n return self._param_quantizers","def param(self):\r\n\r\n return []","def pyranose_names(self):\n output = set()\n for item in self.pyranoses():\n if item in self.pyranose_fac:\n output.add(self.pyranose_fac[item][\"name\"])\n return list(output)","def _fc_in_parameters(self) -> List[str]:\n result = list() # type: List[str]\n\n for param in self.params:\n type_list = param.fc_type()\n for type_name, postfix in type_list:\n result.append('{} {}'.format(type_name, param.name + postfix))\n\n return result","def names(filter=None):","def get_params_list():\n return common.QOL_PARAMS","def current_parameters(self):\n current = []\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n current.append(self.q[core_param].vi_return_param(approx_param))\n return np.array(current)","def get_names(self):\n\n # log.debug(str(inspect.stack()[1][3]) + \" --> OC.get_names()\")\n return [x.options['name'] for x in self.get_list()]","def curate_filter_info(self):\n filter_list = [\n self.sample_name, self.final_id, self.all_variant_count,\n self.filter_min_depth_count, self.filter_max_depth_count,\n self.filter_common_var_count, self.log_mut_count,\n self.cosmic_variant_counts, self.unknown_maf_count\n ]\n return filter_list","def get_param_values(self,obs_name,param):\n\n return self.datasets[obs_name][param]","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def names(self):\n\n allnames = []\n for term in self.terms:\n allnames += term.names()\n return allnames","def names(cls) -> List[str]:","def printChromQual(chromosome:list):\n sum = 0\n for i in range(len(chromosome)):\n machines_list[chromosome[i]].addJob(jobs_list[i])\n sum += jobs_list[i].length\n printMachineStatOut()\n print(\"Optimal solution (sum/num_of_jobs) could be :\",sum/num_of_machines,file=out_file)\n print(\"------------------------------------------------\\n\", file=out_file)","def getConditionNames():\n return _conditions.keys()","def show_parameters(self):\n with np.printoptions(precision=3, suppress=True):\n print('number of wind phase = {}'.format(self.ncomp))\n print('galactic parameter = {}'.format(self.scaling_field))\n print('reference height = {}'.format(self.z0))\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\n params = getattr(self,p)\n print(p)\n for k,v in params.items():\n print(' {} = {}'.format(k,v))","def __str__(self):\n return '%s / %s - %s' % (self.qualifier_name, self.stratigraphic_qualifier, self.level)","def _create_parameter_names(self):\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']","def currentAntennaNames(carmaOnly=False) :\n a=s.getAntennaAssignments()\n namelist = []\n for i in a:\n cname = i.carmaAntennaName\n tname = i.typedAntennaName\n if (carmaOnly) :\n names = i.carmaAntennaName\n else :\n names = \"%s(%s)\" %(cname,tname)\n namelist.append(names)\n return namelist"],"string":"[\n \"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\\n parameters = reduce(\\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\\n qualities,\\n set()\\n )\\n return sorted(list(parameters))\",\n \"def get_parameters_affecting_quality(self, quality: str) -> List[str]:\\n return [p for p, q in self.correlating_pq_tuples if q == quality]\",\n \"def get_qualification_list(self):\\n try:\\n qualifications = self.db_handler.get_qualifications_list()\\n self.logger.write_to_log('qualifications got', 'model')\\n return qualifications\\n except Exception as err:\\n method_name = sys._getframe().f_code.co_name\\n\\n self.logger.write_to_log('exception', 'model')\\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')\",\n \"def parameter_names(self) -> List[str]:\",\n \"def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\\n else: adjust = lambda x: x\\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\\n else: names = [adjust(x.name) for x in self.parameters]\\n if add_self: names = map(lambda x: adjust(self.name) + \\\".\\\" + x, names)\\n return names\",\n \"def parameter_names(self) -> list:\\n parameters = []\\n parameters.extend(self.properties.parameter_names)\\n return parameters\",\n \"def param_names(\\n self, *, include_tp: bool = False, include_gq: bool = False\\n ) -> List[str]:\\n return (\\n self._param_names(self.model, int(include_tp), int(include_gq))\\n .decode(\\\"utf-8\\\")\\n .split(\\\",\\\")\\n )\",\n \"def parameters_names(cls):\\n return cls._Parameters._fields\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def parameters(self):\\n return [i.parameter for i in self.joints.values()]\",\n \"def parameters(self):\\n return [term.parameter for term in self.terms]\",\n \"def parameter_names(self):\\n raise NotImplementedError(\\\"the parameter_names property should \\\"\\n \\\"be defined in the Estimator sub-class\\\")\",\n \"def getResRatioVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.resNames.keys()\",\n \"def list_qualifications() -> Dict[str, Any]:\\n qualifications_schema = QualificationSchema(many=True)\\n qualifications = Qualification.query.all()\\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))\",\n \"def getListOfQualitativeSpecies(self, *args):\\n return _libsbml.QualModelPlugin_getListOfQualitativeSpecies(self, *args)\",\n \"def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\\n return sorted(list(set(expert_knowledge)))\",\n \"def get_param(self, obs_name):\\n return self.datasets[obs_name].keys()\",\n \"def get_param_names(hf):\\n parameters = get_params(hf)\\n return [p.name for p in parameters]\",\n \"def parameterNames(self, p_int): # real signature unknown; restored from __doc__\\n return []\",\n \"def get_paramnames_list(self):\\n # TODO include syselem?\\n\\n query = \\\"SELECT NAME FROM %s\\\" % self.__schema\\n with self.__connection.cursor() as cursor:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return [val['NAME'] for val in result]\",\n \"def get_str_param_names(self):\\n # Exclude self.api and self.names from the command string\\n return self.get_attribute_names(FormattedParameter)\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def getName(self):\\n return _libsbml.QualitativeSpecies_getName(self)\",\n \"def _get_fitted_param_names(self):\\n return self._fitted_param_names\",\n \"def get_qualities_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([q for _, q in self.expert_knowledge])))\",\n \"def GetResourceNames(self):\\r\\n return [x.name for x in self.resources]\",\n \"def getName(self):\\n return _libsbml.QualExtension_getName(self)\",\n \"def parameters(self):\\n return [o.parameters for o in self.obs]\",\n \"def find_qualifications(\\n self, qualification_name: Optional[str] = None\\n ) -> List[Qualification]:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n SELECT * from qualifications\\n WHERE (?1 IS NULL OR qualification_name = ?1)\\n \\\"\\\"\\\",\\n (qualification_name,),\\n )\\n rows = c.fetchall()\\n return [\\n Qualification(\\n self, str(r[\\\"qualification_id\\\"]), row=r, _used_new_call=True\\n )\\n for r in rows\\n ]\",\n \"def return_all_parameter_names():\\n a = list(titles)\\n a.append(r\\\"$\\\\chi^{2}$ per degree of freedom\\\")\\n b = list(labels)\\n b.append(\\\"chi2_per_dof\\\")\\n return a, b\",\n \"def get_param_names(self):\\n return list(self.params.keys())\",\n \"def find_gene_name(qualifiers):\\n if not isinstance(qualifiers, dict):\\n raise TypeError(\\\"Expected qualifier dictionary\\\")\\n for tag in [\\\"protein_id\\\", \\\"locus_tag\\\", \\\"id\\\", \\\"gene\\\", \\\"name\\\", \\\"label\\\"]:\\n if tag in qualifiers:\\n return qualifiers[tag][0]\\n return \\\"N.A.\\\"\",\n \"def name(self):\\n return [o.name for o in self.obs]\",\n \"def _get_param_names_transformed(self):\\r\\n n = self._get_param_names()\\r\\n\\r\\n # remove/concatenate the tied parameter names\\r\\n if len(self.tied_indices):\\r\\n for t in self.tied_indices:\\r\\n n[t[0]] = \\\"<tie>\\\".join([n[tt] for tt in t])\\r\\n remove = np.hstack([t[1:] for t in self.tied_indices])\\r\\n else:\\r\\n remove = np.empty(shape=(0,), dtype=np.int)\\r\\n\\r\\n # also remove the fixed params\\r\\n if len(self.fixed_indices):\\r\\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\\r\\n\\r\\n # add markers to show that some variables are constrained\\r\\n for i, t in zip(self.constrained_indices, self.constraints):\\r\\n for ii in i:\\r\\n n[ii] = n[ii] + t.__str__()\\r\\n\\r\\n n = [nn for i, nn in enumerate(n) if not i in remove]\\r\\n return n\",\n \"def names(self) -> list[str]:\",\n \"def getNames(self) -> List[unicode]:\\n ...\",\n \"def paramset_to_rootnames(paramset):\\n\\n if paramset.name == 'lumi':\\n return 'Lumi'\\n if paramset.is_scalar:\\n if paramset.constrained:\\n return f'alpha_{paramset.name}'\\n return f'{paramset.name}'\\n return [f'gamma_{paramset.name}_{index}' for index in range(paramset.n_parameters)]\",\n \"def return_parameter_names():\\n return list(titles), list(labels)\",\n \"def param_result_as_string_list(self, param_name):\\n p_mean = self.param_dict[param_name][0]\\n p_se = self.param_dict[param_name][1]\\n return [self.activator, self.nbd_site, str(self.rep_index),\\n self.measurement, param_name, str(p_mean),\\n str(p_se)]\",\n \"def params(self):\\n return [p for sublist in [o.params for o in self.obs] for p in sublist]\",\n \"def get_resource_params():\\n return Parameter.list()\",\n \"def params(self) -> Iterable[sympy.Symbol]:\\n for i in range(self.iterations):\\n for p in range(len(self.qubits)):\\n if (self.include_all_z or not\\n numpy.isclose(self.orbital_energies[p], 0)):\\n yield LetterWithSubscripts('U', p, i)\\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\\n if (self.include_all_cz or not\\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\\n yield LetterWithSubscripts('V', p, q, i)\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def getSolRatioVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.solNames.keys()\",\n \"def parameters(self):\\n return []\",\n \"def science_parameter_list(cls):\\n result = []\\n for key in cls.list():\\n if key not in GliderParticleKey.list():\\n result.append(key)\\n\\n return result\",\n \"def parameters_list(self):\\n return [getattr(self.parameters, p) for p in self.parameters_names()]\",\n \"def qparams(self):\\n return self._qparams\",\n \"def parameter_names(self):\\n return [x for x in self.transformations.values() if isinstance(x, str)]\",\n \"def get_param_name(self, param_id, syselem):\\n\\n with self.__connection.cursor() as cursor:\\n query = \\\"SELECT NAME FROM %s WHERE PID= '%s' AND SYSTEM_ELEMENT= '%s'\\\" % (self.__schema, param_id, syselem)\\n cursor.execute(query)\\n result = cursor.fetchone()\\n return result['NAME']\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def names(self) -> List:\\n ...\",\n \"def parameters(self):\\n return [i for i in self.variables if has_roles(i, Parameter)]\",\n \"def getElementName(self):\\n return _libsbml.ListOfQualitativeSpecies_getElementName(self)\",\n \"def parameters(self):\\n #print \\\"in instrument.parameter()\\\"\\n return self._params\",\n \"def getSensorVariableNames(self, product):\\r\\n\\r\\n return []\",\n \"def get(self, *args):\\n return _libsbml.ListOfQualitativeSpecies_get(self, *args)\",\n \"def qualname_candidates(identifier: str, context_qualname: str) -> list[str]:\\n end = len(context_qualname)\\n ret = []\\n while end > 0:\\n ret.append(f\\\"{context_qualname[:end]}.{identifier}\\\")\\n end = context_qualname.rfind(\\\".\\\", 0, end)\\n ret.append(identifier)\\n return ret\",\n \"def getInfoVariableNames(self, product):\\r\\n return []\",\n \"def scopes(self) -> pulumi.Output[Sequence[str]]:\\n return pulumi.get(self, \\\"scopes\\\")\",\n \"def _get_param_row_names(\\n self, env_id: str, env: Environment, env_params: EnvParams\\n ) -> List[str]:\\n if env_id == \\\"HendrixPerishableSubstitutionTwoProduct\\\":\\n return [\\\"a\\\", \\\"b\\\"]\\n else:\\n return []\",\n \"def scopes(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"scopes\\\")\",\n \"def scopes(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"scopes\\\")\",\n \"def find_resonator_names(measurement):\\n res_names = []\\n for k in measurement.keys():\\n if 'R' in k:\\n res_names.append(k)\\n return res_names\",\n \"def get_resource_params(self):\\n return Parameter.list()\",\n \"def get_resource_params(self):\\n return Parameter.list()\",\n \"def getSensorVariableNames(self, product):\\r\\n return []\",\n \"def affecteds_names(self):\\n return [m.name for m in self.affecteds]\",\n \"def getQualitativeSpecies(self):\\n return _libsbml.Output_getQualitativeSpecies(self)\",\n \"def get_strains(names, q_dof):\\n strain_functions = []\\n for n, this_dof in zip(names, q_dof):\\n check_qdof(n, this_dof)\\n if n == 'linear_helix':\\n strain_functions.append(linear_helix_strain)\\n elif n == 'pure_helix':\\n strain_functions.append(pure_helix_strain)\\n elif n == 'torsion_helix':\\n strain_functions.append(torsion_helix_strain)\\n elif n == 'torsion_linear_helix':\\n strain_functions.append(torsion_linear_helix_strain)\\n elif n == 'quadratic':\\n strain_functions.append(quadratic_strain)\\n elif n == 'linear':\\n strain_functions.append(linear_strain)\\n elif n == 'constant':\\n strain_functions.append(constant_strain)\\n elif n == 'full':\\n strain_functions.append(full_strain)\\n else:\\n print(f'{n} is not a defined strain base.')\\n return strain_functions\",\n \"def param(self):\\n return []\",\n \"def param(self):\\n return []\",\n \"def _get_parameters(self) -> list:\\n return self.parameters\",\n \"def sensitivity(self, component = '', parameter = -1, reactor = ''):\\n \\n n = 0\\n if reactor <> '':\\n for reac in self._reactors:\\n if reac.name() == reactor:\\n break\\n else:\\n n = n+1\\n np = self.nSensParams()\\n if parameter >= 0 and parameter < np:\\n return _cantera.reactornet_sensitivity(self.__reactornet_id,\\n component, parameter, n)\\n elif parameter == -1:\\n s = []\\n for m in range(np):\\n s.append(_cantera.reactornet_sensitivity(self.__reactornet_id,\\n component, m, n))\\n return s\\n else:\\n raise CanteraError(\\\"sensitivity requested for illegal parameter number:\\\"+`parameter`)\",\n \"def drug_names():\\n results = set()\\n if 'qry' in request.args and len(request.args['qry']) >= 3:\\n look_for = f\\\"{request.args['qry'].lower()}%\\\"\\n drug_list = FTA.find_by_name(look_for, False )\\n results = set([f\\\"{d.PROPRIETARY_NAME} - {d.NONPROPRIETARY_NAME}\\\" for d in drug_list if d.ACTIVE])\\n\\n results = sorted(list(results))\\n return jsonify(results)\",\n \"def __str__(self):\\r\\n res = [self.Name + ' parameters:']\\r\\n for t in self._tracked_properties:\\r\\n res.append(t + ':' + str(getattr(self, t)))\\r\\n for k, v in sorted(self.Params.items()):\\r\\n res.append(str(k) + ':' + str(v))\\r\\n return '\\\\n'.join(res)\",\n \"def qps(self):\\n from admin.models.qualification_pack import QualificationPack\\n return QualificationPack.objects.filter(\\n occupation=self, is_draft=False\\n )\",\n \"def identifiers(self):\\n identifiers = []\\n\\n for item in self._definition.get('identifiers', []):\\n identifiers.append(Parameter(**item))\\n\\n return identifiers\",\n \"def names(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"names\\\")\",\n \"def names(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"names\\\")\",\n \"def get_suffstat_names():\\n params = ['sum_x', 'sum_x_squared']\\n return params\",\n \"def param_quantizers(self):\\n return self._param_quantizers\",\n \"def param(self):\\r\\n\\r\\n return []\",\n \"def pyranose_names(self):\\n output = set()\\n for item in self.pyranoses():\\n if item in self.pyranose_fac:\\n output.add(self.pyranose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def _fc_in_parameters(self) -> List[str]:\\n result = list() # type: List[str]\\n\\n for param in self.params:\\n type_list = param.fc_type()\\n for type_name, postfix in type_list:\\n result.append('{} {}'.format(type_name, param.name + postfix))\\n\\n return result\",\n \"def names(filter=None):\",\n \"def get_params_list():\\n return common.QOL_PARAMS\",\n \"def current_parameters(self):\\n current = []\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n current.append(self.q[core_param].vi_return_param(approx_param))\\n return np.array(current)\",\n \"def get_names(self):\\n\\n # log.debug(str(inspect.stack()[1][3]) + \\\" --> OC.get_names()\\\")\\n return [x.options['name'] for x in self.get_list()]\",\n \"def curate_filter_info(self):\\n filter_list = [\\n self.sample_name, self.final_id, self.all_variant_count,\\n self.filter_min_depth_count, self.filter_max_depth_count,\\n self.filter_common_var_count, self.log_mut_count,\\n self.cosmic_variant_counts, self.unknown_maf_count\\n ]\\n return filter_list\",\n \"def get_param_values(self,obs_name,param):\\n\\n return self.datasets[obs_name][param]\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def names(self):\\n\\n allnames = []\\n for term in self.terms:\\n allnames += term.names()\\n return allnames\",\n \"def names(cls) -> List[str]:\",\n \"def printChromQual(chromosome:list):\\n sum = 0\\n for i in range(len(chromosome)):\\n machines_list[chromosome[i]].addJob(jobs_list[i])\\n sum += jobs_list[i].length\\n printMachineStatOut()\\n print(\\\"Optimal solution (sum/num_of_jobs) could be :\\\",sum/num_of_machines,file=out_file)\\n print(\\\"------------------------------------------------\\\\n\\\", file=out_file)\",\n \"def getConditionNames():\\n return _conditions.keys()\",\n \"def show_parameters(self):\\n with np.printoptions(precision=3, suppress=True):\\n print('number of wind phase = {}'.format(self.ncomp))\\n print('galactic parameter = {}'.format(self.scaling_field))\\n print('reference height = {}'.format(self.z0))\\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\\n params = getattr(self,p)\\n print(p)\\n for k,v in params.items():\\n print(' {} = {}'.format(k,v))\",\n \"def __str__(self):\\n return '%s / %s - %s' % (self.qualifier_name, self.stratigraphic_qualifier, self.level)\",\n \"def _create_parameter_names(self):\\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']\",\n \"def currentAntennaNames(carmaOnly=False) :\\n a=s.getAntennaAssignments()\\n namelist = []\\n for i in a:\\n cname = i.carmaAntennaName\\n tname = i.typedAntennaName\\n if (carmaOnly) :\\n names = i.carmaAntennaName\\n else :\\n names = \\\"%s(%s)\\\" %(cname,tname)\\n namelist.append(names)\\n return namelist\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7138901","0.65434414","0.60828495","0.5970741","0.5706705","0.56861335","0.5615085","0.5553577","0.5414541","0.53965795","0.5352581","0.5300973","0.52954173","0.52879333","0.5286698","0.5282407","0.5249492","0.5248632","0.52475625","0.52351964","0.5211867","0.5166537","0.5151962","0.51517856","0.51467603","0.51409054","0.5120053","0.5111621","0.5098414","0.5093125","0.5091622","0.5086827","0.5067678","0.50465","0.5042965","0.5008786","0.50078475","0.49884728","0.49871844","0.4986353","0.49717265","0.49713215","0.49685442","0.4943241","0.49324977","0.49277097","0.49267575","0.49232966","0.49223486","0.4911374","0.49071795","0.48971605","0.48866853","0.4885972","0.48824322","0.48745823","0.48709333","0.4867255","0.48630804","0.48570174","0.48554984","0.48549795","0.48549795","0.4847937","0.4842235","0.4842235","0.4833657","0.48279563","0.48262793","0.48256218","0.48253468","0.48253468","0.48171577","0.48120353","0.4808936","0.48040336","0.47999504","0.47948745","0.47924784","0.47924784","0.47812998","0.4778791","0.47775808","0.47767162","0.4774912","0.4772486","0.47698992","0.47633374","0.47548342","0.47521058","0.4749433","0.47481295","0.47481295","0.4726635","0.47210607","0.472072","0.47174916","0.4713529","0.47134793","0.4709601"],"string":"[\n \"0.7138901\",\n \"0.65434414\",\n \"0.60828495\",\n \"0.5970741\",\n \"0.5706705\",\n \"0.56861335\",\n \"0.5615085\",\n \"0.5553577\",\n \"0.5414541\",\n \"0.53965795\",\n \"0.5352581\",\n \"0.5300973\",\n \"0.52954173\",\n \"0.52879333\",\n \"0.5286698\",\n \"0.5282407\",\n \"0.5249492\",\n \"0.5248632\",\n \"0.52475625\",\n \"0.52351964\",\n \"0.5211867\",\n \"0.5166537\",\n \"0.5151962\",\n \"0.51517856\",\n \"0.51467603\",\n \"0.51409054\",\n \"0.5120053\",\n \"0.5111621\",\n \"0.5098414\",\n \"0.5093125\",\n \"0.5091622\",\n \"0.5086827\",\n \"0.5067678\",\n \"0.50465\",\n \"0.5042965\",\n \"0.5008786\",\n \"0.50078475\",\n \"0.49884728\",\n \"0.49871844\",\n \"0.4986353\",\n \"0.49717265\",\n \"0.49713215\",\n \"0.49685442\",\n \"0.4943241\",\n \"0.49324977\",\n \"0.49277097\",\n \"0.49267575\",\n \"0.49232966\",\n \"0.49223486\",\n \"0.4911374\",\n \"0.49071795\",\n \"0.48971605\",\n \"0.48866853\",\n \"0.4885972\",\n \"0.48824322\",\n \"0.48745823\",\n \"0.48709333\",\n \"0.4867255\",\n \"0.48630804\",\n \"0.48570174\",\n \"0.48554984\",\n \"0.48549795\",\n \"0.48549795\",\n \"0.4847937\",\n \"0.4842235\",\n \"0.4842235\",\n \"0.4833657\",\n \"0.48279563\",\n \"0.48262793\",\n \"0.48256218\",\n \"0.48253468\",\n \"0.48253468\",\n \"0.48171577\",\n \"0.48120353\",\n \"0.4808936\",\n \"0.48040336\",\n \"0.47999504\",\n \"0.47948745\",\n \"0.47924784\",\n \"0.47924784\",\n \"0.47812998\",\n \"0.4778791\",\n \"0.47775808\",\n \"0.47767162\",\n \"0.4774912\",\n \"0.4772486\",\n \"0.47698992\",\n \"0.47633374\",\n \"0.47548342\",\n \"0.47521058\",\n \"0.4749433\",\n \"0.47481295\",\n \"0.47481295\",\n \"0.4726635\",\n \"0.47210607\",\n \"0.472072\",\n \"0.47174916\",\n \"0.4713529\",\n \"0.47134793\",\n \"0.4709601\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7769609"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17463,"cells":{"query":{"kind":"string","value":"Returns the names of all parameters are affecting the given quality."},"document":{"kind":"string","value":"def get_parameters_affecting_quality(self, quality: str) -> List[str]:\n return [p for p, q in self.correlating_pq_tuples if q == quality]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_parameters(self, quality):\n if (quality.upper() == 'NAV') or (quality.upper() == 'NAVIGATION'):\n return self._sensor_param_dict['navigation'].copy()\n \n elif (quality.upper() == 'TAC') or (quality.upper() == 'TACTICAL'):\n return self._sensor_param_dict['tactical'].copy()\n \n elif (quality.upper() == 'CON') or (quality.upper() == 'CONSUMER'):\n return self._sensor_param_dict['consumer'].copy()","def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))","def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]","def parameter_names(self) -> List[str]:","def print_quality(self, quality):\n # Find the parameter dictionary for the desired quality level\n \n imu = self.get_parameters(quality)\n \n print '\\n%s Quality \\n-------------------------' % quality.upper()\n print ' %30s' % 'GYRO'\n print ' %22s \\t %g' % ('Wide Band Noise', imu['sigma_w_g'])\n print ' %22s \\t %g' % ('Constant Bias' , imu['sigma_n_g'])\n print ' %22s \\t %g' % ('Bias Stability' , imu['sigma_c_g'])\n print ' %22s \\t %g' % ('Bias Correlation Time', imu['tau_g'])\n print '\\n'\n \n print ' %35s' % 'ACCELEROMETER'\n print ' %22s \\t %g' % ('Wide Band Noise', imu['sigma_w_f'])\n print ' %22s \\t %g' % ('Constant Bias' , imu['sigma_n_f'])\n print ' %22s \\t %g' % ('Bias Stability' , imu['sigma_c_f'])\n print ' %22s \\t %g' % ('Bias Correlation Time', imu['tau_f'])\n print '\\n'","def show_parameters(self):\n with np.printoptions(precision=3, suppress=True):\n print('number of wind phase = {}'.format(self.ncomp))\n print('galactic parameter = {}'.format(self.scaling_field))\n print('reference height = {}'.format(self.z0))\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\n params = getattr(self,p)\n print(p)\n for k,v in params.items():\n print(' {} = {}'.format(k,v))","def potential_parameters(cls):\n return [\"k\", \"length\"]","def parameters_names(cls):\n return cls._Parameters._fields","def quality(self):\n try:\n qid = int((self.tool_metadata or {}).get(\"quality\", 0))\n except:\n qid = 0\n\n # We might be able to get the quality strings from the item's tags\n internal_name, name = \"normal\", \"Normal\"\n if self.tags:\n tags = {x.get('category'): x for x in self.tags}\n if 'Quality' in tags:\n internal_name, name = tags['Quality'].get('internal_name'), tags['Quality'].get('name')\n\n return qid, internal_name, name","def parameter_names(self):\n raise NotImplementedError(\"the parameter_names property should \"\n \"be defined in the Estimator sub-class\")","def parameterNames(self, p_int): # real signature unknown; restored from __doc__\n return []","def potential_parameters(cls):\n return [\"k\", \"angle\"]","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def parameter_names(self) -> list:\n parameters = []\n parameters.extend(self.properties.parameter_names)\n return parameters","def parameters(self):\n #print \"in instrument.parameter()\"\n return self._params","def get_str_param_names(self):\n # Exclude self.api and self.names from the command string\n return self.get_attribute_names(FormattedParameter)","def derived_parameters(cls):\n return ['cgg', 'cdd', 'css', 'cbb', 'vstar', 'gain', 'ft']","def get_param_names(hf):\n parameters = get_params(hf)\n return [p.name for p in parameters]","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"periodicity\", \"phase\", \"idivf\"]","def set_quality(self):\n p = self.suitability + 1.15 * self.fono\n self.quality = np.exp(p) / (1 + np.exp(p))","def derived_parameters(cls):\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']","def potential_parameters(cls):\n return [\"length\", \"distance\"]","def paramDetails(cls):\n return {\n 'dim': (10, 20, 2, 20),\n 'nIter': (1, 10, 2, 5),\n 'lamb': (.1, 1., .1, .05),\n 'alph': (30, 50, 5, 40)\n }","def param_info():\n\n\tgizmo_names = syn.getGizmoNames()\n\n\tfor gizmo in gizmo_names:\n\t\tparams = syn.getParameterNames(gizmo)\n\t#doesnt get all parameters from gizmos i.e. WaveFreq\n\n\t# get all info on the 'WaveFreq' parameter\n\tGIZMO = 'aStim2'\n\tPARAMETER = 'WaveFreq'\n\n\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\n\t#\n\t# # get the array size (should be 100)\n\t# sz = syn.getParameterSize(GIZMO, PARAMETER)\n\t#\n\t# # write values 1 to 50 in second half of buffer\n\t# result = syn.setParameterValues(GIZMO, PARAMETER, np.arange(1, 51), 50)\n\t#\n\t# # read all values from buffer\n\t# syn.getParameterValues(GIZMO, PARAMETER, sz)\n\t#\n\t# # get all info on the 'Go' parameter\n\t# PARAMETER = 'Go'\n\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\n\t#\n\t# # flip the switch\n\t# result = syn.setParameterValue(GIZMO, PARAMETER, 1)\n\t#\n\t# # check the value\n\tfreq = syn.getParameterValue(GIZMO, PARAMETER)\n\tprint('value =', freq)\n\tfreq = [freq]\n\n\t# also verify visually that the switch slipped in the run\n\t# time interface. This state change will be logged just\n\t# like any other variable change and saved with the runtime\n\t# state.\n\n\tnumTrials = 5 #total number of trials across stimuli\n\tISI = [2.0, 3.0, 4.0, 5.0] # ISI in seconds\n\n\t# flash parameters\n\tflash_dur = [.001] # flash durs in seconds (100 ms, 200 ms)\n\tluminance = [[1, 1, 1], [.86, .86, .86], [0, .1, 1]] # white , grayish, purple just for testing\n\n\t# auditory parameters\n\tduration = [.005] # in seconds; pulseDur in TDT\n\tsound_levels = [20.0, 40.0, 60.0, 80.0] # dB; waveAmp in TDT\n\n\t# Auditory on (T/F? if T then A+V, if F then Visual only)\n\tstims = {0: \"auditory_only\",\n\t\t\t 1: \"visual_only\",\n\t\t\t 2: \"A+V\"\n\t\t\t }\n\n\texper = Experiment(numTrials=numTrials, ISI=ISI, flash_dur=flash_dur, luminance=luminance, wave_freq=freq,\n\t\t\t\t\t pulse_dur=duration, wave_amp=sound_levels, stimulus=stims)\n\texper.run_experiment()","def parameters(self):\n return []","def return_all_parameter_names():\n a = list(titles)\n a.append(r\"$\\chi^{2}$ per degree of freedom\")\n b = list(labels)\n b.append(\"chi2_per_dof\")\n return a, b","def get_parameter_estimation_parameters(self, friendly=True):\n #Get the sensitivities task:\n fitTask=self._getTask('parameterFitting')\n fitProblem = fitTask.find(xmlns + 'Problem')\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\n parameters = []\n for subGroup in optimizationItems:\n name = None\n lowerBound = None\n upperBound = None\n startValue = None\n \n for item in subGroup:\n if item.attrib['name'] == 'ObjectCN':\n name = item.attrib['value']\n elif item.attrib['name'] == 'UpperBound':\n upperBound = item.attrib['value']\n elif item.attrib['name'] == 'LowerBound':\n lowerBound = item.attrib['value']\n elif item.attrib['name'] == 'StartValue':\n startValue = item.attrib['value']\n assert name !=None\n assert lowerBound != None\n assert upperBound != None\n assert startValue != None\n \n if friendly:\n #Construct a user-friendly name for the parameter name using regexs\n #Look for a match for global parameters: Vector=Values[Test parameter],\n global_string = r'.*Vector=Values\\[(?P<name>.*)\\].*'\n global_string_re = re.compile(global_string)\n global_match = re.match(global_string_re, name)\n \n if global_match:\n name = global_match.group('name')\n \n #else check for a local match.\n #Vector=Reactions[Reaction] Parameter=k1\n local_string = r'.*Vector=Reactions\\[(?P<reaction>.*)\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\n local_string_re = re.compile(local_string)\n local_match = re.match(local_string_re, name)\n \n if local_match:\n reaction = local_match.group('reaction')\n parameter = local_match.group('parameter')\n name = '(%s).%s'%(reaction, parameter)\n\n parameters.append((name, lowerBound, upperBound, startValue))\n\n return parameters","def parameters(self):\n return [i.parameter for i in self.joints.values()]","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"angle\"]","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def parameter_names(self):\n return [x for x in self.transformations.values() if isinstance(x, str)]","def display_parameters(self):\n l = []\n for param in self.parameters.all():\n if len(param.value) > 16:\n l.append(u\"{}={}...\".format(param.name, param.value[:16]))\n else:\n l.append(u\"{}={}\".format(param.name, param.value))\n return \"; \".join(l)","def _get_fitted_param_names(self):\n return self._fitted_param_names","def summary_parameters(self):\n return {'filters': ', '.join(self.getOption('filters'))}","def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\n else: adjust = lambda x: x\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\n else: names = [adjust(x.name) for x in self.parameters]\n if add_self: names = map(lambda x: adjust(self.name) + \".\" + x, names)\n return names","def parameters(self):\n return {\n 'base':self.base.parameters(),\n 'material':[m.parameters() for m in self.material],\n 'fraction':self.fraction,\n }","def getInfoVariableNames(self, product):\r\n\r\n h = product.getSceneRasterHeight()\r\n\r\n # 10m resolution\r\n if h == 10980:\r\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_10m)\r\n\r\n # 20m resolution\r\n elif h == 5490:\r\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_20m)\r\n\r\n # 20m resolution\r\n elif h == 1830:\r\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_60m)\r\n return []","def params_desc(self):\n return \"{}/{}/{}/{}\".format(\n self.learning_rate, self.movement, self.milestones, self.gamma\n )","def get_param_names(self):\n return list(self.params.keys())","def inspect_state(self):\n for name in self._param_store.get_all_param_names():\n self._logger.info(\"Param [%s]: %r\", name,\n pyro.param(name).data.numpy())","def param_names(\n self, *, include_tp: bool = False, include_gq: bool = False\n ) -> List[str]:\n return (\n self._param_names(self.model, int(include_tp), int(include_gq))\n .decode(\"utf-8\")\n .split(\",\")\n )","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def get_parameters_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([p for p, _ in self.expert_knowledge])))","def current_parameters(self):\n current = []\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n current.append(self.q[core_param].vi_return_param(approx_param))\n return np.array(current)","def get_ext_param_names(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\n ret.append(tmp.decode(\"UTF-8\"))\n\n return ret","def parameters(self):\n ps = super().parameters()\n exclude = set(self.estimator.parameters())\n ps = (p for p in ps if not p in exclude)\n return ps","def supported_parameters(cls):\n raise NotImplementedError()","def parameters(self):\n return [term.parameter for term in self.terms]","def named_trainable_parameters(self) -> List[str]:\n return [name for name, p in self._model.named_parameters() if p.requires_grad]","def print_params(self):\n s = self._list_params()+\"\\n\"\n if 'scale_params' in self.__dict__.keys():\n s += self.scale_params._list_params()+\"\\n\"\n if 'atmospheric_params' in self.__dict__.keys():\n if self.atmospheric_params is not None:\n s += self.atmospheric_params._list_params()+\"\\n\"\n\n if 'atemperature_params' in self.__dict__.keys():\n if self.atemperature_params is not None:\n s += self.atemperature_params._list_params()+\"\\n\"\n\n if 'oceanic_params' in self.__dict__.keys():\n if self.oceanic_params is not None:\n s += self.oceanic_params._list_params()+\"\\n\"\n\n if 'ground_params' in self.__dict__.keys():\n if self.ground_params is not None:\n s += self.ground_params._list_params()+\"\\n\"\n\n if 'gotemperature_params' in self.__dict__.keys():\n if self.gotemperature_params is not None:\n s += self.gotemperature_params._list_params() + \"\\n\"\n\n print(\"Qgs v0.2.8 parameters summary\")\n print(\"=============================\\n\")\n print(s)","def getParameters(self):\n\n current_params = {'taux': self.taux, 'mu': self.mu, 'G': self.G, 'alpha_0': self.alpha_0,\n 'delta': self.delta, 'p': self.p, 'I0': self.I0, 'kparam': self.kparam}\n\n return (current_params)","def parameters(self):\n return [o.parameters for o in self.obs]","def parameters(self):\n return [p for _, a in vars(self).items() for p in self._params(a)]","def supported_qparams(self):\n import re\n return re.findall(r\"\\$\\$\\{(\\w+)\\}\", self.QTEMPLATE)","def search_quality(self,strz):\n\t\tfor q in l_quality: #l_quality = list of allow quality words\n\t\t\tif q in strz:\n\t\t\t\tself.quality=q.replace(\".\",\"\")\n\t\t\t\treturn strz.replace(q,\"\")\n\t\treturn strz","def potential_parameters(cls):\n raise NotImplementedError()","def quality(self):\n return self.plays * self.number","def parameters(self):","def display_parameters(self):\n\n self.logging.debug(\"============\")\n for attr in self.parm_list:\n self.logging.debug(attr.label + \" (\" + attr.when + \")\" + \" = \" + str(attr.value))\n self.logging.debug(\"============\")","def _fit_params(cls) -> List[str]:\n fsigs = set()\n for series_def in cls.__series__:\n fsigs.add(inspect.signature(series_def.fit_func))\n if len(fsigs) > 1:\n raise AnalysisError(\n \"Fit functions specified in the series definition have \"\n \"different function signature. They should receive \"\n \"the same parameter set for multi-objective function fit.\"\n )\n\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\n\n # remove fixed parameters\n if cls.__fixed_parameters__ is not None:\n for fixed_param in cls.__fixed_parameters__:\n try:\n fit_params.remove(fixed_param)\n except ValueError as ex:\n raise AnalysisError(\n f\"Defined fixed parameter {fixed_param} is not a fit function argument.\"\n \"Update series definition to ensure the parameter name is defined with \"\n f\"fit functions. Currently available parameters are {fit_params}.\"\n ) from ex\n\n return fit_params","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"length\", \"distance\"]","def parameters(self):\n raise NotImplementedError('Abstract method \"parameters\" must be '\n 'specialised!')","def science_parameter_list(cls):\n result = []\n for key in cls.list():\n if key not in GliderParticleKey.list():\n result.append(key)\n\n return result","def getInfoVariableNames(self, product):\r\n return []","def get_params_list():\n return common.QOL_PARAMS","def get_required_parameters(self) -> list:\n results = []\n if self.no_params or self.params_optional:\n return []\n else:\n for parameter, parameter_details in self.parameters.items():\n # Fixing issue #92\n # if parameter == \"effect\":\n # continue\n if not parameter_details.default_value:\n results.append(parameter_details.name)\n return results","def _parameter_summary(self, parameters, parameters_to_show=4):\n params = parameters\n if len(parameters) > parameters_to_show:\n params = parameters[:2] + [\"...\"] + parameters[-2:]\n return \", \".join(params)","def getResRatioVarNames( self ):\n\n self.updateAdb( )\n\n return self.resNames.keys()","def qparams(self):\n return self._qparams","def param_quantizers(self):\n return self._param_quantizers","def get_parameter_names(self, exclude_pop_model=False):\n if (self._population_model is None) or exclude_pop_model:\n names = self._mechanistic_model.parameters()\n for error_model in self._error_models:\n names += error_model.get_parameter_names()\n return names\n\n return self._population_model.get_parameter_names()","def param(self):\r\n\r\n return []","def print_param_names(model):\n for (param_name, param) in model.get_parameters().items():\n print(param_name, param.shape)","def _get_parameters(self) -> list:\n return self.parameters","def get_configuration_parameters_names(self):\n return (\n 'timestamp', 'model_name', 'weight_path', 'fit_history_path', 'learning_rate', 'load_weights',\n 'freeze_layers', 'margin', 'hard_sampling_batch_size', 'batch_size',\n 'number_of_validation_imgs', 'input_shape')","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"periodicity\", \"phase\", \"idivf\", \"k_bondorder\"]","def getOptimalParams(self):\n\t\t# Load calibration chain and find optimal for like1\n\t\tcal_data = pd.read_csv(self.database_path, sep=',')\n\t\tparams = cal_data.ix[cal_data['like1'].idxmax()].to_dict()\n\t\tcost = params['like1']\n\t\t# reformat parameters to match original naming\n\t\tparams_reformatted = {}\n\t\tfor k, p in self.cal_params.items():\n\t\t\tparams_reformatted[k] = params['par'+k]\n\n\t\treturn params_reformatted, cost","def param(self):\n return []","def param(self):\n return []","def _cim_quality():\n return {\n 'type' : 'class',\n 'name' : 'cim_quality',\n 'base' : None,\n 'is_abstract' : False,\n 'is_entity' : True,\n 'doc' : 'The starting point for a quality record. It can contain any number of issues and reports. An issue is an open-ended description of some issue about a CIM instance. A record is a prescribed description of some specific quantitative measure that has been applied to a CIM instance.',\n 'properties' : [\n ('meta', 'shared.doc_meta_info', '1.1', None),\n ('reports', 'quality.report', '0.N', None),\n ],\n 'decodings' : [\n ('meta', 'self::cim:cIM_Quality'),\n ('reports', 'child::cim:report'),\n ]\n }","def _get_param_names_transformed(self):\r\n n = self._get_param_names()\r\n\r\n # remove/concatenate the tied parameter names\r\n if len(self.tied_indices):\r\n for t in self.tied_indices:\r\n n[t[0]] = \"<tie>\".join([n[tt] for tt in t])\r\n remove = np.hstack([t[1:] for t in self.tied_indices])\r\n else:\r\n remove = np.empty(shape=(0,), dtype=np.int)\r\n\r\n # also remove the fixed params\r\n if len(self.fixed_indices):\r\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\r\n\r\n # add markers to show that some variables are constrained\r\n for i, t in zip(self.constrained_indices, self.constraints):\r\n for ii in i:\r\n n[ii] = n[ii] + t.__str__()\r\n\r\n n = [nn for i, nn in enumerate(n) if not i in remove]\r\n return n","def parameters_list(self):\n return [getattr(self.parameters, p) for p in self.parameters_names()]","def __str__(self):\n num_active = len([p for p in self if p])\n summary = \"%s has %d parameters with %d active (non-default)\" % \\\n (self.__class__.__name__, len(self), num_active)\n if num_active == 0:\n return summary\n return summary + ':\\n' + '\\n'.join(('%s = %s %s (%s)' % ps)\n for ps in self.param_strs())","def get_optimization_parameters(self, friendly=True):\n #Get the sensitivities task:\n sensTask=self._getTask('optimization')\n sensProblem = sensTask.find(xmlns + 'Problem')\n optimizationItems = sensProblem.find(xmlns + 'ParameterGroup')\n parameters = []\n for subGroup in optimizationItems:\n name = None\n lowerBound = None\n upperBound = None\n startValue = None\n \n for item in subGroup:\n if item.attrib['name'] == 'ObjectCN':\n name = item.attrib['value']\n elif item.attrib['name'] == 'UpperBound':\n upperBound = item.attrib['value']\n elif item.attrib['name'] == 'LowerBound':\n lowerBound = item.attrib['value']\n elif item.attrib['name'] == 'StartValue':\n startValue = item.attrib['value']\n assert name !=None\n assert lowerBound != None\n assert upperBound != None\n assert startValue != None\n \n if friendly:\n #Construct a user-friendly name for the parameter name using regexs\n #Look for a match for global parameters: Vector=Values[Test parameter],\n values_string = r'.*Vector=Values\\[(?P<name>.*)\\].*'\n values_string_re = re.compile(values_string)\n values_match = re.match(values_string_re, name)\n \n if values_match:\n name = 'Values[' + values_match.group('name') + ']'\n \n else:\n #else check for a parameter match.\n #Vector=Reactions[Reaction] Parameter=k1\n parameter_string = r'.*Vector=Reactions\\[(?P<reaction>.*)\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\n parameter_string_re = re.compile(parameter_string)\n parameter_match = re.match(parameter_string_re, name)\n \n if parameter_match:\n reaction = parameter_match.group('reaction')\n parameter = parameter_match.group('parameter')\n name = '(%s).%s'%(reaction, parameter)\n \n else:\n #Try again, this time looking for a string like: Vector=Metabolites[blah]\n metabolites_string = r'.*Vector=Metabolites\\[(?P<name>.*)\\].*'\n metabolites_string_re = re.compile(metabolites_string)\n metabolites_match = re.match(metabolites_string_re, name)\n if metabolites_match:\n name = 'Metabolites[' + metabolites_match.group('name') + ']'\n\n parameters.append((name, lowerBound, upperBound, startValue))\n\n return parameters","def _get_param_names(self):\n temp_params = {'function': self.function, 'target': self.target}\n\n temp_params.update(self.kwargs)\n\n return temp_params","def quality(self) -> int:\n return self._quality","def getParameters(self): #$NON-NLS-1$\r","def parameters(self):\n return [i for i in self.variables if has_roles(i, Parameter)]","def changed_parameters(self):\n return self._changed_parameters","def param(self):\r\n return []","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"length\", \"k_bondorder\", \"length_bondorder\"]","def parameters(self):\n return self.pars","def __str__(self):\n model_parameters = filter(lambda p: p.requires_grad, self.parameters())\n params = sum([np.prod(p.size()) for p in model_parameters])\n return super(BaseModel, self).__str__() + '\\nTrainable parameters: {}'.format(params)\n # print(super(BaseModel, self))","def get_printer_quality(self):\n return self.parent.printer.get_quality()","def parameters(self):\n return self._params","def Qps(self):\n return [elem['Qp'] for elem in self.__compartments]","def parameters(self):\n return self.model.parameters()"],"string":"[\n \"def get_parameters(self, quality):\\n if (quality.upper() == 'NAV') or (quality.upper() == 'NAVIGATION'):\\n return self._sensor_param_dict['navigation'].copy()\\n \\n elif (quality.upper() == 'TAC') or (quality.upper() == 'TACTICAL'):\\n return self._sensor_param_dict['tactical'].copy()\\n \\n elif (quality.upper() == 'CON') or (quality.upper() == 'CONSUMER'):\\n return self._sensor_param_dict['consumer'].copy()\",\n \"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\\n parameters = reduce(\\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\\n qualities,\\n set()\\n )\\n return sorted(list(parameters))\",\n \"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\\n return [q for p, q in self.correlating_pq_tuples if p == parameter]\",\n \"def parameter_names(self) -> List[str]:\",\n \"def print_quality(self, quality):\\n # Find the parameter dictionary for the desired quality level\\n \\n imu = self.get_parameters(quality)\\n \\n print '\\\\n%s Quality \\\\n-------------------------' % quality.upper()\\n print ' %30s' % 'GYRO'\\n print ' %22s \\\\t %g' % ('Wide Band Noise', imu['sigma_w_g'])\\n print ' %22s \\\\t %g' % ('Constant Bias' , imu['sigma_n_g'])\\n print ' %22s \\\\t %g' % ('Bias Stability' , imu['sigma_c_g'])\\n print ' %22s \\\\t %g' % ('Bias Correlation Time', imu['tau_g'])\\n print '\\\\n'\\n \\n print ' %35s' % 'ACCELEROMETER'\\n print ' %22s \\\\t %g' % ('Wide Band Noise', imu['sigma_w_f'])\\n print ' %22s \\\\t %g' % ('Constant Bias' , imu['sigma_n_f'])\\n print ' %22s \\\\t %g' % ('Bias Stability' , imu['sigma_c_f'])\\n print ' %22s \\\\t %g' % ('Bias Correlation Time', imu['tau_f'])\\n print '\\\\n'\",\n \"def show_parameters(self):\\n with np.printoptions(precision=3, suppress=True):\\n print('number of wind phase = {}'.format(self.ncomp))\\n print('galactic parameter = {}'.format(self.scaling_field))\\n print('reference height = {}'.format(self.z0))\\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\\n params = getattr(self,p)\\n print(p)\\n for k,v in params.items():\\n print(' {} = {}'.format(k,v))\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"length\\\"]\",\n \"def parameters_names(cls):\\n return cls._Parameters._fields\",\n \"def quality(self):\\n try:\\n qid = int((self.tool_metadata or {}).get(\\\"quality\\\", 0))\\n except:\\n qid = 0\\n\\n # We might be able to get the quality strings from the item's tags\\n internal_name, name = \\\"normal\\\", \\\"Normal\\\"\\n if self.tags:\\n tags = {x.get('category'): x for x in self.tags}\\n if 'Quality' in tags:\\n internal_name, name = tags['Quality'].get('internal_name'), tags['Quality'].get('name')\\n\\n return qid, internal_name, name\",\n \"def parameter_names(self):\\n raise NotImplementedError(\\\"the parameter_names property should \\\"\\n \\\"be defined in the Estimator sub-class\\\")\",\n \"def parameterNames(self, p_int): # real signature unknown; restored from __doc__\\n return []\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"angle\\\"]\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def parameter_names(self) -> list:\\n parameters = []\\n parameters.extend(self.properties.parameter_names)\\n return parameters\",\n \"def parameters(self):\\n #print \\\"in instrument.parameter()\\\"\\n return self._params\",\n \"def get_str_param_names(self):\\n # Exclude self.api and self.names from the command string\\n return self.get_attribute_names(FormattedParameter)\",\n \"def derived_parameters(cls):\\n return ['cgg', 'cdd', 'css', 'cbb', 'vstar', 'gain', 'ft']\",\n \"def get_param_names(hf):\\n parameters = get_params(hf)\\n return [p.name for p in parameters]\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def set_quality(self):\\n p = self.suitability + 1.15 * self.fono\\n self.quality = np.exp(p) / (1 + np.exp(p))\",\n \"def derived_parameters(cls):\\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']\",\n \"def potential_parameters(cls):\\n return [\\\"length\\\", \\\"distance\\\"]\",\n \"def paramDetails(cls):\\n return {\\n 'dim': (10, 20, 2, 20),\\n 'nIter': (1, 10, 2, 5),\\n 'lamb': (.1, 1., .1, .05),\\n 'alph': (30, 50, 5, 40)\\n }\",\n \"def param_info():\\n\\n\\tgizmo_names = syn.getGizmoNames()\\n\\n\\tfor gizmo in gizmo_names:\\n\\t\\tparams = syn.getParameterNames(gizmo)\\n\\t#doesnt get all parameters from gizmos i.e. WaveFreq\\n\\n\\t# get all info on the 'WaveFreq' parameter\\n\\tGIZMO = 'aStim2'\\n\\tPARAMETER = 'WaveFreq'\\n\\n\\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\\n\\t#\\n\\t# # get the array size (should be 100)\\n\\t# sz = syn.getParameterSize(GIZMO, PARAMETER)\\n\\t#\\n\\t# # write values 1 to 50 in second half of buffer\\n\\t# result = syn.setParameterValues(GIZMO, PARAMETER, np.arange(1, 51), 50)\\n\\t#\\n\\t# # read all values from buffer\\n\\t# syn.getParameterValues(GIZMO, PARAMETER, sz)\\n\\t#\\n\\t# # get all info on the 'Go' parameter\\n\\t# PARAMETER = 'Go'\\n\\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\\n\\t#\\n\\t# # flip the switch\\n\\t# result = syn.setParameterValue(GIZMO, PARAMETER, 1)\\n\\t#\\n\\t# # check the value\\n\\tfreq = syn.getParameterValue(GIZMO, PARAMETER)\\n\\tprint('value =', freq)\\n\\tfreq = [freq]\\n\\n\\t# also verify visually that the switch slipped in the run\\n\\t# time interface. This state change will be logged just\\n\\t# like any other variable change and saved with the runtime\\n\\t# state.\\n\\n\\tnumTrials = 5 #total number of trials across stimuli\\n\\tISI = [2.0, 3.0, 4.0, 5.0] # ISI in seconds\\n\\n\\t# flash parameters\\n\\tflash_dur = [.001] # flash durs in seconds (100 ms, 200 ms)\\n\\tluminance = [[1, 1, 1], [.86, .86, .86], [0, .1, 1]] # white , grayish, purple just for testing\\n\\n\\t# auditory parameters\\n\\tduration = [.005] # in seconds; pulseDur in TDT\\n\\tsound_levels = [20.0, 40.0, 60.0, 80.0] # dB; waveAmp in TDT\\n\\n\\t# Auditory on (T/F? if T then A+V, if F then Visual only)\\n\\tstims = {0: \\\"auditory_only\\\",\\n\\t\\t\\t 1: \\\"visual_only\\\",\\n\\t\\t\\t 2: \\\"A+V\\\"\\n\\t\\t\\t }\\n\\n\\texper = Experiment(numTrials=numTrials, ISI=ISI, flash_dur=flash_dur, luminance=luminance, wave_freq=freq,\\n\\t\\t\\t\\t\\t pulse_dur=duration, wave_amp=sound_levels, stimulus=stims)\\n\\texper.run_experiment()\",\n \"def parameters(self):\\n return []\",\n \"def return_all_parameter_names():\\n a = list(titles)\\n a.append(r\\\"$\\\\chi^{2}$ per degree of freedom\\\")\\n b = list(labels)\\n b.append(\\\"chi2_per_dof\\\")\\n return a, b\",\n \"def get_parameter_estimation_parameters(self, friendly=True):\\n #Get the sensitivities task:\\n fitTask=self._getTask('parameterFitting')\\n fitProblem = fitTask.find(xmlns + 'Problem')\\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\\n parameters = []\\n for subGroup in optimizationItems:\\n name = None\\n lowerBound = None\\n upperBound = None\\n startValue = None\\n \\n for item in subGroup:\\n if item.attrib['name'] == 'ObjectCN':\\n name = item.attrib['value']\\n elif item.attrib['name'] == 'UpperBound':\\n upperBound = item.attrib['value']\\n elif item.attrib['name'] == 'LowerBound':\\n lowerBound = item.attrib['value']\\n elif item.attrib['name'] == 'StartValue':\\n startValue = item.attrib['value']\\n assert name !=None\\n assert lowerBound != None\\n assert upperBound != None\\n assert startValue != None\\n \\n if friendly:\\n #Construct a user-friendly name for the parameter name using regexs\\n #Look for a match for global parameters: Vector=Values[Test parameter],\\n global_string = r'.*Vector=Values\\\\[(?P<name>.*)\\\\].*'\\n global_string_re = re.compile(global_string)\\n global_match = re.match(global_string_re, name)\\n \\n if global_match:\\n name = global_match.group('name')\\n \\n #else check for a local match.\\n #Vector=Reactions[Reaction] Parameter=k1\\n local_string = r'.*Vector=Reactions\\\\[(?P<reaction>.*)\\\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\\n local_string_re = re.compile(local_string)\\n local_match = re.match(local_string_re, name)\\n \\n if local_match:\\n reaction = local_match.group('reaction')\\n parameter = local_match.group('parameter')\\n name = '(%s).%s'%(reaction, parameter)\\n\\n parameters.append((name, lowerBound, upperBound, startValue))\\n\\n return parameters\",\n \"def parameters(self):\\n return [i.parameter for i in self.joints.values()]\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"angle\\\"]\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def parameter_names(self):\\n return [x for x in self.transformations.values() if isinstance(x, str)]\",\n \"def display_parameters(self):\\n l = []\\n for param in self.parameters.all():\\n if len(param.value) > 16:\\n l.append(u\\\"{}={}...\\\".format(param.name, param.value[:16]))\\n else:\\n l.append(u\\\"{}={}\\\".format(param.name, param.value))\\n return \\\"; \\\".join(l)\",\n \"def _get_fitted_param_names(self):\\n return self._fitted_param_names\",\n \"def summary_parameters(self):\\n return {'filters': ', '.join(self.getOption('filters'))}\",\n \"def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\\n else: adjust = lambda x: x\\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\\n else: names = [adjust(x.name) for x in self.parameters]\\n if add_self: names = map(lambda x: adjust(self.name) + \\\".\\\" + x, names)\\n return names\",\n \"def parameters(self):\\n return {\\n 'base':self.base.parameters(),\\n 'material':[m.parameters() for m in self.material],\\n 'fraction':self.fraction,\\n }\",\n \"def getInfoVariableNames(self, product):\\r\\n\\r\\n h = product.getSceneRasterHeight()\\r\\n\\r\\n # 10m resolution\\r\\n if h == 10980:\\r\\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_10m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 5490:\\r\\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_20m)\\r\\n\\r\\n # 20m resolution\\r\\n elif h == 1830:\\r\\n return self.return_available_variables(product, INFO_VARIABLE_NAMES_60m)\\r\\n return []\",\n \"def params_desc(self):\\n return \\\"{}/{}/{}/{}\\\".format(\\n self.learning_rate, self.movement, self.milestones, self.gamma\\n )\",\n \"def get_param_names(self):\\n return list(self.params.keys())\",\n \"def inspect_state(self):\\n for name in self._param_store.get_all_param_names():\\n self._logger.info(\\\"Param [%s]: %r\\\", name,\\n pyro.param(name).data.numpy())\",\n \"def param_names(\\n self, *, include_tp: bool = False, include_gq: bool = False\\n ) -> List[str]:\\n return (\\n self._param_names(self.model, int(include_tp), int(include_gq))\\n .decode(\\\"utf-8\\\")\\n .split(\\\",\\\")\\n )\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def get_parameters_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([p for p, _ in self.expert_knowledge])))\",\n \"def current_parameters(self):\\n current = []\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n current.append(self.q[core_param].vi_return_param(approx_param))\\n return np.array(current)\",\n \"def get_ext_param_names(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\\n ret.append(tmp.decode(\\\"UTF-8\\\"))\\n\\n return ret\",\n \"def parameters(self):\\n ps = super().parameters()\\n exclude = set(self.estimator.parameters())\\n ps = (p for p in ps if not p in exclude)\\n return ps\",\n \"def supported_parameters(cls):\\n raise NotImplementedError()\",\n \"def parameters(self):\\n return [term.parameter for term in self.terms]\",\n \"def named_trainable_parameters(self) -> List[str]:\\n return [name for name, p in self._model.named_parameters() if p.requires_grad]\",\n \"def print_params(self):\\n s = self._list_params()+\\\"\\\\n\\\"\\n if 'scale_params' in self.__dict__.keys():\\n s += self.scale_params._list_params()+\\\"\\\\n\\\"\\n if 'atmospheric_params' in self.__dict__.keys():\\n if self.atmospheric_params is not None:\\n s += self.atmospheric_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'atemperature_params' in self.__dict__.keys():\\n if self.atemperature_params is not None:\\n s += self.atemperature_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'oceanic_params' in self.__dict__.keys():\\n if self.oceanic_params is not None:\\n s += self.oceanic_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'ground_params' in self.__dict__.keys():\\n if self.ground_params is not None:\\n s += self.ground_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'gotemperature_params' in self.__dict__.keys():\\n if self.gotemperature_params is not None:\\n s += self.gotemperature_params._list_params() + \\\"\\\\n\\\"\\n\\n print(\\\"Qgs v0.2.8 parameters summary\\\")\\n print(\\\"=============================\\\\n\\\")\\n print(s)\",\n \"def getParameters(self):\\n\\n current_params = {'taux': self.taux, 'mu': self.mu, 'G': self.G, 'alpha_0': self.alpha_0,\\n 'delta': self.delta, 'p': self.p, 'I0': self.I0, 'kparam': self.kparam}\\n\\n return (current_params)\",\n \"def parameters(self):\\n return [o.parameters for o in self.obs]\",\n \"def parameters(self):\\n return [p for _, a in vars(self).items() for p in self._params(a)]\",\n \"def supported_qparams(self):\\n import re\\n return re.findall(r\\\"\\\\$\\\\$\\\\{(\\\\w+)\\\\}\\\", self.QTEMPLATE)\",\n \"def search_quality(self,strz):\\n\\t\\tfor q in l_quality: #l_quality = list of allow quality words\\n\\t\\t\\tif q in strz:\\n\\t\\t\\t\\tself.quality=q.replace(\\\".\\\",\\\"\\\")\\n\\t\\t\\t\\treturn strz.replace(q,\\\"\\\")\\n\\t\\treturn strz\",\n \"def potential_parameters(cls):\\n raise NotImplementedError()\",\n \"def quality(self):\\n return self.plays * self.number\",\n \"def parameters(self):\",\n \"def display_parameters(self):\\n\\n self.logging.debug(\\\"============\\\")\\n for attr in self.parm_list:\\n self.logging.debug(attr.label + \\\" (\\\" + attr.when + \\\")\\\" + \\\" = \\\" + str(attr.value))\\n self.logging.debug(\\\"============\\\")\",\n \"def _fit_params(cls) -> List[str]:\\n fsigs = set()\\n for series_def in cls.__series__:\\n fsigs.add(inspect.signature(series_def.fit_func))\\n if len(fsigs) > 1:\\n raise AnalysisError(\\n \\\"Fit functions specified in the series definition have \\\"\\n \\\"different function signature. They should receive \\\"\\n \\\"the same parameter set for multi-objective function fit.\\\"\\n )\\n\\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\\n\\n # remove fixed parameters\\n if cls.__fixed_parameters__ is not None:\\n for fixed_param in cls.__fixed_parameters__:\\n try:\\n fit_params.remove(fixed_param)\\n except ValueError as ex:\\n raise AnalysisError(\\n f\\\"Defined fixed parameter {fixed_param} is not a fit function argument.\\\"\\n \\\"Update series definition to ensure the parameter name is defined with \\\"\\n f\\\"fit functions. Currently available parameters are {fit_params}.\\\"\\n ) from ex\\n\\n return fit_params\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"length\\\", \\\"distance\\\"]\",\n \"def parameters(self):\\n raise NotImplementedError('Abstract method \\\"parameters\\\" must be '\\n 'specialised!')\",\n \"def science_parameter_list(cls):\\n result = []\\n for key in cls.list():\\n if key not in GliderParticleKey.list():\\n result.append(key)\\n\\n return result\",\n \"def getInfoVariableNames(self, product):\\r\\n return []\",\n \"def get_params_list():\\n return common.QOL_PARAMS\",\n \"def get_required_parameters(self) -> list:\\n results = []\\n if self.no_params or self.params_optional:\\n return []\\n else:\\n for parameter, parameter_details in self.parameters.items():\\n # Fixing issue #92\\n # if parameter == \\\"effect\\\":\\n # continue\\n if not parameter_details.default_value:\\n results.append(parameter_details.name)\\n return results\",\n \"def _parameter_summary(self, parameters, parameters_to_show=4):\\n params = parameters\\n if len(parameters) > parameters_to_show:\\n params = parameters[:2] + [\\\"...\\\"] + parameters[-2:]\\n return \\\", \\\".join(params)\",\n \"def getResRatioVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.resNames.keys()\",\n \"def qparams(self):\\n return self._qparams\",\n \"def param_quantizers(self):\\n return self._param_quantizers\",\n \"def get_parameter_names(self, exclude_pop_model=False):\\n if (self._population_model is None) or exclude_pop_model:\\n names = self._mechanistic_model.parameters()\\n for error_model in self._error_models:\\n names += error_model.get_parameter_names()\\n return names\\n\\n return self._population_model.get_parameter_names()\",\n \"def param(self):\\r\\n\\r\\n return []\",\n \"def print_param_names(model):\\n for (param_name, param) in model.get_parameters().items():\\n print(param_name, param.shape)\",\n \"def _get_parameters(self) -> list:\\n return self.parameters\",\n \"def get_configuration_parameters_names(self):\\n return (\\n 'timestamp', 'model_name', 'weight_path', 'fit_history_path', 'learning_rate', 'load_weights',\\n 'freeze_layers', 'margin', 'hard_sampling_batch_size', 'batch_size',\\n 'number_of_validation_imgs', 'input_shape')\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\", \\\"k_bondorder\\\"]\",\n \"def getOptimalParams(self):\\n\\t\\t# Load calibration chain and find optimal for like1\\n\\t\\tcal_data = pd.read_csv(self.database_path, sep=',')\\n\\t\\tparams = cal_data.ix[cal_data['like1'].idxmax()].to_dict()\\n\\t\\tcost = params['like1']\\n\\t\\t# reformat parameters to match original naming\\n\\t\\tparams_reformatted = {}\\n\\t\\tfor k, p in self.cal_params.items():\\n\\t\\t\\tparams_reformatted[k] = params['par'+k]\\n\\n\\t\\treturn params_reformatted, cost\",\n \"def param(self):\\n return []\",\n \"def param(self):\\n return []\",\n \"def _cim_quality():\\n return {\\n 'type' : 'class',\\n 'name' : 'cim_quality',\\n 'base' : None,\\n 'is_abstract' : False,\\n 'is_entity' : True,\\n 'doc' : 'The starting point for a quality record. It can contain any number of issues and reports. An issue is an open-ended description of some issue about a CIM instance. A record is a prescribed description of some specific quantitative measure that has been applied to a CIM instance.',\\n 'properties' : [\\n ('meta', 'shared.doc_meta_info', '1.1', None),\\n ('reports', 'quality.report', '0.N', None),\\n ],\\n 'decodings' : [\\n ('meta', 'self::cim:cIM_Quality'),\\n ('reports', 'child::cim:report'),\\n ]\\n }\",\n \"def _get_param_names_transformed(self):\\r\\n n = self._get_param_names()\\r\\n\\r\\n # remove/concatenate the tied parameter names\\r\\n if len(self.tied_indices):\\r\\n for t in self.tied_indices:\\r\\n n[t[0]] = \\\"<tie>\\\".join([n[tt] for tt in t])\\r\\n remove = np.hstack([t[1:] for t in self.tied_indices])\\r\\n else:\\r\\n remove = np.empty(shape=(0,), dtype=np.int)\\r\\n\\r\\n # also remove the fixed params\\r\\n if len(self.fixed_indices):\\r\\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\\r\\n\\r\\n # add markers to show that some variables are constrained\\r\\n for i, t in zip(self.constrained_indices, self.constraints):\\r\\n for ii in i:\\r\\n n[ii] = n[ii] + t.__str__()\\r\\n\\r\\n n = [nn for i, nn in enumerate(n) if not i in remove]\\r\\n return n\",\n \"def parameters_list(self):\\n return [getattr(self.parameters, p) for p in self.parameters_names()]\",\n \"def __str__(self):\\n num_active = len([p for p in self if p])\\n summary = \\\"%s has %d parameters with %d active (non-default)\\\" % \\\\\\n (self.__class__.__name__, len(self), num_active)\\n if num_active == 0:\\n return summary\\n return summary + ':\\\\n' + '\\\\n'.join(('%s = %s %s (%s)' % ps)\\n for ps in self.param_strs())\",\n \"def get_optimization_parameters(self, friendly=True):\\n #Get the sensitivities task:\\n sensTask=self._getTask('optimization')\\n sensProblem = sensTask.find(xmlns + 'Problem')\\n optimizationItems = sensProblem.find(xmlns + 'ParameterGroup')\\n parameters = []\\n for subGroup in optimizationItems:\\n name = None\\n lowerBound = None\\n upperBound = None\\n startValue = None\\n \\n for item in subGroup:\\n if item.attrib['name'] == 'ObjectCN':\\n name = item.attrib['value']\\n elif item.attrib['name'] == 'UpperBound':\\n upperBound = item.attrib['value']\\n elif item.attrib['name'] == 'LowerBound':\\n lowerBound = item.attrib['value']\\n elif item.attrib['name'] == 'StartValue':\\n startValue = item.attrib['value']\\n assert name !=None\\n assert lowerBound != None\\n assert upperBound != None\\n assert startValue != None\\n \\n if friendly:\\n #Construct a user-friendly name for the parameter name using regexs\\n #Look for a match for global parameters: Vector=Values[Test parameter],\\n values_string = r'.*Vector=Values\\\\[(?P<name>.*)\\\\].*'\\n values_string_re = re.compile(values_string)\\n values_match = re.match(values_string_re, name)\\n \\n if values_match:\\n name = 'Values[' + values_match.group('name') + ']'\\n \\n else:\\n #else check for a parameter match.\\n #Vector=Reactions[Reaction] Parameter=k1\\n parameter_string = r'.*Vector=Reactions\\\\[(?P<reaction>.*)\\\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\\n parameter_string_re = re.compile(parameter_string)\\n parameter_match = re.match(parameter_string_re, name)\\n \\n if parameter_match:\\n reaction = parameter_match.group('reaction')\\n parameter = parameter_match.group('parameter')\\n name = '(%s).%s'%(reaction, parameter)\\n \\n else:\\n #Try again, this time looking for a string like: Vector=Metabolites[blah]\\n metabolites_string = r'.*Vector=Metabolites\\\\[(?P<name>.*)\\\\].*'\\n metabolites_string_re = re.compile(metabolites_string)\\n metabolites_match = re.match(metabolites_string_re, name)\\n if metabolites_match:\\n name = 'Metabolites[' + metabolites_match.group('name') + ']'\\n\\n parameters.append((name, lowerBound, upperBound, startValue))\\n\\n return parameters\",\n \"def _get_param_names(self):\\n temp_params = {'function': self.function, 'target': self.target}\\n\\n temp_params.update(self.kwargs)\\n\\n return temp_params\",\n \"def quality(self) -> int:\\n return self._quality\",\n \"def getParameters(self): #$NON-NLS-1$\\r\",\n \"def parameters(self):\\n return [i for i in self.variables if has_roles(i, Parameter)]\",\n \"def changed_parameters(self):\\n return self._changed_parameters\",\n \"def param(self):\\r\\n return []\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"length\\\", \\\"k_bondorder\\\", \\\"length_bondorder\\\"]\",\n \"def parameters(self):\\n return self.pars\",\n \"def __str__(self):\\n model_parameters = filter(lambda p: p.requires_grad, self.parameters())\\n params = sum([np.prod(p.size()) for p in model_parameters])\\n return super(BaseModel, self).__str__() + '\\\\nTrainable parameters: {}'.format(params)\\n # print(super(BaseModel, self))\",\n \"def get_printer_quality(self):\\n return self.parent.printer.get_quality()\",\n \"def parameters(self):\\n return self._params\",\n \"def Qps(self):\\n return [elem['Qp'] for elem in self.__compartments]\",\n \"def parameters(self):\\n return self.model.parameters()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6934809","0.67903876","0.6167672","0.61589646","0.6091067","0.6062602","0.59788996","0.59663045","0.5930198","0.5925212","0.5914016","0.587946","0.57363737","0.5702479","0.57009995","0.57009995","0.5699666","0.5673059","0.5664768","0.5636903","0.5629475","0.5623803","0.5621273","0.5620138","0.56011814","0.55965364","0.5592449","0.55895233","0.5578792","0.5577558","0.5557908","0.5548965","0.55309874","0.5526922","0.55177945","0.5513634","0.5496831","0.54605556","0.5454543","0.5443713","0.54304653","0.5425348","0.5424584","0.5415723","0.5408551","0.5391517","0.5378407","0.5362684","0.5356938","0.53500587","0.5348811","0.5345754","0.53437704","0.5342067","0.5340411","0.53367674","0.53338116","0.53050804","0.5294772","0.5293916","0.52868515","0.52750844","0.5274358","0.52712774","0.5262994","0.52624804","0.5259622","0.52550644","0.5253106","0.5245318","0.5243924","0.52411735","0.5240772","0.52324843","0.5211069","0.52093405","0.5204828","0.51998264","0.5196941","0.5194059","0.5193397","0.5193397","0.51897645","0.5188891","0.5184983","0.5165451","0.5164849","0.51585984","0.51573795","0.5149332","0.51490366","0.5145079","0.51392436","0.51387095","0.5135426","0.5133933","0.5131055","0.51310325","0.51308554","0.51298106"],"string":"[\n \"0.6934809\",\n \"0.67903876\",\n \"0.6167672\",\n \"0.61589646\",\n \"0.6091067\",\n \"0.6062602\",\n \"0.59788996\",\n \"0.59663045\",\n \"0.5930198\",\n \"0.5925212\",\n \"0.5914016\",\n \"0.587946\",\n \"0.57363737\",\n \"0.5702479\",\n \"0.57009995\",\n \"0.57009995\",\n \"0.5699666\",\n \"0.5673059\",\n \"0.5664768\",\n \"0.5636903\",\n \"0.5629475\",\n \"0.5623803\",\n \"0.5621273\",\n \"0.5620138\",\n \"0.56011814\",\n \"0.55965364\",\n \"0.5592449\",\n \"0.55895233\",\n \"0.5578792\",\n \"0.5577558\",\n \"0.5557908\",\n \"0.5548965\",\n \"0.55309874\",\n \"0.5526922\",\n \"0.55177945\",\n \"0.5513634\",\n \"0.5496831\",\n \"0.54605556\",\n \"0.5454543\",\n \"0.5443713\",\n \"0.54304653\",\n \"0.5425348\",\n \"0.5424584\",\n \"0.5415723\",\n \"0.5408551\",\n \"0.5391517\",\n \"0.5378407\",\n \"0.5362684\",\n \"0.5356938\",\n \"0.53500587\",\n \"0.5348811\",\n \"0.5345754\",\n \"0.53437704\",\n \"0.5342067\",\n \"0.5340411\",\n \"0.53367674\",\n \"0.53338116\",\n \"0.53050804\",\n \"0.5294772\",\n \"0.5293916\",\n \"0.52868515\",\n \"0.52750844\",\n \"0.5274358\",\n \"0.52712774\",\n \"0.5262994\",\n \"0.52624804\",\n \"0.5259622\",\n \"0.52550644\",\n \"0.5253106\",\n \"0.5245318\",\n \"0.5243924\",\n \"0.52411735\",\n \"0.5240772\",\n \"0.52324843\",\n \"0.5211069\",\n \"0.52093405\",\n \"0.5204828\",\n \"0.51998264\",\n \"0.5196941\",\n \"0.5194059\",\n \"0.5193397\",\n \"0.5193397\",\n \"0.51897645\",\n \"0.5188891\",\n \"0.5184983\",\n \"0.5165451\",\n \"0.5164849\",\n \"0.51585984\",\n \"0.51573795\",\n \"0.5149332\",\n \"0.51490366\",\n \"0.5145079\",\n \"0.51392436\",\n \"0.51387095\",\n \"0.5135426\",\n \"0.5133933\",\n \"0.5131055\",\n \"0.51310325\",\n \"0.51308554\",\n \"0.51298106\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8470386"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17464,"cells":{"query":{"kind":"string","value":"Returns the names of all parameters are affecting the given qualities."},"document":{"kind":"string","value":"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_parameters_affecting_quality(self, quality: str) -> List[str]:\n return [p for p, q in self.correlating_pq_tuples if q == quality]","def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]","def parameter_names(self) -> List[str]:","def parameters_names(cls):\n return cls._Parameters._fields","def param_names(\n self, *, include_tp: bool = False, include_gq: bool = False\n ) -> List[str]:\n return (\n self._param_names(self.model, int(include_tp), int(include_gq))\n .decode(\"utf-8\")\n .split(\",\")\n )","def parameterNames(self, p_int): # real signature unknown; restored from __doc__\n return []","def parameters(self):\n return [i.parameter for i in self.joints.values()]","def get_param_names(hf):\n parameters = get_params(hf)\n return [p.name for p in parameters]","def parameter_names(self):\n raise NotImplementedError(\"the parameter_names property should \"\n \"be defined in the Estimator sub-class\")","def _get_fitted_param_names(self):\n return self._fitted_param_names","def parameter_names(self) -> list:\n parameters = []\n parameters.extend(self.properties.parameter_names)\n return parameters","def get_str_param_names(self):\n # Exclude self.api and self.names from the command string\n return self.get_attribute_names(FormattedParameter)","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def get_param_names(self):\n return list(self.params.keys())","def parameters(self):\n return [term.parameter for term in self.terms]","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def _get_param_names_transformed(self):\r\n n = self._get_param_names()\r\n\r\n # remove/concatenate the tied parameter names\r\n if len(self.tied_indices):\r\n for t in self.tied_indices:\r\n n[t[0]] = \"<tie>\".join([n[tt] for tt in t])\r\n remove = np.hstack([t[1:] for t in self.tied_indices])\r\n else:\r\n remove = np.empty(shape=(0,), dtype=np.int)\r\n\r\n # also remove the fixed params\r\n if len(self.fixed_indices):\r\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\r\n\r\n # add markers to show that some variables are constrained\r\n for i, t in zip(self.constrained_indices, self.constraints):\r\n for ii in i:\r\n n[ii] = n[ii] + t.__str__()\r\n\r\n n = [nn for i, nn in enumerate(n) if not i in remove]\r\n return n","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def parameters(self):\n return [i for i in self.variables if has_roles(i, Parameter)]","def get_parameter_names(self, exclude_pop_model=False):\n if (self._population_model is None) or exclude_pop_model:\n names = self._mechanistic_model.parameters()\n for error_model in self._error_models:\n names += error_model.get_parameter_names()\n return names\n\n return self._population_model.get_parameter_names()","def _get_param_names(self):\n temp_params = {'function': self.function, 'target': self.target}\n\n temp_params.update(self.kwargs)\n\n return temp_params","def potential_parameters(cls):\n return [\"k\", \"length\"]","def _get_param_names(self):\r\n return sorted([p\r\n for p in self.__dict__\r\n if p != 'additional_args'])","def potential_parameters(cls):\n return [\"length\", \"distance\"]","def parameters(self):\n return [p for _, a in vars(self).items() for p in self._params(a)]","def parameters(self):\n return [o.parameters for o in self.obs]","def get_params_list():\n return common.QOL_PARAMS","def potential_parameters(cls):\n return [\"k\", \"angle\"]","def supported_qparams(self):\n import re\n return re.findall(r\"\\$\\$\\{(\\w+)\\}\", self.QTEMPLATE)","def parameters(self):\n return []","def get_paramnames_list(self):\n # TODO include syselem?\n\n query = \"SELECT NAME FROM %s\" % self.__schema\n with self.__connection.cursor() as cursor:\n cursor.execute(query)\n result = cursor.fetchall()\n return [val['NAME'] for val in result]","def derived_parameters(cls):\n return ['cgg', 'cdd', 'css', 'cbb', 'vstar', 'gain', 'ft']","def derived_parameters(cls):\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']","def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\n else: adjust = lambda x: x\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\n else: names = [adjust(x.name) for x in self.parameters]\n if add_self: names = map(lambda x: adjust(self.name) + \".\" + x, names)\n return names","def qparams(self):\n return self._qparams","def return_all_parameter_names():\n a = list(titles)\n a.append(r\"$\\chi^{2}$ per degree of freedom\")\n b = list(labels)\n b.append(\"chi2_per_dof\")\n return a, b","def get_qualification_list(self):\n try:\n qualifications = self.db_handler.get_qualifications_list()\n self.logger.write_to_log('qualifications got', 'model')\n return qualifications\n except Exception as err:\n method_name = sys._getframe().f_code.co_name\n\n self.logger.write_to_log('exception', 'model')\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')","def names(self):\n result = []\n result.extend(self.positional_arguments)\n if self.arbitary_positional_arguments is not None:\n result.append(self.arbitary_positional_arguments)\n if self.arbitary_keyword_arguments is not None:\n result.append(self.arbitary_keyword_arguments)\n result.extend(self.keyword_arguments)\n return result","def parameter_names(self):\n return [x for x in self.transformations.values() if isinstance(x, str)]","def show_parameters(self):\n with np.printoptions(precision=3, suppress=True):\n print('number of wind phase = {}'.format(self.ncomp))\n print('galactic parameter = {}'.format(self.scaling_field))\n print('reference height = {}'.format(self.z0))\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\n params = getattr(self,p)\n print(p)\n for k,v in params.items():\n print(' {} = {}'.format(k,v))","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"periodicity\", \"phase\", \"idivf\"]","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def get_parameters(self, quality):\n if (quality.upper() == 'NAV') or (quality.upper() == 'NAVIGATION'):\n return self._sensor_param_dict['navigation'].copy()\n \n elif (quality.upper() == 'TAC') or (quality.upper() == 'TACTICAL'):\n return self._sensor_param_dict['tactical'].copy()\n \n elif (quality.upper() == 'CON') or (quality.upper() == 'CONSUMER'):\n return self._sensor_param_dict['consumer'].copy()","def get_original_names_and_sharded_parameters(self):\n orig_named_parameters = []\n for module_name, m in self.named_modules(): # includes self\n if isinstance(m, XlaFullyShardedDataParallel):\n prefix = \"\" if module_name == \"\" else module_name + \".\"\n for p in self.sharded_params:\n n = prefix + p._orig_name\n n = n.replace(\"_fsdp_wrapped_module.\", \"\").replace(\"_fpw_module.\", \"\")\n orig_named_parameters.append((n, p))\n\n return orig_named_parameters","def params(self):\n return [cq.Symbol('theta_0')]","def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\n expert_knowledge = reduce(\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\n qualities,\n set()\n )\n return sorted(list(expert_knowledge))","def get_required_parameters(self) -> list:\n results = []\n if self.no_params or self.params_optional:\n return []\n else:\n for parameter, parameter_details in self.parameters.items():\n # Fixing issue #92\n # if parameter == \"effect\":\n # continue\n if not parameter_details.default_value:\n results.append(parameter_details.name)\n return results","def parameters(self):\n ps = super().parameters()\n exclude = set(self.estimator.parameters())\n ps = (p for p in ps if not p in exclude)\n return ps","def parameters(self):\n return self.vars","def print_param_names(model):\n for (param_name, param) in model.get_parameters().items():\n print(param_name, param.shape)","def param_unc_names(self) -> List[str]:\n return self._param_unc_names(self.model).decode(\"utf-8\").split(\",\")","def display_parameters(self):\n\n self.logging.debug(\"============\")\n for attr in self.parm_list:\n self.logging.debug(attr.label + \" (\" + attr.when + \")\" + \" = \" + str(attr.value))\n self.logging.debug(\"============\")","def parameters_list(self):\n return [getattr(self.parameters, p) for p in self.parameters_names()]","def return_parameter_names():\n return list(titles), list(labels)","def get_resource_params():\n return Parameter.list()","def potential_parameters(cls):\n raise NotImplementedError()","def params(self):\n return [p for sublist in [o.params for o in self.obs] for p in sublist]","def extract_parameters(self) -> Dict[str, Set[str]]:\n regex = \"\\{([A-Za-z0-9_]+)\\}\"\n reserved_parameters = [\n \"output\",\n \"input\",\n \"output_vec\",\n \"input_vec\",\n \"df\",\n \"vec_open\",\n \"vec_close\",\n ]\n parameters = {}\n for scope in self.scopes:\n parameters[scope] = set(\n [\n x\n for x in re.findall(regex, self.call)\n if x not in reserved_parameters\n ]\n )\n return parameters","def named_trainable_parameters(self) -> List[str]:\n return [name for name, p in self._model.named_parameters() if p.requires_grad]","def parameters(self):\n raise NotImplementedError('Abstract method \"parameters\" must be '\n 'specialised!')","def _create_parameter_names(self):\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']","def get_fitted_params(self):\n self.check_is_fitted()\n return {\n name: self._fitted_forecaster.params.get(name)\n for name in self._get_fitted_param_names()\n }","def getConditionNames():\n return _conditions.keys()","def getListOfLocalParameters(self, *args):\n return _libsbml.KineticLaw_getListOfLocalParameters(self, *args)","def parameters(self):\n # encoded in θ\n return self.theta.columns","def supported_parameters(cls):\n raise NotImplementedError()","def _get_params(self, fluents):\n objects_all = set()\n for fluent in fluents:\n objects = fluent.replace(\"(\",\"\").replace(\")\",\"\").split(\" \")[1:]\n objects_all.update(objects)\n\n return objects_all","def parameters(self):\n return {\n 'base':self.base.parameters(),\n 'material':[m.parameters() for m in self.material],\n 'fraction':self.fraction,\n }","def get_derived_paramnames(self):\n names = []\n for name, shape in zip(self.likelihood.child_derived, self.derived_shapes):\n if not isinstance(name, str):\n name = name.__name__\n\n if shape == 0:\n names.append((name, name))\n else:\n names.extend(\n [\n (\n name + self._index_to_string(*ind),\n name + self._index_to_latex(*ind),\n )\n for ind in np.ndindex(*shape)\n ]\n )\n return names","def get_param(self, obs_name):\n return self.datasets[obs_name].keys()","def get_ext_param_names(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\n ret.append(tmp.decode(\"UTF-8\"))\n\n return ret","def get_mandatory_param_names(self):\n all_names = self.params.keys()\n return [name for name in all_names \n if not self.params[name].is_optional]","def _fit_params(cls) -> List[str]:\n fsigs = set()\n for series_def in cls.__series__:\n fsigs.add(inspect.signature(series_def.fit_func))\n if len(fsigs) > 1:\n raise AnalysisError(\n \"Fit functions specified in the series definition have \"\n \"different function signature. They should receive \"\n \"the same parameter set for multi-objective function fit.\"\n )\n\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\n\n # remove fixed parameters\n if cls.__fixed_parameters__ is not None:\n for fixed_param in cls.__fixed_parameters__:\n try:\n fit_params.remove(fixed_param)\n except ValueError as ex:\n raise AnalysisError(\n f\"Defined fixed parameter {fixed_param} is not a fit function argument.\"\n \"Update series definition to ensure the parameter name is defined with \"\n f\"fit functions. Currently available parameters are {fit_params}.\"\n ) from ex\n\n return fit_params","def get_layer_var_names(self):\n return(self.params)","def get_param_names(obj: Union[Type[_BaseTpcpObject], _BaseTpcpObject]) -> List[str]:\n cls = obj if isinstance(obj, type) else type(obj)\n parameters = list(_get_init_defaults(cls).values())\n for p in parameters:\n if p.kind == p.VAR_POSITIONAL:\n raise RuntimeError(\n \"tpcp algorithms and pipelines should always specify their parameters in the signature of their \"\n f\"__init__ (no varargs). {cls} doesn't follow this convention.\"\n )\n # Extract and sort argument names excluding 'self'\n return sorted([p.name for p in parameters])","def param_quantizers(self):\n return self._param_quantizers","def get_resource_params(self):\n return Parameter.list()","def get_resource_params(self):\n return Parameter.list()","def _fc_in_parameters(self) -> List[str]:\n result = list() # type: List[str]\n\n for param in self.params:\n type_list = param.fc_type()\n for type_name, postfix in type_list:\n result.append('{} {}'.format(type_name, param.name + postfix))\n\n return result","def _get_param_names(cls):\n # fetch the constructor\n init = getattr(cls.__init__, 'Database Class', cls.__init__)\n\n if init is object.__init__:\n # no constructor to inspect\n params = []\n else:\n # inspect constructor\n sig = inspect.signature(init)\n parameters = [p for p in sig.parameters.values()\n if p.name != 'self' and\n p.kind != p.VAR_KEYWORD]\n\n for p in parameters:\n if p.kind == p.VAR_POSITIONAL:\n raise RuntimeError(\n 'Database objects should always specify their '\n 'parameters in the signature of their __init__. '\n '{class_} with constructor {signature} does not follow '\n 'this convention.'.format(\n class_=cls,\n signature=sig\n )\n )\n\n # Extract and sort argument names excluding 'self'\n params = sorted([p.name for p in parameters])\n\n return params","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"periodicity\", \"phase\", \"idivf\", \"k_bondorder\"]","def params(self) -> Iterable[sympy.Symbol]:\n for i in range(self.iterations):\n for p in range(len(self.qubits)):\n if (self.include_all_z or not\n numpy.isclose(self.orbital_energies[p], 0)):\n yield LetterWithSubscripts('U', p, i)\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\n if (self.include_all_cz or not\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\n yield LetterWithSubscripts('V', p, q, i)","def parameters(self) -> Mapping[str, str]:\n return pulumi.get(self, \"parameters\")","def get_params(self):\n params = super(JoinFilterQuery, self).get_params()\n params.append(('fq', self._query))\n\n return params","def print_params(self):\n s = self._list_params()+\"\\n\"\n if 'scale_params' in self.__dict__.keys():\n s += self.scale_params._list_params()+\"\\n\"\n if 'atmospheric_params' in self.__dict__.keys():\n if self.atmospheric_params is not None:\n s += self.atmospheric_params._list_params()+\"\\n\"\n\n if 'atemperature_params' in self.__dict__.keys():\n if self.atemperature_params is not None:\n s += self.atemperature_params._list_params()+\"\\n\"\n\n if 'oceanic_params' in self.__dict__.keys():\n if self.oceanic_params is not None:\n s += self.oceanic_params._list_params()+\"\\n\"\n\n if 'ground_params' in self.__dict__.keys():\n if self.ground_params is not None:\n s += self.ground_params._list_params()+\"\\n\"\n\n if 'gotemperature_params' in self.__dict__.keys():\n if self.gotemperature_params is not None:\n s += self.gotemperature_params._list_params() + \"\\n\"\n\n print(\"Qgs v0.2.8 parameters summary\")\n print(\"=============================\\n\")\n print(s)","def get_strains(names, q_dof):\n strain_functions = []\n for n, this_dof in zip(names, q_dof):\n check_qdof(n, this_dof)\n if n == 'linear_helix':\n strain_functions.append(linear_helix_strain)\n elif n == 'pure_helix':\n strain_functions.append(pure_helix_strain)\n elif n == 'torsion_helix':\n strain_functions.append(torsion_helix_strain)\n elif n == 'torsion_linear_helix':\n strain_functions.append(torsion_linear_helix_strain)\n elif n == 'quadratic':\n strain_functions.append(quadratic_strain)\n elif n == 'linear':\n strain_functions.append(linear_strain)\n elif n == 'constant':\n strain_functions.append(constant_strain)\n elif n == 'full':\n strain_functions.append(full_strain)\n else:\n print(f'{n} is not a defined strain base.')\n return strain_functions","def parameters(self):","def get_parameters_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([p for p, _ in self.expert_knowledge])))","def _get_parameters(self) -> list:\n return self.parameters","def current_parameters(self):\n current = []\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n current.append(self.q[core_param].vi_return_param(approx_param))\n return np.array(current)","def parameters(self):\n return self.model.parameters()","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"length\", \"distance\"]","def __str__(self):\r\n res = [self.Name + ' parameters:']\r\n for t in self._tracked_properties:\r\n res.append(t + ':' + str(getattr(self, t)))\r\n for k, v in sorted(self.Params.items()):\r\n res.append(str(k) + ':' + str(v))\r\n return '\\n'.join(res)","def science_parameter_list(cls):\n result = []\n for key in cls.list():\n if key not in GliderParticleKey.list():\n result.append(key)\n\n return result","def _create_parameter_names(self):\n self._parameter_names = self.parameter_schema[\"problem\"][\"names\"]","def find_gene_name(qualifiers):\n if not isinstance(qualifiers, dict):\n raise TypeError(\"Expected qualifier dictionary\")\n for tag in [\"protein_id\", \"locus_tag\", \"id\", \"gene\", \"name\", \"label\"]:\n if tag in qualifiers:\n return qualifiers[tag][0]\n return \"N.A.\"","def inspect_state(self):\n for name in self._param_store.get_all_param_names():\n self._logger.info(\"Param [%s]: %r\", name,\n pyro.param(name).data.numpy())","def log_parameters(self, all_vars):\n del all_vars['self']\n del all_vars['adj_matrix']\n del all_vars['features']\n del all_vars['labels']\n del all_vars['tvt_nids']\n self.logger.info(f'Parameters: {all_vars}')"],"string":"[\n \"def get_parameters_affecting_quality(self, quality: str) -> List[str]:\\n return [p for p, q in self.correlating_pq_tuples if q == quality]\",\n \"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\\n return [q for p, q in self.correlating_pq_tuples if p == parameter]\",\n \"def parameter_names(self) -> List[str]:\",\n \"def parameters_names(cls):\\n return cls._Parameters._fields\",\n \"def param_names(\\n self, *, include_tp: bool = False, include_gq: bool = False\\n ) -> List[str]:\\n return (\\n self._param_names(self.model, int(include_tp), int(include_gq))\\n .decode(\\\"utf-8\\\")\\n .split(\\\",\\\")\\n )\",\n \"def parameterNames(self, p_int): # real signature unknown; restored from __doc__\\n return []\",\n \"def parameters(self):\\n return [i.parameter for i in self.joints.values()]\",\n \"def get_param_names(hf):\\n parameters = get_params(hf)\\n return [p.name for p in parameters]\",\n \"def parameter_names(self):\\n raise NotImplementedError(\\\"the parameter_names property should \\\"\\n \\\"be defined in the Estimator sub-class\\\")\",\n \"def _get_fitted_param_names(self):\\n return self._fitted_param_names\",\n \"def parameter_names(self) -> list:\\n parameters = []\\n parameters.extend(self.properties.parameter_names)\\n return parameters\",\n \"def get_str_param_names(self):\\n # Exclude self.api and self.names from the command string\\n return self.get_attribute_names(FormattedParameter)\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def get_param_names(self):\\n return list(self.params.keys())\",\n \"def parameters(self):\\n return [term.parameter for term in self.terms]\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def _get_param_names_transformed(self):\\r\\n n = self._get_param_names()\\r\\n\\r\\n # remove/concatenate the tied parameter names\\r\\n if len(self.tied_indices):\\r\\n for t in self.tied_indices:\\r\\n n[t[0]] = \\\"<tie>\\\".join([n[tt] for tt in t])\\r\\n remove = np.hstack([t[1:] for t in self.tied_indices])\\r\\n else:\\r\\n remove = np.empty(shape=(0,), dtype=np.int)\\r\\n\\r\\n # also remove the fixed params\\r\\n if len(self.fixed_indices):\\r\\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\\r\\n\\r\\n # add markers to show that some variables are constrained\\r\\n for i, t in zip(self.constrained_indices, self.constraints):\\r\\n for ii in i:\\r\\n n[ii] = n[ii] + t.__str__()\\r\\n\\r\\n n = [nn for i, nn in enumerate(n) if not i in remove]\\r\\n return n\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def parameters(self):\\n return [i for i in self.variables if has_roles(i, Parameter)]\",\n \"def get_parameter_names(self, exclude_pop_model=False):\\n if (self._population_model is None) or exclude_pop_model:\\n names = self._mechanistic_model.parameters()\\n for error_model in self._error_models:\\n names += error_model.get_parameter_names()\\n return names\\n\\n return self._population_model.get_parameter_names()\",\n \"def _get_param_names(self):\\n temp_params = {'function': self.function, 'target': self.target}\\n\\n temp_params.update(self.kwargs)\\n\\n return temp_params\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"length\\\"]\",\n \"def _get_param_names(self):\\r\\n return sorted([p\\r\\n for p in self.__dict__\\r\\n if p != 'additional_args'])\",\n \"def potential_parameters(cls):\\n return [\\\"length\\\", \\\"distance\\\"]\",\n \"def parameters(self):\\n return [p for _, a in vars(self).items() for p in self._params(a)]\",\n \"def parameters(self):\\n return [o.parameters for o in self.obs]\",\n \"def get_params_list():\\n return common.QOL_PARAMS\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"angle\\\"]\",\n \"def supported_qparams(self):\\n import re\\n return re.findall(r\\\"\\\\$\\\\$\\\\{(\\\\w+)\\\\}\\\", self.QTEMPLATE)\",\n \"def parameters(self):\\n return []\",\n \"def get_paramnames_list(self):\\n # TODO include syselem?\\n\\n query = \\\"SELECT NAME FROM %s\\\" % self.__schema\\n with self.__connection.cursor() as cursor:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return [val['NAME'] for val in result]\",\n \"def derived_parameters(cls):\\n return ['cgg', 'cdd', 'css', 'cbb', 'vstar', 'gain', 'ft']\",\n \"def derived_parameters(cls):\\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']\",\n \"def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\\n else: adjust = lambda x: x\\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\\n else: names = [adjust(x.name) for x in self.parameters]\\n if add_self: names = map(lambda x: adjust(self.name) + \\\".\\\" + x, names)\\n return names\",\n \"def qparams(self):\\n return self._qparams\",\n \"def return_all_parameter_names():\\n a = list(titles)\\n a.append(r\\\"$\\\\chi^{2}$ per degree of freedom\\\")\\n b = list(labels)\\n b.append(\\\"chi2_per_dof\\\")\\n return a, b\",\n \"def get_qualification_list(self):\\n try:\\n qualifications = self.db_handler.get_qualifications_list()\\n self.logger.write_to_log('qualifications got', 'model')\\n return qualifications\\n except Exception as err:\\n method_name = sys._getframe().f_code.co_name\\n\\n self.logger.write_to_log('exception', 'model')\\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')\",\n \"def names(self):\\n result = []\\n result.extend(self.positional_arguments)\\n if self.arbitary_positional_arguments is not None:\\n result.append(self.arbitary_positional_arguments)\\n if self.arbitary_keyword_arguments is not None:\\n result.append(self.arbitary_keyword_arguments)\\n result.extend(self.keyword_arguments)\\n return result\",\n \"def parameter_names(self):\\n return [x for x in self.transformations.values() if isinstance(x, str)]\",\n \"def show_parameters(self):\\n with np.printoptions(precision=3, suppress=True):\\n print('number of wind phase = {}'.format(self.ncomp))\\n print('galactic parameter = {}'.format(self.scaling_field))\\n print('reference height = {}'.format(self.z0))\\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\\n params = getattr(self,p)\\n print(p)\\n for k,v in params.items():\\n print(' {} = {}'.format(k,v))\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def get_parameters(self, quality):\\n if (quality.upper() == 'NAV') or (quality.upper() == 'NAVIGATION'):\\n return self._sensor_param_dict['navigation'].copy()\\n \\n elif (quality.upper() == 'TAC') or (quality.upper() == 'TACTICAL'):\\n return self._sensor_param_dict['tactical'].copy()\\n \\n elif (quality.upper() == 'CON') or (quality.upper() == 'CONSUMER'):\\n return self._sensor_param_dict['consumer'].copy()\",\n \"def get_original_names_and_sharded_parameters(self):\\n orig_named_parameters = []\\n for module_name, m in self.named_modules(): # includes self\\n if isinstance(m, XlaFullyShardedDataParallel):\\n prefix = \\\"\\\" if module_name == \\\"\\\" else module_name + \\\".\\\"\\n for p in self.sharded_params:\\n n = prefix + p._orig_name\\n n = n.replace(\\\"_fsdp_wrapped_module.\\\", \\\"\\\").replace(\\\"_fpw_module.\\\", \\\"\\\")\\n orig_named_parameters.append((n, p))\\n\\n return orig_named_parameters\",\n \"def params(self):\\n return [cq.Symbol('theta_0')]\",\n \"def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\\n expert_knowledge = reduce(\\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\\n qualities,\\n set()\\n )\\n return sorted(list(expert_knowledge))\",\n \"def get_required_parameters(self) -> list:\\n results = []\\n if self.no_params or self.params_optional:\\n return []\\n else:\\n for parameter, parameter_details in self.parameters.items():\\n # Fixing issue #92\\n # if parameter == \\\"effect\\\":\\n # continue\\n if not parameter_details.default_value:\\n results.append(parameter_details.name)\\n return results\",\n \"def parameters(self):\\n ps = super().parameters()\\n exclude = set(self.estimator.parameters())\\n ps = (p for p in ps if not p in exclude)\\n return ps\",\n \"def parameters(self):\\n return self.vars\",\n \"def print_param_names(model):\\n for (param_name, param) in model.get_parameters().items():\\n print(param_name, param.shape)\",\n \"def param_unc_names(self) -> List[str]:\\n return self._param_unc_names(self.model).decode(\\\"utf-8\\\").split(\\\",\\\")\",\n \"def display_parameters(self):\\n\\n self.logging.debug(\\\"============\\\")\\n for attr in self.parm_list:\\n self.logging.debug(attr.label + \\\" (\\\" + attr.when + \\\")\\\" + \\\" = \\\" + str(attr.value))\\n self.logging.debug(\\\"============\\\")\",\n \"def parameters_list(self):\\n return [getattr(self.parameters, p) for p in self.parameters_names()]\",\n \"def return_parameter_names():\\n return list(titles), list(labels)\",\n \"def get_resource_params():\\n return Parameter.list()\",\n \"def potential_parameters(cls):\\n raise NotImplementedError()\",\n \"def params(self):\\n return [p for sublist in [o.params for o in self.obs] for p in sublist]\",\n \"def extract_parameters(self) -> Dict[str, Set[str]]:\\n regex = \\\"\\\\{([A-Za-z0-9_]+)\\\\}\\\"\\n reserved_parameters = [\\n \\\"output\\\",\\n \\\"input\\\",\\n \\\"output_vec\\\",\\n \\\"input_vec\\\",\\n \\\"df\\\",\\n \\\"vec_open\\\",\\n \\\"vec_close\\\",\\n ]\\n parameters = {}\\n for scope in self.scopes:\\n parameters[scope] = set(\\n [\\n x\\n for x in re.findall(regex, self.call)\\n if x not in reserved_parameters\\n ]\\n )\\n return parameters\",\n \"def named_trainable_parameters(self) -> List[str]:\\n return [name for name, p in self._model.named_parameters() if p.requires_grad]\",\n \"def parameters(self):\\n raise NotImplementedError('Abstract method \\\"parameters\\\" must be '\\n 'specialised!')\",\n \"def _create_parameter_names(self):\\n self._parameter_names = [key for key in self.parameter_schema.keys() if key != 'num_simulations']\",\n \"def get_fitted_params(self):\\n self.check_is_fitted()\\n return {\\n name: self._fitted_forecaster.params.get(name)\\n for name in self._get_fitted_param_names()\\n }\",\n \"def getConditionNames():\\n return _conditions.keys()\",\n \"def getListOfLocalParameters(self, *args):\\n return _libsbml.KineticLaw_getListOfLocalParameters(self, *args)\",\n \"def parameters(self):\\n # encoded in θ\\n return self.theta.columns\",\n \"def supported_parameters(cls):\\n raise NotImplementedError()\",\n \"def _get_params(self, fluents):\\n objects_all = set()\\n for fluent in fluents:\\n objects = fluent.replace(\\\"(\\\",\\\"\\\").replace(\\\")\\\",\\\"\\\").split(\\\" \\\")[1:]\\n objects_all.update(objects)\\n\\n return objects_all\",\n \"def parameters(self):\\n return {\\n 'base':self.base.parameters(),\\n 'material':[m.parameters() for m in self.material],\\n 'fraction':self.fraction,\\n }\",\n \"def get_derived_paramnames(self):\\n names = []\\n for name, shape in zip(self.likelihood.child_derived, self.derived_shapes):\\n if not isinstance(name, str):\\n name = name.__name__\\n\\n if shape == 0:\\n names.append((name, name))\\n else:\\n names.extend(\\n [\\n (\\n name + self._index_to_string(*ind),\\n name + self._index_to_latex(*ind),\\n )\\n for ind in np.ndindex(*shape)\\n ]\\n )\\n return names\",\n \"def get_param(self, obs_name):\\n return self.datasets[obs_name].keys()\",\n \"def get_ext_param_names(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\\n ret.append(tmp.decode(\\\"UTF-8\\\"))\\n\\n return ret\",\n \"def get_mandatory_param_names(self):\\n all_names = self.params.keys()\\n return [name for name in all_names \\n if not self.params[name].is_optional]\",\n \"def _fit_params(cls) -> List[str]:\\n fsigs = set()\\n for series_def in cls.__series__:\\n fsigs.add(inspect.signature(series_def.fit_func))\\n if len(fsigs) > 1:\\n raise AnalysisError(\\n \\\"Fit functions specified in the series definition have \\\"\\n \\\"different function signature. They should receive \\\"\\n \\\"the same parameter set for multi-objective function fit.\\\"\\n )\\n\\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\\n\\n # remove fixed parameters\\n if cls.__fixed_parameters__ is not None:\\n for fixed_param in cls.__fixed_parameters__:\\n try:\\n fit_params.remove(fixed_param)\\n except ValueError as ex:\\n raise AnalysisError(\\n f\\\"Defined fixed parameter {fixed_param} is not a fit function argument.\\\"\\n \\\"Update series definition to ensure the parameter name is defined with \\\"\\n f\\\"fit functions. Currently available parameters are {fit_params}.\\\"\\n ) from ex\\n\\n return fit_params\",\n \"def get_layer_var_names(self):\\n return(self.params)\",\n \"def get_param_names(obj: Union[Type[_BaseTpcpObject], _BaseTpcpObject]) -> List[str]:\\n cls = obj if isinstance(obj, type) else type(obj)\\n parameters = list(_get_init_defaults(cls).values())\\n for p in parameters:\\n if p.kind == p.VAR_POSITIONAL:\\n raise RuntimeError(\\n \\\"tpcp algorithms and pipelines should always specify their parameters in the signature of their \\\"\\n f\\\"__init__ (no varargs). {cls} doesn't follow this convention.\\\"\\n )\\n # Extract and sort argument names excluding 'self'\\n return sorted([p.name for p in parameters])\",\n \"def param_quantizers(self):\\n return self._param_quantizers\",\n \"def get_resource_params(self):\\n return Parameter.list()\",\n \"def get_resource_params(self):\\n return Parameter.list()\",\n \"def _fc_in_parameters(self) -> List[str]:\\n result = list() # type: List[str]\\n\\n for param in self.params:\\n type_list = param.fc_type()\\n for type_name, postfix in type_list:\\n result.append('{} {}'.format(type_name, param.name + postfix))\\n\\n return result\",\n \"def _get_param_names(cls):\\n # fetch the constructor\\n init = getattr(cls.__init__, 'Database Class', cls.__init__)\\n\\n if init is object.__init__:\\n # no constructor to inspect\\n params = []\\n else:\\n # inspect constructor\\n sig = inspect.signature(init)\\n parameters = [p for p in sig.parameters.values()\\n if p.name != 'self' and\\n p.kind != p.VAR_KEYWORD]\\n\\n for p in parameters:\\n if p.kind == p.VAR_POSITIONAL:\\n raise RuntimeError(\\n 'Database objects should always specify their '\\n 'parameters in the signature of their __init__. '\\n '{class_} with constructor {signature} does not follow '\\n 'this convention.'.format(\\n class_=cls,\\n signature=sig\\n )\\n )\\n\\n # Extract and sort argument names excluding 'self'\\n params = sorted([p.name for p in parameters])\\n\\n return params\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\", \\\"k_bondorder\\\"]\",\n \"def params(self) -> Iterable[sympy.Symbol]:\\n for i in range(self.iterations):\\n for p in range(len(self.qubits)):\\n if (self.include_all_z or not\\n numpy.isclose(self.orbital_energies[p], 0)):\\n yield LetterWithSubscripts('U', p, i)\\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\\n if (self.include_all_cz or not\\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\\n yield LetterWithSubscripts('V', p, q, i)\",\n \"def parameters(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"parameters\\\")\",\n \"def get_params(self):\\n params = super(JoinFilterQuery, self).get_params()\\n params.append(('fq', self._query))\\n\\n return params\",\n \"def print_params(self):\\n s = self._list_params()+\\\"\\\\n\\\"\\n if 'scale_params' in self.__dict__.keys():\\n s += self.scale_params._list_params()+\\\"\\\\n\\\"\\n if 'atmospheric_params' in self.__dict__.keys():\\n if self.atmospheric_params is not None:\\n s += self.atmospheric_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'atemperature_params' in self.__dict__.keys():\\n if self.atemperature_params is not None:\\n s += self.atemperature_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'oceanic_params' in self.__dict__.keys():\\n if self.oceanic_params is not None:\\n s += self.oceanic_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'ground_params' in self.__dict__.keys():\\n if self.ground_params is not None:\\n s += self.ground_params._list_params()+\\\"\\\\n\\\"\\n\\n if 'gotemperature_params' in self.__dict__.keys():\\n if self.gotemperature_params is not None:\\n s += self.gotemperature_params._list_params() + \\\"\\\\n\\\"\\n\\n print(\\\"Qgs v0.2.8 parameters summary\\\")\\n print(\\\"=============================\\\\n\\\")\\n print(s)\",\n \"def get_strains(names, q_dof):\\n strain_functions = []\\n for n, this_dof in zip(names, q_dof):\\n check_qdof(n, this_dof)\\n if n == 'linear_helix':\\n strain_functions.append(linear_helix_strain)\\n elif n == 'pure_helix':\\n strain_functions.append(pure_helix_strain)\\n elif n == 'torsion_helix':\\n strain_functions.append(torsion_helix_strain)\\n elif n == 'torsion_linear_helix':\\n strain_functions.append(torsion_linear_helix_strain)\\n elif n == 'quadratic':\\n strain_functions.append(quadratic_strain)\\n elif n == 'linear':\\n strain_functions.append(linear_strain)\\n elif n == 'constant':\\n strain_functions.append(constant_strain)\\n elif n == 'full':\\n strain_functions.append(full_strain)\\n else:\\n print(f'{n} is not a defined strain base.')\\n return strain_functions\",\n \"def parameters(self):\",\n \"def get_parameters_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([p for p, _ in self.expert_knowledge])))\",\n \"def _get_parameters(self) -> list:\\n return self.parameters\",\n \"def current_parameters(self):\\n current = []\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n current.append(self.q[core_param].vi_return_param(approx_param))\\n return np.array(current)\",\n \"def parameters(self):\\n return self.model.parameters()\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"length\\\", \\\"distance\\\"]\",\n \"def __str__(self):\\r\\n res = [self.Name + ' parameters:']\\r\\n for t in self._tracked_properties:\\r\\n res.append(t + ':' + str(getattr(self, t)))\\r\\n for k, v in sorted(self.Params.items()):\\r\\n res.append(str(k) + ':' + str(v))\\r\\n return '\\\\n'.join(res)\",\n \"def science_parameter_list(cls):\\n result = []\\n for key in cls.list():\\n if key not in GliderParticleKey.list():\\n result.append(key)\\n\\n return result\",\n \"def _create_parameter_names(self):\\n self._parameter_names = self.parameter_schema[\\\"problem\\\"][\\\"names\\\"]\",\n \"def find_gene_name(qualifiers):\\n if not isinstance(qualifiers, dict):\\n raise TypeError(\\\"Expected qualifier dictionary\\\")\\n for tag in [\\\"protein_id\\\", \\\"locus_tag\\\", \\\"id\\\", \\\"gene\\\", \\\"name\\\", \\\"label\\\"]:\\n if tag in qualifiers:\\n return qualifiers[tag][0]\\n return \\\"N.A.\\\"\",\n \"def inspect_state(self):\\n for name in self._param_store.get_all_param_names():\\n self._logger.info(\\\"Param [%s]: %r\\\", name,\\n pyro.param(name).data.numpy())\",\n \"def log_parameters(self, all_vars):\\n del all_vars['self']\\n del all_vars['adj_matrix']\\n del all_vars['features']\\n del all_vars['labels']\\n del all_vars['tvt_nids']\\n self.logger.info(f'Parameters: {all_vars}')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.684624","0.6697432","0.64668196","0.6275496","0.62012315","0.60170823","0.5992412","0.597497","0.5971461","0.5962293","0.5953351","0.5907317","0.58813965","0.5875759","0.58324844","0.57914823","0.5765543","0.57585037","0.5748246","0.5748246","0.5661398","0.5660464","0.559595","0.55902904","0.55680096","0.55584663","0.5547049","0.5524267","0.55196404","0.55166405","0.55115134","0.5498503","0.5489506","0.5485718","0.54798263","0.54778004","0.54777354","0.5457959","0.54452103","0.5443554","0.5440363","0.54358435","0.54262304","0.54151577","0.5395515","0.5394334","0.53811485","0.53645","0.535853","0.53533864","0.53081095","0.5304678","0.5304611","0.5283414","0.52784956","0.5273833","0.5271324","0.52616674","0.52580374","0.5248674","0.52455616","0.52323085","0.5226824","0.52240825","0.5223249","0.5222223","0.5219147","0.51912683","0.5190393","0.5185923","0.5184132","0.5177441","0.5171385","0.51666796","0.51604825","0.51370466","0.51348495","0.51280826","0.51279396","0.51279396","0.51241505","0.51155615","0.5110456","0.5107272","0.51072705","0.50876474","0.50848883","0.50833076","0.50685513","0.5067609","0.5057172","0.50530857","0.5050946","0.50440645","0.50432706","0.50371486","0.5034404","0.5031105","0.50232667","0.50200665"],"string":"[\n \"0.684624\",\n \"0.6697432\",\n \"0.64668196\",\n \"0.6275496\",\n \"0.62012315\",\n \"0.60170823\",\n \"0.5992412\",\n \"0.597497\",\n \"0.5971461\",\n \"0.5962293\",\n \"0.5953351\",\n \"0.5907317\",\n \"0.58813965\",\n \"0.5875759\",\n \"0.58324844\",\n \"0.57914823\",\n \"0.5765543\",\n \"0.57585037\",\n \"0.5748246\",\n \"0.5748246\",\n \"0.5661398\",\n \"0.5660464\",\n \"0.559595\",\n \"0.55902904\",\n \"0.55680096\",\n \"0.55584663\",\n \"0.5547049\",\n \"0.5524267\",\n \"0.55196404\",\n \"0.55166405\",\n \"0.55115134\",\n \"0.5498503\",\n \"0.5489506\",\n \"0.5485718\",\n \"0.54798263\",\n \"0.54778004\",\n \"0.54777354\",\n \"0.5457959\",\n \"0.54452103\",\n \"0.5443554\",\n \"0.5440363\",\n \"0.54358435\",\n \"0.54262304\",\n \"0.54151577\",\n \"0.5395515\",\n \"0.5394334\",\n \"0.53811485\",\n \"0.53645\",\n \"0.535853\",\n \"0.53533864\",\n \"0.53081095\",\n \"0.5304678\",\n \"0.5304611\",\n \"0.5283414\",\n \"0.52784956\",\n \"0.5273833\",\n \"0.5271324\",\n \"0.52616674\",\n \"0.52580374\",\n \"0.5248674\",\n \"0.52455616\",\n \"0.52323085\",\n \"0.5226824\",\n \"0.52240825\",\n \"0.5223249\",\n \"0.5222223\",\n \"0.5219147\",\n \"0.51912683\",\n \"0.5190393\",\n \"0.5185923\",\n \"0.5184132\",\n \"0.5177441\",\n \"0.5171385\",\n \"0.51666796\",\n \"0.51604825\",\n \"0.51370466\",\n \"0.51348495\",\n \"0.51280826\",\n \"0.51279396\",\n \"0.51279396\",\n \"0.51241505\",\n \"0.51155615\",\n \"0.5110456\",\n \"0.5107272\",\n \"0.51072705\",\n \"0.50876474\",\n \"0.50848883\",\n \"0.50833076\",\n \"0.50685513\",\n \"0.5067609\",\n \"0.5057172\",\n \"0.50530857\",\n \"0.5050946\",\n \"0.50440645\",\n \"0.50432706\",\n \"0.50371486\",\n \"0.5034404\",\n \"0.5031105\",\n \"0.50232667\",\n \"0.50200665\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.82784075"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17465,"cells":{"query":{"kind":"string","value":"Returns the names of all parameters that are expert knowledge of the given qualities."},"document":{"kind":"string","value":"def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\n expert_knowledge = reduce(\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\n qualities,\n set()\n )\n return sorted(list(expert_knowledge))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))","def get_parameters_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([p for p, _ in self.expert_knowledge])))","def get_parameters_affecting_quality(self, quality: str) -> List[str]:\n return [p for p, q in self.correlating_pq_tuples if q == quality]","def get_qualities_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([q for _, q in self.expert_knowledge])))","def _get_fitted_param_names(self):\n return self._fitted_param_names","def parameters(self):\n return [term.parameter for term in self.terms]","def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def parameters_names(cls):\n return cls._Parameters._fields","def potential_parameters(cls):\n return [\"k\", \"length\"]","def parameters(self):\n return [i.parameter for i in self.joints.values()]","def parameter_names(self) -> List[str]:","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def parameter_names(self):\n raise NotImplementedError(\"the parameter_names property should \"\n \"be defined in the Estimator sub-class\")","def get_ext_param_names(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\n ret.append(tmp.decode(\"UTF-8\"))\n\n return ret","def supported_qparams(self):\n import re\n return re.findall(r\"\\$\\$\\{(\\w+)\\}\", self.QTEMPLATE)","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def get_param_names(hf):\n parameters = get_params(hf)\n return [p.name for p in parameters]","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"periodicity\", \"phase\", \"idivf\"]","def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\n return sorted(list(set(expert_knowledge)))","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def get_qualification_list(self):\n try:\n qualifications = self.db_handler.get_qualifications_list()\n self.logger.write_to_log('qualifications got', 'model')\n return qualifications\n except Exception as err:\n method_name = sys._getframe().f_code.co_name\n\n self.logger.write_to_log('exception', 'model')\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')","def potential_parameters(cls):\n return [\"length\", \"distance\"]","def potential_parameters(cls):\n return [\"k\", \"angle\"]","def get_parameters(self, quality):\n if (quality.upper() == 'NAV') or (quality.upper() == 'NAVIGATION'):\n return self._sensor_param_dict['navigation'].copy()\n \n elif (quality.upper() == 'TAC') or (quality.upper() == 'TACTICAL'):\n return self._sensor_param_dict['tactical'].copy()\n \n elif (quality.upper() == 'CON') or (quality.upper() == 'CONSUMER'):\n return self._sensor_param_dict['consumer'].copy()","def paramDetails(cls):\n return {\n 'dim': (10, 20, 2, 20),\n 'nIter': (1, 10, 2, 5),\n 'lamb': (.1, 1., .1, .05),\n 'alph': (30, 50, 5, 40)\n }","def get_param_names(self):\n return list(self.params.keys())","def parameters(self):\n return []","def return_all_parameter_names():\n a = list(titles)\n a.append(r\"$\\chi^{2}$ per degree of freedom\")\n b = list(labels)\n b.append(\"chi2_per_dof\")\n return a, b","def supported_parameters(cls):\n raise NotImplementedError()","def param_names(\n self, *, include_tp: bool = False, include_gq: bool = False\n ) -> List[str]:\n return (\n self._param_names(self.model, int(include_tp), int(include_gq))\n .decode(\"utf-8\")\n .split(\",\")\n )","def parameter_names(self):\n return [x for x in self.transformations.values() if isinstance(x, str)]","def potential_parameters(cls):\n raise NotImplementedError()","def get_ext_param_descriptions(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_description(self.xc_func_info, p)\n ret.append(tmp.decode(\"UTF-8\"))\n\n return ret","def qparams(self):\n return self._qparams","def parameters(self):\n return [i for i in self.variables if has_roles(i, Parameter)]","def print_param_names(model):\n for (param_name, param) in model.get_parameters().items():\n print(param_name, param.shape)","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def named_trainable_parameters(self) -> List[str]:\n return [name for name, p in self._model.named_parameters() if p.requires_grad]","def parameterNames(self, p_int): # real signature unknown; restored from __doc__\n return []","def get_params_list():\n return common.QOL_PARAMS","def parameters(self):\n return [p for _, a in vars(self).items() for p in self._params(a)]","def get_mandatory_param_names(self):\n all_names = self.params.keys()\n return [name for name in all_names \n if not self.params[name].is_optional]","def get_required_parameters(self) -> list:\n results = []\n if self.no_params or self.params_optional:\n return []\n else:\n for parameter, parameter_details in self.parameters.items():\n # Fixing issue #92\n # if parameter == \"effect\":\n # continue\n if not parameter_details.default_value:\n results.append(parameter_details.name)\n return results","def show_parameters(self):\n with np.printoptions(precision=3, suppress=True):\n print('number of wind phase = {}'.format(self.ncomp))\n print('galactic parameter = {}'.format(self.scaling_field))\n print('reference height = {}'.format(self.z0))\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\n params = getattr(self,p)\n print(p)\n for k,v in params.items():\n print(' {} = {}'.format(k,v))","def parameter_names(self) -> list:\n parameters = []\n parameters.extend(self.properties.parameter_names)\n return parameters","def _get_param_names(self):\r\n return sorted([p\r\n for p in self.__dict__\r\n if p != 'additional_args'])","def get_str_param_names(self):\n # Exclude self.api and self.names from the command string\n return self.get_attribute_names(FormattedParameter)","def display_parameters(self):\n l = []\n for param in self.parameters.all():\n if len(param.value) > 16:\n l.append(u\"{}={}...\".format(param.name, param.value[:16]))\n else:\n l.append(u\"{}={}\".format(param.name, param.value))\n return \"; \".join(l)","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"length\", \"distance\"]","def list_qualifications() -> Dict[str, Any]:\n qualifications_schema = QualificationSchema(many=True)\n qualifications = Qualification.query.all()\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))","def parameters(self):\n return self.vars","def display_parameters(self):\n\n self.logging.debug(\"============\")\n for attr in self.parm_list:\n self.logging.debug(attr.label + \" (\" + attr.when + \")\" + \" = \" + str(attr.value))\n self.logging.debug(\"============\")","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"periodicity\", \"phase\", \"idivf\", \"k_bondorder\"]","def parameters(self):\n raise NotImplementedError('Abstract method \"parameters\" must be '\n 'specialised!')","def get_fitted_params(self):\n self.check_is_fitted()\n return {\n name: self._fitted_forecaster.params.get(name)\n for name in self._get_fitted_param_names()\n }","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"angle\"]","def get_parameter_names(self, exclude_pop_model=False):\n if (self._population_model is None) or exclude_pop_model:\n names = self._mechanistic_model.parameters()\n for error_model in self._error_models:\n names += error_model.get_parameter_names()\n return names\n\n return self._population_model.get_parameter_names()","def find(self):\n self._get_ids()\n G.debug_(F'Experiments found with matching cross-experiment key and algorithm: {len(self.experiment_ids)}')\n self._get_scored_params()\n self._filter_by_space()\n G.debug_(F'Experiments whose hyperparameters fit in the currently defined space: {len(self.hyperparameters_and_scores)}')\n\n if self.module_name == 'keras':\n if ('model_init_params', 'compile_params', 'optimizer') in self.hyperparameter_space.get_names():\n self._filter_by_guidelines_multi(('model_init_params', 'compile_params', 'optimizer'))\n else:\n self._filter_by_guidelines()\n else:\n self._filter_by_guidelines()\n G.debug_(F'Experiments whose hyperparameters match the current guidelines: {len(self.similar_experiments)}')","def science_parameter_list(cls):\n result = []\n for key in cls.list():\n if key not in GliderParticleKey.list():\n result.append(key)\n\n return result","def get_paramnames_list(self):\n # TODO include syselem?\n\n query = \"SELECT NAME FROM %s\" % self.__schema\n with self.__connection.cursor() as cursor:\n cursor.execute(query)\n result = cursor.fetchall()\n return [val['NAME'] for val in result]","def names(self):\n result = []\n result.extend(self.positional_arguments)\n if self.arbitary_positional_arguments is not None:\n result.append(self.arbitary_positional_arguments)\n if self.arbitary_keyword_arguments is not None:\n result.append(self.arbitary_keyword_arguments)\n result.extend(self.keyword_arguments)\n return result","def _parameter_summary(self, parameters, parameters_to_show=4):\n params = parameters\n if len(parameters) > parameters_to_show:\n params = parameters[:2] + [\"...\"] + parameters[-2:]\n return \", \".join(params)","def parameters(self):\n return [o.parameters for o in self.obs]","def get_design_parameters(self):\n return self.__get_one_type_params(DesignParameter)","def param_unc_names(self) -> List[str]:\n return self._param_unc_names(self.model).decode(\"utf-8\").split(\",\")","def getListOfParameters(self, *args):\n return _libsbml.KineticLaw_getListOfParameters(self, *args)","def get_param_texts(self):\n return self.param_texts","def _external_params():\n list_ext_params = []\n list_ext_params.append(\n (hoomd.md.external.field.Periodic, \"params\",\n list([dict(A=1.5, i=1, w=3.5, p=5),\n dict(A=10, i=0, w=3.4, p=2)]), _evaluate_periodic))\n list_ext_params.append(\n (hoomd.md.external.field.Electric, \"E\", list([\n (1, 0, 0),\n (0, 2, 0),\n ]), _evaluate_electric))\n return list_ext_params","def parameters(self):\n return self.model.parameters()","def supported_parameters(cls):\n return [\"smirks\", \"id\", \"k\", \"length\", \"k_bondorder\", \"length_bondorder\"]","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def _get_param_names_transformed(self):\r\n n = self._get_param_names()\r\n\r\n # remove/concatenate the tied parameter names\r\n if len(self.tied_indices):\r\n for t in self.tied_indices:\r\n n[t[0]] = \"<tie>\".join([n[tt] for tt in t])\r\n remove = np.hstack([t[1:] for t in self.tied_indices])\r\n else:\r\n remove = np.empty(shape=(0,), dtype=np.int)\r\n\r\n # also remove the fixed params\r\n if len(self.fixed_indices):\r\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\r\n\r\n # add markers to show that some variables are constrained\r\n for i, t in zip(self.constrained_indices, self.constraints):\r\n for ii in i:\r\n n[ii] = n[ii] + t.__str__()\r\n\r\n n = [nn for i, nn in enumerate(n) if not i in remove]\r\n return n","def getListOfLocalParameters(self, *args):\n return _libsbml.KineticLaw_getListOfLocalParameters(self, *args)","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"rAfb[1.0,-5.0, 5.0]\");\n self.modelBuilder.doVar(\"rA0[1.0, -5.0, 5.0]\");\n self.modelBuilder.doSet(\"POI\",\"rAfb,rA0\")\n self.modelBuilder.factory_('expr::mAfb(\"@0*@1\",eAfb,rAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0*@1)\",eA0,rA0)')\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def parameters(self):\n return {\n 'base':self.base.parameters(),\n 'material':[m.parameters() for m in self.material],\n 'fraction':self.fraction,\n }","def parameters(self):\n ps = super().parameters()\n exclude = set(self.estimator.parameters())\n ps = (p for p in ps if not p in exclude)\n return ps","def param_strs(self):\n name_len = max(len(p.name) for p in self)\n value_len = max(len(p.value_str) for p in self.params.values())\n units_len = max(len(p.units) for p in self.params.values())\n return [(p.name.ljust(name_len), p.value_str.ljust(value_len),\n p.units.ljust(units_len), p.__doc__)\n for p in self.params.values() if p]","def parameters(self):\n return self.pars","def print_all_params(self, disp=True):\n descriptions = {'general': {}}\n for name, param in self.params.items():\n descriptions['general'][name] = param.get_description()\n\n for comp, comp_obj in self.components.items():\n descriptions[comp] = {}\n for name in comp_obj.get_params():\n descriptions[comp][name] = comp_obj.get_param_description(name)\n return self._print_params(descriptions, disp)","def find_gene_name(qualifiers):\n if not isinstance(qualifiers, dict):\n raise TypeError(\"Expected qualifier dictionary\")\n for tag in [\"protein_id\", \"locus_tag\", \"id\", \"gene\", \"name\", \"label\"]:\n if tag in qualifiers:\n return qualifiers[tag][0]\n return \"N.A.\"","def getParameters(self): #$NON-NLS-1$\r","def getRegressionParams(self, ep):\n if self.lqCNmode == 'eich':\n self.getEichFromEQ(ep)\n self.lqCN = self.lqEich\n\n if self.lqCFmode == 'horacek':\n self.getHoracekFromEQ(ep)\n\n if self.SMode == 'makowski':\n self.getMakowskiFromEQ(ep, self.fG)\n return","def return_parameter_names():\n return list(titles), list(labels)","def _fit_params(cls) -> List[str]:\n fsigs = set()\n for series_def in cls.__series__:\n fsigs.add(inspect.signature(series_def.fit_func))\n if len(fsigs) > 1:\n raise AnalysisError(\n \"Fit functions specified in the series definition have \"\n \"different function signature. They should receive \"\n \"the same parameter set for multi-objective function fit.\"\n )\n\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\n\n # remove fixed parameters\n if cls.__fixed_parameters__ is not None:\n for fixed_param in cls.__fixed_parameters__:\n try:\n fit_params.remove(fixed_param)\n except ValueError as ex:\n raise AnalysisError(\n f\"Defined fixed parameter {fixed_param} is not a fit function argument.\"\n \"Update series definition to ensure the parameter name is defined with \"\n f\"fit functions. Currently available parameters are {fit_params}.\"\n ) from ex\n\n return fit_params","def num_prunable_parameters(self) -> int:\n return sum(l.weight.numel() for l in self.emb_layers) + \\\n sum(weight.numel() for weight in self.emb_projs)","def num_prunable_parameters(self) -> int:\n return sum(l.weight.numel() for l in self.emb_layers) + \\\n sum(weight.numel() for weight in self.emb_projs)","def get_derived_paramnames(self):\n names = []\n for name, shape in zip(self.likelihood.child_derived, self.derived_shapes):\n if not isinstance(name, str):\n name = name.__name__\n\n if shape == 0:\n names.append((name, name))\n else:\n names.extend(\n [\n (\n name + self._index_to_string(*ind),\n name + self._index_to_latex(*ind),\n )\n for ind in np.ndindex(*shape)\n ]\n )\n return names","def parameters(self):\n # encoded in θ\n return self.theta.columns","def _get_param_names(self):\n temp_params = {'function': self.function, 'target': self.target}\n\n temp_params.update(self.kwargs)\n\n return temp_params","def get_resource_params():\n return Parameter.list()","def variables_used (self) :\r\n\t\t## These names possibly contain dimension specification!\r\n\t\treturn self.variable_names","def get_requisite_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_param(self, obs_name):\n return self.datasets[obs_name].keys()","def help(cls):\n print(cls._LIST_PARAMETERS)","def _fc_in_parameters(self) -> List[str]:\n result = list() # type: List[str]\n\n for param in self.params:\n type_list = param.fc_type()\n for type_name, postfix in type_list:\n result.append('{} {}'.format(type_name, param.name + postfix))\n\n return result","def getListOfParameters(self):\n return self.model.getListOfParameters()","def get_input_specs(cls):\n specs = super(Demand, cls).get_input_specs()\n specs.addSub(ValuedParam.get_input_specs('penalty'))\n return specs","def derived_parameters(cls):\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']"],"string":"[\n \"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\\n parameters = reduce(\\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\\n qualities,\\n set()\\n )\\n return sorted(list(parameters))\",\n \"def get_parameters_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([p for p, _ in self.expert_knowledge])))\",\n \"def get_parameters_affecting_quality(self, quality: str) -> List[str]:\\n return [p for p, q in self.correlating_pq_tuples if q == quality]\",\n \"def get_qualities_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([q for _, q in self.expert_knowledge])))\",\n \"def _get_fitted_param_names(self):\\n return self._fitted_param_names\",\n \"def parameters(self):\\n return [term.parameter for term in self.terms]\",\n \"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\\n return [q for p, q in self.correlating_pq_tuples if p == parameter]\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def parameters_names(cls):\\n return cls._Parameters._fields\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"length\\\"]\",\n \"def parameters(self):\\n return [i.parameter for i in self.joints.values()]\",\n \"def parameter_names(self) -> List[str]:\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def parameter_names(self):\\n raise NotImplementedError(\\\"the parameter_names property should \\\"\\n \\\"be defined in the Estimator sub-class\\\")\",\n \"def get_ext_param_names(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\\n ret.append(tmp.decode(\\\"UTF-8\\\"))\\n\\n return ret\",\n \"def supported_qparams(self):\\n import re\\n return re.findall(r\\\"\\\\$\\\\$\\\\{(\\\\w+)\\\\}\\\", self.QTEMPLATE)\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def get_param_names(hf):\\n parameters = get_params(hf)\\n return [p.name for p in parameters]\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\\n return sorted(list(set(expert_knowledge)))\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def get_qualification_list(self):\\n try:\\n qualifications = self.db_handler.get_qualifications_list()\\n self.logger.write_to_log('qualifications got', 'model')\\n return qualifications\\n except Exception as err:\\n method_name = sys._getframe().f_code.co_name\\n\\n self.logger.write_to_log('exception', 'model')\\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')\",\n \"def potential_parameters(cls):\\n return [\\\"length\\\", \\\"distance\\\"]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"angle\\\"]\",\n \"def get_parameters(self, quality):\\n if (quality.upper() == 'NAV') or (quality.upper() == 'NAVIGATION'):\\n return self._sensor_param_dict['navigation'].copy()\\n \\n elif (quality.upper() == 'TAC') or (quality.upper() == 'TACTICAL'):\\n return self._sensor_param_dict['tactical'].copy()\\n \\n elif (quality.upper() == 'CON') or (quality.upper() == 'CONSUMER'):\\n return self._sensor_param_dict['consumer'].copy()\",\n \"def paramDetails(cls):\\n return {\\n 'dim': (10, 20, 2, 20),\\n 'nIter': (1, 10, 2, 5),\\n 'lamb': (.1, 1., .1, .05),\\n 'alph': (30, 50, 5, 40)\\n }\",\n \"def get_param_names(self):\\n return list(self.params.keys())\",\n \"def parameters(self):\\n return []\",\n \"def return_all_parameter_names():\\n a = list(titles)\\n a.append(r\\\"$\\\\chi^{2}$ per degree of freedom\\\")\\n b = list(labels)\\n b.append(\\\"chi2_per_dof\\\")\\n return a, b\",\n \"def supported_parameters(cls):\\n raise NotImplementedError()\",\n \"def param_names(\\n self, *, include_tp: bool = False, include_gq: bool = False\\n ) -> List[str]:\\n return (\\n self._param_names(self.model, int(include_tp), int(include_gq))\\n .decode(\\\"utf-8\\\")\\n .split(\\\",\\\")\\n )\",\n \"def parameter_names(self):\\n return [x for x in self.transformations.values() if isinstance(x, str)]\",\n \"def potential_parameters(cls):\\n raise NotImplementedError()\",\n \"def get_ext_param_descriptions(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_description(self.xc_func_info, p)\\n ret.append(tmp.decode(\\\"UTF-8\\\"))\\n\\n return ret\",\n \"def qparams(self):\\n return self._qparams\",\n \"def parameters(self):\\n return [i for i in self.variables if has_roles(i, Parameter)]\",\n \"def print_param_names(model):\\n for (param_name, param) in model.get_parameters().items():\\n print(param_name, param.shape)\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def named_trainable_parameters(self) -> List[str]:\\n return [name for name, p in self._model.named_parameters() if p.requires_grad]\",\n \"def parameterNames(self, p_int): # real signature unknown; restored from __doc__\\n return []\",\n \"def get_params_list():\\n return common.QOL_PARAMS\",\n \"def parameters(self):\\n return [p for _, a in vars(self).items() for p in self._params(a)]\",\n \"def get_mandatory_param_names(self):\\n all_names = self.params.keys()\\n return [name for name in all_names \\n if not self.params[name].is_optional]\",\n \"def get_required_parameters(self) -> list:\\n results = []\\n if self.no_params or self.params_optional:\\n return []\\n else:\\n for parameter, parameter_details in self.parameters.items():\\n # Fixing issue #92\\n # if parameter == \\\"effect\\\":\\n # continue\\n if not parameter_details.default_value:\\n results.append(parameter_details.name)\\n return results\",\n \"def show_parameters(self):\\n with np.printoptions(precision=3, suppress=True):\\n print('number of wind phase = {}'.format(self.ncomp))\\n print('galactic parameter = {}'.format(self.scaling_field))\\n print('reference height = {}'.format(self.z0))\\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\\n params = getattr(self,p)\\n print(p)\\n for k,v in params.items():\\n print(' {} = {}'.format(k,v))\",\n \"def parameter_names(self) -> list:\\n parameters = []\\n parameters.extend(self.properties.parameter_names)\\n return parameters\",\n \"def _get_param_names(self):\\r\\n return sorted([p\\r\\n for p in self.__dict__\\r\\n if p != 'additional_args'])\",\n \"def get_str_param_names(self):\\n # Exclude self.api and self.names from the command string\\n return self.get_attribute_names(FormattedParameter)\",\n \"def display_parameters(self):\\n l = []\\n for param in self.parameters.all():\\n if len(param.value) > 16:\\n l.append(u\\\"{}={}...\\\".format(param.name, param.value[:16]))\\n else:\\n l.append(u\\\"{}={}\\\".format(param.name, param.value))\\n return \\\"; \\\".join(l)\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"length\\\", \\\"distance\\\"]\",\n \"def list_qualifications() -> Dict[str, Any]:\\n qualifications_schema = QualificationSchema(many=True)\\n qualifications = Qualification.query.all()\\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))\",\n \"def parameters(self):\\n return self.vars\",\n \"def display_parameters(self):\\n\\n self.logging.debug(\\\"============\\\")\\n for attr in self.parm_list:\\n self.logging.debug(attr.label + \\\" (\\\" + attr.when + \\\")\\\" + \\\" = \\\" + str(attr.value))\\n self.logging.debug(\\\"============\\\")\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\", \\\"k_bondorder\\\"]\",\n \"def parameters(self):\\n raise NotImplementedError('Abstract method \\\"parameters\\\" must be '\\n 'specialised!')\",\n \"def get_fitted_params(self):\\n self.check_is_fitted()\\n return {\\n name: self._fitted_forecaster.params.get(name)\\n for name in self._get_fitted_param_names()\\n }\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"angle\\\"]\",\n \"def get_parameter_names(self, exclude_pop_model=False):\\n if (self._population_model is None) or exclude_pop_model:\\n names = self._mechanistic_model.parameters()\\n for error_model in self._error_models:\\n names += error_model.get_parameter_names()\\n return names\\n\\n return self._population_model.get_parameter_names()\",\n \"def find(self):\\n self._get_ids()\\n G.debug_(F'Experiments found with matching cross-experiment key and algorithm: {len(self.experiment_ids)}')\\n self._get_scored_params()\\n self._filter_by_space()\\n G.debug_(F'Experiments whose hyperparameters fit in the currently defined space: {len(self.hyperparameters_and_scores)}')\\n\\n if self.module_name == 'keras':\\n if ('model_init_params', 'compile_params', 'optimizer') in self.hyperparameter_space.get_names():\\n self._filter_by_guidelines_multi(('model_init_params', 'compile_params', 'optimizer'))\\n else:\\n self._filter_by_guidelines()\\n else:\\n self._filter_by_guidelines()\\n G.debug_(F'Experiments whose hyperparameters match the current guidelines: {len(self.similar_experiments)}')\",\n \"def science_parameter_list(cls):\\n result = []\\n for key in cls.list():\\n if key not in GliderParticleKey.list():\\n result.append(key)\\n\\n return result\",\n \"def get_paramnames_list(self):\\n # TODO include syselem?\\n\\n query = \\\"SELECT NAME FROM %s\\\" % self.__schema\\n with self.__connection.cursor() as cursor:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return [val['NAME'] for val in result]\",\n \"def names(self):\\n result = []\\n result.extend(self.positional_arguments)\\n if self.arbitary_positional_arguments is not None:\\n result.append(self.arbitary_positional_arguments)\\n if self.arbitary_keyword_arguments is not None:\\n result.append(self.arbitary_keyword_arguments)\\n result.extend(self.keyword_arguments)\\n return result\",\n \"def _parameter_summary(self, parameters, parameters_to_show=4):\\n params = parameters\\n if len(parameters) > parameters_to_show:\\n params = parameters[:2] + [\\\"...\\\"] + parameters[-2:]\\n return \\\", \\\".join(params)\",\n \"def parameters(self):\\n return [o.parameters for o in self.obs]\",\n \"def get_design_parameters(self):\\n return self.__get_one_type_params(DesignParameter)\",\n \"def param_unc_names(self) -> List[str]:\\n return self._param_unc_names(self.model).decode(\\\"utf-8\\\").split(\\\",\\\")\",\n \"def getListOfParameters(self, *args):\\n return _libsbml.KineticLaw_getListOfParameters(self, *args)\",\n \"def get_param_texts(self):\\n return self.param_texts\",\n \"def _external_params():\\n list_ext_params = []\\n list_ext_params.append(\\n (hoomd.md.external.field.Periodic, \\\"params\\\",\\n list([dict(A=1.5, i=1, w=3.5, p=5),\\n dict(A=10, i=0, w=3.4, p=2)]), _evaluate_periodic))\\n list_ext_params.append(\\n (hoomd.md.external.field.Electric, \\\"E\\\", list([\\n (1, 0, 0),\\n (0, 2, 0),\\n ]), _evaluate_electric))\\n return list_ext_params\",\n \"def parameters(self):\\n return self.model.parameters()\",\n \"def supported_parameters(cls):\\n return [\\\"smirks\\\", \\\"id\\\", \\\"k\\\", \\\"length\\\", \\\"k_bondorder\\\", \\\"length_bondorder\\\"]\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def _get_param_names_transformed(self):\\r\\n n = self._get_param_names()\\r\\n\\r\\n # remove/concatenate the tied parameter names\\r\\n if len(self.tied_indices):\\r\\n for t in self.tied_indices:\\r\\n n[t[0]] = \\\"<tie>\\\".join([n[tt] for tt in t])\\r\\n remove = np.hstack([t[1:] for t in self.tied_indices])\\r\\n else:\\r\\n remove = np.empty(shape=(0,), dtype=np.int)\\r\\n\\r\\n # also remove the fixed params\\r\\n if len(self.fixed_indices):\\r\\n remove = np.hstack((remove, np.hstack(self.fixed_indices)))\\r\\n\\r\\n # add markers to show that some variables are constrained\\r\\n for i, t in zip(self.constrained_indices, self.constraints):\\r\\n for ii in i:\\r\\n n[ii] = n[ii] + t.__str__()\\r\\n\\r\\n n = [nn for i, nn in enumerate(n) if not i in remove]\\r\\n return n\",\n \"def getListOfLocalParameters(self, *args):\\n return _libsbml.KineticLaw_getListOfLocalParameters(self, *args)\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"rAfb[1.0,-5.0, 5.0]\\\");\\n self.modelBuilder.doVar(\\\"rA0[1.0, -5.0, 5.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"rAfb,rA0\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0*@1\\\",eAfb,rAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0*@1)\\\",eA0,rA0)')\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def parameters(self):\\n return {\\n 'base':self.base.parameters(),\\n 'material':[m.parameters() for m in self.material],\\n 'fraction':self.fraction,\\n }\",\n \"def parameters(self):\\n ps = super().parameters()\\n exclude = set(self.estimator.parameters())\\n ps = (p for p in ps if not p in exclude)\\n return ps\",\n \"def param_strs(self):\\n name_len = max(len(p.name) for p in self)\\n value_len = max(len(p.value_str) for p in self.params.values())\\n units_len = max(len(p.units) for p in self.params.values())\\n return [(p.name.ljust(name_len), p.value_str.ljust(value_len),\\n p.units.ljust(units_len), p.__doc__)\\n for p in self.params.values() if p]\",\n \"def parameters(self):\\n return self.pars\",\n \"def print_all_params(self, disp=True):\\n descriptions = {'general': {}}\\n for name, param in self.params.items():\\n descriptions['general'][name] = param.get_description()\\n\\n for comp, comp_obj in self.components.items():\\n descriptions[comp] = {}\\n for name in comp_obj.get_params():\\n descriptions[comp][name] = comp_obj.get_param_description(name)\\n return self._print_params(descriptions, disp)\",\n \"def find_gene_name(qualifiers):\\n if not isinstance(qualifiers, dict):\\n raise TypeError(\\\"Expected qualifier dictionary\\\")\\n for tag in [\\\"protein_id\\\", \\\"locus_tag\\\", \\\"id\\\", \\\"gene\\\", \\\"name\\\", \\\"label\\\"]:\\n if tag in qualifiers:\\n return qualifiers[tag][0]\\n return \\\"N.A.\\\"\",\n \"def getParameters(self): #$NON-NLS-1$\\r\",\n \"def getRegressionParams(self, ep):\\n if self.lqCNmode == 'eich':\\n self.getEichFromEQ(ep)\\n self.lqCN = self.lqEich\\n\\n if self.lqCFmode == 'horacek':\\n self.getHoracekFromEQ(ep)\\n\\n if self.SMode == 'makowski':\\n self.getMakowskiFromEQ(ep, self.fG)\\n return\",\n \"def return_parameter_names():\\n return list(titles), list(labels)\",\n \"def _fit_params(cls) -> List[str]:\\n fsigs = set()\\n for series_def in cls.__series__:\\n fsigs.add(inspect.signature(series_def.fit_func))\\n if len(fsigs) > 1:\\n raise AnalysisError(\\n \\\"Fit functions specified in the series definition have \\\"\\n \\\"different function signature. They should receive \\\"\\n \\\"the same parameter set for multi-objective function fit.\\\"\\n )\\n\\n # remove the first function argument. this is usually x, i.e. not a fit parameter.\\n fit_params = list(list(fsigs)[0].parameters.keys())[1:]\\n\\n # remove fixed parameters\\n if cls.__fixed_parameters__ is not None:\\n for fixed_param in cls.__fixed_parameters__:\\n try:\\n fit_params.remove(fixed_param)\\n except ValueError as ex:\\n raise AnalysisError(\\n f\\\"Defined fixed parameter {fixed_param} is not a fit function argument.\\\"\\n \\\"Update series definition to ensure the parameter name is defined with \\\"\\n f\\\"fit functions. Currently available parameters are {fit_params}.\\\"\\n ) from ex\\n\\n return fit_params\",\n \"def num_prunable_parameters(self) -> int:\\n return sum(l.weight.numel() for l in self.emb_layers) + \\\\\\n sum(weight.numel() for weight in self.emb_projs)\",\n \"def num_prunable_parameters(self) -> int:\\n return sum(l.weight.numel() for l in self.emb_layers) + \\\\\\n sum(weight.numel() for weight in self.emb_projs)\",\n \"def get_derived_paramnames(self):\\n names = []\\n for name, shape in zip(self.likelihood.child_derived, self.derived_shapes):\\n if not isinstance(name, str):\\n name = name.__name__\\n\\n if shape == 0:\\n names.append((name, name))\\n else:\\n names.extend(\\n [\\n (\\n name + self._index_to_string(*ind),\\n name + self._index_to_latex(*ind),\\n )\\n for ind in np.ndindex(*shape)\\n ]\\n )\\n return names\",\n \"def parameters(self):\\n # encoded in θ\\n return self.theta.columns\",\n \"def _get_param_names(self):\\n temp_params = {'function': self.function, 'target': self.target}\\n\\n temp_params.update(self.kwargs)\\n\\n return temp_params\",\n \"def get_resource_params():\\n return Parameter.list()\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names possibly contain dimension specification!\\r\\n\\t\\treturn self.variable_names\",\n \"def get_requisite_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_param(self, obs_name):\\n return self.datasets[obs_name].keys()\",\n \"def help(cls):\\n print(cls._LIST_PARAMETERS)\",\n \"def _fc_in_parameters(self) -> List[str]:\\n result = list() # type: List[str]\\n\\n for param in self.params:\\n type_list = param.fc_type()\\n for type_name, postfix in type_list:\\n result.append('{} {}'.format(type_name, param.name + postfix))\\n\\n return result\",\n \"def getListOfParameters(self):\\n return self.model.getListOfParameters()\",\n \"def get_input_specs(cls):\\n specs = super(Demand, cls).get_input_specs()\\n specs.addSub(ValuedParam.get_input_specs('penalty'))\\n return specs\",\n \"def derived_parameters(cls):\\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7242944","0.6727106","0.6571987","0.63019365","0.6018897","0.60036373","0.57837","0.57691544","0.5760132","0.5750516","0.5735969","0.5735225","0.56694657","0.56694657","0.56474066","0.56144816","0.5601561","0.55747986","0.55496305","0.5520627","0.55122304","0.5489969","0.54870766","0.5476984","0.5465503","0.5437874","0.54369843","0.5421734","0.54205024","0.5419641","0.54195595","0.5394231","0.5362677","0.5354668","0.53470683","0.5325709","0.5307545","0.53024375","0.5298365","0.5288192","0.52755576","0.5269955","0.5264475","0.52622837","0.52585787","0.5207092","0.52004856","0.5190198","0.51789826","0.5165253","0.51554906","0.5135717","0.5133707","0.513061","0.5124133","0.51149446","0.5111589","0.5109815","0.51051325","0.51049423","0.5092805","0.5087588","0.50844365","0.5056771","0.50565535","0.50435776","0.503638","0.5034345","0.5022443","0.5013453","0.5003406","0.49973547","0.4990673","0.49899507","0.49717662","0.49696317","0.49656403","0.4963615","0.49620253","0.49526572","0.4951413","0.49456403","0.49399552","0.49340197","0.4932553","0.49170217","0.4915291","0.4915291","0.4912634","0.49000502","0.48916522","0.48854348","0.48849574","0.4882779","0.48799223","0.48773184","0.48710492","0.48694977","0.48687372","0.4868623"],"string":"[\n \"0.7242944\",\n \"0.6727106\",\n \"0.6571987\",\n \"0.63019365\",\n \"0.6018897\",\n \"0.60036373\",\n \"0.57837\",\n \"0.57691544\",\n \"0.5760132\",\n \"0.5750516\",\n \"0.5735969\",\n \"0.5735225\",\n \"0.56694657\",\n \"0.56694657\",\n \"0.56474066\",\n \"0.56144816\",\n \"0.5601561\",\n \"0.55747986\",\n \"0.55496305\",\n \"0.5520627\",\n \"0.55122304\",\n \"0.5489969\",\n \"0.54870766\",\n \"0.5476984\",\n \"0.5465503\",\n \"0.5437874\",\n \"0.54369843\",\n \"0.5421734\",\n \"0.54205024\",\n \"0.5419641\",\n \"0.54195595\",\n \"0.5394231\",\n \"0.5362677\",\n \"0.5354668\",\n \"0.53470683\",\n \"0.5325709\",\n \"0.5307545\",\n \"0.53024375\",\n \"0.5298365\",\n \"0.5288192\",\n \"0.52755576\",\n \"0.5269955\",\n \"0.5264475\",\n \"0.52622837\",\n \"0.52585787\",\n \"0.5207092\",\n \"0.52004856\",\n \"0.5190198\",\n \"0.51789826\",\n \"0.5165253\",\n \"0.51554906\",\n \"0.5135717\",\n \"0.5133707\",\n \"0.513061\",\n \"0.5124133\",\n \"0.51149446\",\n \"0.5111589\",\n \"0.5109815\",\n \"0.51051325\",\n \"0.51049423\",\n \"0.5092805\",\n \"0.5087588\",\n \"0.50844365\",\n \"0.5056771\",\n \"0.50565535\",\n \"0.50435776\",\n \"0.503638\",\n \"0.5034345\",\n \"0.5022443\",\n \"0.5013453\",\n \"0.5003406\",\n \"0.49973547\",\n \"0.4990673\",\n \"0.49899507\",\n \"0.49717662\",\n \"0.49696317\",\n \"0.49656403\",\n \"0.4963615\",\n \"0.49620253\",\n \"0.49526572\",\n \"0.4951413\",\n \"0.49456403\",\n \"0.49399552\",\n \"0.49340197\",\n \"0.4932553\",\n \"0.49170217\",\n \"0.4915291\",\n \"0.4915291\",\n \"0.4912634\",\n \"0.49000502\",\n \"0.48916522\",\n \"0.48854348\",\n \"0.48849574\",\n \"0.4882779\",\n \"0.48799223\",\n \"0.48773184\",\n \"0.48710492\",\n \"0.48694977\",\n \"0.48687372\",\n \"0.4868623\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6919965"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17466,"cells":{"query":{"kind":"string","value":"Returns the names of all qualties that are expert knowledge of the given parameter."},"document":{"kind":"string","value":"def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:\n expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]\n return sorted(list(set(expert_knowledge)))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]","def get_qualities_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([q for _, q in self.expert_knowledge])))","def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\n expert_knowledge = reduce(\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\n qualities,\n set()\n )\n return sorted(list(expert_knowledge))","def get_parameters_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([p for p, _ in self.expert_knowledge])))","def get_parameters_affecting_quality(self, quality: str) -> List[str]:\n return [p for p, q in self.correlating_pq_tuples if q == quality]","def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))","def get_qualification_list(self):\n try:\n qualifications = self.db_handler.get_qualifications_list()\n self.logger.write_to_log('qualifications got', 'model')\n return qualifications\n except Exception as err:\n method_name = sys._getframe().f_code.co_name\n\n self.logger.write_to_log('exception', 'model')\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')","def list_qualifications() -> Dict[str, Any]:\n qualifications_schema = QualificationSchema(many=True)\n qualifications = Qualification.query.all()\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))","def find(self):\n self._get_ids()\n G.debug_(F'Experiments found with matching cross-experiment key and algorithm: {len(self.experiment_ids)}')\n self._get_scored_params()\n self._filter_by_space()\n G.debug_(F'Experiments whose hyperparameters fit in the currently defined space: {len(self.hyperparameters_and_scores)}')\n\n if self.module_name == 'keras':\n if ('model_init_params', 'compile_params', 'optimizer') in self.hyperparameter_space.get_names():\n self._filter_by_guidelines_multi(('model_init_params', 'compile_params', 'optimizer'))\n else:\n self._filter_by_guidelines()\n else:\n self._filter_by_guidelines()\n G.debug_(F'Experiments whose hyperparameters match the current guidelines: {len(self.similar_experiments)}')","def parameters(self):\n return [term.parameter for term in self.terms]","def getQualitativeSpecies(self):\n return _libsbml.Input_getQualitativeSpecies(self)","def measurements(self):\n exp_type = 'Q_MS_MEASUREMENT'\n path = \"/%s/%s\" % (space, project)\n search = self.transaction.getSearchService()\n exps = search.listExperiments(path)\n return [exp for exp in exps if exp.getExperimentType() == exp_type]","def _get_fitted_param_names(self):\n return self._fitted_param_names","def getQualitativeSpecies(self):\n return _libsbml.Output_getQualitativeSpecies(self)","def describe_qual(df):\n\n categorical = df.dtypes[df.dtypes == \"object\"].index\n df[categorical].describe()","def get_knowledge_category_terms(self):\n return # osid.grading.GradeQueryInspector","def get_evaluation_analysis_types(self, parameters):\n eval_types =[]\n for evaluation_criteria_id in parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"]:\n# for subcriteria in parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"][evaluation_criteria_id]:\n# eval_types.append(subcriteria)\n eval_types.extend(parameters[\"clustering\"][\"evaluation\"][\"evaluation_criteria\"][evaluation_criteria_id].keys())\n return list(set(eval_types))","def find_qualifications(\n self, qualification_name: Optional[str] = None\n ) -> List[Qualification]:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n \"\"\"\n SELECT * from qualifications\n WHERE (?1 IS NULL OR qualification_name = ?1)\n \"\"\",\n (qualification_name,),\n )\n rows = c.fetchall()\n return [\n Qualification(\n self, str(r[\"qualification_id\"]), row=r, _used_new_call=True\n )\n for r in rows\n ]","def getListOfQualitativeSpecies(self, *args):\n return _libsbml.QualModelPlugin_getListOfQualitativeSpecies(self, *args)","def parameters(self):\n return [i.parameter for i in self.joints.values()]","def parameter_names(self) -> List[str]:","def find_gene_name(qualifiers):\n if not isinstance(qualifiers, dict):\n raise TypeError(\"Expected qualifier dictionary\")\n for tag in [\"protein_id\", \"locus_tag\", \"id\", \"gene\", \"name\", \"label\"]:\n if tag in qualifiers:\n return qualifiers[tag][0]\n return \"N.A.\"","def getElementName(self):\n return _libsbml.ListOfQualitativeSpecies_getElementName(self)","def get_requisite_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def getName(self):\n return _libsbml.QualExtension_getName(self)","def getName(self):\n return _libsbml.QualitativeSpecies_getName(self)","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def qps(self):\n from admin.models.qualification_pack import QualificationPack\n return QualificationPack.objects.filter(\n occupation=self, is_draft=False\n )","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def get_pro_independence_parties():\n return ['JxSí', 'CUP', 'JUNTSxCAT', 'ERC-CatSí', 'Govern']","def get_param(self, obs_name):\n return self.datasets[obs_name].keys()","def get_param_name(self, param_id, syselem):\n\n with self.__connection.cursor() as cursor:\n query = \"SELECT NAME FROM %s WHERE PID= '%s' AND SYSTEM_ELEMENT= '%s'\" % (self.__schema, param_id, syselem)\n cursor.execute(query)\n result = cursor.fetchone()\n return result['NAME']","def return_all_parameter_names():\n a = list(titles)\n a.append(r\"$\\chi^{2}$ per degree of freedom\")\n b = list(labels)\n b.append(\"chi2_per_dof\")\n return a, b","def science_parameter_list(cls):\n result = []\n for key in cls.list():\n if key not in GliderParticleKey.list():\n result.append(key)\n\n return result","def get_query_and_evaluation_analysis_types(self, parameters):\n queries = parameters[\"clustering\"][\"evaluation\"][\"query_types\"]\n queries.extend(AnalysisPopulator.get_evaluation_analysis_types(parameters))\n return list(set(queries))","def sensitivity(self, component = '', parameter = -1, reactor = ''):\n \n n = 0\n if reactor <> '':\n for reac in self._reactors:\n if reac.name() == reactor:\n break\n else:\n n = n+1\n np = self.nSensParams()\n if parameter >= 0 and parameter < np:\n return _cantera.reactornet_sensitivity(self.__reactornet_id,\n component, parameter, n)\n elif parameter == -1:\n s = []\n for m in range(np):\n s.append(_cantera.reactornet_sensitivity(self.__reactornet_id,\n component, m, n))\n return s\n else:\n raise CanteraError(\"sensitivity requested for illegal parameter number:\"+`parameter`)","def get_predicates(self):\n query = read_query('structure exploration/predicates')\n response = self._submit_query(query)\n\n return [elem['p']['value'].split('/')[-1] for elem in response]","def getQualitativeSpecies(self, *args):\n return _libsbml.QualModelPlugin_getQualitativeSpecies(self, *args)","def getEssentialList(self):\n return self.essentials","def getNumQualitativeSpecies(self):\n return _libsbml.QualModelPlugin_getNumQualitativeSpecies(self)","def parameter_names(self):\n raise NotImplementedError(\"the parameter_names property should \"\n \"be defined in the Estimator sub-class\")","def get_ext_param_names(self):\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\n\n ret = []\n for p in range(num_param):\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\n ret.append(tmp.decode(\"UTF-8\"))\n\n return ret","def parameters_names(cls):\n return cls._Parameters._fields","def getElementName(self):\n return _libsbml.QualitativeSpecies_getElementName(self)","def ieqs(self):\n return self.inequalities()","def display_skill(self):\n return ', '.join([skill.name for skill in self.skill.all()[:3]])","def get_well_aliases(self):\n return self.info_wells['well'].unique()","def getSymptomsContaining(self, symptom):\n symptoms = set(symptom.split(' '))\n results = []\n for s in self.symptoms_list:\n s_words = set(s.split(' '))\n if symptoms.issubset(s_words):\n results.append(s)\n return results","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def getInfoVariableNames(self, product):\r\n return []","def significant_regressors(self, alpha):\r\n if not self._is_fitted:\r\n raise ValueError(\"Ensure model is fitted first.\")\r\n\r\n if type(alpha) is not float:\r\n raise TypeError(\r\n \"Ensure that alpha is a float number in range (0, 0.1]\")\r\n\r\n if alpha <= 0 or alpha > 0.1:\r\n raise ValueError(\r\n \"Ensure significance level is a float number in range (0, 0.1]\")\r\n\r\n regressor_pvalues = (self._results.pvalues\r\n .drop('const')) # Pandas Series\r\n\r\n # Find the integer indices of the significant regressors\r\n indices_significant = np.where(regressor_pvalues <= alpha)[0]\r\n # Return the names of significant regressors as a list\r\n if indices_significant.size == 0:\r\n return []\r\n else:\r\n return regressor_pvalues.iloc[indices_significant].index.to_list()","def qalist(self):\n return self._palist.qalist","def printChromQual(chromosome:list):\n sum = 0\n for i in range(len(chromosome)):\n machines_list[chromosome[i]].addJob(jobs_list[i])\n sum += jobs_list[i].length\n printMachineStatOut()\n print(\"Optimal solution (sum/num_of_jobs) could be :\",sum/num_of_machines,file=out_file)\n print(\"------------------------------------------------\\n\", file=out_file)","def all_tone_qualities():\n\n tone_qualities = crud.get_tone_qualities()\n\n return render_template('all_tone_qualities.html', tone_qualities=tone_qualities)","def get_assessment_terms(self):\n return # osid.assessment.AssessmentQueryInspector","def FindLHSs(self):\n return self.lhslist","def parameters(self):\n return [i for i in self.variables if has_roles(i, Parameter)]","def get_alphabetical_topics(course_module):\n return sorted(\n course_module.teams_configuration.cleaned_data['team_sets'],\n key=lambda t: t['name'].lower(),\n )","def pyranose_names(self):\n output = set()\n for item in self.pyranoses():\n if item in self.pyranose_fac:\n output.add(self.pyranose_fac[item][\"name\"])\n return list(output)","def get_all_of_experiment(self, experiment_name: str):\n query = (\n f\"SELECT * FROM {self.table_name} WHERE experiment_name='{experiment_name}'\"\n )\n c = self.db.cursor()\n c.execute(query)\n queries = c.fetchall()\n return queries","def names(self):\n return list(item.name for item in self.mechanisms)","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def parameter_names(self) -> list:\n parameters = []\n parameters.extend(self.properties.parameter_names)\n return parameters","def param_result_as_string_list(self, param_name):\n p_mean = self.param_dict[param_name][0]\n p_se = self.param_dict[param_name][1]\n return [self.activator, self.nbd_site, str(self.rep_index),\n self.measurement, param_name, str(p_mean),\n str(p_se)]","def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\n else: adjust = lambda x: x\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\n else: names = [adjust(x.name) for x in self.parameters]\n if add_self: names = map(lambda x: adjust(self.name) + \".\" + x, names)\n return names","def get_param_names(hf):\n parameters = get_params(hf)\n return [p.name for p in parameters]","def inconsistent_entityName(self):\n a = [s for s in self.subjects if len([sa for sa in s.samples if sa.inconsistent_entityName]) > 0]\n if len(a) == 0:\n return None\n return a","def get_parameter_estimation_parameters(self, friendly=True):\n #Get the sensitivities task:\n fitTask=self._getTask('parameterFitting')\n fitProblem = fitTask.find(xmlns + 'Problem')\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\n parameters = []\n for subGroup in optimizationItems:\n name = None\n lowerBound = None\n upperBound = None\n startValue = None\n \n for item in subGroup:\n if item.attrib['name'] == 'ObjectCN':\n name = item.attrib['value']\n elif item.attrib['name'] == 'UpperBound':\n upperBound = item.attrib['value']\n elif item.attrib['name'] == 'LowerBound':\n lowerBound = item.attrib['value']\n elif item.attrib['name'] == 'StartValue':\n startValue = item.attrib['value']\n assert name !=None\n assert lowerBound != None\n assert upperBound != None\n assert startValue != None\n \n if friendly:\n #Construct a user-friendly name for the parameter name using regexs\n #Look for a match for global parameters: Vector=Values[Test parameter],\n global_string = r'.*Vector=Values\\[(?P<name>.*)\\].*'\n global_string_re = re.compile(global_string)\n global_match = re.match(global_string_re, name)\n \n if global_match:\n name = global_match.group('name')\n \n #else check for a local match.\n #Vector=Reactions[Reaction] Parameter=k1\n local_string = r'.*Vector=Reactions\\[(?P<reaction>.*)\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\n local_string_re = re.compile(local_string)\n local_match = re.match(local_string_re, name)\n \n if local_match:\n reaction = local_match.group('reaction')\n parameter = local_match.group('parameter')\n name = '(%s).%s'%(reaction, parameter)\n\n parameters.append((name, lowerBound, upperBound, startValue))\n\n return parameters","def parameter_names(self):\n return [x for x in self.transformations.values() if isinstance(x, str)]","def find_resonator_names(measurement):\n res_names = []\n for k in measurement.keys():\n if 'R' in k:\n res_names.append(k)\n return res_names","def all_present_experiments(self):\n return _yield_subdir_names(self.exp_configs)","def get_quality_checks(dict):\n\n quality_checks = {}\n for type in dict:\n list = []\n for qc in dict[type]:\n list.append(const.get_id(qc))\n quality_checks[type] = list\n\n return quality_checks","def get_cognitive_process_terms(self):\n return # osid.grading.GradeQueryInspector","def get_equivalent_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector","def get_query_specs(self):\n\n person_ext_table_query = {\n cdr_consts.QUERY:\n PERSON_EXT_TABLE_QUERY.render(project=self.project_id,\n dataset=self.dataset_id)\n }\n return [person_ext_table_query]","def paramset_to_rootnames(paramset):\n\n if paramset.name == 'lumi':\n return 'Lumi'\n if paramset.is_scalar:\n if paramset.constrained:\n return f'alpha_{paramset.name}'\n return f'{paramset.name}'\n return [f'gamma_{paramset.name}_{index}' for index in range(paramset.n_parameters)]","def get_paramnames_list(self):\n # TODO include syselem?\n\n query = \"SELECT NAME FROM %s\" % self.__schema\n with self.__connection.cursor() as cursor:\n cursor.execute(query)\n result = cursor.fetchall()\n return [val['NAME'] for val in result]","def get_skill_levels(self):\n return self.model_class.objects.filter(enforced=self.enforced).order_by('-gte')","def parameters(self):\n return []","def queryYM( self, geneName, level ):\n result = [] \n \n # Get a new query on the table\n query = service.new_query(\"Gene\")\n query.add_constraint(\"interactions.participant2\", \"Gene\") \n \n query.add_view( \"primaryIdentifier\", \"symbol\", \"secondaryIdentifier\", \"sgdAlias\", \"name\",\n \"organism.shortName\", \"interactions.details.annotationType\",\n \"interactions.details.phenotype\", \"interactions.details.role1\",\n \"interactions.details.experimentType\", \"interactions.participant2.symbol\",\n \"interactions.participant2.secondaryIdentifier\",\n \"interactions.details.experiment.name\",\n \"interactions.details.relationshipType\" )\n \n query.add_constraint(\"organism.shortName\", \"=\", \"S. cerevisiae\", code = \"B\")\n query.add_constraint(\"Gene\", \"LOOKUP\", geneName, code = \"A\")\n \n for row in query.rows():\n data = level, row[\"primaryIdentifier\"], row[\"symbol\"], row[\"secondaryIdentifier\"], row[\"sgdAlias\"], \\\n row[\"name\"], row[\"organism.shortName\"], row[\"interactions.details.annotationType\"], \\\n row[\"interactions.details.phenotype\"], row[\"interactions.details.role1\"], \\\n row[\"interactions.details.experimentType\"], row[\"interactions.participant2.symbol\"], \\\n row[\"interactions.participant2.secondaryIdentifier\"], \\\n row[\"interactions.details.experiment.name\"], row[\"interactions.details.relationshipType\"]\n item = list(data)\n result.append(item) \n \n return result","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def get_param_re_names(self, param_name_re, syselem):\n\n with self.__connection.cursor() as cursor:\n query = \"SELECT PID FROM %s WHERE NAME LIKE '%s' AND SYSTEM_ELEMENT= '%s'\" % (self.__schema, param_name_re, syselem)\n cursor.execute(query)\n result = cursor.fetchone()\n return result['PID']","def get_suffstat_names():\n params = ['sum_x', 'sum_x_squared']\n return params","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def potential_parameters(cls):\n return [\"k\", \"periodicity\", \"phase\", \"idivf\"]","def possibilities(param):\n print(param)\n exit_list = []\n qmark_indices = []\n word_as_list = list(param)\n for index, letter in enumerate(word_as_list):\n if letter is \"?\":\n qmark_indices.append(index)\n for num in qmark_indices:\n word_as_list[num] = \"1\"\n exit_list.append(''.join(word_as_list))\n word_as_list[num] = \"0\"\n exit_list.append(''.join(word_as_list))\n for num in qmark_indices:\n word_as_list[num] = \"0\"\n exit_list.append(''.join(word_as_list))\n word_as_list[num] = \"1\"\n exit_list.append(''.join(word_as_list))\n new_list = list(set(word for word in exit_list if word.count(\"?\") == 0))\n new_list.sort()\n return new_list","def qparams(self):\n return self._qparams","def get_queryset(self):\n return Objective.objects.filter(perspective__description='Learning and Capacity').order_by('code')","def _get_criteria(self):\n for molecule in self.values():\n molecule.get_criterion()","def parameters(self):\n return [o.parameters for o in self.obs]","def potential_parameters(cls):\n return [\"k\", \"length\"]","def data_skill_list(self):\n data_skill_list = []\n for skill in self.data_skill:\n if 'name' in skill.keys():\n data_skill_list.append(skill['name'])\n return data_skill_list","def get_assumptions(self):\n assumptions = [assumption + ' & ' for assumption in self.assumptions]\n return '(' + ''.join(assumptions)[:-3] + ')'","def get_names(self):\n\n return self.mod_suites.keys()","def paramDetails(cls):\n return {\n 'dim': (10, 20, 2, 20),\n 'nIter': (1, 10, 2, 5),\n 'lamb': (.1, 1., .1, .05),\n 'alph': (30, 50, 5, 40)\n }","def report_edp(self):\n lmfit.report_fit(self.edp_par)\n print(\"chisqr = {0:.3f}\".format(self.edp.chisqr))","def get_explanatory_variables(self):\n return list(self._explanatory_variables)"],"string":"[\n \"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\\n return [q for p, q in self.correlating_pq_tuples if p == parameter]\",\n \"def get_qualities_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([q for _, q in self.expert_knowledge])))\",\n \"def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\\n expert_knowledge = reduce(\\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\\n qualities,\\n set()\\n )\\n return sorted(list(expert_knowledge))\",\n \"def get_parameters_with_expert_knowledge(self) -> List[str]:\\n return sorted(list(set([p for p, _ in self.expert_knowledge])))\",\n \"def get_parameters_affecting_quality(self, quality: str) -> List[str]:\\n return [p for p, q in self.correlating_pq_tuples if q == quality]\",\n \"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\\n parameters = reduce(\\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\\n qualities,\\n set()\\n )\\n return sorted(list(parameters))\",\n \"def get_qualification_list(self):\\n try:\\n qualifications = self.db_handler.get_qualifications_list()\\n self.logger.write_to_log('qualifications got', 'model')\\n return qualifications\\n except Exception as err:\\n method_name = sys._getframe().f_code.co_name\\n\\n self.logger.write_to_log('exception', 'model')\\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')\",\n \"def list_qualifications() -> Dict[str, Any]:\\n qualifications_schema = QualificationSchema(many=True)\\n qualifications = Qualification.query.all()\\n return cast(Dict[str, Any], jsonify(qualifications_schema.dump(qualifications)))\",\n \"def find(self):\\n self._get_ids()\\n G.debug_(F'Experiments found with matching cross-experiment key and algorithm: {len(self.experiment_ids)}')\\n self._get_scored_params()\\n self._filter_by_space()\\n G.debug_(F'Experiments whose hyperparameters fit in the currently defined space: {len(self.hyperparameters_and_scores)}')\\n\\n if self.module_name == 'keras':\\n if ('model_init_params', 'compile_params', 'optimizer') in self.hyperparameter_space.get_names():\\n self._filter_by_guidelines_multi(('model_init_params', 'compile_params', 'optimizer'))\\n else:\\n self._filter_by_guidelines()\\n else:\\n self._filter_by_guidelines()\\n G.debug_(F'Experiments whose hyperparameters match the current guidelines: {len(self.similar_experiments)}')\",\n \"def parameters(self):\\n return [term.parameter for term in self.terms]\",\n \"def getQualitativeSpecies(self):\\n return _libsbml.Input_getQualitativeSpecies(self)\",\n \"def measurements(self):\\n exp_type = 'Q_MS_MEASUREMENT'\\n path = \\\"/%s/%s\\\" % (space, project)\\n search = self.transaction.getSearchService()\\n exps = search.listExperiments(path)\\n return [exp for exp in exps if exp.getExperimentType() == exp_type]\",\n \"def _get_fitted_param_names(self):\\n return self._fitted_param_names\",\n \"def getQualitativeSpecies(self):\\n return _libsbml.Output_getQualitativeSpecies(self)\",\n \"def describe_qual(df):\\n\\n categorical = df.dtypes[df.dtypes == \\\"object\\\"].index\\n df[categorical].describe()\",\n \"def get_knowledge_category_terms(self):\\n return # osid.grading.GradeQueryInspector\",\n \"def get_evaluation_analysis_types(self, parameters):\\n eval_types =[]\\n for evaluation_criteria_id in parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"evaluation_criteria\\\"]:\\n# for subcriteria in parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"evaluation_criteria\\\"][evaluation_criteria_id]:\\n# eval_types.append(subcriteria)\\n eval_types.extend(parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"evaluation_criteria\\\"][evaluation_criteria_id].keys())\\n return list(set(eval_types))\",\n \"def find_qualifications(\\n self, qualification_name: Optional[str] = None\\n ) -> List[Qualification]:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n SELECT * from qualifications\\n WHERE (?1 IS NULL OR qualification_name = ?1)\\n \\\"\\\"\\\",\\n (qualification_name,),\\n )\\n rows = c.fetchall()\\n return [\\n Qualification(\\n self, str(r[\\\"qualification_id\\\"]), row=r, _used_new_call=True\\n )\\n for r in rows\\n ]\",\n \"def getListOfQualitativeSpecies(self, *args):\\n return _libsbml.QualModelPlugin_getListOfQualitativeSpecies(self, *args)\",\n \"def parameters(self):\\n return [i.parameter for i in self.joints.values()]\",\n \"def parameter_names(self) -> List[str]:\",\n \"def find_gene_name(qualifiers):\\n if not isinstance(qualifiers, dict):\\n raise TypeError(\\\"Expected qualifier dictionary\\\")\\n for tag in [\\\"protein_id\\\", \\\"locus_tag\\\", \\\"id\\\", \\\"gene\\\", \\\"name\\\", \\\"label\\\"]:\\n if tag in qualifiers:\\n return qualifiers[tag][0]\\n return \\\"N.A.\\\"\",\n \"def getElementName(self):\\n return _libsbml.ListOfQualitativeSpecies_getElementName(self)\",\n \"def get_requisite_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def getName(self):\\n return _libsbml.QualExtension_getName(self)\",\n \"def getName(self):\\n return _libsbml.QualitativeSpecies_getName(self)\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def qps(self):\\n from admin.models.qualification_pack import QualificationPack\\n return QualificationPack.objects.filter(\\n occupation=self, is_draft=False\\n )\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def get_pro_independence_parties():\\n return ['JxSí', 'CUP', 'JUNTSxCAT', 'ERC-CatSí', 'Govern']\",\n \"def get_param(self, obs_name):\\n return self.datasets[obs_name].keys()\",\n \"def get_param_name(self, param_id, syselem):\\n\\n with self.__connection.cursor() as cursor:\\n query = \\\"SELECT NAME FROM %s WHERE PID= '%s' AND SYSTEM_ELEMENT= '%s'\\\" % (self.__schema, param_id, syselem)\\n cursor.execute(query)\\n result = cursor.fetchone()\\n return result['NAME']\",\n \"def return_all_parameter_names():\\n a = list(titles)\\n a.append(r\\\"$\\\\chi^{2}$ per degree of freedom\\\")\\n b = list(labels)\\n b.append(\\\"chi2_per_dof\\\")\\n return a, b\",\n \"def science_parameter_list(cls):\\n result = []\\n for key in cls.list():\\n if key not in GliderParticleKey.list():\\n result.append(key)\\n\\n return result\",\n \"def get_query_and_evaluation_analysis_types(self, parameters):\\n queries = parameters[\\\"clustering\\\"][\\\"evaluation\\\"][\\\"query_types\\\"]\\n queries.extend(AnalysisPopulator.get_evaluation_analysis_types(parameters))\\n return list(set(queries))\",\n \"def sensitivity(self, component = '', parameter = -1, reactor = ''):\\n \\n n = 0\\n if reactor <> '':\\n for reac in self._reactors:\\n if reac.name() == reactor:\\n break\\n else:\\n n = n+1\\n np = self.nSensParams()\\n if parameter >= 0 and parameter < np:\\n return _cantera.reactornet_sensitivity(self.__reactornet_id,\\n component, parameter, n)\\n elif parameter == -1:\\n s = []\\n for m in range(np):\\n s.append(_cantera.reactornet_sensitivity(self.__reactornet_id,\\n component, m, n))\\n return s\\n else:\\n raise CanteraError(\\\"sensitivity requested for illegal parameter number:\\\"+`parameter`)\",\n \"def get_predicates(self):\\n query = read_query('structure exploration/predicates')\\n response = self._submit_query(query)\\n\\n return [elem['p']['value'].split('/')[-1] for elem in response]\",\n \"def getQualitativeSpecies(self, *args):\\n return _libsbml.QualModelPlugin_getQualitativeSpecies(self, *args)\",\n \"def getEssentialList(self):\\n return self.essentials\",\n \"def getNumQualitativeSpecies(self):\\n return _libsbml.QualModelPlugin_getNumQualitativeSpecies(self)\",\n \"def parameter_names(self):\\n raise NotImplementedError(\\\"the parameter_names property should \\\"\\n \\\"be defined in the Estimator sub-class\\\")\",\n \"def get_ext_param_names(self):\\n num_param = core.xc_func_info_get_n_ext_params(self.xc_func_info)\\n\\n ret = []\\n for p in range(num_param):\\n tmp = core.xc_func_info_get_ext_params_name(self.xc_func_info, p)\\n ret.append(tmp.decode(\\\"UTF-8\\\"))\\n\\n return ret\",\n \"def parameters_names(cls):\\n return cls._Parameters._fields\",\n \"def getElementName(self):\\n return _libsbml.QualitativeSpecies_getElementName(self)\",\n \"def ieqs(self):\\n return self.inequalities()\",\n \"def display_skill(self):\\n return ', '.join([skill.name for skill in self.skill.all()[:3]])\",\n \"def get_well_aliases(self):\\n return self.info_wells['well'].unique()\",\n \"def getSymptomsContaining(self, symptom):\\n symptoms = set(symptom.split(' '))\\n results = []\\n for s in self.symptoms_list:\\n s_words = set(s.split(' '))\\n if symptoms.issubset(s_words):\\n results.append(s)\\n return results\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def getInfoVariableNames(self, product):\\r\\n return []\",\n \"def significant_regressors(self, alpha):\\r\\n if not self._is_fitted:\\r\\n raise ValueError(\\\"Ensure model is fitted first.\\\")\\r\\n\\r\\n if type(alpha) is not float:\\r\\n raise TypeError(\\r\\n \\\"Ensure that alpha is a float number in range (0, 0.1]\\\")\\r\\n\\r\\n if alpha <= 0 or alpha > 0.1:\\r\\n raise ValueError(\\r\\n \\\"Ensure significance level is a float number in range (0, 0.1]\\\")\\r\\n\\r\\n regressor_pvalues = (self._results.pvalues\\r\\n .drop('const')) # Pandas Series\\r\\n\\r\\n # Find the integer indices of the significant regressors\\r\\n indices_significant = np.where(regressor_pvalues <= alpha)[0]\\r\\n # Return the names of significant regressors as a list\\r\\n if indices_significant.size == 0:\\r\\n return []\\r\\n else:\\r\\n return regressor_pvalues.iloc[indices_significant].index.to_list()\",\n \"def qalist(self):\\n return self._palist.qalist\",\n \"def printChromQual(chromosome:list):\\n sum = 0\\n for i in range(len(chromosome)):\\n machines_list[chromosome[i]].addJob(jobs_list[i])\\n sum += jobs_list[i].length\\n printMachineStatOut()\\n print(\\\"Optimal solution (sum/num_of_jobs) could be :\\\",sum/num_of_machines,file=out_file)\\n print(\\\"------------------------------------------------\\\\n\\\", file=out_file)\",\n \"def all_tone_qualities():\\n\\n tone_qualities = crud.get_tone_qualities()\\n\\n return render_template('all_tone_qualities.html', tone_qualities=tone_qualities)\",\n \"def get_assessment_terms(self):\\n return # osid.assessment.AssessmentQueryInspector\",\n \"def FindLHSs(self):\\n return self.lhslist\",\n \"def parameters(self):\\n return [i for i in self.variables if has_roles(i, Parameter)]\",\n \"def get_alphabetical_topics(course_module):\\n return sorted(\\n course_module.teams_configuration.cleaned_data['team_sets'],\\n key=lambda t: t['name'].lower(),\\n )\",\n \"def pyranose_names(self):\\n output = set()\\n for item in self.pyranoses():\\n if item in self.pyranose_fac:\\n output.add(self.pyranose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def get_all_of_experiment(self, experiment_name: str):\\n query = (\\n f\\\"SELECT * FROM {self.table_name} WHERE experiment_name='{experiment_name}'\\\"\\n )\\n c = self.db.cursor()\\n c.execute(query)\\n queries = c.fetchall()\\n return queries\",\n \"def names(self):\\n return list(item.name for item in self.mechanisms)\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def parameter_names(self) -> list:\\n parameters = []\\n parameters.extend(self.properties.parameter_names)\\n return parameters\",\n \"def param_result_as_string_list(self, param_name):\\n p_mean = self.param_dict[param_name][0]\\n p_se = self.param_dict[param_name][1]\\n return [self.activator, self.nbd_site, str(self.rep_index),\\n self.measurement, param_name, str(p_mean),\\n str(p_se)]\",\n \"def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):\\n if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)\\n else: adjust = lambda x: x\\n if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]\\n else: names = [adjust(x.name) for x in self.parameters]\\n if add_self: names = map(lambda x: adjust(self.name) + \\\".\\\" + x, names)\\n return names\",\n \"def get_param_names(hf):\\n parameters = get_params(hf)\\n return [p.name for p in parameters]\",\n \"def inconsistent_entityName(self):\\n a = [s for s in self.subjects if len([sa for sa in s.samples if sa.inconsistent_entityName]) > 0]\\n if len(a) == 0:\\n return None\\n return a\",\n \"def get_parameter_estimation_parameters(self, friendly=True):\\n #Get the sensitivities task:\\n fitTask=self._getTask('parameterFitting')\\n fitProblem = fitTask.find(xmlns + 'Problem')\\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\\n parameters = []\\n for subGroup in optimizationItems:\\n name = None\\n lowerBound = None\\n upperBound = None\\n startValue = None\\n \\n for item in subGroup:\\n if item.attrib['name'] == 'ObjectCN':\\n name = item.attrib['value']\\n elif item.attrib['name'] == 'UpperBound':\\n upperBound = item.attrib['value']\\n elif item.attrib['name'] == 'LowerBound':\\n lowerBound = item.attrib['value']\\n elif item.attrib['name'] == 'StartValue':\\n startValue = item.attrib['value']\\n assert name !=None\\n assert lowerBound != None\\n assert upperBound != None\\n assert startValue != None\\n \\n if friendly:\\n #Construct a user-friendly name for the parameter name using regexs\\n #Look for a match for global parameters: Vector=Values[Test parameter],\\n global_string = r'.*Vector=Values\\\\[(?P<name>.*)\\\\].*'\\n global_string_re = re.compile(global_string)\\n global_match = re.match(global_string_re, name)\\n \\n if global_match:\\n name = global_match.group('name')\\n \\n #else check for a local match.\\n #Vector=Reactions[Reaction] Parameter=k1\\n local_string = r'.*Vector=Reactions\\\\[(?P<reaction>.*)\\\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\\n local_string_re = re.compile(local_string)\\n local_match = re.match(local_string_re, name)\\n \\n if local_match:\\n reaction = local_match.group('reaction')\\n parameter = local_match.group('parameter')\\n name = '(%s).%s'%(reaction, parameter)\\n\\n parameters.append((name, lowerBound, upperBound, startValue))\\n\\n return parameters\",\n \"def parameter_names(self):\\n return [x for x in self.transformations.values() if isinstance(x, str)]\",\n \"def find_resonator_names(measurement):\\n res_names = []\\n for k in measurement.keys():\\n if 'R' in k:\\n res_names.append(k)\\n return res_names\",\n \"def all_present_experiments(self):\\n return _yield_subdir_names(self.exp_configs)\",\n \"def get_quality_checks(dict):\\n\\n quality_checks = {}\\n for type in dict:\\n list = []\\n for qc in dict[type]:\\n list.append(const.get_id(qc))\\n quality_checks[type] = list\\n\\n return quality_checks\",\n \"def get_cognitive_process_terms(self):\\n return # osid.grading.GradeQueryInspector\",\n \"def get_equivalent_objective_terms(self):\\n return # osid.learning.ObjectiveQueryInspector\",\n \"def get_query_specs(self):\\n\\n person_ext_table_query = {\\n cdr_consts.QUERY:\\n PERSON_EXT_TABLE_QUERY.render(project=self.project_id,\\n dataset=self.dataset_id)\\n }\\n return [person_ext_table_query]\",\n \"def paramset_to_rootnames(paramset):\\n\\n if paramset.name == 'lumi':\\n return 'Lumi'\\n if paramset.is_scalar:\\n if paramset.constrained:\\n return f'alpha_{paramset.name}'\\n return f'{paramset.name}'\\n return [f'gamma_{paramset.name}_{index}' for index in range(paramset.n_parameters)]\",\n \"def get_paramnames_list(self):\\n # TODO include syselem?\\n\\n query = \\\"SELECT NAME FROM %s\\\" % self.__schema\\n with self.__connection.cursor() as cursor:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return [val['NAME'] for val in result]\",\n \"def get_skill_levels(self):\\n return self.model_class.objects.filter(enforced=self.enforced).order_by('-gte')\",\n \"def parameters(self):\\n return []\",\n \"def queryYM( self, geneName, level ):\\n result = [] \\n \\n # Get a new query on the table\\n query = service.new_query(\\\"Gene\\\")\\n query.add_constraint(\\\"interactions.participant2\\\", \\\"Gene\\\") \\n \\n query.add_view( \\\"primaryIdentifier\\\", \\\"symbol\\\", \\\"secondaryIdentifier\\\", \\\"sgdAlias\\\", \\\"name\\\",\\n \\\"organism.shortName\\\", \\\"interactions.details.annotationType\\\",\\n \\\"interactions.details.phenotype\\\", \\\"interactions.details.role1\\\",\\n \\\"interactions.details.experimentType\\\", \\\"interactions.participant2.symbol\\\",\\n \\\"interactions.participant2.secondaryIdentifier\\\",\\n \\\"interactions.details.experiment.name\\\",\\n \\\"interactions.details.relationshipType\\\" )\\n \\n query.add_constraint(\\\"organism.shortName\\\", \\\"=\\\", \\\"S. cerevisiae\\\", code = \\\"B\\\")\\n query.add_constraint(\\\"Gene\\\", \\\"LOOKUP\\\", geneName, code = \\\"A\\\")\\n \\n for row in query.rows():\\n data = level, row[\\\"primaryIdentifier\\\"], row[\\\"symbol\\\"], row[\\\"secondaryIdentifier\\\"], row[\\\"sgdAlias\\\"], \\\\\\n row[\\\"name\\\"], row[\\\"organism.shortName\\\"], row[\\\"interactions.details.annotationType\\\"], \\\\\\n row[\\\"interactions.details.phenotype\\\"], row[\\\"interactions.details.role1\\\"], \\\\\\n row[\\\"interactions.details.experimentType\\\"], row[\\\"interactions.participant2.symbol\\\"], \\\\\\n row[\\\"interactions.participant2.secondaryIdentifier\\\"], \\\\\\n row[\\\"interactions.details.experiment.name\\\"], row[\\\"interactions.details.relationshipType\\\"]\\n item = list(data)\\n result.append(item) \\n \\n return result\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def get_param_re_names(self, param_name_re, syselem):\\n\\n with self.__connection.cursor() as cursor:\\n query = \\\"SELECT PID FROM %s WHERE NAME LIKE '%s' AND SYSTEM_ELEMENT= '%s'\\\" % (self.__schema, param_name_re, syselem)\\n cursor.execute(query)\\n result = cursor.fetchone()\\n return result['PID']\",\n \"def get_suffstat_names():\\n params = ['sum_x', 'sum_x_squared']\\n return params\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"periodicity\\\", \\\"phase\\\", \\\"idivf\\\"]\",\n \"def possibilities(param):\\n print(param)\\n exit_list = []\\n qmark_indices = []\\n word_as_list = list(param)\\n for index, letter in enumerate(word_as_list):\\n if letter is \\\"?\\\":\\n qmark_indices.append(index)\\n for num in qmark_indices:\\n word_as_list[num] = \\\"1\\\"\\n exit_list.append(''.join(word_as_list))\\n word_as_list[num] = \\\"0\\\"\\n exit_list.append(''.join(word_as_list))\\n for num in qmark_indices:\\n word_as_list[num] = \\\"0\\\"\\n exit_list.append(''.join(word_as_list))\\n word_as_list[num] = \\\"1\\\"\\n exit_list.append(''.join(word_as_list))\\n new_list = list(set(word for word in exit_list if word.count(\\\"?\\\") == 0))\\n new_list.sort()\\n return new_list\",\n \"def qparams(self):\\n return self._qparams\",\n \"def get_queryset(self):\\n return Objective.objects.filter(perspective__description='Learning and Capacity').order_by('code')\",\n \"def _get_criteria(self):\\n for molecule in self.values():\\n molecule.get_criterion()\",\n \"def parameters(self):\\n return [o.parameters for o in self.obs]\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"length\\\"]\",\n \"def data_skill_list(self):\\n data_skill_list = []\\n for skill in self.data_skill:\\n if 'name' in skill.keys():\\n data_skill_list.append(skill['name'])\\n return data_skill_list\",\n \"def get_assumptions(self):\\n assumptions = [assumption + ' & ' for assumption in self.assumptions]\\n return '(' + ''.join(assumptions)[:-3] + ')'\",\n \"def get_names(self):\\n\\n return self.mod_suites.keys()\",\n \"def paramDetails(cls):\\n return {\\n 'dim': (10, 20, 2, 20),\\n 'nIter': (1, 10, 2, 5),\\n 'lamb': (.1, 1., .1, .05),\\n 'alph': (30, 50, 5, 40)\\n }\",\n \"def report_edp(self):\\n lmfit.report_fit(self.edp_par)\\n print(\\\"chisqr = {0:.3f}\\\".format(self.edp.chisqr))\",\n \"def get_explanatory_variables(self):\\n return list(self._explanatory_variables)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69750583","0.6972947","0.6572471","0.6532671","0.6255699","0.62491757","0.5842264","0.5311929","0.52768415","0.5177957","0.5048739","0.50431305","0.502833","0.49590343","0.49437335","0.4934118","0.49310294","0.49303398","0.49248543","0.4907331","0.48799762","0.48762646","0.48758587","0.48745614","0.48654816","0.4844379","0.48338932","0.48101285","0.4807595","0.48019952","0.47996315","0.47915882","0.47825956","0.47813976","0.47813314","0.47630444","0.47558063","0.47344664","0.47221667","0.47220415","0.46995667","0.468583","0.46726316","0.46633703","0.46632767","0.4639086","0.46318522","0.46249512","0.4612721","0.46065253","0.46065253","0.46065253","0.46002015","0.45971042","0.4594457","0.459226","0.4581046","0.4563142","0.45580667","0.45576575","0.4555652","0.45521304","0.45514205","0.4548512","0.45483816","0.45478418","0.45349762","0.4529659","0.45280784","0.4524216","0.45204607","0.45110244","0.4500729","0.44883254","0.44855464","0.44850782","0.44770876","0.44748664","0.447288","0.44674975","0.44634566","0.4448833","0.4442252","0.4441641","0.44397363","0.44380298","0.44370353","0.44370353","0.4435938","0.44337443","0.4431546","0.44195828","0.44176278","0.4414363","0.43990806","0.43986323","0.43965256","0.43942824","0.4392866","0.43850017"],"string":"[\n \"0.69750583\",\n \"0.6972947\",\n \"0.6572471\",\n \"0.6532671\",\n \"0.6255699\",\n \"0.62491757\",\n \"0.5842264\",\n \"0.5311929\",\n \"0.52768415\",\n \"0.5177957\",\n \"0.5048739\",\n \"0.50431305\",\n \"0.502833\",\n \"0.49590343\",\n \"0.49437335\",\n \"0.4934118\",\n \"0.49310294\",\n \"0.49303398\",\n \"0.49248543\",\n \"0.4907331\",\n \"0.48799762\",\n \"0.48762646\",\n \"0.48758587\",\n \"0.48745614\",\n \"0.48654816\",\n \"0.4844379\",\n \"0.48338932\",\n \"0.48101285\",\n \"0.4807595\",\n \"0.48019952\",\n \"0.47996315\",\n \"0.47915882\",\n \"0.47825956\",\n \"0.47813976\",\n \"0.47813314\",\n \"0.47630444\",\n \"0.47558063\",\n \"0.47344664\",\n \"0.47221667\",\n \"0.47220415\",\n \"0.46995667\",\n \"0.468583\",\n \"0.46726316\",\n \"0.46633703\",\n \"0.46632767\",\n \"0.4639086\",\n \"0.46318522\",\n \"0.46249512\",\n \"0.4612721\",\n \"0.46065253\",\n \"0.46065253\",\n \"0.46065253\",\n \"0.46002015\",\n \"0.45971042\",\n \"0.4594457\",\n \"0.459226\",\n \"0.4581046\",\n \"0.4563142\",\n \"0.45580667\",\n \"0.45576575\",\n \"0.4555652\",\n \"0.45521304\",\n \"0.45514205\",\n \"0.4548512\",\n \"0.45483816\",\n \"0.45478418\",\n \"0.45349762\",\n \"0.4529659\",\n \"0.45280784\",\n \"0.4524216\",\n \"0.45204607\",\n \"0.45110244\",\n \"0.4500729\",\n \"0.44883254\",\n \"0.44855464\",\n \"0.44850782\",\n \"0.44770876\",\n \"0.44748664\",\n \"0.447288\",\n \"0.44674975\",\n \"0.44634566\",\n \"0.4448833\",\n \"0.4442252\",\n \"0.4441641\",\n \"0.44397363\",\n \"0.44380298\",\n \"0.44370353\",\n \"0.44370353\",\n \"0.4435938\",\n \"0.44337443\",\n \"0.4431546\",\n \"0.44195828\",\n \"0.44176278\",\n \"0.4414363\",\n \"0.43990806\",\n \"0.43986323\",\n \"0.43965256\",\n \"0.43942824\",\n \"0.4392866\",\n \"0.43850017\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7195483"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17467,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_EMBRYO state"},"document":{"kind":"string","value":"def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_retrieve_instances_schedule_state(self):\n pass","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_table_false_positives(self):\n pass","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_time_type_state_types(day):\n\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}","def test_time_type_state_is_afternoon(day):\n\n assert day_time_info(day.hours_0).is_afternoon is False\n assert day_time_info(day.hours_1).is_afternoon is False\n assert day_time_info(day.hours_2).is_afternoon is False\n assert day_time_info(day.hours_3).is_afternoon is False\n assert day_time_info(day.hours_4).is_afternoon is False\n assert day_time_info(day.hours_5).is_afternoon is False\n assert day_time_info(day.hours_6).is_afternoon is False\n assert day_time_info(day.hours_7).is_afternoon is False\n assert day_time_info(day.hours_8).is_afternoon is False\n assert day_time_info(day.hours_9).is_afternoon is False\n assert day_time_info(day.hours_10).is_afternoon is False\n assert day_time_info(day.hours_11).is_afternoon is False\n assert day_time_info(day.hours_12).is_afternoon is False\n assert day_time_info(day.hours_13).is_afternoon is True\n assert day_time_info(day.hours_14).is_afternoon is True\n assert day_time_info(day.hours_15).is_afternoon is True\n assert day_time_info(day.hours_16).is_afternoon is True\n assert day_time_info(day.hours_17).is_afternoon is True\n assert day_time_info(day.hours_18).is_afternoon is False\n assert day_time_info(day.hours_19).is_afternoon is False\n assert day_time_info(day.hours_20).is_afternoon is False\n assert day_time_info(day.hours_21).is_afternoon is False\n assert day_time_info(day.hours_22).is_afternoon is False\n assert day_time_info(day.hours_23).is_afternoon is False","def test_time_type_state_is_evening(day):\n\n assert day_time_info(day.hours_0).is_evening is False\n assert day_time_info(day.hours_1).is_evening is False\n assert day_time_info(day.hours_2).is_evening is False\n assert day_time_info(day.hours_3).is_evening is False\n assert day_time_info(day.hours_4).is_evening is False\n assert day_time_info(day.hours_5).is_evening is False\n assert day_time_info(day.hours_6).is_evening is False\n assert day_time_info(day.hours_7).is_evening is False\n assert day_time_info(day.hours_8).is_evening is False\n assert day_time_info(day.hours_9).is_evening is False\n assert day_time_info(day.hours_10).is_evening is False\n assert day_time_info(day.hours_11).is_evening is False\n assert day_time_info(day.hours_12).is_evening is False\n assert day_time_info(day.hours_13).is_evening is False\n assert day_time_info(day.hours_14).is_evening is False\n assert day_time_info(day.hours_15).is_evening is False\n assert day_time_info(day.hours_16).is_evening is False\n assert day_time_info(day.hours_17).is_evening is False\n assert day_time_info(day.hours_18).is_evening is True\n assert day_time_info(day.hours_19).is_evening is True\n assert day_time_info(day.hours_20).is_evening is True\n assert day_time_info(day.hours_21).is_evening is True\n assert day_time_info(day.hours_22).is_evening is True\n assert day_time_info(day.hours_23).is_evening is False","def test_exp_by_states(self):\n api = my_mock.api_mock({\"items\": [{'state': 'Waiting', 'id': 10134},\n {'state': 'Waiting', 'id': 10135},\n {'state': 'Running', 'id': 10130}]})\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\n self.assertEquals(\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\n my_mock.api_mock_stop()","def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_get_state_comparison_stats_ailments(self):\n\n # AilmentType and Ailment breakdown per AilmentType should be in returned stats if present\n ailment_type_headache = AilmentTypeFactory(name='Headache')\n ailment_migraine_headache = AilmentFactory(name='Migraine Headache', type=ailment_type_headache)\n ailment_tension_headache = AilmentFactory(name='Tension Headache', type=ailment_type_headache)\n ailment_type_sprain_or_bruise = AilmentTypeFactory(name='Sprain or Bruise')\n ailment_sprain = AilmentFactory(name='Sprain', type=ailment_type_sprain_or_bruise)\n\n # Texas: 2 with sprain and 2 with nothing\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_sprain)\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 1 with migraine headache, 1 with sprain, 1 with tension headache, and 1 with nothing\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_migraine_headache)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_sprain)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_tension_headache)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n # Mississippi: 2 with migraine headache, 2 with tension headache\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_migraine_headache)\n employee.bureau_states.add(self.mississippi)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_tension_headache)\n employee.bureau_states.add(self.mississippi)\n\n stats = get_state_comparison_stats(number=2)\n\n # With ailment types, ailments should be grouped together\n key = '% With Headache'\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with headache\")\n\n key = '% With Sprain or Bruise'\n expected_output = [('Texas', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with sprain or bruise\"\n )\n\n # Ailments should be treated individually\n key = '% With Migraine Headache'\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with migraine headache\"\n )\n\n key = '% With Tension Headache'\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with tension headache\"\n )\n\n # There should be no breakdown for sprain, because it's the only ailment of that type\n key = '% With Sprain'\n self.assertEqual(len([item for item in stats if key in item]), 0,\n \"There should be no breakdown for an ailment if it's the only one of its type\")","def test_time_type_state_is_midmorning(day):\n\n assert day_time_info(day.hours_0).is_midmorning is False\n assert day_time_info(day.hours_1).is_midmorning is False\n assert day_time_info(day.hours_2).is_midmorning is False\n assert day_time_info(day.hours_3).is_midmorning is False\n assert day_time_info(day.hours_4).is_midmorning is False\n assert day_time_info(day.hours_5).is_midmorning is False\n assert day_time_info(day.hours_6).is_midmorning is False\n assert day_time_info(day.hours_7).is_midmorning is False\n assert day_time_info(day.hours_8).is_midmorning is False\n assert day_time_info(day.hours_9).is_midmorning is False\n assert day_time_info(day.hours_10).is_midmorning is True\n assert day_time_info(day.hours_11).is_midmorning is True\n assert day_time_info(day.hours_12).is_midmorning is False\n assert day_time_info(day.hours_13).is_midmorning is False\n assert day_time_info(day.hours_14).is_midmorning is False\n assert day_time_info(day.hours_15).is_midmorning is False\n assert day_time_info(day.hours_16).is_midmorning is False\n assert day_time_info(day.hours_17).is_midmorning is False\n assert day_time_info(day.hours_18).is_midmorning is False\n assert day_time_info(day.hours_19).is_midmorning is False\n assert day_time_info(day.hours_20).is_midmorning is False\n assert day_time_info(day.hours_21).is_midmorning is False\n assert day_time_info(day.hours_22).is_midmorning is False\n assert day_time_info(day.hours_23).is_midmorning is False","def test_issue_tracked_times(self):\n pass","def test_episode_data(self):\n self.assertEquals(\n self.t['lost']['firstaired'],\n '2004-09-22'\n )","def test_duplicatekeyerror_state_embryo(self):\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_update_team_state(self):\n pass","def test_update_instances_schedule_state(self):\n pass","def test_time_type_state_is_morning(day):\n\n assert day_time_info(day.hours_0).is_morning is False\n assert day_time_info(day.hours_1).is_morning is False\n assert day_time_info(day.hours_2).is_morning is False\n assert day_time_info(day.hours_3).is_morning is False\n assert day_time_info(day.hours_4).is_morning is False\n assert day_time_info(day.hours_5).is_morning is True\n assert day_time_info(day.hours_6).is_morning is True\n assert day_time_info(day.hours_7).is_morning is True\n assert day_time_info(day.hours_8).is_morning is True\n assert day_time_info(day.hours_9).is_morning is True\n assert day_time_info(day.hours_10).is_morning is False\n assert day_time_info(day.hours_11).is_morning is False\n assert day_time_info(day.hours_12).is_morning is False\n assert day_time_info(day.hours_13).is_morning is False\n assert day_time_info(day.hours_14).is_morning is False\n assert day_time_info(day.hours_15).is_morning is False\n assert day_time_info(day.hours_16).is_morning is False\n assert day_time_info(day.hours_17).is_morning is False\n assert day_time_info(day.hours_18).is_morning is False\n assert day_time_info(day.hours_19).is_morning is False\n assert day_time_info(day.hours_20).is_morning is False\n assert day_time_info(day.hours_21).is_morning is False\n assert day_time_info(day.hours_22).is_morning is False\n assert day_time_info(day.hours_23).is_morning is False","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def test_time_type_state_is_night(day):\n\n assert day_time_info(day.hours_0).is_night is True\n assert day_time_info(day.hours_1).is_night is True\n assert day_time_info(day.hours_2).is_night is True\n assert day_time_info(day.hours_3).is_night is True\n assert day_time_info(day.hours_4).is_night is True\n assert day_time_info(day.hours_5).is_night is False\n assert day_time_info(day.hours_6).is_night is False\n assert day_time_info(day.hours_7).is_night is False\n assert day_time_info(day.hours_8).is_night is False\n assert day_time_info(day.hours_9).is_night is False\n assert day_time_info(day.hours_10).is_night is False\n assert day_time_info(day.hours_11).is_night is False\n assert day_time_info(day.hours_12).is_night is False\n assert day_time_info(day.hours_13).is_night is False\n assert day_time_info(day.hours_14).is_night is False\n assert day_time_info(day.hours_15).is_night is False\n assert day_time_info(day.hours_16).is_night is False\n assert day_time_info(day.hours_17).is_night is False\n assert day_time_info(day.hours_18).is_night is False\n assert day_time_info(day.hours_19).is_night is False\n assert day_time_info(day.hours_20).is_night is False\n assert day_time_info(day.hours_21).is_night is False\n assert day_time_info(day.hours_22).is_night is False\n assert day_time_info(day.hours_23).is_night is True","def test_get_field_state_comparisons_tiny(self):\r\n comparison_groupings = get_field_state_comparisons(\r\n self.tiny_dist_matrix_header, self.tiny_dist_matrix,\r\n self.tiny_mapping_header, self.tiny_mapping, self.tiny_field,\r\n ['SampleFieldState1'])\r\n self.assertEqual(comparison_groupings, {})","def test_past_meeting_details(self):\n pass","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def _assert_state(self, state_dict):\n instances = db.instance_get_all(self.context)\n self.assertEqual(len(instances), 1)\n\n if 'vm_state' in state_dict:\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\n if 'task_state' in state_dict:\n self.assertEqual(state_dict['task_state'],\n instances[0]['task_state'])\n if 'power_state' in state_dict:\n self.assertEqual(state_dict['power_state'],\n instances[0]['power_state'])","def test_state_after_failure(self):\n pass","def assert_history(self, rows):\r\n self.assertEqual(self.parse_rows(rows), self.read_history())","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def test_time_field():","def test_get_state_comparison_stats_born_there(self):\n\n key = '% Employees born there'\n\n place_kentucky = PlaceFactory(region=self.kentucky)\n place_texas = PlaceFactory(region=self.texas)\n\n # Mississippi: 0 employees born in Mississippi, 3 born elsewhere\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 2 employees born in Texas, 2 born elsewhere\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=place_texas)\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 3 employees born in Kentucky, 1 employee elsewhere, 3 unknown\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=place_kentucky)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\n employee.bureau_states.add(self.kentucky)\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n expected_output = [('Kentucky', 75), ('Texas', 50)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(top_states, expected_output,\n f\"'{key}' should contain states with the top x % employees born there\")","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def test_update_state4(self):\n pass","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def test_meeting_status(self):\n pass","def test_rt_table(self) -> None:\n expected = Fixtures.next_table()\n expected.description = '\"hello!\" said no one'\n expected.tags.sort()\n\n self.get_proxy().put_table(table=expected)\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\n actual.last_updated_timestamp = None\n actual.tags.sort()\n\n self.assertEqual(expected, actual)","async def test_state_characteristics(hass: HomeAssistant) -> None:\n now = dt_util.utcnow()\n current_time = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\n start_datetime = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\n characteristics: Sequence[dict[str, Any]] = (\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"average_linear\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 10.68,\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"average_step\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 11.36,\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"average_timeless\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"change\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(0),\n \"value_9\": float(round(VALUES_NUMERIC[-1] - VALUES_NUMERIC[0], 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"change_sample\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n round(\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\n / (len(VALUES_NUMERIC) - 1),\n 2,\n )\n ),\n \"unit\": \"°C/sample\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"change_second\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n round(\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\n / (60 * (len(VALUES_NUMERIC) - 1)),\n 2,\n )\n ),\n \"unit\": \"°C/s\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"count\",\n \"value_0\": 0,\n \"value_1\": 1,\n \"value_9\": 9,\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_newest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_oldest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=1)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_value_max\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=2)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_value_min\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=5)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"distance_95_percent_of_values\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(2 * 1.96 * statistics.stdev(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"distance_99_percent_of_values\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(2 * 2.58 * statistics.stdev(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"distance_absolute\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(0),\n \"value_9\": float(max(VALUES_NUMERIC) - min(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"mean\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"median\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(round(statistics.median(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"noisiness\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(sum([3, 4.8, 10.2, 1.2, 5.4, 2.5, 7.3, 8]) / 8, 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"percentile\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 9.2,\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"standard_deviation\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(statistics.stdev(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"sum\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(sum(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"sum_differences\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n sum(\n [\n abs(20 - 17),\n abs(15.2 - 20),\n abs(5 - 15.2),\n abs(3.8 - 5),\n abs(9.2 - 3.8),\n abs(6.7 - 9.2),\n abs(14 - 6.7),\n abs(6 - 14),\n ]\n )\n ),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"sum_differences_nonnegative\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n sum(\n [\n 20 - 17,\n 15.2 - 0,\n 5 - 0,\n 3.8 - 0,\n 9.2 - 3.8,\n 6.7 - 0,\n 14 - 6.7,\n 6 - 0,\n ]\n )\n ),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"total\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(sum(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"value_max\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(max(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"value_min\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(min(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"variance\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(statistics.variance(VALUES_NUMERIC), 2)),\n \"unit\": \"°C²\",\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"average_step\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 50.0,\n \"unit\": \"%\",\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"average_timeless\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": 100.0,\n \"value_9\": float(\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\"on\"), 2)\n ),\n \"unit\": \"%\",\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"count\",\n \"value_0\": 0,\n \"value_1\": 1,\n \"value_9\": len(VALUES_BINARY),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"count_on\",\n \"value_0\": 0,\n \"value_1\": 1,\n \"value_9\": VALUES_BINARY.count(\"on\"),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"count_off\",\n \"value_0\": 0,\n \"value_1\": 0,\n \"value_9\": VALUES_BINARY.count(\"off\"),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"datetime_newest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"datetime_oldest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=1)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"mean\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": 100.0,\n \"value_9\": float(\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\"on\"), 2)\n ),\n \"unit\": \"%\",\n },\n )\n sensors_config = []\n for characteristic in characteristics:\n sensors_config.append(\n {\n \"platform\": \"statistics\",\n \"name\": f\"test_{characteristic['source_sensor_domain']}_{characteristic['name']}\",\n \"entity_id\": f\"{characteristic['source_sensor_domain']}.test_monitored\",\n \"state_characteristic\": characteristic[\"name\"],\n \"max_age\": {\"minutes\": 8}, # 9 values spaces by one minute\n }\n )\n\n with freeze_time(current_time) as freezer:\n assert await async_setup_component(\n hass,\n \"sensor\",\n {\"sensor\": sensors_config},\n )\n await hass.async_block_till_done()\n\n # With all values in buffer\n\n for i in range(len(VALUES_NUMERIC)):\n current_time += timedelta(minutes=1)\n freezer.move_to(current_time)\n async_fire_time_changed(hass, current_time)\n hass.states.async_set(\n \"sensor.test_monitored\",\n str(VALUES_NUMERIC[i]),\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\n )\n hass.states.async_set(\n \"binary_sensor.test_monitored\",\n str(VALUES_BINARY[i]),\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\n )\n await hass.async_block_till_done()\n\n for characteristic in characteristics:\n state = hass.states.get(\n f\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\"\n )\n assert state is not None, (\n \"no state object for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer filled)\"\n )\n assert state.state == str(characteristic[\"value_9\"]), (\n \"value mismatch for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer filled) - \"\n f\"assert {state.state} == {str(characteristic['value_9'])}\"\n )\n assert (\n state.attributes.get(ATTR_UNIT_OF_MEASUREMENT) == characteristic[\"unit\"]\n ), f\"unit mismatch for characteristic '{characteristic['name']}'\"\n\n # With single value in buffer\n\n current_time += timedelta(minutes=8)\n freezer.move_to(current_time)\n async_fire_time_changed(hass, current_time)\n await hass.async_block_till_done()\n\n for characteristic in characteristics:\n state = hass.states.get(\n f\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\"\n )\n assert state is not None, (\n \"no state object for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(one stored value)\"\n )\n assert state.state == str(characteristic[\"value_1\"]), (\n \"value mismatch for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(one stored value) - \"\n f\"assert {state.state} == {str(characteristic['value_1'])}\"\n )\n\n # With empty buffer\n\n current_time += timedelta(minutes=1)\n freezer.move_to(current_time)\n async_fire_time_changed(hass, current_time)\n await hass.async_block_till_done()\n\n for characteristic in characteristics:\n state = hass.states.get(\n f\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\"\n )\n assert state is not None, (\n \"no state object for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer empty)\"\n )\n assert state.state == str(characteristic[\"value_0\"]), (\n \"value mismatch for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer empty) - \"\n f\"assert {state.state} == {str(characteristic['value_0'])}\"\n )","def test_updatePlayerbHist_fold_2(self):\n self.assertEqual(self.player.foldedInd, 1)","def test_fantasy_status_nov_1(league):\n nov_1 = datetime.datetime(2019,11,1)\n players = league.as_of(nov_1).all_players()\n # make sure sammy blais is not a free agent, he was picked up oct 31\n assert(players.loc[6544, 'fantasy_status'] != 'FA')","def test_TimeArray_convert_unit():","def test_past_meetings(self):\n pass","def test_update_state(self):\n pass","def test_update_state2(self):\n pass","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def test_get_team_history(self):\n pass","def make_test_observation_table(observatory_name='HESS', n_obs=10,\n az_range=Angle([0, 360], 'deg'),\n alt_range=Angle([45, 90], 'deg'),\n date_range=(Time('2010-01-01'),\n Time('2015-01-01')),\n use_abs_time=False,\n n_tels_range=(3, 4),\n random_state='random-seed'):\n from ..data import ObservationTable, observatory_locations\n random_state = get_random_state(random_state)\n\n n_obs_start = 1\n\n obs_table = ObservationTable()\n\n # build a time reference as the start of 2010\n dateref = Time('2010-01-01T00:00:00')\n dateref_mjd_fra, dateref_mjd_int = np.modf(dateref.mjd)\n\n # define table header\n obs_table.meta['OBSERVATORY_NAME'] = observatory_name\n obs_table.meta['MJDREFI'] = dateref_mjd_int\n obs_table.meta['MJDREFF'] = dateref_mjd_fra\n if use_abs_time:\n # show the observation times in UTC\n obs_table.meta['TIME_FORMAT'] = 'absolute'\n else:\n # show the observation times in seconds after the reference\n obs_table.meta['TIME_FORMAT'] = 'relative'\n header = obs_table.meta\n\n # obs id\n obs_id = np.arange(n_obs_start, n_obs_start + n_obs)\n obs_table['OBS_ID'] = obs_id\n\n # obs time: 30 min\n ontime = Quantity(30. * np.ones_like(obs_id), 'minute').to('second')\n obs_table['ONTIME'] = ontime\n\n # livetime: 25 min\n time_live = Quantity(25. * np.ones_like(obs_id), 'minute').to('second')\n obs_table['LIVETIME'] = time_live\n\n # start time\n # - random points between the start of 2010 and the end of 2014 (unless\n # otherwise specified)\n # - using the start of 2010 as a reference time for the header of the table\n # - observations restrict to night time (only if specified time interval is\n # more than 1 day)\n # - considering start of astronomical day at midday: implicit in setting\n # the start of the night, when generating random night hours\n datestart = date_range[0]\n dateend = date_range[1]\n time_start = random_state.uniform(datestart.mjd, dateend.mjd, len(obs_id))\n time_start = Time(time_start, format='mjd', scale='utc')\n\n # check if time interval selected is more than 1 day\n if (dateend - datestart).jd > 1.:\n # keep only the integer part (i.e. the day, not the fraction of the day)\n time_start_f, time_start_i = np.modf(time_start.mjd)\n time_start = Time(time_start_i, format='mjd', scale='utc')\n\n # random generation of night hours: 6 h (from 22 h to 4 h), leaving 1/2 h\n # time for the last run to finish\n night_start = Quantity(22., 'hour')\n night_duration = Quantity(5.5, 'hour')\n hour_start = random_state.uniform(night_start.value,\n night_start.value + night_duration.value,\n len(obs_id))\n hour_start = Quantity(hour_start, 'hour')\n\n # add night hour to integer part of MJD\n time_start += hour_start\n\n if use_abs_time:\n # show the observation times in UTC\n time_start = Time(time_start.isot)\n else:\n # show the observation times in seconds after the reference\n time_start = time_relative_to_ref(time_start, header)\n # converting to quantity (better treatment of units)\n time_start = Quantity(time_start.sec, 'second')\n\n obs_table['TSTART'] = time_start\n\n # stop time\n # calculated as TSTART + ONTIME\n if use_abs_time:\n time_stop = Time(obs_table['TSTART'])\n time_stop += TimeDelta(obs_table['ONTIME'])\n else:\n time_stop = TimeDelta(obs_table['TSTART'])\n time_stop += TimeDelta(obs_table['ONTIME'])\n # converting to quantity (better treatment of units)\n time_stop = Quantity(time_stop.sec, 'second')\n\n obs_table['TSTOP'] = time_stop\n\n # az, alt\n # random points in a portion of sphere; default: above 45 deg altitude\n az, alt = sample_sphere(size=len(obs_id),\n lon_range=az_range,\n lat_range=alt_range,\n random_state=random_state)\n az = Angle(az, 'deg')\n alt = Angle(alt, 'deg')\n obs_table['AZ'] = az\n obs_table['ALT'] = alt\n\n # RA, dec\n # derive from az, alt taking into account that alt, az represent the values\n # at the middle of the observation, i.e. at time_ref + (TIME_START + TIME_STOP)/2\n # (or better: time_ref + TIME_START + (TIME_OBSERVATION/2))\n # in use_abs_time mode, the time_ref should not be added, since it's already included\n # in TIME_START and TIME_STOP\n az = Angle(obs_table['AZ'])\n alt = Angle(obs_table['ALT'])\n if use_abs_time:\n obstime = Time(obs_table['TSTART'])\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\n else:\n obstime = time_ref_from_dict(obs_table.meta)\n obstime += TimeDelta(obs_table['TSTART'])\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\n location = observatory_locations[observatory_name]\n altaz_frame = AltAz(obstime=obstime, location=location)\n alt_az_coord = SkyCoord(az, alt, frame=altaz_frame)\n sky_coord = alt_az_coord.transform_to('icrs')\n obs_table['RA'] = sky_coord.ra\n obs_table['DEC'] = sky_coord.dec\n\n # positions\n\n # number of telescopes\n # random integers in a specified range; default: between 3 and 4\n n_tels = random_state.randint(n_tels_range[0], n_tels_range[1] + 1, len(obs_id))\n obs_table['N_TELS'] = n_tels\n\n # muon efficiency\n # random between 0.6 and 1.0\n muoneff = random_state.uniform(low=0.6, high=1.0, size=len(obs_id))\n obs_table['MUONEFF'] = muoneff\n\n return obs_table","def test_cmd_roomstate(self, mock_gametime):\n\n mock_gametime.return_value = _get_timestamp(\"autumn\", \"afternoon\")\n\n # show existing room states (season/time doesn't count)\n\n self.assertEqual(self.room1.room_states, [])\n\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"\",\n f\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\n None\",\n )\n\n # add room states\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"burning\",\n \"Added room state 'burning' to this room.\",\n )\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"windy\",\n \"Added room state 'windy' to this room.\",\n )\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"\",\n f\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\n \"\n \"'burning' and 'windy'\",\n )\n # toggle windy\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"windy\",\n \"Cleared room state 'windy' from this room.\",\n )\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"\",\n f\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\n \"\n \"'burning'\",\n )\n # add a autumn state and make sure we override it\n self.room1.add_desc(\"Autumn description.\", room_state=\"autumn\")\n self.room1.add_desc(\"Spring description.\", room_state=\"spring\")\n\n self.assertEqual(self.room1.get_stateful_desc(), \"Autumn description.\")\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"spring\",\n \"Added room state 'spring' to this room.\",\n )\n self.assertEqual(self.room1.get_stateful_desc(), \"Spring description.\")","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def test_created_and_updated(self):\n state1 = State()\n created = state1.created_at\n updated = state1.updated_at\n state1.save()\n self.assertFalse(updated == state1.updated_at)\n self.assertTrue(created == state1.created_at)","def test_output(self):\n # reproducible arbitrariness\n np.random.seed(123423)\n\n N = 20\n tmax = 30.0\n dt = 1.0\n\n n_steps = int_r(tmax/dt)\n\n table = np.random.randn(N, n_steps)\n G = TableLayer(table)\n\n M = simulation.StateMonitor(G, 'out')\n\n sim = simulation.Simulation(G, M, dt=dt)\n sim.run(tmax)\n\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)","def test_custom_time(self):\n interval = 0.5\n M = simulation.StateMonitor(self.G, 'v', interval=interval)\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, interval)))","def test_time(self):\r\n pass","def test_past_event(self):\n pass","def test_get_current_state(self):\n schedule = parser.parse_schedule(SCHEDULE)\n self.assertEqual(\"a\", schedule.get_current_state(dt(monday, '09:30')))\n self.assertEqual(\"c\", schedule.get_current_state(dt(monday, '08:30')))\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\n lookback=False))","def test_is_approved_no_approval(time_record_factory):\n record = time_record_factory()\n\n assert not record.is_approved","def test_update_state1(self):\n pass","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_booked_event_has_true_flag(self):\r\n user = ViewAfishaTests.mentor\r\n event = EventFactory(city=user.profile.city)\r\n EventParticipantFactory(\r\n event=event,\r\n user=user,\r\n )\r\n\r\n client = self.return_authorized_user_client(user)\r\n response_data = client.get(path=EVENTS_URL, format=\"json\").data\r\n\r\n results = response_data.get(\"results\")\r\n record_dict = results[0]\r\n\r\n self.assertEqual(\r\n \"True\",\r\n str(record_dict.get(\"booked\")),\r\n msg=(\r\n \"Проверьте, что у мероприятий на которые \"\r\n \"пользователь подписан возвращается флаг \"\r\n \"booked': True.\"\r\n ),\r\n )","def event_m20_11_6000():\n \"\"\"State 0,2: [Preset] Door of the living person_SubState\"\"\"\n assert event_m20_11_x77(z42=20110480, z43=600000)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def test_get_habits_trackings_table():\n habits_trackings_table = analytics.habits_trackings_table()\n assert habits_trackings_table == [\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-25', '18:26'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-25', '19:52'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-26', '20:09'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-26', '04:40'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-27', '10:44'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-27', '20:49'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-28', '01:18'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-29', '19:42'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-29', '08:25'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-29', '08:57'),\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-29', '02:07'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-06-29', '04:46'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-30', '05:54'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-30', '12:04'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-01', '05:37'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-01', '07:17'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-01', '12:16'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-02', '22:04'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-02', '23:02'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-03', '07:12'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-04', '08:11'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-05', '10:58'),\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-05', '14:38'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-06', '00:14'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-06', '12:28'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-07', '17:07'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-07', '02:44'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-07', '16:09'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-08', '18:19'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-08', '22:35'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-09', '10:01'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-09', '10:14'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-10', '17:12'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-11', '19:54'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-12', '00:06'),\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-12', '16:12'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-12', '18:43'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-13', '08:02'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-14', '21:32'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-15', '08:37'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-16', '08:32'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-16', '11:59'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-16', '21:14'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-18', '03:03'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-18', '06:59'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-19', '22:50'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-19', '13:05'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-20', '04:59'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-20', '05:52'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-20', '09:00'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-21', '18:44'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-21', '03:55'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-22', '06:52'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-22', '18:27'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-23', '01:28'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-23', '12:17'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-24', '19:10'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-25', '02:18'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-26', '09:53'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-26', '16:59')\n ]","def test_workflows(database, monkeypatch):\n monkeypatch.setattr('dataactcore.utils.sam_recipient.get_sam_props', mock_get_sam_props)\n sess = database.session\n all_duns = ['00000000{}'.format(x) for x in range(1, 5)]\n existing_duns = all_duns[2:]\n for duns in existing_duns:\n sess.add(SAMRecipient(awardee_or_recipient_uniqu=duns))\n sess.commit()\n\n recipient_file = os.path.join(CONFIG_BROKER['path'], 'tests', 'unit', 'data', 'historic_DUNS_export_small.csv')\n\n update_historical_duns.run_sam_batches(recipient_file, sess, block_size=1)\n update_historical_duns.import_historic_recipients(sess)\n\n expected_results = {\n '000000001': {\n 'awardee_or_recipient_uniqu': '000000001',\n 'uei': 'A1',\n 'registration_date': '2004-04-01',\n 'expiration_date': '2013-01-11',\n 'last_sam_mod_date': '2013-01-11',\n 'activation_date': '2012-01-11',\n 'legal_business_name': 'TEST RECIPIENT 1',\n 'address_line_1': 'Test address 1',\n 'address_line_2': 'Test address 2',\n 'city': 'Test city',\n 'state': 'Test state',\n 'zip': 'Test zip',\n 'zip4': 'Test zip4',\n 'country_code': 'Test country',\n 'congressional_district': 'Test congressional district',\n 'business_types_codes': ['A', 'B', 'C'],\n 'business_types': ['Name A', 'Name B', 'Name C'],\n 'dba_name': 'Name 1',\n 'entity_structure': '1A',\n 'ultimate_parent_unique_ide': '999999999',\n 'ultimate_parent_uei': 'Z9',\n 'ultimate_parent_legal_enti': 'Parent Legal Name 1',\n 'high_comp_officer1_full_na': 'Test Exec 1',\n 'high_comp_officer1_amount': '1',\n 'high_comp_officer2_full_na': 'Test Exec 2',\n 'high_comp_officer2_amount': '2',\n 'high_comp_officer3_full_na': 'Test Exec 3',\n 'high_comp_officer3_amount': '3',\n 'high_comp_officer4_full_na': 'Test Exec 4',\n 'high_comp_officer4_amount': '4',\n 'high_comp_officer5_full_na': 'Test Exec 5',\n 'high_comp_officer5_amount': '5'\n },\n '000000002': {\n 'awardee_or_recipient_uniqu': '000000002',\n 'uei': 'B2',\n 'registration_date': '2004-04-02',\n 'expiration_date': '2013-01-12',\n 'last_sam_mod_date': '2013-01-12',\n 'activation_date': '2012-01-12',\n 'legal_business_name': 'TEST RECIPIENT 2',\n 'address_line_1': 'Other Test address 1',\n 'address_line_2': 'Other Test address 2',\n 'city': 'Other Test city',\n 'state': 'Other Test state',\n 'zip': 'Other Test zip',\n 'zip4': 'Other Test zip4',\n 'country_code': 'Other Test country',\n 'congressional_district': 'Other Test congressional district',\n 'business_types_codes': ['D', 'E', 'F'],\n 'business_types': ['Name D', 'Name E', 'Name F'],\n 'dba_name': 'Name 2',\n 'entity_structure': '2B',\n 'ultimate_parent_unique_ide': '999999998',\n 'ultimate_parent_uei': 'Y8',\n 'ultimate_parent_legal_enti': 'Parent Legal Name 2',\n 'high_comp_officer1_full_na': 'Test Other Exec 6',\n 'high_comp_officer1_amount': '6',\n 'high_comp_officer2_full_na': 'Test Other Exec 7',\n 'high_comp_officer2_amount': '7',\n 'high_comp_officer3_full_na': 'Test Other Exec 8',\n 'high_comp_officer3_amount': '8',\n 'high_comp_officer4_full_na': 'Test Other Exec 9',\n 'high_comp_officer4_amount': '9',\n 'high_comp_officer5_full_na': 'Test Other Exec 10',\n 'high_comp_officer5_amount': '10'\n },\n '000000003': {\n 'awardee_or_recipient_uniqu': '000000003',\n 'uei': None,\n 'registration_date': None,\n 'expiration_date': None,\n 'last_sam_mod_date': None,\n 'activation_date': None,\n 'legal_business_name': None,\n 'address_line_1': None,\n 'address_line_2': None,\n 'city': None,\n 'state': None,\n 'zip': None,\n 'zip4': None,\n 'country_code': None,\n 'congressional_district': None,\n 'business_types_codes': None,\n 'business_types': None,\n 'dba_name': None,\n 'entity_structure': None,\n 'ultimate_parent_unique_ide': None,\n 'ultimate_parent_uei': None,\n 'ultimate_parent_legal_enti': None,\n 'high_comp_officer1_full_na': None,\n 'high_comp_officer1_amount': None,\n 'high_comp_officer2_full_na': None,\n 'high_comp_officer2_amount': None,\n 'high_comp_officer3_full_na': None,\n 'high_comp_officer3_amount': None,\n 'high_comp_officer4_full_na': None,\n 'high_comp_officer4_amount': None,\n 'high_comp_officer5_full_na': None,\n 'high_comp_officer5_amount': None\n },\n '000000004': {\n 'awardee_or_recipient_uniqu': '000000004',\n 'uei': None,\n 'registration_date': None,\n 'expiration_date': None,\n 'last_sam_mod_date': None,\n 'activation_date': None,\n 'legal_business_name': None,\n 'address_line_1': None,\n 'address_line_2': None,\n 'city': None,\n 'state': None,\n 'zip': None,\n 'zip4': None,\n 'country_code': None,\n 'congressional_district': None,\n 'business_types_codes': None,\n 'business_types': None,\n 'dba_name': None,\n 'entity_structure': None,\n 'ultimate_parent_unique_ide': None,\n 'ultimate_parent_uei': None,\n 'ultimate_parent_legal_enti': None,\n 'high_comp_officer1_full_na': None,\n 'high_comp_officer1_amount': None,\n 'high_comp_officer2_full_na': None,\n 'high_comp_officer2_amount': None,\n 'high_comp_officer3_full_na': None,\n 'high_comp_officer3_amount': None,\n 'high_comp_officer4_full_na': None,\n 'high_comp_officer4_amount': None,\n 'high_comp_officer5_full_na': None,\n 'high_comp_officer5_amount': None\n }\n }\n results = {}\n for recp_obj in sess.query(SAMRecipient).all():\n results[recp_obj.awardee_or_recipient_uniqu] = {\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\n 'uei': recp_obj.uei,\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\n 'legal_business_name': recp_obj.legal_business_name,\n 'address_line_1': recp_obj.address_line_1,\n 'address_line_2': recp_obj.address_line_2,\n 'city': recp_obj.city,\n 'state': recp_obj.state,\n 'zip': recp_obj.zip,\n 'zip4': recp_obj.zip4,\n 'country_code': recp_obj.country_code,\n 'congressional_district': recp_obj.congressional_district,\n 'business_types_codes': recp_obj.business_types_codes,\n 'business_types': recp_obj.business_types,\n 'dba_name': recp_obj.dba_name,\n 'entity_structure': recp_obj.entity_structure,\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\n }\n assert results == expected_results\n\n # Test to see if truncating the SAM table while keeping the historic reuploads the historic values\n sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).delete(synchronize_session=False)\n\n # Make sure all the historic recipients are removed from the SAMRecipient table\n assert sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).all() == []\n\n # Redo script but don't go through run_sam_batches\n update_historical_duns.clean_historic_recipients(sess)\n update_historical_duns.import_historic_recipients(sess)\n\n results = {}\n for recp_obj in sess.query(SAMRecipient).all():\n results[recp_obj.awardee_or_recipient_uniqu] = {\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\n 'uei': recp_obj.uei,\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\n 'legal_business_name': recp_obj.legal_business_name,\n 'address_line_1': recp_obj.address_line_1,\n 'address_line_2': recp_obj.address_line_2,\n 'city': recp_obj.city,\n 'state': recp_obj.state,\n 'zip': recp_obj.zip,\n 'zip4': recp_obj.zip4,\n 'country_code': recp_obj.country_code,\n 'congressional_district': recp_obj.congressional_district,\n 'business_types_codes': recp_obj.business_types_codes,\n 'business_types': recp_obj.business_types,\n 'dba_name': recp_obj.dba_name,\n 'entity_structure': recp_obj.entity_structure,\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\n }\n assert results == expected_results","def test_full_house_flush_ind(self):","def test_time(self):\n conn = self.database.connection()\n cursor = conn.cursor()\n dialect = self.database.dialect()\n dbapi = self.database.dbapi()\n query = dialect.translate('DROP TABLE test_time')\n try:\n cursor.execute(query)\n except dbapi.Error:\n conn.rollback()\n query = dialect.translate('CREATE TABLE test_time ' \\\n '( value TIME NOT NULL )')\n cursor.execute(query)\n data = []\n rr = random.randrange\n query = 'INSERT INTO test_time VALUES (%s)'\n for i in range(100):\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\n data.append(item)\n cursor.execute(query, (item,))\n query = 'SELECT * FROM test_time'\n cursor.execute(query)\n result = cursor.fetchall()\n for row in result:\n item = row[0]\n assert isinstance(item, datetime.time)\n assert item in data\n data.remove(item)\n query = dialect.translate('DELETE FROM test_time')\n cursor.execute(query)\n query = dialect.translate('DROP TABLE test_time')\n cursor.execute(query)\n conn.commit()","def test_state(self):\n states = self.geographies.find({ 'geoid': config.SUMLEV_STATE })\n\n self.assertEqual(states.count(), 1)\n\n state = states[0]\n\n self.assertEqual(state['sumlev'], config.SUMLEV_STATE)\n self.assertEqual(state['metadata']['NAME'], 'Hawaii')\n self.assertEqual(state['metadata']['STATE'], '15')\n\n pop_2000 = 1211537\n pop_2010 = 1360301\n self._test_totalpop(state, pop_2000, pop_2010)","def test_get_unsuccessful_ta():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\n\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\n assert np.isnan(list_failed[0])","def test_TP_twophase(self):\n with pytest.raises(StateError):\n State(substance=\"water\", T=Q_(373.1242958476844, \"K\"), p=Q_(101325.0, \"Pa\"))","def test_get_state(self):\n\n # test that you can get a state by numerical id\n mdp = MDP()\n mdp.add_state(0)\n self.assertEquals(type(mdp.get_state(0)), State)\n self.assertIn(mdp.get_state(0), mdp.get_state_list())","def test_updatePlayerbHist_fold_1(self):\n self.assertEqual(self.player.bHist[2], [cardutils.BETSTRING_DICT['FOLD']])","def test_match_table_post(self):\n pass","def test_updatePlayerbHist_multiactions_1(self):\n self.assertEqual(self.player.bHist[0][0], cardutils.BETSTRING_DICT['RAISE'])","def test_get_state_comparison_stats_foreign_born(self):\n\n key = '% Foreign-born employees'\n\n us = CountryFactory(code2='US')\n germany = CountryFactory(name='Germany')\n\n # Mississippi: 0 employees born in Germany, 3 in US\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 2 employees born in Germany, 2 in US\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 3 employees born in Germany, 1 employee in US, 3 unknown\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.kentucky)\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n expected_output = [('Kentucky', 75), ('Texas', 50)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(top_states, expected_output,\n \"'{key}' should contain states with the top x % foreign-born employees\")","def test_update_state3(self):\n pass","def test_get_state_list(self):\n\n mdp = MDP(5)\n state_list = mdp.get_state_list()\n\n self.assertEqual(len(state_list), 5)\n self.assertIn(mdp.get_state(0), state_list)","def test_method(self):\n self.amenity.save()\n self.assertTrue(hasattr(self.amenity, \"updated_at\"))","def test_analyze_time(self):\n self.ph5validate.analyze_time()\n self.assertEqual(self.ph5validate.das_time.keys(), [('12183', 1, 500)])\n Dtime = self.ph5validate.das_time[('12183', 1, 500)]\n\n # 3 different deploy time\n self.assertEqual(len(Dtime['time_windows']), 5)\n\n # station 9001\n self.assertEqual(Dtime['time_windows'][0],\n (1550849950, 1550850034, '9001'))\n self.assertEqual(Dtime['time_windows'][1],\n (1550849950, 1550850034, '9001'))\n self.assertEqual(Dtime['time_windows'][2],\n (1550849950, 1550850034, '9001'))\n # station 9002\n self.assertEqual(Dtime['time_windows'][3],\n (1550850043, 1550850093, '9002'))\n # station 9003\n self.assertEqual(Dtime['time_windows'][4],\n (1550850125, 1550850187, '9003'))\n\n self.assertEqual(Dtime['min_deploy_time'],\n [1550849950,\n 'Data exists before deploy time: 7 seconds.'])","def test_from_event_to_delete_state() -> None:\n event = ha.Event(\n EVENT_STATE_CHANGED,\n {\n \"entity_id\": \"sensor.temperature\",\n \"old_state\": ha.State(\"sensor.temperature\", \"18\"),\n \"new_state\": None,\n },\n )\n db_state = States.from_event(event)\n\n assert db_state.entity_id == \"sensor.temperature\"\n assert db_state.state == \"\"\n assert db_state.last_changed_ts is None\n assert db_state.last_updated_ts == event.time_fired.timestamp()","def test_eval_alarm(self):\n def get_state_update_value(h):\n \"\"\"\n \n \"\"\"\n oldstate = h.data['oldState']['stateValue']\n newstate = h.data['newState']['stateValue']\n querydate = h.data['newState']['stateReasonData']['queryDate']\n querydate = utils.parse_strtime(querydate)\n return oldstate, newstate, querydate \n \n test_uuid = str(uuid.uuid4())\n alarmname = \"TestEvalAlarm_\" + test_uuid\n metricname = \"TestEvalMetric_\" + test_uuid\n namespace = \"unittest\"\n unit = \"Percent\"\n dimensions = {\"test_id\":test_uuid}\n threshold = 2.0\n \n # create metric alarm\n alarm = MetricAlarm(name=alarmname, metric=metricname,\n namespace=namespace, statistic=\"Average\",\n comparison=\">\", threshold=threshold,\n period=60, evaluation_periods=1, unit=unit,\n dimensions=dimensions)\n self.synaps.put_metric_alarm(alarm)\n \n # due to put_metric_alarm is asynchronous\n time.sleep(ASYNC_WAIT)\n \n alarm_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0)\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold + 1, timestamp=alarm_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n\n ok_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0) \n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold - 2, timestamp=ok_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n \n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\n history_item_type=\"StateUpdate\")\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\n\n result = map(get_state_update_value, histories)\n \n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\n ('ALARM', 'INSUFFICIENT_DATA', None),\n ('INSUFFICIENT_DATA', 'OK', ok_time),\n ('OK', 'INSUFFICIENT_DATA', None))\n \n failmsg = \"expected: %s real: %s\" % (expected, result)\n \n self.assertEqual(len(result), len(expected), msg=failmsg)\n \n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\n expected):\n self.assertEqual(r_new, e_new, msg=failmsg)\n self.assertEqual(r_old, e_old, msg=failmsg)\n if e_time:\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\n msg=failmsg)\n \n self.synaps.delete_alarms(alarms=[alarmname])","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","async def test_lazy_state_handles_same_last_updated_and_last_changed(\n caplog: pytest.LogCaptureFixture,\n) -> None:\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n row = PropertyMock(\n entity_id=\"sensor.valid\",\n state=\"off\",\n attributes='{\"shared\":true}',\n last_updated_ts=now.timestamp(),\n last_changed_ts=now.timestamp(),\n )\n lstate = LazyState(\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\n )\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n assert lstate.last_updated.timestamp() == row.last_updated_ts\n assert lstate.last_changed.timestamp() == row.last_changed_ts\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2020-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def test_states_repr_without_timestamp() -> None:\n fixed_time = datetime(2016, 7, 9, 11, 0, 0, tzinfo=dt_util.UTC, microsecond=432432)\n states = States(\n entity_id=\"sensor.temp\",\n attributes=None,\n context_id=None,\n context_user_id=None,\n context_parent_id=None,\n origin_idx=None,\n last_updated=fixed_time,\n last_changed=fixed_time,\n last_updated_ts=None,\n last_changed_ts=None,\n )\n assert \"2016-07-09 11:00:00+00:00\" in repr(states)","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_first_timestep(self):\n m = build_model({}, \"simple_supply,two_hours,investment_costs\")\n m.run(build_only=True)\n timestep_0 = \"2005-01-01 00:00\"\n assert m._backend_model.timesteps.ord(timestep_0) == 1","def test_get_times_height():\n times = get_times(date, lng, lat, height)\n for key, value in heightTestTimes.items():\n assert times[key].strftime(\"%Y-%m-%dT%H:%M:%SZ\") == value","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_multiple_states(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory()\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Cancel when created.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.CANCEL, event)\n s1 = context.work_status.value\n self.assertEqual(s1, s.CANCELLED)\n\n # Cancel when started.\n c2 = model.factory(initial_state=s.STARTED)\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(c2, a.CANCEL, event)\n s2 = c2.work_status.value\n self.assertEqual(s2, s.CANCELLED)","def test_from_event_to_db_state_attributes() -> None:\n attrs = {\"this_attr\": True}\n state = ha.State(\"sensor.temperature\", \"18\", attrs)\n event = ha.Event(\n EVENT_STATE_CHANGED,\n {\"entity_id\": \"sensor.temperature\", \"old_state\": None, \"new_state\": state},\n context=state.context,\n )\n db_attrs = StateAttributes()\n dialect = SupportedDialect.MYSQL\n\n db_attrs.shared_attrs = StateAttributes.shared_attrs_bytes_from_event(\n event, {}, {}, dialect\n )\n assert db_attrs.to_native() == attrs","def test_status(self):\n keys = [\n 'receiver time mark count', 'ant type base', 'solution status',\n ...\n ]\n\n for i in xrange(0, 100):\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)","def test_negative_is_active_of_homework():\n assert not expired_hw.is_active()","def test_state(self, mock_req):\n self.setup_api(MOCK_DATA, mock_req)\n now = datetime(1970, month=1, day=1)\n with patch(\"homeassistant.util.dt.now\", return_value=now):\n for name in self.sensor_dict:\n sensor = self.sensor_dict[name][\"sensor\"]\n self.fake_delay(2)\n sensor.update()\n if name == google_wifi.ATTR_LAST_RESTART:\n assert \"1969-12-31 00:00:00\" == sensor.state\n elif name == google_wifi.ATTR_UPTIME:\n assert 1 == sensor.state\n elif name == google_wifi.ATTR_STATUS:\n assert \"Online\" == sensor.state\n else:\n assert \"initial\" == sensor.state","def test_multiple_rules(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory(initial_state=s.STARTED)\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Complete -> completed.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.COMPLETE, event)\n s1 = context.work_status.value\n context_id = context.id\n\n self.assertEqual(s1, s.COMPLETED)\n\n # Complete -> absolutely completed.\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.COMPLETE, event)\n s2 = context.work_status.value\n self.assertEqual(s2, s.ABSOLUTELY_COMPLETED)\n\n # Complete -> ... the same state ...\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n context = model.Model.query.get(context_id)\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(context, a.COMPLETE, event)\n s3 = context.work_status.value\n self.assertEqual(s3, s.ABSOLUTELY_COMPLETED)","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_170518_bad_dbtime(self):\n spc = parser(get_file('PTSDY1_baddbtime.txt'))\n answer = utc(2017, 5, 1, 12, 0)\n for _, outlook in spc.outlook_collections.items():\n self.assertEqual(outlook.expire, answer)","def test_is(self):\n invalid = self.TDTT()\n self.check_invalid_is(invalid)\n\n valid = self.TDTT(when=self.txt_when)\n self.check_valid_is(valid)","def test_time_status(self):\n result = self.test_client.time_status\n\n assert result == \"12312\"","def test_of_agreement(self): \n \n pop_t = world.pop_t \n pop_sub = world.pop_sub \n \n \n Ns = world.subtime.length\n \n dt = world.time.step\n ds = world.subtime.step\n N = round(ds/dt)\n for i in range(Ns):\n numpy.testing.assert_allclose(pop_sub[:,i],pop_t[i*N,:])","def test_from_event_to_db_state() -> None:\n state = ha.State(\n \"sensor.temperature\",\n \"18\",\n context=ha.Context(\n id=\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\",\n parent_id=\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\",\n user_id=\"12345678901234567890123456789012\",\n ),\n )\n event = ha.Event(\n EVENT_STATE_CHANGED,\n {\"entity_id\": \"sensor.temperature\", \"old_state\": None, \"new_state\": state},\n context=state.context,\n )\n assert state.as_dict() == States.from_event(event).to_native().as_dict()","def test_has_ended(self):\r\n self.assertTrue(self.past_show_certs.has_ended())\r\n self.assertTrue(self.past_noshow_certs.has_ended())\r\n self.assertFalse(self.future_show_certs.has_ended())\r\n self.assertFalse(self.future_noshow_certs.has_ended())","async def test_lazy_state_handles_different_last_updated_and_last_changed(\n caplog: pytest.LogCaptureFixture,\n) -> None:\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n row = PropertyMock(\n entity_id=\"sensor.valid\",\n state=\"off\",\n attributes='{\"shared\":true}',\n last_updated_ts=now.timestamp(),\n last_changed_ts=(now - timedelta(seconds=60)).timestamp(),\n )\n lstate = LazyState(\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\n )\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:03:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n assert lstate.last_updated.timestamp() == row.last_updated_ts\n assert lstate.last_changed.timestamp() == row.last_changed_ts\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:03:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }","async def test_state_update(hass):\n assert await setup_multimatic(hass)\n assert_entities_count(hass, 11)\n\n assert hass.states.is_state(\"binary_sensor.dhw_circulation\", \"off\")\n assert hass.states.is_state(\"binary_sensor.room_1_window\", \"off\")\n assert hass.states.is_state(\"binary_sensor.123456789_lock\", \"on\")\n assert hass.states.is_state(\"binary_sensor.123456789_battery\", \"off\")\n assert hass.states.is_state(\"binary_sensor.boiler\", \"off\")\n assert hass.states.is_state(\"binary_sensor.123456789_connectivity\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_update\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_online\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_holiday\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_errors\", \"off\")\n state = hass.states.get(\"binary_sensor.multimatic_holiday\")\n assert state.attributes.get(\"start_date\") is None\n assert state.attributes.get(\"end_date\") is None\n assert state.attributes.get(\"temperature\") is None\n assert hass.states.is_state(\"binary_sensor.multimatic_quick_mode\", \"off\")\n\n dhw = SystemManagerMock.data[\"get_dhw\"]\n dhw.circulation.time_program = time_program(SettingModes.ON, None)\n dhw.circulation.operating_mode = OperatingModes.AUTO\n\n hvac_status = SystemManagerMock.data[\"get_hvac_status\"]\n hvac_status.boiler_status.status_code = \"F11\"\n hvac_status.online = \"OFFLINE\"\n hvac_status.update = \"UPDATE_PENDING\"\n hvac_status.errors = [\n Error(\"device\", \"title\", \"status_code\", \"descr\", datetime.datetime.now())\n ]\n\n rooms = SystemManagerMock.data[\"get_rooms\"]\n rooms[0].devices = [Device(\"Device 1\", \"123456789\", \"VALVE\", True, True)]\n rooms[0].time_program = time_program(None, 20)\n rooms[0].temperature = 22\n rooms[0].target_high = 24\n rooms[0].operating_mode = OperatingModes.AUTO\n rooms[0].child_lock = True\n rooms[0].window_open = True\n\n new_holiday_mode = active_holiday_mode()\n SystemManagerMock.data[\"get_holiday_mode\"] = new_holiday_mode\n SystemManagerMock.data[\"get_quick_mode\"] = QuickModes.HOTWATER_BOOST\n\n await goto_future(hass)\n\n assert_entities_count(hass, 11)\n assert hass.states.is_state(\"binary_sensor.room_1_window\", \"on\")\n assert hass.states.is_state(\"binary_sensor.123456789_lock\", \"off\")\n assert hass.states.is_state(\"binary_sensor.123456789_battery\", \"on\")\n assert hass.states.is_state(\"binary_sensor.boiler\", \"on\")\n assert hass.states.is_state(\"binary_sensor.123456789_connectivity\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_update\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_online\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_errors\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_holiday\", \"on\")\n state = hass.states.get(\"binary_sensor.multimatic_holiday\")\n assert state.attributes[\"start_date\"] == new_holiday_mode.start_date.isoformat()\n assert state.attributes[\"end_date\"] == new_holiday_mode.end_date.isoformat()\n assert state.attributes[\"temperature\"] == new_holiday_mode.target\n assert hass.states.is_state(\"binary_sensor.multimatic_quick_mode\", \"on\")\n state = hass.states.get(\"binary_sensor.multimatic_quick_mode\")\n assert state.attributes[\"quick_mode\"] == QuickModes.HOTWATER_BOOST.name\n assert hass.states.is_state(\"binary_sensor.dhw_circulation\", \"off\")\n\n SystemManagerMock.data[\"get_holiday_mode\"] = HolidayMode(False)\n\n await goto_future(hass)\n assert hass.states.is_state(\"binary_sensor.dhw_circulation\", \"on\")"],"string":"[\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_time_type_state_types(day):\\n\\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}\",\n \"def test_time_type_state_is_afternoon(day):\\n\\n assert day_time_info(day.hours_0).is_afternoon is False\\n assert day_time_info(day.hours_1).is_afternoon is False\\n assert day_time_info(day.hours_2).is_afternoon is False\\n assert day_time_info(day.hours_3).is_afternoon is False\\n assert day_time_info(day.hours_4).is_afternoon is False\\n assert day_time_info(day.hours_5).is_afternoon is False\\n assert day_time_info(day.hours_6).is_afternoon is False\\n assert day_time_info(day.hours_7).is_afternoon is False\\n assert day_time_info(day.hours_8).is_afternoon is False\\n assert day_time_info(day.hours_9).is_afternoon is False\\n assert day_time_info(day.hours_10).is_afternoon is False\\n assert day_time_info(day.hours_11).is_afternoon is False\\n assert day_time_info(day.hours_12).is_afternoon is False\\n assert day_time_info(day.hours_13).is_afternoon is True\\n assert day_time_info(day.hours_14).is_afternoon is True\\n assert day_time_info(day.hours_15).is_afternoon is True\\n assert day_time_info(day.hours_16).is_afternoon is True\\n assert day_time_info(day.hours_17).is_afternoon is True\\n assert day_time_info(day.hours_18).is_afternoon is False\\n assert day_time_info(day.hours_19).is_afternoon is False\\n assert day_time_info(day.hours_20).is_afternoon is False\\n assert day_time_info(day.hours_21).is_afternoon is False\\n assert day_time_info(day.hours_22).is_afternoon is False\\n assert day_time_info(day.hours_23).is_afternoon is False\",\n \"def test_time_type_state_is_evening(day):\\n\\n assert day_time_info(day.hours_0).is_evening is False\\n assert day_time_info(day.hours_1).is_evening is False\\n assert day_time_info(day.hours_2).is_evening is False\\n assert day_time_info(day.hours_3).is_evening is False\\n assert day_time_info(day.hours_4).is_evening is False\\n assert day_time_info(day.hours_5).is_evening is False\\n assert day_time_info(day.hours_6).is_evening is False\\n assert day_time_info(day.hours_7).is_evening is False\\n assert day_time_info(day.hours_8).is_evening is False\\n assert day_time_info(day.hours_9).is_evening is False\\n assert day_time_info(day.hours_10).is_evening is False\\n assert day_time_info(day.hours_11).is_evening is False\\n assert day_time_info(day.hours_12).is_evening is False\\n assert day_time_info(day.hours_13).is_evening is False\\n assert day_time_info(day.hours_14).is_evening is False\\n assert day_time_info(day.hours_15).is_evening is False\\n assert day_time_info(day.hours_16).is_evening is False\\n assert day_time_info(day.hours_17).is_evening is False\\n assert day_time_info(day.hours_18).is_evening is True\\n assert day_time_info(day.hours_19).is_evening is True\\n assert day_time_info(day.hours_20).is_evening is True\\n assert day_time_info(day.hours_21).is_evening is True\\n assert day_time_info(day.hours_22).is_evening is True\\n assert day_time_info(day.hours_23).is_evening is False\",\n \"def test_exp_by_states(self):\\n api = my_mock.api_mock({\\\"items\\\": [{'state': 'Waiting', 'id': 10134},\\n {'state': 'Waiting', 'id': 10135},\\n {'state': 'Running', 'id': 10130}]})\\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\\n self.assertEquals(\\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\\n my_mock.api_mock_stop()\",\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_get_state_comparison_stats_ailments(self):\\n\\n # AilmentType and Ailment breakdown per AilmentType should be in returned stats if present\\n ailment_type_headache = AilmentTypeFactory(name='Headache')\\n ailment_migraine_headache = AilmentFactory(name='Migraine Headache', type=ailment_type_headache)\\n ailment_tension_headache = AilmentFactory(name='Tension Headache', type=ailment_type_headache)\\n ailment_type_sprain_or_bruise = AilmentTypeFactory(name='Sprain or Bruise')\\n ailment_sprain = AilmentFactory(name='Sprain', type=ailment_type_sprain_or_bruise)\\n\\n # Texas: 2 with sprain and 2 with nothing\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_sprain)\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 1 with migraine headache, 1 with sprain, 1 with tension headache, and 1 with nothing\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_migraine_headache)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_sprain)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_tension_headache)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n # Mississippi: 2 with migraine headache, 2 with tension headache\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_migraine_headache)\\n employee.bureau_states.add(self.mississippi)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_tension_headache)\\n employee.bureau_states.add(self.mississippi)\\n\\n stats = get_state_comparison_stats(number=2)\\n\\n # With ailment types, ailments should be grouped together\\n key = '% With Headache'\\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with headache\\\")\\n\\n key = '% With Sprain or Bruise'\\n expected_output = [('Texas', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with sprain or bruise\\\"\\n )\\n\\n # Ailments should be treated individually\\n key = '% With Migraine Headache'\\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with migraine headache\\\"\\n )\\n\\n key = '% With Tension Headache'\\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with tension headache\\\"\\n )\\n\\n # There should be no breakdown for sprain, because it's the only ailment of that type\\n key = '% With Sprain'\\n self.assertEqual(len([item for item in stats if key in item]), 0,\\n \\\"There should be no breakdown for an ailment if it's the only one of its type\\\")\",\n \"def test_time_type_state_is_midmorning(day):\\n\\n assert day_time_info(day.hours_0).is_midmorning is False\\n assert day_time_info(day.hours_1).is_midmorning is False\\n assert day_time_info(day.hours_2).is_midmorning is False\\n assert day_time_info(day.hours_3).is_midmorning is False\\n assert day_time_info(day.hours_4).is_midmorning is False\\n assert day_time_info(day.hours_5).is_midmorning is False\\n assert day_time_info(day.hours_6).is_midmorning is False\\n assert day_time_info(day.hours_7).is_midmorning is False\\n assert day_time_info(day.hours_8).is_midmorning is False\\n assert day_time_info(day.hours_9).is_midmorning is False\\n assert day_time_info(day.hours_10).is_midmorning is True\\n assert day_time_info(day.hours_11).is_midmorning is True\\n assert day_time_info(day.hours_12).is_midmorning is False\\n assert day_time_info(day.hours_13).is_midmorning is False\\n assert day_time_info(day.hours_14).is_midmorning is False\\n assert day_time_info(day.hours_15).is_midmorning is False\\n assert day_time_info(day.hours_16).is_midmorning is False\\n assert day_time_info(day.hours_17).is_midmorning is False\\n assert day_time_info(day.hours_18).is_midmorning is False\\n assert day_time_info(day.hours_19).is_midmorning is False\\n assert day_time_info(day.hours_20).is_midmorning is False\\n assert day_time_info(day.hours_21).is_midmorning is False\\n assert day_time_info(day.hours_22).is_midmorning is False\\n assert day_time_info(day.hours_23).is_midmorning is False\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_episode_data(self):\\n self.assertEquals(\\n self.t['lost']['firstaired'],\\n '2004-09-22'\\n )\",\n \"def test_duplicatekeyerror_state_embryo(self):\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_update_team_state(self):\\n pass\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_time_type_state_is_morning(day):\\n\\n assert day_time_info(day.hours_0).is_morning is False\\n assert day_time_info(day.hours_1).is_morning is False\\n assert day_time_info(day.hours_2).is_morning is False\\n assert day_time_info(day.hours_3).is_morning is False\\n assert day_time_info(day.hours_4).is_morning is False\\n assert day_time_info(day.hours_5).is_morning is True\\n assert day_time_info(day.hours_6).is_morning is True\\n assert day_time_info(day.hours_7).is_morning is True\\n assert day_time_info(day.hours_8).is_morning is True\\n assert day_time_info(day.hours_9).is_morning is True\\n assert day_time_info(day.hours_10).is_morning is False\\n assert day_time_info(day.hours_11).is_morning is False\\n assert day_time_info(day.hours_12).is_morning is False\\n assert day_time_info(day.hours_13).is_morning is False\\n assert day_time_info(day.hours_14).is_morning is False\\n assert day_time_info(day.hours_15).is_morning is False\\n assert day_time_info(day.hours_16).is_morning is False\\n assert day_time_info(day.hours_17).is_morning is False\\n assert day_time_info(day.hours_18).is_morning is False\\n assert day_time_info(day.hours_19).is_morning is False\\n assert day_time_info(day.hours_20).is_morning is False\\n assert day_time_info(day.hours_21).is_morning is False\\n assert day_time_info(day.hours_22).is_morning is False\\n assert day_time_info(day.hours_23).is_morning is False\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def test_time_type_state_is_night(day):\\n\\n assert day_time_info(day.hours_0).is_night is True\\n assert day_time_info(day.hours_1).is_night is True\\n assert day_time_info(day.hours_2).is_night is True\\n assert day_time_info(day.hours_3).is_night is True\\n assert day_time_info(day.hours_4).is_night is True\\n assert day_time_info(day.hours_5).is_night is False\\n assert day_time_info(day.hours_6).is_night is False\\n assert day_time_info(day.hours_7).is_night is False\\n assert day_time_info(day.hours_8).is_night is False\\n assert day_time_info(day.hours_9).is_night is False\\n assert day_time_info(day.hours_10).is_night is False\\n assert day_time_info(day.hours_11).is_night is False\\n assert day_time_info(day.hours_12).is_night is False\\n assert day_time_info(day.hours_13).is_night is False\\n assert day_time_info(day.hours_14).is_night is False\\n assert day_time_info(day.hours_15).is_night is False\\n assert day_time_info(day.hours_16).is_night is False\\n assert day_time_info(day.hours_17).is_night is False\\n assert day_time_info(day.hours_18).is_night is False\\n assert day_time_info(day.hours_19).is_night is False\\n assert day_time_info(day.hours_20).is_night is False\\n assert day_time_info(day.hours_21).is_night is False\\n assert day_time_info(day.hours_22).is_night is False\\n assert day_time_info(day.hours_23).is_night is True\",\n \"def test_get_field_state_comparisons_tiny(self):\\r\\n comparison_groupings = get_field_state_comparisons(\\r\\n self.tiny_dist_matrix_header, self.tiny_dist_matrix,\\r\\n self.tiny_mapping_header, self.tiny_mapping, self.tiny_field,\\r\\n ['SampleFieldState1'])\\r\\n self.assertEqual(comparison_groupings, {})\",\n \"def test_past_meeting_details(self):\\n pass\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def _assert_state(self, state_dict):\\n instances = db.instance_get_all(self.context)\\n self.assertEqual(len(instances), 1)\\n\\n if 'vm_state' in state_dict:\\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\\n if 'task_state' in state_dict:\\n self.assertEqual(state_dict['task_state'],\\n instances[0]['task_state'])\\n if 'power_state' in state_dict:\\n self.assertEqual(state_dict['power_state'],\\n instances[0]['power_state'])\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def assert_history(self, rows):\\r\\n self.assertEqual(self.parse_rows(rows), self.read_history())\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def test_time_field():\",\n \"def test_get_state_comparison_stats_born_there(self):\\n\\n key = '% Employees born there'\\n\\n place_kentucky = PlaceFactory(region=self.kentucky)\\n place_texas = PlaceFactory(region=self.texas)\\n\\n # Mississippi: 0 employees born in Mississippi, 3 born elsewhere\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 2 employees born in Texas, 2 born elsewhere\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=place_texas)\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 3 employees born in Kentucky, 1 employee elsewhere, 3 unknown\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=place_kentucky)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n expected_output = [('Kentucky', 75), ('Texas', 50)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % employees born there\\\")\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def test_update_state4(self):\\n pass\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def test_meeting_status(self):\\n pass\",\n \"def test_rt_table(self) -> None:\\n expected = Fixtures.next_table()\\n expected.description = '\\\"hello!\\\" said no one'\\n expected.tags.sort()\\n\\n self.get_proxy().put_table(table=expected)\\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\\n actual.last_updated_timestamp = None\\n actual.tags.sort()\\n\\n self.assertEqual(expected, actual)\",\n \"async def test_state_characteristics(hass: HomeAssistant) -> None:\\n now = dt_util.utcnow()\\n current_time = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\\n start_datetime = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\\n characteristics: Sequence[dict[str, Any]] = (\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"average_linear\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 10.68,\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"average_step\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 11.36,\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"average_timeless\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"change\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(0),\\n \\\"value_9\\\": float(round(VALUES_NUMERIC[-1] - VALUES_NUMERIC[0], 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"change_sample\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n round(\\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\\n / (len(VALUES_NUMERIC) - 1),\\n 2,\\n )\\n ),\\n \\\"unit\\\": \\\"°C/sample\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"change_second\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n round(\\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\\n / (60 * (len(VALUES_NUMERIC) - 1)),\\n 2,\\n )\\n ),\\n \\\"unit\\\": \\\"°C/s\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"count\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 1,\\n \\\"value_9\\\": 9,\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_newest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_oldest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=1)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_value_max\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=2)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_value_min\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=5)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"distance_95_percent_of_values\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(2 * 1.96 * statistics.stdev(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"distance_99_percent_of_values\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(2 * 2.58 * statistics.stdev(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"distance_absolute\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(0),\\n \\\"value_9\\\": float(max(VALUES_NUMERIC) - min(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"mean\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"median\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(round(statistics.median(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"noisiness\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(sum([3, 4.8, 10.2, 1.2, 5.4, 2.5, 7.3, 8]) / 8, 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"percentile\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 9.2,\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"standard_deviation\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(statistics.stdev(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"sum\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(sum(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"sum_differences\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n sum(\\n [\\n abs(20 - 17),\\n abs(15.2 - 20),\\n abs(5 - 15.2),\\n abs(3.8 - 5),\\n abs(9.2 - 3.8),\\n abs(6.7 - 9.2),\\n abs(14 - 6.7),\\n abs(6 - 14),\\n ]\\n )\\n ),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"sum_differences_nonnegative\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n sum(\\n [\\n 20 - 17,\\n 15.2 - 0,\\n 5 - 0,\\n 3.8 - 0,\\n 9.2 - 3.8,\\n 6.7 - 0,\\n 14 - 6.7,\\n 6 - 0,\\n ]\\n )\\n ),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"total\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(sum(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"value_max\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(max(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"value_min\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(min(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"variance\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(statistics.variance(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C²\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"average_step\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 50.0,\\n \\\"unit\\\": \\\"%\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"average_timeless\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": 100.0,\\n \\\"value_9\\\": float(\\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\\\"on\\\"), 2)\\n ),\\n \\\"unit\\\": \\\"%\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"count\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 1,\\n \\\"value_9\\\": len(VALUES_BINARY),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"count_on\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 1,\\n \\\"value_9\\\": VALUES_BINARY.count(\\\"on\\\"),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"count_off\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 0,\\n \\\"value_9\\\": VALUES_BINARY.count(\\\"off\\\"),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"datetime_newest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"datetime_oldest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=1)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"mean\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": 100.0,\\n \\\"value_9\\\": float(\\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\\\"on\\\"), 2)\\n ),\\n \\\"unit\\\": \\\"%\\\",\\n },\\n )\\n sensors_config = []\\n for characteristic in characteristics:\\n sensors_config.append(\\n {\\n \\\"platform\\\": \\\"statistics\\\",\\n \\\"name\\\": f\\\"test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\",\\n \\\"entity_id\\\": f\\\"{characteristic['source_sensor_domain']}.test_monitored\\\",\\n \\\"state_characteristic\\\": characteristic[\\\"name\\\"],\\n \\\"max_age\\\": {\\\"minutes\\\": 8}, # 9 values spaces by one minute\\n }\\n )\\n\\n with freeze_time(current_time) as freezer:\\n assert await async_setup_component(\\n hass,\\n \\\"sensor\\\",\\n {\\\"sensor\\\": sensors_config},\\n )\\n await hass.async_block_till_done()\\n\\n # With all values in buffer\\n\\n for i in range(len(VALUES_NUMERIC)):\\n current_time += timedelta(minutes=1)\\n freezer.move_to(current_time)\\n async_fire_time_changed(hass, current_time)\\n hass.states.async_set(\\n \\\"sensor.test_monitored\\\",\\n str(VALUES_NUMERIC[i]),\\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\\n )\\n hass.states.async_set(\\n \\\"binary_sensor.test_monitored\\\",\\n str(VALUES_BINARY[i]),\\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\\n )\\n await hass.async_block_till_done()\\n\\n for characteristic in characteristics:\\n state = hass.states.get(\\n f\\\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\"\\n )\\n assert state is not None, (\\n \\\"no state object for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer filled)\\\"\\n )\\n assert state.state == str(characteristic[\\\"value_9\\\"]), (\\n \\\"value mismatch for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer filled) - \\\"\\n f\\\"assert {state.state} == {str(characteristic['value_9'])}\\\"\\n )\\n assert (\\n state.attributes.get(ATTR_UNIT_OF_MEASUREMENT) == characteristic[\\\"unit\\\"]\\n ), f\\\"unit mismatch for characteristic '{characteristic['name']}'\\\"\\n\\n # With single value in buffer\\n\\n current_time += timedelta(minutes=8)\\n freezer.move_to(current_time)\\n async_fire_time_changed(hass, current_time)\\n await hass.async_block_till_done()\\n\\n for characteristic in characteristics:\\n state = hass.states.get(\\n f\\\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\"\\n )\\n assert state is not None, (\\n \\\"no state object for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(one stored value)\\\"\\n )\\n assert state.state == str(characteristic[\\\"value_1\\\"]), (\\n \\\"value mismatch for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(one stored value) - \\\"\\n f\\\"assert {state.state} == {str(characteristic['value_1'])}\\\"\\n )\\n\\n # With empty buffer\\n\\n current_time += timedelta(minutes=1)\\n freezer.move_to(current_time)\\n async_fire_time_changed(hass, current_time)\\n await hass.async_block_till_done()\\n\\n for characteristic in characteristics:\\n state = hass.states.get(\\n f\\\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\"\\n )\\n assert state is not None, (\\n \\\"no state object for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer empty)\\\"\\n )\\n assert state.state == str(characteristic[\\\"value_0\\\"]), (\\n \\\"value mismatch for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer empty) - \\\"\\n f\\\"assert {state.state} == {str(characteristic['value_0'])}\\\"\\n )\",\n \"def test_updatePlayerbHist_fold_2(self):\\n self.assertEqual(self.player.foldedInd, 1)\",\n \"def test_fantasy_status_nov_1(league):\\n nov_1 = datetime.datetime(2019,11,1)\\n players = league.as_of(nov_1).all_players()\\n # make sure sammy blais is not a free agent, he was picked up oct 31\\n assert(players.loc[6544, 'fantasy_status'] != 'FA')\",\n \"def test_TimeArray_convert_unit():\",\n \"def test_past_meetings(self):\\n pass\",\n \"def test_update_state(self):\\n pass\",\n \"def test_update_state2(self):\\n pass\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def test_get_team_history(self):\\n pass\",\n \"def make_test_observation_table(observatory_name='HESS', n_obs=10,\\n az_range=Angle([0, 360], 'deg'),\\n alt_range=Angle([45, 90], 'deg'),\\n date_range=(Time('2010-01-01'),\\n Time('2015-01-01')),\\n use_abs_time=False,\\n n_tels_range=(3, 4),\\n random_state='random-seed'):\\n from ..data import ObservationTable, observatory_locations\\n random_state = get_random_state(random_state)\\n\\n n_obs_start = 1\\n\\n obs_table = ObservationTable()\\n\\n # build a time reference as the start of 2010\\n dateref = Time('2010-01-01T00:00:00')\\n dateref_mjd_fra, dateref_mjd_int = np.modf(dateref.mjd)\\n\\n # define table header\\n obs_table.meta['OBSERVATORY_NAME'] = observatory_name\\n obs_table.meta['MJDREFI'] = dateref_mjd_int\\n obs_table.meta['MJDREFF'] = dateref_mjd_fra\\n if use_abs_time:\\n # show the observation times in UTC\\n obs_table.meta['TIME_FORMAT'] = 'absolute'\\n else:\\n # show the observation times in seconds after the reference\\n obs_table.meta['TIME_FORMAT'] = 'relative'\\n header = obs_table.meta\\n\\n # obs id\\n obs_id = np.arange(n_obs_start, n_obs_start + n_obs)\\n obs_table['OBS_ID'] = obs_id\\n\\n # obs time: 30 min\\n ontime = Quantity(30. * np.ones_like(obs_id), 'minute').to('second')\\n obs_table['ONTIME'] = ontime\\n\\n # livetime: 25 min\\n time_live = Quantity(25. * np.ones_like(obs_id), 'minute').to('second')\\n obs_table['LIVETIME'] = time_live\\n\\n # start time\\n # - random points between the start of 2010 and the end of 2014 (unless\\n # otherwise specified)\\n # - using the start of 2010 as a reference time for the header of the table\\n # - observations restrict to night time (only if specified time interval is\\n # more than 1 day)\\n # - considering start of astronomical day at midday: implicit in setting\\n # the start of the night, when generating random night hours\\n datestart = date_range[0]\\n dateend = date_range[1]\\n time_start = random_state.uniform(datestart.mjd, dateend.mjd, len(obs_id))\\n time_start = Time(time_start, format='mjd', scale='utc')\\n\\n # check if time interval selected is more than 1 day\\n if (dateend - datestart).jd > 1.:\\n # keep only the integer part (i.e. the day, not the fraction of the day)\\n time_start_f, time_start_i = np.modf(time_start.mjd)\\n time_start = Time(time_start_i, format='mjd', scale='utc')\\n\\n # random generation of night hours: 6 h (from 22 h to 4 h), leaving 1/2 h\\n # time for the last run to finish\\n night_start = Quantity(22., 'hour')\\n night_duration = Quantity(5.5, 'hour')\\n hour_start = random_state.uniform(night_start.value,\\n night_start.value + night_duration.value,\\n len(obs_id))\\n hour_start = Quantity(hour_start, 'hour')\\n\\n # add night hour to integer part of MJD\\n time_start += hour_start\\n\\n if use_abs_time:\\n # show the observation times in UTC\\n time_start = Time(time_start.isot)\\n else:\\n # show the observation times in seconds after the reference\\n time_start = time_relative_to_ref(time_start, header)\\n # converting to quantity (better treatment of units)\\n time_start = Quantity(time_start.sec, 'second')\\n\\n obs_table['TSTART'] = time_start\\n\\n # stop time\\n # calculated as TSTART + ONTIME\\n if use_abs_time:\\n time_stop = Time(obs_table['TSTART'])\\n time_stop += TimeDelta(obs_table['ONTIME'])\\n else:\\n time_stop = TimeDelta(obs_table['TSTART'])\\n time_stop += TimeDelta(obs_table['ONTIME'])\\n # converting to quantity (better treatment of units)\\n time_stop = Quantity(time_stop.sec, 'second')\\n\\n obs_table['TSTOP'] = time_stop\\n\\n # az, alt\\n # random points in a portion of sphere; default: above 45 deg altitude\\n az, alt = sample_sphere(size=len(obs_id),\\n lon_range=az_range,\\n lat_range=alt_range,\\n random_state=random_state)\\n az = Angle(az, 'deg')\\n alt = Angle(alt, 'deg')\\n obs_table['AZ'] = az\\n obs_table['ALT'] = alt\\n\\n # RA, dec\\n # derive from az, alt taking into account that alt, az represent the values\\n # at the middle of the observation, i.e. at time_ref + (TIME_START + TIME_STOP)/2\\n # (or better: time_ref + TIME_START + (TIME_OBSERVATION/2))\\n # in use_abs_time mode, the time_ref should not be added, since it's already included\\n # in TIME_START and TIME_STOP\\n az = Angle(obs_table['AZ'])\\n alt = Angle(obs_table['ALT'])\\n if use_abs_time:\\n obstime = Time(obs_table['TSTART'])\\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\\n else:\\n obstime = time_ref_from_dict(obs_table.meta)\\n obstime += TimeDelta(obs_table['TSTART'])\\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\\n location = observatory_locations[observatory_name]\\n altaz_frame = AltAz(obstime=obstime, location=location)\\n alt_az_coord = SkyCoord(az, alt, frame=altaz_frame)\\n sky_coord = alt_az_coord.transform_to('icrs')\\n obs_table['RA'] = sky_coord.ra\\n obs_table['DEC'] = sky_coord.dec\\n\\n # positions\\n\\n # number of telescopes\\n # random integers in a specified range; default: between 3 and 4\\n n_tels = random_state.randint(n_tels_range[0], n_tels_range[1] + 1, len(obs_id))\\n obs_table['N_TELS'] = n_tels\\n\\n # muon efficiency\\n # random between 0.6 and 1.0\\n muoneff = random_state.uniform(low=0.6, high=1.0, size=len(obs_id))\\n obs_table['MUONEFF'] = muoneff\\n\\n return obs_table\",\n \"def test_cmd_roomstate(self, mock_gametime):\\n\\n mock_gametime.return_value = _get_timestamp(\\\"autumn\\\", \\\"afternoon\\\")\\n\\n # show existing room states (season/time doesn't count)\\n\\n self.assertEqual(self.room1.room_states, [])\\n\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"\\\",\\n f\\\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\\\n None\\\",\\n )\\n\\n # add room states\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"burning\\\",\\n \\\"Added room state 'burning' to this room.\\\",\\n )\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"windy\\\",\\n \\\"Added room state 'windy' to this room.\\\",\\n )\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"\\\",\\n f\\\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\\\n \\\"\\n \\\"'burning' and 'windy'\\\",\\n )\\n # toggle windy\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"windy\\\",\\n \\\"Cleared room state 'windy' from this room.\\\",\\n )\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"\\\",\\n f\\\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\\\n \\\"\\n \\\"'burning'\\\",\\n )\\n # add a autumn state and make sure we override it\\n self.room1.add_desc(\\\"Autumn description.\\\", room_state=\\\"autumn\\\")\\n self.room1.add_desc(\\\"Spring description.\\\", room_state=\\\"spring\\\")\\n\\n self.assertEqual(self.room1.get_stateful_desc(), \\\"Autumn description.\\\")\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"spring\\\",\\n \\\"Added room state 'spring' to this room.\\\",\\n )\\n self.assertEqual(self.room1.get_stateful_desc(), \\\"Spring description.\\\")\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def test_created_and_updated(self):\\n state1 = State()\\n created = state1.created_at\\n updated = state1.updated_at\\n state1.save()\\n self.assertFalse(updated == state1.updated_at)\\n self.assertTrue(created == state1.created_at)\",\n \"def test_output(self):\\n # reproducible arbitrariness\\n np.random.seed(123423)\\n\\n N = 20\\n tmax = 30.0\\n dt = 1.0\\n\\n n_steps = int_r(tmax/dt)\\n\\n table = np.random.randn(N, n_steps)\\n G = TableLayer(table)\\n\\n M = simulation.StateMonitor(G, 'out')\\n\\n sim = simulation.Simulation(G, M, dt=dt)\\n sim.run(tmax)\\n\\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)\",\n \"def test_custom_time(self):\\n interval = 0.5\\n M = simulation.StateMonitor(self.G, 'v', interval=interval)\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, interval)))\",\n \"def test_time(self):\\r\\n pass\",\n \"def test_past_event(self):\\n pass\",\n \"def test_get_current_state(self):\\n schedule = parser.parse_schedule(SCHEDULE)\\n self.assertEqual(\\\"a\\\", schedule.get_current_state(dt(monday, '09:30')))\\n self.assertEqual(\\\"c\\\", schedule.get_current_state(dt(monday, '08:30')))\\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\\n lookback=False))\",\n \"def test_is_approved_no_approval(time_record_factory):\\n record = time_record_factory()\\n\\n assert not record.is_approved\",\n \"def test_update_state1(self):\\n pass\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_booked_event_has_true_flag(self):\\r\\n user = ViewAfishaTests.mentor\\r\\n event = EventFactory(city=user.profile.city)\\r\\n EventParticipantFactory(\\r\\n event=event,\\r\\n user=user,\\r\\n )\\r\\n\\r\\n client = self.return_authorized_user_client(user)\\r\\n response_data = client.get(path=EVENTS_URL, format=\\\"json\\\").data\\r\\n\\r\\n results = response_data.get(\\\"results\\\")\\r\\n record_dict = results[0]\\r\\n\\r\\n self.assertEqual(\\r\\n \\\"True\\\",\\r\\n str(record_dict.get(\\\"booked\\\")),\\r\\n msg=(\\r\\n \\\"Проверьте, что у мероприятий на которые \\\"\\r\\n \\\"пользователь подписан возвращается флаг \\\"\\r\\n \\\"booked': True.\\\"\\r\\n ),\\r\\n )\",\n \"def event_m20_11_6000():\\n \\\"\\\"\\\"State 0,2: [Preset] Door of the living person_SubState\\\"\\\"\\\"\\n assert event_m20_11_x77(z42=20110480, z43=600000)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def test_get_habits_trackings_table():\\n habits_trackings_table = analytics.habits_trackings_table()\\n assert habits_trackings_table == [\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-25', '18:26'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-25', '19:52'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-26', '20:09'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-26', '04:40'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-27', '10:44'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-27', '20:49'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-28', '01:18'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-29', '19:42'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-29', '08:25'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-29', '08:57'),\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-29', '02:07'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-06-29', '04:46'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-30', '05:54'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-30', '12:04'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-01', '05:37'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-01', '07:17'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-01', '12:16'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-02', '22:04'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-02', '23:02'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-03', '07:12'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-04', '08:11'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-05', '10:58'),\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-05', '14:38'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-06', '00:14'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-06', '12:28'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-07', '17:07'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-07', '02:44'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-07', '16:09'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-08', '18:19'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-08', '22:35'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-09', '10:01'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-09', '10:14'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-10', '17:12'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-11', '19:54'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-12', '00:06'),\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-12', '16:12'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-12', '18:43'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-13', '08:02'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-14', '21:32'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-15', '08:37'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-16', '08:32'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-16', '11:59'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-16', '21:14'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-18', '03:03'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-18', '06:59'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-19', '22:50'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-19', '13:05'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-20', '04:59'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-20', '05:52'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-20', '09:00'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-21', '18:44'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-21', '03:55'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-22', '06:52'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-22', '18:27'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-23', '01:28'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-23', '12:17'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-24', '19:10'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-25', '02:18'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-26', '09:53'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-26', '16:59')\\n ]\",\n \"def test_workflows(database, monkeypatch):\\n monkeypatch.setattr('dataactcore.utils.sam_recipient.get_sam_props', mock_get_sam_props)\\n sess = database.session\\n all_duns = ['00000000{}'.format(x) for x in range(1, 5)]\\n existing_duns = all_duns[2:]\\n for duns in existing_duns:\\n sess.add(SAMRecipient(awardee_or_recipient_uniqu=duns))\\n sess.commit()\\n\\n recipient_file = os.path.join(CONFIG_BROKER['path'], 'tests', 'unit', 'data', 'historic_DUNS_export_small.csv')\\n\\n update_historical_duns.run_sam_batches(recipient_file, sess, block_size=1)\\n update_historical_duns.import_historic_recipients(sess)\\n\\n expected_results = {\\n '000000001': {\\n 'awardee_or_recipient_uniqu': '000000001',\\n 'uei': 'A1',\\n 'registration_date': '2004-04-01',\\n 'expiration_date': '2013-01-11',\\n 'last_sam_mod_date': '2013-01-11',\\n 'activation_date': '2012-01-11',\\n 'legal_business_name': 'TEST RECIPIENT 1',\\n 'address_line_1': 'Test address 1',\\n 'address_line_2': 'Test address 2',\\n 'city': 'Test city',\\n 'state': 'Test state',\\n 'zip': 'Test zip',\\n 'zip4': 'Test zip4',\\n 'country_code': 'Test country',\\n 'congressional_district': 'Test congressional district',\\n 'business_types_codes': ['A', 'B', 'C'],\\n 'business_types': ['Name A', 'Name B', 'Name C'],\\n 'dba_name': 'Name 1',\\n 'entity_structure': '1A',\\n 'ultimate_parent_unique_ide': '999999999',\\n 'ultimate_parent_uei': 'Z9',\\n 'ultimate_parent_legal_enti': 'Parent Legal Name 1',\\n 'high_comp_officer1_full_na': 'Test Exec 1',\\n 'high_comp_officer1_amount': '1',\\n 'high_comp_officer2_full_na': 'Test Exec 2',\\n 'high_comp_officer2_amount': '2',\\n 'high_comp_officer3_full_na': 'Test Exec 3',\\n 'high_comp_officer3_amount': '3',\\n 'high_comp_officer4_full_na': 'Test Exec 4',\\n 'high_comp_officer4_amount': '4',\\n 'high_comp_officer5_full_na': 'Test Exec 5',\\n 'high_comp_officer5_amount': '5'\\n },\\n '000000002': {\\n 'awardee_or_recipient_uniqu': '000000002',\\n 'uei': 'B2',\\n 'registration_date': '2004-04-02',\\n 'expiration_date': '2013-01-12',\\n 'last_sam_mod_date': '2013-01-12',\\n 'activation_date': '2012-01-12',\\n 'legal_business_name': 'TEST RECIPIENT 2',\\n 'address_line_1': 'Other Test address 1',\\n 'address_line_2': 'Other Test address 2',\\n 'city': 'Other Test city',\\n 'state': 'Other Test state',\\n 'zip': 'Other Test zip',\\n 'zip4': 'Other Test zip4',\\n 'country_code': 'Other Test country',\\n 'congressional_district': 'Other Test congressional district',\\n 'business_types_codes': ['D', 'E', 'F'],\\n 'business_types': ['Name D', 'Name E', 'Name F'],\\n 'dba_name': 'Name 2',\\n 'entity_structure': '2B',\\n 'ultimate_parent_unique_ide': '999999998',\\n 'ultimate_parent_uei': 'Y8',\\n 'ultimate_parent_legal_enti': 'Parent Legal Name 2',\\n 'high_comp_officer1_full_na': 'Test Other Exec 6',\\n 'high_comp_officer1_amount': '6',\\n 'high_comp_officer2_full_na': 'Test Other Exec 7',\\n 'high_comp_officer2_amount': '7',\\n 'high_comp_officer3_full_na': 'Test Other Exec 8',\\n 'high_comp_officer3_amount': '8',\\n 'high_comp_officer4_full_na': 'Test Other Exec 9',\\n 'high_comp_officer4_amount': '9',\\n 'high_comp_officer5_full_na': 'Test Other Exec 10',\\n 'high_comp_officer5_amount': '10'\\n },\\n '000000003': {\\n 'awardee_or_recipient_uniqu': '000000003',\\n 'uei': None,\\n 'registration_date': None,\\n 'expiration_date': None,\\n 'last_sam_mod_date': None,\\n 'activation_date': None,\\n 'legal_business_name': None,\\n 'address_line_1': None,\\n 'address_line_2': None,\\n 'city': None,\\n 'state': None,\\n 'zip': None,\\n 'zip4': None,\\n 'country_code': None,\\n 'congressional_district': None,\\n 'business_types_codes': None,\\n 'business_types': None,\\n 'dba_name': None,\\n 'entity_structure': None,\\n 'ultimate_parent_unique_ide': None,\\n 'ultimate_parent_uei': None,\\n 'ultimate_parent_legal_enti': None,\\n 'high_comp_officer1_full_na': None,\\n 'high_comp_officer1_amount': None,\\n 'high_comp_officer2_full_na': None,\\n 'high_comp_officer2_amount': None,\\n 'high_comp_officer3_full_na': None,\\n 'high_comp_officer3_amount': None,\\n 'high_comp_officer4_full_na': None,\\n 'high_comp_officer4_amount': None,\\n 'high_comp_officer5_full_na': None,\\n 'high_comp_officer5_amount': None\\n },\\n '000000004': {\\n 'awardee_or_recipient_uniqu': '000000004',\\n 'uei': None,\\n 'registration_date': None,\\n 'expiration_date': None,\\n 'last_sam_mod_date': None,\\n 'activation_date': None,\\n 'legal_business_name': None,\\n 'address_line_1': None,\\n 'address_line_2': None,\\n 'city': None,\\n 'state': None,\\n 'zip': None,\\n 'zip4': None,\\n 'country_code': None,\\n 'congressional_district': None,\\n 'business_types_codes': None,\\n 'business_types': None,\\n 'dba_name': None,\\n 'entity_structure': None,\\n 'ultimate_parent_unique_ide': None,\\n 'ultimate_parent_uei': None,\\n 'ultimate_parent_legal_enti': None,\\n 'high_comp_officer1_full_na': None,\\n 'high_comp_officer1_amount': None,\\n 'high_comp_officer2_full_na': None,\\n 'high_comp_officer2_amount': None,\\n 'high_comp_officer3_full_na': None,\\n 'high_comp_officer3_amount': None,\\n 'high_comp_officer4_full_na': None,\\n 'high_comp_officer4_amount': None,\\n 'high_comp_officer5_full_na': None,\\n 'high_comp_officer5_amount': None\\n }\\n }\\n results = {}\\n for recp_obj in sess.query(SAMRecipient).all():\\n results[recp_obj.awardee_or_recipient_uniqu] = {\\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\\n 'uei': recp_obj.uei,\\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\\n 'legal_business_name': recp_obj.legal_business_name,\\n 'address_line_1': recp_obj.address_line_1,\\n 'address_line_2': recp_obj.address_line_2,\\n 'city': recp_obj.city,\\n 'state': recp_obj.state,\\n 'zip': recp_obj.zip,\\n 'zip4': recp_obj.zip4,\\n 'country_code': recp_obj.country_code,\\n 'congressional_district': recp_obj.congressional_district,\\n 'business_types_codes': recp_obj.business_types_codes,\\n 'business_types': recp_obj.business_types,\\n 'dba_name': recp_obj.dba_name,\\n 'entity_structure': recp_obj.entity_structure,\\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\\n }\\n assert results == expected_results\\n\\n # Test to see if truncating the SAM table while keeping the historic reuploads the historic values\\n sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).delete(synchronize_session=False)\\n\\n # Make sure all the historic recipients are removed from the SAMRecipient table\\n assert sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).all() == []\\n\\n # Redo script but don't go through run_sam_batches\\n update_historical_duns.clean_historic_recipients(sess)\\n update_historical_duns.import_historic_recipients(sess)\\n\\n results = {}\\n for recp_obj in sess.query(SAMRecipient).all():\\n results[recp_obj.awardee_or_recipient_uniqu] = {\\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\\n 'uei': recp_obj.uei,\\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\\n 'legal_business_name': recp_obj.legal_business_name,\\n 'address_line_1': recp_obj.address_line_1,\\n 'address_line_2': recp_obj.address_line_2,\\n 'city': recp_obj.city,\\n 'state': recp_obj.state,\\n 'zip': recp_obj.zip,\\n 'zip4': recp_obj.zip4,\\n 'country_code': recp_obj.country_code,\\n 'congressional_district': recp_obj.congressional_district,\\n 'business_types_codes': recp_obj.business_types_codes,\\n 'business_types': recp_obj.business_types,\\n 'dba_name': recp_obj.dba_name,\\n 'entity_structure': recp_obj.entity_structure,\\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\\n }\\n assert results == expected_results\",\n \"def test_full_house_flush_ind(self):\",\n \"def test_time(self):\\n conn = self.database.connection()\\n cursor = conn.cursor()\\n dialect = self.database.dialect()\\n dbapi = self.database.dbapi()\\n query = dialect.translate('DROP TABLE test_time')\\n try:\\n cursor.execute(query)\\n except dbapi.Error:\\n conn.rollback()\\n query = dialect.translate('CREATE TABLE test_time ' \\\\\\n '( value TIME NOT NULL )')\\n cursor.execute(query)\\n data = []\\n rr = random.randrange\\n query = 'INSERT INTO test_time VALUES (%s)'\\n for i in range(100):\\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\\n data.append(item)\\n cursor.execute(query, (item,))\\n query = 'SELECT * FROM test_time'\\n cursor.execute(query)\\n result = cursor.fetchall()\\n for row in result:\\n item = row[0]\\n assert isinstance(item, datetime.time)\\n assert item in data\\n data.remove(item)\\n query = dialect.translate('DELETE FROM test_time')\\n cursor.execute(query)\\n query = dialect.translate('DROP TABLE test_time')\\n cursor.execute(query)\\n conn.commit()\",\n \"def test_state(self):\\n states = self.geographies.find({ 'geoid': config.SUMLEV_STATE })\\n\\n self.assertEqual(states.count(), 1)\\n\\n state = states[0]\\n\\n self.assertEqual(state['sumlev'], config.SUMLEV_STATE)\\n self.assertEqual(state['metadata']['NAME'], 'Hawaii')\\n self.assertEqual(state['metadata']['STATE'], '15')\\n\\n pop_2000 = 1211537\\n pop_2010 = 1360301\\n self._test_totalpop(state, pop_2000, pop_2010)\",\n \"def test_get_unsuccessful_ta():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\\n\\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\\n assert np.isnan(list_failed[0])\",\n \"def test_TP_twophase(self):\\n with pytest.raises(StateError):\\n State(substance=\\\"water\\\", T=Q_(373.1242958476844, \\\"K\\\"), p=Q_(101325.0, \\\"Pa\\\"))\",\n \"def test_get_state(self):\\n\\n # test that you can get a state by numerical id\\n mdp = MDP()\\n mdp.add_state(0)\\n self.assertEquals(type(mdp.get_state(0)), State)\\n self.assertIn(mdp.get_state(0), mdp.get_state_list())\",\n \"def test_updatePlayerbHist_fold_1(self):\\n self.assertEqual(self.player.bHist[2], [cardutils.BETSTRING_DICT['FOLD']])\",\n \"def test_match_table_post(self):\\n pass\",\n \"def test_updatePlayerbHist_multiactions_1(self):\\n self.assertEqual(self.player.bHist[0][0], cardutils.BETSTRING_DICT['RAISE'])\",\n \"def test_get_state_comparison_stats_foreign_born(self):\\n\\n key = '% Foreign-born employees'\\n\\n us = CountryFactory(code2='US')\\n germany = CountryFactory(name='Germany')\\n\\n # Mississippi: 0 employees born in Germany, 3 in US\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 2 employees born in Germany, 2 in US\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 3 employees born in Germany, 1 employee in US, 3 unknown\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n expected_output = [('Kentucky', 75), ('Texas', 50)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(top_states, expected_output,\\n \\\"'{key}' should contain states with the top x % foreign-born employees\\\")\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_get_state_list(self):\\n\\n mdp = MDP(5)\\n state_list = mdp.get_state_list()\\n\\n self.assertEqual(len(state_list), 5)\\n self.assertIn(mdp.get_state(0), state_list)\",\n \"def test_method(self):\\n self.amenity.save()\\n self.assertTrue(hasattr(self.amenity, \\\"updated_at\\\"))\",\n \"def test_analyze_time(self):\\n self.ph5validate.analyze_time()\\n self.assertEqual(self.ph5validate.das_time.keys(), [('12183', 1, 500)])\\n Dtime = self.ph5validate.das_time[('12183', 1, 500)]\\n\\n # 3 different deploy time\\n self.assertEqual(len(Dtime['time_windows']), 5)\\n\\n # station 9001\\n self.assertEqual(Dtime['time_windows'][0],\\n (1550849950, 1550850034, '9001'))\\n self.assertEqual(Dtime['time_windows'][1],\\n (1550849950, 1550850034, '9001'))\\n self.assertEqual(Dtime['time_windows'][2],\\n (1550849950, 1550850034, '9001'))\\n # station 9002\\n self.assertEqual(Dtime['time_windows'][3],\\n (1550850043, 1550850093, '9002'))\\n # station 9003\\n self.assertEqual(Dtime['time_windows'][4],\\n (1550850125, 1550850187, '9003'))\\n\\n self.assertEqual(Dtime['min_deploy_time'],\\n [1550849950,\\n 'Data exists before deploy time: 7 seconds.'])\",\n \"def test_from_event_to_delete_state() -> None:\\n event = ha.Event(\\n EVENT_STATE_CHANGED,\\n {\\n \\\"entity_id\\\": \\\"sensor.temperature\\\",\\n \\\"old_state\\\": ha.State(\\\"sensor.temperature\\\", \\\"18\\\"),\\n \\\"new_state\\\": None,\\n },\\n )\\n db_state = States.from_event(event)\\n\\n assert db_state.entity_id == \\\"sensor.temperature\\\"\\n assert db_state.state == \\\"\\\"\\n assert db_state.last_changed_ts is None\\n assert db_state.last_updated_ts == event.time_fired.timestamp()\",\n \"def test_eval_alarm(self):\\n def get_state_update_value(h):\\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\\"\\n oldstate = h.data['oldState']['stateValue']\\n newstate = h.data['newState']['stateValue']\\n querydate = h.data['newState']['stateReasonData']['queryDate']\\n querydate = utils.parse_strtime(querydate)\\n return oldstate, newstate, querydate \\n \\n test_uuid = str(uuid.uuid4())\\n alarmname = \\\"TestEvalAlarm_\\\" + test_uuid\\n metricname = \\\"TestEvalMetric_\\\" + test_uuid\\n namespace = \\\"unittest\\\"\\n unit = \\\"Percent\\\"\\n dimensions = {\\\"test_id\\\":test_uuid}\\n threshold = 2.0\\n \\n # create metric alarm\\n alarm = MetricAlarm(name=alarmname, metric=metricname,\\n namespace=namespace, statistic=\\\"Average\\\",\\n comparison=\\\">\\\", threshold=threshold,\\n period=60, evaluation_periods=1, unit=unit,\\n dimensions=dimensions)\\n self.synaps.put_metric_alarm(alarm)\\n \\n # due to put_metric_alarm is asynchronous\\n time.sleep(ASYNC_WAIT)\\n \\n alarm_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0)\\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold + 1, timestamp=alarm_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n\\n ok_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0) \\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold - 2, timestamp=ok_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n \\n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\\n history_item_type=\\\"StateUpdate\\\")\\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\\n\\n result = map(get_state_update_value, histories)\\n \\n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\\n ('ALARM', 'INSUFFICIENT_DATA', None),\\n ('INSUFFICIENT_DATA', 'OK', ok_time),\\n ('OK', 'INSUFFICIENT_DATA', None))\\n \\n failmsg = \\\"expected: %s real: %s\\\" % (expected, result)\\n \\n self.assertEqual(len(result), len(expected), msg=failmsg)\\n \\n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\\n expected):\\n self.assertEqual(r_new, e_new, msg=failmsg)\\n self.assertEqual(r_old, e_old, msg=failmsg)\\n if e_time:\\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\\n msg=failmsg)\\n \\n self.synaps.delete_alarms(alarms=[alarmname])\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"async def test_lazy_state_handles_same_last_updated_and_last_changed(\\n caplog: pytest.LogCaptureFixture,\\n) -> None:\\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n row = PropertyMock(\\n entity_id=\\\"sensor.valid\\\",\\n state=\\\"off\\\",\\n attributes='{\\\"shared\\\":true}',\\n last_updated_ts=now.timestamp(),\\n last_changed_ts=now.timestamp(),\\n )\\n lstate = LazyState(\\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\\n )\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n assert lstate.last_updated.timestamp() == row.last_updated_ts\\n assert lstate.last_changed.timestamp() == row.last_changed_ts\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def test_states_repr_without_timestamp() -> None:\\n fixed_time = datetime(2016, 7, 9, 11, 0, 0, tzinfo=dt_util.UTC, microsecond=432432)\\n states = States(\\n entity_id=\\\"sensor.temp\\\",\\n attributes=None,\\n context_id=None,\\n context_user_id=None,\\n context_parent_id=None,\\n origin_idx=None,\\n last_updated=fixed_time,\\n last_changed=fixed_time,\\n last_updated_ts=None,\\n last_changed_ts=None,\\n )\\n assert \\\"2016-07-09 11:00:00+00:00\\\" in repr(states)\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_first_timestep(self):\\n m = build_model({}, \\\"simple_supply,two_hours,investment_costs\\\")\\n m.run(build_only=True)\\n timestep_0 = \\\"2005-01-01 00:00\\\"\\n assert m._backend_model.timesteps.ord(timestep_0) == 1\",\n \"def test_get_times_height():\\n times = get_times(date, lng, lat, height)\\n for key, value in heightTestTimes.items():\\n assert times[key].strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") == value\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_multiple_states(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory()\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Cancel when created.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.CANCEL, event)\\n s1 = context.work_status.value\\n self.assertEqual(s1, s.CANCELLED)\\n\\n # Cancel when started.\\n c2 = model.factory(initial_state=s.STARTED)\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(c2, a.CANCEL, event)\\n s2 = c2.work_status.value\\n self.assertEqual(s2, s.CANCELLED)\",\n \"def test_from_event_to_db_state_attributes() -> None:\\n attrs = {\\\"this_attr\\\": True}\\n state = ha.State(\\\"sensor.temperature\\\", \\\"18\\\", attrs)\\n event = ha.Event(\\n EVENT_STATE_CHANGED,\\n {\\\"entity_id\\\": \\\"sensor.temperature\\\", \\\"old_state\\\": None, \\\"new_state\\\": state},\\n context=state.context,\\n )\\n db_attrs = StateAttributes()\\n dialect = SupportedDialect.MYSQL\\n\\n db_attrs.shared_attrs = StateAttributes.shared_attrs_bytes_from_event(\\n event, {}, {}, dialect\\n )\\n assert db_attrs.to_native() == attrs\",\n \"def test_status(self):\\n keys = [\\n 'receiver time mark count', 'ant type base', 'solution status',\\n ...\\n ]\\n\\n for i in xrange(0, 100):\\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)\",\n \"def test_negative_is_active_of_homework():\\n assert not expired_hw.is_active()\",\n \"def test_state(self, mock_req):\\n self.setup_api(MOCK_DATA, mock_req)\\n now = datetime(1970, month=1, day=1)\\n with patch(\\\"homeassistant.util.dt.now\\\", return_value=now):\\n for name in self.sensor_dict:\\n sensor = self.sensor_dict[name][\\\"sensor\\\"]\\n self.fake_delay(2)\\n sensor.update()\\n if name == google_wifi.ATTR_LAST_RESTART:\\n assert \\\"1969-12-31 00:00:00\\\" == sensor.state\\n elif name == google_wifi.ATTR_UPTIME:\\n assert 1 == sensor.state\\n elif name == google_wifi.ATTR_STATUS:\\n assert \\\"Online\\\" == sensor.state\\n else:\\n assert \\\"initial\\\" == sensor.state\",\n \"def test_multiple_rules(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory(initial_state=s.STARTED)\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Complete -> completed.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.COMPLETE, event)\\n s1 = context.work_status.value\\n context_id = context.id\\n\\n self.assertEqual(s1, s.COMPLETED)\\n\\n # Complete -> absolutely completed.\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.COMPLETE, event)\\n s2 = context.work_status.value\\n self.assertEqual(s2, s.ABSOLUTELY_COMPLETED)\\n\\n # Complete -> ... the same state ...\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n context = model.Model.query.get(context_id)\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(context, a.COMPLETE, event)\\n s3 = context.work_status.value\\n self.assertEqual(s3, s.ABSOLUTELY_COMPLETED)\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_170518_bad_dbtime(self):\\n spc = parser(get_file('PTSDY1_baddbtime.txt'))\\n answer = utc(2017, 5, 1, 12, 0)\\n for _, outlook in spc.outlook_collections.items():\\n self.assertEqual(outlook.expire, answer)\",\n \"def test_is(self):\\n invalid = self.TDTT()\\n self.check_invalid_is(invalid)\\n\\n valid = self.TDTT(when=self.txt_when)\\n self.check_valid_is(valid)\",\n \"def test_time_status(self):\\n result = self.test_client.time_status\\n\\n assert result == \\\"12312\\\"\",\n \"def test_of_agreement(self): \\n \\n pop_t = world.pop_t \\n pop_sub = world.pop_sub \\n \\n \\n Ns = world.subtime.length\\n \\n dt = world.time.step\\n ds = world.subtime.step\\n N = round(ds/dt)\\n for i in range(Ns):\\n numpy.testing.assert_allclose(pop_sub[:,i],pop_t[i*N,:])\",\n \"def test_from_event_to_db_state() -> None:\\n state = ha.State(\\n \\\"sensor.temperature\\\",\\n \\\"18\\\",\\n context=ha.Context(\\n id=\\\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\\\",\\n parent_id=\\\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\\\",\\n user_id=\\\"12345678901234567890123456789012\\\",\\n ),\\n )\\n event = ha.Event(\\n EVENT_STATE_CHANGED,\\n {\\\"entity_id\\\": \\\"sensor.temperature\\\", \\\"old_state\\\": None, \\\"new_state\\\": state},\\n context=state.context,\\n )\\n assert state.as_dict() == States.from_event(event).to_native().as_dict()\",\n \"def test_has_ended(self):\\r\\n self.assertTrue(self.past_show_certs.has_ended())\\r\\n self.assertTrue(self.past_noshow_certs.has_ended())\\r\\n self.assertFalse(self.future_show_certs.has_ended())\\r\\n self.assertFalse(self.future_noshow_certs.has_ended())\",\n \"async def test_lazy_state_handles_different_last_updated_and_last_changed(\\n caplog: pytest.LogCaptureFixture,\\n) -> None:\\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n row = PropertyMock(\\n entity_id=\\\"sensor.valid\\\",\\n state=\\\"off\\\",\\n attributes='{\\\"shared\\\":true}',\\n last_updated_ts=now.timestamp(),\\n last_changed_ts=(now - timedelta(seconds=60)).timestamp(),\\n )\\n lstate = LazyState(\\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\\n )\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:03:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n assert lstate.last_updated.timestamp() == row.last_updated_ts\\n assert lstate.last_changed.timestamp() == row.last_changed_ts\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:03:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\",\n \"async def test_state_update(hass):\\n assert await setup_multimatic(hass)\\n assert_entities_count(hass, 11)\\n\\n assert hass.states.is_state(\\\"binary_sensor.dhw_circulation\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.room_1_window\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_lock\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_battery\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.boiler\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_connectivity\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_update\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_online\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_holiday\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_errors\\\", \\\"off\\\")\\n state = hass.states.get(\\\"binary_sensor.multimatic_holiday\\\")\\n assert state.attributes.get(\\\"start_date\\\") is None\\n assert state.attributes.get(\\\"end_date\\\") is None\\n assert state.attributes.get(\\\"temperature\\\") is None\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_quick_mode\\\", \\\"off\\\")\\n\\n dhw = SystemManagerMock.data[\\\"get_dhw\\\"]\\n dhw.circulation.time_program = time_program(SettingModes.ON, None)\\n dhw.circulation.operating_mode = OperatingModes.AUTO\\n\\n hvac_status = SystemManagerMock.data[\\\"get_hvac_status\\\"]\\n hvac_status.boiler_status.status_code = \\\"F11\\\"\\n hvac_status.online = \\\"OFFLINE\\\"\\n hvac_status.update = \\\"UPDATE_PENDING\\\"\\n hvac_status.errors = [\\n Error(\\\"device\\\", \\\"title\\\", \\\"status_code\\\", \\\"descr\\\", datetime.datetime.now())\\n ]\\n\\n rooms = SystemManagerMock.data[\\\"get_rooms\\\"]\\n rooms[0].devices = [Device(\\\"Device 1\\\", \\\"123456789\\\", \\\"VALVE\\\", True, True)]\\n rooms[0].time_program = time_program(None, 20)\\n rooms[0].temperature = 22\\n rooms[0].target_high = 24\\n rooms[0].operating_mode = OperatingModes.AUTO\\n rooms[0].child_lock = True\\n rooms[0].window_open = True\\n\\n new_holiday_mode = active_holiday_mode()\\n SystemManagerMock.data[\\\"get_holiday_mode\\\"] = new_holiday_mode\\n SystemManagerMock.data[\\\"get_quick_mode\\\"] = QuickModes.HOTWATER_BOOST\\n\\n await goto_future(hass)\\n\\n assert_entities_count(hass, 11)\\n assert hass.states.is_state(\\\"binary_sensor.room_1_window\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_lock\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_battery\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.boiler\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_connectivity\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_update\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_online\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_errors\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_holiday\\\", \\\"on\\\")\\n state = hass.states.get(\\\"binary_sensor.multimatic_holiday\\\")\\n assert state.attributes[\\\"start_date\\\"] == new_holiday_mode.start_date.isoformat()\\n assert state.attributes[\\\"end_date\\\"] == new_holiday_mode.end_date.isoformat()\\n assert state.attributes[\\\"temperature\\\"] == new_holiday_mode.target\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_quick_mode\\\", \\\"on\\\")\\n state = hass.states.get(\\\"binary_sensor.multimatic_quick_mode\\\")\\n assert state.attributes[\\\"quick_mode\\\"] == QuickModes.HOTWATER_BOOST.name\\n assert hass.states.is_state(\\\"binary_sensor.dhw_circulation\\\", \\\"off\\\")\\n\\n SystemManagerMock.data[\\\"get_holiday_mode\\\"] = HolidayMode(False)\\n\\n await goto_future(hass)\\n assert hass.states.is_state(\\\"binary_sensor.dhw_circulation\\\", \\\"on\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6052409","0.60238993","0.60199136","0.5990552","0.59654564","0.59051305","0.58771414","0.587141","0.5824163","0.57776195","0.575167","0.57320946","0.57115203","0.57089746","0.57080287","0.56987756","0.5684115","0.5668643","0.564905","0.56462383","0.5624186","0.5575393","0.55537695","0.5505268","0.54985476","0.5489427","0.54696864","0.5461627","0.54490864","0.5433699","0.54332614","0.5432792","0.54217726","0.5421053","0.541184","0.5398063","0.5397379","0.5394865","0.5380041","0.53692317","0.5357282","0.5353086","0.53493005","0.5348852","0.53447056","0.5344","0.53282106","0.5326239","0.53258187","0.53212637","0.5320721","0.53188753","0.5315504","0.53040284","0.5302646","0.53017604","0.52859455","0.5279591","0.5279202","0.52753556","0.52717054","0.52710325","0.52511185","0.52232003","0.5219207","0.5218135","0.52121073","0.5209475","0.52078134","0.52025634","0.51971936","0.51828575","0.5172181","0.51594514","0.5151996","0.5145199","0.5135852","0.5133792","0.51330495","0.513123","0.5131125","0.5112613","0.5095338","0.5092126","0.5089274","0.5083183","0.5082421","0.50781256","0.5072881","0.50701374","0.5066181","0.50627196","0.5062379","0.50614965","0.50595814","0.50583947","0.5057267","0.5055498","0.505193","0.50479084"],"string":"[\n \"0.6052409\",\n \"0.60238993\",\n \"0.60199136\",\n \"0.5990552\",\n \"0.59654564\",\n \"0.59051305\",\n \"0.58771414\",\n \"0.587141\",\n \"0.5824163\",\n \"0.57776195\",\n \"0.575167\",\n \"0.57320946\",\n \"0.57115203\",\n \"0.57089746\",\n \"0.57080287\",\n \"0.56987756\",\n \"0.5684115\",\n \"0.5668643\",\n \"0.564905\",\n \"0.56462383\",\n \"0.5624186\",\n \"0.5575393\",\n \"0.55537695\",\n \"0.5505268\",\n \"0.54985476\",\n \"0.5489427\",\n \"0.54696864\",\n \"0.5461627\",\n \"0.54490864\",\n \"0.5433699\",\n \"0.54332614\",\n \"0.5432792\",\n \"0.54217726\",\n \"0.5421053\",\n \"0.541184\",\n \"0.5398063\",\n \"0.5397379\",\n \"0.5394865\",\n \"0.5380041\",\n \"0.53692317\",\n \"0.5357282\",\n \"0.5353086\",\n \"0.53493005\",\n \"0.5348852\",\n \"0.53447056\",\n \"0.5344\",\n \"0.53282106\",\n \"0.5326239\",\n \"0.53258187\",\n \"0.53212637\",\n \"0.5320721\",\n \"0.53188753\",\n \"0.5315504\",\n \"0.53040284\",\n \"0.5302646\",\n \"0.53017604\",\n \"0.52859455\",\n \"0.5279591\",\n \"0.5279202\",\n \"0.52753556\",\n \"0.52717054\",\n \"0.52710325\",\n \"0.52511185\",\n \"0.52232003\",\n \"0.5219207\",\n \"0.5218135\",\n \"0.52121073\",\n \"0.5209475\",\n \"0.52078134\",\n \"0.52025634\",\n \"0.51971936\",\n \"0.51828575\",\n \"0.5172181\",\n \"0.51594514\",\n \"0.5151996\",\n \"0.5145199\",\n \"0.5135852\",\n \"0.5133792\",\n \"0.51330495\",\n \"0.513123\",\n \"0.5131125\",\n \"0.5112613\",\n \"0.5095338\",\n \"0.5092126\",\n \"0.5089274\",\n \"0.5083183\",\n \"0.5082421\",\n \"0.50781256\",\n \"0.5072881\",\n \"0.50701374\",\n \"0.5066181\",\n \"0.50627196\",\n \"0.5062379\",\n \"0.50614965\",\n \"0.50595814\",\n \"0.50583947\",\n \"0.5057267\",\n \"0.5055498\",\n \"0.505193\",\n \"0.50479084\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68013006"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17468,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_EMBRYO state"},"document":{"kind":"string","value":"def test_duplicatekeyerror_state_embryo(self):\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_retrieve_instances_schedule_state(self):\n pass","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_table_false_positives(self):\n pass","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_time_type_state_types(day):\n\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}","def test_time_type_state_is_afternoon(day):\n\n assert day_time_info(day.hours_0).is_afternoon is False\n assert day_time_info(day.hours_1).is_afternoon is False\n assert day_time_info(day.hours_2).is_afternoon is False\n assert day_time_info(day.hours_3).is_afternoon is False\n assert day_time_info(day.hours_4).is_afternoon is False\n assert day_time_info(day.hours_5).is_afternoon is False\n assert day_time_info(day.hours_6).is_afternoon is False\n assert day_time_info(day.hours_7).is_afternoon is False\n assert day_time_info(day.hours_8).is_afternoon is False\n assert day_time_info(day.hours_9).is_afternoon is False\n assert day_time_info(day.hours_10).is_afternoon is False\n assert day_time_info(day.hours_11).is_afternoon is False\n assert day_time_info(day.hours_12).is_afternoon is False\n assert day_time_info(day.hours_13).is_afternoon is True\n assert day_time_info(day.hours_14).is_afternoon is True\n assert day_time_info(day.hours_15).is_afternoon is True\n assert day_time_info(day.hours_16).is_afternoon is True\n assert day_time_info(day.hours_17).is_afternoon is True\n assert day_time_info(day.hours_18).is_afternoon is False\n assert day_time_info(day.hours_19).is_afternoon is False\n assert day_time_info(day.hours_20).is_afternoon is False\n assert day_time_info(day.hours_21).is_afternoon is False\n assert day_time_info(day.hours_22).is_afternoon is False\n assert day_time_info(day.hours_23).is_afternoon is False","def test_time_type_state_is_evening(day):\n\n assert day_time_info(day.hours_0).is_evening is False\n assert day_time_info(day.hours_1).is_evening is False\n assert day_time_info(day.hours_2).is_evening is False\n assert day_time_info(day.hours_3).is_evening is False\n assert day_time_info(day.hours_4).is_evening is False\n assert day_time_info(day.hours_5).is_evening is False\n assert day_time_info(day.hours_6).is_evening is False\n assert day_time_info(day.hours_7).is_evening is False\n assert day_time_info(day.hours_8).is_evening is False\n assert day_time_info(day.hours_9).is_evening is False\n assert day_time_info(day.hours_10).is_evening is False\n assert day_time_info(day.hours_11).is_evening is False\n assert day_time_info(day.hours_12).is_evening is False\n assert day_time_info(day.hours_13).is_evening is False\n assert day_time_info(day.hours_14).is_evening is False\n assert day_time_info(day.hours_15).is_evening is False\n assert day_time_info(day.hours_16).is_evening is False\n assert day_time_info(day.hours_17).is_evening is False\n assert day_time_info(day.hours_18).is_evening is True\n assert day_time_info(day.hours_19).is_evening is True\n assert day_time_info(day.hours_20).is_evening is True\n assert day_time_info(day.hours_21).is_evening is True\n assert day_time_info(day.hours_22).is_evening is True\n assert day_time_info(day.hours_23).is_evening is False","def test_exp_by_states(self):\n api = my_mock.api_mock({\"items\": [{'state': 'Waiting', 'id': 10134},\n {'state': 'Waiting', 'id': 10135},\n {'state': 'Running', 'id': 10130}]})\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\n self.assertEquals(\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\n my_mock.api_mock_stop()","def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_get_state_comparison_stats_ailments(self):\n\n # AilmentType and Ailment breakdown per AilmentType should be in returned stats if present\n ailment_type_headache = AilmentTypeFactory(name='Headache')\n ailment_migraine_headache = AilmentFactory(name='Migraine Headache', type=ailment_type_headache)\n ailment_tension_headache = AilmentFactory(name='Tension Headache', type=ailment_type_headache)\n ailment_type_sprain_or_bruise = AilmentTypeFactory(name='Sprain or Bruise')\n ailment_sprain = AilmentFactory(name='Sprain', type=ailment_type_sprain_or_bruise)\n\n # Texas: 2 with sprain and 2 with nothing\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_sprain)\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 1 with migraine headache, 1 with sprain, 1 with tension headache, and 1 with nothing\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_migraine_headache)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_sprain)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_tension_headache)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n # Mississippi: 2 with migraine headache, 2 with tension headache\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_migraine_headache)\n employee.bureau_states.add(self.mississippi)\n for _ in range(2):\n employee = EmployeeFactory()\n employee.ailments.add(ailment_tension_headache)\n employee.bureau_states.add(self.mississippi)\n\n stats = get_state_comparison_stats(number=2)\n\n # With ailment types, ailments should be grouped together\n key = '% With Headache'\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with headache\")\n\n key = '% With Sprain or Bruise'\n expected_output = [('Texas', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with sprain or bruise\"\n )\n\n # Ailments should be treated individually\n key = '% With Migraine Headache'\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with migraine headache\"\n )\n\n key = '% With Tension Headache'\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\n top_states = self.get_state_stats_for_key(stats, key)\n self.assertListEqual(\n top_states, expected_output,\n f\"'{key}' should contain states with the top x % of employees with tension headache\"\n )\n\n # There should be no breakdown for sprain, because it's the only ailment of that type\n key = '% With Sprain'\n self.assertEqual(len([item for item in stats if key in item]), 0,\n \"There should be no breakdown for an ailment if it's the only one of its type\")","def test_time_type_state_is_midmorning(day):\n\n assert day_time_info(day.hours_0).is_midmorning is False\n assert day_time_info(day.hours_1).is_midmorning is False\n assert day_time_info(day.hours_2).is_midmorning is False\n assert day_time_info(day.hours_3).is_midmorning is False\n assert day_time_info(day.hours_4).is_midmorning is False\n assert day_time_info(day.hours_5).is_midmorning is False\n assert day_time_info(day.hours_6).is_midmorning is False\n assert day_time_info(day.hours_7).is_midmorning is False\n assert day_time_info(day.hours_8).is_midmorning is False\n assert day_time_info(day.hours_9).is_midmorning is False\n assert day_time_info(day.hours_10).is_midmorning is True\n assert day_time_info(day.hours_11).is_midmorning is True\n assert day_time_info(day.hours_12).is_midmorning is False\n assert day_time_info(day.hours_13).is_midmorning is False\n assert day_time_info(day.hours_14).is_midmorning is False\n assert day_time_info(day.hours_15).is_midmorning is False\n assert day_time_info(day.hours_16).is_midmorning is False\n assert day_time_info(day.hours_17).is_midmorning is False\n assert day_time_info(day.hours_18).is_midmorning is False\n assert day_time_info(day.hours_19).is_midmorning is False\n assert day_time_info(day.hours_20).is_midmorning is False\n assert day_time_info(day.hours_21).is_midmorning is False\n assert day_time_info(day.hours_22).is_midmorning is False\n assert day_time_info(day.hours_23).is_midmorning is False","def test_issue_tracked_times(self):\n pass","def test_episode_data(self):\n self.assertEquals(\n self.t['lost']['firstaired'],\n '2004-09-22'\n )","def test_update_team_state(self):\n pass","def test_update_instances_schedule_state(self):\n pass","def test_time_type_state_is_morning(day):\n\n assert day_time_info(day.hours_0).is_morning is False\n assert day_time_info(day.hours_1).is_morning is False\n assert day_time_info(day.hours_2).is_morning is False\n assert day_time_info(day.hours_3).is_morning is False\n assert day_time_info(day.hours_4).is_morning is False\n assert day_time_info(day.hours_5).is_morning is True\n assert day_time_info(day.hours_6).is_morning is True\n assert day_time_info(day.hours_7).is_morning is True\n assert day_time_info(day.hours_8).is_morning is True\n assert day_time_info(day.hours_9).is_morning is True\n assert day_time_info(day.hours_10).is_morning is False\n assert day_time_info(day.hours_11).is_morning is False\n assert day_time_info(day.hours_12).is_morning is False\n assert day_time_info(day.hours_13).is_morning is False\n assert day_time_info(day.hours_14).is_morning is False\n assert day_time_info(day.hours_15).is_morning is False\n assert day_time_info(day.hours_16).is_morning is False\n assert day_time_info(day.hours_17).is_morning is False\n assert day_time_info(day.hours_18).is_morning is False\n assert day_time_info(day.hours_19).is_morning is False\n assert day_time_info(day.hours_20).is_morning is False\n assert day_time_info(day.hours_21).is_morning is False\n assert day_time_info(day.hours_22).is_morning is False\n assert day_time_info(day.hours_23).is_morning is False","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def test_time_type_state_is_night(day):\n\n assert day_time_info(day.hours_0).is_night is True\n assert day_time_info(day.hours_1).is_night is True\n assert day_time_info(day.hours_2).is_night is True\n assert day_time_info(day.hours_3).is_night is True\n assert day_time_info(day.hours_4).is_night is True\n assert day_time_info(day.hours_5).is_night is False\n assert day_time_info(day.hours_6).is_night is False\n assert day_time_info(day.hours_7).is_night is False\n assert day_time_info(day.hours_8).is_night is False\n assert day_time_info(day.hours_9).is_night is False\n assert day_time_info(day.hours_10).is_night is False\n assert day_time_info(day.hours_11).is_night is False\n assert day_time_info(day.hours_12).is_night is False\n assert day_time_info(day.hours_13).is_night is False\n assert day_time_info(day.hours_14).is_night is False\n assert day_time_info(day.hours_15).is_night is False\n assert day_time_info(day.hours_16).is_night is False\n assert day_time_info(day.hours_17).is_night is False\n assert day_time_info(day.hours_18).is_night is False\n assert day_time_info(day.hours_19).is_night is False\n assert day_time_info(day.hours_20).is_night is False\n assert day_time_info(day.hours_21).is_night is False\n assert day_time_info(day.hours_22).is_night is False\n assert day_time_info(day.hours_23).is_night is True","def test_get_field_state_comparisons_tiny(self):\r\n comparison_groupings = get_field_state_comparisons(\r\n self.tiny_dist_matrix_header, self.tiny_dist_matrix,\r\n self.tiny_mapping_header, self.tiny_mapping, self.tiny_field,\r\n ['SampleFieldState1'])\r\n self.assertEqual(comparison_groupings, {})","def test_past_meeting_details(self):\n pass","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def _assert_state(self, state_dict):\n instances = db.instance_get_all(self.context)\n self.assertEqual(len(instances), 1)\n\n if 'vm_state' in state_dict:\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\n if 'task_state' in state_dict:\n self.assertEqual(state_dict['task_state'],\n instances[0]['task_state'])\n if 'power_state' in state_dict:\n self.assertEqual(state_dict['power_state'],\n instances[0]['power_state'])","def test_state_after_failure(self):\n pass","def assert_history(self, rows):\r\n self.assertEqual(self.parse_rows(rows), self.read_history())","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def test_time_field():","def test_get_state_comparison_stats_born_there(self):\n\n key = '% Employees born there'\n\n place_kentucky = PlaceFactory(region=self.kentucky)\n place_texas = PlaceFactory(region=self.texas)\n\n # Mississippi: 0 employees born in Mississippi, 3 born elsewhere\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 2 employees born in Texas, 2 born elsewhere\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=place_texas)\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 3 employees born in Kentucky, 1 employee elsewhere, 3 unknown\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=place_kentucky)\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\n employee.bureau_states.add(self.kentucky)\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n expected_output = [('Kentucky', 75), ('Texas', 50)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(top_states, expected_output,\n f\"'{key}' should contain states with the top x % employees born there\")","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def test_update_state4(self):\n pass","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def test_meeting_status(self):\n pass","def test_rt_table(self) -> None:\n expected = Fixtures.next_table()\n expected.description = '\"hello!\" said no one'\n expected.tags.sort()\n\n self.get_proxy().put_table(table=expected)\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\n actual.last_updated_timestamp = None\n actual.tags.sort()\n\n self.assertEqual(expected, actual)","async def test_state_characteristics(hass: HomeAssistant) -> None:\n now = dt_util.utcnow()\n current_time = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\n start_datetime = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\n characteristics: Sequence[dict[str, Any]] = (\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"average_linear\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 10.68,\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"average_step\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 11.36,\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"average_timeless\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"change\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(0),\n \"value_9\": float(round(VALUES_NUMERIC[-1] - VALUES_NUMERIC[0], 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"change_sample\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n round(\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\n / (len(VALUES_NUMERIC) - 1),\n 2,\n )\n ),\n \"unit\": \"°C/sample\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"change_second\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n round(\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\n / (60 * (len(VALUES_NUMERIC) - 1)),\n 2,\n )\n ),\n \"unit\": \"°C/s\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"count\",\n \"value_0\": 0,\n \"value_1\": 1,\n \"value_9\": 9,\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_newest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_oldest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=1)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_value_max\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=2)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"datetime_value_min\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=5)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"distance_95_percent_of_values\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(2 * 1.96 * statistics.stdev(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"distance_99_percent_of_values\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(2 * 2.58 * statistics.stdev(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"distance_absolute\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(0),\n \"value_9\": float(max(VALUES_NUMERIC) - min(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"mean\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"median\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(round(statistics.median(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"noisiness\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(sum([3, 4.8, 10.2, 1.2, 5.4, 2.5, 7.3, 8]) / 8, 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"percentile\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 9.2,\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"standard_deviation\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(statistics.stdev(VALUES_NUMERIC), 2)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"sum\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(sum(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"sum_differences\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n sum(\n [\n abs(20 - 17),\n abs(15.2 - 20),\n abs(5 - 15.2),\n abs(3.8 - 5),\n abs(9.2 - 3.8),\n abs(6.7 - 9.2),\n abs(14 - 6.7),\n abs(6 - 14),\n ]\n )\n ),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"sum_differences_nonnegative\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(\n sum(\n [\n 20 - 17,\n 15.2 - 0,\n 5 - 0,\n 3.8 - 0,\n 9.2 - 3.8,\n 6.7 - 0,\n 14 - 6.7,\n 6 - 0,\n ]\n )\n ),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"total\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(sum(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"value_max\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(max(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"value_min\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": float(VALUES_NUMERIC[-1]),\n \"value_9\": float(min(VALUES_NUMERIC)),\n \"unit\": \"°C\",\n },\n {\n \"source_sensor_domain\": \"sensor\",\n \"name\": \"variance\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": float(round(statistics.variance(VALUES_NUMERIC), 2)),\n \"unit\": \"°C²\",\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"average_step\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": STATE_UNKNOWN,\n \"value_9\": 50.0,\n \"unit\": \"%\",\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"average_timeless\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": 100.0,\n \"value_9\": float(\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\"on\"), 2)\n ),\n \"unit\": \"%\",\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"count\",\n \"value_0\": 0,\n \"value_1\": 1,\n \"value_9\": len(VALUES_BINARY),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"count_on\",\n \"value_0\": 0,\n \"value_1\": 1,\n \"value_9\": VALUES_BINARY.count(\"on\"),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"count_off\",\n \"value_0\": 0,\n \"value_1\": 0,\n \"value_9\": VALUES_BINARY.count(\"off\"),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"datetime_newest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"datetime_oldest\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": (start_datetime + timedelta(minutes=9)).isoformat(),\n \"value_9\": (start_datetime + timedelta(minutes=1)).isoformat(),\n \"unit\": None,\n },\n {\n \"source_sensor_domain\": \"binary_sensor\",\n \"name\": \"mean\",\n \"value_0\": STATE_UNKNOWN,\n \"value_1\": 100.0,\n \"value_9\": float(\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\"on\"), 2)\n ),\n \"unit\": \"%\",\n },\n )\n sensors_config = []\n for characteristic in characteristics:\n sensors_config.append(\n {\n \"platform\": \"statistics\",\n \"name\": f\"test_{characteristic['source_sensor_domain']}_{characteristic['name']}\",\n \"entity_id\": f\"{characteristic['source_sensor_domain']}.test_monitored\",\n \"state_characteristic\": characteristic[\"name\"],\n \"max_age\": {\"minutes\": 8}, # 9 values spaces by one minute\n }\n )\n\n with freeze_time(current_time) as freezer:\n assert await async_setup_component(\n hass,\n \"sensor\",\n {\"sensor\": sensors_config},\n )\n await hass.async_block_till_done()\n\n # With all values in buffer\n\n for i in range(len(VALUES_NUMERIC)):\n current_time += timedelta(minutes=1)\n freezer.move_to(current_time)\n async_fire_time_changed(hass, current_time)\n hass.states.async_set(\n \"sensor.test_monitored\",\n str(VALUES_NUMERIC[i]),\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\n )\n hass.states.async_set(\n \"binary_sensor.test_monitored\",\n str(VALUES_BINARY[i]),\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\n )\n await hass.async_block_till_done()\n\n for characteristic in characteristics:\n state = hass.states.get(\n f\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\"\n )\n assert state is not None, (\n \"no state object for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer filled)\"\n )\n assert state.state == str(characteristic[\"value_9\"]), (\n \"value mismatch for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer filled) - \"\n f\"assert {state.state} == {str(characteristic['value_9'])}\"\n )\n assert (\n state.attributes.get(ATTR_UNIT_OF_MEASUREMENT) == characteristic[\"unit\"]\n ), f\"unit mismatch for characteristic '{characteristic['name']}'\"\n\n # With single value in buffer\n\n current_time += timedelta(minutes=8)\n freezer.move_to(current_time)\n async_fire_time_changed(hass, current_time)\n await hass.async_block_till_done()\n\n for characteristic in characteristics:\n state = hass.states.get(\n f\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\"\n )\n assert state is not None, (\n \"no state object for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(one stored value)\"\n )\n assert state.state == str(characteristic[\"value_1\"]), (\n \"value mismatch for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(one stored value) - \"\n f\"assert {state.state} == {str(characteristic['value_1'])}\"\n )\n\n # With empty buffer\n\n current_time += timedelta(minutes=1)\n freezer.move_to(current_time)\n async_fire_time_changed(hass, current_time)\n await hass.async_block_till_done()\n\n for characteristic in characteristics:\n state = hass.states.get(\n f\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\"\n )\n assert state is not None, (\n \"no state object for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer empty)\"\n )\n assert state.state == str(characteristic[\"value_0\"]), (\n \"value mismatch for characteristic \"\n f\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \"\n \"(buffer empty) - \"\n f\"assert {state.state} == {str(characteristic['value_0'])}\"\n )","def test_updatePlayerbHist_fold_2(self):\n self.assertEqual(self.player.foldedInd, 1)","def test_fantasy_status_nov_1(league):\n nov_1 = datetime.datetime(2019,11,1)\n players = league.as_of(nov_1).all_players()\n # make sure sammy blais is not a free agent, he was picked up oct 31\n assert(players.loc[6544, 'fantasy_status'] != 'FA')","def test_TimeArray_convert_unit():","def test_past_meetings(self):\n pass","def test_update_state(self):\n pass","def test_update_state2(self):\n pass","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def test_get_team_history(self):\n pass","def make_test_observation_table(observatory_name='HESS', n_obs=10,\n az_range=Angle([0, 360], 'deg'),\n alt_range=Angle([45, 90], 'deg'),\n date_range=(Time('2010-01-01'),\n Time('2015-01-01')),\n use_abs_time=False,\n n_tels_range=(3, 4),\n random_state='random-seed'):\n from ..data import ObservationTable, observatory_locations\n random_state = get_random_state(random_state)\n\n n_obs_start = 1\n\n obs_table = ObservationTable()\n\n # build a time reference as the start of 2010\n dateref = Time('2010-01-01T00:00:00')\n dateref_mjd_fra, dateref_mjd_int = np.modf(dateref.mjd)\n\n # define table header\n obs_table.meta['OBSERVATORY_NAME'] = observatory_name\n obs_table.meta['MJDREFI'] = dateref_mjd_int\n obs_table.meta['MJDREFF'] = dateref_mjd_fra\n if use_abs_time:\n # show the observation times in UTC\n obs_table.meta['TIME_FORMAT'] = 'absolute'\n else:\n # show the observation times in seconds after the reference\n obs_table.meta['TIME_FORMAT'] = 'relative'\n header = obs_table.meta\n\n # obs id\n obs_id = np.arange(n_obs_start, n_obs_start + n_obs)\n obs_table['OBS_ID'] = obs_id\n\n # obs time: 30 min\n ontime = Quantity(30. * np.ones_like(obs_id), 'minute').to('second')\n obs_table['ONTIME'] = ontime\n\n # livetime: 25 min\n time_live = Quantity(25. * np.ones_like(obs_id), 'minute').to('second')\n obs_table['LIVETIME'] = time_live\n\n # start time\n # - random points between the start of 2010 and the end of 2014 (unless\n # otherwise specified)\n # - using the start of 2010 as a reference time for the header of the table\n # - observations restrict to night time (only if specified time interval is\n # more than 1 day)\n # - considering start of astronomical day at midday: implicit in setting\n # the start of the night, when generating random night hours\n datestart = date_range[0]\n dateend = date_range[1]\n time_start = random_state.uniform(datestart.mjd, dateend.mjd, len(obs_id))\n time_start = Time(time_start, format='mjd', scale='utc')\n\n # check if time interval selected is more than 1 day\n if (dateend - datestart).jd > 1.:\n # keep only the integer part (i.e. the day, not the fraction of the day)\n time_start_f, time_start_i = np.modf(time_start.mjd)\n time_start = Time(time_start_i, format='mjd', scale='utc')\n\n # random generation of night hours: 6 h (from 22 h to 4 h), leaving 1/2 h\n # time for the last run to finish\n night_start = Quantity(22., 'hour')\n night_duration = Quantity(5.5, 'hour')\n hour_start = random_state.uniform(night_start.value,\n night_start.value + night_duration.value,\n len(obs_id))\n hour_start = Quantity(hour_start, 'hour')\n\n # add night hour to integer part of MJD\n time_start += hour_start\n\n if use_abs_time:\n # show the observation times in UTC\n time_start = Time(time_start.isot)\n else:\n # show the observation times in seconds after the reference\n time_start = time_relative_to_ref(time_start, header)\n # converting to quantity (better treatment of units)\n time_start = Quantity(time_start.sec, 'second')\n\n obs_table['TSTART'] = time_start\n\n # stop time\n # calculated as TSTART + ONTIME\n if use_abs_time:\n time_stop = Time(obs_table['TSTART'])\n time_stop += TimeDelta(obs_table['ONTIME'])\n else:\n time_stop = TimeDelta(obs_table['TSTART'])\n time_stop += TimeDelta(obs_table['ONTIME'])\n # converting to quantity (better treatment of units)\n time_stop = Quantity(time_stop.sec, 'second')\n\n obs_table['TSTOP'] = time_stop\n\n # az, alt\n # random points in a portion of sphere; default: above 45 deg altitude\n az, alt = sample_sphere(size=len(obs_id),\n lon_range=az_range,\n lat_range=alt_range,\n random_state=random_state)\n az = Angle(az, 'deg')\n alt = Angle(alt, 'deg')\n obs_table['AZ'] = az\n obs_table['ALT'] = alt\n\n # RA, dec\n # derive from az, alt taking into account that alt, az represent the values\n # at the middle of the observation, i.e. at time_ref + (TIME_START + TIME_STOP)/2\n # (or better: time_ref + TIME_START + (TIME_OBSERVATION/2))\n # in use_abs_time mode, the time_ref should not be added, since it's already included\n # in TIME_START and TIME_STOP\n az = Angle(obs_table['AZ'])\n alt = Angle(obs_table['ALT'])\n if use_abs_time:\n obstime = Time(obs_table['TSTART'])\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\n else:\n obstime = time_ref_from_dict(obs_table.meta)\n obstime += TimeDelta(obs_table['TSTART'])\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\n location = observatory_locations[observatory_name]\n altaz_frame = AltAz(obstime=obstime, location=location)\n alt_az_coord = SkyCoord(az, alt, frame=altaz_frame)\n sky_coord = alt_az_coord.transform_to('icrs')\n obs_table['RA'] = sky_coord.ra\n obs_table['DEC'] = sky_coord.dec\n\n # positions\n\n # number of telescopes\n # random integers in a specified range; default: between 3 and 4\n n_tels = random_state.randint(n_tels_range[0], n_tels_range[1] + 1, len(obs_id))\n obs_table['N_TELS'] = n_tels\n\n # muon efficiency\n # random between 0.6 and 1.0\n muoneff = random_state.uniform(low=0.6, high=1.0, size=len(obs_id))\n obs_table['MUONEFF'] = muoneff\n\n return obs_table","def test_cmd_roomstate(self, mock_gametime):\n\n mock_gametime.return_value = _get_timestamp(\"autumn\", \"afternoon\")\n\n # show existing room states (season/time doesn't count)\n\n self.assertEqual(self.room1.room_states, [])\n\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"\",\n f\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\n None\",\n )\n\n # add room states\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"burning\",\n \"Added room state 'burning' to this room.\",\n )\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"windy\",\n \"Added room state 'windy' to this room.\",\n )\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"\",\n f\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\n \"\n \"'burning' and 'windy'\",\n )\n # toggle windy\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"windy\",\n \"Cleared room state 'windy' from this room.\",\n )\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"\",\n f\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\n \"\n \"'burning'\",\n )\n # add a autumn state and make sure we override it\n self.room1.add_desc(\"Autumn description.\", room_state=\"autumn\")\n self.room1.add_desc(\"Spring description.\", room_state=\"spring\")\n\n self.assertEqual(self.room1.get_stateful_desc(), \"Autumn description.\")\n self.call(\n extended_room.CmdExtendedRoomState(),\n \"spring\",\n \"Added room state 'spring' to this room.\",\n )\n self.assertEqual(self.room1.get_stateful_desc(), \"Spring description.\")","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def test_created_and_updated(self):\n state1 = State()\n created = state1.created_at\n updated = state1.updated_at\n state1.save()\n self.assertFalse(updated == state1.updated_at)\n self.assertTrue(created == state1.created_at)","def test_output(self):\n # reproducible arbitrariness\n np.random.seed(123423)\n\n N = 20\n tmax = 30.0\n dt = 1.0\n\n n_steps = int_r(tmax/dt)\n\n table = np.random.randn(N, n_steps)\n G = TableLayer(table)\n\n M = simulation.StateMonitor(G, 'out')\n\n sim = simulation.Simulation(G, M, dt=dt)\n sim.run(tmax)\n\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)","def test_custom_time(self):\n interval = 0.5\n M = simulation.StateMonitor(self.G, 'v', interval=interval)\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, interval)))","def test_time(self):\r\n pass","def test_past_event(self):\n pass","def test_get_current_state(self):\n schedule = parser.parse_schedule(SCHEDULE)\n self.assertEqual(\"a\", schedule.get_current_state(dt(monday, '09:30')))\n self.assertEqual(\"c\", schedule.get_current_state(dt(monday, '08:30')))\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\n lookback=False))","def test_is_approved_no_approval(time_record_factory):\n record = time_record_factory()\n\n assert not record.is_approved","def test_update_state1(self):\n pass","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_booked_event_has_true_flag(self):\r\n user = ViewAfishaTests.mentor\r\n event = EventFactory(city=user.profile.city)\r\n EventParticipantFactory(\r\n event=event,\r\n user=user,\r\n )\r\n\r\n client = self.return_authorized_user_client(user)\r\n response_data = client.get(path=EVENTS_URL, format=\"json\").data\r\n\r\n results = response_data.get(\"results\")\r\n record_dict = results[0]\r\n\r\n self.assertEqual(\r\n \"True\",\r\n str(record_dict.get(\"booked\")),\r\n msg=(\r\n \"Проверьте, что у мероприятий на которые \"\r\n \"пользователь подписан возвращается флаг \"\r\n \"booked': True.\"\r\n ),\r\n )","def event_m20_11_6000():\n \"\"\"State 0,2: [Preset] Door of the living person_SubState\"\"\"\n assert event_m20_11_x77(z42=20110480, z43=600000)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def test_get_habits_trackings_table():\n habits_trackings_table = analytics.habits_trackings_table()\n assert habits_trackings_table == [\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-25', '18:26'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-25', '19:52'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-26', '20:09'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-26', '04:40'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-27', '10:44'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-27', '20:49'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-28', '01:18'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-29', '19:42'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-29', '08:25'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-29', '08:57'),\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-29', '02:07'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-06-29', '04:46'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-30', '05:54'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-30', '12:04'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-01', '05:37'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-01', '07:17'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-01', '12:16'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-02', '22:04'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-02', '23:02'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-03', '07:12'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-04', '08:11'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-05', '10:58'),\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-05', '14:38'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-06', '00:14'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-06', '12:28'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-07', '17:07'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-07', '02:44'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-07', '16:09'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-08', '18:19'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-08', '22:35'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-09', '10:01'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-09', '10:14'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-10', '17:12'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-11', '19:54'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-12', '00:06'),\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-12', '16:12'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-12', '18:43'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-13', '08:02'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-14', '21:32'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-15', '08:37'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-16', '08:32'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-16', '11:59'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-16', '21:14'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-18', '03:03'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-18', '06:59'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-19', '22:50'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-19', '13:05'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-20', '04:59'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-20', '05:52'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-20', '09:00'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-21', '18:44'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-21', '03:55'),\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-22', '06:52'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-22', '18:27'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-23', '01:28'),\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-23', '12:17'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-24', '19:10'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-25', '02:18'),\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-26', '09:53'),\n (5, 'Learn French', 'weekly', 'Fluent in french',\n 'Practice the 4 skills', '2021-07-26', '16:59')\n ]","def test_workflows(database, monkeypatch):\n monkeypatch.setattr('dataactcore.utils.sam_recipient.get_sam_props', mock_get_sam_props)\n sess = database.session\n all_duns = ['00000000{}'.format(x) for x in range(1, 5)]\n existing_duns = all_duns[2:]\n for duns in existing_duns:\n sess.add(SAMRecipient(awardee_or_recipient_uniqu=duns))\n sess.commit()\n\n recipient_file = os.path.join(CONFIG_BROKER['path'], 'tests', 'unit', 'data', 'historic_DUNS_export_small.csv')\n\n update_historical_duns.run_sam_batches(recipient_file, sess, block_size=1)\n update_historical_duns.import_historic_recipients(sess)\n\n expected_results = {\n '000000001': {\n 'awardee_or_recipient_uniqu': '000000001',\n 'uei': 'A1',\n 'registration_date': '2004-04-01',\n 'expiration_date': '2013-01-11',\n 'last_sam_mod_date': '2013-01-11',\n 'activation_date': '2012-01-11',\n 'legal_business_name': 'TEST RECIPIENT 1',\n 'address_line_1': 'Test address 1',\n 'address_line_2': 'Test address 2',\n 'city': 'Test city',\n 'state': 'Test state',\n 'zip': 'Test zip',\n 'zip4': 'Test zip4',\n 'country_code': 'Test country',\n 'congressional_district': 'Test congressional district',\n 'business_types_codes': ['A', 'B', 'C'],\n 'business_types': ['Name A', 'Name B', 'Name C'],\n 'dba_name': 'Name 1',\n 'entity_structure': '1A',\n 'ultimate_parent_unique_ide': '999999999',\n 'ultimate_parent_uei': 'Z9',\n 'ultimate_parent_legal_enti': 'Parent Legal Name 1',\n 'high_comp_officer1_full_na': 'Test Exec 1',\n 'high_comp_officer1_amount': '1',\n 'high_comp_officer2_full_na': 'Test Exec 2',\n 'high_comp_officer2_amount': '2',\n 'high_comp_officer3_full_na': 'Test Exec 3',\n 'high_comp_officer3_amount': '3',\n 'high_comp_officer4_full_na': 'Test Exec 4',\n 'high_comp_officer4_amount': '4',\n 'high_comp_officer5_full_na': 'Test Exec 5',\n 'high_comp_officer5_amount': '5'\n },\n '000000002': {\n 'awardee_or_recipient_uniqu': '000000002',\n 'uei': 'B2',\n 'registration_date': '2004-04-02',\n 'expiration_date': '2013-01-12',\n 'last_sam_mod_date': '2013-01-12',\n 'activation_date': '2012-01-12',\n 'legal_business_name': 'TEST RECIPIENT 2',\n 'address_line_1': 'Other Test address 1',\n 'address_line_2': 'Other Test address 2',\n 'city': 'Other Test city',\n 'state': 'Other Test state',\n 'zip': 'Other Test zip',\n 'zip4': 'Other Test zip4',\n 'country_code': 'Other Test country',\n 'congressional_district': 'Other Test congressional district',\n 'business_types_codes': ['D', 'E', 'F'],\n 'business_types': ['Name D', 'Name E', 'Name F'],\n 'dba_name': 'Name 2',\n 'entity_structure': '2B',\n 'ultimate_parent_unique_ide': '999999998',\n 'ultimate_parent_uei': 'Y8',\n 'ultimate_parent_legal_enti': 'Parent Legal Name 2',\n 'high_comp_officer1_full_na': 'Test Other Exec 6',\n 'high_comp_officer1_amount': '6',\n 'high_comp_officer2_full_na': 'Test Other Exec 7',\n 'high_comp_officer2_amount': '7',\n 'high_comp_officer3_full_na': 'Test Other Exec 8',\n 'high_comp_officer3_amount': '8',\n 'high_comp_officer4_full_na': 'Test Other Exec 9',\n 'high_comp_officer4_amount': '9',\n 'high_comp_officer5_full_na': 'Test Other Exec 10',\n 'high_comp_officer5_amount': '10'\n },\n '000000003': {\n 'awardee_or_recipient_uniqu': '000000003',\n 'uei': None,\n 'registration_date': None,\n 'expiration_date': None,\n 'last_sam_mod_date': None,\n 'activation_date': None,\n 'legal_business_name': None,\n 'address_line_1': None,\n 'address_line_2': None,\n 'city': None,\n 'state': None,\n 'zip': None,\n 'zip4': None,\n 'country_code': None,\n 'congressional_district': None,\n 'business_types_codes': None,\n 'business_types': None,\n 'dba_name': None,\n 'entity_structure': None,\n 'ultimate_parent_unique_ide': None,\n 'ultimate_parent_uei': None,\n 'ultimate_parent_legal_enti': None,\n 'high_comp_officer1_full_na': None,\n 'high_comp_officer1_amount': None,\n 'high_comp_officer2_full_na': None,\n 'high_comp_officer2_amount': None,\n 'high_comp_officer3_full_na': None,\n 'high_comp_officer3_amount': None,\n 'high_comp_officer4_full_na': None,\n 'high_comp_officer4_amount': None,\n 'high_comp_officer5_full_na': None,\n 'high_comp_officer5_amount': None\n },\n '000000004': {\n 'awardee_or_recipient_uniqu': '000000004',\n 'uei': None,\n 'registration_date': None,\n 'expiration_date': None,\n 'last_sam_mod_date': None,\n 'activation_date': None,\n 'legal_business_name': None,\n 'address_line_1': None,\n 'address_line_2': None,\n 'city': None,\n 'state': None,\n 'zip': None,\n 'zip4': None,\n 'country_code': None,\n 'congressional_district': None,\n 'business_types_codes': None,\n 'business_types': None,\n 'dba_name': None,\n 'entity_structure': None,\n 'ultimate_parent_unique_ide': None,\n 'ultimate_parent_uei': None,\n 'ultimate_parent_legal_enti': None,\n 'high_comp_officer1_full_na': None,\n 'high_comp_officer1_amount': None,\n 'high_comp_officer2_full_na': None,\n 'high_comp_officer2_amount': None,\n 'high_comp_officer3_full_na': None,\n 'high_comp_officer3_amount': None,\n 'high_comp_officer4_full_na': None,\n 'high_comp_officer4_amount': None,\n 'high_comp_officer5_full_na': None,\n 'high_comp_officer5_amount': None\n }\n }\n results = {}\n for recp_obj in sess.query(SAMRecipient).all():\n results[recp_obj.awardee_or_recipient_uniqu] = {\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\n 'uei': recp_obj.uei,\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\n 'legal_business_name': recp_obj.legal_business_name,\n 'address_line_1': recp_obj.address_line_1,\n 'address_line_2': recp_obj.address_line_2,\n 'city': recp_obj.city,\n 'state': recp_obj.state,\n 'zip': recp_obj.zip,\n 'zip4': recp_obj.zip4,\n 'country_code': recp_obj.country_code,\n 'congressional_district': recp_obj.congressional_district,\n 'business_types_codes': recp_obj.business_types_codes,\n 'business_types': recp_obj.business_types,\n 'dba_name': recp_obj.dba_name,\n 'entity_structure': recp_obj.entity_structure,\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\n }\n assert results == expected_results\n\n # Test to see if truncating the SAM table while keeping the historic reuploads the historic values\n sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).delete(synchronize_session=False)\n\n # Make sure all the historic recipients are removed from the SAMRecipient table\n assert sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).all() == []\n\n # Redo script but don't go through run_sam_batches\n update_historical_duns.clean_historic_recipients(sess)\n update_historical_duns.import_historic_recipients(sess)\n\n results = {}\n for recp_obj in sess.query(SAMRecipient).all():\n results[recp_obj.awardee_or_recipient_uniqu] = {\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\n 'uei': recp_obj.uei,\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\n 'legal_business_name': recp_obj.legal_business_name,\n 'address_line_1': recp_obj.address_line_1,\n 'address_line_2': recp_obj.address_line_2,\n 'city': recp_obj.city,\n 'state': recp_obj.state,\n 'zip': recp_obj.zip,\n 'zip4': recp_obj.zip4,\n 'country_code': recp_obj.country_code,\n 'congressional_district': recp_obj.congressional_district,\n 'business_types_codes': recp_obj.business_types_codes,\n 'business_types': recp_obj.business_types,\n 'dba_name': recp_obj.dba_name,\n 'entity_structure': recp_obj.entity_structure,\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\n }\n assert results == expected_results","def test_full_house_flush_ind(self):","def test_time(self):\n conn = self.database.connection()\n cursor = conn.cursor()\n dialect = self.database.dialect()\n dbapi = self.database.dbapi()\n query = dialect.translate('DROP TABLE test_time')\n try:\n cursor.execute(query)\n except dbapi.Error:\n conn.rollback()\n query = dialect.translate('CREATE TABLE test_time ' \\\n '( value TIME NOT NULL )')\n cursor.execute(query)\n data = []\n rr = random.randrange\n query = 'INSERT INTO test_time VALUES (%s)'\n for i in range(100):\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\n data.append(item)\n cursor.execute(query, (item,))\n query = 'SELECT * FROM test_time'\n cursor.execute(query)\n result = cursor.fetchall()\n for row in result:\n item = row[0]\n assert isinstance(item, datetime.time)\n assert item in data\n data.remove(item)\n query = dialect.translate('DELETE FROM test_time')\n cursor.execute(query)\n query = dialect.translate('DROP TABLE test_time')\n cursor.execute(query)\n conn.commit()","def test_state(self):\n states = self.geographies.find({ 'geoid': config.SUMLEV_STATE })\n\n self.assertEqual(states.count(), 1)\n\n state = states[0]\n\n self.assertEqual(state['sumlev'], config.SUMLEV_STATE)\n self.assertEqual(state['metadata']['NAME'], 'Hawaii')\n self.assertEqual(state['metadata']['STATE'], '15')\n\n pop_2000 = 1211537\n pop_2010 = 1360301\n self._test_totalpop(state, pop_2000, pop_2010)","def test_get_unsuccessful_ta():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\n\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\n assert np.isnan(list_failed[0])","def test_TP_twophase(self):\n with pytest.raises(StateError):\n State(substance=\"water\", T=Q_(373.1242958476844, \"K\"), p=Q_(101325.0, \"Pa\"))","def test_get_state(self):\n\n # test that you can get a state by numerical id\n mdp = MDP()\n mdp.add_state(0)\n self.assertEquals(type(mdp.get_state(0)), State)\n self.assertIn(mdp.get_state(0), mdp.get_state_list())","def test_updatePlayerbHist_fold_1(self):\n self.assertEqual(self.player.bHist[2], [cardutils.BETSTRING_DICT['FOLD']])","def test_match_table_post(self):\n pass","def test_updatePlayerbHist_multiactions_1(self):\n self.assertEqual(self.player.bHist[0][0], cardutils.BETSTRING_DICT['RAISE'])","def test_get_state_comparison_stats_foreign_born(self):\n\n key = '% Foreign-born employees'\n\n us = CountryFactory(code2='US')\n germany = CountryFactory(name='Germany')\n\n # Mississippi: 0 employees born in Germany, 3 in US\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.mississippi)\n\n # Texas: 2 employees born in Germany, 2 in US\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\n employee.bureau_states.add(self.texas)\n for _ in range(2):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.texas)\n\n # Kentucky: 3 employees born in Germany, 1 employee in US, 3 unknown\n for _ in range(3):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\n employee.bureau_states.add(self.kentucky)\n for _ in range(1):\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\n employee.bureau_states.add(self.kentucky)\n for _ in range(3):\n employee = EmployeeFactory()\n employee.bureau_states.add(self.kentucky)\n\n expected_output = [('Kentucky', 75), ('Texas', 50)]\n\n stats = get_state_comparison_stats(number=2)\n top_states = self.get_state_stats_for_key(stats, key)\n\n self.assertListEqual(top_states, expected_output,\n \"'{key}' should contain states with the top x % foreign-born employees\")","def test_update_state3(self):\n pass","def test_get_state_list(self):\n\n mdp = MDP(5)\n state_list = mdp.get_state_list()\n\n self.assertEqual(len(state_list), 5)\n self.assertIn(mdp.get_state(0), state_list)","def test_method(self):\n self.amenity.save()\n self.assertTrue(hasattr(self.amenity, \"updated_at\"))","def test_analyze_time(self):\n self.ph5validate.analyze_time()\n self.assertEqual(self.ph5validate.das_time.keys(), [('12183', 1, 500)])\n Dtime = self.ph5validate.das_time[('12183', 1, 500)]\n\n # 3 different deploy time\n self.assertEqual(len(Dtime['time_windows']), 5)\n\n # station 9001\n self.assertEqual(Dtime['time_windows'][0],\n (1550849950, 1550850034, '9001'))\n self.assertEqual(Dtime['time_windows'][1],\n (1550849950, 1550850034, '9001'))\n self.assertEqual(Dtime['time_windows'][2],\n (1550849950, 1550850034, '9001'))\n # station 9002\n self.assertEqual(Dtime['time_windows'][3],\n (1550850043, 1550850093, '9002'))\n # station 9003\n self.assertEqual(Dtime['time_windows'][4],\n (1550850125, 1550850187, '9003'))\n\n self.assertEqual(Dtime['min_deploy_time'],\n [1550849950,\n 'Data exists before deploy time: 7 seconds.'])","def test_from_event_to_delete_state() -> None:\n event = ha.Event(\n EVENT_STATE_CHANGED,\n {\n \"entity_id\": \"sensor.temperature\",\n \"old_state\": ha.State(\"sensor.temperature\", \"18\"),\n \"new_state\": None,\n },\n )\n db_state = States.from_event(event)\n\n assert db_state.entity_id == \"sensor.temperature\"\n assert db_state.state == \"\"\n assert db_state.last_changed_ts is None\n assert db_state.last_updated_ts == event.time_fired.timestamp()","def test_eval_alarm(self):\n def get_state_update_value(h):\n \"\"\"\n \n \"\"\"\n oldstate = h.data['oldState']['stateValue']\n newstate = h.data['newState']['stateValue']\n querydate = h.data['newState']['stateReasonData']['queryDate']\n querydate = utils.parse_strtime(querydate)\n return oldstate, newstate, querydate \n \n test_uuid = str(uuid.uuid4())\n alarmname = \"TestEvalAlarm_\" + test_uuid\n metricname = \"TestEvalMetric_\" + test_uuid\n namespace = \"unittest\"\n unit = \"Percent\"\n dimensions = {\"test_id\":test_uuid}\n threshold = 2.0\n \n # create metric alarm\n alarm = MetricAlarm(name=alarmname, metric=metricname,\n namespace=namespace, statistic=\"Average\",\n comparison=\">\", threshold=threshold,\n period=60, evaluation_periods=1, unit=unit,\n dimensions=dimensions)\n self.synaps.put_metric_alarm(alarm)\n \n # due to put_metric_alarm is asynchronous\n time.sleep(ASYNC_WAIT)\n \n alarm_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0)\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold + 1, timestamp=alarm_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n\n ok_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0) \n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold - 2, timestamp=ok_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n \n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\n history_item_type=\"StateUpdate\")\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\n\n result = map(get_state_update_value, histories)\n \n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\n ('ALARM', 'INSUFFICIENT_DATA', None),\n ('INSUFFICIENT_DATA', 'OK', ok_time),\n ('OK', 'INSUFFICIENT_DATA', None))\n \n failmsg = \"expected: %s real: %s\" % (expected, result)\n \n self.assertEqual(len(result), len(expected), msg=failmsg)\n \n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\n expected):\n self.assertEqual(r_new, e_new, msg=failmsg)\n self.assertEqual(r_old, e_old, msg=failmsg)\n if e_time:\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\n msg=failmsg)\n \n self.synaps.delete_alarms(alarms=[alarmname])","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","async def test_lazy_state_handles_same_last_updated_and_last_changed(\n caplog: pytest.LogCaptureFixture,\n) -> None:\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n row = PropertyMock(\n entity_id=\"sensor.valid\",\n state=\"off\",\n attributes='{\"shared\":true}',\n last_updated_ts=now.timestamp(),\n last_changed_ts=now.timestamp(),\n )\n lstate = LazyState(\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\n )\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n assert lstate.last_updated.timestamp() == row.last_updated_ts\n assert lstate.last_changed.timestamp() == row.last_changed_ts\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2020-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def test_states_repr_without_timestamp() -> None:\n fixed_time = datetime(2016, 7, 9, 11, 0, 0, tzinfo=dt_util.UTC, microsecond=432432)\n states = States(\n entity_id=\"sensor.temp\",\n attributes=None,\n context_id=None,\n context_user_id=None,\n context_parent_id=None,\n origin_idx=None,\n last_updated=fixed_time,\n last_changed=fixed_time,\n last_updated_ts=None,\n last_changed_ts=None,\n )\n assert \"2016-07-09 11:00:00+00:00\" in repr(states)","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_first_timestep(self):\n m = build_model({}, \"simple_supply,two_hours,investment_costs\")\n m.run(build_only=True)\n timestep_0 = \"2005-01-01 00:00\"\n assert m._backend_model.timesteps.ord(timestep_0) == 1","def test_get_times_height():\n times = get_times(date, lng, lat, height)\n for key, value in heightTestTimes.items():\n assert times[key].strftime(\"%Y-%m-%dT%H:%M:%SZ\") == value","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_multiple_states(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory()\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Cancel when created.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.CANCEL, event)\n s1 = context.work_status.value\n self.assertEqual(s1, s.CANCELLED)\n\n # Cancel when started.\n c2 = model.factory(initial_state=s.STARTED)\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(c2, a.CANCEL, event)\n s2 = c2.work_status.value\n self.assertEqual(s2, s.CANCELLED)","def test_from_event_to_db_state_attributes() -> None:\n attrs = {\"this_attr\": True}\n state = ha.State(\"sensor.temperature\", \"18\", attrs)\n event = ha.Event(\n EVENT_STATE_CHANGED,\n {\"entity_id\": \"sensor.temperature\", \"old_state\": None, \"new_state\": state},\n context=state.context,\n )\n db_attrs = StateAttributes()\n dialect = SupportedDialect.MYSQL\n\n db_attrs.shared_attrs = StateAttributes.shared_attrs_bytes_from_event(\n event, {}, {}, dialect\n )\n assert db_attrs.to_native() == attrs","def test_status(self):\n keys = [\n 'receiver time mark count', 'ant type base', 'solution status',\n ...\n ]\n\n for i in xrange(0, 100):\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)","def test_negative_is_active_of_homework():\n assert not expired_hw.is_active()","def test_state(self, mock_req):\n self.setup_api(MOCK_DATA, mock_req)\n now = datetime(1970, month=1, day=1)\n with patch(\"homeassistant.util.dt.now\", return_value=now):\n for name in self.sensor_dict:\n sensor = self.sensor_dict[name][\"sensor\"]\n self.fake_delay(2)\n sensor.update()\n if name == google_wifi.ATTR_LAST_RESTART:\n assert \"1969-12-31 00:00:00\" == sensor.state\n elif name == google_wifi.ATTR_UPTIME:\n assert 1 == sensor.state\n elif name == google_wifi.ATTR_STATUS:\n assert \"Online\" == sensor.state\n else:\n assert \"initial\" == sensor.state","def test_multiple_rules(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory(initial_state=s.STARTED)\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Complete -> completed.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.COMPLETE, event)\n s1 = context.work_status.value\n context_id = context.id\n\n self.assertEqual(s1, s.COMPLETED)\n\n # Complete -> absolutely completed.\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.COMPLETE, event)\n s2 = context.work_status.value\n self.assertEqual(s2, s.ABSOLUTELY_COMPLETED)\n\n # Complete -> ... the same state ...\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n context = model.Model.query.get(context_id)\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(context, a.COMPLETE, event)\n s3 = context.work_status.value\n self.assertEqual(s3, s.ABSOLUTELY_COMPLETED)","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_170518_bad_dbtime(self):\n spc = parser(get_file('PTSDY1_baddbtime.txt'))\n answer = utc(2017, 5, 1, 12, 0)\n for _, outlook in spc.outlook_collections.items():\n self.assertEqual(outlook.expire, answer)","def test_is(self):\n invalid = self.TDTT()\n self.check_invalid_is(invalid)\n\n valid = self.TDTT(when=self.txt_when)\n self.check_valid_is(valid)","def test_time_status(self):\n result = self.test_client.time_status\n\n assert result == \"12312\"","def test_of_agreement(self): \n \n pop_t = world.pop_t \n pop_sub = world.pop_sub \n \n \n Ns = world.subtime.length\n \n dt = world.time.step\n ds = world.subtime.step\n N = round(ds/dt)\n for i in range(Ns):\n numpy.testing.assert_allclose(pop_sub[:,i],pop_t[i*N,:])","def test_from_event_to_db_state() -> None:\n state = ha.State(\n \"sensor.temperature\",\n \"18\",\n context=ha.Context(\n id=\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\",\n parent_id=\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\",\n user_id=\"12345678901234567890123456789012\",\n ),\n )\n event = ha.Event(\n EVENT_STATE_CHANGED,\n {\"entity_id\": \"sensor.temperature\", \"old_state\": None, \"new_state\": state},\n context=state.context,\n )\n assert state.as_dict() == States.from_event(event).to_native().as_dict()","def test_has_ended(self):\r\n self.assertTrue(self.past_show_certs.has_ended())\r\n self.assertTrue(self.past_noshow_certs.has_ended())\r\n self.assertFalse(self.future_show_certs.has_ended())\r\n self.assertFalse(self.future_noshow_certs.has_ended())","async def test_lazy_state_handles_different_last_updated_and_last_changed(\n caplog: pytest.LogCaptureFixture,\n) -> None:\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n row = PropertyMock(\n entity_id=\"sensor.valid\",\n state=\"off\",\n attributes='{\"shared\":true}',\n last_updated_ts=now.timestamp(),\n last_changed_ts=(now - timedelta(seconds=60)).timestamp(),\n )\n lstate = LazyState(\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\n )\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:03:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n assert lstate.last_updated.timestamp() == row.last_updated_ts\n assert lstate.last_changed.timestamp() == row.last_changed_ts\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:03:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }","async def test_state_update(hass):\n assert await setup_multimatic(hass)\n assert_entities_count(hass, 11)\n\n assert hass.states.is_state(\"binary_sensor.dhw_circulation\", \"off\")\n assert hass.states.is_state(\"binary_sensor.room_1_window\", \"off\")\n assert hass.states.is_state(\"binary_sensor.123456789_lock\", \"on\")\n assert hass.states.is_state(\"binary_sensor.123456789_battery\", \"off\")\n assert hass.states.is_state(\"binary_sensor.boiler\", \"off\")\n assert hass.states.is_state(\"binary_sensor.123456789_connectivity\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_update\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_online\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_holiday\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_errors\", \"off\")\n state = hass.states.get(\"binary_sensor.multimatic_holiday\")\n assert state.attributes.get(\"start_date\") is None\n assert state.attributes.get(\"end_date\") is None\n assert state.attributes.get(\"temperature\") is None\n assert hass.states.is_state(\"binary_sensor.multimatic_quick_mode\", \"off\")\n\n dhw = SystemManagerMock.data[\"get_dhw\"]\n dhw.circulation.time_program = time_program(SettingModes.ON, None)\n dhw.circulation.operating_mode = OperatingModes.AUTO\n\n hvac_status = SystemManagerMock.data[\"get_hvac_status\"]\n hvac_status.boiler_status.status_code = \"F11\"\n hvac_status.online = \"OFFLINE\"\n hvac_status.update = \"UPDATE_PENDING\"\n hvac_status.errors = [\n Error(\"device\", \"title\", \"status_code\", \"descr\", datetime.datetime.now())\n ]\n\n rooms = SystemManagerMock.data[\"get_rooms\"]\n rooms[0].devices = [Device(\"Device 1\", \"123456789\", \"VALVE\", True, True)]\n rooms[0].time_program = time_program(None, 20)\n rooms[0].temperature = 22\n rooms[0].target_high = 24\n rooms[0].operating_mode = OperatingModes.AUTO\n rooms[0].child_lock = True\n rooms[0].window_open = True\n\n new_holiday_mode = active_holiday_mode()\n SystemManagerMock.data[\"get_holiday_mode\"] = new_holiday_mode\n SystemManagerMock.data[\"get_quick_mode\"] = QuickModes.HOTWATER_BOOST\n\n await goto_future(hass)\n\n assert_entities_count(hass, 11)\n assert hass.states.is_state(\"binary_sensor.room_1_window\", \"on\")\n assert hass.states.is_state(\"binary_sensor.123456789_lock\", \"off\")\n assert hass.states.is_state(\"binary_sensor.123456789_battery\", \"on\")\n assert hass.states.is_state(\"binary_sensor.boiler\", \"on\")\n assert hass.states.is_state(\"binary_sensor.123456789_connectivity\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_update\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_system_online\", \"off\")\n assert hass.states.is_state(\"binary_sensor.multimatic_errors\", \"on\")\n assert hass.states.is_state(\"binary_sensor.multimatic_holiday\", \"on\")\n state = hass.states.get(\"binary_sensor.multimatic_holiday\")\n assert state.attributes[\"start_date\"] == new_holiday_mode.start_date.isoformat()\n assert state.attributes[\"end_date\"] == new_holiday_mode.end_date.isoformat()\n assert state.attributes[\"temperature\"] == new_holiday_mode.target\n assert hass.states.is_state(\"binary_sensor.multimatic_quick_mode\", \"on\")\n state = hass.states.get(\"binary_sensor.multimatic_quick_mode\")\n assert state.attributes[\"quick_mode\"] == QuickModes.HOTWATER_BOOST.name\n assert hass.states.is_state(\"binary_sensor.dhw_circulation\", \"off\")\n\n SystemManagerMock.data[\"get_holiday_mode\"] = HolidayMode(False)\n\n await goto_future(hass)\n assert hass.states.is_state(\"binary_sensor.dhw_circulation\", \"on\")"],"string":"[\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_time_type_state_types(day):\\n\\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}\",\n \"def test_time_type_state_is_afternoon(day):\\n\\n assert day_time_info(day.hours_0).is_afternoon is False\\n assert day_time_info(day.hours_1).is_afternoon is False\\n assert day_time_info(day.hours_2).is_afternoon is False\\n assert day_time_info(day.hours_3).is_afternoon is False\\n assert day_time_info(day.hours_4).is_afternoon is False\\n assert day_time_info(day.hours_5).is_afternoon is False\\n assert day_time_info(day.hours_6).is_afternoon is False\\n assert day_time_info(day.hours_7).is_afternoon is False\\n assert day_time_info(day.hours_8).is_afternoon is False\\n assert day_time_info(day.hours_9).is_afternoon is False\\n assert day_time_info(day.hours_10).is_afternoon is False\\n assert day_time_info(day.hours_11).is_afternoon is False\\n assert day_time_info(day.hours_12).is_afternoon is False\\n assert day_time_info(day.hours_13).is_afternoon is True\\n assert day_time_info(day.hours_14).is_afternoon is True\\n assert day_time_info(day.hours_15).is_afternoon is True\\n assert day_time_info(day.hours_16).is_afternoon is True\\n assert day_time_info(day.hours_17).is_afternoon is True\\n assert day_time_info(day.hours_18).is_afternoon is False\\n assert day_time_info(day.hours_19).is_afternoon is False\\n assert day_time_info(day.hours_20).is_afternoon is False\\n assert day_time_info(day.hours_21).is_afternoon is False\\n assert day_time_info(day.hours_22).is_afternoon is False\\n assert day_time_info(day.hours_23).is_afternoon is False\",\n \"def test_time_type_state_is_evening(day):\\n\\n assert day_time_info(day.hours_0).is_evening is False\\n assert day_time_info(day.hours_1).is_evening is False\\n assert day_time_info(day.hours_2).is_evening is False\\n assert day_time_info(day.hours_3).is_evening is False\\n assert day_time_info(day.hours_4).is_evening is False\\n assert day_time_info(day.hours_5).is_evening is False\\n assert day_time_info(day.hours_6).is_evening is False\\n assert day_time_info(day.hours_7).is_evening is False\\n assert day_time_info(day.hours_8).is_evening is False\\n assert day_time_info(day.hours_9).is_evening is False\\n assert day_time_info(day.hours_10).is_evening is False\\n assert day_time_info(day.hours_11).is_evening is False\\n assert day_time_info(day.hours_12).is_evening is False\\n assert day_time_info(day.hours_13).is_evening is False\\n assert day_time_info(day.hours_14).is_evening is False\\n assert day_time_info(day.hours_15).is_evening is False\\n assert day_time_info(day.hours_16).is_evening is False\\n assert day_time_info(day.hours_17).is_evening is False\\n assert day_time_info(day.hours_18).is_evening is True\\n assert day_time_info(day.hours_19).is_evening is True\\n assert day_time_info(day.hours_20).is_evening is True\\n assert day_time_info(day.hours_21).is_evening is True\\n assert day_time_info(day.hours_22).is_evening is True\\n assert day_time_info(day.hours_23).is_evening is False\",\n \"def test_exp_by_states(self):\\n api = my_mock.api_mock({\\\"items\\\": [{'state': 'Waiting', 'id': 10134},\\n {'state': 'Waiting', 'id': 10135},\\n {'state': 'Running', 'id': 10130}]})\\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\\n self.assertEquals(\\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\\n my_mock.api_mock_stop()\",\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_get_state_comparison_stats_ailments(self):\\n\\n # AilmentType and Ailment breakdown per AilmentType should be in returned stats if present\\n ailment_type_headache = AilmentTypeFactory(name='Headache')\\n ailment_migraine_headache = AilmentFactory(name='Migraine Headache', type=ailment_type_headache)\\n ailment_tension_headache = AilmentFactory(name='Tension Headache', type=ailment_type_headache)\\n ailment_type_sprain_or_bruise = AilmentTypeFactory(name='Sprain or Bruise')\\n ailment_sprain = AilmentFactory(name='Sprain', type=ailment_type_sprain_or_bruise)\\n\\n # Texas: 2 with sprain and 2 with nothing\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_sprain)\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 1 with migraine headache, 1 with sprain, 1 with tension headache, and 1 with nothing\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_migraine_headache)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_sprain)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_tension_headache)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n # Mississippi: 2 with migraine headache, 2 with tension headache\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_migraine_headache)\\n employee.bureau_states.add(self.mississippi)\\n for _ in range(2):\\n employee = EmployeeFactory()\\n employee.ailments.add(ailment_tension_headache)\\n employee.bureau_states.add(self.mississippi)\\n\\n stats = get_state_comparison_stats(number=2)\\n\\n # With ailment types, ailments should be grouped together\\n key = '% With Headache'\\n expected_output = [('Mississippi', 100), ('Kentucky', 50)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with headache\\\")\\n\\n key = '% With Sprain or Bruise'\\n expected_output = [('Texas', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with sprain or bruise\\\"\\n )\\n\\n # Ailments should be treated individually\\n key = '% With Migraine Headache'\\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with migraine headache\\\"\\n )\\n\\n key = '% With Tension Headache'\\n expected_output = [('Mississippi', 50), ('Kentucky', 25)]\\n top_states = self.get_state_stats_for_key(stats, key)\\n self.assertListEqual(\\n top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % of employees with tension headache\\\"\\n )\\n\\n # There should be no breakdown for sprain, because it's the only ailment of that type\\n key = '% With Sprain'\\n self.assertEqual(len([item for item in stats if key in item]), 0,\\n \\\"There should be no breakdown for an ailment if it's the only one of its type\\\")\",\n \"def test_time_type_state_is_midmorning(day):\\n\\n assert day_time_info(day.hours_0).is_midmorning is False\\n assert day_time_info(day.hours_1).is_midmorning is False\\n assert day_time_info(day.hours_2).is_midmorning is False\\n assert day_time_info(day.hours_3).is_midmorning is False\\n assert day_time_info(day.hours_4).is_midmorning is False\\n assert day_time_info(day.hours_5).is_midmorning is False\\n assert day_time_info(day.hours_6).is_midmorning is False\\n assert day_time_info(day.hours_7).is_midmorning is False\\n assert day_time_info(day.hours_8).is_midmorning is False\\n assert day_time_info(day.hours_9).is_midmorning is False\\n assert day_time_info(day.hours_10).is_midmorning is True\\n assert day_time_info(day.hours_11).is_midmorning is True\\n assert day_time_info(day.hours_12).is_midmorning is False\\n assert day_time_info(day.hours_13).is_midmorning is False\\n assert day_time_info(day.hours_14).is_midmorning is False\\n assert day_time_info(day.hours_15).is_midmorning is False\\n assert day_time_info(day.hours_16).is_midmorning is False\\n assert day_time_info(day.hours_17).is_midmorning is False\\n assert day_time_info(day.hours_18).is_midmorning is False\\n assert day_time_info(day.hours_19).is_midmorning is False\\n assert day_time_info(day.hours_20).is_midmorning is False\\n assert day_time_info(day.hours_21).is_midmorning is False\\n assert day_time_info(day.hours_22).is_midmorning is False\\n assert day_time_info(day.hours_23).is_midmorning is False\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_episode_data(self):\\n self.assertEquals(\\n self.t['lost']['firstaired'],\\n '2004-09-22'\\n )\",\n \"def test_update_team_state(self):\\n pass\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_time_type_state_is_morning(day):\\n\\n assert day_time_info(day.hours_0).is_morning is False\\n assert day_time_info(day.hours_1).is_morning is False\\n assert day_time_info(day.hours_2).is_morning is False\\n assert day_time_info(day.hours_3).is_morning is False\\n assert day_time_info(day.hours_4).is_morning is False\\n assert day_time_info(day.hours_5).is_morning is True\\n assert day_time_info(day.hours_6).is_morning is True\\n assert day_time_info(day.hours_7).is_morning is True\\n assert day_time_info(day.hours_8).is_morning is True\\n assert day_time_info(day.hours_9).is_morning is True\\n assert day_time_info(day.hours_10).is_morning is False\\n assert day_time_info(day.hours_11).is_morning is False\\n assert day_time_info(day.hours_12).is_morning is False\\n assert day_time_info(day.hours_13).is_morning is False\\n assert day_time_info(day.hours_14).is_morning is False\\n assert day_time_info(day.hours_15).is_morning is False\\n assert day_time_info(day.hours_16).is_morning is False\\n assert day_time_info(day.hours_17).is_morning is False\\n assert day_time_info(day.hours_18).is_morning is False\\n assert day_time_info(day.hours_19).is_morning is False\\n assert day_time_info(day.hours_20).is_morning is False\\n assert day_time_info(day.hours_21).is_morning is False\\n assert day_time_info(day.hours_22).is_morning is False\\n assert day_time_info(day.hours_23).is_morning is False\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def test_time_type_state_is_night(day):\\n\\n assert day_time_info(day.hours_0).is_night is True\\n assert day_time_info(day.hours_1).is_night is True\\n assert day_time_info(day.hours_2).is_night is True\\n assert day_time_info(day.hours_3).is_night is True\\n assert day_time_info(day.hours_4).is_night is True\\n assert day_time_info(day.hours_5).is_night is False\\n assert day_time_info(day.hours_6).is_night is False\\n assert day_time_info(day.hours_7).is_night is False\\n assert day_time_info(day.hours_8).is_night is False\\n assert day_time_info(day.hours_9).is_night is False\\n assert day_time_info(day.hours_10).is_night is False\\n assert day_time_info(day.hours_11).is_night is False\\n assert day_time_info(day.hours_12).is_night is False\\n assert day_time_info(day.hours_13).is_night is False\\n assert day_time_info(day.hours_14).is_night is False\\n assert day_time_info(day.hours_15).is_night is False\\n assert day_time_info(day.hours_16).is_night is False\\n assert day_time_info(day.hours_17).is_night is False\\n assert day_time_info(day.hours_18).is_night is False\\n assert day_time_info(day.hours_19).is_night is False\\n assert day_time_info(day.hours_20).is_night is False\\n assert day_time_info(day.hours_21).is_night is False\\n assert day_time_info(day.hours_22).is_night is False\\n assert day_time_info(day.hours_23).is_night is True\",\n \"def test_get_field_state_comparisons_tiny(self):\\r\\n comparison_groupings = get_field_state_comparisons(\\r\\n self.tiny_dist_matrix_header, self.tiny_dist_matrix,\\r\\n self.tiny_mapping_header, self.tiny_mapping, self.tiny_field,\\r\\n ['SampleFieldState1'])\\r\\n self.assertEqual(comparison_groupings, {})\",\n \"def test_past_meeting_details(self):\\n pass\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def _assert_state(self, state_dict):\\n instances = db.instance_get_all(self.context)\\n self.assertEqual(len(instances), 1)\\n\\n if 'vm_state' in state_dict:\\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\\n if 'task_state' in state_dict:\\n self.assertEqual(state_dict['task_state'],\\n instances[0]['task_state'])\\n if 'power_state' in state_dict:\\n self.assertEqual(state_dict['power_state'],\\n instances[0]['power_state'])\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def assert_history(self, rows):\\r\\n self.assertEqual(self.parse_rows(rows), self.read_history())\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def test_time_field():\",\n \"def test_get_state_comparison_stats_born_there(self):\\n\\n key = '% Employees born there'\\n\\n place_kentucky = PlaceFactory(region=self.kentucky)\\n place_texas = PlaceFactory(region=self.texas)\\n\\n # Mississippi: 0 employees born in Mississippi, 3 born elsewhere\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 2 employees born in Texas, 2 born elsewhere\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=place_texas)\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 3 employees born in Kentucky, 1 employee elsewhere, 3 unknown\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=place_kentucky)\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory())\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n expected_output = [('Kentucky', 75), ('Texas', 50)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(top_states, expected_output,\\n f\\\"'{key}' should contain states with the top x % employees born there\\\")\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def test_update_state4(self):\\n pass\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def test_meeting_status(self):\\n pass\",\n \"def test_rt_table(self) -> None:\\n expected = Fixtures.next_table()\\n expected.description = '\\\"hello!\\\" said no one'\\n expected.tags.sort()\\n\\n self.get_proxy().put_table(table=expected)\\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\\n actual.last_updated_timestamp = None\\n actual.tags.sort()\\n\\n self.assertEqual(expected, actual)\",\n \"async def test_state_characteristics(hass: HomeAssistant) -> None:\\n now = dt_util.utcnow()\\n current_time = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\\n start_datetime = datetime(now.year + 1, 8, 2, 12, 23, 42, tzinfo=dt_util.UTC)\\n characteristics: Sequence[dict[str, Any]] = (\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"average_linear\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 10.68,\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"average_step\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 11.36,\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"average_timeless\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"change\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(0),\\n \\\"value_9\\\": float(round(VALUES_NUMERIC[-1] - VALUES_NUMERIC[0], 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"change_sample\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n round(\\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\\n / (len(VALUES_NUMERIC) - 1),\\n 2,\\n )\\n ),\\n \\\"unit\\\": \\\"°C/sample\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"change_second\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n round(\\n (VALUES_NUMERIC[-1] - VALUES_NUMERIC[0])\\n / (60 * (len(VALUES_NUMERIC) - 1)),\\n 2,\\n )\\n ),\\n \\\"unit\\\": \\\"°C/s\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"count\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 1,\\n \\\"value_9\\\": 9,\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_newest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_oldest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=1)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_value_max\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=2)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"datetime_value_min\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=5)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"distance_95_percent_of_values\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(2 * 1.96 * statistics.stdev(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"distance_99_percent_of_values\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(2 * 2.58 * statistics.stdev(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"distance_absolute\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(0),\\n \\\"value_9\\\": float(max(VALUES_NUMERIC) - min(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"mean\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(round(sum(VALUES_NUMERIC) / len(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"median\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(round(statistics.median(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"noisiness\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(sum([3, 4.8, 10.2, 1.2, 5.4, 2.5, 7.3, 8]) / 8, 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"percentile\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 9.2,\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"standard_deviation\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(statistics.stdev(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"sum\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(sum(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"sum_differences\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n sum(\\n [\\n abs(20 - 17),\\n abs(15.2 - 20),\\n abs(5 - 15.2),\\n abs(3.8 - 5),\\n abs(9.2 - 3.8),\\n abs(6.7 - 9.2),\\n abs(14 - 6.7),\\n abs(6 - 14),\\n ]\\n )\\n ),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"sum_differences_nonnegative\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(\\n sum(\\n [\\n 20 - 17,\\n 15.2 - 0,\\n 5 - 0,\\n 3.8 - 0,\\n 9.2 - 3.8,\\n 6.7 - 0,\\n 14 - 6.7,\\n 6 - 0,\\n ]\\n )\\n ),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"total\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(sum(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"value_max\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(max(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"value_min\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": float(VALUES_NUMERIC[-1]),\\n \\\"value_9\\\": float(min(VALUES_NUMERIC)),\\n \\\"unit\\\": \\\"°C\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"sensor\\\",\\n \\\"name\\\": \\\"variance\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": float(round(statistics.variance(VALUES_NUMERIC), 2)),\\n \\\"unit\\\": \\\"°C²\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"average_step\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": STATE_UNKNOWN,\\n \\\"value_9\\\": 50.0,\\n \\\"unit\\\": \\\"%\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"average_timeless\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": 100.0,\\n \\\"value_9\\\": float(\\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\\\"on\\\"), 2)\\n ),\\n \\\"unit\\\": \\\"%\\\",\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"count\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 1,\\n \\\"value_9\\\": len(VALUES_BINARY),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"count_on\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 1,\\n \\\"value_9\\\": VALUES_BINARY.count(\\\"on\\\"),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"count_off\\\",\\n \\\"value_0\\\": 0,\\n \\\"value_1\\\": 0,\\n \\\"value_9\\\": VALUES_BINARY.count(\\\"off\\\"),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"datetime_newest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"datetime_oldest\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": (start_datetime + timedelta(minutes=9)).isoformat(),\\n \\\"value_9\\\": (start_datetime + timedelta(minutes=1)).isoformat(),\\n \\\"unit\\\": None,\\n },\\n {\\n \\\"source_sensor_domain\\\": \\\"binary_sensor\\\",\\n \\\"name\\\": \\\"mean\\\",\\n \\\"value_0\\\": STATE_UNKNOWN,\\n \\\"value_1\\\": 100.0,\\n \\\"value_9\\\": float(\\n round(100 / len(VALUES_BINARY) * VALUES_BINARY.count(\\\"on\\\"), 2)\\n ),\\n \\\"unit\\\": \\\"%\\\",\\n },\\n )\\n sensors_config = []\\n for characteristic in characteristics:\\n sensors_config.append(\\n {\\n \\\"platform\\\": \\\"statistics\\\",\\n \\\"name\\\": f\\\"test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\",\\n \\\"entity_id\\\": f\\\"{characteristic['source_sensor_domain']}.test_monitored\\\",\\n \\\"state_characteristic\\\": characteristic[\\\"name\\\"],\\n \\\"max_age\\\": {\\\"minutes\\\": 8}, # 9 values spaces by one minute\\n }\\n )\\n\\n with freeze_time(current_time) as freezer:\\n assert await async_setup_component(\\n hass,\\n \\\"sensor\\\",\\n {\\\"sensor\\\": sensors_config},\\n )\\n await hass.async_block_till_done()\\n\\n # With all values in buffer\\n\\n for i in range(len(VALUES_NUMERIC)):\\n current_time += timedelta(minutes=1)\\n freezer.move_to(current_time)\\n async_fire_time_changed(hass, current_time)\\n hass.states.async_set(\\n \\\"sensor.test_monitored\\\",\\n str(VALUES_NUMERIC[i]),\\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\\n )\\n hass.states.async_set(\\n \\\"binary_sensor.test_monitored\\\",\\n str(VALUES_BINARY[i]),\\n {ATTR_UNIT_OF_MEASUREMENT: UnitOfTemperature.CELSIUS},\\n )\\n await hass.async_block_till_done()\\n\\n for characteristic in characteristics:\\n state = hass.states.get(\\n f\\\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\"\\n )\\n assert state is not None, (\\n \\\"no state object for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer filled)\\\"\\n )\\n assert state.state == str(characteristic[\\\"value_9\\\"]), (\\n \\\"value mismatch for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer filled) - \\\"\\n f\\\"assert {state.state} == {str(characteristic['value_9'])}\\\"\\n )\\n assert (\\n state.attributes.get(ATTR_UNIT_OF_MEASUREMENT) == characteristic[\\\"unit\\\"]\\n ), f\\\"unit mismatch for characteristic '{characteristic['name']}'\\\"\\n\\n # With single value in buffer\\n\\n current_time += timedelta(minutes=8)\\n freezer.move_to(current_time)\\n async_fire_time_changed(hass, current_time)\\n await hass.async_block_till_done()\\n\\n for characteristic in characteristics:\\n state = hass.states.get(\\n f\\\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\"\\n )\\n assert state is not None, (\\n \\\"no state object for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(one stored value)\\\"\\n )\\n assert state.state == str(characteristic[\\\"value_1\\\"]), (\\n \\\"value mismatch for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(one stored value) - \\\"\\n f\\\"assert {state.state} == {str(characteristic['value_1'])}\\\"\\n )\\n\\n # With empty buffer\\n\\n current_time += timedelta(minutes=1)\\n freezer.move_to(current_time)\\n async_fire_time_changed(hass, current_time)\\n await hass.async_block_till_done()\\n\\n for characteristic in characteristics:\\n state = hass.states.get(\\n f\\\"sensor.test_{characteristic['source_sensor_domain']}_{characteristic['name']}\\\"\\n )\\n assert state is not None, (\\n \\\"no state object for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer empty)\\\"\\n )\\n assert state.state == str(characteristic[\\\"value_0\\\"]), (\\n \\\"value mismatch for characteristic \\\"\\n f\\\"'{characteristic['source_sensor_domain']}/{characteristic['name']}' \\\"\\n \\\"(buffer empty) - \\\"\\n f\\\"assert {state.state} == {str(characteristic['value_0'])}\\\"\\n )\",\n \"def test_updatePlayerbHist_fold_2(self):\\n self.assertEqual(self.player.foldedInd, 1)\",\n \"def test_fantasy_status_nov_1(league):\\n nov_1 = datetime.datetime(2019,11,1)\\n players = league.as_of(nov_1).all_players()\\n # make sure sammy blais is not a free agent, he was picked up oct 31\\n assert(players.loc[6544, 'fantasy_status'] != 'FA')\",\n \"def test_TimeArray_convert_unit():\",\n \"def test_past_meetings(self):\\n pass\",\n \"def test_update_state(self):\\n pass\",\n \"def test_update_state2(self):\\n pass\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def test_get_team_history(self):\\n pass\",\n \"def make_test_observation_table(observatory_name='HESS', n_obs=10,\\n az_range=Angle([0, 360], 'deg'),\\n alt_range=Angle([45, 90], 'deg'),\\n date_range=(Time('2010-01-01'),\\n Time('2015-01-01')),\\n use_abs_time=False,\\n n_tels_range=(3, 4),\\n random_state='random-seed'):\\n from ..data import ObservationTable, observatory_locations\\n random_state = get_random_state(random_state)\\n\\n n_obs_start = 1\\n\\n obs_table = ObservationTable()\\n\\n # build a time reference as the start of 2010\\n dateref = Time('2010-01-01T00:00:00')\\n dateref_mjd_fra, dateref_mjd_int = np.modf(dateref.mjd)\\n\\n # define table header\\n obs_table.meta['OBSERVATORY_NAME'] = observatory_name\\n obs_table.meta['MJDREFI'] = dateref_mjd_int\\n obs_table.meta['MJDREFF'] = dateref_mjd_fra\\n if use_abs_time:\\n # show the observation times in UTC\\n obs_table.meta['TIME_FORMAT'] = 'absolute'\\n else:\\n # show the observation times in seconds after the reference\\n obs_table.meta['TIME_FORMAT'] = 'relative'\\n header = obs_table.meta\\n\\n # obs id\\n obs_id = np.arange(n_obs_start, n_obs_start + n_obs)\\n obs_table['OBS_ID'] = obs_id\\n\\n # obs time: 30 min\\n ontime = Quantity(30. * np.ones_like(obs_id), 'minute').to('second')\\n obs_table['ONTIME'] = ontime\\n\\n # livetime: 25 min\\n time_live = Quantity(25. * np.ones_like(obs_id), 'minute').to('second')\\n obs_table['LIVETIME'] = time_live\\n\\n # start time\\n # - random points between the start of 2010 and the end of 2014 (unless\\n # otherwise specified)\\n # - using the start of 2010 as a reference time for the header of the table\\n # - observations restrict to night time (only if specified time interval is\\n # more than 1 day)\\n # - considering start of astronomical day at midday: implicit in setting\\n # the start of the night, when generating random night hours\\n datestart = date_range[0]\\n dateend = date_range[1]\\n time_start = random_state.uniform(datestart.mjd, dateend.mjd, len(obs_id))\\n time_start = Time(time_start, format='mjd', scale='utc')\\n\\n # check if time interval selected is more than 1 day\\n if (dateend - datestart).jd > 1.:\\n # keep only the integer part (i.e. the day, not the fraction of the day)\\n time_start_f, time_start_i = np.modf(time_start.mjd)\\n time_start = Time(time_start_i, format='mjd', scale='utc')\\n\\n # random generation of night hours: 6 h (from 22 h to 4 h), leaving 1/2 h\\n # time for the last run to finish\\n night_start = Quantity(22., 'hour')\\n night_duration = Quantity(5.5, 'hour')\\n hour_start = random_state.uniform(night_start.value,\\n night_start.value + night_duration.value,\\n len(obs_id))\\n hour_start = Quantity(hour_start, 'hour')\\n\\n # add night hour to integer part of MJD\\n time_start += hour_start\\n\\n if use_abs_time:\\n # show the observation times in UTC\\n time_start = Time(time_start.isot)\\n else:\\n # show the observation times in seconds after the reference\\n time_start = time_relative_to_ref(time_start, header)\\n # converting to quantity (better treatment of units)\\n time_start = Quantity(time_start.sec, 'second')\\n\\n obs_table['TSTART'] = time_start\\n\\n # stop time\\n # calculated as TSTART + ONTIME\\n if use_abs_time:\\n time_stop = Time(obs_table['TSTART'])\\n time_stop += TimeDelta(obs_table['ONTIME'])\\n else:\\n time_stop = TimeDelta(obs_table['TSTART'])\\n time_stop += TimeDelta(obs_table['ONTIME'])\\n # converting to quantity (better treatment of units)\\n time_stop = Quantity(time_stop.sec, 'second')\\n\\n obs_table['TSTOP'] = time_stop\\n\\n # az, alt\\n # random points in a portion of sphere; default: above 45 deg altitude\\n az, alt = sample_sphere(size=len(obs_id),\\n lon_range=az_range,\\n lat_range=alt_range,\\n random_state=random_state)\\n az = Angle(az, 'deg')\\n alt = Angle(alt, 'deg')\\n obs_table['AZ'] = az\\n obs_table['ALT'] = alt\\n\\n # RA, dec\\n # derive from az, alt taking into account that alt, az represent the values\\n # at the middle of the observation, i.e. at time_ref + (TIME_START + TIME_STOP)/2\\n # (or better: time_ref + TIME_START + (TIME_OBSERVATION/2))\\n # in use_abs_time mode, the time_ref should not be added, since it's already included\\n # in TIME_START and TIME_STOP\\n az = Angle(obs_table['AZ'])\\n alt = Angle(obs_table['ALT'])\\n if use_abs_time:\\n obstime = Time(obs_table['TSTART'])\\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\\n else:\\n obstime = time_ref_from_dict(obs_table.meta)\\n obstime += TimeDelta(obs_table['TSTART'])\\n obstime += TimeDelta(obs_table['ONTIME']) / 2.\\n location = observatory_locations[observatory_name]\\n altaz_frame = AltAz(obstime=obstime, location=location)\\n alt_az_coord = SkyCoord(az, alt, frame=altaz_frame)\\n sky_coord = alt_az_coord.transform_to('icrs')\\n obs_table['RA'] = sky_coord.ra\\n obs_table['DEC'] = sky_coord.dec\\n\\n # positions\\n\\n # number of telescopes\\n # random integers in a specified range; default: between 3 and 4\\n n_tels = random_state.randint(n_tels_range[0], n_tels_range[1] + 1, len(obs_id))\\n obs_table['N_TELS'] = n_tels\\n\\n # muon efficiency\\n # random between 0.6 and 1.0\\n muoneff = random_state.uniform(low=0.6, high=1.0, size=len(obs_id))\\n obs_table['MUONEFF'] = muoneff\\n\\n return obs_table\",\n \"def test_cmd_roomstate(self, mock_gametime):\\n\\n mock_gametime.return_value = _get_timestamp(\\\"autumn\\\", \\\"afternoon\\\")\\n\\n # show existing room states (season/time doesn't count)\\n\\n self.assertEqual(self.room1.room_states, [])\\n\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"\\\",\\n f\\\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\\\n None\\\",\\n )\\n\\n # add room states\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"burning\\\",\\n \\\"Added room state 'burning' to this room.\\\",\\n )\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"windy\\\",\\n \\\"Added room state 'windy' to this room.\\\",\\n )\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"\\\",\\n f\\\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\\\n \\\"\\n \\\"'burning' and 'windy'\\\",\\n )\\n # toggle windy\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"windy\\\",\\n \\\"Cleared room state 'windy' from this room.\\\",\\n )\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"\\\",\\n f\\\"Room states (not counting automatic time/season) on Room(#{self.room1.id}):\\\\n \\\"\\n \\\"'burning'\\\",\\n )\\n # add a autumn state and make sure we override it\\n self.room1.add_desc(\\\"Autumn description.\\\", room_state=\\\"autumn\\\")\\n self.room1.add_desc(\\\"Spring description.\\\", room_state=\\\"spring\\\")\\n\\n self.assertEqual(self.room1.get_stateful_desc(), \\\"Autumn description.\\\")\\n self.call(\\n extended_room.CmdExtendedRoomState(),\\n \\\"spring\\\",\\n \\\"Added room state 'spring' to this room.\\\",\\n )\\n self.assertEqual(self.room1.get_stateful_desc(), \\\"Spring description.\\\")\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def test_created_and_updated(self):\\n state1 = State()\\n created = state1.created_at\\n updated = state1.updated_at\\n state1.save()\\n self.assertFalse(updated == state1.updated_at)\\n self.assertTrue(created == state1.created_at)\",\n \"def test_output(self):\\n # reproducible arbitrariness\\n np.random.seed(123423)\\n\\n N = 20\\n tmax = 30.0\\n dt = 1.0\\n\\n n_steps = int_r(tmax/dt)\\n\\n table = np.random.randn(N, n_steps)\\n G = TableLayer(table)\\n\\n M = simulation.StateMonitor(G, 'out')\\n\\n sim = simulation.Simulation(G, M, dt=dt)\\n sim.run(tmax)\\n\\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)\",\n \"def test_custom_time(self):\\n interval = 0.5\\n M = simulation.StateMonitor(self.G, 'v', interval=interval)\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, interval)))\",\n \"def test_time(self):\\r\\n pass\",\n \"def test_past_event(self):\\n pass\",\n \"def test_get_current_state(self):\\n schedule = parser.parse_schedule(SCHEDULE)\\n self.assertEqual(\\\"a\\\", schedule.get_current_state(dt(monday, '09:30')))\\n self.assertEqual(\\\"c\\\", schedule.get_current_state(dt(monday, '08:30')))\\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\\n lookback=False))\",\n \"def test_is_approved_no_approval(time_record_factory):\\n record = time_record_factory()\\n\\n assert not record.is_approved\",\n \"def test_update_state1(self):\\n pass\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_booked_event_has_true_flag(self):\\r\\n user = ViewAfishaTests.mentor\\r\\n event = EventFactory(city=user.profile.city)\\r\\n EventParticipantFactory(\\r\\n event=event,\\r\\n user=user,\\r\\n )\\r\\n\\r\\n client = self.return_authorized_user_client(user)\\r\\n response_data = client.get(path=EVENTS_URL, format=\\\"json\\\").data\\r\\n\\r\\n results = response_data.get(\\\"results\\\")\\r\\n record_dict = results[0]\\r\\n\\r\\n self.assertEqual(\\r\\n \\\"True\\\",\\r\\n str(record_dict.get(\\\"booked\\\")),\\r\\n msg=(\\r\\n \\\"Проверьте, что у мероприятий на которые \\\"\\r\\n \\\"пользователь подписан возвращается флаг \\\"\\r\\n \\\"booked': True.\\\"\\r\\n ),\\r\\n )\",\n \"def event_m20_11_6000():\\n \\\"\\\"\\\"State 0,2: [Preset] Door of the living person_SubState\\\"\\\"\\\"\\n assert event_m20_11_x77(z42=20110480, z43=600000)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def test_get_habits_trackings_table():\\n habits_trackings_table = analytics.habits_trackings_table()\\n assert habits_trackings_table == [\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-25', '18:26'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-25', '19:52'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-26', '20:09'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-26', '04:40'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-27', '10:44'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-27', '20:49'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-28', '01:18'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-06-29', '19:42'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-29', '08:25'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-29', '08:57'),\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-06-29', '02:07'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-06-29', '04:46'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-06-30', '05:54'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-06-30', '12:04'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-01', '05:37'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-01', '07:17'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-01', '12:16'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-02', '22:04'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-02', '23:02'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-03', '07:12'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-04', '08:11'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-05', '10:58'),\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-05', '14:38'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-06', '00:14'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-06', '12:28'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-07', '17:07'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-07', '02:44'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-07', '16:09'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-08', '18:19'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-08', '22:35'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-09', '10:01'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-09', '10:14'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-10', '17:12'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-11', '19:54'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-12', '00:06'),\\n (2, 'Run', 'weekly', 'Improved fitness', 'Jogging and sprinting', '2021-07-12', '16:12'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-12', '18:43'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-13', '08:02'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-14', '21:32'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-15', '08:37'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-16', '08:32'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-16', '11:59'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-16', '21:14'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-18', '03:03'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-18', '06:59'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-19', '22:50'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-19', '13:05'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-20', '04:59'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-20', '05:52'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-20', '09:00'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-21', '18:44'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-21', '03:55'),\\n (3, 'Read', 'daily', '12 books in a year', 'Classics and dystopian', '2021-07-22', '06:52'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-22', '18:27'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-23', '01:28'),\\n (1, 'Yoga', 'daily', 'Be more flexible', 'A low-impact activity', '2021-07-23', '12:17'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-24', '19:10'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-25', '02:18'),\\n (4, 'Meditation', 'daily', 'Training awareness', '20 minutes', '2021-07-26', '09:53'),\\n (5, 'Learn French', 'weekly', 'Fluent in french',\\n 'Practice the 4 skills', '2021-07-26', '16:59')\\n ]\",\n \"def test_workflows(database, monkeypatch):\\n monkeypatch.setattr('dataactcore.utils.sam_recipient.get_sam_props', mock_get_sam_props)\\n sess = database.session\\n all_duns = ['00000000{}'.format(x) for x in range(1, 5)]\\n existing_duns = all_duns[2:]\\n for duns in existing_duns:\\n sess.add(SAMRecipient(awardee_or_recipient_uniqu=duns))\\n sess.commit()\\n\\n recipient_file = os.path.join(CONFIG_BROKER['path'], 'tests', 'unit', 'data', 'historic_DUNS_export_small.csv')\\n\\n update_historical_duns.run_sam_batches(recipient_file, sess, block_size=1)\\n update_historical_duns.import_historic_recipients(sess)\\n\\n expected_results = {\\n '000000001': {\\n 'awardee_or_recipient_uniqu': '000000001',\\n 'uei': 'A1',\\n 'registration_date': '2004-04-01',\\n 'expiration_date': '2013-01-11',\\n 'last_sam_mod_date': '2013-01-11',\\n 'activation_date': '2012-01-11',\\n 'legal_business_name': 'TEST RECIPIENT 1',\\n 'address_line_1': 'Test address 1',\\n 'address_line_2': 'Test address 2',\\n 'city': 'Test city',\\n 'state': 'Test state',\\n 'zip': 'Test zip',\\n 'zip4': 'Test zip4',\\n 'country_code': 'Test country',\\n 'congressional_district': 'Test congressional district',\\n 'business_types_codes': ['A', 'B', 'C'],\\n 'business_types': ['Name A', 'Name B', 'Name C'],\\n 'dba_name': 'Name 1',\\n 'entity_structure': '1A',\\n 'ultimate_parent_unique_ide': '999999999',\\n 'ultimate_parent_uei': 'Z9',\\n 'ultimate_parent_legal_enti': 'Parent Legal Name 1',\\n 'high_comp_officer1_full_na': 'Test Exec 1',\\n 'high_comp_officer1_amount': '1',\\n 'high_comp_officer2_full_na': 'Test Exec 2',\\n 'high_comp_officer2_amount': '2',\\n 'high_comp_officer3_full_na': 'Test Exec 3',\\n 'high_comp_officer3_amount': '3',\\n 'high_comp_officer4_full_na': 'Test Exec 4',\\n 'high_comp_officer4_amount': '4',\\n 'high_comp_officer5_full_na': 'Test Exec 5',\\n 'high_comp_officer5_amount': '5'\\n },\\n '000000002': {\\n 'awardee_or_recipient_uniqu': '000000002',\\n 'uei': 'B2',\\n 'registration_date': '2004-04-02',\\n 'expiration_date': '2013-01-12',\\n 'last_sam_mod_date': '2013-01-12',\\n 'activation_date': '2012-01-12',\\n 'legal_business_name': 'TEST RECIPIENT 2',\\n 'address_line_1': 'Other Test address 1',\\n 'address_line_2': 'Other Test address 2',\\n 'city': 'Other Test city',\\n 'state': 'Other Test state',\\n 'zip': 'Other Test zip',\\n 'zip4': 'Other Test zip4',\\n 'country_code': 'Other Test country',\\n 'congressional_district': 'Other Test congressional district',\\n 'business_types_codes': ['D', 'E', 'F'],\\n 'business_types': ['Name D', 'Name E', 'Name F'],\\n 'dba_name': 'Name 2',\\n 'entity_structure': '2B',\\n 'ultimate_parent_unique_ide': '999999998',\\n 'ultimate_parent_uei': 'Y8',\\n 'ultimate_parent_legal_enti': 'Parent Legal Name 2',\\n 'high_comp_officer1_full_na': 'Test Other Exec 6',\\n 'high_comp_officer1_amount': '6',\\n 'high_comp_officer2_full_na': 'Test Other Exec 7',\\n 'high_comp_officer2_amount': '7',\\n 'high_comp_officer3_full_na': 'Test Other Exec 8',\\n 'high_comp_officer3_amount': '8',\\n 'high_comp_officer4_full_na': 'Test Other Exec 9',\\n 'high_comp_officer4_amount': '9',\\n 'high_comp_officer5_full_na': 'Test Other Exec 10',\\n 'high_comp_officer5_amount': '10'\\n },\\n '000000003': {\\n 'awardee_or_recipient_uniqu': '000000003',\\n 'uei': None,\\n 'registration_date': None,\\n 'expiration_date': None,\\n 'last_sam_mod_date': None,\\n 'activation_date': None,\\n 'legal_business_name': None,\\n 'address_line_1': None,\\n 'address_line_2': None,\\n 'city': None,\\n 'state': None,\\n 'zip': None,\\n 'zip4': None,\\n 'country_code': None,\\n 'congressional_district': None,\\n 'business_types_codes': None,\\n 'business_types': None,\\n 'dba_name': None,\\n 'entity_structure': None,\\n 'ultimate_parent_unique_ide': None,\\n 'ultimate_parent_uei': None,\\n 'ultimate_parent_legal_enti': None,\\n 'high_comp_officer1_full_na': None,\\n 'high_comp_officer1_amount': None,\\n 'high_comp_officer2_full_na': None,\\n 'high_comp_officer2_amount': None,\\n 'high_comp_officer3_full_na': None,\\n 'high_comp_officer3_amount': None,\\n 'high_comp_officer4_full_na': None,\\n 'high_comp_officer4_amount': None,\\n 'high_comp_officer5_full_na': None,\\n 'high_comp_officer5_amount': None\\n },\\n '000000004': {\\n 'awardee_or_recipient_uniqu': '000000004',\\n 'uei': None,\\n 'registration_date': None,\\n 'expiration_date': None,\\n 'last_sam_mod_date': None,\\n 'activation_date': None,\\n 'legal_business_name': None,\\n 'address_line_1': None,\\n 'address_line_2': None,\\n 'city': None,\\n 'state': None,\\n 'zip': None,\\n 'zip4': None,\\n 'country_code': None,\\n 'congressional_district': None,\\n 'business_types_codes': None,\\n 'business_types': None,\\n 'dba_name': None,\\n 'entity_structure': None,\\n 'ultimate_parent_unique_ide': None,\\n 'ultimate_parent_uei': None,\\n 'ultimate_parent_legal_enti': None,\\n 'high_comp_officer1_full_na': None,\\n 'high_comp_officer1_amount': None,\\n 'high_comp_officer2_full_na': None,\\n 'high_comp_officer2_amount': None,\\n 'high_comp_officer3_full_na': None,\\n 'high_comp_officer3_amount': None,\\n 'high_comp_officer4_full_na': None,\\n 'high_comp_officer4_amount': None,\\n 'high_comp_officer5_full_na': None,\\n 'high_comp_officer5_amount': None\\n }\\n }\\n results = {}\\n for recp_obj in sess.query(SAMRecipient).all():\\n results[recp_obj.awardee_or_recipient_uniqu] = {\\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\\n 'uei': recp_obj.uei,\\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\\n 'legal_business_name': recp_obj.legal_business_name,\\n 'address_line_1': recp_obj.address_line_1,\\n 'address_line_2': recp_obj.address_line_2,\\n 'city': recp_obj.city,\\n 'state': recp_obj.state,\\n 'zip': recp_obj.zip,\\n 'zip4': recp_obj.zip4,\\n 'country_code': recp_obj.country_code,\\n 'congressional_district': recp_obj.congressional_district,\\n 'business_types_codes': recp_obj.business_types_codes,\\n 'business_types': recp_obj.business_types,\\n 'dba_name': recp_obj.dba_name,\\n 'entity_structure': recp_obj.entity_structure,\\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\\n }\\n assert results == expected_results\\n\\n # Test to see if truncating the SAM table while keeping the historic reuploads the historic values\\n sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).delete(synchronize_session=False)\\n\\n # Make sure all the historic recipients are removed from the SAMRecipient table\\n assert sess.query(SAMRecipient).filter(SAMRecipient.historic.is_(True)).all() == []\\n\\n # Redo script but don't go through run_sam_batches\\n update_historical_duns.clean_historic_recipients(sess)\\n update_historical_duns.import_historic_recipients(sess)\\n\\n results = {}\\n for recp_obj in sess.query(SAMRecipient).all():\\n results[recp_obj.awardee_or_recipient_uniqu] = {\\n 'awardee_or_recipient_uniqu': recp_obj.awardee_or_recipient_uniqu,\\n 'uei': recp_obj.uei,\\n 'registration_date': str(recp_obj.registration_date) if recp_obj.registration_date else None,\\n 'expiration_date': str(recp_obj.expiration_date) if recp_obj.expiration_date else None,\\n 'last_sam_mod_date': str(recp_obj.last_sam_mod_date) if recp_obj.last_sam_mod_date else None,\\n 'activation_date': str(recp_obj.activation_date) if recp_obj.activation_date else None,\\n 'legal_business_name': recp_obj.legal_business_name,\\n 'address_line_1': recp_obj.address_line_1,\\n 'address_line_2': recp_obj.address_line_2,\\n 'city': recp_obj.city,\\n 'state': recp_obj.state,\\n 'zip': recp_obj.zip,\\n 'zip4': recp_obj.zip4,\\n 'country_code': recp_obj.country_code,\\n 'congressional_district': recp_obj.congressional_district,\\n 'business_types_codes': recp_obj.business_types_codes,\\n 'business_types': recp_obj.business_types,\\n 'dba_name': recp_obj.dba_name,\\n 'entity_structure': recp_obj.entity_structure,\\n 'ultimate_parent_unique_ide': recp_obj.ultimate_parent_unique_ide,\\n 'ultimate_parent_uei': recp_obj.ultimate_parent_uei,\\n 'ultimate_parent_legal_enti': recp_obj.ultimate_parent_legal_enti,\\n 'high_comp_officer1_full_na': recp_obj.high_comp_officer1_full_na,\\n 'high_comp_officer1_amount': recp_obj.high_comp_officer1_amount,\\n 'high_comp_officer2_full_na': recp_obj.high_comp_officer2_full_na,\\n 'high_comp_officer2_amount': recp_obj.high_comp_officer2_amount,\\n 'high_comp_officer3_full_na': recp_obj.high_comp_officer3_full_na,\\n 'high_comp_officer3_amount': recp_obj.high_comp_officer3_amount,\\n 'high_comp_officer4_full_na': recp_obj.high_comp_officer4_full_na,\\n 'high_comp_officer4_amount': recp_obj.high_comp_officer4_amount,\\n 'high_comp_officer5_full_na': recp_obj.high_comp_officer5_full_na,\\n 'high_comp_officer5_amount': recp_obj.high_comp_officer5_amount\\n }\\n assert results == expected_results\",\n \"def test_full_house_flush_ind(self):\",\n \"def test_time(self):\\n conn = self.database.connection()\\n cursor = conn.cursor()\\n dialect = self.database.dialect()\\n dbapi = self.database.dbapi()\\n query = dialect.translate('DROP TABLE test_time')\\n try:\\n cursor.execute(query)\\n except dbapi.Error:\\n conn.rollback()\\n query = dialect.translate('CREATE TABLE test_time ' \\\\\\n '( value TIME NOT NULL )')\\n cursor.execute(query)\\n data = []\\n rr = random.randrange\\n query = 'INSERT INTO test_time VALUES (%s)'\\n for i in range(100):\\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\\n data.append(item)\\n cursor.execute(query, (item,))\\n query = 'SELECT * FROM test_time'\\n cursor.execute(query)\\n result = cursor.fetchall()\\n for row in result:\\n item = row[0]\\n assert isinstance(item, datetime.time)\\n assert item in data\\n data.remove(item)\\n query = dialect.translate('DELETE FROM test_time')\\n cursor.execute(query)\\n query = dialect.translate('DROP TABLE test_time')\\n cursor.execute(query)\\n conn.commit()\",\n \"def test_state(self):\\n states = self.geographies.find({ 'geoid': config.SUMLEV_STATE })\\n\\n self.assertEqual(states.count(), 1)\\n\\n state = states[0]\\n\\n self.assertEqual(state['sumlev'], config.SUMLEV_STATE)\\n self.assertEqual(state['metadata']['NAME'], 'Hawaii')\\n self.assertEqual(state['metadata']['STATE'], '15')\\n\\n pop_2000 = 1211537\\n pop_2010 = 1360301\\n self._test_totalpop(state, pop_2000, pop_2010)\",\n \"def test_get_unsuccessful_ta():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\\n\\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\\n assert np.isnan(list_failed[0])\",\n \"def test_TP_twophase(self):\\n with pytest.raises(StateError):\\n State(substance=\\\"water\\\", T=Q_(373.1242958476844, \\\"K\\\"), p=Q_(101325.0, \\\"Pa\\\"))\",\n \"def test_get_state(self):\\n\\n # test that you can get a state by numerical id\\n mdp = MDP()\\n mdp.add_state(0)\\n self.assertEquals(type(mdp.get_state(0)), State)\\n self.assertIn(mdp.get_state(0), mdp.get_state_list())\",\n \"def test_updatePlayerbHist_fold_1(self):\\n self.assertEqual(self.player.bHist[2], [cardutils.BETSTRING_DICT['FOLD']])\",\n \"def test_match_table_post(self):\\n pass\",\n \"def test_updatePlayerbHist_multiactions_1(self):\\n self.assertEqual(self.player.bHist[0][0], cardutils.BETSTRING_DICT['RAISE'])\",\n \"def test_get_state_comparison_stats_foreign_born(self):\\n\\n key = '% Foreign-born employees'\\n\\n us = CountryFactory(code2='US')\\n germany = CountryFactory(name='Germany')\\n\\n # Mississippi: 0 employees born in Germany, 3 in US\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.mississippi)\\n\\n # Texas: 2 employees born in Germany, 2 in US\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\\n employee.bureau_states.add(self.texas)\\n for _ in range(2):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.texas)\\n\\n # Kentucky: 3 employees born in Germany, 1 employee in US, 3 unknown\\n for _ in range(3):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=germany))\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(1):\\n employee = EmployeeFactory(place_of_birth=PlaceFactory(country=us))\\n employee.bureau_states.add(self.kentucky)\\n for _ in range(3):\\n employee = EmployeeFactory()\\n employee.bureau_states.add(self.kentucky)\\n\\n expected_output = [('Kentucky', 75), ('Texas', 50)]\\n\\n stats = get_state_comparison_stats(number=2)\\n top_states = self.get_state_stats_for_key(stats, key)\\n\\n self.assertListEqual(top_states, expected_output,\\n \\\"'{key}' should contain states with the top x % foreign-born employees\\\")\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_get_state_list(self):\\n\\n mdp = MDP(5)\\n state_list = mdp.get_state_list()\\n\\n self.assertEqual(len(state_list), 5)\\n self.assertIn(mdp.get_state(0), state_list)\",\n \"def test_method(self):\\n self.amenity.save()\\n self.assertTrue(hasattr(self.amenity, \\\"updated_at\\\"))\",\n \"def test_analyze_time(self):\\n self.ph5validate.analyze_time()\\n self.assertEqual(self.ph5validate.das_time.keys(), [('12183', 1, 500)])\\n Dtime = self.ph5validate.das_time[('12183', 1, 500)]\\n\\n # 3 different deploy time\\n self.assertEqual(len(Dtime['time_windows']), 5)\\n\\n # station 9001\\n self.assertEqual(Dtime['time_windows'][0],\\n (1550849950, 1550850034, '9001'))\\n self.assertEqual(Dtime['time_windows'][1],\\n (1550849950, 1550850034, '9001'))\\n self.assertEqual(Dtime['time_windows'][2],\\n (1550849950, 1550850034, '9001'))\\n # station 9002\\n self.assertEqual(Dtime['time_windows'][3],\\n (1550850043, 1550850093, '9002'))\\n # station 9003\\n self.assertEqual(Dtime['time_windows'][4],\\n (1550850125, 1550850187, '9003'))\\n\\n self.assertEqual(Dtime['min_deploy_time'],\\n [1550849950,\\n 'Data exists before deploy time: 7 seconds.'])\",\n \"def test_from_event_to_delete_state() -> None:\\n event = ha.Event(\\n EVENT_STATE_CHANGED,\\n {\\n \\\"entity_id\\\": \\\"sensor.temperature\\\",\\n \\\"old_state\\\": ha.State(\\\"sensor.temperature\\\", \\\"18\\\"),\\n \\\"new_state\\\": None,\\n },\\n )\\n db_state = States.from_event(event)\\n\\n assert db_state.entity_id == \\\"sensor.temperature\\\"\\n assert db_state.state == \\\"\\\"\\n assert db_state.last_changed_ts is None\\n assert db_state.last_updated_ts == event.time_fired.timestamp()\",\n \"def test_eval_alarm(self):\\n def get_state_update_value(h):\\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\\"\\n oldstate = h.data['oldState']['stateValue']\\n newstate = h.data['newState']['stateValue']\\n querydate = h.data['newState']['stateReasonData']['queryDate']\\n querydate = utils.parse_strtime(querydate)\\n return oldstate, newstate, querydate \\n \\n test_uuid = str(uuid.uuid4())\\n alarmname = \\\"TestEvalAlarm_\\\" + test_uuid\\n metricname = \\\"TestEvalMetric_\\\" + test_uuid\\n namespace = \\\"unittest\\\"\\n unit = \\\"Percent\\\"\\n dimensions = {\\\"test_id\\\":test_uuid}\\n threshold = 2.0\\n \\n # create metric alarm\\n alarm = MetricAlarm(name=alarmname, metric=metricname,\\n namespace=namespace, statistic=\\\"Average\\\",\\n comparison=\\\">\\\", threshold=threshold,\\n period=60, evaluation_periods=1, unit=unit,\\n dimensions=dimensions)\\n self.synaps.put_metric_alarm(alarm)\\n \\n # due to put_metric_alarm is asynchronous\\n time.sleep(ASYNC_WAIT)\\n \\n alarm_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0)\\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold + 1, timestamp=alarm_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n\\n ok_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0) \\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold - 2, timestamp=ok_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n \\n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\\n history_item_type=\\\"StateUpdate\\\")\\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\\n\\n result = map(get_state_update_value, histories)\\n \\n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\\n ('ALARM', 'INSUFFICIENT_DATA', None),\\n ('INSUFFICIENT_DATA', 'OK', ok_time),\\n ('OK', 'INSUFFICIENT_DATA', None))\\n \\n failmsg = \\\"expected: %s real: %s\\\" % (expected, result)\\n \\n self.assertEqual(len(result), len(expected), msg=failmsg)\\n \\n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\\n expected):\\n self.assertEqual(r_new, e_new, msg=failmsg)\\n self.assertEqual(r_old, e_old, msg=failmsg)\\n if e_time:\\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\\n msg=failmsg)\\n \\n self.synaps.delete_alarms(alarms=[alarmname])\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"async def test_lazy_state_handles_same_last_updated_and_last_changed(\\n caplog: pytest.LogCaptureFixture,\\n) -> None:\\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n row = PropertyMock(\\n entity_id=\\\"sensor.valid\\\",\\n state=\\\"off\\\",\\n attributes='{\\\"shared\\\":true}',\\n last_updated_ts=now.timestamp(),\\n last_changed_ts=now.timestamp(),\\n )\\n lstate = LazyState(\\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\\n )\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n assert lstate.last_updated.timestamp() == row.last_updated_ts\\n assert lstate.last_changed.timestamp() == row.last_changed_ts\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def test_states_repr_without_timestamp() -> None:\\n fixed_time = datetime(2016, 7, 9, 11, 0, 0, tzinfo=dt_util.UTC, microsecond=432432)\\n states = States(\\n entity_id=\\\"sensor.temp\\\",\\n attributes=None,\\n context_id=None,\\n context_user_id=None,\\n context_parent_id=None,\\n origin_idx=None,\\n last_updated=fixed_time,\\n last_changed=fixed_time,\\n last_updated_ts=None,\\n last_changed_ts=None,\\n )\\n assert \\\"2016-07-09 11:00:00+00:00\\\" in repr(states)\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_first_timestep(self):\\n m = build_model({}, \\\"simple_supply,two_hours,investment_costs\\\")\\n m.run(build_only=True)\\n timestep_0 = \\\"2005-01-01 00:00\\\"\\n assert m._backend_model.timesteps.ord(timestep_0) == 1\",\n \"def test_get_times_height():\\n times = get_times(date, lng, lat, height)\\n for key, value in heightTestTimes.items():\\n assert times[key].strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") == value\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_multiple_states(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory()\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Cancel when created.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.CANCEL, event)\\n s1 = context.work_status.value\\n self.assertEqual(s1, s.CANCELLED)\\n\\n # Cancel when started.\\n c2 = model.factory(initial_state=s.STARTED)\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(c2, a.CANCEL, event)\\n s2 = c2.work_status.value\\n self.assertEqual(s2, s.CANCELLED)\",\n \"def test_from_event_to_db_state_attributes() -> None:\\n attrs = {\\\"this_attr\\\": True}\\n state = ha.State(\\\"sensor.temperature\\\", \\\"18\\\", attrs)\\n event = ha.Event(\\n EVENT_STATE_CHANGED,\\n {\\\"entity_id\\\": \\\"sensor.temperature\\\", \\\"old_state\\\": None, \\\"new_state\\\": state},\\n context=state.context,\\n )\\n db_attrs = StateAttributes()\\n dialect = SupportedDialect.MYSQL\\n\\n db_attrs.shared_attrs = StateAttributes.shared_attrs_bytes_from_event(\\n event, {}, {}, dialect\\n )\\n assert db_attrs.to_native() == attrs\",\n \"def test_status(self):\\n keys = [\\n 'receiver time mark count', 'ant type base', 'solution status',\\n ...\\n ]\\n\\n for i in xrange(0, 100):\\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)\",\n \"def test_negative_is_active_of_homework():\\n assert not expired_hw.is_active()\",\n \"def test_state(self, mock_req):\\n self.setup_api(MOCK_DATA, mock_req)\\n now = datetime(1970, month=1, day=1)\\n with patch(\\\"homeassistant.util.dt.now\\\", return_value=now):\\n for name in self.sensor_dict:\\n sensor = self.sensor_dict[name][\\\"sensor\\\"]\\n self.fake_delay(2)\\n sensor.update()\\n if name == google_wifi.ATTR_LAST_RESTART:\\n assert \\\"1969-12-31 00:00:00\\\" == sensor.state\\n elif name == google_wifi.ATTR_UPTIME:\\n assert 1 == sensor.state\\n elif name == google_wifi.ATTR_STATUS:\\n assert \\\"Online\\\" == sensor.state\\n else:\\n assert \\\"initial\\\" == sensor.state\",\n \"def test_multiple_rules(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory(initial_state=s.STARTED)\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Complete -> completed.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.COMPLETE, event)\\n s1 = context.work_status.value\\n context_id = context.id\\n\\n self.assertEqual(s1, s.COMPLETED)\\n\\n # Complete -> absolutely completed.\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.COMPLETE, event)\\n s2 = context.work_status.value\\n self.assertEqual(s2, s.ABSOLUTELY_COMPLETED)\\n\\n # Complete -> ... the same state ...\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n context = model.Model.query.get(context_id)\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(context, a.COMPLETE, event)\\n s3 = context.work_status.value\\n self.assertEqual(s3, s.ABSOLUTELY_COMPLETED)\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_170518_bad_dbtime(self):\\n spc = parser(get_file('PTSDY1_baddbtime.txt'))\\n answer = utc(2017, 5, 1, 12, 0)\\n for _, outlook in spc.outlook_collections.items():\\n self.assertEqual(outlook.expire, answer)\",\n \"def test_is(self):\\n invalid = self.TDTT()\\n self.check_invalid_is(invalid)\\n\\n valid = self.TDTT(when=self.txt_when)\\n self.check_valid_is(valid)\",\n \"def test_time_status(self):\\n result = self.test_client.time_status\\n\\n assert result == \\\"12312\\\"\",\n \"def test_of_agreement(self): \\n \\n pop_t = world.pop_t \\n pop_sub = world.pop_sub \\n \\n \\n Ns = world.subtime.length\\n \\n dt = world.time.step\\n ds = world.subtime.step\\n N = round(ds/dt)\\n for i in range(Ns):\\n numpy.testing.assert_allclose(pop_sub[:,i],pop_t[i*N,:])\",\n \"def test_from_event_to_db_state() -> None:\\n state = ha.State(\\n \\\"sensor.temperature\\\",\\n \\\"18\\\",\\n context=ha.Context(\\n id=\\\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\\\",\\n parent_id=\\\"01EYQZJXZ5Z1Z1Z1Z1Z1Z1Z1Z1\\\",\\n user_id=\\\"12345678901234567890123456789012\\\",\\n ),\\n )\\n event = ha.Event(\\n EVENT_STATE_CHANGED,\\n {\\\"entity_id\\\": \\\"sensor.temperature\\\", \\\"old_state\\\": None, \\\"new_state\\\": state},\\n context=state.context,\\n )\\n assert state.as_dict() == States.from_event(event).to_native().as_dict()\",\n \"def test_has_ended(self):\\r\\n self.assertTrue(self.past_show_certs.has_ended())\\r\\n self.assertTrue(self.past_noshow_certs.has_ended())\\r\\n self.assertFalse(self.future_show_certs.has_ended())\\r\\n self.assertFalse(self.future_noshow_certs.has_ended())\",\n \"async def test_lazy_state_handles_different_last_updated_and_last_changed(\\n caplog: pytest.LogCaptureFixture,\\n) -> None:\\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n row = PropertyMock(\\n entity_id=\\\"sensor.valid\\\",\\n state=\\\"off\\\",\\n attributes='{\\\"shared\\\":true}',\\n last_updated_ts=now.timestamp(),\\n last_changed_ts=(now - timedelta(seconds=60)).timestamp(),\\n )\\n lstate = LazyState(\\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\\n )\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:03:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n assert lstate.last_updated.timestamp() == row.last_updated_ts\\n assert lstate.last_changed.timestamp() == row.last_changed_ts\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:03:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\",\n \"async def test_state_update(hass):\\n assert await setup_multimatic(hass)\\n assert_entities_count(hass, 11)\\n\\n assert hass.states.is_state(\\\"binary_sensor.dhw_circulation\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.room_1_window\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_lock\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_battery\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.boiler\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_connectivity\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_update\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_online\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_holiday\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_errors\\\", \\\"off\\\")\\n state = hass.states.get(\\\"binary_sensor.multimatic_holiday\\\")\\n assert state.attributes.get(\\\"start_date\\\") is None\\n assert state.attributes.get(\\\"end_date\\\") is None\\n assert state.attributes.get(\\\"temperature\\\") is None\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_quick_mode\\\", \\\"off\\\")\\n\\n dhw = SystemManagerMock.data[\\\"get_dhw\\\"]\\n dhw.circulation.time_program = time_program(SettingModes.ON, None)\\n dhw.circulation.operating_mode = OperatingModes.AUTO\\n\\n hvac_status = SystemManagerMock.data[\\\"get_hvac_status\\\"]\\n hvac_status.boiler_status.status_code = \\\"F11\\\"\\n hvac_status.online = \\\"OFFLINE\\\"\\n hvac_status.update = \\\"UPDATE_PENDING\\\"\\n hvac_status.errors = [\\n Error(\\\"device\\\", \\\"title\\\", \\\"status_code\\\", \\\"descr\\\", datetime.datetime.now())\\n ]\\n\\n rooms = SystemManagerMock.data[\\\"get_rooms\\\"]\\n rooms[0].devices = [Device(\\\"Device 1\\\", \\\"123456789\\\", \\\"VALVE\\\", True, True)]\\n rooms[0].time_program = time_program(None, 20)\\n rooms[0].temperature = 22\\n rooms[0].target_high = 24\\n rooms[0].operating_mode = OperatingModes.AUTO\\n rooms[0].child_lock = True\\n rooms[0].window_open = True\\n\\n new_holiday_mode = active_holiday_mode()\\n SystemManagerMock.data[\\\"get_holiday_mode\\\"] = new_holiday_mode\\n SystemManagerMock.data[\\\"get_quick_mode\\\"] = QuickModes.HOTWATER_BOOST\\n\\n await goto_future(hass)\\n\\n assert_entities_count(hass, 11)\\n assert hass.states.is_state(\\\"binary_sensor.room_1_window\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_lock\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_battery\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.boiler\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.123456789_connectivity\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_update\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_system_online\\\", \\\"off\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_errors\\\", \\\"on\\\")\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_holiday\\\", \\\"on\\\")\\n state = hass.states.get(\\\"binary_sensor.multimatic_holiday\\\")\\n assert state.attributes[\\\"start_date\\\"] == new_holiday_mode.start_date.isoformat()\\n assert state.attributes[\\\"end_date\\\"] == new_holiday_mode.end_date.isoformat()\\n assert state.attributes[\\\"temperature\\\"] == new_holiday_mode.target\\n assert hass.states.is_state(\\\"binary_sensor.multimatic_quick_mode\\\", \\\"on\\\")\\n state = hass.states.get(\\\"binary_sensor.multimatic_quick_mode\\\")\\n assert state.attributes[\\\"quick_mode\\\"] == QuickModes.HOTWATER_BOOST.name\\n assert hass.states.is_state(\\\"binary_sensor.dhw_circulation\\\", \\\"off\\\")\\n\\n SystemManagerMock.data[\\\"get_holiday_mode\\\"] = HolidayMode(False)\\n\\n await goto_future(hass)\\n assert hass.states.is_state(\\\"binary_sensor.dhw_circulation\\\", \\\"on\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68013006","0.6052409","0.60238993","0.60199136","0.5990552","0.59654564","0.59051305","0.58771414","0.587141","0.5824163","0.57776195","0.575167","0.57320946","0.57115203","0.57089746","0.57080287","0.5684115","0.5668643","0.564905","0.56462383","0.5624186","0.5575393","0.55537695","0.5505268","0.54985476","0.5489427","0.54696864","0.5461627","0.54490864","0.5433699","0.54332614","0.5432792","0.54217726","0.5421053","0.541184","0.5398063","0.5397379","0.5394865","0.5380041","0.53692317","0.5357282","0.5353086","0.53493005","0.5348852","0.53447056","0.5344","0.53282106","0.5326239","0.53258187","0.53212637","0.5320721","0.53188753","0.5315504","0.53040284","0.5302646","0.53017604","0.52859455","0.5279591","0.5279202","0.52753556","0.52717054","0.52710325","0.52511185","0.52232003","0.5219207","0.5218135","0.52121073","0.5209475","0.52078134","0.52025634","0.51971936","0.51828575","0.5172181","0.51594514","0.5151996","0.5145199","0.5135852","0.5133792","0.51330495","0.513123","0.5131125","0.5112613","0.5095338","0.5092126","0.5089274","0.5083183","0.5082421","0.50781256","0.5072881","0.50701374","0.5066181","0.50627196","0.5062379","0.50614965","0.50595814","0.50583947","0.5057267","0.5055498","0.505193","0.50479084"],"string":"[\n \"0.68013006\",\n \"0.6052409\",\n \"0.60238993\",\n \"0.60199136\",\n \"0.5990552\",\n \"0.59654564\",\n \"0.59051305\",\n \"0.58771414\",\n \"0.587141\",\n \"0.5824163\",\n \"0.57776195\",\n \"0.575167\",\n \"0.57320946\",\n \"0.57115203\",\n \"0.57089746\",\n \"0.57080287\",\n \"0.5684115\",\n \"0.5668643\",\n \"0.564905\",\n \"0.56462383\",\n \"0.5624186\",\n \"0.5575393\",\n \"0.55537695\",\n \"0.5505268\",\n \"0.54985476\",\n \"0.5489427\",\n \"0.54696864\",\n \"0.5461627\",\n \"0.54490864\",\n \"0.5433699\",\n \"0.54332614\",\n \"0.5432792\",\n \"0.54217726\",\n \"0.5421053\",\n \"0.541184\",\n \"0.5398063\",\n \"0.5397379\",\n \"0.5394865\",\n \"0.5380041\",\n \"0.53692317\",\n \"0.5357282\",\n \"0.5353086\",\n \"0.53493005\",\n \"0.5348852\",\n \"0.53447056\",\n \"0.5344\",\n \"0.53282106\",\n \"0.5326239\",\n \"0.53258187\",\n \"0.53212637\",\n \"0.5320721\",\n \"0.53188753\",\n \"0.5315504\",\n \"0.53040284\",\n \"0.5302646\",\n \"0.53017604\",\n \"0.52859455\",\n \"0.5279591\",\n \"0.5279202\",\n \"0.52753556\",\n \"0.52717054\",\n \"0.52710325\",\n \"0.52511185\",\n \"0.52232003\",\n \"0.5219207\",\n \"0.5218135\",\n \"0.52121073\",\n \"0.5209475\",\n \"0.52078134\",\n \"0.52025634\",\n \"0.51971936\",\n \"0.51828575\",\n \"0.5172181\",\n \"0.51594514\",\n \"0.5151996\",\n \"0.5145199\",\n \"0.5135852\",\n \"0.5133792\",\n \"0.51330495\",\n \"0.513123\",\n \"0.5131125\",\n \"0.5112613\",\n \"0.5095338\",\n \"0.5092126\",\n \"0.5089274\",\n \"0.5083183\",\n \"0.5082421\",\n \"0.50781256\",\n \"0.5072881\",\n \"0.50701374\",\n \"0.5066181\",\n \"0.50627196\",\n \"0.5062379\",\n \"0.50614965\",\n \"0.50595814\",\n \"0.50583947\",\n \"0.5057267\",\n \"0.5055498\",\n \"0.505193\",\n \"0.50479084\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56987756"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":17469,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_IN_PROGRESS state"},"document":{"kind":"string","value":"def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_update_single_row_if_status_is_in_progress(self):\n first = generate_mock_result(status='IN_PROGRESS', success=False)\n self.db.insert_single_result(first)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'IN_PROGRESS')\n second = generate_mock_result(status='SUCCESS', success=True)\n self.db.insert_single_result(second)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'SUCCESS')","def _wait_for_table_status(self, expected='ACTIVE'):\n\n achieved_state = False\n while not achieved_state:\n table_description = self.client.describe_table(TableName=self.table_name)\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\n current_status = table_description['Table']['TableStatus']\n achieved_state = current_status == expected\n sleep(1)","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_retrieve_instances_schedule_state(self):\n pass","def insync_and_state_check(self):\n self.step('verifying tables are properly synced on all endpoints')\n is_ok = True\n limit, count = 10, 0\n while count < limit:\n try:\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\n assert rc == 200, f\"something wrong happened when checking state table {rc}\"\n for state in state_check['data']:\n if not state['in_sync'] == True or not state['state'] == 'loaded':\n print(f\"found state which was not in_sync=True & 'loaded {state}, retrying\")\n is_ok = False\n self.sync_job_check()\n break\n if is_ok:\n break\n count+=1\n except Exception as e:\n print(f\"something wrong happened when checking state table\")\n break","def _compute_queue_state(self):\n for record in self:\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\n record.state = \"completed\"\n elif record.queue_line_draft_records == record.queue_line_total_records:\n record.state = \"draft\"\n elif record.queue_line_total_records == record.queue_line_fail_records:\n record.state = \"failed\"\n else:\n record.state = \"partially_completed\"","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_get_unsuccessful_ta():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\n\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\n assert np.isnan(list_failed[0])","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def test_issue_tracked_times(self):\n pass","def test_update_task_states(self):\r\n changed = self.combinedoe.update_task_states()\r\n self.assertFalse(changed)\r\n\r\n current_task = self.combinedoe.current_task\r\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\r\n changed = self.combinedoe.update_task_states()\r\n\r\n self.assertTrue(changed)","def test_update_instances_schedule_state(self):\n pass","def test_state_after_failure(self):\n pass","def test_get_refresh_job_status(self):\n pass","def load_status_table():","def update_status(cls):\n for job in cls.query.filter(cls.finished == False):\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\n dynamo = DynamoIngestionStatusClient()\n num_urls_left = 0\n for url in urls_left:\n dynamo_url = dynamo.get(url.url)\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\n # will never be processed, so ignore for our purposes\n url.processed = True\n else:\n num_urls_left += 1\n if num_hits_left+num_urls_left == 0:\n job.finished = True\n print '*** Job ID: %s is complete ***' % str(job.id)\n\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\n if not job.finished:\n print 'unprocessed URLs: %i' % num_urls_left\n print 'outstanding HITs: %i\\n' % num_hits_left\n session.flush()","def test_mark_incompleted(self):\n event = Event.objects.all()[0]\n\n todo = TodoItem.objects.create(\n event=event, completed=True, title=\"Test TODO2\",\n due=datetime.date.today(), additional=\"\",\n )\n\n assert todo.completed is True\n\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\n todo.refresh_from_db()\n\n assert todo.completed is False","def _check_completed(self):\n current_rung_df = self.sieve_board.loc[\n self.sieve_board['status'].isin(\n [StatusType.WAITTING, StatusType.RUNNING])\n ]\n if current_rung_df.empty:\n return True\n else:\n return False","def test_update_state(self):\n # add a task\n self.add(title=\"Sample task doing\", description=\"for sample\", state=\"doing\")\n task = Task.query.filter_by(title='Sample task doing').first()\n\n # change task to todo\n old_id = task.id\n self.update_state(id=old_id, state='todo')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'todo')\n\n # change task to done\n old_id = task.id\n self.update_state(id=old_id, state='done')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'done')","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_time_successful(self):\n\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\n '2016-01-10T00:00:00Z',\n '2016-01-13T00:00:00Z',\n 'source')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 2)\n for result in results:\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])","def test_pending_job(self):\n\n pending_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"pending\"))\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def check_states(results):\n assert results[\"states\"][\"device0\"] == {\"counter\": 10, \"rts\": 0}\n assert results[\"states\"][\"device1\"] == {\"counter\": 10, \"rts\": 0}","def test_running_job(self):\n\n running_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"running\"))\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_update_state(self):\n pass","def get_time_in_status(self, issue, state):\n \n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\n\n if ((issue['fields']['issuetype']['name'] == \"Sub-task\") and (issue['fields']['status']['name'] == \"In Progress\")) or \\\n ((issue['fields']['issuetype']['name'] == \"New Feature\") and (issue['fields']['status']['name'] not in [\"Open\", \"In Progress\", \"Reopened\", \"Resolved\", \"Closed\"])):\n print \"Getting time in status for %s\" % issue['fields']['status']['name']\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\n # First state create time same as issue created datetime \n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\") # Current item create datetime\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n diff = nicd - cicdt # Time the issue has been in that state\n \n # If there's a changelog history go thru it else use issue create date and current date for time in status\n # If there's no changelog/histories then we're still in first state, nothing has changed\n if not len(issue['changelog']['histories']):\n print \"no changelog for %s\" % issue['key']\n self.issue_objs[issue['key']].time_in_status = diff.days\n else:\n # If there is a changelog look for the value \"status\", changes may be something other than status\n print \"There's a changelog for issue %s\" % issue['key']\n# print json.dumps(issue, indent=4)\n \n # Go thru changelog histories to see if any of the changes are \"status\" for this state, also get last status or sprint if New Feature\n status_change_found = False\n for histories_item in issue['changelog']['histories']:\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n print \"There's a status change\"\n status_change_found = True\n \n # Get oldest sprint and status within config.change_period days ago for New Features \n if (issue['fields']['issuetype']['name'] == \"New Feature\") and \\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\").date() # histories item create datetime\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\n print \"change too old %s, getting next histories item\" % hicdt\n continue # If older than config.change_period days then we don't care, get next histories item\n else:\n print \"recent change %s \" % hicdt\n if histories_item['items'][0]['field'] == 'Milestone(s)':\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\n print \"last_sprint %s\" % self.issue_objs[issue['key']].last_sprint\n break # Got the oldest change within change_period\n if histories_item['items'][0]['field'] == 'status':\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\n print \"last_status %s\" % self.issue_objs[issue['key']].last_status\n break # Got the oldest change within change_period\n \n # If there was a change in status calculate time in status from changelog\n if(status_change_found):\n total_days = 0 # Accumulate all the diffs for each time in this state\n # Go thru changelog histories to see if this issue was in this state before.\n for i, histories_item in enumerate(issue['changelog']['histories']):\n # All the different kinds of things that have changed, we're only interested in 'status'\n \n # Display all the changelog history items\n if (0):\n for k, v in histories_item.items():\n print \"issue['changelog']['histories'] key: %s value: %s\" % (k, v)\n print \"issue['changelog']['histories']['items'] %s\" % histories_item['items']\n for item in histories_item['items']:\n print \"histories_item['items'] %s\" % item\n print \"histories_item['items'][0] %s\" %histories_item['items'][0]\n for item2 in histories_item['items'][0]:\n print \"histories_item['items'][0] %s %s\" % (item2, histories_item['items'][0][item2])\n \n # If status record we're interested in is found, look for next status change or use current date for end date\n # If status \"Pending\" for New Features or \"Open\" for Sub-tasks there is no change entry for that, it's first state, use created date.\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n current_status_create_datetime = histories_item['created'].split('.')[0]\n \n # Go thru future history changes until the next status change\n status_found = False\n for j in range(i+1, len(issue['changelog']['histories'])):\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\n print \"%s. next histories item created: %s\" % (j, next_item_create_date)\n status_found = True\n break # Break the for loop, next status found\n \n if not status_found: \n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \n print \"No next histories item, using today's date: %s\" % (next_item_create_date)\n \n cscdt = dt.strptime(current_status_create_datetime, \"%Y-%m-%dT%H:%M:%S\") # Current status create datetime\n nicdt = dt.strptime(next_item_create_date, \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n \n print \"Current status create date for %s: %s\" % (issue['key'], cscdt)\n print \"Next item create date for %s: %s\" % (issue['key'], nicdt)\n \n diff = nicdt - cscdt # The number of days the issue has been in that state\n total_days += diff.days # Accumulate all the times this issue has been in this state.\n self.issue_objs[issue['key']].time_in_status = total_days\n else:\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now","def _wait_for_state(self, task_id, current, new):\n state = result = None\n start = time.time()\n # 45 seconds is a long time.\n # TODO(maruel): Make task_runner use exponential backoff instead of\n # hardcoded 10s/30s, which makes these tests tremendously slower than\n # necessary.\n # https://crbug.com/825500\n while time.time() - start < 45.:\n result = self.client.task_result(task_id)\n state = result[u'state']\n if state == new:\n break\n self.assertEqual(current, state, result)\n time.sleep(0.01)\n self.assertEqual(new, state, result)\n return result","def query_job_progress():\n pass","def __checkInited(self, date=None, tkey=None, ttype=None):\n if ttype == 'crontab':\n return False\n if date is None:\n date = self.ddate\n\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\n if doc.get(\"status\") == \"waiting\":\n return True\n return False","def task_status():\n pass","def test_exp_by_states(self):\n api = my_mock.api_mock({\"items\": [{'state': 'Waiting', 'id': 10134},\n {'state': 'Waiting', 'id': 10135},\n {'state': 'Running', 'id': 10130}]})\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\n self.assertEquals(\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\n my_mock.api_mock_stop()","def test_source_time_successful(self):\n\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 1)\n for result in results:\n self.assertTrue(result['id'] in [self.job1.id])","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_completed(self):\n return False","def test_print_progress_flag(self, capsys):\n file = os.path.join(\"tests\", \"data\", \"geolife\", \"geolife_staypoints.csv\")\n staypoints = ti.read_staypoints_csv(file, tz=\"utc\", index_col=\"id\", crs=\"epsg:4326\")\n\n staypoints.as_staypoints.generate_locations(print_progress=True)\n captured_print = capsys.readouterr()\n assert captured_print.err != \"\"\n\n staypoints.as_staypoints.generate_locations(print_progress=False)\n captured_print = capsys.readouterr()\n assert captured_print.err == \"\"","def stalled(self):\n oneHourAgo = datetime.today() - timedelta(hours = 1)\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def test_pending_job(self):\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\n pending_job.pop(\"status\")\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_update_state1(self):\n pass","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def test_running_job(self):\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def pre_step(self,status):\n self.t0 = time.time()\n pass","def test_status_done_basic(self):\n\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\n FORSETI_CONNECT = 'sqlite:///{}'.format(\n get_db_file_path('forseti_1_basic.db'))\n\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\n FORSETI_CONNECT)\n self.source = 'FORSETI'\n self.model_manager = self.service_config.model_manager\n self.model_name = self.model_manager.create(name=self.source)\n\n scoped_session, data_access = self.model_manager.get(self.model_name)\n with scoped_session as session:\n\n importer_cls = importer.by_source(self.source)\n import_runner = importer_cls(\n session,\n self.model_manager.model(self.model_name, expunge=False),\n data_access,\n self.service_config)\n import_runner.run()\n\n model = self.model_manager.model(self.model_name)\n self.assertEqual(model.state,\n 'PARTIAL_SUCCESS',\n 'Model state should be set to PARTIAL_SUCCESS')","def test_completed():\n assert complete == 1\n assert errorflag == 0","def test_service_initiated():\n assert \"ready\" in bkt_outcome_unwind.index()","def test_update_state4(self):\n pass","def task_scanned(now_task):","def test_notBeforeBefore(self):\n dbpool, qpool, clock, performerChosen = self._setupPools()\n\n @transactionally(dbpool.pool.connection)\n def check(txn):\n return qpool.enqueueWork(\n txn, DummyWorkItem, a=3, b=9,\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\n )\n\n yield check\n\n clock.advance(1000)\n # Advance far beyond the given timestamp.\n self.assertEquals(performerChosen, [True])\n\n # Wait for job\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\n clock.advance(1)\n\n # Work item complete\n self.assertTrue(DummyWorkItem.results == {1: 12})","def test_getting_state_parallel(self):\n no_replicates = 25\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\n for i in range(no_replicates):\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)","def test_state(self, mock_req):\n self.setup_api(MOCK_DATA, mock_req)\n now = datetime(1970, month=1, day=1)\n with patch(\"homeassistant.util.dt.now\", return_value=now):\n for name in self.sensor_dict:\n sensor = self.sensor_dict[name][\"sensor\"]\n self.fake_delay(2)\n sensor.update()\n if name == google_wifi.ATTR_LAST_RESTART:\n assert \"1969-12-31 00:00:00\" == sensor.state\n elif name == google_wifi.ATTR_UPTIME:\n assert 1 == sensor.state\n elif name == google_wifi.ATTR_STATUS:\n assert \"Online\" == sensor.state\n else:\n assert \"initial\" == sensor.state","def test_table_false_positives(self):\n pass","def test_delete_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.delete_problem_state('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def testingProgressFunc(state, action, text, tick):\n pass\n #print \"testingProgressFunc\", state, action, text, tick","def test_time_status(self):\n result = self.test_client.time_status\n\n assert result == \"12312\"","def update_task_state(mapper, conn, target):\r\n sql_query = ('select count(id) from task_run \\\r\n where task_run.task_id=%s') % target.task_id\r\n n_answers = conn.scalar(sql_query)\r\n sql_query = ('select n_answers from task \\\r\n where task.id=%s') % target.task_id\r\n task_n_answers = conn.scalar(sql_query)\r\n if (n_answers) >= task_n_answers:\r\n sql_query = (\"UPDATE task SET state=\\'completed\\' \\\r\n where id=%s\") % target.task_id\r\n conn.execute(sql_query)","def test_act_ii(self):\n # setup\n processing_time = 5.0\n self.transaction_behaviour.processing = \"some_dialogue\"\n self.transaction_behaviour.max_processing = 120\n self.transaction_behaviour.processing_time = processing_time\n\n # operation\n self.transaction_behaviour.act()\n\n # after\n self.assert_quantity_in_outbox(0)\n assert (\n self.transaction_behaviour.processing_time\n == processing_time + self.transaction_behaviour.tick_interval\n )","def in_progress(self):\n return False","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_update_state2(self):\n pass","def set_task_in_progress(self):\n\n tasks = self._get_all_tasks()\n\n task_id = tasks[self.tasks_view.currentRow()].Id\n\n self.tasks_flow.set_status(task_id, 1)\n\n # Refresh the table\n self.write_tasks_table()","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def policy_checker(name):\n\n policy = PolicyModel.query(PolicyModel.Name == name).get()\n if not policy:\n logging.error(\"Policy %s not found!\", name)\n return \"not found\", 404\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\n if not schedule:\n logging.error(\"Schedule %s not found!\", policy.Schedule)\n return \"not found\", 404\n logging.debug(\n \"Time at Timezone %s is %s\",\n schedule.Timezone,\n tz.get_time_at_timezone(schedule.Timezone),\n )\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\n logging.debug(\"Working on day %s hour %s\", day, hour)\n arr = np.asarray(schedule.Schedule[\"__ndarray__\"], dtype=np.int).flatten()\n matrix_size = schedule.Schedule[\"Shape\"][0] * schedule.Schedule[\"Shape\"][1]\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\n now = arr[day * 24 + hour]\n prev = arr[prev]\n logging.info(\"Previous state %s current %s\", prev, now)\n if now == prev:\n # do nothing\n logging.info(\"Conditions are met, Nothing should be done for %s\", name)\n return \"ok\", 200\n else:\n # stop/start\n logging.info(\"State is changing for %s to %s\", name, now)\n # for each tag lets do it\n\n task_client = tasks_v2.CloudTasksClient()\n task = {\n \"app_engine_http_request\": { # Specify the type of request.\n \"http_method\": \"POST\",\n \"relative_uri\": \"/tasks/change_state\",\n }\n }\n parent = task_client.queue_path(\n queue=\"zorya-tasks\",\n project=utils.get_project_id(),\n location=location.get_location(),\n )\n for tag in policy.Tags:\n for project in policy.Projects:\n payload = {\n \"project\": project,\n \"tagkey\": next(iter(tag)),\n \"tagvalue\": tag[next(iter(tag))],\n \"action\": str(now),\n }\n task[\"app_engine_http_request\"][\"body\"] = (\n json.dumps(payload)\n ).encode()\n response = task_client.create_task(parent, task)\n logging.debug(\"Task %s enqueued\", response.name)\n return \"ok\", 200","def test_get_current_state(self):\n schedule = parser.parse_schedule(SCHEDULE)\n self.assertEqual(\"a\", schedule.get_current_state(dt(monday, '09:30')))\n self.assertEqual(\"c\", schedule.get_current_state(dt(monday, '08:30')))\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\n lookback=False))","def mark_as_in_progress(self, task):\n raise NotImplementedError('')","def test_reset_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.reset_problem_attempts('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def test_check_contributing_state_completed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=1)\r\n user = UserFactory.create()\r\n TaskRunFactory.create_batch(1, task=task, user=user)\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def test_status(self):\n keys = [\n 'receiver time mark count', 'ant type base', 'solution status',\n ...\n ]\n\n for i in xrange(0, 100):\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)","def test_is_approved_no_approval(time_record_factory):\n record = time_record_factory()\n\n assert not record.is_approved","def test_default_status(self) -> None:\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\n self.assertEqual(self.report.has_started(), False)","def testoptstatus(self): \r\n assert len(self.data.optstatus) == len(self.data.geovalues)\r\n assert self.data.optstatus[0] == self.data.OPT_NEW\r\n for i in range(1, len(self.data.optstatus)-1):\r\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\r\n assert self.data.optstatus[-1] == self.data.OPT_DONE","def test__validate_status__0():\n for input_value, expected_output in (\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\n ):\n output = validate_status(input_value)\n vampytest.assert_is(output, expected_output)","def test_update_state3(self):\n pass","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def testPreProcessResourceState(self):\n resource_id_1 = \"resource_id_1\"\n task_id_1 = \"task_id_1\"\n json_out = {resource_id_1: [task_id_1]}\n\n # Test that the resource id is properly added with associated task\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\n\n # Test that the additional task id is appended to resource id\n task_id_2 = \"task_id_2\"\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\n\n # Test that additional resource id is added into state file\n resource_id_2 = \"resource_id_2\"\n json_out_3 = {\n resource_id_1: [task_id_1, task_id_2],\n resource_id_2: [task_id_1]\n }\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\n\n # Test that same task id is not duplicated into state file\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)","def test_meeting_live_stream_status_update(self):\n pass","def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def check_state(self):\n pass","def _server_poll_expcompleted_(self):\n #print \"class Princeton_CCD function _server_poll_expcompleted_\" \n try:\n last_state = self.polled_running\n except (AttributeError,UnboundLocalError):\n self.polled_running = False\n last_state = False\n self.polled_running = self.query_running()\n if (not bool(last_state) and bool(self.polled_running)):\n self.begin_acq_time = time.time()\n #print self.query_running(), last_state\n #if ((last_state == True) and (self.polled_running == False)): CP\n if (bool(last_state) and not bool(self.polled_running)):\n self.end_acq_time = time.time()\n return True\n else:\n return False","def test_ensure_not_ts_ts(self):\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')","def check_status():\n logger.debug(\"Starting the check_status() routine.\")\n\n url = \"https://www.toggl.com/api/v8/time_entries/current\"\n token = os.environ[\"TOGGL_API_TOKEN\"]\n auth_token = base64.b64encode(f\"{token}:api_token\".encode()).decode()\n resp = requests.get(url, headers={\"Authorization\": \"Basic \" + auth_token})\n\n cols = \"id\", \"duration\", \"description\"\n status = {k: v for k, v in (resp.json()[\"data\"] or {}).items() if k in cols}\n logger.debug(f\"{'Something' if 'id' in status else 'Something'} is being tracked.\")\n\n return status","def _assert_state(self, state_dict):\n instances = db.instance_get_all(self.context)\n self.assertEqual(len(instances), 1)\n\n if 'vm_state' in state_dict:\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\n if 'task_state' in state_dict:\n self.assertEqual(state_dict['task_state'],\n instances[0]['task_state'])\n if 'power_state' in state_dict:\n self.assertEqual(state_dict['power_state'],\n instances[0]['power_state'])","def test_project_state_counters(self):\n #build counters:\n management.call_command('rebuild_counters')\n\n self.client.force_authenticate(self.global_user_1)\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\n\n for project_data in data:\n self.assertIn('state', project_data)\n project_state_data = project_data['state']\n if project_state_data:\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\n self.assertEqual(\n project_state_data['numberOfEnrolledLessons'],\n project_state_obj.lesson_states.count()\n )\n self.assertEqual(\n project_state_data['numberOfCompletedLessons'],\n project_state_obj.lesson_states.filter(is_completed=True).count()\n )","def test_multiple_states(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory()\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Cancel when created.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.CANCEL, event)\n s1 = context.work_status.value\n self.assertEqual(s1, s.CANCELLED)\n\n # Cancel when started.\n c2 = model.factory(initial_state=s.STARTED)\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(c2, a.CANCEL, event)\n s2 = c2.work_status.value\n self.assertEqual(s2, s.CANCELLED)","def test_active_and_date_range(self):\n\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(len(result['results'][0]['job_counts']), 0)","def test_send_state_event_nonoverwriting(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": True}, tok=u1token, state_key=\"tabby\"\n )\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": False}, tok=u1token, state_key=\"moggy\"\n )\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 1,\n )","def test_check_contributing_state_completed_user_not_contributed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=2)\r\n TaskRunFactory.create_batch(2, task=task)\r\n user = UserFactory.create()\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def check_indicator_files(tasks):\n\n for task in tasks:\n if task[\"status\"]==\"unknown\":\n if os.path.exists(task[\"result\"]):\n task[\"status\"]=\"previously completed\"\n else:\n task[\"status\"]=\"to do\"\n return","def test_timeout_processing(self):\n # setup\n self.transaction_behaviour.processing_time = None\n\n # operation\n self.transaction_behaviour._timeout_processing()\n\n # after\n self.assert_quantity_in_outbox(0)","def test_is_finished(self):\n experiment = Experiment(TasksMock())\n self.assertEquals(False, experiment.is_finished())\n for _ in range(0, 17):\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(True, experiment.is_finished())","def test_active(self):\n\n url = '/%s/job-types/status/?is_active=true' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def poll(self):\n\n query = f\"sacct -j {self.jobid} -o State -n -X -P\"\n if self.cluster:\n query += f\" --clusters={self.cluster}\"\n\n cmd = BuildTestCommand(query)\n cmd.execute()\n\n logger.debug(f\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\")\n job_state = cmd.get_output()\n self._state = \"\".join(job_state).rstrip()\n logger.debug(f\"JobID: '{self.jobid}' job state:{self._state}\")","def test_assessment_import_states(self):\n self.import_file(\"assessment_full_no_warnings.csv\")\n response = self.import_file(\"assessment_update_intermediate.csv\")\n expected_errors = {\n \"Assessment\": {\n \"block_errors\": set(),\n \"block_warnings\": set(),\n \"row_errors\": set(),\n \"row_warnings\": set(),\n }\n }\n self._check_csv_response(response, expected_errors)\n\n assessments = {r.slug: r for r in models.Assessment.query.all()}\n self.assertEqual(assessments[\"Assessment 60\"].status,\n models.Assessment.START_STATE)\n self.assertEqual(assessments[\"Assessment 61\"].status,\n models.Assessment.PROGRESS_STATE)\n self.assertEqual(assessments[\"Assessment 62\"].status,\n models.Assessment.DONE_STATE)\n self.assertEqual(assessments[\"Assessment 63\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 64\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 3\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 4\"].status,\n models.Assessment.FINAL_STATE)\n\n # Check that there is only one attachment left\n asmt1 = assessments[\"Assessment 1\"]\n self.assertEqual({\"a.b.com\", \"c d com\"},\n {i.title for i in asmt1.document_url})\n self.assertEqual({u'evidence title 1'},\n {i.title for i in asmt1.document_evidence})"],"string":"[\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_update_single_row_if_status_is_in_progress(self):\\n first = generate_mock_result(status='IN_PROGRESS', success=False)\\n self.db.insert_single_result(first)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'IN_PROGRESS')\\n second = generate_mock_result(status='SUCCESS', success=True)\\n self.db.insert_single_result(second)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'SUCCESS')\",\n \"def _wait_for_table_status(self, expected='ACTIVE'):\\n\\n achieved_state = False\\n while not achieved_state:\\n table_description = self.client.describe_table(TableName=self.table_name)\\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\\n current_status = table_description['Table']['TableStatus']\\n achieved_state = current_status == expected\\n sleep(1)\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def insync_and_state_check(self):\\n self.step('verifying tables are properly synced on all endpoints')\\n is_ok = True\\n limit, count = 10, 0\\n while count < limit:\\n try:\\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\\n assert rc == 200, f\\\"something wrong happened when checking state table {rc}\\\"\\n for state in state_check['data']:\\n if not state['in_sync'] == True or not state['state'] == 'loaded':\\n print(f\\\"found state which was not in_sync=True & 'loaded {state}, retrying\\\")\\n is_ok = False\\n self.sync_job_check()\\n break\\n if is_ok:\\n break\\n count+=1\\n except Exception as e:\\n print(f\\\"something wrong happened when checking state table\\\")\\n break\",\n \"def _compute_queue_state(self):\\n for record in self:\\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\\n record.state = \\\"completed\\\"\\n elif record.queue_line_draft_records == record.queue_line_total_records:\\n record.state = \\\"draft\\\"\\n elif record.queue_line_total_records == record.queue_line_fail_records:\\n record.state = \\\"failed\\\"\\n else:\\n record.state = \\\"partially_completed\\\"\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_get_unsuccessful_ta():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\\n\\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\\n assert np.isnan(list_failed[0])\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_update_task_states(self):\\r\\n changed = self.combinedoe.update_task_states()\\r\\n self.assertFalse(changed)\\r\\n\\r\\n current_task = self.combinedoe.current_task\\r\\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\\r\\n changed = self.combinedoe.update_task_states()\\r\\n\\r\\n self.assertTrue(changed)\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def load_status_table():\",\n \"def update_status(cls):\\n for job in cls.query.filter(cls.finished == False):\\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\\n dynamo = DynamoIngestionStatusClient()\\n num_urls_left = 0\\n for url in urls_left:\\n dynamo_url = dynamo.get(url.url)\\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\\n # will never be processed, so ignore for our purposes\\n url.processed = True\\n else:\\n num_urls_left += 1\\n if num_hits_left+num_urls_left == 0:\\n job.finished = True\\n print '*** Job ID: %s is complete ***' % str(job.id)\\n\\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\\n if not job.finished:\\n print 'unprocessed URLs: %i' % num_urls_left\\n print 'outstanding HITs: %i\\\\n' % num_hits_left\\n session.flush()\",\n \"def test_mark_incompleted(self):\\n event = Event.objects.all()[0]\\n\\n todo = TodoItem.objects.create(\\n event=event, completed=True, title=\\\"Test TODO2\\\",\\n due=datetime.date.today(), additional=\\\"\\\",\\n )\\n\\n assert todo.completed is True\\n\\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\\n todo.refresh_from_db()\\n\\n assert todo.completed is False\",\n \"def _check_completed(self):\\n current_rung_df = self.sieve_board.loc[\\n self.sieve_board['status'].isin(\\n [StatusType.WAITTING, StatusType.RUNNING])\\n ]\\n if current_rung_df.empty:\\n return True\\n else:\\n return False\",\n \"def test_update_state(self):\\n # add a task\\n self.add(title=\\\"Sample task doing\\\", description=\\\"for sample\\\", state=\\\"doing\\\")\\n task = Task.query.filter_by(title='Sample task doing').first()\\n\\n # change task to todo\\n old_id = task.id\\n self.update_state(id=old_id, state='todo')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'todo')\\n\\n # change task to done\\n old_id = task.id\\n self.update_state(id=old_id, state='done')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'done')\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_time_successful(self):\\n\\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\\n '2016-01-10T00:00:00Z',\\n '2016-01-13T00:00:00Z',\\n 'source')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 2)\\n for result in results:\\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])\",\n \"def test_pending_job(self):\\n\\n pending_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"pending\\\"))\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def check_states(results):\\n assert results[\\\"states\\\"][\\\"device0\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\\n assert results[\\\"states\\\"][\\\"device1\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\",\n \"def test_running_job(self):\\n\\n running_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"running\\\"))\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_update_state(self):\\n pass\",\n \"def get_time_in_status(self, issue, state):\\n \\n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\\n\\n if ((issue['fields']['issuetype']['name'] == \\\"Sub-task\\\") and (issue['fields']['status']['name'] == \\\"In Progress\\\")) or \\\\\\n ((issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and (issue['fields']['status']['name'] not in [\\\"Open\\\", \\\"In Progress\\\", \\\"Reopened\\\", \\\"Resolved\\\", \\\"Closed\\\"])):\\n print \\\"Getting time in status for %s\\\" % issue['fields']['status']['name']\\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\\n # First state create time same as issue created datetime \\n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\") # Current item create datetime\\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n diff = nicd - cicdt # Time the issue has been in that state\\n \\n # If there's a changelog history go thru it else use issue create date and current date for time in status\\n # If there's no changelog/histories then we're still in first state, nothing has changed\\n if not len(issue['changelog']['histories']):\\n print \\\"no changelog for %s\\\" % issue['key']\\n self.issue_objs[issue['key']].time_in_status = diff.days\\n else:\\n # If there is a changelog look for the value \\\"status\\\", changes may be something other than status\\n print \\\"There's a changelog for issue %s\\\" % issue['key']\\n# print json.dumps(issue, indent=4)\\n \\n # Go thru changelog histories to see if any of the changes are \\\"status\\\" for this state, also get last status or sprint if New Feature\\n status_change_found = False\\n for histories_item in issue['changelog']['histories']:\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n print \\\"There's a status change\\\"\\n status_change_found = True\\n \\n # Get oldest sprint and status within config.change_period days ago for New Features \\n if (issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and \\\\\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\").date() # histories item create datetime\\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\\n print \\\"change too old %s, getting next histories item\\\" % hicdt\\n continue # If older than config.change_period days then we don't care, get next histories item\\n else:\\n print \\\"recent change %s \\\" % hicdt\\n if histories_item['items'][0]['field'] == 'Milestone(s)':\\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\\n print \\\"last_sprint %s\\\" % self.issue_objs[issue['key']].last_sprint\\n break # Got the oldest change within change_period\\n if histories_item['items'][0]['field'] == 'status':\\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\\n print \\\"last_status %s\\\" % self.issue_objs[issue['key']].last_status\\n break # Got the oldest change within change_period\\n \\n # If there was a change in status calculate time in status from changelog\\n if(status_change_found):\\n total_days = 0 # Accumulate all the diffs for each time in this state\\n # Go thru changelog histories to see if this issue was in this state before.\\n for i, histories_item in enumerate(issue['changelog']['histories']):\\n # All the different kinds of things that have changed, we're only interested in 'status'\\n \\n # Display all the changelog history items\\n if (0):\\n for k, v in histories_item.items():\\n print \\\"issue['changelog']['histories'] key: %s value: %s\\\" % (k, v)\\n print \\\"issue['changelog']['histories']['items'] %s\\\" % histories_item['items']\\n for item in histories_item['items']:\\n print \\\"histories_item['items'] %s\\\" % item\\n print \\\"histories_item['items'][0] %s\\\" %histories_item['items'][0]\\n for item2 in histories_item['items'][0]:\\n print \\\"histories_item['items'][0] %s %s\\\" % (item2, histories_item['items'][0][item2])\\n \\n # If status record we're interested in is found, look for next status change or use current date for end date\\n # If status \\\"Pending\\\" for New Features or \\\"Open\\\" for Sub-tasks there is no change entry for that, it's first state, use created date.\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n current_status_create_datetime = histories_item['created'].split('.')[0]\\n \\n # Go thru future history changes until the next status change\\n status_found = False\\n for j in range(i+1, len(issue['changelog']['histories'])):\\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\\n print \\\"%s. next histories item created: %s\\\" % (j, next_item_create_date)\\n status_found = True\\n break # Break the for loop, next status found\\n \\n if not status_found: \\n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \\n print \\\"No next histories item, using today's date: %s\\\" % (next_item_create_date)\\n \\n cscdt = dt.strptime(current_status_create_datetime, \\\"%Y-%m-%dT%H:%M:%S\\\") # Current status create datetime\\n nicdt = dt.strptime(next_item_create_date, \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n \\n print \\\"Current status create date for %s: %s\\\" % (issue['key'], cscdt)\\n print \\\"Next item create date for %s: %s\\\" % (issue['key'], nicdt)\\n \\n diff = nicdt - cscdt # The number of days the issue has been in that state\\n total_days += diff.days # Accumulate all the times this issue has been in this state.\\n self.issue_objs[issue['key']].time_in_status = total_days\\n else:\\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now\",\n \"def _wait_for_state(self, task_id, current, new):\\n state = result = None\\n start = time.time()\\n # 45 seconds is a long time.\\n # TODO(maruel): Make task_runner use exponential backoff instead of\\n # hardcoded 10s/30s, which makes these tests tremendously slower than\\n # necessary.\\n # https://crbug.com/825500\\n while time.time() - start < 45.:\\n result = self.client.task_result(task_id)\\n state = result[u'state']\\n if state == new:\\n break\\n self.assertEqual(current, state, result)\\n time.sleep(0.01)\\n self.assertEqual(new, state, result)\\n return result\",\n \"def query_job_progress():\\n pass\",\n \"def __checkInited(self, date=None, tkey=None, ttype=None):\\n if ttype == 'crontab':\\n return False\\n if date is None:\\n date = self.ddate\\n\\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\\n if doc.get(\\\"status\\\") == \\\"waiting\\\":\\n return True\\n return False\",\n \"def task_status():\\n pass\",\n \"def test_exp_by_states(self):\\n api = my_mock.api_mock({\\\"items\\\": [{'state': 'Waiting', 'id': 10134},\\n {'state': 'Waiting', 'id': 10135},\\n {'state': 'Running', 'id': 10130}]})\\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\\n self.assertEquals(\\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\\n my_mock.api_mock_stop()\",\n \"def test_source_time_successful(self):\\n\\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 1)\\n for result in results:\\n self.assertTrue(result['id'] in [self.job1.id])\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_completed(self):\\n return False\",\n \"def test_print_progress_flag(self, capsys):\\n file = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"geolife\\\", \\\"geolife_staypoints.csv\\\")\\n staypoints = ti.read_staypoints_csv(file, tz=\\\"utc\\\", index_col=\\\"id\\\", crs=\\\"epsg:4326\\\")\\n\\n staypoints.as_staypoints.generate_locations(print_progress=True)\\n captured_print = capsys.readouterr()\\n assert captured_print.err != \\\"\\\"\\n\\n staypoints.as_staypoints.generate_locations(print_progress=False)\\n captured_print = capsys.readouterr()\\n assert captured_print.err == \\\"\\\"\",\n \"def stalled(self):\\n oneHourAgo = datetime.today() - timedelta(hours = 1)\\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def test_pending_job(self):\\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\\n pending_job.pop(\\\"status\\\")\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_update_state1(self):\\n pass\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def test_running_job(self):\\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def pre_step(self,status):\\n self.t0 = time.time()\\n pass\",\n \"def test_status_done_basic(self):\\n\\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\\n FORSETI_CONNECT = 'sqlite:///{}'.format(\\n get_db_file_path('forseti_1_basic.db'))\\n\\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\\n FORSETI_CONNECT)\\n self.source = 'FORSETI'\\n self.model_manager = self.service_config.model_manager\\n self.model_name = self.model_manager.create(name=self.source)\\n\\n scoped_session, data_access = self.model_manager.get(self.model_name)\\n with scoped_session as session:\\n\\n importer_cls = importer.by_source(self.source)\\n import_runner = importer_cls(\\n session,\\n self.model_manager.model(self.model_name, expunge=False),\\n data_access,\\n self.service_config)\\n import_runner.run()\\n\\n model = self.model_manager.model(self.model_name)\\n self.assertEqual(model.state,\\n 'PARTIAL_SUCCESS',\\n 'Model state should be set to PARTIAL_SUCCESS')\",\n \"def test_completed():\\n assert complete == 1\\n assert errorflag == 0\",\n \"def test_service_initiated():\\n assert \\\"ready\\\" in bkt_outcome_unwind.index()\",\n \"def test_update_state4(self):\\n pass\",\n \"def task_scanned(now_task):\",\n \"def test_notBeforeBefore(self):\\n dbpool, qpool, clock, performerChosen = self._setupPools()\\n\\n @transactionally(dbpool.pool.connection)\\n def check(txn):\\n return qpool.enqueueWork(\\n txn, DummyWorkItem, a=3, b=9,\\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\\n )\\n\\n yield check\\n\\n clock.advance(1000)\\n # Advance far beyond the given timestamp.\\n self.assertEquals(performerChosen, [True])\\n\\n # Wait for job\\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\\n clock.advance(1)\\n\\n # Work item complete\\n self.assertTrue(DummyWorkItem.results == {1: 12})\",\n \"def test_getting_state_parallel(self):\\n no_replicates = 25\\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\\n for i in range(no_replicates):\\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)\",\n \"def test_state(self, mock_req):\\n self.setup_api(MOCK_DATA, mock_req)\\n now = datetime(1970, month=1, day=1)\\n with patch(\\\"homeassistant.util.dt.now\\\", return_value=now):\\n for name in self.sensor_dict:\\n sensor = self.sensor_dict[name][\\\"sensor\\\"]\\n self.fake_delay(2)\\n sensor.update()\\n if name == google_wifi.ATTR_LAST_RESTART:\\n assert \\\"1969-12-31 00:00:00\\\" == sensor.state\\n elif name == google_wifi.ATTR_UPTIME:\\n assert 1 == sensor.state\\n elif name == google_wifi.ATTR_STATUS:\\n assert \\\"Online\\\" == sensor.state\\n else:\\n assert \\\"initial\\\" == sensor.state\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_delete_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.delete_problem_state('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def testingProgressFunc(state, action, text, tick):\\n pass\\n #print \\\"testingProgressFunc\\\", state, action, text, tick\",\n \"def test_time_status(self):\\n result = self.test_client.time_status\\n\\n assert result == \\\"12312\\\"\",\n \"def update_task_state(mapper, conn, target):\\r\\n sql_query = ('select count(id) from task_run \\\\\\r\\n where task_run.task_id=%s') % target.task_id\\r\\n n_answers = conn.scalar(sql_query)\\r\\n sql_query = ('select n_answers from task \\\\\\r\\n where task.id=%s') % target.task_id\\r\\n task_n_answers = conn.scalar(sql_query)\\r\\n if (n_answers) >= task_n_answers:\\r\\n sql_query = (\\\"UPDATE task SET state=\\\\'completed\\\\' \\\\\\r\\n where id=%s\\\") % target.task_id\\r\\n conn.execute(sql_query)\",\n \"def test_act_ii(self):\\n # setup\\n processing_time = 5.0\\n self.transaction_behaviour.processing = \\\"some_dialogue\\\"\\n self.transaction_behaviour.max_processing = 120\\n self.transaction_behaviour.processing_time = processing_time\\n\\n # operation\\n self.transaction_behaviour.act()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\\n assert (\\n self.transaction_behaviour.processing_time\\n == processing_time + self.transaction_behaviour.tick_interval\\n )\",\n \"def in_progress(self):\\n return False\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_update_state2(self):\\n pass\",\n \"def set_task_in_progress(self):\\n\\n tasks = self._get_all_tasks()\\n\\n task_id = tasks[self.tasks_view.currentRow()].Id\\n\\n self.tasks_flow.set_status(task_id, 1)\\n\\n # Refresh the table\\n self.write_tasks_table()\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def policy_checker(name):\\n\\n policy = PolicyModel.query(PolicyModel.Name == name).get()\\n if not policy:\\n logging.error(\\\"Policy %s not found!\\\", name)\\n return \\\"not found\\\", 404\\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\\n if not schedule:\\n logging.error(\\\"Schedule %s not found!\\\", policy.Schedule)\\n return \\\"not found\\\", 404\\n logging.debug(\\n \\\"Time at Timezone %s is %s\\\",\\n schedule.Timezone,\\n tz.get_time_at_timezone(schedule.Timezone),\\n )\\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\\n logging.debug(\\\"Working on day %s hour %s\\\", day, hour)\\n arr = np.asarray(schedule.Schedule[\\\"__ndarray__\\\"], dtype=np.int).flatten()\\n matrix_size = schedule.Schedule[\\\"Shape\\\"][0] * schedule.Schedule[\\\"Shape\\\"][1]\\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\\n now = arr[day * 24 + hour]\\n prev = arr[prev]\\n logging.info(\\\"Previous state %s current %s\\\", prev, now)\\n if now == prev:\\n # do nothing\\n logging.info(\\\"Conditions are met, Nothing should be done for %s\\\", name)\\n return \\\"ok\\\", 200\\n else:\\n # stop/start\\n logging.info(\\\"State is changing for %s to %s\\\", name, now)\\n # for each tag lets do it\\n\\n task_client = tasks_v2.CloudTasksClient()\\n task = {\\n \\\"app_engine_http_request\\\": { # Specify the type of request.\\n \\\"http_method\\\": \\\"POST\\\",\\n \\\"relative_uri\\\": \\\"/tasks/change_state\\\",\\n }\\n }\\n parent = task_client.queue_path(\\n queue=\\\"zorya-tasks\\\",\\n project=utils.get_project_id(),\\n location=location.get_location(),\\n )\\n for tag in policy.Tags:\\n for project in policy.Projects:\\n payload = {\\n \\\"project\\\": project,\\n \\\"tagkey\\\": next(iter(tag)),\\n \\\"tagvalue\\\": tag[next(iter(tag))],\\n \\\"action\\\": str(now),\\n }\\n task[\\\"app_engine_http_request\\\"][\\\"body\\\"] = (\\n json.dumps(payload)\\n ).encode()\\n response = task_client.create_task(parent, task)\\n logging.debug(\\\"Task %s enqueued\\\", response.name)\\n return \\\"ok\\\", 200\",\n \"def test_get_current_state(self):\\n schedule = parser.parse_schedule(SCHEDULE)\\n self.assertEqual(\\\"a\\\", schedule.get_current_state(dt(monday, '09:30')))\\n self.assertEqual(\\\"c\\\", schedule.get_current_state(dt(monday, '08:30')))\\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\\n lookback=False))\",\n \"def mark_as_in_progress(self, task):\\n raise NotImplementedError('')\",\n \"def test_reset_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.reset_problem_attempts('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def test_check_contributing_state_completed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=1)\\r\\n user = UserFactory.create()\\r\\n TaskRunFactory.create_batch(1, task=task, user=user)\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def test_status(self):\\n keys = [\\n 'receiver time mark count', 'ant type base', 'solution status',\\n ...\\n ]\\n\\n for i in xrange(0, 100):\\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)\",\n \"def test_is_approved_no_approval(time_record_factory):\\n record = time_record_factory()\\n\\n assert not record.is_approved\",\n \"def test_default_status(self) -> None:\\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\\n self.assertEqual(self.report.has_started(), False)\",\n \"def testoptstatus(self): \\r\\n assert len(self.data.optstatus) == len(self.data.geovalues)\\r\\n assert self.data.optstatus[0] == self.data.OPT_NEW\\r\\n for i in range(1, len(self.data.optstatus)-1):\\r\\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\\r\\n assert self.data.optstatus[-1] == self.data.OPT_DONE\",\n \"def test__validate_status__0():\\n for input_value, expected_output in (\\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\\n ):\\n output = validate_status(input_value)\\n vampytest.assert_is(output, expected_output)\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def testPreProcessResourceState(self):\\n resource_id_1 = \\\"resource_id_1\\\"\\n task_id_1 = \\\"task_id_1\\\"\\n json_out = {resource_id_1: [task_id_1]}\\n\\n # Test that the resource id is properly added with associated task\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\\n\\n # Test that the additional task id is appended to resource id\\n task_id_2 = \\\"task_id_2\\\"\\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\\n\\n # Test that additional resource id is added into state file\\n resource_id_2 = \\\"resource_id_2\\\"\\n json_out_3 = {\\n resource_id_1: [task_id_1, task_id_2],\\n resource_id_2: [task_id_1]\\n }\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\\n\\n # Test that same task id is not duplicated into state file\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\",\n \"def test_meeting_live_stream_status_update(self):\\n pass\",\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def check_state(self):\\n pass\",\n \"def _server_poll_expcompleted_(self):\\n #print \\\"class Princeton_CCD function _server_poll_expcompleted_\\\" \\n try:\\n last_state = self.polled_running\\n except (AttributeError,UnboundLocalError):\\n self.polled_running = False\\n last_state = False\\n self.polled_running = self.query_running()\\n if (not bool(last_state) and bool(self.polled_running)):\\n self.begin_acq_time = time.time()\\n #print self.query_running(), last_state\\n #if ((last_state == True) and (self.polled_running == False)): CP\\n if (bool(last_state) and not bool(self.polled_running)):\\n self.end_acq_time = time.time()\\n return True\\n else:\\n return False\",\n \"def test_ensure_not_ts_ts(self):\\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')\",\n \"def check_status():\\n logger.debug(\\\"Starting the check_status() routine.\\\")\\n\\n url = \\\"https://www.toggl.com/api/v8/time_entries/current\\\"\\n token = os.environ[\\\"TOGGL_API_TOKEN\\\"]\\n auth_token = base64.b64encode(f\\\"{token}:api_token\\\".encode()).decode()\\n resp = requests.get(url, headers={\\\"Authorization\\\": \\\"Basic \\\" + auth_token})\\n\\n cols = \\\"id\\\", \\\"duration\\\", \\\"description\\\"\\n status = {k: v for k, v in (resp.json()[\\\"data\\\"] or {}).items() if k in cols}\\n logger.debug(f\\\"{'Something' if 'id' in status else 'Something'} is being tracked.\\\")\\n\\n return status\",\n \"def _assert_state(self, state_dict):\\n instances = db.instance_get_all(self.context)\\n self.assertEqual(len(instances), 1)\\n\\n if 'vm_state' in state_dict:\\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\\n if 'task_state' in state_dict:\\n self.assertEqual(state_dict['task_state'],\\n instances[0]['task_state'])\\n if 'power_state' in state_dict:\\n self.assertEqual(state_dict['power_state'],\\n instances[0]['power_state'])\",\n \"def test_project_state_counters(self):\\n #build counters:\\n management.call_command('rebuild_counters')\\n\\n self.client.force_authenticate(self.global_user_1)\\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\\n\\n for project_data in data:\\n self.assertIn('state', project_data)\\n project_state_data = project_data['state']\\n if project_state_data:\\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\\n self.assertEqual(\\n project_state_data['numberOfEnrolledLessons'],\\n project_state_obj.lesson_states.count()\\n )\\n self.assertEqual(\\n project_state_data['numberOfCompletedLessons'],\\n project_state_obj.lesson_states.filter(is_completed=True).count()\\n )\",\n \"def test_multiple_states(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory()\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Cancel when created.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.CANCEL, event)\\n s1 = context.work_status.value\\n self.assertEqual(s1, s.CANCELLED)\\n\\n # Cancel when started.\\n c2 = model.factory(initial_state=s.STARTED)\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(c2, a.CANCEL, event)\\n s2 = c2.work_status.value\\n self.assertEqual(s2, s.CANCELLED)\",\n \"def test_active_and_date_range(self):\\n\\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(len(result['results'][0]['job_counts']), 0)\",\n \"def test_send_state_event_nonoverwriting(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": True}, tok=u1token, state_key=\\\"tabby\\\"\\n )\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": False}, tok=u1token, state_key=\\\"moggy\\\"\\n )\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 1,\\n )\",\n \"def test_check_contributing_state_completed_user_not_contributed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=2)\\r\\n TaskRunFactory.create_batch(2, task=task)\\r\\n user = UserFactory.create()\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def check_indicator_files(tasks):\\n\\n for task in tasks:\\n if task[\\\"status\\\"]==\\\"unknown\\\":\\n if os.path.exists(task[\\\"result\\\"]):\\n task[\\\"status\\\"]=\\\"previously completed\\\"\\n else:\\n task[\\\"status\\\"]=\\\"to do\\\"\\n return\",\n \"def test_timeout_processing(self):\\n # setup\\n self.transaction_behaviour.processing_time = None\\n\\n # operation\\n self.transaction_behaviour._timeout_processing()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\",\n \"def test_is_finished(self):\\n experiment = Experiment(TasksMock())\\n self.assertEquals(False, experiment.is_finished())\\n for _ in range(0, 17):\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(True, experiment.is_finished())\",\n \"def test_active(self):\\n\\n url = '/%s/job-types/status/?is_active=true' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def poll(self):\\n\\n query = f\\\"sacct -j {self.jobid} -o State -n -X -P\\\"\\n if self.cluster:\\n query += f\\\" --clusters={self.cluster}\\\"\\n\\n cmd = BuildTestCommand(query)\\n cmd.execute()\\n\\n logger.debug(f\\\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\\\")\\n job_state = cmd.get_output()\\n self._state = \\\"\\\".join(job_state).rstrip()\\n logger.debug(f\\\"JobID: '{self.jobid}' job state:{self._state}\\\")\",\n \"def test_assessment_import_states(self):\\n self.import_file(\\\"assessment_full_no_warnings.csv\\\")\\n response = self.import_file(\\\"assessment_update_intermediate.csv\\\")\\n expected_errors = {\\n \\\"Assessment\\\": {\\n \\\"block_errors\\\": set(),\\n \\\"block_warnings\\\": set(),\\n \\\"row_errors\\\": set(),\\n \\\"row_warnings\\\": set(),\\n }\\n }\\n self._check_csv_response(response, expected_errors)\\n\\n assessments = {r.slug: r for r in models.Assessment.query.all()}\\n self.assertEqual(assessments[\\\"Assessment 60\\\"].status,\\n models.Assessment.START_STATE)\\n self.assertEqual(assessments[\\\"Assessment 61\\\"].status,\\n models.Assessment.PROGRESS_STATE)\\n self.assertEqual(assessments[\\\"Assessment 62\\\"].status,\\n models.Assessment.DONE_STATE)\\n self.assertEqual(assessments[\\\"Assessment 63\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 64\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 3\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 4\\\"].status,\\n models.Assessment.FINAL_STATE)\\n\\n # Check that there is only one attachment left\\n asmt1 = assessments[\\\"Assessment 1\\\"]\\n self.assertEqual({\\\"a.b.com\\\", \\\"c d com\\\"},\\n {i.title for i in asmt1.document_url})\\n self.assertEqual({u'evidence title 1'},\\n {i.title for i in asmt1.document_evidence})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71193224","0.6860334","0.66941416","0.628547","0.6147146","0.6099557","0.60727316","0.59782106","0.59703326","0.59561163","0.58597606","0.5858871","0.5750034","0.57215893","0.57205486","0.5681524","0.5675636","0.56590813","0.5616347","0.56143284","0.558176","0.5567624","0.55467683","0.5534509","0.5526768","0.5515089","0.550762","0.55065536","0.55008715","0.54807657","0.54804677","0.54558194","0.5450302","0.54500717","0.543557","0.54271126","0.54120195","0.5381252","0.5371506","0.5367457","0.53505445","0.53492796","0.53492117","0.53475666","0.53466445","0.53451306","0.53333145","0.5332616","0.5311005","0.5305092","0.53039515","0.53019696","0.52922636","0.52862763","0.52830493","0.5278864","0.5255235","0.5245685","0.52438986","0.52436084","0.52395296","0.5238431","0.5236099","0.5232433","0.5231263","0.5230454","0.5218501","0.52094007","0.5208183","0.52060616","0.5202795","0.5202125","0.51943576","0.5189145","0.5179092","0.51789975","0.51763767","0.5176183","0.51746553","0.5167336","0.5164447","0.51635677","0.5163309","0.51626724","0.5161","0.51603633","0.5159166","0.5158292","0.5155792","0.5146473","0.5145949","0.51427597","0.5135461","0.5134001","0.5133516","0.5127348","0.51268166","0.51153547","0.51101816","0.51032335"],"string":"[\n \"0.71193224\",\n \"0.6860334\",\n \"0.66941416\",\n \"0.628547\",\n \"0.6147146\",\n \"0.6099557\",\n \"0.60727316\",\n \"0.59782106\",\n \"0.59703326\",\n \"0.59561163\",\n \"0.58597606\",\n \"0.5858871\",\n \"0.5750034\",\n \"0.57215893\",\n \"0.57205486\",\n \"0.5681524\",\n \"0.5675636\",\n \"0.56590813\",\n \"0.5616347\",\n \"0.56143284\",\n \"0.558176\",\n \"0.5567624\",\n \"0.55467683\",\n \"0.5534509\",\n \"0.5526768\",\n \"0.5515089\",\n \"0.550762\",\n \"0.55065536\",\n \"0.55008715\",\n \"0.54807657\",\n \"0.54804677\",\n \"0.54558194\",\n \"0.5450302\",\n \"0.54500717\",\n \"0.543557\",\n \"0.54271126\",\n \"0.54120195\",\n \"0.5381252\",\n \"0.5371506\",\n \"0.5367457\",\n \"0.53505445\",\n \"0.53492796\",\n \"0.53492117\",\n \"0.53475666\",\n \"0.53466445\",\n \"0.53451306\",\n \"0.53333145\",\n \"0.5332616\",\n \"0.5311005\",\n \"0.5305092\",\n \"0.53039515\",\n \"0.53019696\",\n \"0.52922636\",\n \"0.52862763\",\n \"0.52830493\",\n \"0.5278864\",\n \"0.5255235\",\n \"0.5245685\",\n \"0.52438986\",\n \"0.52436084\",\n \"0.52395296\",\n \"0.5238431\",\n \"0.5236099\",\n \"0.5232433\",\n \"0.5231263\",\n \"0.5230454\",\n \"0.5218501\",\n \"0.52094007\",\n \"0.5208183\",\n \"0.52060616\",\n \"0.5202795\",\n \"0.5202125\",\n \"0.51943576\",\n \"0.5189145\",\n \"0.5179092\",\n \"0.51789975\",\n \"0.51763767\",\n \"0.5176183\",\n \"0.51746553\",\n \"0.5167336\",\n \"0.5164447\",\n \"0.51635677\",\n \"0.5163309\",\n \"0.51626724\",\n \"0.5161\",\n \"0.51603633\",\n \"0.5159166\",\n \"0.5158292\",\n \"0.5155792\",\n \"0.5146473\",\n \"0.5145949\",\n \"0.51427597\",\n \"0.5135461\",\n \"0.5134001\",\n \"0.5133516\",\n \"0.5127348\",\n \"0.51268166\",\n \"0.51153547\",\n \"0.51101816\",\n \"0.51032335\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7257185"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17470,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_IN_PROGRESS state"},"document":{"kind":"string","value":"def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_update_single_row_if_status_is_in_progress(self):\n first = generate_mock_result(status='IN_PROGRESS', success=False)\n self.db.insert_single_result(first)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'IN_PROGRESS')\n second = generate_mock_result(status='SUCCESS', success=True)\n self.db.insert_single_result(second)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'SUCCESS')","def _wait_for_table_status(self, expected='ACTIVE'):\n\n achieved_state = False\n while not achieved_state:\n table_description = self.client.describe_table(TableName=self.table_name)\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\n current_status = table_description['Table']['TableStatus']\n achieved_state = current_status == expected\n sleep(1)","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_retrieve_instances_schedule_state(self):\n pass","def insync_and_state_check(self):\n self.step('verifying tables are properly synced on all endpoints')\n is_ok = True\n limit, count = 10, 0\n while count < limit:\n try:\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\n assert rc == 200, f\"something wrong happened when checking state table {rc}\"\n for state in state_check['data']:\n if not state['in_sync'] == True or not state['state'] == 'loaded':\n print(f\"found state which was not in_sync=True & 'loaded {state}, retrying\")\n is_ok = False\n self.sync_job_check()\n break\n if is_ok:\n break\n count+=1\n except Exception as e:\n print(f\"something wrong happened when checking state table\")\n break","def _compute_queue_state(self):\n for record in self:\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\n record.state = \"completed\"\n elif record.queue_line_draft_records == record.queue_line_total_records:\n record.state = \"draft\"\n elif record.queue_line_total_records == record.queue_line_fail_records:\n record.state = \"failed\"\n else:\n record.state = \"partially_completed\"","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_get_unsuccessful_ta():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\n\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\n assert np.isnan(list_failed[0])","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_issue_tracked_times(self):\n pass","def test_update_task_states(self):\r\n changed = self.combinedoe.update_task_states()\r\n self.assertFalse(changed)\r\n\r\n current_task = self.combinedoe.current_task\r\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\r\n changed = self.combinedoe.update_task_states()\r\n\r\n self.assertTrue(changed)","def test_update_instances_schedule_state(self):\n pass","def test_state_after_failure(self):\n pass","def test_get_refresh_job_status(self):\n pass","def load_status_table():","def update_status(cls):\n for job in cls.query.filter(cls.finished == False):\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\n dynamo = DynamoIngestionStatusClient()\n num_urls_left = 0\n for url in urls_left:\n dynamo_url = dynamo.get(url.url)\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\n # will never be processed, so ignore for our purposes\n url.processed = True\n else:\n num_urls_left += 1\n if num_hits_left+num_urls_left == 0:\n job.finished = True\n print '*** Job ID: %s is complete ***' % str(job.id)\n\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\n if not job.finished:\n print 'unprocessed URLs: %i' % num_urls_left\n print 'outstanding HITs: %i\\n' % num_hits_left\n session.flush()","def test_mark_incompleted(self):\n event = Event.objects.all()[0]\n\n todo = TodoItem.objects.create(\n event=event, completed=True, title=\"Test TODO2\",\n due=datetime.date.today(), additional=\"\",\n )\n\n assert todo.completed is True\n\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\n todo.refresh_from_db()\n\n assert todo.completed is False","def _check_completed(self):\n current_rung_df = self.sieve_board.loc[\n self.sieve_board['status'].isin(\n [StatusType.WAITTING, StatusType.RUNNING])\n ]\n if current_rung_df.empty:\n return True\n else:\n return False","def test_update_state(self):\n # add a task\n self.add(title=\"Sample task doing\", description=\"for sample\", state=\"doing\")\n task = Task.query.filter_by(title='Sample task doing').first()\n\n # change task to todo\n old_id = task.id\n self.update_state(id=old_id, state='todo')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'todo')\n\n # change task to done\n old_id = task.id\n self.update_state(id=old_id, state='done')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'done')","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_pending_job(self):\n\n pending_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"pending\"))\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_time_successful(self):\n\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\n '2016-01-10T00:00:00Z',\n '2016-01-13T00:00:00Z',\n 'source')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 2)\n for result in results:\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])","def check_states(results):\n assert results[\"states\"][\"device0\"] == {\"counter\": 10, \"rts\": 0}\n assert results[\"states\"][\"device1\"] == {\"counter\": 10, \"rts\": 0}","def test_running_job(self):\n\n running_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"running\"))\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_update_state(self):\n pass","def get_time_in_status(self, issue, state):\n \n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\n\n if ((issue['fields']['issuetype']['name'] == \"Sub-task\") and (issue['fields']['status']['name'] == \"In Progress\")) or \\\n ((issue['fields']['issuetype']['name'] == \"New Feature\") and (issue['fields']['status']['name'] not in [\"Open\", \"In Progress\", \"Reopened\", \"Resolved\", \"Closed\"])):\n print \"Getting time in status for %s\" % issue['fields']['status']['name']\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\n # First state create time same as issue created datetime \n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\") # Current item create datetime\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n diff = nicd - cicdt # Time the issue has been in that state\n \n # If there's a changelog history go thru it else use issue create date and current date for time in status\n # If there's no changelog/histories then we're still in first state, nothing has changed\n if not len(issue['changelog']['histories']):\n print \"no changelog for %s\" % issue['key']\n self.issue_objs[issue['key']].time_in_status = diff.days\n else:\n # If there is a changelog look for the value \"status\", changes may be something other than status\n print \"There's a changelog for issue %s\" % issue['key']\n# print json.dumps(issue, indent=4)\n \n # Go thru changelog histories to see if any of the changes are \"status\" for this state, also get last status or sprint if New Feature\n status_change_found = False\n for histories_item in issue['changelog']['histories']:\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n print \"There's a status change\"\n status_change_found = True\n \n # Get oldest sprint and status within config.change_period days ago for New Features \n if (issue['fields']['issuetype']['name'] == \"New Feature\") and \\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\").date() # histories item create datetime\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\n print \"change too old %s, getting next histories item\" % hicdt\n continue # If older than config.change_period days then we don't care, get next histories item\n else:\n print \"recent change %s \" % hicdt\n if histories_item['items'][0]['field'] == 'Milestone(s)':\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\n print \"last_sprint %s\" % self.issue_objs[issue['key']].last_sprint\n break # Got the oldest change within change_period\n if histories_item['items'][0]['field'] == 'status':\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\n print \"last_status %s\" % self.issue_objs[issue['key']].last_status\n break # Got the oldest change within change_period\n \n # If there was a change in status calculate time in status from changelog\n if(status_change_found):\n total_days = 0 # Accumulate all the diffs for each time in this state\n # Go thru changelog histories to see if this issue was in this state before.\n for i, histories_item in enumerate(issue['changelog']['histories']):\n # All the different kinds of things that have changed, we're only interested in 'status'\n \n # Display all the changelog history items\n if (0):\n for k, v in histories_item.items():\n print \"issue['changelog']['histories'] key: %s value: %s\" % (k, v)\n print \"issue['changelog']['histories']['items'] %s\" % histories_item['items']\n for item in histories_item['items']:\n print \"histories_item['items'] %s\" % item\n print \"histories_item['items'][0] %s\" %histories_item['items'][0]\n for item2 in histories_item['items'][0]:\n print \"histories_item['items'][0] %s %s\" % (item2, histories_item['items'][0][item2])\n \n # If status record we're interested in is found, look for next status change or use current date for end date\n # If status \"Pending\" for New Features or \"Open\" for Sub-tasks there is no change entry for that, it's first state, use created date.\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n current_status_create_datetime = histories_item['created'].split('.')[0]\n \n # Go thru future history changes until the next status change\n status_found = False\n for j in range(i+1, len(issue['changelog']['histories'])):\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\n print \"%s. next histories item created: %s\" % (j, next_item_create_date)\n status_found = True\n break # Break the for loop, next status found\n \n if not status_found: \n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \n print \"No next histories item, using today's date: %s\" % (next_item_create_date)\n \n cscdt = dt.strptime(current_status_create_datetime, \"%Y-%m-%dT%H:%M:%S\") # Current status create datetime\n nicdt = dt.strptime(next_item_create_date, \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n \n print \"Current status create date for %s: %s\" % (issue['key'], cscdt)\n print \"Next item create date for %s: %s\" % (issue['key'], nicdt)\n \n diff = nicdt - cscdt # The number of days the issue has been in that state\n total_days += diff.days # Accumulate all the times this issue has been in this state.\n self.issue_objs[issue['key']].time_in_status = total_days\n else:\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now","def query_job_progress():\n pass","def _wait_for_state(self, task_id, current, new):\n state = result = None\n start = time.time()\n # 45 seconds is a long time.\n # TODO(maruel): Make task_runner use exponential backoff instead of\n # hardcoded 10s/30s, which makes these tests tremendously slower than\n # necessary.\n # https://crbug.com/825500\n while time.time() - start < 45.:\n result = self.client.task_result(task_id)\n state = result[u'state']\n if state == new:\n break\n self.assertEqual(current, state, result)\n time.sleep(0.01)\n self.assertEqual(new, state, result)\n return result","def __checkInited(self, date=None, tkey=None, ttype=None):\n if ttype == 'crontab':\n return False\n if date is None:\n date = self.ddate\n\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\n if doc.get(\"status\") == \"waiting\":\n return True\n return False","def task_status():\n pass","def test_exp_by_states(self):\n api = my_mock.api_mock({\"items\": [{'state': 'Waiting', 'id': 10134},\n {'state': 'Waiting', 'id': 10135},\n {'state': 'Running', 'id': 10130}]})\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\n self.assertEquals(\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\n my_mock.api_mock_stop()","def test_source_time_successful(self):\n\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 1)\n for result in results:\n self.assertTrue(result['id'] in [self.job1.id])","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_completed(self):\n return False","def test_print_progress_flag(self, capsys):\n file = os.path.join(\"tests\", \"data\", \"geolife\", \"geolife_staypoints.csv\")\n staypoints = ti.read_staypoints_csv(file, tz=\"utc\", index_col=\"id\", crs=\"epsg:4326\")\n\n staypoints.as_staypoints.generate_locations(print_progress=True)\n captured_print = capsys.readouterr()\n assert captured_print.err != \"\"\n\n staypoints.as_staypoints.generate_locations(print_progress=False)\n captured_print = capsys.readouterr()\n assert captured_print.err == \"\"","def stalled(self):\n oneHourAgo = datetime.today() - timedelta(hours = 1)\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def test_pending_job(self):\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\n pending_job.pop(\"status\")\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_update_state1(self):\n pass","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def pre_step(self,status):\n self.t0 = time.time()\n pass","def test_running_job(self):\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_status_done_basic(self):\n\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\n FORSETI_CONNECT = 'sqlite:///{}'.format(\n get_db_file_path('forseti_1_basic.db'))\n\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\n FORSETI_CONNECT)\n self.source = 'FORSETI'\n self.model_manager = self.service_config.model_manager\n self.model_name = self.model_manager.create(name=self.source)\n\n scoped_session, data_access = self.model_manager.get(self.model_name)\n with scoped_session as session:\n\n importer_cls = importer.by_source(self.source)\n import_runner = importer_cls(\n session,\n self.model_manager.model(self.model_name, expunge=False),\n data_access,\n self.service_config)\n import_runner.run()\n\n model = self.model_manager.model(self.model_name)\n self.assertEqual(model.state,\n 'PARTIAL_SUCCESS',\n 'Model state should be set to PARTIAL_SUCCESS')","def test_completed():\n assert complete == 1\n assert errorflag == 0","def test_service_initiated():\n assert \"ready\" in bkt_outcome_unwind.index()","def test_update_state4(self):\n pass","def task_scanned(now_task):","def test_notBeforeBefore(self):\n dbpool, qpool, clock, performerChosen = self._setupPools()\n\n @transactionally(dbpool.pool.connection)\n def check(txn):\n return qpool.enqueueWork(\n txn, DummyWorkItem, a=3, b=9,\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\n )\n\n yield check\n\n clock.advance(1000)\n # Advance far beyond the given timestamp.\n self.assertEquals(performerChosen, [True])\n\n # Wait for job\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\n clock.advance(1)\n\n # Work item complete\n self.assertTrue(DummyWorkItem.results == {1: 12})","def test_getting_state_parallel(self):\n no_replicates = 25\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\n for i in range(no_replicates):\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)","def test_state(self, mock_req):\n self.setup_api(MOCK_DATA, mock_req)\n now = datetime(1970, month=1, day=1)\n with patch(\"homeassistant.util.dt.now\", return_value=now):\n for name in self.sensor_dict:\n sensor = self.sensor_dict[name][\"sensor\"]\n self.fake_delay(2)\n sensor.update()\n if name == google_wifi.ATTR_LAST_RESTART:\n assert \"1969-12-31 00:00:00\" == sensor.state\n elif name == google_wifi.ATTR_UPTIME:\n assert 1 == sensor.state\n elif name == google_wifi.ATTR_STATUS:\n assert \"Online\" == sensor.state\n else:\n assert \"initial\" == sensor.state","def test_table_false_positives(self):\n pass","def test_delete_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.delete_problem_state('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def testingProgressFunc(state, action, text, tick):\n pass\n #print \"testingProgressFunc\", state, action, text, tick","def test_time_status(self):\n result = self.test_client.time_status\n\n assert result == \"12312\"","def update_task_state(mapper, conn, target):\r\n sql_query = ('select count(id) from task_run \\\r\n where task_run.task_id=%s') % target.task_id\r\n n_answers = conn.scalar(sql_query)\r\n sql_query = ('select n_answers from task \\\r\n where task.id=%s') % target.task_id\r\n task_n_answers = conn.scalar(sql_query)\r\n if (n_answers) >= task_n_answers:\r\n sql_query = (\"UPDATE task SET state=\\'completed\\' \\\r\n where id=%s\") % target.task_id\r\n conn.execute(sql_query)","def test_act_ii(self):\n # setup\n processing_time = 5.0\n self.transaction_behaviour.processing = \"some_dialogue\"\n self.transaction_behaviour.max_processing = 120\n self.transaction_behaviour.processing_time = processing_time\n\n # operation\n self.transaction_behaviour.act()\n\n # after\n self.assert_quantity_in_outbox(0)\n assert (\n self.transaction_behaviour.processing_time\n == processing_time + self.transaction_behaviour.tick_interval\n )","def in_progress(self):\n return False","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_update_state2(self):\n pass","def set_task_in_progress(self):\n\n tasks = self._get_all_tasks()\n\n task_id = tasks[self.tasks_view.currentRow()].Id\n\n self.tasks_flow.set_status(task_id, 1)\n\n # Refresh the table\n self.write_tasks_table()","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def policy_checker(name):\n\n policy = PolicyModel.query(PolicyModel.Name == name).get()\n if not policy:\n logging.error(\"Policy %s not found!\", name)\n return \"not found\", 404\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\n if not schedule:\n logging.error(\"Schedule %s not found!\", policy.Schedule)\n return \"not found\", 404\n logging.debug(\n \"Time at Timezone %s is %s\",\n schedule.Timezone,\n tz.get_time_at_timezone(schedule.Timezone),\n )\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\n logging.debug(\"Working on day %s hour %s\", day, hour)\n arr = np.asarray(schedule.Schedule[\"__ndarray__\"], dtype=np.int).flatten()\n matrix_size = schedule.Schedule[\"Shape\"][0] * schedule.Schedule[\"Shape\"][1]\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\n now = arr[day * 24 + hour]\n prev = arr[prev]\n logging.info(\"Previous state %s current %s\", prev, now)\n if now == prev:\n # do nothing\n logging.info(\"Conditions are met, Nothing should be done for %s\", name)\n return \"ok\", 200\n else:\n # stop/start\n logging.info(\"State is changing for %s to %s\", name, now)\n # for each tag lets do it\n\n task_client = tasks_v2.CloudTasksClient()\n task = {\n \"app_engine_http_request\": { # Specify the type of request.\n \"http_method\": \"POST\",\n \"relative_uri\": \"/tasks/change_state\",\n }\n }\n parent = task_client.queue_path(\n queue=\"zorya-tasks\",\n project=utils.get_project_id(),\n location=location.get_location(),\n )\n for tag in policy.Tags:\n for project in policy.Projects:\n payload = {\n \"project\": project,\n \"tagkey\": next(iter(tag)),\n \"tagvalue\": tag[next(iter(tag))],\n \"action\": str(now),\n }\n task[\"app_engine_http_request\"][\"body\"] = (\n json.dumps(payload)\n ).encode()\n response = task_client.create_task(parent, task)\n logging.debug(\"Task %s enqueued\", response.name)\n return \"ok\", 200","def test_get_current_state(self):\n schedule = parser.parse_schedule(SCHEDULE)\n self.assertEqual(\"a\", schedule.get_current_state(dt(monday, '09:30')))\n self.assertEqual(\"c\", schedule.get_current_state(dt(monday, '08:30')))\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\n lookback=False))","def mark_as_in_progress(self, task):\n raise NotImplementedError('')","def test_reset_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.reset_problem_attempts('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def test_check_contributing_state_completed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=1)\r\n user = UserFactory.create()\r\n TaskRunFactory.create_batch(1, task=task, user=user)\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def test_status(self):\n keys = [\n 'receiver time mark count', 'ant type base', 'solution status',\n ...\n ]\n\n for i in xrange(0, 100):\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)","def test_default_status(self) -> None:\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\n self.assertEqual(self.report.has_started(), False)","def test_is_approved_no_approval(time_record_factory):\n record = time_record_factory()\n\n assert not record.is_approved","def test__validate_status__0():\n for input_value, expected_output in (\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\n ):\n output = validate_status(input_value)\n vampytest.assert_is(output, expected_output)","def testoptstatus(self): \r\n assert len(self.data.optstatus) == len(self.data.geovalues)\r\n assert self.data.optstatus[0] == self.data.OPT_NEW\r\n for i in range(1, len(self.data.optstatus)-1):\r\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\r\n assert self.data.optstatus[-1] == self.data.OPT_DONE","def test_update_state3(self):\n pass","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def testPreProcessResourceState(self):\n resource_id_1 = \"resource_id_1\"\n task_id_1 = \"task_id_1\"\n json_out = {resource_id_1: [task_id_1]}\n\n # Test that the resource id is properly added with associated task\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\n\n # Test that the additional task id is appended to resource id\n task_id_2 = \"task_id_2\"\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\n\n # Test that additional resource id is added into state file\n resource_id_2 = \"resource_id_2\"\n json_out_3 = {\n resource_id_1: [task_id_1, task_id_2],\n resource_id_2: [task_id_1]\n }\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\n\n # Test that same task id is not duplicated into state file\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)","def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_meeting_live_stream_status_update(self):\n pass","def check_state(self):\n pass","def _server_poll_expcompleted_(self):\n #print \"class Princeton_CCD function _server_poll_expcompleted_\" \n try:\n last_state = self.polled_running\n except (AttributeError,UnboundLocalError):\n self.polled_running = False\n last_state = False\n self.polled_running = self.query_running()\n if (not bool(last_state) and bool(self.polled_running)):\n self.begin_acq_time = time.time()\n #print self.query_running(), last_state\n #if ((last_state == True) and (self.polled_running == False)): CP\n if (bool(last_state) and not bool(self.polled_running)):\n self.end_acq_time = time.time()\n return True\n else:\n return False","def test_ensure_not_ts_ts(self):\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')","def check_status():\n logger.debug(\"Starting the check_status() routine.\")\n\n url = \"https://www.toggl.com/api/v8/time_entries/current\"\n token = os.environ[\"TOGGL_API_TOKEN\"]\n auth_token = base64.b64encode(f\"{token}:api_token\".encode()).decode()\n resp = requests.get(url, headers={\"Authorization\": \"Basic \" + auth_token})\n\n cols = \"id\", \"duration\", \"description\"\n status = {k: v for k, v in (resp.json()[\"data\"] or {}).items() if k in cols}\n logger.debug(f\"{'Something' if 'id' in status else 'Something'} is being tracked.\")\n\n return status","def _assert_state(self, state_dict):\n instances = db.instance_get_all(self.context)\n self.assertEqual(len(instances), 1)\n\n if 'vm_state' in state_dict:\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\n if 'task_state' in state_dict:\n self.assertEqual(state_dict['task_state'],\n instances[0]['task_state'])\n if 'power_state' in state_dict:\n self.assertEqual(state_dict['power_state'],\n instances[0]['power_state'])","def test_project_state_counters(self):\n #build counters:\n management.call_command('rebuild_counters')\n\n self.client.force_authenticate(self.global_user_1)\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\n\n for project_data in data:\n self.assertIn('state', project_data)\n project_state_data = project_data['state']\n if project_state_data:\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\n self.assertEqual(\n project_state_data['numberOfEnrolledLessons'],\n project_state_obj.lesson_states.count()\n )\n self.assertEqual(\n project_state_data['numberOfCompletedLessons'],\n project_state_obj.lesson_states.filter(is_completed=True).count()\n )","def test_multiple_states(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory()\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Cancel when created.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.CANCEL, event)\n s1 = context.work_status.value\n self.assertEqual(s1, s.CANCELLED)\n\n # Cancel when started.\n c2 = model.factory(initial_state=s.STARTED)\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(c2, a.CANCEL, event)\n s2 = c2.work_status.value\n self.assertEqual(s2, s.CANCELLED)","def test_active_and_date_range(self):\n\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(len(result['results'][0]['job_counts']), 0)","def test_send_state_event_nonoverwriting(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": True}, tok=u1token, state_key=\"tabby\"\n )\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": False}, tok=u1token, state_key=\"moggy\"\n )\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 1,\n )","def test_check_contributing_state_completed_user_not_contributed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=2)\r\n TaskRunFactory.create_batch(2, task=task)\r\n user = UserFactory.create()\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def check_indicator_files(tasks):\n\n for task in tasks:\n if task[\"status\"]==\"unknown\":\n if os.path.exists(task[\"result\"]):\n task[\"status\"]=\"previously completed\"\n else:\n task[\"status\"]=\"to do\"\n return","def test_timeout_processing(self):\n # setup\n self.transaction_behaviour.processing_time = None\n\n # operation\n self.transaction_behaviour._timeout_processing()\n\n # after\n self.assert_quantity_in_outbox(0)","def test_is_finished(self):\n experiment = Experiment(TasksMock())\n self.assertEquals(False, experiment.is_finished())\n for _ in range(0, 17):\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(True, experiment.is_finished())","def test_active(self):\n\n url = '/%s/job-types/status/?is_active=true' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def poll(self):\n\n query = f\"sacct -j {self.jobid} -o State -n -X -P\"\n if self.cluster:\n query += f\" --clusters={self.cluster}\"\n\n cmd = BuildTestCommand(query)\n cmd.execute()\n\n logger.debug(f\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\")\n job_state = cmd.get_output()\n self._state = \"\".join(job_state).rstrip()\n logger.debug(f\"JobID: '{self.jobid}' job state:{self._state}\")","def test_assessment_import_states(self):\n self.import_file(\"assessment_full_no_warnings.csv\")\n response = self.import_file(\"assessment_update_intermediate.csv\")\n expected_errors = {\n \"Assessment\": {\n \"block_errors\": set(),\n \"block_warnings\": set(),\n \"row_errors\": set(),\n \"row_warnings\": set(),\n }\n }\n self._check_csv_response(response, expected_errors)\n\n assessments = {r.slug: r for r in models.Assessment.query.all()}\n self.assertEqual(assessments[\"Assessment 60\"].status,\n models.Assessment.START_STATE)\n self.assertEqual(assessments[\"Assessment 61\"].status,\n models.Assessment.PROGRESS_STATE)\n self.assertEqual(assessments[\"Assessment 62\"].status,\n models.Assessment.DONE_STATE)\n self.assertEqual(assessments[\"Assessment 63\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 64\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 3\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 4\"].status,\n models.Assessment.FINAL_STATE)\n\n # Check that there is only one attachment left\n asmt1 = assessments[\"Assessment 1\"]\n self.assertEqual({\"a.b.com\", \"c d com\"},\n {i.title for i in asmt1.document_url})\n self.assertEqual({u'evidence title 1'},\n {i.title for i in asmt1.document_evidence})"],"string":"[\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_update_single_row_if_status_is_in_progress(self):\\n first = generate_mock_result(status='IN_PROGRESS', success=False)\\n self.db.insert_single_result(first)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'IN_PROGRESS')\\n second = generate_mock_result(status='SUCCESS', success=True)\\n self.db.insert_single_result(second)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'SUCCESS')\",\n \"def _wait_for_table_status(self, expected='ACTIVE'):\\n\\n achieved_state = False\\n while not achieved_state:\\n table_description = self.client.describe_table(TableName=self.table_name)\\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\\n current_status = table_description['Table']['TableStatus']\\n achieved_state = current_status == expected\\n sleep(1)\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def insync_and_state_check(self):\\n self.step('verifying tables are properly synced on all endpoints')\\n is_ok = True\\n limit, count = 10, 0\\n while count < limit:\\n try:\\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\\n assert rc == 200, f\\\"something wrong happened when checking state table {rc}\\\"\\n for state in state_check['data']:\\n if not state['in_sync'] == True or not state['state'] == 'loaded':\\n print(f\\\"found state which was not in_sync=True & 'loaded {state}, retrying\\\")\\n is_ok = False\\n self.sync_job_check()\\n break\\n if is_ok:\\n break\\n count+=1\\n except Exception as e:\\n print(f\\\"something wrong happened when checking state table\\\")\\n break\",\n \"def _compute_queue_state(self):\\n for record in self:\\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\\n record.state = \\\"completed\\\"\\n elif record.queue_line_draft_records == record.queue_line_total_records:\\n record.state = \\\"draft\\\"\\n elif record.queue_line_total_records == record.queue_line_fail_records:\\n record.state = \\\"failed\\\"\\n else:\\n record.state = \\\"partially_completed\\\"\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_get_unsuccessful_ta():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\\n\\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\\n assert np.isnan(list_failed[0])\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_update_task_states(self):\\r\\n changed = self.combinedoe.update_task_states()\\r\\n self.assertFalse(changed)\\r\\n\\r\\n current_task = self.combinedoe.current_task\\r\\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\\r\\n changed = self.combinedoe.update_task_states()\\r\\n\\r\\n self.assertTrue(changed)\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def load_status_table():\",\n \"def update_status(cls):\\n for job in cls.query.filter(cls.finished == False):\\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\\n dynamo = DynamoIngestionStatusClient()\\n num_urls_left = 0\\n for url in urls_left:\\n dynamo_url = dynamo.get(url.url)\\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\\n # will never be processed, so ignore for our purposes\\n url.processed = True\\n else:\\n num_urls_left += 1\\n if num_hits_left+num_urls_left == 0:\\n job.finished = True\\n print '*** Job ID: %s is complete ***' % str(job.id)\\n\\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\\n if not job.finished:\\n print 'unprocessed URLs: %i' % num_urls_left\\n print 'outstanding HITs: %i\\\\n' % num_hits_left\\n session.flush()\",\n \"def test_mark_incompleted(self):\\n event = Event.objects.all()[0]\\n\\n todo = TodoItem.objects.create(\\n event=event, completed=True, title=\\\"Test TODO2\\\",\\n due=datetime.date.today(), additional=\\\"\\\",\\n )\\n\\n assert todo.completed is True\\n\\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\\n todo.refresh_from_db()\\n\\n assert todo.completed is False\",\n \"def _check_completed(self):\\n current_rung_df = self.sieve_board.loc[\\n self.sieve_board['status'].isin(\\n [StatusType.WAITTING, StatusType.RUNNING])\\n ]\\n if current_rung_df.empty:\\n return True\\n else:\\n return False\",\n \"def test_update_state(self):\\n # add a task\\n self.add(title=\\\"Sample task doing\\\", description=\\\"for sample\\\", state=\\\"doing\\\")\\n task = Task.query.filter_by(title='Sample task doing').first()\\n\\n # change task to todo\\n old_id = task.id\\n self.update_state(id=old_id, state='todo')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'todo')\\n\\n # change task to done\\n old_id = task.id\\n self.update_state(id=old_id, state='done')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'done')\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_pending_job(self):\\n\\n pending_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"pending\\\"))\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_time_successful(self):\\n\\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\\n '2016-01-10T00:00:00Z',\\n '2016-01-13T00:00:00Z',\\n 'source')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 2)\\n for result in results:\\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])\",\n \"def check_states(results):\\n assert results[\\\"states\\\"][\\\"device0\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\\n assert results[\\\"states\\\"][\\\"device1\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\",\n \"def test_running_job(self):\\n\\n running_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"running\\\"))\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_update_state(self):\\n pass\",\n \"def get_time_in_status(self, issue, state):\\n \\n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\\n\\n if ((issue['fields']['issuetype']['name'] == \\\"Sub-task\\\") and (issue['fields']['status']['name'] == \\\"In Progress\\\")) or \\\\\\n ((issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and (issue['fields']['status']['name'] not in [\\\"Open\\\", \\\"In Progress\\\", \\\"Reopened\\\", \\\"Resolved\\\", \\\"Closed\\\"])):\\n print \\\"Getting time in status for %s\\\" % issue['fields']['status']['name']\\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\\n # First state create time same as issue created datetime \\n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\") # Current item create datetime\\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n diff = nicd - cicdt # Time the issue has been in that state\\n \\n # If there's a changelog history go thru it else use issue create date and current date for time in status\\n # If there's no changelog/histories then we're still in first state, nothing has changed\\n if not len(issue['changelog']['histories']):\\n print \\\"no changelog for %s\\\" % issue['key']\\n self.issue_objs[issue['key']].time_in_status = diff.days\\n else:\\n # If there is a changelog look for the value \\\"status\\\", changes may be something other than status\\n print \\\"There's a changelog for issue %s\\\" % issue['key']\\n# print json.dumps(issue, indent=4)\\n \\n # Go thru changelog histories to see if any of the changes are \\\"status\\\" for this state, also get last status or sprint if New Feature\\n status_change_found = False\\n for histories_item in issue['changelog']['histories']:\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n print \\\"There's a status change\\\"\\n status_change_found = True\\n \\n # Get oldest sprint and status within config.change_period days ago for New Features \\n if (issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and \\\\\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\").date() # histories item create datetime\\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\\n print \\\"change too old %s, getting next histories item\\\" % hicdt\\n continue # If older than config.change_period days then we don't care, get next histories item\\n else:\\n print \\\"recent change %s \\\" % hicdt\\n if histories_item['items'][0]['field'] == 'Milestone(s)':\\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\\n print \\\"last_sprint %s\\\" % self.issue_objs[issue['key']].last_sprint\\n break # Got the oldest change within change_period\\n if histories_item['items'][0]['field'] == 'status':\\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\\n print \\\"last_status %s\\\" % self.issue_objs[issue['key']].last_status\\n break # Got the oldest change within change_period\\n \\n # If there was a change in status calculate time in status from changelog\\n if(status_change_found):\\n total_days = 0 # Accumulate all the diffs for each time in this state\\n # Go thru changelog histories to see if this issue was in this state before.\\n for i, histories_item in enumerate(issue['changelog']['histories']):\\n # All the different kinds of things that have changed, we're only interested in 'status'\\n \\n # Display all the changelog history items\\n if (0):\\n for k, v in histories_item.items():\\n print \\\"issue['changelog']['histories'] key: %s value: %s\\\" % (k, v)\\n print \\\"issue['changelog']['histories']['items'] %s\\\" % histories_item['items']\\n for item in histories_item['items']:\\n print \\\"histories_item['items'] %s\\\" % item\\n print \\\"histories_item['items'][0] %s\\\" %histories_item['items'][0]\\n for item2 in histories_item['items'][0]:\\n print \\\"histories_item['items'][0] %s %s\\\" % (item2, histories_item['items'][0][item2])\\n \\n # If status record we're interested in is found, look for next status change or use current date for end date\\n # If status \\\"Pending\\\" for New Features or \\\"Open\\\" for Sub-tasks there is no change entry for that, it's first state, use created date.\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n current_status_create_datetime = histories_item['created'].split('.')[0]\\n \\n # Go thru future history changes until the next status change\\n status_found = False\\n for j in range(i+1, len(issue['changelog']['histories'])):\\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\\n print \\\"%s. next histories item created: %s\\\" % (j, next_item_create_date)\\n status_found = True\\n break # Break the for loop, next status found\\n \\n if not status_found: \\n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \\n print \\\"No next histories item, using today's date: %s\\\" % (next_item_create_date)\\n \\n cscdt = dt.strptime(current_status_create_datetime, \\\"%Y-%m-%dT%H:%M:%S\\\") # Current status create datetime\\n nicdt = dt.strptime(next_item_create_date, \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n \\n print \\\"Current status create date for %s: %s\\\" % (issue['key'], cscdt)\\n print \\\"Next item create date for %s: %s\\\" % (issue['key'], nicdt)\\n \\n diff = nicdt - cscdt # The number of days the issue has been in that state\\n total_days += diff.days # Accumulate all the times this issue has been in this state.\\n self.issue_objs[issue['key']].time_in_status = total_days\\n else:\\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now\",\n \"def query_job_progress():\\n pass\",\n \"def _wait_for_state(self, task_id, current, new):\\n state = result = None\\n start = time.time()\\n # 45 seconds is a long time.\\n # TODO(maruel): Make task_runner use exponential backoff instead of\\n # hardcoded 10s/30s, which makes these tests tremendously slower than\\n # necessary.\\n # https://crbug.com/825500\\n while time.time() - start < 45.:\\n result = self.client.task_result(task_id)\\n state = result[u'state']\\n if state == new:\\n break\\n self.assertEqual(current, state, result)\\n time.sleep(0.01)\\n self.assertEqual(new, state, result)\\n return result\",\n \"def __checkInited(self, date=None, tkey=None, ttype=None):\\n if ttype == 'crontab':\\n return False\\n if date is None:\\n date = self.ddate\\n\\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\\n if doc.get(\\\"status\\\") == \\\"waiting\\\":\\n return True\\n return False\",\n \"def task_status():\\n pass\",\n \"def test_exp_by_states(self):\\n api = my_mock.api_mock({\\\"items\\\": [{'state': 'Waiting', 'id': 10134},\\n {'state': 'Waiting', 'id': 10135},\\n {'state': 'Running', 'id': 10130}]})\\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\\n self.assertEquals(\\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\\n my_mock.api_mock_stop()\",\n \"def test_source_time_successful(self):\\n\\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 1)\\n for result in results:\\n self.assertTrue(result['id'] in [self.job1.id])\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_completed(self):\\n return False\",\n \"def test_print_progress_flag(self, capsys):\\n file = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"geolife\\\", \\\"geolife_staypoints.csv\\\")\\n staypoints = ti.read_staypoints_csv(file, tz=\\\"utc\\\", index_col=\\\"id\\\", crs=\\\"epsg:4326\\\")\\n\\n staypoints.as_staypoints.generate_locations(print_progress=True)\\n captured_print = capsys.readouterr()\\n assert captured_print.err != \\\"\\\"\\n\\n staypoints.as_staypoints.generate_locations(print_progress=False)\\n captured_print = capsys.readouterr()\\n assert captured_print.err == \\\"\\\"\",\n \"def stalled(self):\\n oneHourAgo = datetime.today() - timedelta(hours = 1)\\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def test_pending_job(self):\\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\\n pending_job.pop(\\\"status\\\")\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_update_state1(self):\\n pass\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def pre_step(self,status):\\n self.t0 = time.time()\\n pass\",\n \"def test_running_job(self):\\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_status_done_basic(self):\\n\\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\\n FORSETI_CONNECT = 'sqlite:///{}'.format(\\n get_db_file_path('forseti_1_basic.db'))\\n\\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\\n FORSETI_CONNECT)\\n self.source = 'FORSETI'\\n self.model_manager = self.service_config.model_manager\\n self.model_name = self.model_manager.create(name=self.source)\\n\\n scoped_session, data_access = self.model_manager.get(self.model_name)\\n with scoped_session as session:\\n\\n importer_cls = importer.by_source(self.source)\\n import_runner = importer_cls(\\n session,\\n self.model_manager.model(self.model_name, expunge=False),\\n data_access,\\n self.service_config)\\n import_runner.run()\\n\\n model = self.model_manager.model(self.model_name)\\n self.assertEqual(model.state,\\n 'PARTIAL_SUCCESS',\\n 'Model state should be set to PARTIAL_SUCCESS')\",\n \"def test_completed():\\n assert complete == 1\\n assert errorflag == 0\",\n \"def test_service_initiated():\\n assert \\\"ready\\\" in bkt_outcome_unwind.index()\",\n \"def test_update_state4(self):\\n pass\",\n \"def task_scanned(now_task):\",\n \"def test_notBeforeBefore(self):\\n dbpool, qpool, clock, performerChosen = self._setupPools()\\n\\n @transactionally(dbpool.pool.connection)\\n def check(txn):\\n return qpool.enqueueWork(\\n txn, DummyWorkItem, a=3, b=9,\\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\\n )\\n\\n yield check\\n\\n clock.advance(1000)\\n # Advance far beyond the given timestamp.\\n self.assertEquals(performerChosen, [True])\\n\\n # Wait for job\\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\\n clock.advance(1)\\n\\n # Work item complete\\n self.assertTrue(DummyWorkItem.results == {1: 12})\",\n \"def test_getting_state_parallel(self):\\n no_replicates = 25\\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\\n for i in range(no_replicates):\\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)\",\n \"def test_state(self, mock_req):\\n self.setup_api(MOCK_DATA, mock_req)\\n now = datetime(1970, month=1, day=1)\\n with patch(\\\"homeassistant.util.dt.now\\\", return_value=now):\\n for name in self.sensor_dict:\\n sensor = self.sensor_dict[name][\\\"sensor\\\"]\\n self.fake_delay(2)\\n sensor.update()\\n if name == google_wifi.ATTR_LAST_RESTART:\\n assert \\\"1969-12-31 00:00:00\\\" == sensor.state\\n elif name == google_wifi.ATTR_UPTIME:\\n assert 1 == sensor.state\\n elif name == google_wifi.ATTR_STATUS:\\n assert \\\"Online\\\" == sensor.state\\n else:\\n assert \\\"initial\\\" == sensor.state\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_delete_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.delete_problem_state('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def testingProgressFunc(state, action, text, tick):\\n pass\\n #print \\\"testingProgressFunc\\\", state, action, text, tick\",\n \"def test_time_status(self):\\n result = self.test_client.time_status\\n\\n assert result == \\\"12312\\\"\",\n \"def update_task_state(mapper, conn, target):\\r\\n sql_query = ('select count(id) from task_run \\\\\\r\\n where task_run.task_id=%s') % target.task_id\\r\\n n_answers = conn.scalar(sql_query)\\r\\n sql_query = ('select n_answers from task \\\\\\r\\n where task.id=%s') % target.task_id\\r\\n task_n_answers = conn.scalar(sql_query)\\r\\n if (n_answers) >= task_n_answers:\\r\\n sql_query = (\\\"UPDATE task SET state=\\\\'completed\\\\' \\\\\\r\\n where id=%s\\\") % target.task_id\\r\\n conn.execute(sql_query)\",\n \"def test_act_ii(self):\\n # setup\\n processing_time = 5.0\\n self.transaction_behaviour.processing = \\\"some_dialogue\\\"\\n self.transaction_behaviour.max_processing = 120\\n self.transaction_behaviour.processing_time = processing_time\\n\\n # operation\\n self.transaction_behaviour.act()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\\n assert (\\n self.transaction_behaviour.processing_time\\n == processing_time + self.transaction_behaviour.tick_interval\\n )\",\n \"def in_progress(self):\\n return False\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_update_state2(self):\\n pass\",\n \"def set_task_in_progress(self):\\n\\n tasks = self._get_all_tasks()\\n\\n task_id = tasks[self.tasks_view.currentRow()].Id\\n\\n self.tasks_flow.set_status(task_id, 1)\\n\\n # Refresh the table\\n self.write_tasks_table()\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def policy_checker(name):\\n\\n policy = PolicyModel.query(PolicyModel.Name == name).get()\\n if not policy:\\n logging.error(\\\"Policy %s not found!\\\", name)\\n return \\\"not found\\\", 404\\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\\n if not schedule:\\n logging.error(\\\"Schedule %s not found!\\\", policy.Schedule)\\n return \\\"not found\\\", 404\\n logging.debug(\\n \\\"Time at Timezone %s is %s\\\",\\n schedule.Timezone,\\n tz.get_time_at_timezone(schedule.Timezone),\\n )\\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\\n logging.debug(\\\"Working on day %s hour %s\\\", day, hour)\\n arr = np.asarray(schedule.Schedule[\\\"__ndarray__\\\"], dtype=np.int).flatten()\\n matrix_size = schedule.Schedule[\\\"Shape\\\"][0] * schedule.Schedule[\\\"Shape\\\"][1]\\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\\n now = arr[day * 24 + hour]\\n prev = arr[prev]\\n logging.info(\\\"Previous state %s current %s\\\", prev, now)\\n if now == prev:\\n # do nothing\\n logging.info(\\\"Conditions are met, Nothing should be done for %s\\\", name)\\n return \\\"ok\\\", 200\\n else:\\n # stop/start\\n logging.info(\\\"State is changing for %s to %s\\\", name, now)\\n # for each tag lets do it\\n\\n task_client = tasks_v2.CloudTasksClient()\\n task = {\\n \\\"app_engine_http_request\\\": { # Specify the type of request.\\n \\\"http_method\\\": \\\"POST\\\",\\n \\\"relative_uri\\\": \\\"/tasks/change_state\\\",\\n }\\n }\\n parent = task_client.queue_path(\\n queue=\\\"zorya-tasks\\\",\\n project=utils.get_project_id(),\\n location=location.get_location(),\\n )\\n for tag in policy.Tags:\\n for project in policy.Projects:\\n payload = {\\n \\\"project\\\": project,\\n \\\"tagkey\\\": next(iter(tag)),\\n \\\"tagvalue\\\": tag[next(iter(tag))],\\n \\\"action\\\": str(now),\\n }\\n task[\\\"app_engine_http_request\\\"][\\\"body\\\"] = (\\n json.dumps(payload)\\n ).encode()\\n response = task_client.create_task(parent, task)\\n logging.debug(\\\"Task %s enqueued\\\", response.name)\\n return \\\"ok\\\", 200\",\n \"def test_get_current_state(self):\\n schedule = parser.parse_schedule(SCHEDULE)\\n self.assertEqual(\\\"a\\\", schedule.get_current_state(dt(monday, '09:30')))\\n self.assertEqual(\\\"c\\\", schedule.get_current_state(dt(monday, '08:30')))\\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\\n lookback=False))\",\n \"def mark_as_in_progress(self, task):\\n raise NotImplementedError('')\",\n \"def test_reset_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.reset_problem_attempts('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def test_check_contributing_state_completed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=1)\\r\\n user = UserFactory.create()\\r\\n TaskRunFactory.create_batch(1, task=task, user=user)\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def test_status(self):\\n keys = [\\n 'receiver time mark count', 'ant type base', 'solution status',\\n ...\\n ]\\n\\n for i in xrange(0, 100):\\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)\",\n \"def test_default_status(self) -> None:\\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\\n self.assertEqual(self.report.has_started(), False)\",\n \"def test_is_approved_no_approval(time_record_factory):\\n record = time_record_factory()\\n\\n assert not record.is_approved\",\n \"def test__validate_status__0():\\n for input_value, expected_output in (\\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\\n ):\\n output = validate_status(input_value)\\n vampytest.assert_is(output, expected_output)\",\n \"def testoptstatus(self): \\r\\n assert len(self.data.optstatus) == len(self.data.geovalues)\\r\\n assert self.data.optstatus[0] == self.data.OPT_NEW\\r\\n for i in range(1, len(self.data.optstatus)-1):\\r\\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\\r\\n assert self.data.optstatus[-1] == self.data.OPT_DONE\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def testPreProcessResourceState(self):\\n resource_id_1 = \\\"resource_id_1\\\"\\n task_id_1 = \\\"task_id_1\\\"\\n json_out = {resource_id_1: [task_id_1]}\\n\\n # Test that the resource id is properly added with associated task\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\\n\\n # Test that the additional task id is appended to resource id\\n task_id_2 = \\\"task_id_2\\\"\\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\\n\\n # Test that additional resource id is added into state file\\n resource_id_2 = \\\"resource_id_2\\\"\\n json_out_3 = {\\n resource_id_1: [task_id_1, task_id_2],\\n resource_id_2: [task_id_1]\\n }\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\\n\\n # Test that same task id is not duplicated into state file\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\",\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_meeting_live_stream_status_update(self):\\n pass\",\n \"def check_state(self):\\n pass\",\n \"def _server_poll_expcompleted_(self):\\n #print \\\"class Princeton_CCD function _server_poll_expcompleted_\\\" \\n try:\\n last_state = self.polled_running\\n except (AttributeError,UnboundLocalError):\\n self.polled_running = False\\n last_state = False\\n self.polled_running = self.query_running()\\n if (not bool(last_state) and bool(self.polled_running)):\\n self.begin_acq_time = time.time()\\n #print self.query_running(), last_state\\n #if ((last_state == True) and (self.polled_running == False)): CP\\n if (bool(last_state) and not bool(self.polled_running)):\\n self.end_acq_time = time.time()\\n return True\\n else:\\n return False\",\n \"def test_ensure_not_ts_ts(self):\\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')\",\n \"def check_status():\\n logger.debug(\\\"Starting the check_status() routine.\\\")\\n\\n url = \\\"https://www.toggl.com/api/v8/time_entries/current\\\"\\n token = os.environ[\\\"TOGGL_API_TOKEN\\\"]\\n auth_token = base64.b64encode(f\\\"{token}:api_token\\\".encode()).decode()\\n resp = requests.get(url, headers={\\\"Authorization\\\": \\\"Basic \\\" + auth_token})\\n\\n cols = \\\"id\\\", \\\"duration\\\", \\\"description\\\"\\n status = {k: v for k, v in (resp.json()[\\\"data\\\"] or {}).items() if k in cols}\\n logger.debug(f\\\"{'Something' if 'id' in status else 'Something'} is being tracked.\\\")\\n\\n return status\",\n \"def _assert_state(self, state_dict):\\n instances = db.instance_get_all(self.context)\\n self.assertEqual(len(instances), 1)\\n\\n if 'vm_state' in state_dict:\\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\\n if 'task_state' in state_dict:\\n self.assertEqual(state_dict['task_state'],\\n instances[0]['task_state'])\\n if 'power_state' in state_dict:\\n self.assertEqual(state_dict['power_state'],\\n instances[0]['power_state'])\",\n \"def test_project_state_counters(self):\\n #build counters:\\n management.call_command('rebuild_counters')\\n\\n self.client.force_authenticate(self.global_user_1)\\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\\n\\n for project_data in data:\\n self.assertIn('state', project_data)\\n project_state_data = project_data['state']\\n if project_state_data:\\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\\n self.assertEqual(\\n project_state_data['numberOfEnrolledLessons'],\\n project_state_obj.lesson_states.count()\\n )\\n self.assertEqual(\\n project_state_data['numberOfCompletedLessons'],\\n project_state_obj.lesson_states.filter(is_completed=True).count()\\n )\",\n \"def test_multiple_states(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory()\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Cancel when created.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.CANCEL, event)\\n s1 = context.work_status.value\\n self.assertEqual(s1, s.CANCELLED)\\n\\n # Cancel when started.\\n c2 = model.factory(initial_state=s.STARTED)\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(c2, a.CANCEL, event)\\n s2 = c2.work_status.value\\n self.assertEqual(s2, s.CANCELLED)\",\n \"def test_active_and_date_range(self):\\n\\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(len(result['results'][0]['job_counts']), 0)\",\n \"def test_send_state_event_nonoverwriting(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": True}, tok=u1token, state_key=\\\"tabby\\\"\\n )\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": False}, tok=u1token, state_key=\\\"moggy\\\"\\n )\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 1,\\n )\",\n \"def test_check_contributing_state_completed_user_not_contributed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=2)\\r\\n TaskRunFactory.create_batch(2, task=task)\\r\\n user = UserFactory.create()\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def check_indicator_files(tasks):\\n\\n for task in tasks:\\n if task[\\\"status\\\"]==\\\"unknown\\\":\\n if os.path.exists(task[\\\"result\\\"]):\\n task[\\\"status\\\"]=\\\"previously completed\\\"\\n else:\\n task[\\\"status\\\"]=\\\"to do\\\"\\n return\",\n \"def test_timeout_processing(self):\\n # setup\\n self.transaction_behaviour.processing_time = None\\n\\n # operation\\n self.transaction_behaviour._timeout_processing()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\",\n \"def test_is_finished(self):\\n experiment = Experiment(TasksMock())\\n self.assertEquals(False, experiment.is_finished())\\n for _ in range(0, 17):\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(True, experiment.is_finished())\",\n \"def test_active(self):\\n\\n url = '/%s/job-types/status/?is_active=true' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def poll(self):\\n\\n query = f\\\"sacct -j {self.jobid} -o State -n -X -P\\\"\\n if self.cluster:\\n query += f\\\" --clusters={self.cluster}\\\"\\n\\n cmd = BuildTestCommand(query)\\n cmd.execute()\\n\\n logger.debug(f\\\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\\\")\\n job_state = cmd.get_output()\\n self._state = \\\"\\\".join(job_state).rstrip()\\n logger.debug(f\\\"JobID: '{self.jobid}' job state:{self._state}\\\")\",\n \"def test_assessment_import_states(self):\\n self.import_file(\\\"assessment_full_no_warnings.csv\\\")\\n response = self.import_file(\\\"assessment_update_intermediate.csv\\\")\\n expected_errors = {\\n \\\"Assessment\\\": {\\n \\\"block_errors\\\": set(),\\n \\\"block_warnings\\\": set(),\\n \\\"row_errors\\\": set(),\\n \\\"row_warnings\\\": set(),\\n }\\n }\\n self._check_csv_response(response, expected_errors)\\n\\n assessments = {r.slug: r for r in models.Assessment.query.all()}\\n self.assertEqual(assessments[\\\"Assessment 60\\\"].status,\\n models.Assessment.START_STATE)\\n self.assertEqual(assessments[\\\"Assessment 61\\\"].status,\\n models.Assessment.PROGRESS_STATE)\\n self.assertEqual(assessments[\\\"Assessment 62\\\"].status,\\n models.Assessment.DONE_STATE)\\n self.assertEqual(assessments[\\\"Assessment 63\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 64\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 3\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 4\\\"].status,\\n models.Assessment.FINAL_STATE)\\n\\n # Check that there is only one attachment left\\n asmt1 = assessments[\\\"Assessment 1\\\"]\\n self.assertEqual({\\\"a.b.com\\\", \\\"c d com\\\"},\\n {i.title for i in asmt1.document_url})\\n self.assertEqual({u'evidence title 1'},\\n {i.title for i in asmt1.document_evidence})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7257716","0.6861403","0.6694048","0.6286829","0.61476827","0.6099142","0.6072867","0.59780765","0.5971016","0.595613","0.5860175","0.58586013","0.5750365","0.5722227","0.57212484","0.5681915","0.56768733","0.56603575","0.5617645","0.56150514","0.5581628","0.5568725","0.5546942","0.5533718","0.55280554","0.55153114","0.55074936","0.5506668","0.5500458","0.54812","0.5481189","0.5455569","0.5451551","0.545138","0.54343754","0.54280937","0.5412749","0.5380607","0.5371121","0.536731","0.53514564","0.53506863","0.53494036","0.5348718","0.53472334","0.5344583","0.53339547","0.5333885","0.53119737","0.53047115","0.53037494","0.53024095","0.5292097","0.5286541","0.5283738","0.5278938","0.52546245","0.52463114","0.52447915","0.5243245","0.5240098","0.52392054","0.5236918","0.52328086","0.5231965","0.5231962","0.5218364","0.5209338","0.52089345","0.5207472","0.5204317","0.5200914","0.5193996","0.51880616","0.51798755","0.51780987","0.5177101","0.5176744","0.5175223","0.5167225","0.516509","0.5164577","0.51636326","0.5162743","0.5161201","0.5160347","0.51588315","0.5157218","0.51553965","0.5148234","0.5145533","0.5143466","0.5135572","0.5134765","0.51347274","0.5127283","0.51271003","0.5115242","0.51110834","0.5103544"],"string":"[\n \"0.7257716\",\n \"0.6861403\",\n \"0.6694048\",\n \"0.6286829\",\n \"0.61476827\",\n \"0.6099142\",\n \"0.6072867\",\n \"0.59780765\",\n \"0.5971016\",\n \"0.595613\",\n \"0.5860175\",\n \"0.58586013\",\n \"0.5750365\",\n \"0.5722227\",\n \"0.57212484\",\n \"0.5681915\",\n \"0.56768733\",\n \"0.56603575\",\n \"0.5617645\",\n \"0.56150514\",\n \"0.5581628\",\n \"0.5568725\",\n \"0.5546942\",\n \"0.5533718\",\n \"0.55280554\",\n \"0.55153114\",\n \"0.55074936\",\n \"0.5506668\",\n \"0.5500458\",\n \"0.54812\",\n \"0.5481189\",\n \"0.5455569\",\n \"0.5451551\",\n \"0.545138\",\n \"0.54343754\",\n \"0.54280937\",\n \"0.5412749\",\n \"0.5380607\",\n \"0.5371121\",\n \"0.536731\",\n \"0.53514564\",\n \"0.53506863\",\n \"0.53494036\",\n \"0.5348718\",\n \"0.53472334\",\n \"0.5344583\",\n \"0.53339547\",\n \"0.5333885\",\n \"0.53119737\",\n \"0.53047115\",\n \"0.53037494\",\n \"0.53024095\",\n \"0.5292097\",\n \"0.5286541\",\n \"0.5283738\",\n \"0.5278938\",\n \"0.52546245\",\n \"0.52463114\",\n \"0.52447915\",\n \"0.5243245\",\n \"0.5240098\",\n \"0.52392054\",\n \"0.5236918\",\n \"0.52328086\",\n \"0.5231965\",\n \"0.5231962\",\n \"0.5218364\",\n \"0.5209338\",\n \"0.52089345\",\n \"0.5207472\",\n \"0.5204317\",\n \"0.5200914\",\n \"0.5193996\",\n \"0.51880616\",\n \"0.51798755\",\n \"0.51780987\",\n \"0.5177101\",\n \"0.5176744\",\n \"0.5175223\",\n \"0.5167225\",\n \"0.516509\",\n \"0.5164577\",\n \"0.51636326\",\n \"0.5162743\",\n \"0.5161201\",\n \"0.5160347\",\n \"0.51588315\",\n \"0.5157218\",\n \"0.51553965\",\n \"0.5148234\",\n \"0.5145533\",\n \"0.5143466\",\n \"0.5135572\",\n \"0.5134765\",\n \"0.51347274\",\n \"0.5127283\",\n \"0.51271003\",\n \"0.5115242\",\n \"0.51110834\",\n \"0.5103544\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71204054"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17471,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_IN_PROGRESS state"},"document":{"kind":"string","value":"def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_update_single_row_if_status_is_in_progress(self):\n first = generate_mock_result(status='IN_PROGRESS', success=False)\n self.db.insert_single_result(first)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'IN_PROGRESS')\n second = generate_mock_result(status='SUCCESS', success=True)\n self.db.insert_single_result(second)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'SUCCESS')","def _wait_for_table_status(self, expected='ACTIVE'):\n\n achieved_state = False\n while not achieved_state:\n table_description = self.client.describe_table(TableName=self.table_name)\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\n current_status = table_description['Table']['TableStatus']\n achieved_state = current_status == expected\n sleep(1)","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_retrieve_instances_schedule_state(self):\n pass","def insync_and_state_check(self):\n self.step('verifying tables are properly synced on all endpoints')\n is_ok = True\n limit, count = 10, 0\n while count < limit:\n try:\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\n assert rc == 200, f\"something wrong happened when checking state table {rc}\"\n for state in state_check['data']:\n if not state['in_sync'] == True or not state['state'] == 'loaded':\n print(f\"found state which was not in_sync=True & 'loaded {state}, retrying\")\n is_ok = False\n self.sync_job_check()\n break\n if is_ok:\n break\n count+=1\n except Exception as e:\n print(f\"something wrong happened when checking state table\")\n break","def _compute_queue_state(self):\n for record in self:\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\n record.state = \"completed\"\n elif record.queue_line_draft_records == record.queue_line_total_records:\n record.state = \"draft\"\n elif record.queue_line_total_records == record.queue_line_fail_records:\n record.state = \"failed\"\n else:\n record.state = \"partially_completed\"","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_get_unsuccessful_ta():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\n\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\n assert np.isnan(list_failed[0])","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def test_issue_tracked_times(self):\n pass","def test_update_task_states(self):\r\n changed = self.combinedoe.update_task_states()\r\n self.assertFalse(changed)\r\n\r\n current_task = self.combinedoe.current_task\r\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\r\n changed = self.combinedoe.update_task_states()\r\n\r\n self.assertTrue(changed)","def test_update_instances_schedule_state(self):\n pass","def test_state_after_failure(self):\n pass","def test_get_refresh_job_status(self):\n pass","def load_status_table():","def update_status(cls):\n for job in cls.query.filter(cls.finished == False):\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\n dynamo = DynamoIngestionStatusClient()\n num_urls_left = 0\n for url in urls_left:\n dynamo_url = dynamo.get(url.url)\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\n # will never be processed, so ignore for our purposes\n url.processed = True\n else:\n num_urls_left += 1\n if num_hits_left+num_urls_left == 0:\n job.finished = True\n print '*** Job ID: %s is complete ***' % str(job.id)\n\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\n if not job.finished:\n print 'unprocessed URLs: %i' % num_urls_left\n print 'outstanding HITs: %i\\n' % num_hits_left\n session.flush()","def test_mark_incompleted(self):\n event = Event.objects.all()[0]\n\n todo = TodoItem.objects.create(\n event=event, completed=True, title=\"Test TODO2\",\n due=datetime.date.today(), additional=\"\",\n )\n\n assert todo.completed is True\n\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\n todo.refresh_from_db()\n\n assert todo.completed is False","def _check_completed(self):\n current_rung_df = self.sieve_board.loc[\n self.sieve_board['status'].isin(\n [StatusType.WAITTING, StatusType.RUNNING])\n ]\n if current_rung_df.empty:\n return True\n else:\n return False","def test_update_state(self):\n # add a task\n self.add(title=\"Sample task doing\", description=\"for sample\", state=\"doing\")\n task = Task.query.filter_by(title='Sample task doing').first()\n\n # change task to todo\n old_id = task.id\n self.update_state(id=old_id, state='todo')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'todo')\n\n # change task to done\n old_id = task.id\n self.update_state(id=old_id, state='done')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'done')","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_time_successful(self):\n\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\n '2016-01-10T00:00:00Z',\n '2016-01-13T00:00:00Z',\n 'source')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 2)\n for result in results:\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])","def test_pending_job(self):\n\n pending_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"pending\"))\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def check_states(results):\n assert results[\"states\"][\"device0\"] == {\"counter\": 10, \"rts\": 0}\n assert results[\"states\"][\"device1\"] == {\"counter\": 10, \"rts\": 0}","def test_running_job(self):\n\n running_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"running\"))\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_update_state(self):\n pass","def get_time_in_status(self, issue, state):\n \n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\n\n if ((issue['fields']['issuetype']['name'] == \"Sub-task\") and (issue['fields']['status']['name'] == \"In Progress\")) or \\\n ((issue['fields']['issuetype']['name'] == \"New Feature\") and (issue['fields']['status']['name'] not in [\"Open\", \"In Progress\", \"Reopened\", \"Resolved\", \"Closed\"])):\n print \"Getting time in status for %s\" % issue['fields']['status']['name']\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\n # First state create time same as issue created datetime \n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\") # Current item create datetime\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n diff = nicd - cicdt # Time the issue has been in that state\n \n # If there's a changelog history go thru it else use issue create date and current date for time in status\n # If there's no changelog/histories then we're still in first state, nothing has changed\n if not len(issue['changelog']['histories']):\n print \"no changelog for %s\" % issue['key']\n self.issue_objs[issue['key']].time_in_status = diff.days\n else:\n # If there is a changelog look for the value \"status\", changes may be something other than status\n print \"There's a changelog for issue %s\" % issue['key']\n# print json.dumps(issue, indent=4)\n \n # Go thru changelog histories to see if any of the changes are \"status\" for this state, also get last status or sprint if New Feature\n status_change_found = False\n for histories_item in issue['changelog']['histories']:\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n print \"There's a status change\"\n status_change_found = True\n \n # Get oldest sprint and status within config.change_period days ago for New Features \n if (issue['fields']['issuetype']['name'] == \"New Feature\") and \\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\").date() # histories item create datetime\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\n print \"change too old %s, getting next histories item\" % hicdt\n continue # If older than config.change_period days then we don't care, get next histories item\n else:\n print \"recent change %s \" % hicdt\n if histories_item['items'][0]['field'] == 'Milestone(s)':\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\n print \"last_sprint %s\" % self.issue_objs[issue['key']].last_sprint\n break # Got the oldest change within change_period\n if histories_item['items'][0]['field'] == 'status':\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\n print \"last_status %s\" % self.issue_objs[issue['key']].last_status\n break # Got the oldest change within change_period\n \n # If there was a change in status calculate time in status from changelog\n if(status_change_found):\n total_days = 0 # Accumulate all the diffs for each time in this state\n # Go thru changelog histories to see if this issue was in this state before.\n for i, histories_item in enumerate(issue['changelog']['histories']):\n # All the different kinds of things that have changed, we're only interested in 'status'\n \n # Display all the changelog history items\n if (0):\n for k, v in histories_item.items():\n print \"issue['changelog']['histories'] key: %s value: %s\" % (k, v)\n print \"issue['changelog']['histories']['items'] %s\" % histories_item['items']\n for item in histories_item['items']:\n print \"histories_item['items'] %s\" % item\n print \"histories_item['items'][0] %s\" %histories_item['items'][0]\n for item2 in histories_item['items'][0]:\n print \"histories_item['items'][0] %s %s\" % (item2, histories_item['items'][0][item2])\n \n # If status record we're interested in is found, look for next status change or use current date for end date\n # If status \"Pending\" for New Features or \"Open\" for Sub-tasks there is no change entry for that, it's first state, use created date.\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n current_status_create_datetime = histories_item['created'].split('.')[0]\n \n # Go thru future history changes until the next status change\n status_found = False\n for j in range(i+1, len(issue['changelog']['histories'])):\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\n print \"%s. next histories item created: %s\" % (j, next_item_create_date)\n status_found = True\n break # Break the for loop, next status found\n \n if not status_found: \n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \n print \"No next histories item, using today's date: %s\" % (next_item_create_date)\n \n cscdt = dt.strptime(current_status_create_datetime, \"%Y-%m-%dT%H:%M:%S\") # Current status create datetime\n nicdt = dt.strptime(next_item_create_date, \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n \n print \"Current status create date for %s: %s\" % (issue['key'], cscdt)\n print \"Next item create date for %s: %s\" % (issue['key'], nicdt)\n \n diff = nicdt - cscdt # The number of days the issue has been in that state\n total_days += diff.days # Accumulate all the times this issue has been in this state.\n self.issue_objs[issue['key']].time_in_status = total_days\n else:\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now","def _wait_for_state(self, task_id, current, new):\n state = result = None\n start = time.time()\n # 45 seconds is a long time.\n # TODO(maruel): Make task_runner use exponential backoff instead of\n # hardcoded 10s/30s, which makes these tests tremendously slower than\n # necessary.\n # https://crbug.com/825500\n while time.time() - start < 45.:\n result = self.client.task_result(task_id)\n state = result[u'state']\n if state == new:\n break\n self.assertEqual(current, state, result)\n time.sleep(0.01)\n self.assertEqual(new, state, result)\n return result","def query_job_progress():\n pass","def __checkInited(self, date=None, tkey=None, ttype=None):\n if ttype == 'crontab':\n return False\n if date is None:\n date = self.ddate\n\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\n if doc.get(\"status\") == \"waiting\":\n return True\n return False","def task_status():\n pass","def test_exp_by_states(self):\n api = my_mock.api_mock({\"items\": [{'state': 'Waiting', 'id': 10134},\n {'state': 'Waiting', 'id': 10135},\n {'state': 'Running', 'id': 10130}]})\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\n self.assertEquals(\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\n my_mock.api_mock_stop()","def test_source_time_successful(self):\n\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 1)\n for result in results:\n self.assertTrue(result['id'] in [self.job1.id])","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_completed(self):\n return False","def test_print_progress_flag(self, capsys):\n file = os.path.join(\"tests\", \"data\", \"geolife\", \"geolife_staypoints.csv\")\n staypoints = ti.read_staypoints_csv(file, tz=\"utc\", index_col=\"id\", crs=\"epsg:4326\")\n\n staypoints.as_staypoints.generate_locations(print_progress=True)\n captured_print = capsys.readouterr()\n assert captured_print.err != \"\"\n\n staypoints.as_staypoints.generate_locations(print_progress=False)\n captured_print = capsys.readouterr()\n assert captured_print.err == \"\"","def stalled(self):\n oneHourAgo = datetime.today() - timedelta(hours = 1)\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def test_pending_job(self):\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\n pending_job.pop(\"status\")\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_update_state1(self):\n pass","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def test_running_job(self):\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def pre_step(self,status):\n self.t0 = time.time()\n pass","def test_status_done_basic(self):\n\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\n FORSETI_CONNECT = 'sqlite:///{}'.format(\n get_db_file_path('forseti_1_basic.db'))\n\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\n FORSETI_CONNECT)\n self.source = 'FORSETI'\n self.model_manager = self.service_config.model_manager\n self.model_name = self.model_manager.create(name=self.source)\n\n scoped_session, data_access = self.model_manager.get(self.model_name)\n with scoped_session as session:\n\n importer_cls = importer.by_source(self.source)\n import_runner = importer_cls(\n session,\n self.model_manager.model(self.model_name, expunge=False),\n data_access,\n self.service_config)\n import_runner.run()\n\n model = self.model_manager.model(self.model_name)\n self.assertEqual(model.state,\n 'PARTIAL_SUCCESS',\n 'Model state should be set to PARTIAL_SUCCESS')","def test_completed():\n assert complete == 1\n assert errorflag == 0","def test_service_initiated():\n assert \"ready\" in bkt_outcome_unwind.index()","def test_update_state4(self):\n pass","def task_scanned(now_task):","def test_notBeforeBefore(self):\n dbpool, qpool, clock, performerChosen = self._setupPools()\n\n @transactionally(dbpool.pool.connection)\n def check(txn):\n return qpool.enqueueWork(\n txn, DummyWorkItem, a=3, b=9,\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\n )\n\n yield check\n\n clock.advance(1000)\n # Advance far beyond the given timestamp.\n self.assertEquals(performerChosen, [True])\n\n # Wait for job\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\n clock.advance(1)\n\n # Work item complete\n self.assertTrue(DummyWorkItem.results == {1: 12})","def test_getting_state_parallel(self):\n no_replicates = 25\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\n for i in range(no_replicates):\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)","def test_state(self, mock_req):\n self.setup_api(MOCK_DATA, mock_req)\n now = datetime(1970, month=1, day=1)\n with patch(\"homeassistant.util.dt.now\", return_value=now):\n for name in self.sensor_dict:\n sensor = self.sensor_dict[name][\"sensor\"]\n self.fake_delay(2)\n sensor.update()\n if name == google_wifi.ATTR_LAST_RESTART:\n assert \"1969-12-31 00:00:00\" == sensor.state\n elif name == google_wifi.ATTR_UPTIME:\n assert 1 == sensor.state\n elif name == google_wifi.ATTR_STATUS:\n assert \"Online\" == sensor.state\n else:\n assert \"initial\" == sensor.state","def test_table_false_positives(self):\n pass","def test_delete_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.delete_problem_state('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def testingProgressFunc(state, action, text, tick):\n pass\n #print \"testingProgressFunc\", state, action, text, tick","def test_time_status(self):\n result = self.test_client.time_status\n\n assert result == \"12312\"","def update_task_state(mapper, conn, target):\r\n sql_query = ('select count(id) from task_run \\\r\n where task_run.task_id=%s') % target.task_id\r\n n_answers = conn.scalar(sql_query)\r\n sql_query = ('select n_answers from task \\\r\n where task.id=%s') % target.task_id\r\n task_n_answers = conn.scalar(sql_query)\r\n if (n_answers) >= task_n_answers:\r\n sql_query = (\"UPDATE task SET state=\\'completed\\' \\\r\n where id=%s\") % target.task_id\r\n conn.execute(sql_query)","def test_act_ii(self):\n # setup\n processing_time = 5.0\n self.transaction_behaviour.processing = \"some_dialogue\"\n self.transaction_behaviour.max_processing = 120\n self.transaction_behaviour.processing_time = processing_time\n\n # operation\n self.transaction_behaviour.act()\n\n # after\n self.assert_quantity_in_outbox(0)\n assert (\n self.transaction_behaviour.processing_time\n == processing_time + self.transaction_behaviour.tick_interval\n )","def in_progress(self):\n return False","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_update_state2(self):\n pass","def set_task_in_progress(self):\n\n tasks = self._get_all_tasks()\n\n task_id = tasks[self.tasks_view.currentRow()].Id\n\n self.tasks_flow.set_status(task_id, 1)\n\n # Refresh the table\n self.write_tasks_table()","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def policy_checker(name):\n\n policy = PolicyModel.query(PolicyModel.Name == name).get()\n if not policy:\n logging.error(\"Policy %s not found!\", name)\n return \"not found\", 404\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\n if not schedule:\n logging.error(\"Schedule %s not found!\", policy.Schedule)\n return \"not found\", 404\n logging.debug(\n \"Time at Timezone %s is %s\",\n schedule.Timezone,\n tz.get_time_at_timezone(schedule.Timezone),\n )\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\n logging.debug(\"Working on day %s hour %s\", day, hour)\n arr = np.asarray(schedule.Schedule[\"__ndarray__\"], dtype=np.int).flatten()\n matrix_size = schedule.Schedule[\"Shape\"][0] * schedule.Schedule[\"Shape\"][1]\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\n now = arr[day * 24 + hour]\n prev = arr[prev]\n logging.info(\"Previous state %s current %s\", prev, now)\n if now == prev:\n # do nothing\n logging.info(\"Conditions are met, Nothing should be done for %s\", name)\n return \"ok\", 200\n else:\n # stop/start\n logging.info(\"State is changing for %s to %s\", name, now)\n # for each tag lets do it\n\n task_client = tasks_v2.CloudTasksClient()\n task = {\n \"app_engine_http_request\": { # Specify the type of request.\n \"http_method\": \"POST\",\n \"relative_uri\": \"/tasks/change_state\",\n }\n }\n parent = task_client.queue_path(\n queue=\"zorya-tasks\",\n project=utils.get_project_id(),\n location=location.get_location(),\n )\n for tag in policy.Tags:\n for project in policy.Projects:\n payload = {\n \"project\": project,\n \"tagkey\": next(iter(tag)),\n \"tagvalue\": tag[next(iter(tag))],\n \"action\": str(now),\n }\n task[\"app_engine_http_request\"][\"body\"] = (\n json.dumps(payload)\n ).encode()\n response = task_client.create_task(parent, task)\n logging.debug(\"Task %s enqueued\", response.name)\n return \"ok\", 200","def test_get_current_state(self):\n schedule = parser.parse_schedule(SCHEDULE)\n self.assertEqual(\"a\", schedule.get_current_state(dt(monday, '09:30')))\n self.assertEqual(\"c\", schedule.get_current_state(dt(monday, '08:30')))\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\n lookback=False))","def mark_as_in_progress(self, task):\n raise NotImplementedError('')","def test_reset_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.reset_problem_attempts('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def test_check_contributing_state_completed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=1)\r\n user = UserFactory.create()\r\n TaskRunFactory.create_batch(1, task=task, user=user)\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def test_status(self):\n keys = [\n 'receiver time mark count', 'ant type base', 'solution status',\n ...\n ]\n\n for i in xrange(0, 100):\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)","def test_is_approved_no_approval(time_record_factory):\n record = time_record_factory()\n\n assert not record.is_approved","def test_default_status(self) -> None:\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\n self.assertEqual(self.report.has_started(), False)","def testoptstatus(self): \r\n assert len(self.data.optstatus) == len(self.data.geovalues)\r\n assert self.data.optstatus[0] == self.data.OPT_NEW\r\n for i in range(1, len(self.data.optstatus)-1):\r\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\r\n assert self.data.optstatus[-1] == self.data.OPT_DONE","def test__validate_status__0():\n for input_value, expected_output in (\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\n ):\n output = validate_status(input_value)\n vampytest.assert_is(output, expected_output)","def test_update_state3(self):\n pass","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def testPreProcessResourceState(self):\n resource_id_1 = \"resource_id_1\"\n task_id_1 = \"task_id_1\"\n json_out = {resource_id_1: [task_id_1]}\n\n # Test that the resource id is properly added with associated task\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\n\n # Test that the additional task id is appended to resource id\n task_id_2 = \"task_id_2\"\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\n\n # Test that additional resource id is added into state file\n resource_id_2 = \"resource_id_2\"\n json_out_3 = {\n resource_id_1: [task_id_1, task_id_2],\n resource_id_2: [task_id_1]\n }\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\n\n # Test that same task id is not duplicated into state file\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)","def test_meeting_live_stream_status_update(self):\n pass","def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def check_state(self):\n pass","def _server_poll_expcompleted_(self):\n #print \"class Princeton_CCD function _server_poll_expcompleted_\" \n try:\n last_state = self.polled_running\n except (AttributeError,UnboundLocalError):\n self.polled_running = False\n last_state = False\n self.polled_running = self.query_running()\n if (not bool(last_state) and bool(self.polled_running)):\n self.begin_acq_time = time.time()\n #print self.query_running(), last_state\n #if ((last_state == True) and (self.polled_running == False)): CP\n if (bool(last_state) and not bool(self.polled_running)):\n self.end_acq_time = time.time()\n return True\n else:\n return False","def test_ensure_not_ts_ts(self):\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')","def check_status():\n logger.debug(\"Starting the check_status() routine.\")\n\n url = \"https://www.toggl.com/api/v8/time_entries/current\"\n token = os.environ[\"TOGGL_API_TOKEN\"]\n auth_token = base64.b64encode(f\"{token}:api_token\".encode()).decode()\n resp = requests.get(url, headers={\"Authorization\": \"Basic \" + auth_token})\n\n cols = \"id\", \"duration\", \"description\"\n status = {k: v for k, v in (resp.json()[\"data\"] or {}).items() if k in cols}\n logger.debug(f\"{'Something' if 'id' in status else 'Something'} is being tracked.\")\n\n return status","def _assert_state(self, state_dict):\n instances = db.instance_get_all(self.context)\n self.assertEqual(len(instances), 1)\n\n if 'vm_state' in state_dict:\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\n if 'task_state' in state_dict:\n self.assertEqual(state_dict['task_state'],\n instances[0]['task_state'])\n if 'power_state' in state_dict:\n self.assertEqual(state_dict['power_state'],\n instances[0]['power_state'])","def test_project_state_counters(self):\n #build counters:\n management.call_command('rebuild_counters')\n\n self.client.force_authenticate(self.global_user_1)\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\n\n for project_data in data:\n self.assertIn('state', project_data)\n project_state_data = project_data['state']\n if project_state_data:\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\n self.assertEqual(\n project_state_data['numberOfEnrolledLessons'],\n project_state_obj.lesson_states.count()\n )\n self.assertEqual(\n project_state_data['numberOfCompletedLessons'],\n project_state_obj.lesson_states.filter(is_completed=True).count()\n )","def test_multiple_states(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory()\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Cancel when created.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.CANCEL, event)\n s1 = context.work_status.value\n self.assertEqual(s1, s.CANCELLED)\n\n # Cancel when started.\n c2 = model.factory(initial_state=s.STARTED)\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(c2, a.CANCEL, event)\n s2 = c2.work_status.value\n self.assertEqual(s2, s.CANCELLED)","def test_active_and_date_range(self):\n\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(len(result['results'][0]['job_counts']), 0)","def test_send_state_event_nonoverwriting(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": True}, tok=u1token, state_key=\"tabby\"\n )\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": False}, tok=u1token, state_key=\"moggy\"\n )\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 1,\n )","def test_check_contributing_state_completed_user_not_contributed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=2)\r\n TaskRunFactory.create_batch(2, task=task)\r\n user = UserFactory.create()\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def check_indicator_files(tasks):\n\n for task in tasks:\n if task[\"status\"]==\"unknown\":\n if os.path.exists(task[\"result\"]):\n task[\"status\"]=\"previously completed\"\n else:\n task[\"status\"]=\"to do\"\n return","def test_timeout_processing(self):\n # setup\n self.transaction_behaviour.processing_time = None\n\n # operation\n self.transaction_behaviour._timeout_processing()\n\n # after\n self.assert_quantity_in_outbox(0)","def test_is_finished(self):\n experiment = Experiment(TasksMock())\n self.assertEquals(False, experiment.is_finished())\n for _ in range(0, 17):\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(True, experiment.is_finished())","def test_active(self):\n\n url = '/%s/job-types/status/?is_active=true' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def poll(self):\n\n query = f\"sacct -j {self.jobid} -o State -n -X -P\"\n if self.cluster:\n query += f\" --clusters={self.cluster}\"\n\n cmd = BuildTestCommand(query)\n cmd.execute()\n\n logger.debug(f\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\")\n job_state = cmd.get_output()\n self._state = \"\".join(job_state).rstrip()\n logger.debug(f\"JobID: '{self.jobid}' job state:{self._state}\")","def test_assessment_import_states(self):\n self.import_file(\"assessment_full_no_warnings.csv\")\n response = self.import_file(\"assessment_update_intermediate.csv\")\n expected_errors = {\n \"Assessment\": {\n \"block_errors\": set(),\n \"block_warnings\": set(),\n \"row_errors\": set(),\n \"row_warnings\": set(),\n }\n }\n self._check_csv_response(response, expected_errors)\n\n assessments = {r.slug: r for r in models.Assessment.query.all()}\n self.assertEqual(assessments[\"Assessment 60\"].status,\n models.Assessment.START_STATE)\n self.assertEqual(assessments[\"Assessment 61\"].status,\n models.Assessment.PROGRESS_STATE)\n self.assertEqual(assessments[\"Assessment 62\"].status,\n models.Assessment.DONE_STATE)\n self.assertEqual(assessments[\"Assessment 63\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 64\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 3\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 4\"].status,\n models.Assessment.FINAL_STATE)\n\n # Check that there is only one attachment left\n asmt1 = assessments[\"Assessment 1\"]\n self.assertEqual({\"a.b.com\", \"c d com\"},\n {i.title for i in asmt1.document_url})\n self.assertEqual({u'evidence title 1'},\n {i.title for i in asmt1.document_evidence})"],"string":"[\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_update_single_row_if_status_is_in_progress(self):\\n first = generate_mock_result(status='IN_PROGRESS', success=False)\\n self.db.insert_single_result(first)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'IN_PROGRESS')\\n second = generate_mock_result(status='SUCCESS', success=True)\\n self.db.insert_single_result(second)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'SUCCESS')\",\n \"def _wait_for_table_status(self, expected='ACTIVE'):\\n\\n achieved_state = False\\n while not achieved_state:\\n table_description = self.client.describe_table(TableName=self.table_name)\\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\\n current_status = table_description['Table']['TableStatus']\\n achieved_state = current_status == expected\\n sleep(1)\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def insync_and_state_check(self):\\n self.step('verifying tables are properly synced on all endpoints')\\n is_ok = True\\n limit, count = 10, 0\\n while count < limit:\\n try:\\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\\n assert rc == 200, f\\\"something wrong happened when checking state table {rc}\\\"\\n for state in state_check['data']:\\n if not state['in_sync'] == True or not state['state'] == 'loaded':\\n print(f\\\"found state which was not in_sync=True & 'loaded {state}, retrying\\\")\\n is_ok = False\\n self.sync_job_check()\\n break\\n if is_ok:\\n break\\n count+=1\\n except Exception as e:\\n print(f\\\"something wrong happened when checking state table\\\")\\n break\",\n \"def _compute_queue_state(self):\\n for record in self:\\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\\n record.state = \\\"completed\\\"\\n elif record.queue_line_draft_records == record.queue_line_total_records:\\n record.state = \\\"draft\\\"\\n elif record.queue_line_total_records == record.queue_line_fail_records:\\n record.state = \\\"failed\\\"\\n else:\\n record.state = \\\"partially_completed\\\"\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_get_unsuccessful_ta():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\\n\\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\\n assert np.isnan(list_failed[0])\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_update_task_states(self):\\r\\n changed = self.combinedoe.update_task_states()\\r\\n self.assertFalse(changed)\\r\\n\\r\\n current_task = self.combinedoe.current_task\\r\\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\\r\\n changed = self.combinedoe.update_task_states()\\r\\n\\r\\n self.assertTrue(changed)\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def load_status_table():\",\n \"def update_status(cls):\\n for job in cls.query.filter(cls.finished == False):\\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\\n dynamo = DynamoIngestionStatusClient()\\n num_urls_left = 0\\n for url in urls_left:\\n dynamo_url = dynamo.get(url.url)\\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\\n # will never be processed, so ignore for our purposes\\n url.processed = True\\n else:\\n num_urls_left += 1\\n if num_hits_left+num_urls_left == 0:\\n job.finished = True\\n print '*** Job ID: %s is complete ***' % str(job.id)\\n\\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\\n if not job.finished:\\n print 'unprocessed URLs: %i' % num_urls_left\\n print 'outstanding HITs: %i\\\\n' % num_hits_left\\n session.flush()\",\n \"def test_mark_incompleted(self):\\n event = Event.objects.all()[0]\\n\\n todo = TodoItem.objects.create(\\n event=event, completed=True, title=\\\"Test TODO2\\\",\\n due=datetime.date.today(), additional=\\\"\\\",\\n )\\n\\n assert todo.completed is True\\n\\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\\n todo.refresh_from_db()\\n\\n assert todo.completed is False\",\n \"def _check_completed(self):\\n current_rung_df = self.sieve_board.loc[\\n self.sieve_board['status'].isin(\\n [StatusType.WAITTING, StatusType.RUNNING])\\n ]\\n if current_rung_df.empty:\\n return True\\n else:\\n return False\",\n \"def test_update_state(self):\\n # add a task\\n self.add(title=\\\"Sample task doing\\\", description=\\\"for sample\\\", state=\\\"doing\\\")\\n task = Task.query.filter_by(title='Sample task doing').first()\\n\\n # change task to todo\\n old_id = task.id\\n self.update_state(id=old_id, state='todo')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'todo')\\n\\n # change task to done\\n old_id = task.id\\n self.update_state(id=old_id, state='done')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'done')\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_time_successful(self):\\n\\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\\n '2016-01-10T00:00:00Z',\\n '2016-01-13T00:00:00Z',\\n 'source')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 2)\\n for result in results:\\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])\",\n \"def test_pending_job(self):\\n\\n pending_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"pending\\\"))\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def check_states(results):\\n assert results[\\\"states\\\"][\\\"device0\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\\n assert results[\\\"states\\\"][\\\"device1\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\",\n \"def test_running_job(self):\\n\\n running_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"running\\\"))\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_update_state(self):\\n pass\",\n \"def get_time_in_status(self, issue, state):\\n \\n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\\n\\n if ((issue['fields']['issuetype']['name'] == \\\"Sub-task\\\") and (issue['fields']['status']['name'] == \\\"In Progress\\\")) or \\\\\\n ((issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and (issue['fields']['status']['name'] not in [\\\"Open\\\", \\\"In Progress\\\", \\\"Reopened\\\", \\\"Resolved\\\", \\\"Closed\\\"])):\\n print \\\"Getting time in status for %s\\\" % issue['fields']['status']['name']\\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\\n # First state create time same as issue created datetime \\n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\") # Current item create datetime\\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n diff = nicd - cicdt # Time the issue has been in that state\\n \\n # If there's a changelog history go thru it else use issue create date and current date for time in status\\n # If there's no changelog/histories then we're still in first state, nothing has changed\\n if not len(issue['changelog']['histories']):\\n print \\\"no changelog for %s\\\" % issue['key']\\n self.issue_objs[issue['key']].time_in_status = diff.days\\n else:\\n # If there is a changelog look for the value \\\"status\\\", changes may be something other than status\\n print \\\"There's a changelog for issue %s\\\" % issue['key']\\n# print json.dumps(issue, indent=4)\\n \\n # Go thru changelog histories to see if any of the changes are \\\"status\\\" for this state, also get last status or sprint if New Feature\\n status_change_found = False\\n for histories_item in issue['changelog']['histories']:\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n print \\\"There's a status change\\\"\\n status_change_found = True\\n \\n # Get oldest sprint and status within config.change_period days ago for New Features \\n if (issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and \\\\\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\").date() # histories item create datetime\\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\\n print \\\"change too old %s, getting next histories item\\\" % hicdt\\n continue # If older than config.change_period days then we don't care, get next histories item\\n else:\\n print \\\"recent change %s \\\" % hicdt\\n if histories_item['items'][0]['field'] == 'Milestone(s)':\\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\\n print \\\"last_sprint %s\\\" % self.issue_objs[issue['key']].last_sprint\\n break # Got the oldest change within change_period\\n if histories_item['items'][0]['field'] == 'status':\\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\\n print \\\"last_status %s\\\" % self.issue_objs[issue['key']].last_status\\n break # Got the oldest change within change_period\\n \\n # If there was a change in status calculate time in status from changelog\\n if(status_change_found):\\n total_days = 0 # Accumulate all the diffs for each time in this state\\n # Go thru changelog histories to see if this issue was in this state before.\\n for i, histories_item in enumerate(issue['changelog']['histories']):\\n # All the different kinds of things that have changed, we're only interested in 'status'\\n \\n # Display all the changelog history items\\n if (0):\\n for k, v in histories_item.items():\\n print \\\"issue['changelog']['histories'] key: %s value: %s\\\" % (k, v)\\n print \\\"issue['changelog']['histories']['items'] %s\\\" % histories_item['items']\\n for item in histories_item['items']:\\n print \\\"histories_item['items'] %s\\\" % item\\n print \\\"histories_item['items'][0] %s\\\" %histories_item['items'][0]\\n for item2 in histories_item['items'][0]:\\n print \\\"histories_item['items'][0] %s %s\\\" % (item2, histories_item['items'][0][item2])\\n \\n # If status record we're interested in is found, look for next status change or use current date for end date\\n # If status \\\"Pending\\\" for New Features or \\\"Open\\\" for Sub-tasks there is no change entry for that, it's first state, use created date.\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n current_status_create_datetime = histories_item['created'].split('.')[0]\\n \\n # Go thru future history changes until the next status change\\n status_found = False\\n for j in range(i+1, len(issue['changelog']['histories'])):\\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\\n print \\\"%s. next histories item created: %s\\\" % (j, next_item_create_date)\\n status_found = True\\n break # Break the for loop, next status found\\n \\n if not status_found: \\n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \\n print \\\"No next histories item, using today's date: %s\\\" % (next_item_create_date)\\n \\n cscdt = dt.strptime(current_status_create_datetime, \\\"%Y-%m-%dT%H:%M:%S\\\") # Current status create datetime\\n nicdt = dt.strptime(next_item_create_date, \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n \\n print \\\"Current status create date for %s: %s\\\" % (issue['key'], cscdt)\\n print \\\"Next item create date for %s: %s\\\" % (issue['key'], nicdt)\\n \\n diff = nicdt - cscdt # The number of days the issue has been in that state\\n total_days += diff.days # Accumulate all the times this issue has been in this state.\\n self.issue_objs[issue['key']].time_in_status = total_days\\n else:\\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now\",\n \"def _wait_for_state(self, task_id, current, new):\\n state = result = None\\n start = time.time()\\n # 45 seconds is a long time.\\n # TODO(maruel): Make task_runner use exponential backoff instead of\\n # hardcoded 10s/30s, which makes these tests tremendously slower than\\n # necessary.\\n # https://crbug.com/825500\\n while time.time() - start < 45.:\\n result = self.client.task_result(task_id)\\n state = result[u'state']\\n if state == new:\\n break\\n self.assertEqual(current, state, result)\\n time.sleep(0.01)\\n self.assertEqual(new, state, result)\\n return result\",\n \"def query_job_progress():\\n pass\",\n \"def __checkInited(self, date=None, tkey=None, ttype=None):\\n if ttype == 'crontab':\\n return False\\n if date is None:\\n date = self.ddate\\n\\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\\n if doc.get(\\\"status\\\") == \\\"waiting\\\":\\n return True\\n return False\",\n \"def task_status():\\n pass\",\n \"def test_exp_by_states(self):\\n api = my_mock.api_mock({\\\"items\\\": [{'state': 'Waiting', 'id': 10134},\\n {'state': 'Waiting', 'id': 10135},\\n {'state': 'Running', 'id': 10130}]})\\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\\n self.assertEquals(\\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\\n my_mock.api_mock_stop()\",\n \"def test_source_time_successful(self):\\n\\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 1)\\n for result in results:\\n self.assertTrue(result['id'] in [self.job1.id])\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_completed(self):\\n return False\",\n \"def test_print_progress_flag(self, capsys):\\n file = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"geolife\\\", \\\"geolife_staypoints.csv\\\")\\n staypoints = ti.read_staypoints_csv(file, tz=\\\"utc\\\", index_col=\\\"id\\\", crs=\\\"epsg:4326\\\")\\n\\n staypoints.as_staypoints.generate_locations(print_progress=True)\\n captured_print = capsys.readouterr()\\n assert captured_print.err != \\\"\\\"\\n\\n staypoints.as_staypoints.generate_locations(print_progress=False)\\n captured_print = capsys.readouterr()\\n assert captured_print.err == \\\"\\\"\",\n \"def stalled(self):\\n oneHourAgo = datetime.today() - timedelta(hours = 1)\\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def test_pending_job(self):\\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\\n pending_job.pop(\\\"status\\\")\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_update_state1(self):\\n pass\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def test_running_job(self):\\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def pre_step(self,status):\\n self.t0 = time.time()\\n pass\",\n \"def test_status_done_basic(self):\\n\\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\\n FORSETI_CONNECT = 'sqlite:///{}'.format(\\n get_db_file_path('forseti_1_basic.db'))\\n\\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\\n FORSETI_CONNECT)\\n self.source = 'FORSETI'\\n self.model_manager = self.service_config.model_manager\\n self.model_name = self.model_manager.create(name=self.source)\\n\\n scoped_session, data_access = self.model_manager.get(self.model_name)\\n with scoped_session as session:\\n\\n importer_cls = importer.by_source(self.source)\\n import_runner = importer_cls(\\n session,\\n self.model_manager.model(self.model_name, expunge=False),\\n data_access,\\n self.service_config)\\n import_runner.run()\\n\\n model = self.model_manager.model(self.model_name)\\n self.assertEqual(model.state,\\n 'PARTIAL_SUCCESS',\\n 'Model state should be set to PARTIAL_SUCCESS')\",\n \"def test_completed():\\n assert complete == 1\\n assert errorflag == 0\",\n \"def test_service_initiated():\\n assert \\\"ready\\\" in bkt_outcome_unwind.index()\",\n \"def test_update_state4(self):\\n pass\",\n \"def task_scanned(now_task):\",\n \"def test_notBeforeBefore(self):\\n dbpool, qpool, clock, performerChosen = self._setupPools()\\n\\n @transactionally(dbpool.pool.connection)\\n def check(txn):\\n return qpool.enqueueWork(\\n txn, DummyWorkItem, a=3, b=9,\\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\\n )\\n\\n yield check\\n\\n clock.advance(1000)\\n # Advance far beyond the given timestamp.\\n self.assertEquals(performerChosen, [True])\\n\\n # Wait for job\\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\\n clock.advance(1)\\n\\n # Work item complete\\n self.assertTrue(DummyWorkItem.results == {1: 12})\",\n \"def test_getting_state_parallel(self):\\n no_replicates = 25\\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\\n for i in range(no_replicates):\\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)\",\n \"def test_state(self, mock_req):\\n self.setup_api(MOCK_DATA, mock_req)\\n now = datetime(1970, month=1, day=1)\\n with patch(\\\"homeassistant.util.dt.now\\\", return_value=now):\\n for name in self.sensor_dict:\\n sensor = self.sensor_dict[name][\\\"sensor\\\"]\\n self.fake_delay(2)\\n sensor.update()\\n if name == google_wifi.ATTR_LAST_RESTART:\\n assert \\\"1969-12-31 00:00:00\\\" == sensor.state\\n elif name == google_wifi.ATTR_UPTIME:\\n assert 1 == sensor.state\\n elif name == google_wifi.ATTR_STATUS:\\n assert \\\"Online\\\" == sensor.state\\n else:\\n assert \\\"initial\\\" == sensor.state\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_delete_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.delete_problem_state('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def testingProgressFunc(state, action, text, tick):\\n pass\\n #print \\\"testingProgressFunc\\\", state, action, text, tick\",\n \"def test_time_status(self):\\n result = self.test_client.time_status\\n\\n assert result == \\\"12312\\\"\",\n \"def update_task_state(mapper, conn, target):\\r\\n sql_query = ('select count(id) from task_run \\\\\\r\\n where task_run.task_id=%s') % target.task_id\\r\\n n_answers = conn.scalar(sql_query)\\r\\n sql_query = ('select n_answers from task \\\\\\r\\n where task.id=%s') % target.task_id\\r\\n task_n_answers = conn.scalar(sql_query)\\r\\n if (n_answers) >= task_n_answers:\\r\\n sql_query = (\\\"UPDATE task SET state=\\\\'completed\\\\' \\\\\\r\\n where id=%s\\\") % target.task_id\\r\\n conn.execute(sql_query)\",\n \"def test_act_ii(self):\\n # setup\\n processing_time = 5.0\\n self.transaction_behaviour.processing = \\\"some_dialogue\\\"\\n self.transaction_behaviour.max_processing = 120\\n self.transaction_behaviour.processing_time = processing_time\\n\\n # operation\\n self.transaction_behaviour.act()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\\n assert (\\n self.transaction_behaviour.processing_time\\n == processing_time + self.transaction_behaviour.tick_interval\\n )\",\n \"def in_progress(self):\\n return False\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_update_state2(self):\\n pass\",\n \"def set_task_in_progress(self):\\n\\n tasks = self._get_all_tasks()\\n\\n task_id = tasks[self.tasks_view.currentRow()].Id\\n\\n self.tasks_flow.set_status(task_id, 1)\\n\\n # Refresh the table\\n self.write_tasks_table()\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def policy_checker(name):\\n\\n policy = PolicyModel.query(PolicyModel.Name == name).get()\\n if not policy:\\n logging.error(\\\"Policy %s not found!\\\", name)\\n return \\\"not found\\\", 404\\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\\n if not schedule:\\n logging.error(\\\"Schedule %s not found!\\\", policy.Schedule)\\n return \\\"not found\\\", 404\\n logging.debug(\\n \\\"Time at Timezone %s is %s\\\",\\n schedule.Timezone,\\n tz.get_time_at_timezone(schedule.Timezone),\\n )\\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\\n logging.debug(\\\"Working on day %s hour %s\\\", day, hour)\\n arr = np.asarray(schedule.Schedule[\\\"__ndarray__\\\"], dtype=np.int).flatten()\\n matrix_size = schedule.Schedule[\\\"Shape\\\"][0] * schedule.Schedule[\\\"Shape\\\"][1]\\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\\n now = arr[day * 24 + hour]\\n prev = arr[prev]\\n logging.info(\\\"Previous state %s current %s\\\", prev, now)\\n if now == prev:\\n # do nothing\\n logging.info(\\\"Conditions are met, Nothing should be done for %s\\\", name)\\n return \\\"ok\\\", 200\\n else:\\n # stop/start\\n logging.info(\\\"State is changing for %s to %s\\\", name, now)\\n # for each tag lets do it\\n\\n task_client = tasks_v2.CloudTasksClient()\\n task = {\\n \\\"app_engine_http_request\\\": { # Specify the type of request.\\n \\\"http_method\\\": \\\"POST\\\",\\n \\\"relative_uri\\\": \\\"/tasks/change_state\\\",\\n }\\n }\\n parent = task_client.queue_path(\\n queue=\\\"zorya-tasks\\\",\\n project=utils.get_project_id(),\\n location=location.get_location(),\\n )\\n for tag in policy.Tags:\\n for project in policy.Projects:\\n payload = {\\n \\\"project\\\": project,\\n \\\"tagkey\\\": next(iter(tag)),\\n \\\"tagvalue\\\": tag[next(iter(tag))],\\n \\\"action\\\": str(now),\\n }\\n task[\\\"app_engine_http_request\\\"][\\\"body\\\"] = (\\n json.dumps(payload)\\n ).encode()\\n response = task_client.create_task(parent, task)\\n logging.debug(\\\"Task %s enqueued\\\", response.name)\\n return \\\"ok\\\", 200\",\n \"def test_get_current_state(self):\\n schedule = parser.parse_schedule(SCHEDULE)\\n self.assertEqual(\\\"a\\\", schedule.get_current_state(dt(monday, '09:30')))\\n self.assertEqual(\\\"c\\\", schedule.get_current_state(dt(monday, '08:30')))\\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\\n lookback=False))\",\n \"def mark_as_in_progress(self, task):\\n raise NotImplementedError('')\",\n \"def test_reset_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.reset_problem_attempts('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def test_check_contributing_state_completed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=1)\\r\\n user = UserFactory.create()\\r\\n TaskRunFactory.create_batch(1, task=task, user=user)\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def test_status(self):\\n keys = [\\n 'receiver time mark count', 'ant type base', 'solution status',\\n ...\\n ]\\n\\n for i in xrange(0, 100):\\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)\",\n \"def test_is_approved_no_approval(time_record_factory):\\n record = time_record_factory()\\n\\n assert not record.is_approved\",\n \"def test_default_status(self) -> None:\\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\\n self.assertEqual(self.report.has_started(), False)\",\n \"def testoptstatus(self): \\r\\n assert len(self.data.optstatus) == len(self.data.geovalues)\\r\\n assert self.data.optstatus[0] == self.data.OPT_NEW\\r\\n for i in range(1, len(self.data.optstatus)-1):\\r\\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\\r\\n assert self.data.optstatus[-1] == self.data.OPT_DONE\",\n \"def test__validate_status__0():\\n for input_value, expected_output in (\\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\\n ):\\n output = validate_status(input_value)\\n vampytest.assert_is(output, expected_output)\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def testPreProcessResourceState(self):\\n resource_id_1 = \\\"resource_id_1\\\"\\n task_id_1 = \\\"task_id_1\\\"\\n json_out = {resource_id_1: [task_id_1]}\\n\\n # Test that the resource id is properly added with associated task\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\\n\\n # Test that the additional task id is appended to resource id\\n task_id_2 = \\\"task_id_2\\\"\\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\\n\\n # Test that additional resource id is added into state file\\n resource_id_2 = \\\"resource_id_2\\\"\\n json_out_3 = {\\n resource_id_1: [task_id_1, task_id_2],\\n resource_id_2: [task_id_1]\\n }\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\\n\\n # Test that same task id is not duplicated into state file\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\",\n \"def test_meeting_live_stream_status_update(self):\\n pass\",\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def check_state(self):\\n pass\",\n \"def _server_poll_expcompleted_(self):\\n #print \\\"class Princeton_CCD function _server_poll_expcompleted_\\\" \\n try:\\n last_state = self.polled_running\\n except (AttributeError,UnboundLocalError):\\n self.polled_running = False\\n last_state = False\\n self.polled_running = self.query_running()\\n if (not bool(last_state) and bool(self.polled_running)):\\n self.begin_acq_time = time.time()\\n #print self.query_running(), last_state\\n #if ((last_state == True) and (self.polled_running == False)): CP\\n if (bool(last_state) and not bool(self.polled_running)):\\n self.end_acq_time = time.time()\\n return True\\n else:\\n return False\",\n \"def test_ensure_not_ts_ts(self):\\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')\",\n \"def check_status():\\n logger.debug(\\\"Starting the check_status() routine.\\\")\\n\\n url = \\\"https://www.toggl.com/api/v8/time_entries/current\\\"\\n token = os.environ[\\\"TOGGL_API_TOKEN\\\"]\\n auth_token = base64.b64encode(f\\\"{token}:api_token\\\".encode()).decode()\\n resp = requests.get(url, headers={\\\"Authorization\\\": \\\"Basic \\\" + auth_token})\\n\\n cols = \\\"id\\\", \\\"duration\\\", \\\"description\\\"\\n status = {k: v for k, v in (resp.json()[\\\"data\\\"] or {}).items() if k in cols}\\n logger.debug(f\\\"{'Something' if 'id' in status else 'Something'} is being tracked.\\\")\\n\\n return status\",\n \"def _assert_state(self, state_dict):\\n instances = db.instance_get_all(self.context)\\n self.assertEqual(len(instances), 1)\\n\\n if 'vm_state' in state_dict:\\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\\n if 'task_state' in state_dict:\\n self.assertEqual(state_dict['task_state'],\\n instances[0]['task_state'])\\n if 'power_state' in state_dict:\\n self.assertEqual(state_dict['power_state'],\\n instances[0]['power_state'])\",\n \"def test_project_state_counters(self):\\n #build counters:\\n management.call_command('rebuild_counters')\\n\\n self.client.force_authenticate(self.global_user_1)\\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\\n\\n for project_data in data:\\n self.assertIn('state', project_data)\\n project_state_data = project_data['state']\\n if project_state_data:\\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\\n self.assertEqual(\\n project_state_data['numberOfEnrolledLessons'],\\n project_state_obj.lesson_states.count()\\n )\\n self.assertEqual(\\n project_state_data['numberOfCompletedLessons'],\\n project_state_obj.lesson_states.filter(is_completed=True).count()\\n )\",\n \"def test_multiple_states(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory()\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Cancel when created.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.CANCEL, event)\\n s1 = context.work_status.value\\n self.assertEqual(s1, s.CANCELLED)\\n\\n # Cancel when started.\\n c2 = model.factory(initial_state=s.STARTED)\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(c2, a.CANCEL, event)\\n s2 = c2.work_status.value\\n self.assertEqual(s2, s.CANCELLED)\",\n \"def test_active_and_date_range(self):\\n\\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(len(result['results'][0]['job_counts']), 0)\",\n \"def test_send_state_event_nonoverwriting(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": True}, tok=u1token, state_key=\\\"tabby\\\"\\n )\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": False}, tok=u1token, state_key=\\\"moggy\\\"\\n )\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 1,\\n )\",\n \"def test_check_contributing_state_completed_user_not_contributed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=2)\\r\\n TaskRunFactory.create_batch(2, task=task)\\r\\n user = UserFactory.create()\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def check_indicator_files(tasks):\\n\\n for task in tasks:\\n if task[\\\"status\\\"]==\\\"unknown\\\":\\n if os.path.exists(task[\\\"result\\\"]):\\n task[\\\"status\\\"]=\\\"previously completed\\\"\\n else:\\n task[\\\"status\\\"]=\\\"to do\\\"\\n return\",\n \"def test_timeout_processing(self):\\n # setup\\n self.transaction_behaviour.processing_time = None\\n\\n # operation\\n self.transaction_behaviour._timeout_processing()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\",\n \"def test_is_finished(self):\\n experiment = Experiment(TasksMock())\\n self.assertEquals(False, experiment.is_finished())\\n for _ in range(0, 17):\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(True, experiment.is_finished())\",\n \"def test_active(self):\\n\\n url = '/%s/job-types/status/?is_active=true' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def poll(self):\\n\\n query = f\\\"sacct -j {self.jobid} -o State -n -X -P\\\"\\n if self.cluster:\\n query += f\\\" --clusters={self.cluster}\\\"\\n\\n cmd = BuildTestCommand(query)\\n cmd.execute()\\n\\n logger.debug(f\\\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\\\")\\n job_state = cmd.get_output()\\n self._state = \\\"\\\".join(job_state).rstrip()\\n logger.debug(f\\\"JobID: '{self.jobid}' job state:{self._state}\\\")\",\n \"def test_assessment_import_states(self):\\n self.import_file(\\\"assessment_full_no_warnings.csv\\\")\\n response = self.import_file(\\\"assessment_update_intermediate.csv\\\")\\n expected_errors = {\\n \\\"Assessment\\\": {\\n \\\"block_errors\\\": set(),\\n \\\"block_warnings\\\": set(),\\n \\\"row_errors\\\": set(),\\n \\\"row_warnings\\\": set(),\\n }\\n }\\n self._check_csv_response(response, expected_errors)\\n\\n assessments = {r.slug: r for r in models.Assessment.query.all()}\\n self.assertEqual(assessments[\\\"Assessment 60\\\"].status,\\n models.Assessment.START_STATE)\\n self.assertEqual(assessments[\\\"Assessment 61\\\"].status,\\n models.Assessment.PROGRESS_STATE)\\n self.assertEqual(assessments[\\\"Assessment 62\\\"].status,\\n models.Assessment.DONE_STATE)\\n self.assertEqual(assessments[\\\"Assessment 63\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 64\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 3\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 4\\\"].status,\\n models.Assessment.FINAL_STATE)\\n\\n # Check that there is only one attachment left\\n asmt1 = assessments[\\\"Assessment 1\\\"]\\n self.assertEqual({\\\"a.b.com\\\", \\\"c d com\\\"},\\n {i.title for i in asmt1.document_url})\\n self.assertEqual({u'evidence title 1'},\\n {i.title for i in asmt1.document_evidence})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7257185","0.71193224","0.6860334","0.628547","0.6147146","0.6099557","0.60727316","0.59782106","0.59703326","0.59561163","0.58597606","0.5858871","0.5750034","0.57215893","0.57205486","0.5681524","0.5675636","0.56590813","0.5616347","0.56143284","0.558176","0.5567624","0.55467683","0.5534509","0.5526768","0.5515089","0.550762","0.55065536","0.55008715","0.54807657","0.54804677","0.54558194","0.5450302","0.54500717","0.543557","0.54271126","0.54120195","0.5381252","0.5371506","0.5367457","0.53505445","0.53492796","0.53492117","0.53475666","0.53466445","0.53451306","0.53333145","0.5332616","0.5311005","0.5305092","0.53039515","0.53019696","0.52922636","0.52862763","0.52830493","0.5278864","0.5255235","0.5245685","0.52438986","0.52436084","0.52395296","0.5238431","0.5236099","0.5232433","0.5231263","0.5230454","0.5218501","0.52094007","0.5208183","0.52060616","0.5202795","0.5202125","0.51943576","0.5189145","0.5179092","0.51789975","0.51763767","0.5176183","0.51746553","0.5167336","0.5164447","0.51635677","0.5163309","0.51626724","0.5161","0.51603633","0.5159166","0.5158292","0.5155792","0.5146473","0.5145949","0.51427597","0.5135461","0.5134001","0.5133516","0.5127348","0.51268166","0.51153547","0.51101816","0.51032335"],"string":"[\n \"0.7257185\",\n \"0.71193224\",\n \"0.6860334\",\n \"0.628547\",\n \"0.6147146\",\n \"0.6099557\",\n \"0.60727316\",\n \"0.59782106\",\n \"0.59703326\",\n \"0.59561163\",\n \"0.58597606\",\n \"0.5858871\",\n \"0.5750034\",\n \"0.57215893\",\n \"0.57205486\",\n \"0.5681524\",\n \"0.5675636\",\n \"0.56590813\",\n \"0.5616347\",\n \"0.56143284\",\n \"0.558176\",\n \"0.5567624\",\n \"0.55467683\",\n \"0.5534509\",\n \"0.5526768\",\n \"0.5515089\",\n \"0.550762\",\n \"0.55065536\",\n \"0.55008715\",\n \"0.54807657\",\n \"0.54804677\",\n \"0.54558194\",\n \"0.5450302\",\n \"0.54500717\",\n \"0.543557\",\n \"0.54271126\",\n \"0.54120195\",\n \"0.5381252\",\n \"0.5371506\",\n \"0.5367457\",\n \"0.53505445\",\n \"0.53492796\",\n \"0.53492117\",\n \"0.53475666\",\n \"0.53466445\",\n \"0.53451306\",\n \"0.53333145\",\n \"0.5332616\",\n \"0.5311005\",\n \"0.5305092\",\n \"0.53039515\",\n \"0.53019696\",\n \"0.52922636\",\n \"0.52862763\",\n \"0.52830493\",\n \"0.5278864\",\n \"0.5255235\",\n \"0.5245685\",\n \"0.52438986\",\n \"0.52436084\",\n \"0.52395296\",\n \"0.5238431\",\n \"0.5236099\",\n \"0.5232433\",\n \"0.5231263\",\n \"0.5230454\",\n \"0.5218501\",\n \"0.52094007\",\n \"0.5208183\",\n \"0.52060616\",\n \"0.5202795\",\n \"0.5202125\",\n \"0.51943576\",\n \"0.5189145\",\n \"0.5179092\",\n \"0.51789975\",\n \"0.51763767\",\n \"0.5176183\",\n \"0.51746553\",\n \"0.5167336\",\n \"0.5164447\",\n \"0.51635677\",\n \"0.5163309\",\n \"0.51626724\",\n \"0.5161\",\n \"0.51603633\",\n \"0.5159166\",\n \"0.5158292\",\n \"0.5155792\",\n \"0.5146473\",\n \"0.5145949\",\n \"0.51427597\",\n \"0.5135461\",\n \"0.5134001\",\n \"0.5133516\",\n \"0.5127348\",\n \"0.51268166\",\n \"0.51153547\",\n \"0.51101816\",\n \"0.51032335\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66941416"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":17472,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_IN_PROGRESS state"},"document":{"kind":"string","value":"def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_update_single_row_if_status_is_in_progress(self):\n first = generate_mock_result(status='IN_PROGRESS', success=False)\n self.db.insert_single_result(first)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'IN_PROGRESS')\n second = generate_mock_result(status='SUCCESS', success=True)\n self.db.insert_single_result(second)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'SUCCESS')","def _wait_for_table_status(self, expected='ACTIVE'):\n\n achieved_state = False\n while not achieved_state:\n table_description = self.client.describe_table(TableName=self.table_name)\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\n current_status = table_description['Table']['TableStatus']\n achieved_state = current_status == expected\n sleep(1)","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_retrieve_instances_schedule_state(self):\n pass","def insync_and_state_check(self):\n self.step('verifying tables are properly synced on all endpoints')\n is_ok = True\n limit, count = 10, 0\n while count < limit:\n try:\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\n assert rc == 200, f\"something wrong happened when checking state table {rc}\"\n for state in state_check['data']:\n if not state['in_sync'] == True or not state['state'] == 'loaded':\n print(f\"found state which was not in_sync=True & 'loaded {state}, retrying\")\n is_ok = False\n self.sync_job_check()\n break\n if is_ok:\n break\n count+=1\n except Exception as e:\n print(f\"something wrong happened when checking state table\")\n break","def _compute_queue_state(self):\n for record in self:\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\n record.state = \"completed\"\n elif record.queue_line_draft_records == record.queue_line_total_records:\n record.state = \"draft\"\n elif record.queue_line_total_records == record.queue_line_fail_records:\n record.state = \"failed\"\n else:\n record.state = \"partially_completed\"","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_get_unsuccessful_ta():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\n\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\n assert np.isnan(list_failed[0])","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def test_issue_tracked_times(self):\n pass","def test_update_task_states(self):\r\n changed = self.combinedoe.update_task_states()\r\n self.assertFalse(changed)\r\n\r\n current_task = self.combinedoe.current_task\r\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\r\n changed = self.combinedoe.update_task_states()\r\n\r\n self.assertTrue(changed)","def test_update_instances_schedule_state(self):\n pass","def test_state_after_failure(self):\n pass","def test_get_refresh_job_status(self):\n pass","def load_status_table():","def update_status(cls):\n for job in cls.query.filter(cls.finished == False):\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\n dynamo = DynamoIngestionStatusClient()\n num_urls_left = 0\n for url in urls_left:\n dynamo_url = dynamo.get(url.url)\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\n # will never be processed, so ignore for our purposes\n url.processed = True\n else:\n num_urls_left += 1\n if num_hits_left+num_urls_left == 0:\n job.finished = True\n print '*** Job ID: %s is complete ***' % str(job.id)\n\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\n if not job.finished:\n print 'unprocessed URLs: %i' % num_urls_left\n print 'outstanding HITs: %i\\n' % num_hits_left\n session.flush()","def test_mark_incompleted(self):\n event = Event.objects.all()[0]\n\n todo = TodoItem.objects.create(\n event=event, completed=True, title=\"Test TODO2\",\n due=datetime.date.today(), additional=\"\",\n )\n\n assert todo.completed is True\n\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\n todo.refresh_from_db()\n\n assert todo.completed is False","def _check_completed(self):\n current_rung_df = self.sieve_board.loc[\n self.sieve_board['status'].isin(\n [StatusType.WAITTING, StatusType.RUNNING])\n ]\n if current_rung_df.empty:\n return True\n else:\n return False","def test_update_state(self):\n # add a task\n self.add(title=\"Sample task doing\", description=\"for sample\", state=\"doing\")\n task = Task.query.filter_by(title='Sample task doing').first()\n\n # change task to todo\n old_id = task.id\n self.update_state(id=old_id, state='todo')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'todo')\n\n # change task to done\n old_id = task.id\n self.update_state(id=old_id, state='done')\n task = Task.query.filter_by(title='Sample task doing').first()\n self.assertEqual(task.id, old_id)\n self.assertEqual(task.state, 'done')","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_time_successful(self):\n\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\n '2016-01-10T00:00:00Z',\n '2016-01-13T00:00:00Z',\n 'source')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 2)\n for result in results:\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])","def test_pending_job(self):\n\n pending_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"pending\"))\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def check_states(results):\n assert results[\"states\"][\"device0\"] == {\"counter\": 10, \"rts\": 0}\n assert results[\"states\"][\"device1\"] == {\"counter\": 10, \"rts\": 0}","def test_running_job(self):\n\n running_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"running\"))\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_update_state(self):\n pass","def get_time_in_status(self, issue, state):\n \n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\n\n if ((issue['fields']['issuetype']['name'] == \"Sub-task\") and (issue['fields']['status']['name'] == \"In Progress\")) or \\\n ((issue['fields']['issuetype']['name'] == \"New Feature\") and (issue['fields']['status']['name'] not in [\"Open\", \"In Progress\", \"Reopened\", \"Resolved\", \"Closed\"])):\n print \"Getting time in status for %s\" % issue['fields']['status']['name']\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\n # First state create time same as issue created datetime \n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\") # Current item create datetime\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n diff = nicd - cicdt # Time the issue has been in that state\n \n # If there's a changelog history go thru it else use issue create date and current date for time in status\n # If there's no changelog/histories then we're still in first state, nothing has changed\n if not len(issue['changelog']['histories']):\n print \"no changelog for %s\" % issue['key']\n self.issue_objs[issue['key']].time_in_status = diff.days\n else:\n # If there is a changelog look for the value \"status\", changes may be something other than status\n print \"There's a changelog for issue %s\" % issue['key']\n# print json.dumps(issue, indent=4)\n \n # Go thru changelog histories to see if any of the changes are \"status\" for this state, also get last status or sprint if New Feature\n status_change_found = False\n for histories_item in issue['changelog']['histories']:\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n print \"There's a status change\"\n status_change_found = True\n \n # Get oldest sprint and status within config.change_period days ago for New Features \n if (issue['fields']['issuetype']['name'] == \"New Feature\") and \\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \"%Y-%m-%dT%H:%M:%S\").date() # histories item create datetime\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\n print \"change too old %s, getting next histories item\" % hicdt\n continue # If older than config.change_period days then we don't care, get next histories item\n else:\n print \"recent change %s \" % hicdt\n if histories_item['items'][0]['field'] == 'Milestone(s)':\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\n print \"last_sprint %s\" % self.issue_objs[issue['key']].last_sprint\n break # Got the oldest change within change_period\n if histories_item['items'][0]['field'] == 'status':\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\n print \"last_status %s\" % self.issue_objs[issue['key']].last_status\n break # Got the oldest change within change_period\n \n # If there was a change in status calculate time in status from changelog\n if(status_change_found):\n total_days = 0 # Accumulate all the diffs for each time in this state\n # Go thru changelog histories to see if this issue was in this state before.\n for i, histories_item in enumerate(issue['changelog']['histories']):\n # All the different kinds of things that have changed, we're only interested in 'status'\n \n # Display all the changelog history items\n if (0):\n for k, v in histories_item.items():\n print \"issue['changelog']['histories'] key: %s value: %s\" % (k, v)\n print \"issue['changelog']['histories']['items'] %s\" % histories_item['items']\n for item in histories_item['items']:\n print \"histories_item['items'] %s\" % item\n print \"histories_item['items'][0] %s\" %histories_item['items'][0]\n for item2 in histories_item['items'][0]:\n print \"histories_item['items'][0] %s %s\" % (item2, histories_item['items'][0][item2])\n \n # If status record we're interested in is found, look for next status change or use current date for end date\n # If status \"Pending\" for New Features or \"Open\" for Sub-tasks there is no change entry for that, it's first state, use created date.\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\n current_status_create_datetime = histories_item['created'].split('.')[0]\n \n # Go thru future history changes until the next status change\n status_found = False\n for j in range(i+1, len(issue['changelog']['histories'])):\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\n print \"%s. next histories item created: %s\" % (j, next_item_create_date)\n status_found = True\n break # Break the for loop, next status found\n \n if not status_found: \n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \n print \"No next histories item, using today's date: %s\" % (next_item_create_date)\n \n cscdt = dt.strptime(current_status_create_datetime, \"%Y-%m-%dT%H:%M:%S\") # Current status create datetime\n nicdt = dt.strptime(next_item_create_date, \"%Y-%m-%dT%H:%M:%S\") # Next item create datetime\n \n print \"Current status create date for %s: %s\" % (issue['key'], cscdt)\n print \"Next item create date for %s: %s\" % (issue['key'], nicdt)\n \n diff = nicdt - cscdt # The number of days the issue has been in that state\n total_days += diff.days # Accumulate all the times this issue has been in this state.\n self.issue_objs[issue['key']].time_in_status = total_days\n else:\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now","def _wait_for_state(self, task_id, current, new):\n state = result = None\n start = time.time()\n # 45 seconds is a long time.\n # TODO(maruel): Make task_runner use exponential backoff instead of\n # hardcoded 10s/30s, which makes these tests tremendously slower than\n # necessary.\n # https://crbug.com/825500\n while time.time() - start < 45.:\n result = self.client.task_result(task_id)\n state = result[u'state']\n if state == new:\n break\n self.assertEqual(current, state, result)\n time.sleep(0.01)\n self.assertEqual(new, state, result)\n return result","def query_job_progress():\n pass","def __checkInited(self, date=None, tkey=None, ttype=None):\n if ttype == 'crontab':\n return False\n if date is None:\n date = self.ddate\n\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\n if doc.get(\"status\") == \"waiting\":\n return True\n return False","def task_status():\n pass","def test_exp_by_states(self):\n api = my_mock.api_mock({\"items\": [{'state': 'Waiting', 'id': 10134},\n {'state': 'Waiting', 'id': 10135},\n {'state': 'Running', 'id': 10130}]})\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\n self.assertEquals(\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\n my_mock.api_mock_stop()","def test_source_time_successful(self):\n\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 1)\n for result in results:\n self.assertTrue(result['id'] in [self.job1.id])","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_completed(self):\n return False","def test_print_progress_flag(self, capsys):\n file = os.path.join(\"tests\", \"data\", \"geolife\", \"geolife_staypoints.csv\")\n staypoints = ti.read_staypoints_csv(file, tz=\"utc\", index_col=\"id\", crs=\"epsg:4326\")\n\n staypoints.as_staypoints.generate_locations(print_progress=True)\n captured_print = capsys.readouterr()\n assert captured_print.err != \"\"\n\n staypoints.as_staypoints.generate_locations(print_progress=False)\n captured_print = capsys.readouterr()\n assert captured_print.err == \"\"","def stalled(self):\n oneHourAgo = datetime.today() - timedelta(hours = 1)\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def test_pending_job(self):\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\n pending_job.pop(\"status\")\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_update_state1(self):\n pass","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def test_running_job(self):\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def pre_step(self,status):\n self.t0 = time.time()\n pass","def test_status_done_basic(self):\n\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\n FORSETI_CONNECT = 'sqlite:///{}'.format(\n get_db_file_path('forseti_1_basic.db'))\n\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\n FORSETI_CONNECT)\n self.source = 'FORSETI'\n self.model_manager = self.service_config.model_manager\n self.model_name = self.model_manager.create(name=self.source)\n\n scoped_session, data_access = self.model_manager.get(self.model_name)\n with scoped_session as session:\n\n importer_cls = importer.by_source(self.source)\n import_runner = importer_cls(\n session,\n self.model_manager.model(self.model_name, expunge=False),\n data_access,\n self.service_config)\n import_runner.run()\n\n model = self.model_manager.model(self.model_name)\n self.assertEqual(model.state,\n 'PARTIAL_SUCCESS',\n 'Model state should be set to PARTIAL_SUCCESS')","def test_completed():\n assert complete == 1\n assert errorflag == 0","def test_service_initiated():\n assert \"ready\" in bkt_outcome_unwind.index()","def test_update_state4(self):\n pass","def task_scanned(now_task):","def test_notBeforeBefore(self):\n dbpool, qpool, clock, performerChosen = self._setupPools()\n\n @transactionally(dbpool.pool.connection)\n def check(txn):\n return qpool.enqueueWork(\n txn, DummyWorkItem, a=3, b=9,\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\n )\n\n yield check\n\n clock.advance(1000)\n # Advance far beyond the given timestamp.\n self.assertEquals(performerChosen, [True])\n\n # Wait for job\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\n clock.advance(1)\n\n # Work item complete\n self.assertTrue(DummyWorkItem.results == {1: 12})","def test_getting_state_parallel(self):\n no_replicates = 25\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\n for i in range(no_replicates):\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)","def test_state(self, mock_req):\n self.setup_api(MOCK_DATA, mock_req)\n now = datetime(1970, month=1, day=1)\n with patch(\"homeassistant.util.dt.now\", return_value=now):\n for name in self.sensor_dict:\n sensor = self.sensor_dict[name][\"sensor\"]\n self.fake_delay(2)\n sensor.update()\n if name == google_wifi.ATTR_LAST_RESTART:\n assert \"1969-12-31 00:00:00\" == sensor.state\n elif name == google_wifi.ATTR_UPTIME:\n assert 1 == sensor.state\n elif name == google_wifi.ATTR_STATUS:\n assert \"Online\" == sensor.state\n else:\n assert \"initial\" == sensor.state","def test_table_false_positives(self):\n pass","def test_delete_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.delete_problem_state('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def testingProgressFunc(state, action, text, tick):\n pass\n #print \"testingProgressFunc\", state, action, text, tick","def test_time_status(self):\n result = self.test_client.time_status\n\n assert result == \"12312\"","def update_task_state(mapper, conn, target):\r\n sql_query = ('select count(id) from task_run \\\r\n where task_run.task_id=%s') % target.task_id\r\n n_answers = conn.scalar(sql_query)\r\n sql_query = ('select n_answers from task \\\r\n where task.id=%s') % target.task_id\r\n task_n_answers = conn.scalar(sql_query)\r\n if (n_answers) >= task_n_answers:\r\n sql_query = (\"UPDATE task SET state=\\'completed\\' \\\r\n where id=%s\") % target.task_id\r\n conn.execute(sql_query)","def test_act_ii(self):\n # setup\n processing_time = 5.0\n self.transaction_behaviour.processing = \"some_dialogue\"\n self.transaction_behaviour.max_processing = 120\n self.transaction_behaviour.processing_time = processing_time\n\n # operation\n self.transaction_behaviour.act()\n\n # after\n self.assert_quantity_in_outbox(0)\n assert (\n self.transaction_behaviour.processing_time\n == processing_time + self.transaction_behaviour.tick_interval\n )","def in_progress(self):\n return False","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_update_state2(self):\n pass","def set_task_in_progress(self):\n\n tasks = self._get_all_tasks()\n\n task_id = tasks[self.tasks_view.currentRow()].Id\n\n self.tasks_flow.set_status(task_id, 1)\n\n # Refresh the table\n self.write_tasks_table()","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def policy_checker(name):\n\n policy = PolicyModel.query(PolicyModel.Name == name).get()\n if not policy:\n logging.error(\"Policy %s not found!\", name)\n return \"not found\", 404\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\n if not schedule:\n logging.error(\"Schedule %s not found!\", policy.Schedule)\n return \"not found\", 404\n logging.debug(\n \"Time at Timezone %s is %s\",\n schedule.Timezone,\n tz.get_time_at_timezone(schedule.Timezone),\n )\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\n logging.debug(\"Working on day %s hour %s\", day, hour)\n arr = np.asarray(schedule.Schedule[\"__ndarray__\"], dtype=np.int).flatten()\n matrix_size = schedule.Schedule[\"Shape\"][0] * schedule.Schedule[\"Shape\"][1]\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\n now = arr[day * 24 + hour]\n prev = arr[prev]\n logging.info(\"Previous state %s current %s\", prev, now)\n if now == prev:\n # do nothing\n logging.info(\"Conditions are met, Nothing should be done for %s\", name)\n return \"ok\", 200\n else:\n # stop/start\n logging.info(\"State is changing for %s to %s\", name, now)\n # for each tag lets do it\n\n task_client = tasks_v2.CloudTasksClient()\n task = {\n \"app_engine_http_request\": { # Specify the type of request.\n \"http_method\": \"POST\",\n \"relative_uri\": \"/tasks/change_state\",\n }\n }\n parent = task_client.queue_path(\n queue=\"zorya-tasks\",\n project=utils.get_project_id(),\n location=location.get_location(),\n )\n for tag in policy.Tags:\n for project in policy.Projects:\n payload = {\n \"project\": project,\n \"tagkey\": next(iter(tag)),\n \"tagvalue\": tag[next(iter(tag))],\n \"action\": str(now),\n }\n task[\"app_engine_http_request\"][\"body\"] = (\n json.dumps(payload)\n ).encode()\n response = task_client.create_task(parent, task)\n logging.debug(\"Task %s enqueued\", response.name)\n return \"ok\", 200","def test_get_current_state(self):\n schedule = parser.parse_schedule(SCHEDULE)\n self.assertEqual(\"a\", schedule.get_current_state(dt(monday, '09:30')))\n self.assertEqual(\"c\", schedule.get_current_state(dt(monday, '08:30')))\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\n lookback=False))","def mark_as_in_progress(self, task):\n raise NotImplementedError('')","def test_reset_non_problem(self):\r\n location = self.problem_section.location\r\n instructor_task = self.reset_problem_attempts('instructor', location)\r\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\r\n self.assertEqual(instructor_task.task_state, SUCCESS)","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def test_check_contributing_state_completed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=1)\r\n user = UserFactory.create()\r\n TaskRunFactory.create_batch(1, task=task, user=user)\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def test_status(self):\n keys = [\n 'receiver time mark count', 'ant type base', 'solution status',\n ...\n ]\n\n for i in xrange(0, 100):\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)","def test_is_approved_no_approval(time_record_factory):\n record = time_record_factory()\n\n assert not record.is_approved","def test_default_status(self) -> None:\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\n self.assertEqual(self.report.has_started(), False)","def testoptstatus(self): \r\n assert len(self.data.optstatus) == len(self.data.geovalues)\r\n assert self.data.optstatus[0] == self.data.OPT_NEW\r\n for i in range(1, len(self.data.optstatus)-1):\r\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\r\n assert self.data.optstatus[-1] == self.data.OPT_DONE","def test__validate_status__0():\n for input_value, expected_output in (\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\n ):\n output = validate_status(input_value)\n vampytest.assert_is(output, expected_output)","def test_update_state3(self):\n pass","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def testPreProcessResourceState(self):\n resource_id_1 = \"resource_id_1\"\n task_id_1 = \"task_id_1\"\n json_out = {resource_id_1: [task_id_1]}\n\n # Test that the resource id is properly added with associated task\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\n\n # Test that the additional task id is appended to resource id\n task_id_2 = \"task_id_2\"\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\n\n # Test that additional resource id is added into state file\n resource_id_2 = \"resource_id_2\"\n json_out_3 = {\n resource_id_1: [task_id_1, task_id_2],\n resource_id_2: [task_id_1]\n }\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\n\n # Test that same task id is not duplicated into state file\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)","def test_meeting_live_stream_status_update(self):\n pass","def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def check_state(self):\n pass","def _server_poll_expcompleted_(self):\n #print \"class Princeton_CCD function _server_poll_expcompleted_\" \n try:\n last_state = self.polled_running\n except (AttributeError,UnboundLocalError):\n self.polled_running = False\n last_state = False\n self.polled_running = self.query_running()\n if (not bool(last_state) and bool(self.polled_running)):\n self.begin_acq_time = time.time()\n #print self.query_running(), last_state\n #if ((last_state == True) and (self.polled_running == False)): CP\n if (bool(last_state) and not bool(self.polled_running)):\n self.end_acq_time = time.time()\n return True\n else:\n return False","def test_ensure_not_ts_ts(self):\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')","def check_status():\n logger.debug(\"Starting the check_status() routine.\")\n\n url = \"https://www.toggl.com/api/v8/time_entries/current\"\n token = os.environ[\"TOGGL_API_TOKEN\"]\n auth_token = base64.b64encode(f\"{token}:api_token\".encode()).decode()\n resp = requests.get(url, headers={\"Authorization\": \"Basic \" + auth_token})\n\n cols = \"id\", \"duration\", \"description\"\n status = {k: v for k, v in (resp.json()[\"data\"] or {}).items() if k in cols}\n logger.debug(f\"{'Something' if 'id' in status else 'Something'} is being tracked.\")\n\n return status","def _assert_state(self, state_dict):\n instances = db.instance_get_all(self.context)\n self.assertEqual(len(instances), 1)\n\n if 'vm_state' in state_dict:\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\n if 'task_state' in state_dict:\n self.assertEqual(state_dict['task_state'],\n instances[0]['task_state'])\n if 'power_state' in state_dict:\n self.assertEqual(state_dict['power_state'],\n instances[0]['power_state'])","def test_project_state_counters(self):\n #build counters:\n management.call_command('rebuild_counters')\n\n self.client.force_authenticate(self.global_user_1)\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\n\n for project_data in data:\n self.assertIn('state', project_data)\n project_state_data = project_data['state']\n if project_state_data:\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\n self.assertEqual(\n project_state_data['numberOfEnrolledLessons'],\n project_state_obj.lesson_states.count()\n )\n self.assertEqual(\n project_state_data['numberOfCompletedLessons'],\n project_state_obj.lesson_states.filter(is_completed=True).count()\n )","def test_multiple_states(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory()\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Cancel when created.\n state_changer = request.state_changer\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n state_changer.perform(context, a.CANCEL, event)\n s1 = context.work_status.value\n self.assertEqual(s1, s.CANCELLED)\n\n # Cancel when started.\n c2 = model.factory(initial_state=s.STARTED)\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n with transaction.manager:\n bm.Session.add(event)\n state_changer.perform(c2, a.CANCEL, event)\n s2 = c2.work_status.value\n self.assertEqual(s2, s.CANCELLED)","def test_active_and_date_range(self):\n\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(len(result['results'][0]['job_counts']), 0)","def test_send_state_event_nonoverwriting(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": True}, tok=u1token, state_key=\"tabby\"\n )\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.send_state(\n r1, \"cat.hissing\", {\"value\": False}, tok=u1token, state_key=\"moggy\"\n )\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 1,\n )","def test_check_contributing_state_completed_user_not_contributed(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=2)\r\n TaskRunFactory.create_batch(2, task=task)\r\n user = UserFactory.create()\r\n\r\n contributing_state = helpers.check_contributing_state(app_id=app.id,\r\n user_id=user.id)\r\n\r\n assert task.state == 'completed', task.state\r\n assert contributing_state == 'completed', contributing_state","def check_indicator_files(tasks):\n\n for task in tasks:\n if task[\"status\"]==\"unknown\":\n if os.path.exists(task[\"result\"]):\n task[\"status\"]=\"previously completed\"\n else:\n task[\"status\"]=\"to do\"\n return","def test_timeout_processing(self):\n # setup\n self.transaction_behaviour.processing_time = None\n\n # operation\n self.transaction_behaviour._timeout_processing()\n\n # after\n self.assert_quantity_in_outbox(0)","def test_is_finished(self):\n experiment = Experiment(TasksMock())\n self.assertEquals(False, experiment.is_finished())\n for _ in range(0, 17):\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(True, experiment.is_finished())","def test_active(self):\n\n url = '/%s/job-types/status/?is_active=true' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def poll(self):\n\n query = f\"sacct -j {self.jobid} -o State -n -X -P\"\n if self.cluster:\n query += f\" --clusters={self.cluster}\"\n\n cmd = BuildTestCommand(query)\n cmd.execute()\n\n logger.debug(f\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\")\n job_state = cmd.get_output()\n self._state = \"\".join(job_state).rstrip()\n logger.debug(f\"JobID: '{self.jobid}' job state:{self._state}\")","def test_assessment_import_states(self):\n self.import_file(\"assessment_full_no_warnings.csv\")\n response = self.import_file(\"assessment_update_intermediate.csv\")\n expected_errors = {\n \"Assessment\": {\n \"block_errors\": set(),\n \"block_warnings\": set(),\n \"row_errors\": set(),\n \"row_warnings\": set(),\n }\n }\n self._check_csv_response(response, expected_errors)\n\n assessments = {r.slug: r for r in models.Assessment.query.all()}\n self.assertEqual(assessments[\"Assessment 60\"].status,\n models.Assessment.START_STATE)\n self.assertEqual(assessments[\"Assessment 61\"].status,\n models.Assessment.PROGRESS_STATE)\n self.assertEqual(assessments[\"Assessment 62\"].status,\n models.Assessment.DONE_STATE)\n self.assertEqual(assessments[\"Assessment 63\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 64\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 3\"].status,\n models.Assessment.FINAL_STATE)\n self.assertEqual(assessments[\"Assessment 4\"].status,\n models.Assessment.FINAL_STATE)\n\n # Check that there is only one attachment left\n asmt1 = assessments[\"Assessment 1\"]\n self.assertEqual({\"a.b.com\", \"c d com\"},\n {i.title for i in asmt1.document_url})\n self.assertEqual({u'evidence title 1'},\n {i.title for i in asmt1.document_evidence})"],"string":"[\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_update_single_row_if_status_is_in_progress(self):\\n first = generate_mock_result(status='IN_PROGRESS', success=False)\\n self.db.insert_single_result(first)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'IN_PROGRESS')\\n second = generate_mock_result(status='SUCCESS', success=True)\\n self.db.insert_single_result(second)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'SUCCESS')\",\n \"def _wait_for_table_status(self, expected='ACTIVE'):\\n\\n achieved_state = False\\n while not achieved_state:\\n table_description = self.client.describe_table(TableName=self.table_name)\\n logging.debug('Waiting for DynamoDB table %s to become %s.',self.table_name,expected)\\n current_status = table_description['Table']['TableStatus']\\n achieved_state = current_status == expected\\n sleep(1)\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def insync_and_state_check(self):\\n self.step('verifying tables are properly synced on all endpoints')\\n is_ok = True\\n limit, count = 10, 0\\n while count < limit:\\n try:\\n state_check, rc = self.probe('/cluster/pyql/table/state/select')\\n assert rc == 200, f\\\"something wrong happened when checking state table {rc}\\\"\\n for state in state_check['data']:\\n if not state['in_sync'] == True or not state['state'] == 'loaded':\\n print(f\\\"found state which was not in_sync=True & 'loaded {state}, retrying\\\")\\n is_ok = False\\n self.sync_job_check()\\n break\\n if is_ok:\\n break\\n count+=1\\n except Exception as e:\\n print(f\\\"something wrong happened when checking state table\\\")\\n break\",\n \"def _compute_queue_state(self):\\n for record in self:\\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\\n record.state = \\\"completed\\\"\\n elif record.queue_line_draft_records == record.queue_line_total_records:\\n record.state = \\\"draft\\\"\\n elif record.queue_line_total_records == record.queue_line_fail_records:\\n record.state = \\\"failed\\\"\\n else:\\n record.state = \\\"partially_completed\\\"\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_get_unsuccessful_ta():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\\n\\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\\n assert np.isnan(list_failed[0])\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_update_task_states(self):\\r\\n changed = self.combinedoe.update_task_states()\\r\\n self.assertFalse(changed)\\r\\n\\r\\n current_task = self.combinedoe.current_task\\r\\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\\r\\n changed = self.combinedoe.update_task_states()\\r\\n\\r\\n self.assertTrue(changed)\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def load_status_table():\",\n \"def update_status(cls):\\n for job in cls.query.filter(cls.finished == False):\\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\\n dynamo = DynamoIngestionStatusClient()\\n num_urls_left = 0\\n for url in urls_left:\\n dynamo_url = dynamo.get(url.url)\\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\\n # will never be processed, so ignore for our purposes\\n url.processed = True\\n else:\\n num_urls_left += 1\\n if num_hits_left+num_urls_left == 0:\\n job.finished = True\\n print '*** Job ID: %s is complete ***' % str(job.id)\\n\\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\\n if not job.finished:\\n print 'unprocessed URLs: %i' % num_urls_left\\n print 'outstanding HITs: %i\\\\n' % num_hits_left\\n session.flush()\",\n \"def test_mark_incompleted(self):\\n event = Event.objects.all()[0]\\n\\n todo = TodoItem.objects.create(\\n event=event, completed=True, title=\\\"Test TODO2\\\",\\n due=datetime.date.today(), additional=\\\"\\\",\\n )\\n\\n assert todo.completed is True\\n\\n self.client.get(reverse('todo_mark_incompleted', args=[todo.pk]))\\n todo.refresh_from_db()\\n\\n assert todo.completed is False\",\n \"def _check_completed(self):\\n current_rung_df = self.sieve_board.loc[\\n self.sieve_board['status'].isin(\\n [StatusType.WAITTING, StatusType.RUNNING])\\n ]\\n if current_rung_df.empty:\\n return True\\n else:\\n return False\",\n \"def test_update_state(self):\\n # add a task\\n self.add(title=\\\"Sample task doing\\\", description=\\\"for sample\\\", state=\\\"doing\\\")\\n task = Task.query.filter_by(title='Sample task doing').first()\\n\\n # change task to todo\\n old_id = task.id\\n self.update_state(id=old_id, state='todo')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'todo')\\n\\n # change task to done\\n old_id = task.id\\n self.update_state(id=old_id, state='done')\\n task = Task.query.filter_by(title='Sample task doing').first()\\n self.assertEqual(task.id, old_id)\\n self.assertEqual(task.state, 'done')\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_time_successful(self):\\n\\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\\n '2016-01-10T00:00:00Z',\\n '2016-01-13T00:00:00Z',\\n 'source')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 2)\\n for result in results:\\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])\",\n \"def test_pending_job(self):\\n\\n pending_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"pending\\\"))\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def check_states(results):\\n assert results[\\\"states\\\"][\\\"device0\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\\n assert results[\\\"states\\\"][\\\"device1\\\"] == {\\\"counter\\\": 10, \\\"rts\\\": 0}\",\n \"def test_running_job(self):\\n\\n running_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"running\\\"))\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_update_state(self):\\n pass\",\n \"def get_time_in_status(self, issue, state):\\n \\n# current_datetime = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime\\n\\n if ((issue['fields']['issuetype']['name'] == \\\"Sub-task\\\") and (issue['fields']['status']['name'] == \\\"In Progress\\\")) or \\\\\\n ((issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and (issue['fields']['status']['name'] not in [\\\"Open\\\", \\\"In Progress\\\", \\\"Reopened\\\", \\\"Resolved\\\", \\\"Closed\\\"])):\\n print \\\"Getting time in status for %s\\\" % issue['fields']['status']['name']\\n # At first assume state is first, last and only state in case no changelog, if there's a changelog we'll revise later.\\n # First state create time same as issue created datetime \\n cicdt = dt.strptime(issue['fields']['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\") # Current item create datetime\\n nicd = dt.strptime(str(dt.now()).split('.')[0].replace(' ', 'T'), \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n diff = nicd - cicdt # Time the issue has been in that state\\n \\n # If there's a changelog history go thru it else use issue create date and current date for time in status\\n # If there's no changelog/histories then we're still in first state, nothing has changed\\n if not len(issue['changelog']['histories']):\\n print \\\"no changelog for %s\\\" % issue['key']\\n self.issue_objs[issue['key']].time_in_status = diff.days\\n else:\\n # If there is a changelog look for the value \\\"status\\\", changes may be something other than status\\n print \\\"There's a changelog for issue %s\\\" % issue['key']\\n# print json.dumps(issue, indent=4)\\n \\n # Go thru changelog histories to see if any of the changes are \\\"status\\\" for this state, also get last status or sprint if New Feature\\n status_change_found = False\\n for histories_item in issue['changelog']['histories']:\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n print \\\"There's a status change\\\"\\n status_change_found = True\\n \\n # Get oldest sprint and status within config.change_period days ago for New Features \\n if (issue['fields']['issuetype']['name'] == \\\"New Feature\\\") and \\\\\\n ((histories_item['items'][0]['field'] == 'Milestone(s)') or (histories_item['items'][0]['field'] == 'status')):\\n hicdt = dt.strptime(histories_item['created'].split('.')[0], \\\"%Y-%m-%dT%H:%M:%S\\\").date() # histories item create datetime\\n if hicdt < date.today()-timedelta(days=int(self.config.get('default', 'change_period'))):\\n print \\\"change too old %s, getting next histories item\\\" % hicdt\\n continue # If older than config.change_period days then we don't care, get next histories item\\n else:\\n print \\\"recent change %s \\\" % hicdt\\n if histories_item['items'][0]['field'] == 'Milestone(s)':\\n self.issue_objs[issue['key']].last_sprint = histories_item['items'][0]['fromString'] # last sprint if issue less that config.change_period old\\n print \\\"last_sprint %s\\\" % self.issue_objs[issue['key']].last_sprint\\n break # Got the oldest change within change_period\\n if histories_item['items'][0]['field'] == 'status':\\n self.issue_objs[issue['key']].last_status = histories_item['items'][0]['fromString'] # last status if issue less that config.change_period old\\n print \\\"last_status %s\\\" % self.issue_objs[issue['key']].last_status\\n break # Got the oldest change within change_period\\n \\n # If there was a change in status calculate time in status from changelog\\n if(status_change_found):\\n total_days = 0 # Accumulate all the diffs for each time in this state\\n # Go thru changelog histories to see if this issue was in this state before.\\n for i, histories_item in enumerate(issue['changelog']['histories']):\\n # All the different kinds of things that have changed, we're only interested in 'status'\\n \\n # Display all the changelog history items\\n if (0):\\n for k, v in histories_item.items():\\n print \\\"issue['changelog']['histories'] key: %s value: %s\\\" % (k, v)\\n print \\\"issue['changelog']['histories']['items'] %s\\\" % histories_item['items']\\n for item in histories_item['items']:\\n print \\\"histories_item['items'] %s\\\" % item\\n print \\\"histories_item['items'][0] %s\\\" %histories_item['items'][0]\\n for item2 in histories_item['items'][0]:\\n print \\\"histories_item['items'][0] %s %s\\\" % (item2, histories_item['items'][0][item2])\\n \\n # If status record we're interested in is found, look for next status change or use current date for end date\\n # If status \\\"Pending\\\" for New Features or \\\"Open\\\" for Sub-tasks there is no change entry for that, it's first state, use created date.\\n if ((histories_item['items'][0]['field'] == 'status') and (histories_item['items'][0]['toString'] == state)):\\n current_status_create_datetime = histories_item['created'].split('.')[0]\\n \\n # Go thru future history changes until the next status change\\n status_found = False\\n for j in range(i+1, len(issue['changelog']['histories'])):\\n if (issue['changelog']['histories'][j]['items'][0]['field'] == 'status'):\\n next_item_create_date = issue['changelog']['histories'][j]['created'].split('.')[0] # Only next status change not any change\\n print \\\"%s. next histories item created: %s\\\" % (j, next_item_create_date)\\n status_found = True\\n break # Break the for loop, next status found\\n \\n if not status_found: \\n next_item_create_date = str(dt.now()).split('.')[0].replace(' ', 'T') # The current datetime \\n print \\\"No next histories item, using today's date: %s\\\" % (next_item_create_date)\\n \\n cscdt = dt.strptime(current_status_create_datetime, \\\"%Y-%m-%dT%H:%M:%S\\\") # Current status create datetime\\n nicdt = dt.strptime(next_item_create_date, \\\"%Y-%m-%dT%H:%M:%S\\\") # Next item create datetime\\n \\n print \\\"Current status create date for %s: %s\\\" % (issue['key'], cscdt)\\n print \\\"Next item create date for %s: %s\\\" % (issue['key'], nicdt)\\n \\n diff = nicdt - cscdt # The number of days the issue has been in that state\\n total_days += diff.days # Accumulate all the times this issue has been in this state.\\n self.issue_objs[issue['key']].time_in_status = total_days\\n else:\\n self.issue_objs[issue['key']].time_in_status = diff.days # No change in status, days in status is issue created date to now\",\n \"def _wait_for_state(self, task_id, current, new):\\n state = result = None\\n start = time.time()\\n # 45 seconds is a long time.\\n # TODO(maruel): Make task_runner use exponential backoff instead of\\n # hardcoded 10s/30s, which makes these tests tremendously slower than\\n # necessary.\\n # https://crbug.com/825500\\n while time.time() - start < 45.:\\n result = self.client.task_result(task_id)\\n state = result[u'state']\\n if state == new:\\n break\\n self.assertEqual(current, state, result)\\n time.sleep(0.01)\\n self.assertEqual(new, state, result)\\n return result\",\n \"def query_job_progress():\\n pass\",\n \"def __checkInited(self, date=None, tkey=None, ttype=None):\\n if ttype == 'crontab':\\n return False\\n if date is None:\\n date = self.ddate\\n\\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\\n if doc.get(\\\"status\\\") == \\\"waiting\\\":\\n return True\\n return False\",\n \"def task_status():\\n pass\",\n \"def test_exp_by_states(self):\\n api = my_mock.api_mock({\\\"items\\\": [{'state': 'Waiting', 'id': 10134},\\n {'state': 'Waiting', 'id': 10135},\\n {'state': 'Running', 'id': 10130}]})\\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\\n self.assertEquals(\\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\\n my_mock.api_mock_stop()\",\n \"def test_source_time_successful(self):\\n\\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 1)\\n for result in results:\\n self.assertTrue(result['id'] in [self.job1.id])\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_completed(self):\\n return False\",\n \"def test_print_progress_flag(self, capsys):\\n file = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"geolife\\\", \\\"geolife_staypoints.csv\\\")\\n staypoints = ti.read_staypoints_csv(file, tz=\\\"utc\\\", index_col=\\\"id\\\", crs=\\\"epsg:4326\\\")\\n\\n staypoints.as_staypoints.generate_locations(print_progress=True)\\n captured_print = capsys.readouterr()\\n assert captured_print.err != \\\"\\\"\\n\\n staypoints.as_staypoints.generate_locations(print_progress=False)\\n captured_print = capsys.readouterr()\\n assert captured_print.err == \\\"\\\"\",\n \"def stalled(self):\\n oneHourAgo = datetime.today() - timedelta(hours = 1)\\n return self.filter(status=m.Update.IN_PROGRESS, requestedAt__lte=oneHourAgo)\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def test_pending_job(self):\\n pending_job = json.loads(BASE_JSON % ('null', 'null', 0, 1433166609))[0]\\n pending_job.pop(\\\"status\\\")\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_update_state1(self):\\n pass\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def test_running_job(self):\\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def pre_step(self,status):\\n self.t0 = time.time()\\n pass\",\n \"def test_status_done_basic(self):\\n\\n EXPLAIN_CONNECT = 'sqlite:///:memory:'\\n FORSETI_CONNECT = 'sqlite:///{}'.format(\\n get_db_file_path('forseti_1_basic.db'))\\n\\n self.service_config = ServiceConfig(EXPLAIN_CONNECT,\\n FORSETI_CONNECT)\\n self.source = 'FORSETI'\\n self.model_manager = self.service_config.model_manager\\n self.model_name = self.model_manager.create(name=self.source)\\n\\n scoped_session, data_access = self.model_manager.get(self.model_name)\\n with scoped_session as session:\\n\\n importer_cls = importer.by_source(self.source)\\n import_runner = importer_cls(\\n session,\\n self.model_manager.model(self.model_name, expunge=False),\\n data_access,\\n self.service_config)\\n import_runner.run()\\n\\n model = self.model_manager.model(self.model_name)\\n self.assertEqual(model.state,\\n 'PARTIAL_SUCCESS',\\n 'Model state should be set to PARTIAL_SUCCESS')\",\n \"def test_completed():\\n assert complete == 1\\n assert errorflag == 0\",\n \"def test_service_initiated():\\n assert \\\"ready\\\" in bkt_outcome_unwind.index()\",\n \"def test_update_state4(self):\\n pass\",\n \"def task_scanned(now_task):\",\n \"def test_notBeforeBefore(self):\\n dbpool, qpool, clock, performerChosen = self._setupPools()\\n\\n @transactionally(dbpool.pool.connection)\\n def check(txn):\\n return qpool.enqueueWork(\\n txn, DummyWorkItem, a=3, b=9,\\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\\n )\\n\\n yield check\\n\\n clock.advance(1000)\\n # Advance far beyond the given timestamp.\\n self.assertEquals(performerChosen, [True])\\n\\n # Wait for job\\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\\n clock.advance(1)\\n\\n # Work item complete\\n self.assertTrue(DummyWorkItem.results == {1: 12})\",\n \"def test_getting_state_parallel(self):\\n no_replicates = 25\\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\\n for i in range(no_replicates):\\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)\",\n \"def test_state(self, mock_req):\\n self.setup_api(MOCK_DATA, mock_req)\\n now = datetime(1970, month=1, day=1)\\n with patch(\\\"homeassistant.util.dt.now\\\", return_value=now):\\n for name in self.sensor_dict:\\n sensor = self.sensor_dict[name][\\\"sensor\\\"]\\n self.fake_delay(2)\\n sensor.update()\\n if name == google_wifi.ATTR_LAST_RESTART:\\n assert \\\"1969-12-31 00:00:00\\\" == sensor.state\\n elif name == google_wifi.ATTR_UPTIME:\\n assert 1 == sensor.state\\n elif name == google_wifi.ATTR_STATUS:\\n assert \\\"Online\\\" == sensor.state\\n else:\\n assert \\\"initial\\\" == sensor.state\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_delete_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.delete_problem_state('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def testingProgressFunc(state, action, text, tick):\\n pass\\n #print \\\"testingProgressFunc\\\", state, action, text, tick\",\n \"def test_time_status(self):\\n result = self.test_client.time_status\\n\\n assert result == \\\"12312\\\"\",\n \"def update_task_state(mapper, conn, target):\\r\\n sql_query = ('select count(id) from task_run \\\\\\r\\n where task_run.task_id=%s') % target.task_id\\r\\n n_answers = conn.scalar(sql_query)\\r\\n sql_query = ('select n_answers from task \\\\\\r\\n where task.id=%s') % target.task_id\\r\\n task_n_answers = conn.scalar(sql_query)\\r\\n if (n_answers) >= task_n_answers:\\r\\n sql_query = (\\\"UPDATE task SET state=\\\\'completed\\\\' \\\\\\r\\n where id=%s\\\") % target.task_id\\r\\n conn.execute(sql_query)\",\n \"def test_act_ii(self):\\n # setup\\n processing_time = 5.0\\n self.transaction_behaviour.processing = \\\"some_dialogue\\\"\\n self.transaction_behaviour.max_processing = 120\\n self.transaction_behaviour.processing_time = processing_time\\n\\n # operation\\n self.transaction_behaviour.act()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\\n assert (\\n self.transaction_behaviour.processing_time\\n == processing_time + self.transaction_behaviour.tick_interval\\n )\",\n \"def in_progress(self):\\n return False\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_update_state2(self):\\n pass\",\n \"def set_task_in_progress(self):\\n\\n tasks = self._get_all_tasks()\\n\\n task_id = tasks[self.tasks_view.currentRow()].Id\\n\\n self.tasks_flow.set_status(task_id, 1)\\n\\n # Refresh the table\\n self.write_tasks_table()\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def policy_checker(name):\\n\\n policy = PolicyModel.query(PolicyModel.Name == name).get()\\n if not policy:\\n logging.error(\\\"Policy %s not found!\\\", name)\\n return \\\"not found\\\", 404\\n schedule = SchedulesModel.query(SchedulesModel.Name == policy.Schedule).get()\\n if not schedule:\\n logging.error(\\\"Schedule %s not found!\\\", policy.Schedule)\\n return \\\"not found\\\", 404\\n logging.debug(\\n \\\"Time at Timezone %s is %s\\\",\\n schedule.Timezone,\\n tz.get_time_at_timezone(schedule.Timezone),\\n )\\n day, hour = tz.convert_time_to_index(tz.get_time_at_timezone(schedule.Timezone))\\n logging.debug(\\\"Working on day %s hour %s\\\", day, hour)\\n arr = np.asarray(schedule.Schedule[\\\"__ndarray__\\\"], dtype=np.int).flatten()\\n matrix_size = schedule.Schedule[\\\"Shape\\\"][0] * schedule.Schedule[\\\"Shape\\\"][1]\\n prev = utils.get_prev_idx(day * 24 + hour, matrix_size)\\n now = arr[day * 24 + hour]\\n prev = arr[prev]\\n logging.info(\\\"Previous state %s current %s\\\", prev, now)\\n if now == prev:\\n # do nothing\\n logging.info(\\\"Conditions are met, Nothing should be done for %s\\\", name)\\n return \\\"ok\\\", 200\\n else:\\n # stop/start\\n logging.info(\\\"State is changing for %s to %s\\\", name, now)\\n # for each tag lets do it\\n\\n task_client = tasks_v2.CloudTasksClient()\\n task = {\\n \\\"app_engine_http_request\\\": { # Specify the type of request.\\n \\\"http_method\\\": \\\"POST\\\",\\n \\\"relative_uri\\\": \\\"/tasks/change_state\\\",\\n }\\n }\\n parent = task_client.queue_path(\\n queue=\\\"zorya-tasks\\\",\\n project=utils.get_project_id(),\\n location=location.get_location(),\\n )\\n for tag in policy.Tags:\\n for project in policy.Projects:\\n payload = {\\n \\\"project\\\": project,\\n \\\"tagkey\\\": next(iter(tag)),\\n \\\"tagvalue\\\": tag[next(iter(tag))],\\n \\\"action\\\": str(now),\\n }\\n task[\\\"app_engine_http_request\\\"][\\\"body\\\"] = (\\n json.dumps(payload)\\n ).encode()\\n response = task_client.create_task(parent, task)\\n logging.debug(\\\"Task %s enqueued\\\", response.name)\\n return \\\"ok\\\", 200\",\n \"def test_get_current_state(self):\\n schedule = parser.parse_schedule(SCHEDULE)\\n self.assertEqual(\\\"a\\\", schedule.get_current_state(dt(monday, '09:30')))\\n self.assertEqual(\\\"c\\\", schedule.get_current_state(dt(monday, '08:30')))\\n self.assertIsNone(schedule.get_current_state(dt(saturday, '09:30'),\\n lookback=False))\",\n \"def mark_as_in_progress(self, task):\\n raise NotImplementedError('')\",\n \"def test_reset_non_problem(self):\\r\\n location = self.problem_section.location\\r\\n instructor_task = self.reset_problem_attempts('instructor', location)\\r\\n instructor_task = InstructorTask.objects.get(id=instructor_task.id)\\r\\n self.assertEqual(instructor_task.task_state, SUCCESS)\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def test_check_contributing_state_completed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=1)\\r\\n user = UserFactory.create()\\r\\n TaskRunFactory.create_batch(1, task=task, user=user)\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def test_status(self):\\n keys = [\\n 'receiver time mark count', 'ant type base', 'solution status',\\n ...\\n ]\\n\\n for i in xrange(0, 100):\\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)\",\n \"def test_is_approved_no_approval(time_record_factory):\\n record = time_record_factory()\\n\\n assert not record.is_approved\",\n \"def test_default_status(self) -> None:\\n self.assertEqual(self.report.status, Report.BackTestStatus.PENDING)\\n self.assertEqual(self.report.has_started(), False)\",\n \"def testoptstatus(self): \\r\\n assert len(self.data.optstatus) == len(self.data.geovalues)\\r\\n assert self.data.optstatus[0] == self.data.OPT_NEW\\r\\n for i in range(1, len(self.data.optstatus)-1):\\r\\n assert self.data.optstatus[i] == self.data.OPT_UNKNOWN\\r\\n assert self.data.optstatus[-1] == self.data.OPT_DONE\",\n \"def test__validate_status__0():\\n for input_value, expected_output in (\\n (ScheduledEventStatus.active, ScheduledEventStatus.active),\\n (ScheduledEventStatus.active.value, ScheduledEventStatus.active)\\n ):\\n output = validate_status(input_value)\\n vampytest.assert_is(output, expected_output)\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def testPreProcessResourceState(self):\\n resource_id_1 = \\\"resource_id_1\\\"\\n task_id_1 = \\\"task_id_1\\\"\\n json_out = {resource_id_1: [task_id_1]}\\n\\n # Test that the resource id is properly added with associated task\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\\n\\n # Test that the additional task id is appended to resource id\\n task_id_2 = \\\"task_id_2\\\"\\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\\n\\n # Test that additional resource id is added into state file\\n resource_id_2 = \\\"resource_id_2\\\"\\n json_out_3 = {\\n resource_id_1: [task_id_1, task_id_2],\\n resource_id_2: [task_id_1]\\n }\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\\n\\n # Test that same task id is not duplicated into state file\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\",\n \"def test_meeting_live_stream_status_update(self):\\n pass\",\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def check_state(self):\\n pass\",\n \"def _server_poll_expcompleted_(self):\\n #print \\\"class Princeton_CCD function _server_poll_expcompleted_\\\" \\n try:\\n last_state = self.polled_running\\n except (AttributeError,UnboundLocalError):\\n self.polled_running = False\\n last_state = False\\n self.polled_running = self.query_running()\\n if (not bool(last_state) and bool(self.polled_running)):\\n self.begin_acq_time = time.time()\\n #print self.query_running(), last_state\\n #if ((last_state == True) and (self.polled_running == False)): CP\\n if (bool(last_state) and not bool(self.polled_running)):\\n self.end_acq_time = time.time()\\n return True\\n else:\\n return False\",\n \"def test_ensure_not_ts_ts(self):\\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')\",\n \"def check_status():\\n logger.debug(\\\"Starting the check_status() routine.\\\")\\n\\n url = \\\"https://www.toggl.com/api/v8/time_entries/current\\\"\\n token = os.environ[\\\"TOGGL_API_TOKEN\\\"]\\n auth_token = base64.b64encode(f\\\"{token}:api_token\\\".encode()).decode()\\n resp = requests.get(url, headers={\\\"Authorization\\\": \\\"Basic \\\" + auth_token})\\n\\n cols = \\\"id\\\", \\\"duration\\\", \\\"description\\\"\\n status = {k: v for k, v in (resp.json()[\\\"data\\\"] or {}).items() if k in cols}\\n logger.debug(f\\\"{'Something' if 'id' in status else 'Something'} is being tracked.\\\")\\n\\n return status\",\n \"def _assert_state(self, state_dict):\\n instances = db.instance_get_all(self.context)\\n self.assertEqual(len(instances), 1)\\n\\n if 'vm_state' in state_dict:\\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\\n if 'task_state' in state_dict:\\n self.assertEqual(state_dict['task_state'],\\n instances[0]['task_state'])\\n if 'power_state' in state_dict:\\n self.assertEqual(state_dict['power_state'],\\n instances[0]['power_state'])\",\n \"def test_project_state_counters(self):\\n #build counters:\\n management.call_command('rebuild_counters')\\n\\n self.client.force_authenticate(self.global_user_1)\\n data = self.client.get(self.api_project_list_url, {'user': 'current'}).data['results']\\n\\n for project_data in data:\\n self.assertIn('state', project_data)\\n project_state_data = project_data['state']\\n if project_state_data:\\n project_state_obj = ProjectState.objects.get(project=project_state_data['id'], user=project_state_data['userId'])\\n self.assertEqual(project_state_data['numberOfProjectLessons'], project_state_obj.project.lessons.count())\\n self.assertEqual(\\n project_state_data['numberOfEnrolledLessons'],\\n project_state_obj.lesson_states.count()\\n )\\n self.assertEqual(\\n project_state_data['numberOfCompletedLessons'],\\n project_state_obj.lesson_states.filter(is_completed=True).count()\\n )\",\n \"def test_multiple_states(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory()\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Cancel when created.\\n state_changer = request.state_changer\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n state_changer.perform(context, a.CANCEL, event)\\n s1 = context.work_status.value\\n self.assertEqual(s1, s.CANCELLED)\\n\\n # Cancel when started.\\n c2 = model.factory(initial_state=s.STARTED)\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n with transaction.manager:\\n bm.Session.add(event)\\n state_changer.perform(c2, a.CANCEL, event)\\n s2 = c2.work_status.value\\n self.assertEqual(s2, s.CANCELLED)\",\n \"def test_active_and_date_range(self):\\n\\n url = '/%s/job-types/status/?is_active=true&started=%s&ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(len(result['results'][0]['job_counts']), 0)\",\n \"def test_send_state_event_nonoverwriting(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": True}, tok=u1token, state_key=\\\"tabby\\\"\\n )\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.send_state(\\n r1, \\\"cat.hissing\\\", {\\\"value\\\": False}, tok=u1token, state_key=\\\"moggy\\\"\\n )\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 1,\\n )\",\n \"def test_check_contributing_state_completed_user_not_contributed(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=2)\\r\\n TaskRunFactory.create_batch(2, task=task)\\r\\n user = UserFactory.create()\\r\\n\\r\\n contributing_state = helpers.check_contributing_state(app_id=app.id,\\r\\n user_id=user.id)\\r\\n\\r\\n assert task.state == 'completed', task.state\\r\\n assert contributing_state == 'completed', contributing_state\",\n \"def check_indicator_files(tasks):\\n\\n for task in tasks:\\n if task[\\\"status\\\"]==\\\"unknown\\\":\\n if os.path.exists(task[\\\"result\\\"]):\\n task[\\\"status\\\"]=\\\"previously completed\\\"\\n else:\\n task[\\\"status\\\"]=\\\"to do\\\"\\n return\",\n \"def test_timeout_processing(self):\\n # setup\\n self.transaction_behaviour.processing_time = None\\n\\n # operation\\n self.transaction_behaviour._timeout_processing()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\",\n \"def test_is_finished(self):\\n experiment = Experiment(TasksMock())\\n self.assertEquals(False, experiment.is_finished())\\n for _ in range(0, 17):\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(True, experiment.is_finished())\",\n \"def test_active(self):\\n\\n url = '/%s/job-types/status/?is_active=true' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def poll(self):\\n\\n query = f\\\"sacct -j {self.jobid} -o State -n -X -P\\\"\\n if self.cluster:\\n query += f\\\" --clusters={self.cluster}\\\"\\n\\n cmd = BuildTestCommand(query)\\n cmd.execute()\\n\\n logger.debug(f\\\"Querying JobID: '{self.jobid}' Job State by running: '{query}'\\\")\\n job_state = cmd.get_output()\\n self._state = \\\"\\\".join(job_state).rstrip()\\n logger.debug(f\\\"JobID: '{self.jobid}' job state:{self._state}\\\")\",\n \"def test_assessment_import_states(self):\\n self.import_file(\\\"assessment_full_no_warnings.csv\\\")\\n response = self.import_file(\\\"assessment_update_intermediate.csv\\\")\\n expected_errors = {\\n \\\"Assessment\\\": {\\n \\\"block_errors\\\": set(),\\n \\\"block_warnings\\\": set(),\\n \\\"row_errors\\\": set(),\\n \\\"row_warnings\\\": set(),\\n }\\n }\\n self._check_csv_response(response, expected_errors)\\n\\n assessments = {r.slug: r for r in models.Assessment.query.all()}\\n self.assertEqual(assessments[\\\"Assessment 60\\\"].status,\\n models.Assessment.START_STATE)\\n self.assertEqual(assessments[\\\"Assessment 61\\\"].status,\\n models.Assessment.PROGRESS_STATE)\\n self.assertEqual(assessments[\\\"Assessment 62\\\"].status,\\n models.Assessment.DONE_STATE)\\n self.assertEqual(assessments[\\\"Assessment 63\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 64\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 3\\\"].status,\\n models.Assessment.FINAL_STATE)\\n self.assertEqual(assessments[\\\"Assessment 4\\\"].status,\\n models.Assessment.FINAL_STATE)\\n\\n # Check that there is only one attachment left\\n asmt1 = assessments[\\\"Assessment 1\\\"]\\n self.assertEqual({\\\"a.b.com\\\", \\\"c d com\\\"},\\n {i.title for i in asmt1.document_url})\\n self.assertEqual({u'evidence title 1'},\\n {i.title for i in asmt1.document_evidence})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7257185","0.71193224","0.66941416","0.628547","0.6147146","0.6099557","0.60727316","0.59782106","0.59703326","0.59561163","0.58597606","0.5858871","0.5750034","0.57215893","0.57205486","0.5681524","0.5675636","0.56590813","0.5616347","0.56143284","0.558176","0.5567624","0.55467683","0.5534509","0.5526768","0.5515089","0.550762","0.55065536","0.55008715","0.54807657","0.54804677","0.54558194","0.5450302","0.54500717","0.543557","0.54271126","0.54120195","0.5381252","0.5371506","0.5367457","0.53505445","0.53492796","0.53492117","0.53475666","0.53466445","0.53451306","0.53333145","0.5332616","0.5311005","0.5305092","0.53039515","0.53019696","0.52922636","0.52862763","0.52830493","0.5278864","0.5255235","0.5245685","0.52438986","0.52436084","0.52395296","0.5238431","0.5236099","0.5232433","0.5231263","0.5230454","0.5218501","0.52094007","0.5208183","0.52060616","0.5202795","0.5202125","0.51943576","0.5189145","0.5179092","0.51789975","0.51763767","0.5176183","0.51746553","0.5167336","0.5164447","0.51635677","0.5163309","0.51626724","0.5161","0.51603633","0.5159166","0.5158292","0.5155792","0.5146473","0.5145949","0.51427597","0.5135461","0.5134001","0.5133516","0.5127348","0.51268166","0.51153547","0.51101816","0.51032335"],"string":"[\n \"0.7257185\",\n \"0.71193224\",\n \"0.66941416\",\n \"0.628547\",\n \"0.6147146\",\n \"0.6099557\",\n \"0.60727316\",\n \"0.59782106\",\n \"0.59703326\",\n \"0.59561163\",\n \"0.58597606\",\n \"0.5858871\",\n \"0.5750034\",\n \"0.57215893\",\n \"0.57205486\",\n \"0.5681524\",\n \"0.5675636\",\n \"0.56590813\",\n \"0.5616347\",\n \"0.56143284\",\n \"0.558176\",\n \"0.5567624\",\n \"0.55467683\",\n \"0.5534509\",\n \"0.5526768\",\n \"0.5515089\",\n \"0.550762\",\n \"0.55065536\",\n \"0.55008715\",\n \"0.54807657\",\n \"0.54804677\",\n \"0.54558194\",\n \"0.5450302\",\n \"0.54500717\",\n \"0.543557\",\n \"0.54271126\",\n \"0.54120195\",\n \"0.5381252\",\n \"0.5371506\",\n \"0.5367457\",\n \"0.53505445\",\n \"0.53492796\",\n \"0.53492117\",\n \"0.53475666\",\n \"0.53466445\",\n \"0.53451306\",\n \"0.53333145\",\n \"0.5332616\",\n \"0.5311005\",\n \"0.5305092\",\n \"0.53039515\",\n \"0.53019696\",\n \"0.52922636\",\n \"0.52862763\",\n \"0.52830493\",\n \"0.5278864\",\n \"0.5255235\",\n \"0.5245685\",\n \"0.52438986\",\n \"0.52436084\",\n \"0.52395296\",\n \"0.5238431\",\n \"0.5236099\",\n \"0.5232433\",\n \"0.5231263\",\n \"0.5230454\",\n \"0.5218501\",\n \"0.52094007\",\n \"0.5208183\",\n \"0.52060616\",\n \"0.5202795\",\n \"0.5202125\",\n \"0.51943576\",\n \"0.5189145\",\n \"0.5179092\",\n \"0.51789975\",\n \"0.51763767\",\n \"0.5176183\",\n \"0.51746553\",\n \"0.5167336\",\n \"0.5164447\",\n \"0.51635677\",\n \"0.5163309\",\n \"0.51626724\",\n \"0.5161\",\n \"0.51603633\",\n \"0.5159166\",\n \"0.5158292\",\n \"0.5155792\",\n \"0.5146473\",\n \"0.5145949\",\n \"0.51427597\",\n \"0.5135461\",\n \"0.5134001\",\n \"0.5133516\",\n \"0.5127348\",\n \"0.51268166\",\n \"0.51153547\",\n \"0.51101816\",\n \"0.51032335\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6860334"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":17473,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_FINAL_RUN state"},"document":{"kind":"string","value":"def test_processed_state_final_run(self):\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_FINAL_RUN,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=1).get_tree(any(str))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def IsFinal(self):\n return self.state in FINAL_TEST_RUN_STATES","def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_run_ended(self):","def test_cancelled_state_final_run(self):\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_CANCELED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_FINAL_RUN,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_state_after_failure(self):\n pass","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def finished_tests(self):\n self.testing = 0","def test_runner_with_db(dataset, time_start, time_diff):\n\n session = dataset\n\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\n replay_rate = 1.0 \n\n db_connector_test = DataBaseConnector(session=session, \n table_name='timeseries_dataset', \n time_column='timestamp', \n start_date=time_start,\n end_date=end_date)\n\n runner = CentralRunner(db_connection=db_connector_test, \n output_system='mock_output_systerm', \n start_time=time_start, \n end_time=end_date,\n replay_rate=replay_rate )\n\n\n results_test = [\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 0), 'text': 'bob', 'value': 10.0},\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'cat', 'value':-10.0},\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'eat', 'value': 12.1}\n ]\n \n # test that the trigger_release is working right\n # expect 1\n start = time.perf_counter()\n \n code_start = datetime.datetime.now()\n\n # we need to retink the way that we trigger this....\n runner._trigger_release(result_set=results_test, code_start=code_start, replay_start_time=time_start, \n batch=(datetime.datetime(2021, 1, 1, 10, 1, 0), datetime.datetime(2021, 1, 1, 10, 1, 1)), \n replay_rate=replay_rate)\n \n end = time.perf_counter()\n\n code_time = end - start\n assert int(code_time) == time_diff","def test_issue_tracked_times(self):\n pass","def test_update_instances_schedule_state(self):\n pass","def test_retrieve_instances_schedule_state(self):\n pass","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_clean_run(self):\n Historical_ROAs_Parsed_Table(clear=True)\n with Historical_ROAs_Table(clear=True) as t:\n Historical_ROAs_Parser().run()\n assert t.get_count() > 2000000","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_is_finished(self):\n experiment = Experiment(TasksMock())\n self.assertEquals(False, experiment.is_finished())\n for _ in range(0, 17):\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(True, experiment.is_finished())","def test_output(self):\n # reproducible arbitrariness\n np.random.seed(123423)\n\n N = 20\n tmax = 30.0\n dt = 1.0\n\n n_steps = int_r(tmax/dt)\n\n table = np.random.randn(N, n_steps)\n G = TableLayer(table)\n\n M = simulation.StateMonitor(G, 'out')\n\n sim = simulation.Simulation(G, M, dt=dt)\n sim.run(tmax)\n\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def test_update_state4(self):\n pass","def test_run(self):\n class MockProvider(BaseCoverageProvider):\n SERVICE_NAME = \"I do nothing\"\n was_run = False\n\n def run_once_and_update_timestamp(self):\n \"\"\"Set a variable.\"\"\"\n self.was_run = True\n return None\n\n provider = MockProvider(self._db)\n result = provider.run()\n\n # run_once_and_update_timestamp() was called.\n assert True == provider.was_run\n\n # run() returned a CoverageProviderProgress with basic\n # timing information, since run_once_and_update_timestamp()\n # didn't provide anything.\n assert isinstance(result, CoverageProviderProgress)\n now = utc_now()\n assert result.start < result.finish\n for time in (result.start, result.finish):\n assert (now - time).total_seconds() < 5","def test_completed():\n assert complete == 1\n assert errorflag == 0","def test_update_state3(self):\n pass","def test_update_state(self):\n pass","def test_log_last_completed_datetime(self):\n initial_count = CostUsageReportStatus.objects.count()\n saver = ReportStatsDBAccessor(\"myreport\", self.manifest_id)\n saver.log_last_completed_datetime()\n self.assertIsNotNone(saver.get_last_completed_datetime())\n saver.delete()\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)","def test_run_started(self):","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_9999_last(self):\n self.lasttest = True","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_completed(self):\n return False","def get_exist_phase_timestamp(self):\r\n csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\r\n if not os.path.isfile(csv_path):\r\n print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\r\n exit(-1)\r\n converter = {'benchmarkPhase': str, 'streamNumber': int, 'queryNumber': int,\r\n 'epochStartTimestamp': 'int64', 'epochEndTimestamp': 'int64'}\r\n df = pd.read_csv(csv_path, delimiter=';').loc[:,\r\n ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\r\n phase_ts = OrderedDict()\r\n is_exist = False\r\n for phase in self.phase_name:\r\n benchmark_phase = (df['benchmarkPhase'] == phase)\r\n if any(benchmark_phase): # whether this phase exist in the BB logs\r\n if phase == 'POWER_TEST': # power test overall and each query\r\n stream_num = ((df['streamNumber']) == 0)\r\n mask = benchmark_phase & stream_num\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 2] = 0 # fill 0 to blank value\r\n elif phase == 'THROUGHPUT_TEST_1': # throughput test overall and each stream\r\n query_num = (pd.isnull(df['queryNumber']))\r\n mask = benchmark_phase & query_num\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 1:3] = -1 # file -1 to overall throughput test\r\n phase_ts[phase].iloc[1:, 2] = 0 # fill 0 to blank value\r\n else: # other phases\r\n stream_num = (pd.isnull(df['streamNumber']))\r\n query_num = (pd.isnull(df['queryNumber']))\r\n mask = benchmark_phase & stream_num & query_num\r\n # phase_start = line['epochStartTimestamp'].values / 1000\r\n # phase_end = line['epochEndTimestamp'].values / 1000\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 1:3] = 0 # fill 0 to blank value\r\n phase_ts[phase] = phase_ts[phase].astype(converter)\r\n is_exist = True\r\n if is_exist:\r\n return phase_ts\r\n else:\r\n print('It seems BigBenchTimes.csv in {0} does not include any TPCx-BB phases, ' \\\r\n 'existing...'.format(self.bb_log_path))\r\n exit(-1)","def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_update_state2(self):\n pass","def test_service_initiated():\n assert \"ready\" in bkt_outcome_unwind.index()","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def validation_epoch_end(self, validation_step_outputs):\n self.epoch_end_toc = time()\n logger.info(f\"This epoch took {int(self.epoch_end_toc - self.epoch_start_tic)} s\")\n return super().validation_epoch_end(validation_step_outputs)","def test_initialization_of_homework_result_created():\n expected = datetime.datetime.now()\n assert result_1.created - expected < datetime.timedelta(seconds=1)","def test_taskrun_updates_task_state(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=2)\r\n url = '/api/taskrun?api_key=%s' % app.owner.api_key\r\n\r\n # Post first taskrun\r\n data = dict(\r\n app_id=task.app_id,\r\n task_id=task.id,\r\n info='my task result')\r\n datajson = json.dumps(data)\r\n tmp = self.app.post(url, data=datajson)\r\n r_taskrun = json.loads(tmp.data)\r\n\r\n assert tmp.status_code == 200, r_taskrun\r\n err_msg = \"Task state should be different from completed\"\r\n assert task.state == 'ongoing', err_msg\r\n\r\n # Post second taskrun\r\n url = '/api/taskrun'\r\n data = dict(\r\n app_id=task.app_id,\r\n task_id=task.id,\r\n info='my task result anon')\r\n datajson = json.dumps(data)\r\n tmp = self.app.post(url, data=datajson)\r\n r_taskrun = json.loads(tmp.data)\r\n\r\n assert tmp.status_code == 200, r_taskrun\r\n err_msg = \"Task state should be equal to completed\"\r\n assert task.state == 'completed', err_msg","def test_rt_table(self) -> None:\n expected = Fixtures.next_table()\n expected.description = '\"hello!\" said no one'\n expected.tags.sort()\n\n self.get_proxy().put_table(table=expected)\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\n actual.last_updated_timestamp = None\n actual.tags.sort()\n\n self.assertEqual(expected, actual)","def test_update_state1(self):\n pass","def test_update_splits():\n assert new_log.iloc[0]['start'] == 0","def on_test_epoch_end(self) -> None:\n self.log_dict(self.test_metrics.compute())\n self.test_metrics.reset()","def test_run_date(self):\n Historical_ROAs_Parsed_Table(clear=True)\n\n date = '2019-08-01'\n \n with Historical_ROAs_Table(clear=True) as t:\n Historical_ROAs_Parser().run(datetime.strptime(date, '%Y-%m-%d'))\n sql = f\"SELECT COUNT(*) FROM {t.name} WHERE date_added = '{date}'\"\n \n # unless ROAs are retroactively added, this should be constant\n assert t.get_count(sql) == 95743","def __exit__(self, _type, value, traceback):\n try:\n if not self.record_finish:\n return\n print >>sys.stderr, 'record', self.record\n dutset = {'last_finish_time':time()}\n if not self.record:\n return\n upd = {'end_time': time(), 'modification_time':time()}\n\n if value: # i.e. , if test failed:\n upd['failure'] = repr(value)\n upd['exception'] = value.__class__.__name__\n if not isinstance(value, KeyboardInterrupt):\n print 'HEADLINE: exception', upd['exception'], value\n for clause in format_exception(_type, value, traceback):\n for line in clause.split('\\n'):\n print 'CRASH:', line\n else:\n upd['infrastructure_problem'] = True\n upd['whiteboard'] = '[infrastructure] test interrupted'\n if self.reinstall_on_failure:\n dutset['test_failed'] = True\n tnext = time() + 300\n print 'INFO: test failed, so will reinstall machine at', \\\n asctime(localtime(tnext))\n\n if self.failed: #some test suite failed\n upd['failure'] = 'test failed'\n\n self.mdb.results.update({'_id':self.result_id}, {'$set':upd})\n classify = process_result(self.mdb.results.find_one({'_id':self.result_id}))\n print 'HEADLINE:', classify, self.full_description()\n\n get_track().updates.save({'result_id':self.result_id,\n 'action':'experiment finished'})\n\n if self.dut_id:\n self.mdb.duts.update({'_id':self.dut_id}, \n {'$unset': {'control_pid':1, 'result_id':1,\n 'control_command_line':1},\n '$set': dutset})\n if self.build:\n recount(self.build)\n if classify == 'infrastructure_problems':\n pass\n else:\n col = 'green' if classify == 'passes' else 'red'\n finally:\n if self.record_queue:\n self.record_queue.put('finish')\n self.record_queue.close()\n self.record_queue.join_thread()\n if self.stream_process:\n self.stream_process.join()\n if self.stdout_filter:\n self.stdout_filter.del_callback(self)","def test_log_last_started_datetime(self):\n initial_count = CostUsageReportStatus.objects.count()\n saver = ReportStatsDBAccessor(\"myreport\", self.manifest_id)\n saver.log_last_started_datetime()\n self.assertIsNotNone(saver.get_last_started_datetime())\n saver.delete()\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)","def test_full_house_flush_ind(self):","def test_snapshot(self):\n array = np.arange(1000)\n df = DataFlow.from_numpy(array).shuffle().snapshot()\n with DataFlowContext(df).as_default() as ctx:\n # shuffled data flow should be ready after df.shuffle()\n array2 = df.all()[0]\n self.assertFalse(np.all(array2 == array))\n np.testing.assert_array_equal(np.sort(array2), array)\n\n # after each reset_epoch, the snapshot data flow should be same\n ctx.reset_epoch()\n array3 = df.all()[0]\n self.assertFalse(np.all(array3 == array))\n np.testing.assert_array_equal(array3, array2)","def test_runner_full_loop(caplog, dataset):\n caplog.set_level(logging.DEBUG)\n\n session = dataset\n\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 0)\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\n replay_rate = 1 \n \n db_connector_test = DataBaseConnector(session=session, \n table_name='timeseries_dataset', \n time_column='timestamp', \n start_date=start_date,\n end_date=end_date)\n\n test_publisher = ConsolePublisher()\n\n runner = CentralRunner(db_connection=db_connector_test, \n output_system=test_publisher, \n start_time=start_date, \n end_time=end_date,\n replay_rate=replay_rate )\n\n start = time.perf_counter()\n \n runner.run()\n\n end = time.perf_counter()\n\n code_time = end - start\n \n print(code_time)\n \n assert int(code_time) == 4","def test_retrospective_the_prospective_checkpointed_timestamps(\n self, test_type, stage_pair, pm\n ):\n\n stage1, stage2 = stage_pair\n pm.timestamp(checkpoint=stage1, finished=True)\n assert stage1 == pm.prev_checkpoint\n assert pm.curr_checkpoint is None\n pm.timestamp(checkpoint=stage2, finished=False)\n\n if test_type == FILES_TEST:\n expected = [checkpoint_filepath(stage1, pm)]\n assert set(expected) == set(fetch_checkpoint_files(pm))\n else:\n assert pm.prev_checkpoint is None\n assert stage2 == pm.curr_checkpoint","def testing_phase(self):\r\n self.test_dataloader = self.get_dataloader(self.test_h5_path, \"test set\")\r\n self.get_ts_properties()\r\n\r\n self.restart_epoch = util.get_restart_epoch()\r\n print(f\"* Loading model from previous saved state (Epoch {self.restart_epoch}).\", flush=True)\r\n self.model = torch.load(\r\n self.C.job_dir + f\"model_restart_{self.restart_epoch}.pth\"\r\n )\r\n\r\n self.model.eval()\r\n with torch.no_grad():\r\n self.generate_graphs(n_samples=self.C.n_samples)\r\n\r\n print(\"* Evaluating model.\", flush=True)\r\n anal.evaluate_model(valid_dataloader=self.test_dataloader,\r\n train_dataloader=self.train_dataloader,\r\n nll_per_action=self.nll_per_action,\r\n model=self.model)\r\n\r\n self.print_time_elapsed()","def on_test_batch_end(self, batch, logs=None):","def test_epoch_end(self, outputs: Any) -> None:\n self.log_dict(self.test_metrics.compute())\n self.test_metrics.reset()","def test_update_team_state(self):\n pass","def test_prospective_then_retrospective_checkpointed_timestamps(\n self, test_type, stage_pair, pm\n ):\n\n stage1, stage2 = stage_pair\n pm.timestamp(checkpoint=stage1, finished=False)\n assert stage1 == pm.curr_checkpoint\n pm.timestamp(checkpoint=stage2, finished=True)\n\n if test_type == FILES_TEST:\n checkpoint_files = fetch_checkpoint_files(pm)\n expected = [checkpoint_filepath(stage2, pm)]\n assert set(expected) == set(checkpoint_files)\n else:\n # Current checkpoint will be reset by second (retrospective)\n # timestamp call.\n assert stage2 == pm.prev_checkpoint\n assert pm.curr_checkpoint is None","def test_list_runs(self):\n pass","def test_has_ended(self):\r\n self.assertTrue(self.past_show_certs.has_ended())\r\n self.assertTrue(self.past_noshow_certs.has_ended())\r\n self.assertFalse(self.future_show_certs.has_ended())\r\n self.assertFalse(self.future_noshow_certs.has_ended())","def emit(self, record):\n self.eng.dispose()\n trys = 3\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\"__\")}\n for t in range(trys):\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\n failed = False\n try:\n with self.eng.connect() as con:\n # formating values to convert from python or parsl to db standards\n info['task_fail_history'] = str(info.get('task_fail_history', None))\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\n run_id = info['run_id']\n\n # if workflow or task has completed, update their entries with the time.\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\n con.execute(up)\n return\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\n workflow = self.meta.tables['task']\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\n .where(workflow.c.run_id == run_id)\n con.execute(up)\n\n # create workflows table if this is a new database without one\n if 'workflows' not in self.meta.tables.keys():\n workflows = create_workflows_table(self.meta)\n self.meta.create_all(con)\n # if this is the first sight of the workflow, add it to the workflows table\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\n workflows = self.meta.tables['workflows']\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\n con.execute(ins)\n\n # if log has task counts, update the workflow entry in the workflows table\n if 'tasks_completed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n if 'tasks_failed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n\n # create task table if this is a new run without one\n if 'task' not in self.meta.tables.keys():\n workflow = create_task_table(self.meta)\n self.meta.create_all(con)\n\n # check to make sure it is a task log and not just a workflow overview log\n if info.get('task_id', None) is not None:\n # if this is the first sight of the task in the workflow, add it to the workflow table\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\n if 'psutil_process_pid' in info.keys():\n # this is the race condition that a resource log is before a status log so ignore this resource update\n return\n\n workflow = self.meta.tables['task']\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\n con.execute(ins)\n\n if 'task_status' in info.keys():\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\n if 'task_status' not in self.meta.tables.keys():\n task_status_table = create_task_status_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n # if this status table already exists, just insert the update\n else:\n task_status_table = self.meta.tables['task_status']\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n return\n\n if 'psutil_process_pid' in info.keys():\n # if this is a task resource update then handle that, if the resource table DNE then create it\n if 'task_resources' not in self.meta.tables.keys():\n task_resource_table = create_task_resource_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n # if this resource table already exists, just insert the update\n else:\n task_resource_table = self.meta.tables['task_resources']\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n return\n\n except Exception as e:\n logger.error(\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\".format(t, str(e)))\n failed = True\n time.sleep(5)\n if not failed:\n return","def end_of_test_epoch_hook(self):\n pass","def test_experiment(experiment):\n run = experiment.run()\n assert run.status == 'COMPLETED'","def test_validate_self_final_state_transitions(self):\n with nose.assert_raises(exceptions.FinalStateError):\n self.dtm1.transitions['q4'] = {'0': ('q4', '0', 'L')}\n self.dtm1.validate_self()","def test_table_false_positives(self):\n pass","def test_total_time_no_end_time(time_record_factory):\n d = datetime.datetime(2018, 10, 1, 15, 26)\n t = time_record_factory(time_start=d, time_end=None)\n expected = datetime.timedelta(0)\n assert t.total_time == expected","def test_get_last_tracking_insertation_():\n insert_habit()\n insert_day()\n trackings_table = analytics.trackings_table()\n assert trackings_table[60][:-1] == (6, str(date.today()))\n delete_habit()","def done(self):\n log.debug(\"Test run concluded\")\n if self._startTime is not None:\n self.report['startTime'] = self._startTime\n self.report['runTime'] = time.time() - self._startTime\n self.report['testsRun'] = self.testsRun\n self.report['tests'] = self._tests\n self.writeReport()","def on_test_end(self, logs=None):","def event_m20_11_6000():\n \"\"\"State 0,2: [Preset] Door of the living person_SubState\"\"\"\n assert event_m20_11_x77(z42=20110480, z43=600000)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def has_finished():","def test_queryset_total_time(time_record_factory):\n now = timezone.now()\n later = now + datetime.timedelta(hours=1)\n\n time_record_factory(time_end=later, time_start=now)\n time_record_factory(time_end=later, time_start=now)\n\n expected = sum(\n (record.delta for record in models.TimeRecord.objects.with_deltas()),\n datetime.timedelta(0),\n )\n\n assert models.TimeRecord.objects.total_time() == expected","def test_sequence_done(self):\n self.t(\"1,2 done\")\n code, out, err = self.t(\"_get 1.status 2.status\")\n self.assertEqual(\"completed completed\\n\", out)","def testTimestamps(self):\n predicate = \"metadata:predicate\"\n subject = \"aff4:/metadata:8\"\n\n # Extend the range of valid timestamps returned from the table to account\n # for potential clock skew.\n start = long(time.time() - 60) * 1e6\n data_store.DB.Set(subject, predicate, \"1\", token=self.token)\n\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\n\n # Check the time is reasonable\n end = long(time.time() + 60) * 1e6\n\n self.assert_(ts >= start and ts <= end)\n self.assertEqual(stored, \"1\")","def newTestRun(self):\n sql = \"INSERT INTO testrun VALUES (NULL, %s, %s)\"\n self.cursor.execute(sql, (self.timestamp, self.server))\n self.db.commit()\n self.testid = self.cursor.lastrowid","def test_archive_commitlog_point_in_time(self):\n self.run_archive_commitlog(restore_point_in_time=True)","def final(self, state):\n deltaReward = state.getScore() - self.lastState.getScore()\n self.observeTransition(self.lastState, self.lastAction, state, deltaReward)\n self.stopEpisode()\n\n # Make sure we have this var\n if not 'episodeStartTime' in self.__dict__:\n self.episodeStartTime = time.time()\n if not 'lastWindowAccumRewards' in self.__dict__:\n self.lastWindowAccumRewards = 0.0\n self.lastWindowAccumRewards += state.getScore()\n\n NUM_EPS_UPDATE = 100\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\n print 'Reinforcement Learning Status:'\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\n if self.episodesSoFar <= self.numTraining:\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\n print '\\tCompleted %d out of %d training episodes' % (\n self.episodesSoFar,self.numTraining)\n print '\\tAverage Rewards over all training: %.2f' % (\n trainAvg)\n else:\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\n print '\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining)\n print '\\tAverage Rewards over testing: %.2f' % testAvg\n print '\\tAverage Rewards for last %d episodes: %.2f' % (\n NUM_EPS_UPDATE,windowAvg)\n print '\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime)\n self.lastWindowAccumRewards = 0.0\n self.episodeStartTime = time.time()\n\n if self.episodesSoFar == self.numTraining:\n msg = 'Training Done (turning off epsilon and alpha)'\n print '%s\\n%s' % (msg,'-' * len(msg))","def test_total_time_with_start_and_end_times(time_record_factory):\n ts = datetime.datetime(2018, 10, 1, 9, 0)\n te = datetime.datetime(2018, 10, 1, 17, 0)\n\n t = time_record_factory(time_start=ts, time_end=te)\n\n expected = te-ts\n\n assert t.total_time == expected","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def assertion_started(self, func):","def TableFinish(current_time, nextGroup_endTime, table_type):\n if (current_time in nextGroup_endTime.values()):\n for n in list(nextGroup_endTime.keys()):\n if current_time == int(nextGroup_endTime[n]):\n if len(table_type)==6:\n table_type[n].cleanTable()\n elif len(table_type)==4:\n table_type[n-6].cleanTable()\n elif len(table_type)==2:\n table_type[n-10].cleanTable()","async def test_timings(hass: ha.HomeAssistant, skip_setup):\n\n for fname in glob.glob(test_config_dir + \"timing_*.yaml\"):\n print(f\"Processing: {fname}\")\n config = CONFIG_SCHEMA(load_yaml_config_file(fname))\n\n if ha.DOMAIN in config:\n await async_process_ha_core_config(hass, config[ha.DOMAIN])\n coordinator = IUCoordinator(hass).load(config[DOMAIN])\n\n next_time = dt_util.utcnow()\n interval = coordinator.track_interval()\n while not coordinator.tester._initialised or coordinator.tester.is_testing:\n await coordinator._async_timer(next_time)\n next_time += interval\n\n assert (\n coordinator.tester.total_events\n == coordinator.tester.total_checks\n == coordinator.tester.total_results\n )\n assert coordinator.tester.total_errors == 0\n print(\n \"Finished: {0}; events: {1}; checks: {2}; errors: {3}; time: {4:.2f}s\".format(\n fname,\n coordinator.tester.total_events,\n coordinator.tester.total_checks,\n coordinator.tester.total_errors,\n coordinator.tester.total_time,\n )\n )","def assert_history(self, rows):\r\n self.assertEqual(self.parse_rows(rows), self.read_history())","def test_same_unique_crash_type_with_same_state(self):\n self.testcases[0].security_flag = False\n self.testcases[0].crash_type = 'Timeout'\n self.testcases[0].crash_state = 'abcde'\n self.testcases[0].timestamp = datetime.datetime.utcfromtimestamp(0)\n self.testcases[1].security_flag = False\n self.testcases[1].crash_type = 'Timeout'\n self.testcases[1].crash_state = 'abcde'\n self.testcases[1].timestamp = datetime.datetime.utcfromtimestamp(1)\n\n for t in self.testcases:\n t.put()\n\n grouper.group_testcases()\n\n testcases = []\n for testcase_id in data_handler.get_open_testcase_id_iterator():\n testcases.append(data_handler.get_testcase_by_id(testcase_id))\n\n self.assertEqual(len(testcases), 1)\n self.assertEqual(testcases[0].group_id, 0)\n self.assertTrue(testcases[0].is_leader)","def test_noTimedEventsInsertion(self):\n self.test_noTimedEventsExtraction()\n self._testInsertion()","def timestep(self):\n self.survive()\n self.move()\n self.set_survival_probability(self.vital_var)\n self.reproduce()\n # self.save_frame()\n if self.time % self.save_state_frequency == 0:\n self.save_state(self.state_file.format(self.time))\n if self.time % self.save_stats_frequency == 0:\n self.write_stats()\n # self.connected_component()\n self.time += 1\n\n if self.pop_size > 0:\n return True\n else:\n return False","def test_eval_alarm(self):\n def get_state_update_value(h):\n \"\"\"\n \n \"\"\"\n oldstate = h.data['oldState']['stateValue']\n newstate = h.data['newState']['stateValue']\n querydate = h.data['newState']['stateReasonData']['queryDate']\n querydate = utils.parse_strtime(querydate)\n return oldstate, newstate, querydate \n \n test_uuid = str(uuid.uuid4())\n alarmname = \"TestEvalAlarm_\" + test_uuid\n metricname = \"TestEvalMetric_\" + test_uuid\n namespace = \"unittest\"\n unit = \"Percent\"\n dimensions = {\"test_id\":test_uuid}\n threshold = 2.0\n \n # create metric alarm\n alarm = MetricAlarm(name=alarmname, metric=metricname,\n namespace=namespace, statistic=\"Average\",\n comparison=\">\", threshold=threshold,\n period=60, evaluation_periods=1, unit=unit,\n dimensions=dimensions)\n self.synaps.put_metric_alarm(alarm)\n \n # due to put_metric_alarm is asynchronous\n time.sleep(ASYNC_WAIT)\n \n alarm_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0)\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold + 1, timestamp=alarm_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n\n ok_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0) \n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold - 2, timestamp=ok_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n \n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\n history_item_type=\"StateUpdate\")\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\n\n result = map(get_state_update_value, histories)\n \n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\n ('ALARM', 'INSUFFICIENT_DATA', None),\n ('INSUFFICIENT_DATA', 'OK', ok_time),\n ('OK', 'INSUFFICIENT_DATA', None))\n \n failmsg = \"expected: %s real: %s\" % (expected, result)\n \n self.assertEqual(len(result), len(expected), msg=failmsg)\n \n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\n expected):\n self.assertEqual(r_new, e_new, msg=failmsg)\n self.assertEqual(r_old, e_old, msg=failmsg)\n if e_time:\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\n msg=failmsg)\n \n self.synaps.delete_alarms(alarms=[alarmname])","def test_update_task_states(self):\r\n changed = self.combinedoe.update_task_states()\r\n self.assertFalse(changed)\r\n\r\n current_task = self.combinedoe.current_task\r\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\r\n changed = self.combinedoe.update_task_states()\r\n\r\n self.assertTrue(changed)","def verify_execution_results(state, collation):\n state.commit()\n\n validate_transaction_tree(collation)\n\n if collation.header.post_state_root != state.trie.root_hash:\n raise ValueError('State root mismatch: header %s computed %s' %\n (encode_hex(collation.header.post_state_root), encode_hex(state.trie.root_hash)))\n if collation.header.receipts_root != mk_receipt_sha(state.receipts):\n raise ValueError('Receipt root mismatch: header %s computed %s, computed %d, %d receipts' %\n (encode_hex(collation.header.receipts_root), encode_hex(mk_receipt_sha(state.receipts)),\n state.gas_used, len(state.receipts)))\n\n return True","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def get_raw_data(trace: Trace, cmdl_args):\n # Retrieves states dataframe with the intresting columns\n df_state = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.START.value,\n STATE_COLS.END.value, STATE_COLS.VAL.value]]\n\n # Computes the elapse times of each state\n df_state['el_time'] = df_state[STATE_COLS.END.value] - df_state[STATE_COLS.START.value]\n\n # Removes start and end columns from rows cause we don't need them. Load the data into memory.\n df_state = df_state.drop(columns=[STATE_COLS.START.value, STATE_COLS.END.value]).compute()\n\n # Computes runtime (in us)\n runtime = df_state.groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])['el_time'].sum().max() / 1000\n # Filters rows by useful and groups dataframe by process\n df_state_useful_grouped = df_state.loc[df_state[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])\n # Computes useful average time (in us)\n useful_av = df_state_useful_grouped['el_time'].sum().mean() / 1000\n # Computes useful max time (in us)\n useful_max = df_state_useful_grouped['el_time'].sum().max() / 1000\n # Computes useful tot time (in us)\n useful_tot = df_state_useful_grouped['el_time'].sum().sum() / 1000\n\n # Dimemas simulation for ideal times\n if cmdl_args.dimemas:\n runtime_id, useful_id = get_ideal_data(trace.metadata.path, len(trace.metadata.cpu_list))\n else:\n runtime_id = float('NaN')\n useful_id = float('NaN')\n\n\n # Loads only meaningful columns from df_states and filters useful rows\n df_state_useful = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.END.value, STATE_COLS.VAL.value]]\n df_state_useful = df_state_useful.loc[df_state_useful[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].drop(columns=STATE_COLS.VAL.value).compute()\n\n # Loads only meaningful columns from df_events\n df_event = trace.df_event[[EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value, EVENT_COLS.TIME.value,\n EVENT_COLS.EVTYPE.value, EVENT_COLS.EVVAL.value]]\n\n # Filters for PAPI_TOT_INS and set as index the process identifier\n df_event_ins = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_INS.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\n\n # Gets total useful instructions by grouping and applying a custom filtering function\n useful_ins = df_event_ins.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\n\n # Filter for PAPI_TOT_CYC and set as indexes the process identifier\n df_event_cyc = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_CYC.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\n\n # Gets total useful cycles by grouping and applying a custom filtering function\n useful_cyc= df_event_cyc.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\n\n # Computes average IPC\n try:\n ipc = useful_ins / useful_cyc\n except ValueError:\n ipc = float('NaN')\n # Computes average frequency\n try:\n freq = useful_cyc / useful_tot / 1000\n except ValueError:\n freq = float('NaN')\n\n return ipc, freq, runtime, runtime_id, useful_av, useful_max, useful_tot, useful_id, useful_ins, useful_cyc","async def test_lazy_state_handles_same_last_updated_and_last_changed(\n caplog: pytest.LogCaptureFixture,\n) -> None:\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n row = PropertyMock(\n entity_id=\"sensor.valid\",\n state=\"off\",\n attributes='{\"shared\":true}',\n last_updated_ts=now.timestamp(),\n last_changed_ts=now.timestamp(),\n )\n lstate = LazyState(\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\n )\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n assert lstate.last_updated.timestamp() == row.last_updated_ts\n assert lstate.last_changed.timestamp() == row.last_changed_ts\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2020-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }","def test_time_type_state_is_evening(day):\n\n assert day_time_info(day.hours_0).is_evening is False\n assert day_time_info(day.hours_1).is_evening is False\n assert day_time_info(day.hours_2).is_evening is False\n assert day_time_info(day.hours_3).is_evening is False\n assert day_time_info(day.hours_4).is_evening is False\n assert day_time_info(day.hours_5).is_evening is False\n assert day_time_info(day.hours_6).is_evening is False\n assert day_time_info(day.hours_7).is_evening is False\n assert day_time_info(day.hours_8).is_evening is False\n assert day_time_info(day.hours_9).is_evening is False\n assert day_time_info(day.hours_10).is_evening is False\n assert day_time_info(day.hours_11).is_evening is False\n assert day_time_info(day.hours_12).is_evening is False\n assert day_time_info(day.hours_13).is_evening is False\n assert day_time_info(day.hours_14).is_evening is False\n assert day_time_info(day.hours_15).is_evening is False\n assert day_time_info(day.hours_16).is_evening is False\n assert day_time_info(day.hours_17).is_evening is False\n assert day_time_info(day.hours_18).is_evening is True\n assert day_time_info(day.hours_19).is_evening is True\n assert day_time_info(day.hours_20).is_evening is True\n assert day_time_info(day.hours_21).is_evening is True\n assert day_time_info(day.hours_22).is_evening is True\n assert day_time_info(day.hours_23).is_evening is False","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def state(self):\n retval = \"FINISHED\"\n for testrun in self.testruns:\n # we have to check for this because deleting testruns doesn't always delete them out of testrun groups\n if not isinstance(testrun, bson.DBRef):\n assert isinstance(testrun, Testrun)\n if testrun.state == \"TO_BE_RUN\":\n return \"TO_BE_RUN\" # lowest possible state, return immediately\n elif testrun.state == \"RUNNING\":\n retval = \"RUNNING\" # we don't have to worry about overwriting lower state TO_BE_RUN\n return retval","def test_post_processing_time():\r\n cmd = ShdlcCmdGetErrorState(clear=False)\r\n assert type(cmd.post_processing_time) is float\r\n assert cmd.post_processing_time == 0.0","def test_time_type_state_is_afternoon(day):\n\n assert day_time_info(day.hours_0).is_afternoon is False\n assert day_time_info(day.hours_1).is_afternoon is False\n assert day_time_info(day.hours_2).is_afternoon is False\n assert day_time_info(day.hours_3).is_afternoon is False\n assert day_time_info(day.hours_4).is_afternoon is False\n assert day_time_info(day.hours_5).is_afternoon is False\n assert day_time_info(day.hours_6).is_afternoon is False\n assert day_time_info(day.hours_7).is_afternoon is False\n assert day_time_info(day.hours_8).is_afternoon is False\n assert day_time_info(day.hours_9).is_afternoon is False\n assert day_time_info(day.hours_10).is_afternoon is False\n assert day_time_info(day.hours_11).is_afternoon is False\n assert day_time_info(day.hours_12).is_afternoon is False\n assert day_time_info(day.hours_13).is_afternoon is True\n assert day_time_info(day.hours_14).is_afternoon is True\n assert day_time_info(day.hours_15).is_afternoon is True\n assert day_time_info(day.hours_16).is_afternoon is True\n assert day_time_info(day.hours_17).is_afternoon is True\n assert day_time_info(day.hours_18).is_afternoon is False\n assert day_time_info(day.hours_19).is_afternoon is False\n assert day_time_info(day.hours_20).is_afternoon is False\n assert day_time_info(day.hours_21).is_afternoon is False\n assert day_time_info(day.hours_22).is_afternoon is False\n assert day_time_info(day.hours_23).is_afternoon is False","def event_m20_11_83000():\n \"\"\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\"\"\"\n assert event_m20_11_x53(z73=2011300, z74=2011399)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def test_same_unique_crash_type_with_different_state(self):\n self.testcases[0].security_flag = False\n self.testcases[0].crash_type = 'Timeout'\n self.testcases[0].crash_state = 'abcdef'\n self.testcases[1].security_flag = False\n self.testcases[1].crash_type = 'Timeout'\n self.testcases[1].crash_state = 'abcde'\n\n for t in self.testcases:\n t.put()\n\n grouper.group_testcases()\n\n for index, t in enumerate(self.testcases):\n self.testcases[index] = data_handler.get_testcase_by_id(t.key.id())\n self.assertEqual(self.testcases[index].group_id, 0)\n self.assertTrue(self.testcases[index].is_leader)","def test_runner_full_loop_big(caplog, big_dataset):\n caplog.set_level(logging.INFO)\n\n session = big_dataset\n\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 3)\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 6)\n replay_rate = 1 \n\n db_connector_test = DataBaseConnector(session=session, \n table_name='timeseries_dataset', \n time_column='timestamp', \n start_date=start_date,\n end_date=end_date)\n\n debug_publisher = FilePublisher()\n\n runner = CentralRunner(db_connection=db_connector_test, \n output_system=debug_publisher, \n start_time=start_date, \n end_time=end_date,\n replay_rate=replay_rate )\n\n start = time.perf_counter()\n \n runner.run()\n\n end = time.perf_counter()\n\n code_time = end - start\n assert int(code_time) == 2\n\n ## Test results\n assert os.path.exists('test_tmp/17-05-2020_13-00-03')\n assert os.path.exists('test_tmp/17-05-2020_13-00-05')\n\n assert len(os.listdir('test_tmp/17-05-2020_13-00-03')) == 9\n assert len(os.listdir('test_tmp/17-05-2020_13-00-05')) == 2"],"string":"[\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def IsFinal(self):\\n return self.state in FINAL_TEST_RUN_STATES\",\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_run_ended(self):\",\n \"def test_cancelled_state_final_run(self):\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_CANCELED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_FINAL_RUN,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def finished_tests(self):\\n self.testing = 0\",\n \"def test_runner_with_db(dataset, time_start, time_diff):\\n\\n session = dataset\\n\\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\\n replay_rate = 1.0 \\n\\n db_connector_test = DataBaseConnector(session=session, \\n table_name='timeseries_dataset', \\n time_column='timestamp', \\n start_date=time_start,\\n end_date=end_date)\\n\\n runner = CentralRunner(db_connection=db_connector_test, \\n output_system='mock_output_systerm', \\n start_time=time_start, \\n end_time=end_date,\\n replay_rate=replay_rate )\\n\\n\\n results_test = [\\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 0), 'text': 'bob', 'value': 10.0},\\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'cat', 'value':-10.0},\\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'eat', 'value': 12.1}\\n ]\\n \\n # test that the trigger_release is working right\\n # expect 1\\n start = time.perf_counter()\\n \\n code_start = datetime.datetime.now()\\n\\n # we need to retink the way that we trigger this....\\n runner._trigger_release(result_set=results_test, code_start=code_start, replay_start_time=time_start, \\n batch=(datetime.datetime(2021, 1, 1, 10, 1, 0), datetime.datetime(2021, 1, 1, 10, 1, 1)), \\n replay_rate=replay_rate)\\n \\n end = time.perf_counter()\\n\\n code_time = end - start\\n assert int(code_time) == time_diff\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_clean_run(self):\\n Historical_ROAs_Parsed_Table(clear=True)\\n with Historical_ROAs_Table(clear=True) as t:\\n Historical_ROAs_Parser().run()\\n assert t.get_count() > 2000000\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_is_finished(self):\\n experiment = Experiment(TasksMock())\\n self.assertEquals(False, experiment.is_finished())\\n for _ in range(0, 17):\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(True, experiment.is_finished())\",\n \"def test_output(self):\\n # reproducible arbitrariness\\n np.random.seed(123423)\\n\\n N = 20\\n tmax = 30.0\\n dt = 1.0\\n\\n n_steps = int_r(tmax/dt)\\n\\n table = np.random.randn(N, n_steps)\\n G = TableLayer(table)\\n\\n M = simulation.StateMonitor(G, 'out')\\n\\n sim = simulation.Simulation(G, M, dt=dt)\\n sim.run(tmax)\\n\\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def test_update_state4(self):\\n pass\",\n \"def test_run(self):\\n class MockProvider(BaseCoverageProvider):\\n SERVICE_NAME = \\\"I do nothing\\\"\\n was_run = False\\n\\n def run_once_and_update_timestamp(self):\\n \\\"\\\"\\\"Set a variable.\\\"\\\"\\\"\\n self.was_run = True\\n return None\\n\\n provider = MockProvider(self._db)\\n result = provider.run()\\n\\n # run_once_and_update_timestamp() was called.\\n assert True == provider.was_run\\n\\n # run() returned a CoverageProviderProgress with basic\\n # timing information, since run_once_and_update_timestamp()\\n # didn't provide anything.\\n assert isinstance(result, CoverageProviderProgress)\\n now = utc_now()\\n assert result.start < result.finish\\n for time in (result.start, result.finish):\\n assert (now - time).total_seconds() < 5\",\n \"def test_completed():\\n assert complete == 1\\n assert errorflag == 0\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_update_state(self):\\n pass\",\n \"def test_log_last_completed_datetime(self):\\n initial_count = CostUsageReportStatus.objects.count()\\n saver = ReportStatsDBAccessor(\\\"myreport\\\", self.manifest_id)\\n saver.log_last_completed_datetime()\\n self.assertIsNotNone(saver.get_last_completed_datetime())\\n saver.delete()\\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)\",\n \"def test_run_started(self):\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_9999_last(self):\\n self.lasttest = True\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_completed(self):\\n return False\",\n \"def get_exist_phase_timestamp(self):\\r\\n csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\\r\\n if not os.path.isfile(csv_path):\\r\\n print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\\r\\n exit(-1)\\r\\n converter = {'benchmarkPhase': str, 'streamNumber': int, 'queryNumber': int,\\r\\n 'epochStartTimestamp': 'int64', 'epochEndTimestamp': 'int64'}\\r\\n df = pd.read_csv(csv_path, delimiter=';').loc[:,\\r\\n ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\\r\\n phase_ts = OrderedDict()\\r\\n is_exist = False\\r\\n for phase in self.phase_name:\\r\\n benchmark_phase = (df['benchmarkPhase'] == phase)\\r\\n if any(benchmark_phase): # whether this phase exist in the BB logs\\r\\n if phase == 'POWER_TEST': # power test overall and each query\\r\\n stream_num = ((df['streamNumber']) == 0)\\r\\n mask = benchmark_phase & stream_num\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 2] = 0 # fill 0 to blank value\\r\\n elif phase == 'THROUGHPUT_TEST_1': # throughput test overall and each stream\\r\\n query_num = (pd.isnull(df['queryNumber']))\\r\\n mask = benchmark_phase & query_num\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 1:3] = -1 # file -1 to overall throughput test\\r\\n phase_ts[phase].iloc[1:, 2] = 0 # fill 0 to blank value\\r\\n else: # other phases\\r\\n stream_num = (pd.isnull(df['streamNumber']))\\r\\n query_num = (pd.isnull(df['queryNumber']))\\r\\n mask = benchmark_phase & stream_num & query_num\\r\\n # phase_start = line['epochStartTimestamp'].values / 1000\\r\\n # phase_end = line['epochEndTimestamp'].values / 1000\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 1:3] = 0 # fill 0 to blank value\\r\\n phase_ts[phase] = phase_ts[phase].astype(converter)\\r\\n is_exist = True\\r\\n if is_exist:\\r\\n return phase_ts\\r\\n else:\\r\\n print('It seems BigBenchTimes.csv in {0} does not include any TPCx-BB phases, ' \\\\\\r\\n 'existing...'.format(self.bb_log_path))\\r\\n exit(-1)\",\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_update_state2(self):\\n pass\",\n \"def test_service_initiated():\\n assert \\\"ready\\\" in bkt_outcome_unwind.index()\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def validation_epoch_end(self, validation_step_outputs):\\n self.epoch_end_toc = time()\\n logger.info(f\\\"This epoch took {int(self.epoch_end_toc - self.epoch_start_tic)} s\\\")\\n return super().validation_epoch_end(validation_step_outputs)\",\n \"def test_initialization_of_homework_result_created():\\n expected = datetime.datetime.now()\\n assert result_1.created - expected < datetime.timedelta(seconds=1)\",\n \"def test_taskrun_updates_task_state(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=2)\\r\\n url = '/api/taskrun?api_key=%s' % app.owner.api_key\\r\\n\\r\\n # Post first taskrun\\r\\n data = dict(\\r\\n app_id=task.app_id,\\r\\n task_id=task.id,\\r\\n info='my task result')\\r\\n datajson = json.dumps(data)\\r\\n tmp = self.app.post(url, data=datajson)\\r\\n r_taskrun = json.loads(tmp.data)\\r\\n\\r\\n assert tmp.status_code == 200, r_taskrun\\r\\n err_msg = \\\"Task state should be different from completed\\\"\\r\\n assert task.state == 'ongoing', err_msg\\r\\n\\r\\n # Post second taskrun\\r\\n url = '/api/taskrun'\\r\\n data = dict(\\r\\n app_id=task.app_id,\\r\\n task_id=task.id,\\r\\n info='my task result anon')\\r\\n datajson = json.dumps(data)\\r\\n tmp = self.app.post(url, data=datajson)\\r\\n r_taskrun = json.loads(tmp.data)\\r\\n\\r\\n assert tmp.status_code == 200, r_taskrun\\r\\n err_msg = \\\"Task state should be equal to completed\\\"\\r\\n assert task.state == 'completed', err_msg\",\n \"def test_rt_table(self) -> None:\\n expected = Fixtures.next_table()\\n expected.description = '\\\"hello!\\\" said no one'\\n expected.tags.sort()\\n\\n self.get_proxy().put_table(table=expected)\\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\\n actual.last_updated_timestamp = None\\n actual.tags.sort()\\n\\n self.assertEqual(expected, actual)\",\n \"def test_update_state1(self):\\n pass\",\n \"def test_update_splits():\\n assert new_log.iloc[0]['start'] == 0\",\n \"def on_test_epoch_end(self) -> None:\\n self.log_dict(self.test_metrics.compute())\\n self.test_metrics.reset()\",\n \"def test_run_date(self):\\n Historical_ROAs_Parsed_Table(clear=True)\\n\\n date = '2019-08-01'\\n \\n with Historical_ROAs_Table(clear=True) as t:\\n Historical_ROAs_Parser().run(datetime.strptime(date, '%Y-%m-%d'))\\n sql = f\\\"SELECT COUNT(*) FROM {t.name} WHERE date_added = '{date}'\\\"\\n \\n # unless ROAs are retroactively added, this should be constant\\n assert t.get_count(sql) == 95743\",\n \"def __exit__(self, _type, value, traceback):\\n try:\\n if not self.record_finish:\\n return\\n print >>sys.stderr, 'record', self.record\\n dutset = {'last_finish_time':time()}\\n if not self.record:\\n return\\n upd = {'end_time': time(), 'modification_time':time()}\\n\\n if value: # i.e. , if test failed:\\n upd['failure'] = repr(value)\\n upd['exception'] = value.__class__.__name__\\n if not isinstance(value, KeyboardInterrupt):\\n print 'HEADLINE: exception', upd['exception'], value\\n for clause in format_exception(_type, value, traceback):\\n for line in clause.split('\\\\n'):\\n print 'CRASH:', line\\n else:\\n upd['infrastructure_problem'] = True\\n upd['whiteboard'] = '[infrastructure] test interrupted'\\n if self.reinstall_on_failure:\\n dutset['test_failed'] = True\\n tnext = time() + 300\\n print 'INFO: test failed, so will reinstall machine at', \\\\\\n asctime(localtime(tnext))\\n\\n if self.failed: #some test suite failed\\n upd['failure'] = 'test failed'\\n\\n self.mdb.results.update({'_id':self.result_id}, {'$set':upd})\\n classify = process_result(self.mdb.results.find_one({'_id':self.result_id}))\\n print 'HEADLINE:', classify, self.full_description()\\n\\n get_track().updates.save({'result_id':self.result_id,\\n 'action':'experiment finished'})\\n\\n if self.dut_id:\\n self.mdb.duts.update({'_id':self.dut_id}, \\n {'$unset': {'control_pid':1, 'result_id':1,\\n 'control_command_line':1},\\n '$set': dutset})\\n if self.build:\\n recount(self.build)\\n if classify == 'infrastructure_problems':\\n pass\\n else:\\n col = 'green' if classify == 'passes' else 'red'\\n finally:\\n if self.record_queue:\\n self.record_queue.put('finish')\\n self.record_queue.close()\\n self.record_queue.join_thread()\\n if self.stream_process:\\n self.stream_process.join()\\n if self.stdout_filter:\\n self.stdout_filter.del_callback(self)\",\n \"def test_log_last_started_datetime(self):\\n initial_count = CostUsageReportStatus.objects.count()\\n saver = ReportStatsDBAccessor(\\\"myreport\\\", self.manifest_id)\\n saver.log_last_started_datetime()\\n self.assertIsNotNone(saver.get_last_started_datetime())\\n saver.delete()\\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)\",\n \"def test_full_house_flush_ind(self):\",\n \"def test_snapshot(self):\\n array = np.arange(1000)\\n df = DataFlow.from_numpy(array).shuffle().snapshot()\\n with DataFlowContext(df).as_default() as ctx:\\n # shuffled data flow should be ready after df.shuffle()\\n array2 = df.all()[0]\\n self.assertFalse(np.all(array2 == array))\\n np.testing.assert_array_equal(np.sort(array2), array)\\n\\n # after each reset_epoch, the snapshot data flow should be same\\n ctx.reset_epoch()\\n array3 = df.all()[0]\\n self.assertFalse(np.all(array3 == array))\\n np.testing.assert_array_equal(array3, array2)\",\n \"def test_runner_full_loop(caplog, dataset):\\n caplog.set_level(logging.DEBUG)\\n\\n session = dataset\\n\\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 0)\\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\\n replay_rate = 1 \\n \\n db_connector_test = DataBaseConnector(session=session, \\n table_name='timeseries_dataset', \\n time_column='timestamp', \\n start_date=start_date,\\n end_date=end_date)\\n\\n test_publisher = ConsolePublisher()\\n\\n runner = CentralRunner(db_connection=db_connector_test, \\n output_system=test_publisher, \\n start_time=start_date, \\n end_time=end_date,\\n replay_rate=replay_rate )\\n\\n start = time.perf_counter()\\n \\n runner.run()\\n\\n end = time.perf_counter()\\n\\n code_time = end - start\\n \\n print(code_time)\\n \\n assert int(code_time) == 4\",\n \"def test_retrospective_the_prospective_checkpointed_timestamps(\\n self, test_type, stage_pair, pm\\n ):\\n\\n stage1, stage2 = stage_pair\\n pm.timestamp(checkpoint=stage1, finished=True)\\n assert stage1 == pm.prev_checkpoint\\n assert pm.curr_checkpoint is None\\n pm.timestamp(checkpoint=stage2, finished=False)\\n\\n if test_type == FILES_TEST:\\n expected = [checkpoint_filepath(stage1, pm)]\\n assert set(expected) == set(fetch_checkpoint_files(pm))\\n else:\\n assert pm.prev_checkpoint is None\\n assert stage2 == pm.curr_checkpoint\",\n \"def testing_phase(self):\\r\\n self.test_dataloader = self.get_dataloader(self.test_h5_path, \\\"test set\\\")\\r\\n self.get_ts_properties()\\r\\n\\r\\n self.restart_epoch = util.get_restart_epoch()\\r\\n print(f\\\"* Loading model from previous saved state (Epoch {self.restart_epoch}).\\\", flush=True)\\r\\n self.model = torch.load(\\r\\n self.C.job_dir + f\\\"model_restart_{self.restart_epoch}.pth\\\"\\r\\n )\\r\\n\\r\\n self.model.eval()\\r\\n with torch.no_grad():\\r\\n self.generate_graphs(n_samples=self.C.n_samples)\\r\\n\\r\\n print(\\\"* Evaluating model.\\\", flush=True)\\r\\n anal.evaluate_model(valid_dataloader=self.test_dataloader,\\r\\n train_dataloader=self.train_dataloader,\\r\\n nll_per_action=self.nll_per_action,\\r\\n model=self.model)\\r\\n\\r\\n self.print_time_elapsed()\",\n \"def on_test_batch_end(self, batch, logs=None):\",\n \"def test_epoch_end(self, outputs: Any) -> None:\\n self.log_dict(self.test_metrics.compute())\\n self.test_metrics.reset()\",\n \"def test_update_team_state(self):\\n pass\",\n \"def test_prospective_then_retrospective_checkpointed_timestamps(\\n self, test_type, stage_pair, pm\\n ):\\n\\n stage1, stage2 = stage_pair\\n pm.timestamp(checkpoint=stage1, finished=False)\\n assert stage1 == pm.curr_checkpoint\\n pm.timestamp(checkpoint=stage2, finished=True)\\n\\n if test_type == FILES_TEST:\\n checkpoint_files = fetch_checkpoint_files(pm)\\n expected = [checkpoint_filepath(stage2, pm)]\\n assert set(expected) == set(checkpoint_files)\\n else:\\n # Current checkpoint will be reset by second (retrospective)\\n # timestamp call.\\n assert stage2 == pm.prev_checkpoint\\n assert pm.curr_checkpoint is None\",\n \"def test_list_runs(self):\\n pass\",\n \"def test_has_ended(self):\\r\\n self.assertTrue(self.past_show_certs.has_ended())\\r\\n self.assertTrue(self.past_noshow_certs.has_ended())\\r\\n self.assertFalse(self.future_show_certs.has_ended())\\r\\n self.assertFalse(self.future_noshow_certs.has_ended())\",\n \"def emit(self, record):\\n self.eng.dispose()\\n trys = 3\\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\\\"__\\\")}\\n for t in range(trys):\\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\\n failed = False\\n try:\\n with self.eng.connect() as con:\\n # formating values to convert from python or parsl to db standards\\n info['task_fail_history'] = str(info.get('task_fail_history', None))\\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\\n run_id = info['run_id']\\n\\n # if workflow or task has completed, update their entries with the time.\\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n return\\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\\n workflow = self.meta.tables['task']\\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\\\\n .where(workflow.c.run_id == run_id)\\n con.execute(up)\\n\\n # create workflows table if this is a new database without one\\n if 'workflows' not in self.meta.tables.keys():\\n workflows = create_workflows_table(self.meta)\\n self.meta.create_all(con)\\n # if this is the first sight of the workflow, add it to the workflows table\\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\\n workflows = self.meta.tables['workflows']\\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\\n con.execute(ins)\\n\\n # if log has task counts, update the workflow entry in the workflows table\\n if 'tasks_completed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n if 'tasks_failed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n\\n # create task table if this is a new run without one\\n if 'task' not in self.meta.tables.keys():\\n workflow = create_task_table(self.meta)\\n self.meta.create_all(con)\\n\\n # check to make sure it is a task log and not just a workflow overview log\\n if info.get('task_id', None) is not None:\\n # if this is the first sight of the task in the workflow, add it to the workflow table\\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\\n if 'psutil_process_pid' in info.keys():\\n # this is the race condition that a resource log is before a status log so ignore this resource update\\n return\\n\\n workflow = self.meta.tables['task']\\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\\n con.execute(ins)\\n\\n if 'task_status' in info.keys():\\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\\n if 'task_status' not in self.meta.tables.keys():\\n task_status_table = create_task_status_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n # if this status table already exists, just insert the update\\n else:\\n task_status_table = self.meta.tables['task_status']\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n return\\n\\n if 'psutil_process_pid' in info.keys():\\n # if this is a task resource update then handle that, if the resource table DNE then create it\\n if 'task_resources' not in self.meta.tables.keys():\\n task_resource_table = create_task_resource_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n # if this resource table already exists, just insert the update\\n else:\\n task_resource_table = self.meta.tables['task_resources']\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n return\\n\\n except Exception as e:\\n logger.error(\\\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\\\".format(t, str(e)))\\n failed = True\\n time.sleep(5)\\n if not failed:\\n return\",\n \"def end_of_test_epoch_hook(self):\\n pass\",\n \"def test_experiment(experiment):\\n run = experiment.run()\\n assert run.status == 'COMPLETED'\",\n \"def test_validate_self_final_state_transitions(self):\\n with nose.assert_raises(exceptions.FinalStateError):\\n self.dtm1.transitions['q4'] = {'0': ('q4', '0', 'L')}\\n self.dtm1.validate_self()\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_total_time_no_end_time(time_record_factory):\\n d = datetime.datetime(2018, 10, 1, 15, 26)\\n t = time_record_factory(time_start=d, time_end=None)\\n expected = datetime.timedelta(0)\\n assert t.total_time == expected\",\n \"def test_get_last_tracking_insertation_():\\n insert_habit()\\n insert_day()\\n trackings_table = analytics.trackings_table()\\n assert trackings_table[60][:-1] == (6, str(date.today()))\\n delete_habit()\",\n \"def done(self):\\n log.debug(\\\"Test run concluded\\\")\\n if self._startTime is not None:\\n self.report['startTime'] = self._startTime\\n self.report['runTime'] = time.time() - self._startTime\\n self.report['testsRun'] = self.testsRun\\n self.report['tests'] = self._tests\\n self.writeReport()\",\n \"def on_test_end(self, logs=None):\",\n \"def event_m20_11_6000():\\n \\\"\\\"\\\"State 0,2: [Preset] Door of the living person_SubState\\\"\\\"\\\"\\n assert event_m20_11_x77(z42=20110480, z43=600000)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def has_finished():\",\n \"def test_queryset_total_time(time_record_factory):\\n now = timezone.now()\\n later = now + datetime.timedelta(hours=1)\\n\\n time_record_factory(time_end=later, time_start=now)\\n time_record_factory(time_end=later, time_start=now)\\n\\n expected = sum(\\n (record.delta for record in models.TimeRecord.objects.with_deltas()),\\n datetime.timedelta(0),\\n )\\n\\n assert models.TimeRecord.objects.total_time() == expected\",\n \"def test_sequence_done(self):\\n self.t(\\\"1,2 done\\\")\\n code, out, err = self.t(\\\"_get 1.status 2.status\\\")\\n self.assertEqual(\\\"completed completed\\\\n\\\", out)\",\n \"def testTimestamps(self):\\n predicate = \\\"metadata:predicate\\\"\\n subject = \\\"aff4:/metadata:8\\\"\\n\\n # Extend the range of valid timestamps returned from the table to account\\n # for potential clock skew.\\n start = long(time.time() - 60) * 1e6\\n data_store.DB.Set(subject, predicate, \\\"1\\\", token=self.token)\\n\\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\\n\\n # Check the time is reasonable\\n end = long(time.time() + 60) * 1e6\\n\\n self.assert_(ts >= start and ts <= end)\\n self.assertEqual(stored, \\\"1\\\")\",\n \"def newTestRun(self):\\n sql = \\\"INSERT INTO testrun VALUES (NULL, %s, %s)\\\"\\n self.cursor.execute(sql, (self.timestamp, self.server))\\n self.db.commit()\\n self.testid = self.cursor.lastrowid\",\n \"def test_archive_commitlog_point_in_time(self):\\n self.run_archive_commitlog(restore_point_in_time=True)\",\n \"def final(self, state):\\n deltaReward = state.getScore() - self.lastState.getScore()\\n self.observeTransition(self.lastState, self.lastAction, state, deltaReward)\\n self.stopEpisode()\\n\\n # Make sure we have this var\\n if not 'episodeStartTime' in self.__dict__:\\n self.episodeStartTime = time.time()\\n if not 'lastWindowAccumRewards' in self.__dict__:\\n self.lastWindowAccumRewards = 0.0\\n self.lastWindowAccumRewards += state.getScore()\\n\\n NUM_EPS_UPDATE = 100\\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\\n print 'Reinforcement Learning Status:'\\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\\n if self.episodesSoFar <= self.numTraining:\\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\\n print '\\\\tCompleted %d out of %d training episodes' % (\\n self.episodesSoFar,self.numTraining)\\n print '\\\\tAverage Rewards over all training: %.2f' % (\\n trainAvg)\\n else:\\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\\n print '\\\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining)\\n print '\\\\tAverage Rewards over testing: %.2f' % testAvg\\n print '\\\\tAverage Rewards for last %d episodes: %.2f' % (\\n NUM_EPS_UPDATE,windowAvg)\\n print '\\\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime)\\n self.lastWindowAccumRewards = 0.0\\n self.episodeStartTime = time.time()\\n\\n if self.episodesSoFar == self.numTraining:\\n msg = 'Training Done (turning off epsilon and alpha)'\\n print '%s\\\\n%s' % (msg,'-' * len(msg))\",\n \"def test_total_time_with_start_and_end_times(time_record_factory):\\n ts = datetime.datetime(2018, 10, 1, 9, 0)\\n te = datetime.datetime(2018, 10, 1, 17, 0)\\n\\n t = time_record_factory(time_start=ts, time_end=te)\\n\\n expected = te-ts\\n\\n assert t.total_time == expected\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def assertion_started(self, func):\",\n \"def TableFinish(current_time, nextGroup_endTime, table_type):\\n if (current_time in nextGroup_endTime.values()):\\n for n in list(nextGroup_endTime.keys()):\\n if current_time == int(nextGroup_endTime[n]):\\n if len(table_type)==6:\\n table_type[n].cleanTable()\\n elif len(table_type)==4:\\n table_type[n-6].cleanTable()\\n elif len(table_type)==2:\\n table_type[n-10].cleanTable()\",\n \"async def test_timings(hass: ha.HomeAssistant, skip_setup):\\n\\n for fname in glob.glob(test_config_dir + \\\"timing_*.yaml\\\"):\\n print(f\\\"Processing: {fname}\\\")\\n config = CONFIG_SCHEMA(load_yaml_config_file(fname))\\n\\n if ha.DOMAIN in config:\\n await async_process_ha_core_config(hass, config[ha.DOMAIN])\\n coordinator = IUCoordinator(hass).load(config[DOMAIN])\\n\\n next_time = dt_util.utcnow()\\n interval = coordinator.track_interval()\\n while not coordinator.tester._initialised or coordinator.tester.is_testing:\\n await coordinator._async_timer(next_time)\\n next_time += interval\\n\\n assert (\\n coordinator.tester.total_events\\n == coordinator.tester.total_checks\\n == coordinator.tester.total_results\\n )\\n assert coordinator.tester.total_errors == 0\\n print(\\n \\\"Finished: {0}; events: {1}; checks: {2}; errors: {3}; time: {4:.2f}s\\\".format(\\n fname,\\n coordinator.tester.total_events,\\n coordinator.tester.total_checks,\\n coordinator.tester.total_errors,\\n coordinator.tester.total_time,\\n )\\n )\",\n \"def assert_history(self, rows):\\r\\n self.assertEqual(self.parse_rows(rows), self.read_history())\",\n \"def test_same_unique_crash_type_with_same_state(self):\\n self.testcases[0].security_flag = False\\n self.testcases[0].crash_type = 'Timeout'\\n self.testcases[0].crash_state = 'abcde'\\n self.testcases[0].timestamp = datetime.datetime.utcfromtimestamp(0)\\n self.testcases[1].security_flag = False\\n self.testcases[1].crash_type = 'Timeout'\\n self.testcases[1].crash_state = 'abcde'\\n self.testcases[1].timestamp = datetime.datetime.utcfromtimestamp(1)\\n\\n for t in self.testcases:\\n t.put()\\n\\n grouper.group_testcases()\\n\\n testcases = []\\n for testcase_id in data_handler.get_open_testcase_id_iterator():\\n testcases.append(data_handler.get_testcase_by_id(testcase_id))\\n\\n self.assertEqual(len(testcases), 1)\\n self.assertEqual(testcases[0].group_id, 0)\\n self.assertTrue(testcases[0].is_leader)\",\n \"def test_noTimedEventsInsertion(self):\\n self.test_noTimedEventsExtraction()\\n self._testInsertion()\",\n \"def timestep(self):\\n self.survive()\\n self.move()\\n self.set_survival_probability(self.vital_var)\\n self.reproduce()\\n # self.save_frame()\\n if self.time % self.save_state_frequency == 0:\\n self.save_state(self.state_file.format(self.time))\\n if self.time % self.save_stats_frequency == 0:\\n self.write_stats()\\n # self.connected_component()\\n self.time += 1\\n\\n if self.pop_size > 0:\\n return True\\n else:\\n return False\",\n \"def test_eval_alarm(self):\\n def get_state_update_value(h):\\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\\"\\n oldstate = h.data['oldState']['stateValue']\\n newstate = h.data['newState']['stateValue']\\n querydate = h.data['newState']['stateReasonData']['queryDate']\\n querydate = utils.parse_strtime(querydate)\\n return oldstate, newstate, querydate \\n \\n test_uuid = str(uuid.uuid4())\\n alarmname = \\\"TestEvalAlarm_\\\" + test_uuid\\n metricname = \\\"TestEvalMetric_\\\" + test_uuid\\n namespace = \\\"unittest\\\"\\n unit = \\\"Percent\\\"\\n dimensions = {\\\"test_id\\\":test_uuid}\\n threshold = 2.0\\n \\n # create metric alarm\\n alarm = MetricAlarm(name=alarmname, metric=metricname,\\n namespace=namespace, statistic=\\\"Average\\\",\\n comparison=\\\">\\\", threshold=threshold,\\n period=60, evaluation_periods=1, unit=unit,\\n dimensions=dimensions)\\n self.synaps.put_metric_alarm(alarm)\\n \\n # due to put_metric_alarm is asynchronous\\n time.sleep(ASYNC_WAIT)\\n \\n alarm_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0)\\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold + 1, timestamp=alarm_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n\\n ok_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0) \\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold - 2, timestamp=ok_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n \\n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\\n history_item_type=\\\"StateUpdate\\\")\\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\\n\\n result = map(get_state_update_value, histories)\\n \\n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\\n ('ALARM', 'INSUFFICIENT_DATA', None),\\n ('INSUFFICIENT_DATA', 'OK', ok_time),\\n ('OK', 'INSUFFICIENT_DATA', None))\\n \\n failmsg = \\\"expected: %s real: %s\\\" % (expected, result)\\n \\n self.assertEqual(len(result), len(expected), msg=failmsg)\\n \\n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\\n expected):\\n self.assertEqual(r_new, e_new, msg=failmsg)\\n self.assertEqual(r_old, e_old, msg=failmsg)\\n if e_time:\\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\\n msg=failmsg)\\n \\n self.synaps.delete_alarms(alarms=[alarmname])\",\n \"def test_update_task_states(self):\\r\\n changed = self.combinedoe.update_task_states()\\r\\n self.assertFalse(changed)\\r\\n\\r\\n current_task = self.combinedoe.current_task\\r\\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\\r\\n changed = self.combinedoe.update_task_states()\\r\\n\\r\\n self.assertTrue(changed)\",\n \"def verify_execution_results(state, collation):\\n state.commit()\\n\\n validate_transaction_tree(collation)\\n\\n if collation.header.post_state_root != state.trie.root_hash:\\n raise ValueError('State root mismatch: header %s computed %s' %\\n (encode_hex(collation.header.post_state_root), encode_hex(state.trie.root_hash)))\\n if collation.header.receipts_root != mk_receipt_sha(state.receipts):\\n raise ValueError('Receipt root mismatch: header %s computed %s, computed %d, %d receipts' %\\n (encode_hex(collation.header.receipts_root), encode_hex(mk_receipt_sha(state.receipts)),\\n state.gas_used, len(state.receipts)))\\n\\n return True\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def get_raw_data(trace: Trace, cmdl_args):\\n # Retrieves states dataframe with the intresting columns\\n df_state = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.START.value,\\n STATE_COLS.END.value, STATE_COLS.VAL.value]]\\n\\n # Computes the elapse times of each state\\n df_state['el_time'] = df_state[STATE_COLS.END.value] - df_state[STATE_COLS.START.value]\\n\\n # Removes start and end columns from rows cause we don't need them. Load the data into memory.\\n df_state = df_state.drop(columns=[STATE_COLS.START.value, STATE_COLS.END.value]).compute()\\n\\n # Computes runtime (in us)\\n runtime = df_state.groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])['el_time'].sum().max() / 1000\\n # Filters rows by useful and groups dataframe by process\\n df_state_useful_grouped = df_state.loc[df_state[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])\\n # Computes useful average time (in us)\\n useful_av = df_state_useful_grouped['el_time'].sum().mean() / 1000\\n # Computes useful max time (in us)\\n useful_max = df_state_useful_grouped['el_time'].sum().max() / 1000\\n # Computes useful tot time (in us)\\n useful_tot = df_state_useful_grouped['el_time'].sum().sum() / 1000\\n\\n # Dimemas simulation for ideal times\\n if cmdl_args.dimemas:\\n runtime_id, useful_id = get_ideal_data(trace.metadata.path, len(trace.metadata.cpu_list))\\n else:\\n runtime_id = float('NaN')\\n useful_id = float('NaN')\\n\\n\\n # Loads only meaningful columns from df_states and filters useful rows\\n df_state_useful = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.END.value, STATE_COLS.VAL.value]]\\n df_state_useful = df_state_useful.loc[df_state_useful[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].drop(columns=STATE_COLS.VAL.value).compute()\\n\\n # Loads only meaningful columns from df_events\\n df_event = trace.df_event[[EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value, EVENT_COLS.TIME.value,\\n EVENT_COLS.EVTYPE.value, EVENT_COLS.EVVAL.value]]\\n\\n # Filters for PAPI_TOT_INS and set as index the process identifier\\n df_event_ins = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_INS.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\\n\\n # Gets total useful instructions by grouping and applying a custom filtering function\\n useful_ins = df_event_ins.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\\n\\n # Filter for PAPI_TOT_CYC and set as indexes the process identifier\\n df_event_cyc = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_CYC.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\\n\\n # Gets total useful cycles by grouping and applying a custom filtering function\\n useful_cyc= df_event_cyc.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\\n\\n # Computes average IPC\\n try:\\n ipc = useful_ins / useful_cyc\\n except ValueError:\\n ipc = float('NaN')\\n # Computes average frequency\\n try:\\n freq = useful_cyc / useful_tot / 1000\\n except ValueError:\\n freq = float('NaN')\\n\\n return ipc, freq, runtime, runtime_id, useful_av, useful_max, useful_tot, useful_id, useful_ins, useful_cyc\",\n \"async def test_lazy_state_handles_same_last_updated_and_last_changed(\\n caplog: pytest.LogCaptureFixture,\\n) -> None:\\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n row = PropertyMock(\\n entity_id=\\\"sensor.valid\\\",\\n state=\\\"off\\\",\\n attributes='{\\\"shared\\\":true}',\\n last_updated_ts=now.timestamp(),\\n last_changed_ts=now.timestamp(),\\n )\\n lstate = LazyState(\\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\\n )\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n assert lstate.last_updated.timestamp() == row.last_updated_ts\\n assert lstate.last_changed.timestamp() == row.last_changed_ts\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\",\n \"def test_time_type_state_is_evening(day):\\n\\n assert day_time_info(day.hours_0).is_evening is False\\n assert day_time_info(day.hours_1).is_evening is False\\n assert day_time_info(day.hours_2).is_evening is False\\n assert day_time_info(day.hours_3).is_evening is False\\n assert day_time_info(day.hours_4).is_evening is False\\n assert day_time_info(day.hours_5).is_evening is False\\n assert day_time_info(day.hours_6).is_evening is False\\n assert day_time_info(day.hours_7).is_evening is False\\n assert day_time_info(day.hours_8).is_evening is False\\n assert day_time_info(day.hours_9).is_evening is False\\n assert day_time_info(day.hours_10).is_evening is False\\n assert day_time_info(day.hours_11).is_evening is False\\n assert day_time_info(day.hours_12).is_evening is False\\n assert day_time_info(day.hours_13).is_evening is False\\n assert day_time_info(day.hours_14).is_evening is False\\n assert day_time_info(day.hours_15).is_evening is False\\n assert day_time_info(day.hours_16).is_evening is False\\n assert day_time_info(day.hours_17).is_evening is False\\n assert day_time_info(day.hours_18).is_evening is True\\n assert day_time_info(day.hours_19).is_evening is True\\n assert day_time_info(day.hours_20).is_evening is True\\n assert day_time_info(day.hours_21).is_evening is True\\n assert day_time_info(day.hours_22).is_evening is True\\n assert day_time_info(day.hours_23).is_evening is False\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def state(self):\\n retval = \\\"FINISHED\\\"\\n for testrun in self.testruns:\\n # we have to check for this because deleting testruns doesn't always delete them out of testrun groups\\n if not isinstance(testrun, bson.DBRef):\\n assert isinstance(testrun, Testrun)\\n if testrun.state == \\\"TO_BE_RUN\\\":\\n return \\\"TO_BE_RUN\\\" # lowest possible state, return immediately\\n elif testrun.state == \\\"RUNNING\\\":\\n retval = \\\"RUNNING\\\" # we don't have to worry about overwriting lower state TO_BE_RUN\\n return retval\",\n \"def test_post_processing_time():\\r\\n cmd = ShdlcCmdGetErrorState(clear=False)\\r\\n assert type(cmd.post_processing_time) is float\\r\\n assert cmd.post_processing_time == 0.0\",\n \"def test_time_type_state_is_afternoon(day):\\n\\n assert day_time_info(day.hours_0).is_afternoon is False\\n assert day_time_info(day.hours_1).is_afternoon is False\\n assert day_time_info(day.hours_2).is_afternoon is False\\n assert day_time_info(day.hours_3).is_afternoon is False\\n assert day_time_info(day.hours_4).is_afternoon is False\\n assert day_time_info(day.hours_5).is_afternoon is False\\n assert day_time_info(day.hours_6).is_afternoon is False\\n assert day_time_info(day.hours_7).is_afternoon is False\\n assert day_time_info(day.hours_8).is_afternoon is False\\n assert day_time_info(day.hours_9).is_afternoon is False\\n assert day_time_info(day.hours_10).is_afternoon is False\\n assert day_time_info(day.hours_11).is_afternoon is False\\n assert day_time_info(day.hours_12).is_afternoon is False\\n assert day_time_info(day.hours_13).is_afternoon is True\\n assert day_time_info(day.hours_14).is_afternoon is True\\n assert day_time_info(day.hours_15).is_afternoon is True\\n assert day_time_info(day.hours_16).is_afternoon is True\\n assert day_time_info(day.hours_17).is_afternoon is True\\n assert day_time_info(day.hours_18).is_afternoon is False\\n assert day_time_info(day.hours_19).is_afternoon is False\\n assert day_time_info(day.hours_20).is_afternoon is False\\n assert day_time_info(day.hours_21).is_afternoon is False\\n assert day_time_info(day.hours_22).is_afternoon is False\\n assert day_time_info(day.hours_23).is_afternoon is False\",\n \"def event_m20_11_83000():\\n \\\"\\\"\\\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\\\"\\\"\\\"\\n assert event_m20_11_x53(z73=2011300, z74=2011399)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def test_same_unique_crash_type_with_different_state(self):\\n self.testcases[0].security_flag = False\\n self.testcases[0].crash_type = 'Timeout'\\n self.testcases[0].crash_state = 'abcdef'\\n self.testcases[1].security_flag = False\\n self.testcases[1].crash_type = 'Timeout'\\n self.testcases[1].crash_state = 'abcde'\\n\\n for t in self.testcases:\\n t.put()\\n\\n grouper.group_testcases()\\n\\n for index, t in enumerate(self.testcases):\\n self.testcases[index] = data_handler.get_testcase_by_id(t.key.id())\\n self.assertEqual(self.testcases[index].group_id, 0)\\n self.assertTrue(self.testcases[index].is_leader)\",\n \"def test_runner_full_loop_big(caplog, big_dataset):\\n caplog.set_level(logging.INFO)\\n\\n session = big_dataset\\n\\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 3)\\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 6)\\n replay_rate = 1 \\n\\n db_connector_test = DataBaseConnector(session=session, \\n table_name='timeseries_dataset', \\n time_column='timestamp', \\n start_date=start_date,\\n end_date=end_date)\\n\\n debug_publisher = FilePublisher()\\n\\n runner = CentralRunner(db_connection=db_connector_test, \\n output_system=debug_publisher, \\n start_time=start_date, \\n end_time=end_date,\\n replay_rate=replay_rate )\\n\\n start = time.perf_counter()\\n \\n runner.run()\\n\\n end = time.perf_counter()\\n\\n code_time = end - start\\n assert int(code_time) == 2\\n\\n ## Test results\\n assert os.path.exists('test_tmp/17-05-2020_13-00-03')\\n assert os.path.exists('test_tmp/17-05-2020_13-00-05')\\n\\n assert len(os.listdir('test_tmp/17-05-2020_13-00-03')) == 9\\n assert len(os.listdir('test_tmp/17-05-2020_13-00-05')) == 2\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66075","0.6143854","0.61161435","0.61069274","0.6051925","0.59604484","0.58838767","0.58052","0.5779052","0.5769028","0.57552785","0.5731349","0.5673748","0.56650174","0.56536967","0.5641838","0.56204814","0.5584373","0.5582211","0.55599564","0.55164605","0.5514844","0.5505435","0.5491299","0.5471248","0.5445622","0.5426274","0.54235333","0.5415118","0.53671294","0.5358964","0.5357287","0.5351717","0.53280383","0.53181636","0.5313106","0.53113395","0.5295853","0.52942896","0.5292141","0.5280726","0.5280635","0.52692693","0.5265327","0.5264658","0.5262992","0.5256355","0.5243309","0.5239129","0.5230553","0.52266675","0.5210496","0.52005434","0.51999927","0.5196027","0.5194009","0.5182567","0.5165497","0.5160515","0.5159152","0.51581466","0.5155134","0.5151885","0.5149598","0.5147008","0.5145836","0.51408327","0.51407903","0.51344246","0.5133375","0.51244164","0.51157814","0.51131964","0.51128364","0.5108012","0.5106096","0.5102417","0.5100133","0.50925434","0.50886023","0.5082891","0.508287","0.50791764","0.5070331","0.5066678","0.5057765","0.50567085","0.5048314","0.50471705","0.50443745","0.504284","0.50280803","0.502648","0.502427","0.50234973","0.50141066","0.50123394","0.5011273","0.50094247","0.50067174"],"string":"[\n \"0.66075\",\n \"0.6143854\",\n \"0.61161435\",\n \"0.61069274\",\n \"0.6051925\",\n \"0.59604484\",\n \"0.58838767\",\n \"0.58052\",\n \"0.5779052\",\n \"0.5769028\",\n \"0.57552785\",\n \"0.5731349\",\n \"0.5673748\",\n \"0.56650174\",\n \"0.56536967\",\n \"0.5641838\",\n \"0.56204814\",\n \"0.5584373\",\n \"0.5582211\",\n \"0.55599564\",\n \"0.55164605\",\n \"0.5514844\",\n \"0.5505435\",\n \"0.5491299\",\n \"0.5471248\",\n \"0.5445622\",\n \"0.5426274\",\n \"0.54235333\",\n \"0.5415118\",\n \"0.53671294\",\n \"0.5358964\",\n \"0.5357287\",\n \"0.5351717\",\n \"0.53280383\",\n \"0.53181636\",\n \"0.5313106\",\n \"0.53113395\",\n \"0.5295853\",\n \"0.52942896\",\n \"0.5292141\",\n \"0.5280726\",\n \"0.5280635\",\n \"0.52692693\",\n \"0.5265327\",\n \"0.5264658\",\n \"0.5262992\",\n \"0.5256355\",\n \"0.5243309\",\n \"0.5239129\",\n \"0.5230553\",\n \"0.52266675\",\n \"0.5210496\",\n \"0.52005434\",\n \"0.51999927\",\n \"0.5196027\",\n \"0.5194009\",\n \"0.5182567\",\n \"0.5165497\",\n \"0.5160515\",\n \"0.5159152\",\n \"0.51581466\",\n \"0.5155134\",\n \"0.5151885\",\n \"0.5149598\",\n \"0.5147008\",\n \"0.5145836\",\n \"0.51408327\",\n \"0.51407903\",\n \"0.51344246\",\n \"0.5133375\",\n \"0.51244164\",\n \"0.51157814\",\n \"0.51131964\",\n \"0.51128364\",\n \"0.5108012\",\n \"0.5106096\",\n \"0.5102417\",\n \"0.5100133\",\n \"0.50925434\",\n \"0.50886023\",\n \"0.5082891\",\n \"0.508287\",\n \"0.50791764\",\n \"0.5070331\",\n \"0.5066678\",\n \"0.5057765\",\n \"0.50567085\",\n \"0.5048314\",\n \"0.50471705\",\n \"0.50443745\",\n \"0.504284\",\n \"0.50280803\",\n \"0.502648\",\n \"0.502427\",\n \"0.50234973\",\n \"0.50141066\",\n \"0.50123394\",\n \"0.5011273\",\n \"0.50094247\",\n \"0.50067174\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65768546"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17474,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_FINAL_RUN state"},"document":{"kind":"string","value":"def test_cancelled_state_final_run(self):\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_CANCELED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_FINAL_RUN,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_processed_state_final_run(self):\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_FINAL_RUN,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=1).get_tree(any(str))","def IsFinal(self):\n return self.state in FINAL_TEST_RUN_STATES","def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_run_ended(self):","def test_state_after_failure(self):\n pass","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def finished_tests(self):\n self.testing = 0","def test_runner_with_db(dataset, time_start, time_diff):\n\n session = dataset\n\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\n replay_rate = 1.0 \n\n db_connector_test = DataBaseConnector(session=session, \n table_name='timeseries_dataset', \n time_column='timestamp', \n start_date=time_start,\n end_date=end_date)\n\n runner = CentralRunner(db_connection=db_connector_test, \n output_system='mock_output_systerm', \n start_time=time_start, \n end_time=end_date,\n replay_rate=replay_rate )\n\n\n results_test = [\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 0), 'text': 'bob', 'value': 10.0},\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'cat', 'value':-10.0},\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'eat', 'value': 12.1}\n ]\n \n # test that the trigger_release is working right\n # expect 1\n start = time.perf_counter()\n \n code_start = datetime.datetime.now()\n\n # we need to retink the way that we trigger this....\n runner._trigger_release(result_set=results_test, code_start=code_start, replay_start_time=time_start, \n batch=(datetime.datetime(2021, 1, 1, 10, 1, 0), datetime.datetime(2021, 1, 1, 10, 1, 1)), \n replay_rate=replay_rate)\n \n end = time.perf_counter()\n\n code_time = end - start\n assert int(code_time) == time_diff","def test_issue_tracked_times(self):\n pass","def test_update_instances_schedule_state(self):\n pass","def test_retrieve_instances_schedule_state(self):\n pass","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_clean_run(self):\n Historical_ROAs_Parsed_Table(clear=True)\n with Historical_ROAs_Table(clear=True) as t:\n Historical_ROAs_Parser().run()\n assert t.get_count() > 2000000","def test_is_finished(self):\n experiment = Experiment(TasksMock())\n self.assertEquals(False, experiment.is_finished())\n for _ in range(0, 17):\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_down(time.time())\n self.assertEquals(False, experiment.is_finished())\n experiment.press_b_up(time.time())\n self.assertEquals(True, experiment.is_finished())","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def test_output(self):\n # reproducible arbitrariness\n np.random.seed(123423)\n\n N = 20\n tmax = 30.0\n dt = 1.0\n\n n_steps = int_r(tmax/dt)\n\n table = np.random.randn(N, n_steps)\n G = TableLayer(table)\n\n M = simulation.StateMonitor(G, 'out')\n\n sim = simulation.Simulation(G, M, dt=dt)\n sim.run(tmax)\n\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)","def test_update_state4(self):\n pass","def test_run(self):\n class MockProvider(BaseCoverageProvider):\n SERVICE_NAME = \"I do nothing\"\n was_run = False\n\n def run_once_and_update_timestamp(self):\n \"\"\"Set a variable.\"\"\"\n self.was_run = True\n return None\n\n provider = MockProvider(self._db)\n result = provider.run()\n\n # run_once_and_update_timestamp() was called.\n assert True == provider.was_run\n\n # run() returned a CoverageProviderProgress with basic\n # timing information, since run_once_and_update_timestamp()\n # didn't provide anything.\n assert isinstance(result, CoverageProviderProgress)\n now = utc_now()\n assert result.start < result.finish\n for time in (result.start, result.finish):\n assert (now - time).total_seconds() < 5","def test_completed():\n assert complete == 1\n assert errorflag == 0","def test_update_state3(self):\n pass","def test_update_state(self):\n pass","def test_log_last_completed_datetime(self):\n initial_count = CostUsageReportStatus.objects.count()\n saver = ReportStatsDBAccessor(\"myreport\", self.manifest_id)\n saver.log_last_completed_datetime()\n self.assertIsNotNone(saver.get_last_completed_datetime())\n saver.delete()\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)","def test_run_started(self):","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_9999_last(self):\n self.lasttest = True","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def test_completed(self):\n return False","def get_exist_phase_timestamp(self):\r\n csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\r\n if not os.path.isfile(csv_path):\r\n print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\r\n exit(-1)\r\n converter = {'benchmarkPhase': str, 'streamNumber': int, 'queryNumber': int,\r\n 'epochStartTimestamp': 'int64', 'epochEndTimestamp': 'int64'}\r\n df = pd.read_csv(csv_path, delimiter=';').loc[:,\r\n ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\r\n phase_ts = OrderedDict()\r\n is_exist = False\r\n for phase in self.phase_name:\r\n benchmark_phase = (df['benchmarkPhase'] == phase)\r\n if any(benchmark_phase): # whether this phase exist in the BB logs\r\n if phase == 'POWER_TEST': # power test overall and each query\r\n stream_num = ((df['streamNumber']) == 0)\r\n mask = benchmark_phase & stream_num\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 2] = 0 # fill 0 to blank value\r\n elif phase == 'THROUGHPUT_TEST_1': # throughput test overall and each stream\r\n query_num = (pd.isnull(df['queryNumber']))\r\n mask = benchmark_phase & query_num\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 1:3] = -1 # file -1 to overall throughput test\r\n phase_ts[phase].iloc[1:, 2] = 0 # fill 0 to blank value\r\n else: # other phases\r\n stream_num = (pd.isnull(df['streamNumber']))\r\n query_num = (pd.isnull(df['queryNumber']))\r\n mask = benchmark_phase & stream_num & query_num\r\n # phase_start = line['epochStartTimestamp'].values / 1000\r\n # phase_end = line['epochEndTimestamp'].values / 1000\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 1:3] = 0 # fill 0 to blank value\r\n phase_ts[phase] = phase_ts[phase].astype(converter)\r\n is_exist = True\r\n if is_exist:\r\n return phase_ts\r\n else:\r\n print('It seems BigBenchTimes.csv in {0} does not include any TPCx-BB phases, ' \\\r\n 'existing...'.format(self.bb_log_path))\r\n exit(-1)","def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_update_state2(self):\n pass","def test_service_initiated():\n assert \"ready\" in bkt_outcome_unwind.index()","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def validation_epoch_end(self, validation_step_outputs):\n self.epoch_end_toc = time()\n logger.info(f\"This epoch took {int(self.epoch_end_toc - self.epoch_start_tic)} s\")\n return super().validation_epoch_end(validation_step_outputs)","def test_initialization_of_homework_result_created():\n expected = datetime.datetime.now()\n assert result_1.created - expected < datetime.timedelta(seconds=1)","def test_taskrun_updates_task_state(self):\r\n app = AppFactory.create()\r\n task = TaskFactory.create(app=app, n_answers=2)\r\n url = '/api/taskrun?api_key=%s' % app.owner.api_key\r\n\r\n # Post first taskrun\r\n data = dict(\r\n app_id=task.app_id,\r\n task_id=task.id,\r\n info='my task result')\r\n datajson = json.dumps(data)\r\n tmp = self.app.post(url, data=datajson)\r\n r_taskrun = json.loads(tmp.data)\r\n\r\n assert tmp.status_code == 200, r_taskrun\r\n err_msg = \"Task state should be different from completed\"\r\n assert task.state == 'ongoing', err_msg\r\n\r\n # Post second taskrun\r\n url = '/api/taskrun'\r\n data = dict(\r\n app_id=task.app_id,\r\n task_id=task.id,\r\n info='my task result anon')\r\n datajson = json.dumps(data)\r\n tmp = self.app.post(url, data=datajson)\r\n r_taskrun = json.loads(tmp.data)\r\n\r\n assert tmp.status_code == 200, r_taskrun\r\n err_msg = \"Task state should be equal to completed\"\r\n assert task.state == 'completed', err_msg","def test_rt_table(self) -> None:\n expected = Fixtures.next_table()\n expected.description = '\"hello!\" said no one'\n expected.tags.sort()\n\n self.get_proxy().put_table(table=expected)\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\n actual.last_updated_timestamp = None\n actual.tags.sort()\n\n self.assertEqual(expected, actual)","def test_update_splits():\n assert new_log.iloc[0]['start'] == 0","def test_update_state1(self):\n pass","def on_test_epoch_end(self) -> None:\n self.log_dict(self.test_metrics.compute())\n self.test_metrics.reset()","def test_run_date(self):\n Historical_ROAs_Parsed_Table(clear=True)\n\n date = '2019-08-01'\n \n with Historical_ROAs_Table(clear=True) as t:\n Historical_ROAs_Parser().run(datetime.strptime(date, '%Y-%m-%d'))\n sql = f\"SELECT COUNT(*) FROM {t.name} WHERE date_added = '{date}'\"\n \n # unless ROAs are retroactively added, this should be constant\n assert t.get_count(sql) == 95743","def __exit__(self, _type, value, traceback):\n try:\n if not self.record_finish:\n return\n print >>sys.stderr, 'record', self.record\n dutset = {'last_finish_time':time()}\n if not self.record:\n return\n upd = {'end_time': time(), 'modification_time':time()}\n\n if value: # i.e. , if test failed:\n upd['failure'] = repr(value)\n upd['exception'] = value.__class__.__name__\n if not isinstance(value, KeyboardInterrupt):\n print 'HEADLINE: exception', upd['exception'], value\n for clause in format_exception(_type, value, traceback):\n for line in clause.split('\\n'):\n print 'CRASH:', line\n else:\n upd['infrastructure_problem'] = True\n upd['whiteboard'] = '[infrastructure] test interrupted'\n if self.reinstall_on_failure:\n dutset['test_failed'] = True\n tnext = time() + 300\n print 'INFO: test failed, so will reinstall machine at', \\\n asctime(localtime(tnext))\n\n if self.failed: #some test suite failed\n upd['failure'] = 'test failed'\n\n self.mdb.results.update({'_id':self.result_id}, {'$set':upd})\n classify = process_result(self.mdb.results.find_one({'_id':self.result_id}))\n print 'HEADLINE:', classify, self.full_description()\n\n get_track().updates.save({'result_id':self.result_id,\n 'action':'experiment finished'})\n\n if self.dut_id:\n self.mdb.duts.update({'_id':self.dut_id}, \n {'$unset': {'control_pid':1, 'result_id':1,\n 'control_command_line':1},\n '$set': dutset})\n if self.build:\n recount(self.build)\n if classify == 'infrastructure_problems':\n pass\n else:\n col = 'green' if classify == 'passes' else 'red'\n finally:\n if self.record_queue:\n self.record_queue.put('finish')\n self.record_queue.close()\n self.record_queue.join_thread()\n if self.stream_process:\n self.stream_process.join()\n if self.stdout_filter:\n self.stdout_filter.del_callback(self)","def test_log_last_started_datetime(self):\n initial_count = CostUsageReportStatus.objects.count()\n saver = ReportStatsDBAccessor(\"myreport\", self.manifest_id)\n saver.log_last_started_datetime()\n self.assertIsNotNone(saver.get_last_started_datetime())\n saver.delete()\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)","def test_full_house_flush_ind(self):","def test_snapshot(self):\n array = np.arange(1000)\n df = DataFlow.from_numpy(array).shuffle().snapshot()\n with DataFlowContext(df).as_default() as ctx:\n # shuffled data flow should be ready after df.shuffle()\n array2 = df.all()[0]\n self.assertFalse(np.all(array2 == array))\n np.testing.assert_array_equal(np.sort(array2), array)\n\n # after each reset_epoch, the snapshot data flow should be same\n ctx.reset_epoch()\n array3 = df.all()[0]\n self.assertFalse(np.all(array3 == array))\n np.testing.assert_array_equal(array3, array2)","def test_runner_full_loop(caplog, dataset):\n caplog.set_level(logging.DEBUG)\n\n session = dataset\n\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 0)\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\n replay_rate = 1 \n \n db_connector_test = DataBaseConnector(session=session, \n table_name='timeseries_dataset', \n time_column='timestamp', \n start_date=start_date,\n end_date=end_date)\n\n test_publisher = ConsolePublisher()\n\n runner = CentralRunner(db_connection=db_connector_test, \n output_system=test_publisher, \n start_time=start_date, \n end_time=end_date,\n replay_rate=replay_rate )\n\n start = time.perf_counter()\n \n runner.run()\n\n end = time.perf_counter()\n\n code_time = end - start\n \n print(code_time)\n \n assert int(code_time) == 4","def test_retrospective_the_prospective_checkpointed_timestamps(\n self, test_type, stage_pair, pm\n ):\n\n stage1, stage2 = stage_pair\n pm.timestamp(checkpoint=stage1, finished=True)\n assert stage1 == pm.prev_checkpoint\n assert pm.curr_checkpoint is None\n pm.timestamp(checkpoint=stage2, finished=False)\n\n if test_type == FILES_TEST:\n expected = [checkpoint_filepath(stage1, pm)]\n assert set(expected) == set(fetch_checkpoint_files(pm))\n else:\n assert pm.prev_checkpoint is None\n assert stage2 == pm.curr_checkpoint","def testing_phase(self):\r\n self.test_dataloader = self.get_dataloader(self.test_h5_path, \"test set\")\r\n self.get_ts_properties()\r\n\r\n self.restart_epoch = util.get_restart_epoch()\r\n print(f\"* Loading model from previous saved state (Epoch {self.restart_epoch}).\", flush=True)\r\n self.model = torch.load(\r\n self.C.job_dir + f\"model_restart_{self.restart_epoch}.pth\"\r\n )\r\n\r\n self.model.eval()\r\n with torch.no_grad():\r\n self.generate_graphs(n_samples=self.C.n_samples)\r\n\r\n print(\"* Evaluating model.\", flush=True)\r\n anal.evaluate_model(valid_dataloader=self.test_dataloader,\r\n train_dataloader=self.train_dataloader,\r\n nll_per_action=self.nll_per_action,\r\n model=self.model)\r\n\r\n self.print_time_elapsed()","def on_test_batch_end(self, batch, logs=None):","def test_epoch_end(self, outputs: Any) -> None:\n self.log_dict(self.test_metrics.compute())\n self.test_metrics.reset()","def test_update_team_state(self):\n pass","def test_prospective_then_retrospective_checkpointed_timestamps(\n self, test_type, stage_pair, pm\n ):\n\n stage1, stage2 = stage_pair\n pm.timestamp(checkpoint=stage1, finished=False)\n assert stage1 == pm.curr_checkpoint\n pm.timestamp(checkpoint=stage2, finished=True)\n\n if test_type == FILES_TEST:\n checkpoint_files = fetch_checkpoint_files(pm)\n expected = [checkpoint_filepath(stage2, pm)]\n assert set(expected) == set(checkpoint_files)\n else:\n # Current checkpoint will be reset by second (retrospective)\n # timestamp call.\n assert stage2 == pm.prev_checkpoint\n assert pm.curr_checkpoint is None","def test_list_runs(self):\n pass","def test_has_ended(self):\r\n self.assertTrue(self.past_show_certs.has_ended())\r\n self.assertTrue(self.past_noshow_certs.has_ended())\r\n self.assertFalse(self.future_show_certs.has_ended())\r\n self.assertFalse(self.future_noshow_certs.has_ended())","def emit(self, record):\n self.eng.dispose()\n trys = 3\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\"__\")}\n for t in range(trys):\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\n failed = False\n try:\n with self.eng.connect() as con:\n # formating values to convert from python or parsl to db standards\n info['task_fail_history'] = str(info.get('task_fail_history', None))\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\n run_id = info['run_id']\n\n # if workflow or task has completed, update their entries with the time.\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\n con.execute(up)\n return\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\n workflow = self.meta.tables['task']\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\n .where(workflow.c.run_id == run_id)\n con.execute(up)\n\n # create workflows table if this is a new database without one\n if 'workflows' not in self.meta.tables.keys():\n workflows = create_workflows_table(self.meta)\n self.meta.create_all(con)\n # if this is the first sight of the workflow, add it to the workflows table\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\n workflows = self.meta.tables['workflows']\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\n con.execute(ins)\n\n # if log has task counts, update the workflow entry in the workflows table\n if 'tasks_completed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n if 'tasks_failed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n\n # create task table if this is a new run without one\n if 'task' not in self.meta.tables.keys():\n workflow = create_task_table(self.meta)\n self.meta.create_all(con)\n\n # check to make sure it is a task log and not just a workflow overview log\n if info.get('task_id', None) is not None:\n # if this is the first sight of the task in the workflow, add it to the workflow table\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\n if 'psutil_process_pid' in info.keys():\n # this is the race condition that a resource log is before a status log so ignore this resource update\n return\n\n workflow = self.meta.tables['task']\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\n con.execute(ins)\n\n if 'task_status' in info.keys():\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\n if 'task_status' not in self.meta.tables.keys():\n task_status_table = create_task_status_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n # if this status table already exists, just insert the update\n else:\n task_status_table = self.meta.tables['task_status']\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n return\n\n if 'psutil_process_pid' in info.keys():\n # if this is a task resource update then handle that, if the resource table DNE then create it\n if 'task_resources' not in self.meta.tables.keys():\n task_resource_table = create_task_resource_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n # if this resource table already exists, just insert the update\n else:\n task_resource_table = self.meta.tables['task_resources']\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n return\n\n except Exception as e:\n logger.error(\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\".format(t, str(e)))\n failed = True\n time.sleep(5)\n if not failed:\n return","def end_of_test_epoch_hook(self):\n pass","def test_experiment(experiment):\n run = experiment.run()\n assert run.status == 'COMPLETED'","def test_total_time_no_end_time(time_record_factory):\n d = datetime.datetime(2018, 10, 1, 15, 26)\n t = time_record_factory(time_start=d, time_end=None)\n expected = datetime.timedelta(0)\n assert t.total_time == expected","def test_validate_self_final_state_transitions(self):\n with nose.assert_raises(exceptions.FinalStateError):\n self.dtm1.transitions['q4'] = {'0': ('q4', '0', 'L')}\n self.dtm1.validate_self()","def test_table_false_positives(self):\n pass","def test_get_last_tracking_insertation_():\n insert_habit()\n insert_day()\n trackings_table = analytics.trackings_table()\n assert trackings_table[60][:-1] == (6, str(date.today()))\n delete_habit()","def done(self):\n log.debug(\"Test run concluded\")\n if self._startTime is not None:\n self.report['startTime'] = self._startTime\n self.report['runTime'] = time.time() - self._startTime\n self.report['testsRun'] = self.testsRun\n self.report['tests'] = self._tests\n self.writeReport()","def on_test_end(self, logs=None):","def event_m20_11_6000():\n \"\"\"State 0,2: [Preset] Door of the living person_SubState\"\"\"\n assert event_m20_11_x77(z42=20110480, z43=600000)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def has_finished():","def test_queryset_total_time(time_record_factory):\n now = timezone.now()\n later = now + datetime.timedelta(hours=1)\n\n time_record_factory(time_end=later, time_start=now)\n time_record_factory(time_end=later, time_start=now)\n\n expected = sum(\n (record.delta for record in models.TimeRecord.objects.with_deltas()),\n datetime.timedelta(0),\n )\n\n assert models.TimeRecord.objects.total_time() == expected","def testTimestamps(self):\n predicate = \"metadata:predicate\"\n subject = \"aff4:/metadata:8\"\n\n # Extend the range of valid timestamps returned from the table to account\n # for potential clock skew.\n start = long(time.time() - 60) * 1e6\n data_store.DB.Set(subject, predicate, \"1\", token=self.token)\n\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\n\n # Check the time is reasonable\n end = long(time.time() + 60) * 1e6\n\n self.assert_(ts >= start and ts <= end)\n self.assertEqual(stored, \"1\")","def test_sequence_done(self):\n self.t(\"1,2 done\")\n code, out, err = self.t(\"_get 1.status 2.status\")\n self.assertEqual(\"completed completed\\n\", out)","def newTestRun(self):\n sql = \"INSERT INTO testrun VALUES (NULL, %s, %s)\"\n self.cursor.execute(sql, (self.timestamp, self.server))\n self.db.commit()\n self.testid = self.cursor.lastrowid","def test_archive_commitlog_point_in_time(self):\n self.run_archive_commitlog(restore_point_in_time=True)","def test_total_time_with_start_and_end_times(time_record_factory):\n ts = datetime.datetime(2018, 10, 1, 9, 0)\n te = datetime.datetime(2018, 10, 1, 17, 0)\n\n t = time_record_factory(time_start=ts, time_end=te)\n\n expected = te-ts\n\n assert t.total_time == expected","def final(self, state):\n deltaReward = state.getScore() - self.lastState.getScore()\n self.observeTransition(self.lastState, self.lastAction, state, deltaReward)\n self.stopEpisode()\n\n # Make sure we have this var\n if not 'episodeStartTime' in self.__dict__:\n self.episodeStartTime = time.time()\n if not 'lastWindowAccumRewards' in self.__dict__:\n self.lastWindowAccumRewards = 0.0\n self.lastWindowAccumRewards += state.getScore()\n\n NUM_EPS_UPDATE = 100\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\n print 'Reinforcement Learning Status:'\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\n if self.episodesSoFar <= self.numTraining:\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\n print '\\tCompleted %d out of %d training episodes' % (\n self.episodesSoFar,self.numTraining)\n print '\\tAverage Rewards over all training: %.2f' % (\n trainAvg)\n else:\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\n print '\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining)\n print '\\tAverage Rewards over testing: %.2f' % testAvg\n print '\\tAverage Rewards for last %d episodes: %.2f' % (\n NUM_EPS_UPDATE,windowAvg)\n print '\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime)\n self.lastWindowAccumRewards = 0.0\n self.episodeStartTime = time.time()\n\n if self.episodesSoFar == self.numTraining:\n msg = 'Training Done (turning off epsilon and alpha)'\n print '%s\\n%s' % (msg,'-' * len(msg))","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def assertion_started(self, func):","async def test_timings(hass: ha.HomeAssistant, skip_setup):\n\n for fname in glob.glob(test_config_dir + \"timing_*.yaml\"):\n print(f\"Processing: {fname}\")\n config = CONFIG_SCHEMA(load_yaml_config_file(fname))\n\n if ha.DOMAIN in config:\n await async_process_ha_core_config(hass, config[ha.DOMAIN])\n coordinator = IUCoordinator(hass).load(config[DOMAIN])\n\n next_time = dt_util.utcnow()\n interval = coordinator.track_interval()\n while not coordinator.tester._initialised or coordinator.tester.is_testing:\n await coordinator._async_timer(next_time)\n next_time += interval\n\n assert (\n coordinator.tester.total_events\n == coordinator.tester.total_checks\n == coordinator.tester.total_results\n )\n assert coordinator.tester.total_errors == 0\n print(\n \"Finished: {0}; events: {1}; checks: {2}; errors: {3}; time: {4:.2f}s\".format(\n fname,\n coordinator.tester.total_events,\n coordinator.tester.total_checks,\n coordinator.tester.total_errors,\n coordinator.tester.total_time,\n )\n )","def TableFinish(current_time, nextGroup_endTime, table_type):\n if (current_time in nextGroup_endTime.values()):\n for n in list(nextGroup_endTime.keys()):\n if current_time == int(nextGroup_endTime[n]):\n if len(table_type)==6:\n table_type[n].cleanTable()\n elif len(table_type)==4:\n table_type[n-6].cleanTable()\n elif len(table_type)==2:\n table_type[n-10].cleanTable()","def assert_history(self, rows):\r\n self.assertEqual(self.parse_rows(rows), self.read_history())","def test_same_unique_crash_type_with_same_state(self):\n self.testcases[0].security_flag = False\n self.testcases[0].crash_type = 'Timeout'\n self.testcases[0].crash_state = 'abcde'\n self.testcases[0].timestamp = datetime.datetime.utcfromtimestamp(0)\n self.testcases[1].security_flag = False\n self.testcases[1].crash_type = 'Timeout'\n self.testcases[1].crash_state = 'abcde'\n self.testcases[1].timestamp = datetime.datetime.utcfromtimestamp(1)\n\n for t in self.testcases:\n t.put()\n\n grouper.group_testcases()\n\n testcases = []\n for testcase_id in data_handler.get_open_testcase_id_iterator():\n testcases.append(data_handler.get_testcase_by_id(testcase_id))\n\n self.assertEqual(len(testcases), 1)\n self.assertEqual(testcases[0].group_id, 0)\n self.assertTrue(testcases[0].is_leader)","def test_noTimedEventsInsertion(self):\n self.test_noTimedEventsExtraction()\n self._testInsertion()","def timestep(self):\n self.survive()\n self.move()\n self.set_survival_probability(self.vital_var)\n self.reproduce()\n # self.save_frame()\n if self.time % self.save_state_frequency == 0:\n self.save_state(self.state_file.format(self.time))\n if self.time % self.save_stats_frequency == 0:\n self.write_stats()\n # self.connected_component()\n self.time += 1\n\n if self.pop_size > 0:\n return True\n else:\n return False","def test_eval_alarm(self):\n def get_state_update_value(h):\n \"\"\"\n \n \"\"\"\n oldstate = h.data['oldState']['stateValue']\n newstate = h.data['newState']['stateValue']\n querydate = h.data['newState']['stateReasonData']['queryDate']\n querydate = utils.parse_strtime(querydate)\n return oldstate, newstate, querydate \n \n test_uuid = str(uuid.uuid4())\n alarmname = \"TestEvalAlarm_\" + test_uuid\n metricname = \"TestEvalMetric_\" + test_uuid\n namespace = \"unittest\"\n unit = \"Percent\"\n dimensions = {\"test_id\":test_uuid}\n threshold = 2.0\n \n # create metric alarm\n alarm = MetricAlarm(name=alarmname, metric=metricname,\n namespace=namespace, statistic=\"Average\",\n comparison=\">\", threshold=threshold,\n period=60, evaluation_periods=1, unit=unit,\n dimensions=dimensions)\n self.synaps.put_metric_alarm(alarm)\n \n # due to put_metric_alarm is asynchronous\n time.sleep(ASYNC_WAIT)\n \n alarm_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0)\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold + 1, timestamp=alarm_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n\n ok_time = datetime.datetime.utcnow().replace(second=0,\n microsecond=0) \n self.synaps.put_metric_data(namespace=namespace, name=metricname,\n value=threshold - 2, timestamp=ok_time,\n unit=unit, dimensions=dimensions)\n\n time.sleep(60 * 5)\n \n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\n history_item_type=\"StateUpdate\")\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\n\n result = map(get_state_update_value, histories)\n \n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\n ('ALARM', 'INSUFFICIENT_DATA', None),\n ('INSUFFICIENT_DATA', 'OK', ok_time),\n ('OK', 'INSUFFICIENT_DATA', None))\n \n failmsg = \"expected: %s real: %s\" % (expected, result)\n \n self.assertEqual(len(result), len(expected), msg=failmsg)\n \n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\n expected):\n self.assertEqual(r_new, e_new, msg=failmsg)\n self.assertEqual(r_old, e_old, msg=failmsg)\n if e_time:\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\n msg=failmsg)\n \n self.synaps.delete_alarms(alarms=[alarmname])","def test_update_task_states(self):\r\n changed = self.combinedoe.update_task_states()\r\n self.assertFalse(changed)\r\n\r\n current_task = self.combinedoe.current_task\r\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\r\n changed = self.combinedoe.update_task_states()\r\n\r\n self.assertTrue(changed)","def verify_execution_results(state, collation):\n state.commit()\n\n validate_transaction_tree(collation)\n\n if collation.header.post_state_root != state.trie.root_hash:\n raise ValueError('State root mismatch: header %s computed %s' %\n (encode_hex(collation.header.post_state_root), encode_hex(state.trie.root_hash)))\n if collation.header.receipts_root != mk_receipt_sha(state.receipts):\n raise ValueError('Receipt root mismatch: header %s computed %s, computed %d, %d receipts' %\n (encode_hex(collation.header.receipts_root), encode_hex(mk_receipt_sha(state.receipts)),\n state.gas_used, len(state.receipts)))\n\n return True","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def get_raw_data(trace: Trace, cmdl_args):\n # Retrieves states dataframe with the intresting columns\n df_state = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.START.value,\n STATE_COLS.END.value, STATE_COLS.VAL.value]]\n\n # Computes the elapse times of each state\n df_state['el_time'] = df_state[STATE_COLS.END.value] - df_state[STATE_COLS.START.value]\n\n # Removes start and end columns from rows cause we don't need them. Load the data into memory.\n df_state = df_state.drop(columns=[STATE_COLS.START.value, STATE_COLS.END.value]).compute()\n\n # Computes runtime (in us)\n runtime = df_state.groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])['el_time'].sum().max() / 1000\n # Filters rows by useful and groups dataframe by process\n df_state_useful_grouped = df_state.loc[df_state[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])\n # Computes useful average time (in us)\n useful_av = df_state_useful_grouped['el_time'].sum().mean() / 1000\n # Computes useful max time (in us)\n useful_max = df_state_useful_grouped['el_time'].sum().max() / 1000\n # Computes useful tot time (in us)\n useful_tot = df_state_useful_grouped['el_time'].sum().sum() / 1000\n\n # Dimemas simulation for ideal times\n if cmdl_args.dimemas:\n runtime_id, useful_id = get_ideal_data(trace.metadata.path, len(trace.metadata.cpu_list))\n else:\n runtime_id = float('NaN')\n useful_id = float('NaN')\n\n\n # Loads only meaningful columns from df_states and filters useful rows\n df_state_useful = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.END.value, STATE_COLS.VAL.value]]\n df_state_useful = df_state_useful.loc[df_state_useful[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].drop(columns=STATE_COLS.VAL.value).compute()\n\n # Loads only meaningful columns from df_events\n df_event = trace.df_event[[EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value, EVENT_COLS.TIME.value,\n EVENT_COLS.EVTYPE.value, EVENT_COLS.EVVAL.value]]\n\n # Filters for PAPI_TOT_INS and set as index the process identifier\n df_event_ins = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_INS.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\n\n # Gets total useful instructions by grouping and applying a custom filtering function\n useful_ins = df_event_ins.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\n\n # Filter for PAPI_TOT_CYC and set as indexes the process identifier\n df_event_cyc = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_CYC.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\n\n # Gets total useful cycles by grouping and applying a custom filtering function\n useful_cyc= df_event_cyc.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\n\n # Computes average IPC\n try:\n ipc = useful_ins / useful_cyc\n except ValueError:\n ipc = float('NaN')\n # Computes average frequency\n try:\n freq = useful_cyc / useful_tot / 1000\n except ValueError:\n freq = float('NaN')\n\n return ipc, freq, runtime, runtime_id, useful_av, useful_max, useful_tot, useful_id, useful_ins, useful_cyc","def test_time_type_state_is_evening(day):\n\n assert day_time_info(day.hours_0).is_evening is False\n assert day_time_info(day.hours_1).is_evening is False\n assert day_time_info(day.hours_2).is_evening is False\n assert day_time_info(day.hours_3).is_evening is False\n assert day_time_info(day.hours_4).is_evening is False\n assert day_time_info(day.hours_5).is_evening is False\n assert day_time_info(day.hours_6).is_evening is False\n assert day_time_info(day.hours_7).is_evening is False\n assert day_time_info(day.hours_8).is_evening is False\n assert day_time_info(day.hours_9).is_evening is False\n assert day_time_info(day.hours_10).is_evening is False\n assert day_time_info(day.hours_11).is_evening is False\n assert day_time_info(day.hours_12).is_evening is False\n assert day_time_info(day.hours_13).is_evening is False\n assert day_time_info(day.hours_14).is_evening is False\n assert day_time_info(day.hours_15).is_evening is False\n assert day_time_info(day.hours_16).is_evening is False\n assert day_time_info(day.hours_17).is_evening is False\n assert day_time_info(day.hours_18).is_evening is True\n assert day_time_info(day.hours_19).is_evening is True\n assert day_time_info(day.hours_20).is_evening is True\n assert day_time_info(day.hours_21).is_evening is True\n assert day_time_info(day.hours_22).is_evening is True\n assert day_time_info(day.hours_23).is_evening is False","async def test_lazy_state_handles_same_last_updated_and_last_changed(\n caplog: pytest.LogCaptureFixture,\n) -> None:\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n row = PropertyMock(\n entity_id=\"sensor.valid\",\n state=\"off\",\n attributes='{\"shared\":true}',\n last_updated_ts=now.timestamp(),\n last_changed_ts=now.timestamp(),\n )\n lstate = LazyState(\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\n )\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n assert lstate.last_updated.timestamp() == row.last_updated_ts\n assert lstate.last_changed.timestamp() == row.last_changed_ts\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2021-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2021-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\n assert lstate.as_dict() == {\n \"attributes\": {\"shared\": True},\n \"entity_id\": \"sensor.valid\",\n \"last_changed\": \"2020-06-12T03:04:01.000323+00:00\",\n \"last_updated\": \"2020-06-12T03:04:01.000323+00:00\",\n \"state\": \"off\",\n }","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def state(self):\n retval = \"FINISHED\"\n for testrun in self.testruns:\n # we have to check for this because deleting testruns doesn't always delete them out of testrun groups\n if not isinstance(testrun, bson.DBRef):\n assert isinstance(testrun, Testrun)\n if testrun.state == \"TO_BE_RUN\":\n return \"TO_BE_RUN\" # lowest possible state, return immediately\n elif testrun.state == \"RUNNING\":\n retval = \"RUNNING\" # we don't have to worry about overwriting lower state TO_BE_RUN\n return retval","def test_post_processing_time():\r\n cmd = ShdlcCmdGetErrorState(clear=False)\r\n assert type(cmd.post_processing_time) is float\r\n assert cmd.post_processing_time == 0.0","def test_time_type_state_is_afternoon(day):\n\n assert day_time_info(day.hours_0).is_afternoon is False\n assert day_time_info(day.hours_1).is_afternoon is False\n assert day_time_info(day.hours_2).is_afternoon is False\n assert day_time_info(day.hours_3).is_afternoon is False\n assert day_time_info(day.hours_4).is_afternoon is False\n assert day_time_info(day.hours_5).is_afternoon is False\n assert day_time_info(day.hours_6).is_afternoon is False\n assert day_time_info(day.hours_7).is_afternoon is False\n assert day_time_info(day.hours_8).is_afternoon is False\n assert day_time_info(day.hours_9).is_afternoon is False\n assert day_time_info(day.hours_10).is_afternoon is False\n assert day_time_info(day.hours_11).is_afternoon is False\n assert day_time_info(day.hours_12).is_afternoon is False\n assert day_time_info(day.hours_13).is_afternoon is True\n assert day_time_info(day.hours_14).is_afternoon is True\n assert day_time_info(day.hours_15).is_afternoon is True\n assert day_time_info(day.hours_16).is_afternoon is True\n assert day_time_info(day.hours_17).is_afternoon is True\n assert day_time_info(day.hours_18).is_afternoon is False\n assert day_time_info(day.hours_19).is_afternoon is False\n assert day_time_info(day.hours_20).is_afternoon is False\n assert day_time_info(day.hours_21).is_afternoon is False\n assert day_time_info(day.hours_22).is_afternoon is False\n assert day_time_info(day.hours_23).is_afternoon is False","def test_same_unique_crash_type_with_different_state(self):\n self.testcases[0].security_flag = False\n self.testcases[0].crash_type = 'Timeout'\n self.testcases[0].crash_state = 'abcdef'\n self.testcases[1].security_flag = False\n self.testcases[1].crash_type = 'Timeout'\n self.testcases[1].crash_state = 'abcde'\n\n for t in self.testcases:\n t.put()\n\n grouper.group_testcases()\n\n for index, t in enumerate(self.testcases):\n self.testcases[index] = data_handler.get_testcase_by_id(t.key.id())\n self.assertEqual(self.testcases[index].group_id, 0)\n self.assertTrue(self.testcases[index].is_leader)","def event_m20_11_83000():\n \"\"\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\"\"\"\n assert event_m20_11_x53(z73=2011300, z74=2011399)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def test_runner_full_loop_big(caplog, big_dataset):\n caplog.set_level(logging.INFO)\n\n session = big_dataset\n\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 3)\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 6)\n replay_rate = 1 \n\n db_connector_test = DataBaseConnector(session=session, \n table_name='timeseries_dataset', \n time_column='timestamp', \n start_date=start_date,\n end_date=end_date)\n\n debug_publisher = FilePublisher()\n\n runner = CentralRunner(db_connection=db_connector_test, \n output_system=debug_publisher, \n start_time=start_date, \n end_time=end_date,\n replay_rate=replay_rate )\n\n start = time.perf_counter()\n \n runner.run()\n\n end = time.perf_counter()\n\n code_time = end - start\n assert int(code_time) == 2\n\n ## Test results\n assert os.path.exists('test_tmp/17-05-2020_13-00-03')\n assert os.path.exists('test_tmp/17-05-2020_13-00-05')\n\n assert len(os.listdir('test_tmp/17-05-2020_13-00-03')) == 9\n assert len(os.listdir('test_tmp/17-05-2020_13-00-05')) == 2"],"string":"[\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_processed_state_final_run(self):\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_FINAL_RUN,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=1).get_tree(any(str))\",\n \"def IsFinal(self):\\n return self.state in FINAL_TEST_RUN_STATES\",\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_run_ended(self):\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def finished_tests(self):\\n self.testing = 0\",\n \"def test_runner_with_db(dataset, time_start, time_diff):\\n\\n session = dataset\\n\\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\\n replay_rate = 1.0 \\n\\n db_connector_test = DataBaseConnector(session=session, \\n table_name='timeseries_dataset', \\n time_column='timestamp', \\n start_date=time_start,\\n end_date=end_date)\\n\\n runner = CentralRunner(db_connection=db_connector_test, \\n output_system='mock_output_systerm', \\n start_time=time_start, \\n end_time=end_date,\\n replay_rate=replay_rate )\\n\\n\\n results_test = [\\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 0), 'text': 'bob', 'value': 10.0},\\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'cat', 'value':-10.0},\\n {'timestamp': datetime.datetime(2021, 1, 1, 10, 1, 1), 'text': 'eat', 'value': 12.1}\\n ]\\n \\n # test that the trigger_release is working right\\n # expect 1\\n start = time.perf_counter()\\n \\n code_start = datetime.datetime.now()\\n\\n # we need to retink the way that we trigger this....\\n runner._trigger_release(result_set=results_test, code_start=code_start, replay_start_time=time_start, \\n batch=(datetime.datetime(2021, 1, 1, 10, 1, 0), datetime.datetime(2021, 1, 1, 10, 1, 1)), \\n replay_rate=replay_rate)\\n \\n end = time.perf_counter()\\n\\n code_time = end - start\\n assert int(code_time) == time_diff\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_state_processed(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_PROCESSED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_clean_run(self):\\n Historical_ROAs_Parsed_Table(clear=True)\\n with Historical_ROAs_Table(clear=True) as t:\\n Historical_ROAs_Parser().run()\\n assert t.get_count() > 2000000\",\n \"def test_is_finished(self):\\n experiment = Experiment(TasksMock())\\n self.assertEquals(False, experiment.is_finished())\\n for _ in range(0, 17):\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_down(time.time())\\n self.assertEquals(False, experiment.is_finished())\\n experiment.press_b_up(time.time())\\n self.assertEquals(True, experiment.is_finished())\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def test_output(self):\\n # reproducible arbitrariness\\n np.random.seed(123423)\\n\\n N = 20\\n tmax = 30.0\\n dt = 1.0\\n\\n n_steps = int_r(tmax/dt)\\n\\n table = np.random.randn(N, n_steps)\\n G = TableLayer(table)\\n\\n M = simulation.StateMonitor(G, 'out')\\n\\n sim = simulation.Simulation(G, M, dt=dt)\\n sim.run(tmax)\\n\\n self.assertLess(np.max(np.abs(M.out - table)), 1e-6)\",\n \"def test_update_state4(self):\\n pass\",\n \"def test_run(self):\\n class MockProvider(BaseCoverageProvider):\\n SERVICE_NAME = \\\"I do nothing\\\"\\n was_run = False\\n\\n def run_once_and_update_timestamp(self):\\n \\\"\\\"\\\"Set a variable.\\\"\\\"\\\"\\n self.was_run = True\\n return None\\n\\n provider = MockProvider(self._db)\\n result = provider.run()\\n\\n # run_once_and_update_timestamp() was called.\\n assert True == provider.was_run\\n\\n # run() returned a CoverageProviderProgress with basic\\n # timing information, since run_once_and_update_timestamp()\\n # didn't provide anything.\\n assert isinstance(result, CoverageProviderProgress)\\n now = utc_now()\\n assert result.start < result.finish\\n for time in (result.start, result.finish):\\n assert (now - time).total_seconds() < 5\",\n \"def test_completed():\\n assert complete == 1\\n assert errorflag == 0\",\n \"def test_update_state3(self):\\n pass\",\n \"def test_update_state(self):\\n pass\",\n \"def test_log_last_completed_datetime(self):\\n initial_count = CostUsageReportStatus.objects.count()\\n saver = ReportStatsDBAccessor(\\\"myreport\\\", self.manifest_id)\\n saver.log_last_completed_datetime()\\n self.assertIsNotNone(saver.get_last_completed_datetime())\\n saver.delete()\\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)\",\n \"def test_run_started(self):\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_9999_last(self):\\n self.lasttest = True\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def test_completed(self):\\n return False\",\n \"def get_exist_phase_timestamp(self):\\r\\n csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\\r\\n if not os.path.isfile(csv_path):\\r\\n print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\\r\\n exit(-1)\\r\\n converter = {'benchmarkPhase': str, 'streamNumber': int, 'queryNumber': int,\\r\\n 'epochStartTimestamp': 'int64', 'epochEndTimestamp': 'int64'}\\r\\n df = pd.read_csv(csv_path, delimiter=';').loc[:,\\r\\n ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\\r\\n phase_ts = OrderedDict()\\r\\n is_exist = False\\r\\n for phase in self.phase_name:\\r\\n benchmark_phase = (df['benchmarkPhase'] == phase)\\r\\n if any(benchmark_phase): # whether this phase exist in the BB logs\\r\\n if phase == 'POWER_TEST': # power test overall and each query\\r\\n stream_num = ((df['streamNumber']) == 0)\\r\\n mask = benchmark_phase & stream_num\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 2] = 0 # fill 0 to blank value\\r\\n elif phase == 'THROUGHPUT_TEST_1': # throughput test overall and each stream\\r\\n query_num = (pd.isnull(df['queryNumber']))\\r\\n mask = benchmark_phase & query_num\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 1:3] = -1 # file -1 to overall throughput test\\r\\n phase_ts[phase].iloc[1:, 2] = 0 # fill 0 to blank value\\r\\n else: # other phases\\r\\n stream_num = (pd.isnull(df['streamNumber']))\\r\\n query_num = (pd.isnull(df['queryNumber']))\\r\\n mask = benchmark_phase & stream_num & query_num\\r\\n # phase_start = line['epochStartTimestamp'].values / 1000\\r\\n # phase_end = line['epochEndTimestamp'].values / 1000\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 1:3] = 0 # fill 0 to blank value\\r\\n phase_ts[phase] = phase_ts[phase].astype(converter)\\r\\n is_exist = True\\r\\n if is_exist:\\r\\n return phase_ts\\r\\n else:\\r\\n print('It seems BigBenchTimes.csv in {0} does not include any TPCx-BB phases, ' \\\\\\r\\n 'existing...'.format(self.bb_log_path))\\r\\n exit(-1)\",\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_update_state2(self):\\n pass\",\n \"def test_service_initiated():\\n assert \\\"ready\\\" in bkt_outcome_unwind.index()\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def validation_epoch_end(self, validation_step_outputs):\\n self.epoch_end_toc = time()\\n logger.info(f\\\"This epoch took {int(self.epoch_end_toc - self.epoch_start_tic)} s\\\")\\n return super().validation_epoch_end(validation_step_outputs)\",\n \"def test_initialization_of_homework_result_created():\\n expected = datetime.datetime.now()\\n assert result_1.created - expected < datetime.timedelta(seconds=1)\",\n \"def test_taskrun_updates_task_state(self):\\r\\n app = AppFactory.create()\\r\\n task = TaskFactory.create(app=app, n_answers=2)\\r\\n url = '/api/taskrun?api_key=%s' % app.owner.api_key\\r\\n\\r\\n # Post first taskrun\\r\\n data = dict(\\r\\n app_id=task.app_id,\\r\\n task_id=task.id,\\r\\n info='my task result')\\r\\n datajson = json.dumps(data)\\r\\n tmp = self.app.post(url, data=datajson)\\r\\n r_taskrun = json.loads(tmp.data)\\r\\n\\r\\n assert tmp.status_code == 200, r_taskrun\\r\\n err_msg = \\\"Task state should be different from completed\\\"\\r\\n assert task.state == 'ongoing', err_msg\\r\\n\\r\\n # Post second taskrun\\r\\n url = '/api/taskrun'\\r\\n data = dict(\\r\\n app_id=task.app_id,\\r\\n task_id=task.id,\\r\\n info='my task result anon')\\r\\n datajson = json.dumps(data)\\r\\n tmp = self.app.post(url, data=datajson)\\r\\n r_taskrun = json.loads(tmp.data)\\r\\n\\r\\n assert tmp.status_code == 200, r_taskrun\\r\\n err_msg = \\\"Task state should be equal to completed\\\"\\r\\n assert task.state == 'completed', err_msg\",\n \"def test_rt_table(self) -> None:\\n expected = Fixtures.next_table()\\n expected.description = '\\\"hello!\\\" said no one'\\n expected.tags.sort()\\n\\n self.get_proxy().put_table(table=expected)\\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\\n actual.last_updated_timestamp = None\\n actual.tags.sort()\\n\\n self.assertEqual(expected, actual)\",\n \"def test_update_splits():\\n assert new_log.iloc[0]['start'] == 0\",\n \"def test_update_state1(self):\\n pass\",\n \"def on_test_epoch_end(self) -> None:\\n self.log_dict(self.test_metrics.compute())\\n self.test_metrics.reset()\",\n \"def test_run_date(self):\\n Historical_ROAs_Parsed_Table(clear=True)\\n\\n date = '2019-08-01'\\n \\n with Historical_ROAs_Table(clear=True) as t:\\n Historical_ROAs_Parser().run(datetime.strptime(date, '%Y-%m-%d'))\\n sql = f\\\"SELECT COUNT(*) FROM {t.name} WHERE date_added = '{date}'\\\"\\n \\n # unless ROAs are retroactively added, this should be constant\\n assert t.get_count(sql) == 95743\",\n \"def __exit__(self, _type, value, traceback):\\n try:\\n if not self.record_finish:\\n return\\n print >>sys.stderr, 'record', self.record\\n dutset = {'last_finish_time':time()}\\n if not self.record:\\n return\\n upd = {'end_time': time(), 'modification_time':time()}\\n\\n if value: # i.e. , if test failed:\\n upd['failure'] = repr(value)\\n upd['exception'] = value.__class__.__name__\\n if not isinstance(value, KeyboardInterrupt):\\n print 'HEADLINE: exception', upd['exception'], value\\n for clause in format_exception(_type, value, traceback):\\n for line in clause.split('\\\\n'):\\n print 'CRASH:', line\\n else:\\n upd['infrastructure_problem'] = True\\n upd['whiteboard'] = '[infrastructure] test interrupted'\\n if self.reinstall_on_failure:\\n dutset['test_failed'] = True\\n tnext = time() + 300\\n print 'INFO: test failed, so will reinstall machine at', \\\\\\n asctime(localtime(tnext))\\n\\n if self.failed: #some test suite failed\\n upd['failure'] = 'test failed'\\n\\n self.mdb.results.update({'_id':self.result_id}, {'$set':upd})\\n classify = process_result(self.mdb.results.find_one({'_id':self.result_id}))\\n print 'HEADLINE:', classify, self.full_description()\\n\\n get_track().updates.save({'result_id':self.result_id,\\n 'action':'experiment finished'})\\n\\n if self.dut_id:\\n self.mdb.duts.update({'_id':self.dut_id}, \\n {'$unset': {'control_pid':1, 'result_id':1,\\n 'control_command_line':1},\\n '$set': dutset})\\n if self.build:\\n recount(self.build)\\n if classify == 'infrastructure_problems':\\n pass\\n else:\\n col = 'green' if classify == 'passes' else 'red'\\n finally:\\n if self.record_queue:\\n self.record_queue.put('finish')\\n self.record_queue.close()\\n self.record_queue.join_thread()\\n if self.stream_process:\\n self.stream_process.join()\\n if self.stdout_filter:\\n self.stdout_filter.del_callback(self)\",\n \"def test_log_last_started_datetime(self):\\n initial_count = CostUsageReportStatus.objects.count()\\n saver = ReportStatsDBAccessor(\\\"myreport\\\", self.manifest_id)\\n saver.log_last_started_datetime()\\n self.assertIsNotNone(saver.get_last_started_datetime())\\n saver.delete()\\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)\",\n \"def test_full_house_flush_ind(self):\",\n \"def test_snapshot(self):\\n array = np.arange(1000)\\n df = DataFlow.from_numpy(array).shuffle().snapshot()\\n with DataFlowContext(df).as_default() as ctx:\\n # shuffled data flow should be ready after df.shuffle()\\n array2 = df.all()[0]\\n self.assertFalse(np.all(array2 == array))\\n np.testing.assert_array_equal(np.sort(array2), array)\\n\\n # after each reset_epoch, the snapshot data flow should be same\\n ctx.reset_epoch()\\n array3 = df.all()[0]\\n self.assertFalse(np.all(array3 == array))\\n np.testing.assert_array_equal(array3, array2)\",\n \"def test_runner_full_loop(caplog, dataset):\\n caplog.set_level(logging.DEBUG)\\n\\n session = dataset\\n\\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 0)\\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 5)\\n replay_rate = 1 \\n \\n db_connector_test = DataBaseConnector(session=session, \\n table_name='timeseries_dataset', \\n time_column='timestamp', \\n start_date=start_date,\\n end_date=end_date)\\n\\n test_publisher = ConsolePublisher()\\n\\n runner = CentralRunner(db_connection=db_connector_test, \\n output_system=test_publisher, \\n start_time=start_date, \\n end_time=end_date,\\n replay_rate=replay_rate )\\n\\n start = time.perf_counter()\\n \\n runner.run()\\n\\n end = time.perf_counter()\\n\\n code_time = end - start\\n \\n print(code_time)\\n \\n assert int(code_time) == 4\",\n \"def test_retrospective_the_prospective_checkpointed_timestamps(\\n self, test_type, stage_pair, pm\\n ):\\n\\n stage1, stage2 = stage_pair\\n pm.timestamp(checkpoint=stage1, finished=True)\\n assert stage1 == pm.prev_checkpoint\\n assert pm.curr_checkpoint is None\\n pm.timestamp(checkpoint=stage2, finished=False)\\n\\n if test_type == FILES_TEST:\\n expected = [checkpoint_filepath(stage1, pm)]\\n assert set(expected) == set(fetch_checkpoint_files(pm))\\n else:\\n assert pm.prev_checkpoint is None\\n assert stage2 == pm.curr_checkpoint\",\n \"def testing_phase(self):\\r\\n self.test_dataloader = self.get_dataloader(self.test_h5_path, \\\"test set\\\")\\r\\n self.get_ts_properties()\\r\\n\\r\\n self.restart_epoch = util.get_restart_epoch()\\r\\n print(f\\\"* Loading model from previous saved state (Epoch {self.restart_epoch}).\\\", flush=True)\\r\\n self.model = torch.load(\\r\\n self.C.job_dir + f\\\"model_restart_{self.restart_epoch}.pth\\\"\\r\\n )\\r\\n\\r\\n self.model.eval()\\r\\n with torch.no_grad():\\r\\n self.generate_graphs(n_samples=self.C.n_samples)\\r\\n\\r\\n print(\\\"* Evaluating model.\\\", flush=True)\\r\\n anal.evaluate_model(valid_dataloader=self.test_dataloader,\\r\\n train_dataloader=self.train_dataloader,\\r\\n nll_per_action=self.nll_per_action,\\r\\n model=self.model)\\r\\n\\r\\n self.print_time_elapsed()\",\n \"def on_test_batch_end(self, batch, logs=None):\",\n \"def test_epoch_end(self, outputs: Any) -> None:\\n self.log_dict(self.test_metrics.compute())\\n self.test_metrics.reset()\",\n \"def test_update_team_state(self):\\n pass\",\n \"def test_prospective_then_retrospective_checkpointed_timestamps(\\n self, test_type, stage_pair, pm\\n ):\\n\\n stage1, stage2 = stage_pair\\n pm.timestamp(checkpoint=stage1, finished=False)\\n assert stage1 == pm.curr_checkpoint\\n pm.timestamp(checkpoint=stage2, finished=True)\\n\\n if test_type == FILES_TEST:\\n checkpoint_files = fetch_checkpoint_files(pm)\\n expected = [checkpoint_filepath(stage2, pm)]\\n assert set(expected) == set(checkpoint_files)\\n else:\\n # Current checkpoint will be reset by second (retrospective)\\n # timestamp call.\\n assert stage2 == pm.prev_checkpoint\\n assert pm.curr_checkpoint is None\",\n \"def test_list_runs(self):\\n pass\",\n \"def test_has_ended(self):\\r\\n self.assertTrue(self.past_show_certs.has_ended())\\r\\n self.assertTrue(self.past_noshow_certs.has_ended())\\r\\n self.assertFalse(self.future_show_certs.has_ended())\\r\\n self.assertFalse(self.future_noshow_certs.has_ended())\",\n \"def emit(self, record):\\n self.eng.dispose()\\n trys = 3\\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\\\"__\\\")}\\n for t in range(trys):\\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\\n failed = False\\n try:\\n with self.eng.connect() as con:\\n # formating values to convert from python or parsl to db standards\\n info['task_fail_history'] = str(info.get('task_fail_history', None))\\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\\n run_id = info['run_id']\\n\\n # if workflow or task has completed, update their entries with the time.\\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n return\\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\\n workflow = self.meta.tables['task']\\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\\\\n .where(workflow.c.run_id == run_id)\\n con.execute(up)\\n\\n # create workflows table if this is a new database without one\\n if 'workflows' not in self.meta.tables.keys():\\n workflows = create_workflows_table(self.meta)\\n self.meta.create_all(con)\\n # if this is the first sight of the workflow, add it to the workflows table\\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\\n workflows = self.meta.tables['workflows']\\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\\n con.execute(ins)\\n\\n # if log has task counts, update the workflow entry in the workflows table\\n if 'tasks_completed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n if 'tasks_failed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n\\n # create task table if this is a new run without one\\n if 'task' not in self.meta.tables.keys():\\n workflow = create_task_table(self.meta)\\n self.meta.create_all(con)\\n\\n # check to make sure it is a task log and not just a workflow overview log\\n if info.get('task_id', None) is not None:\\n # if this is the first sight of the task in the workflow, add it to the workflow table\\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\\n if 'psutil_process_pid' in info.keys():\\n # this is the race condition that a resource log is before a status log so ignore this resource update\\n return\\n\\n workflow = self.meta.tables['task']\\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\\n con.execute(ins)\\n\\n if 'task_status' in info.keys():\\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\\n if 'task_status' not in self.meta.tables.keys():\\n task_status_table = create_task_status_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n # if this status table already exists, just insert the update\\n else:\\n task_status_table = self.meta.tables['task_status']\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n return\\n\\n if 'psutil_process_pid' in info.keys():\\n # if this is a task resource update then handle that, if the resource table DNE then create it\\n if 'task_resources' not in self.meta.tables.keys():\\n task_resource_table = create_task_resource_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n # if this resource table already exists, just insert the update\\n else:\\n task_resource_table = self.meta.tables['task_resources']\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n return\\n\\n except Exception as e:\\n logger.error(\\\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\\\".format(t, str(e)))\\n failed = True\\n time.sleep(5)\\n if not failed:\\n return\",\n \"def end_of_test_epoch_hook(self):\\n pass\",\n \"def test_experiment(experiment):\\n run = experiment.run()\\n assert run.status == 'COMPLETED'\",\n \"def test_total_time_no_end_time(time_record_factory):\\n d = datetime.datetime(2018, 10, 1, 15, 26)\\n t = time_record_factory(time_start=d, time_end=None)\\n expected = datetime.timedelta(0)\\n assert t.total_time == expected\",\n \"def test_validate_self_final_state_transitions(self):\\n with nose.assert_raises(exceptions.FinalStateError):\\n self.dtm1.transitions['q4'] = {'0': ('q4', '0', 'L')}\\n self.dtm1.validate_self()\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_get_last_tracking_insertation_():\\n insert_habit()\\n insert_day()\\n trackings_table = analytics.trackings_table()\\n assert trackings_table[60][:-1] == (6, str(date.today()))\\n delete_habit()\",\n \"def done(self):\\n log.debug(\\\"Test run concluded\\\")\\n if self._startTime is not None:\\n self.report['startTime'] = self._startTime\\n self.report['runTime'] = time.time() - self._startTime\\n self.report['testsRun'] = self.testsRun\\n self.report['tests'] = self._tests\\n self.writeReport()\",\n \"def on_test_end(self, logs=None):\",\n \"def event_m20_11_6000():\\n \\\"\\\"\\\"State 0,2: [Preset] Door of the living person_SubState\\\"\\\"\\\"\\n assert event_m20_11_x77(z42=20110480, z43=600000)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def has_finished():\",\n \"def test_queryset_total_time(time_record_factory):\\n now = timezone.now()\\n later = now + datetime.timedelta(hours=1)\\n\\n time_record_factory(time_end=later, time_start=now)\\n time_record_factory(time_end=later, time_start=now)\\n\\n expected = sum(\\n (record.delta for record in models.TimeRecord.objects.with_deltas()),\\n datetime.timedelta(0),\\n )\\n\\n assert models.TimeRecord.objects.total_time() == expected\",\n \"def testTimestamps(self):\\n predicate = \\\"metadata:predicate\\\"\\n subject = \\\"aff4:/metadata:8\\\"\\n\\n # Extend the range of valid timestamps returned from the table to account\\n # for potential clock skew.\\n start = long(time.time() - 60) * 1e6\\n data_store.DB.Set(subject, predicate, \\\"1\\\", token=self.token)\\n\\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\\n\\n # Check the time is reasonable\\n end = long(time.time() + 60) * 1e6\\n\\n self.assert_(ts >= start and ts <= end)\\n self.assertEqual(stored, \\\"1\\\")\",\n \"def test_sequence_done(self):\\n self.t(\\\"1,2 done\\\")\\n code, out, err = self.t(\\\"_get 1.status 2.status\\\")\\n self.assertEqual(\\\"completed completed\\\\n\\\", out)\",\n \"def newTestRun(self):\\n sql = \\\"INSERT INTO testrun VALUES (NULL, %s, %s)\\\"\\n self.cursor.execute(sql, (self.timestamp, self.server))\\n self.db.commit()\\n self.testid = self.cursor.lastrowid\",\n \"def test_archive_commitlog_point_in_time(self):\\n self.run_archive_commitlog(restore_point_in_time=True)\",\n \"def test_total_time_with_start_and_end_times(time_record_factory):\\n ts = datetime.datetime(2018, 10, 1, 9, 0)\\n te = datetime.datetime(2018, 10, 1, 17, 0)\\n\\n t = time_record_factory(time_start=ts, time_end=te)\\n\\n expected = te-ts\\n\\n assert t.total_time == expected\",\n \"def final(self, state):\\n deltaReward = state.getScore() - self.lastState.getScore()\\n self.observeTransition(self.lastState, self.lastAction, state, deltaReward)\\n self.stopEpisode()\\n\\n # Make sure we have this var\\n if not 'episodeStartTime' in self.__dict__:\\n self.episodeStartTime = time.time()\\n if not 'lastWindowAccumRewards' in self.__dict__:\\n self.lastWindowAccumRewards = 0.0\\n self.lastWindowAccumRewards += state.getScore()\\n\\n NUM_EPS_UPDATE = 100\\n if self.episodesSoFar % NUM_EPS_UPDATE == 0:\\n print 'Reinforcement Learning Status:'\\n windowAvg = self.lastWindowAccumRewards / float(NUM_EPS_UPDATE)\\n if self.episodesSoFar <= self.numTraining:\\n trainAvg = self.accumTrainRewards / float(self.episodesSoFar)\\n print '\\\\tCompleted %d out of %d training episodes' % (\\n self.episodesSoFar,self.numTraining)\\n print '\\\\tAverage Rewards over all training: %.2f' % (\\n trainAvg)\\n else:\\n testAvg = float(self.accumTestRewards) / (self.episodesSoFar - self.numTraining)\\n print '\\\\tCompleted %d test episodes' % (self.episodesSoFar - self.numTraining)\\n print '\\\\tAverage Rewards over testing: %.2f' % testAvg\\n print '\\\\tAverage Rewards for last %d episodes: %.2f' % (\\n NUM_EPS_UPDATE,windowAvg)\\n print '\\\\tEpisode took %.2f seconds' % (time.time() - self.episodeStartTime)\\n self.lastWindowAccumRewards = 0.0\\n self.episodeStartTime = time.time()\\n\\n if self.episodesSoFar == self.numTraining:\\n msg = 'Training Done (turning off epsilon and alpha)'\\n print '%s\\\\n%s' % (msg,'-' * len(msg))\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def assertion_started(self, func):\",\n \"async def test_timings(hass: ha.HomeAssistant, skip_setup):\\n\\n for fname in glob.glob(test_config_dir + \\\"timing_*.yaml\\\"):\\n print(f\\\"Processing: {fname}\\\")\\n config = CONFIG_SCHEMA(load_yaml_config_file(fname))\\n\\n if ha.DOMAIN in config:\\n await async_process_ha_core_config(hass, config[ha.DOMAIN])\\n coordinator = IUCoordinator(hass).load(config[DOMAIN])\\n\\n next_time = dt_util.utcnow()\\n interval = coordinator.track_interval()\\n while not coordinator.tester._initialised or coordinator.tester.is_testing:\\n await coordinator._async_timer(next_time)\\n next_time += interval\\n\\n assert (\\n coordinator.tester.total_events\\n == coordinator.tester.total_checks\\n == coordinator.tester.total_results\\n )\\n assert coordinator.tester.total_errors == 0\\n print(\\n \\\"Finished: {0}; events: {1}; checks: {2}; errors: {3}; time: {4:.2f}s\\\".format(\\n fname,\\n coordinator.tester.total_events,\\n coordinator.tester.total_checks,\\n coordinator.tester.total_errors,\\n coordinator.tester.total_time,\\n )\\n )\",\n \"def TableFinish(current_time, nextGroup_endTime, table_type):\\n if (current_time in nextGroup_endTime.values()):\\n for n in list(nextGroup_endTime.keys()):\\n if current_time == int(nextGroup_endTime[n]):\\n if len(table_type)==6:\\n table_type[n].cleanTable()\\n elif len(table_type)==4:\\n table_type[n-6].cleanTable()\\n elif len(table_type)==2:\\n table_type[n-10].cleanTable()\",\n \"def assert_history(self, rows):\\r\\n self.assertEqual(self.parse_rows(rows), self.read_history())\",\n \"def test_same_unique_crash_type_with_same_state(self):\\n self.testcases[0].security_flag = False\\n self.testcases[0].crash_type = 'Timeout'\\n self.testcases[0].crash_state = 'abcde'\\n self.testcases[0].timestamp = datetime.datetime.utcfromtimestamp(0)\\n self.testcases[1].security_flag = False\\n self.testcases[1].crash_type = 'Timeout'\\n self.testcases[1].crash_state = 'abcde'\\n self.testcases[1].timestamp = datetime.datetime.utcfromtimestamp(1)\\n\\n for t in self.testcases:\\n t.put()\\n\\n grouper.group_testcases()\\n\\n testcases = []\\n for testcase_id in data_handler.get_open_testcase_id_iterator():\\n testcases.append(data_handler.get_testcase_by_id(testcase_id))\\n\\n self.assertEqual(len(testcases), 1)\\n self.assertEqual(testcases[0].group_id, 0)\\n self.assertTrue(testcases[0].is_leader)\",\n \"def test_noTimedEventsInsertion(self):\\n self.test_noTimedEventsExtraction()\\n self._testInsertion()\",\n \"def timestep(self):\\n self.survive()\\n self.move()\\n self.set_survival_probability(self.vital_var)\\n self.reproduce()\\n # self.save_frame()\\n if self.time % self.save_state_frequency == 0:\\n self.save_state(self.state_file.format(self.time))\\n if self.time % self.save_stats_frequency == 0:\\n self.write_stats()\\n # self.connected_component()\\n self.time += 1\\n\\n if self.pop_size > 0:\\n return True\\n else:\\n return False\",\n \"def test_eval_alarm(self):\\n def get_state_update_value(h):\\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\\"\\n oldstate = h.data['oldState']['stateValue']\\n newstate = h.data['newState']['stateValue']\\n querydate = h.data['newState']['stateReasonData']['queryDate']\\n querydate = utils.parse_strtime(querydate)\\n return oldstate, newstate, querydate \\n \\n test_uuid = str(uuid.uuid4())\\n alarmname = \\\"TestEvalAlarm_\\\" + test_uuid\\n metricname = \\\"TestEvalMetric_\\\" + test_uuid\\n namespace = \\\"unittest\\\"\\n unit = \\\"Percent\\\"\\n dimensions = {\\\"test_id\\\":test_uuid}\\n threshold = 2.0\\n \\n # create metric alarm\\n alarm = MetricAlarm(name=alarmname, metric=metricname,\\n namespace=namespace, statistic=\\\"Average\\\",\\n comparison=\\\">\\\", threshold=threshold,\\n period=60, evaluation_periods=1, unit=unit,\\n dimensions=dimensions)\\n self.synaps.put_metric_alarm(alarm)\\n \\n # due to put_metric_alarm is asynchronous\\n time.sleep(ASYNC_WAIT)\\n \\n alarm_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0)\\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold + 1, timestamp=alarm_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n\\n ok_time = datetime.datetime.utcnow().replace(second=0,\\n microsecond=0) \\n self.synaps.put_metric_data(namespace=namespace, name=metricname,\\n value=threshold - 2, timestamp=ok_time,\\n unit=unit, dimensions=dimensions)\\n\\n time.sleep(60 * 5)\\n \\n histories = self.synaps.describe_alarm_history(alarm_name=alarmname,\\n history_item_type=\\\"StateUpdate\\\")\\n histories.sort(cmp=lambda a, b: cmp(a.timestamp, b.timestamp))\\n\\n result = map(get_state_update_value, histories)\\n \\n expected = (('INSUFFICIENT_DATA', 'ALARM', alarm_time),\\n ('ALARM', 'INSUFFICIENT_DATA', None),\\n ('INSUFFICIENT_DATA', 'OK', ok_time),\\n ('OK', 'INSUFFICIENT_DATA', None))\\n \\n failmsg = \\\"expected: %s real: %s\\\" % (expected, result)\\n \\n self.assertEqual(len(result), len(expected), msg=failmsg)\\n \\n for ((r_new, r_old, r_time), (e_new, e_old, e_time)) in zip(result,\\n expected):\\n self.assertEqual(r_new, e_new, msg=failmsg)\\n self.assertEqual(r_old, e_old, msg=failmsg)\\n if e_time:\\n self.assertTrue((r_time - e_time) < timedelta(seconds=300),\\n msg=failmsg)\\n \\n self.synaps.delete_alarms(alarms=[alarmname])\",\n \"def test_update_task_states(self):\\r\\n changed = self.combinedoe.update_task_states()\\r\\n self.assertFalse(changed)\\r\\n\\r\\n current_task = self.combinedoe.current_task\\r\\n current_task.change_state(CombinedOpenEndedV1Module.DONE)\\r\\n changed = self.combinedoe.update_task_states()\\r\\n\\r\\n self.assertTrue(changed)\",\n \"def verify_execution_results(state, collation):\\n state.commit()\\n\\n validate_transaction_tree(collation)\\n\\n if collation.header.post_state_root != state.trie.root_hash:\\n raise ValueError('State root mismatch: header %s computed %s' %\\n (encode_hex(collation.header.post_state_root), encode_hex(state.trie.root_hash)))\\n if collation.header.receipts_root != mk_receipt_sha(state.receipts):\\n raise ValueError('Receipt root mismatch: header %s computed %s, computed %d, %d receipts' %\\n (encode_hex(collation.header.receipts_root), encode_hex(mk_receipt_sha(state.receipts)),\\n state.gas_used, len(state.receipts)))\\n\\n return True\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def get_raw_data(trace: Trace, cmdl_args):\\n # Retrieves states dataframe with the intresting columns\\n df_state = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.START.value,\\n STATE_COLS.END.value, STATE_COLS.VAL.value]]\\n\\n # Computes the elapse times of each state\\n df_state['el_time'] = df_state[STATE_COLS.END.value] - df_state[STATE_COLS.START.value]\\n\\n # Removes start and end columns from rows cause we don't need them. Load the data into memory.\\n df_state = df_state.drop(columns=[STATE_COLS.START.value, STATE_COLS.END.value]).compute()\\n\\n # Computes runtime (in us)\\n runtime = df_state.groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])['el_time'].sum().max() / 1000\\n # Filters rows by useful and groups dataframe by process\\n df_state_useful_grouped = df_state.loc[df_state[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].groupby([STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value])\\n # Computes useful average time (in us)\\n useful_av = df_state_useful_grouped['el_time'].sum().mean() / 1000\\n # Computes useful max time (in us)\\n useful_max = df_state_useful_grouped['el_time'].sum().max() / 1000\\n # Computes useful tot time (in us)\\n useful_tot = df_state_useful_grouped['el_time'].sum().sum() / 1000\\n\\n # Dimemas simulation for ideal times\\n if cmdl_args.dimemas:\\n runtime_id, useful_id = get_ideal_data(trace.metadata.path, len(trace.metadata.cpu_list))\\n else:\\n runtime_id = float('NaN')\\n useful_id = float('NaN')\\n\\n\\n # Loads only meaningful columns from df_states and filters useful rows\\n df_state_useful = trace.df_state[[STATE_COLS.APP.value, STATE_COLS.TASK.value, STATE_COLS.THREAD.value, STATE_COLS.END.value, STATE_COLS.VAL.value]]\\n df_state_useful = df_state_useful.loc[df_state_useful[STATE_COLS.VAL.value] == STATE_VALUES.RUNNING.value].drop(columns=STATE_COLS.VAL.value).compute()\\n\\n # Loads only meaningful columns from df_events\\n df_event = trace.df_event[[EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value, EVENT_COLS.TIME.value,\\n EVENT_COLS.EVTYPE.value, EVENT_COLS.EVVAL.value]]\\n\\n # Filters for PAPI_TOT_INS and set as index the process identifier\\n df_event_ins = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_INS.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\\n\\n # Gets total useful instructions by grouping and applying a custom filtering function\\n useful_ins = df_event_ins.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\\n\\n # Filter for PAPI_TOT_CYC and set as indexes the process identifier\\n df_event_cyc = df_event.loc[df_event[EVENT_COLS.EVTYPE.value] == EVENT_VALUES.PAPI_TOT_CYC.value].drop(columns=EVENT_COLS.EVTYPE.value).compute().set_index([EVENT_COLS.APP.value, EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value])\\n\\n # Gets total useful cycles by grouping and applying a custom filtering function\\n useful_cyc= df_event_cyc.groupby([EVENT_COLS.APP.value,EVENT_COLS.TASK.value, EVENT_COLS.THREAD.value]).apply(is_useful, useful_states=df_state_useful)[EVENT_COLS.EVVAL.value].sum()\\n\\n # Computes average IPC\\n try:\\n ipc = useful_ins / useful_cyc\\n except ValueError:\\n ipc = float('NaN')\\n # Computes average frequency\\n try:\\n freq = useful_cyc / useful_tot / 1000\\n except ValueError:\\n freq = float('NaN')\\n\\n return ipc, freq, runtime, runtime_id, useful_av, useful_max, useful_tot, useful_id, useful_ins, useful_cyc\",\n \"def test_time_type_state_is_evening(day):\\n\\n assert day_time_info(day.hours_0).is_evening is False\\n assert day_time_info(day.hours_1).is_evening is False\\n assert day_time_info(day.hours_2).is_evening is False\\n assert day_time_info(day.hours_3).is_evening is False\\n assert day_time_info(day.hours_4).is_evening is False\\n assert day_time_info(day.hours_5).is_evening is False\\n assert day_time_info(day.hours_6).is_evening is False\\n assert day_time_info(day.hours_7).is_evening is False\\n assert day_time_info(day.hours_8).is_evening is False\\n assert day_time_info(day.hours_9).is_evening is False\\n assert day_time_info(day.hours_10).is_evening is False\\n assert day_time_info(day.hours_11).is_evening is False\\n assert day_time_info(day.hours_12).is_evening is False\\n assert day_time_info(day.hours_13).is_evening is False\\n assert day_time_info(day.hours_14).is_evening is False\\n assert day_time_info(day.hours_15).is_evening is False\\n assert day_time_info(day.hours_16).is_evening is False\\n assert day_time_info(day.hours_17).is_evening is False\\n assert day_time_info(day.hours_18).is_evening is True\\n assert day_time_info(day.hours_19).is_evening is True\\n assert day_time_info(day.hours_20).is_evening is True\\n assert day_time_info(day.hours_21).is_evening is True\\n assert day_time_info(day.hours_22).is_evening is True\\n assert day_time_info(day.hours_23).is_evening is False\",\n \"async def test_lazy_state_handles_same_last_updated_and_last_changed(\\n caplog: pytest.LogCaptureFixture,\\n) -> None:\\n now = datetime(2021, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n row = PropertyMock(\\n entity_id=\\\"sensor.valid\\\",\\n state=\\\"off\\\",\\n attributes='{\\\"shared\\\":true}',\\n last_updated_ts=now.timestamp(),\\n last_changed_ts=now.timestamp(),\\n )\\n lstate = LazyState(\\n row, {}, None, row.entity_id, row.state, row.last_updated_ts, False\\n )\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n assert lstate.last_updated.timestamp() == row.last_updated_ts\\n assert lstate.last_changed.timestamp() == row.last_changed_ts\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_updated = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2021-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\\n lstate.last_changed = datetime(2020, 6, 12, 3, 4, 1, 323, tzinfo=dt_util.UTC)\\n assert lstate.as_dict() == {\\n \\\"attributes\\\": {\\\"shared\\\": True},\\n \\\"entity_id\\\": \\\"sensor.valid\\\",\\n \\\"last_changed\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"last_updated\\\": \\\"2020-06-12T03:04:01.000323+00:00\\\",\\n \\\"state\\\": \\\"off\\\",\\n }\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def state(self):\\n retval = \\\"FINISHED\\\"\\n for testrun in self.testruns:\\n # we have to check for this because deleting testruns doesn't always delete them out of testrun groups\\n if not isinstance(testrun, bson.DBRef):\\n assert isinstance(testrun, Testrun)\\n if testrun.state == \\\"TO_BE_RUN\\\":\\n return \\\"TO_BE_RUN\\\" # lowest possible state, return immediately\\n elif testrun.state == \\\"RUNNING\\\":\\n retval = \\\"RUNNING\\\" # we don't have to worry about overwriting lower state TO_BE_RUN\\n return retval\",\n \"def test_post_processing_time():\\r\\n cmd = ShdlcCmdGetErrorState(clear=False)\\r\\n assert type(cmd.post_processing_time) is float\\r\\n assert cmd.post_processing_time == 0.0\",\n \"def test_time_type_state_is_afternoon(day):\\n\\n assert day_time_info(day.hours_0).is_afternoon is False\\n assert day_time_info(day.hours_1).is_afternoon is False\\n assert day_time_info(day.hours_2).is_afternoon is False\\n assert day_time_info(day.hours_3).is_afternoon is False\\n assert day_time_info(day.hours_4).is_afternoon is False\\n assert day_time_info(day.hours_5).is_afternoon is False\\n assert day_time_info(day.hours_6).is_afternoon is False\\n assert day_time_info(day.hours_7).is_afternoon is False\\n assert day_time_info(day.hours_8).is_afternoon is False\\n assert day_time_info(day.hours_9).is_afternoon is False\\n assert day_time_info(day.hours_10).is_afternoon is False\\n assert day_time_info(day.hours_11).is_afternoon is False\\n assert day_time_info(day.hours_12).is_afternoon is False\\n assert day_time_info(day.hours_13).is_afternoon is True\\n assert day_time_info(day.hours_14).is_afternoon is True\\n assert day_time_info(day.hours_15).is_afternoon is True\\n assert day_time_info(day.hours_16).is_afternoon is True\\n assert day_time_info(day.hours_17).is_afternoon is True\\n assert day_time_info(day.hours_18).is_afternoon is False\\n assert day_time_info(day.hours_19).is_afternoon is False\\n assert day_time_info(day.hours_20).is_afternoon is False\\n assert day_time_info(day.hours_21).is_afternoon is False\\n assert day_time_info(day.hours_22).is_afternoon is False\\n assert day_time_info(day.hours_23).is_afternoon is False\",\n \"def test_same_unique_crash_type_with_different_state(self):\\n self.testcases[0].security_flag = False\\n self.testcases[0].crash_type = 'Timeout'\\n self.testcases[0].crash_state = 'abcdef'\\n self.testcases[1].security_flag = False\\n self.testcases[1].crash_type = 'Timeout'\\n self.testcases[1].crash_state = 'abcde'\\n\\n for t in self.testcases:\\n t.put()\\n\\n grouper.group_testcases()\\n\\n for index, t in enumerate(self.testcases):\\n self.testcases[index] = data_handler.get_testcase_by_id(t.key.id())\\n self.assertEqual(self.testcases[index].group_id, 0)\\n self.assertTrue(self.testcases[index].is_leader)\",\n \"def event_m20_11_83000():\\n \\\"\\\"\\\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\\\"\\\"\\\"\\n assert event_m20_11_x53(z73=2011300, z74=2011399)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def test_runner_full_loop_big(caplog, big_dataset):\\n caplog.set_level(logging.INFO)\\n\\n session = big_dataset\\n\\n start_date = datetime.datetime(2020, 5, 17, 13, 0, 3)\\n end_date = datetime.datetime(2020, 5, 17, 13, 0, 6)\\n replay_rate = 1 \\n\\n db_connector_test = DataBaseConnector(session=session, \\n table_name='timeseries_dataset', \\n time_column='timestamp', \\n start_date=start_date,\\n end_date=end_date)\\n\\n debug_publisher = FilePublisher()\\n\\n runner = CentralRunner(db_connection=db_connector_test, \\n output_system=debug_publisher, \\n start_time=start_date, \\n end_time=end_date,\\n replay_rate=replay_rate )\\n\\n start = time.perf_counter()\\n \\n runner.run()\\n\\n end = time.perf_counter()\\n\\n code_time = end - start\\n assert int(code_time) == 2\\n\\n ## Test results\\n assert os.path.exists('test_tmp/17-05-2020_13-00-03')\\n assert os.path.exists('test_tmp/17-05-2020_13-00-05')\\n\\n assert len(os.listdir('test_tmp/17-05-2020_13-00-03')) == 9\\n assert len(os.listdir('test_tmp/17-05-2020_13-00-05')) == 2\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66093767","0.6576982","0.6142489","0.6118232","0.6109181","0.6053264","0.5884454","0.5807409","0.57819825","0.5768662","0.5756515","0.57349944","0.56748885","0.56665564","0.5654893","0.5644546","0.5621295","0.55847925","0.55834424","0.5561242","0.5518169","0.5516259","0.5504033","0.5492939","0.5471564","0.5444255","0.54255176","0.5425316","0.54166967","0.53698844","0.536093","0.53576416","0.5352122","0.53285515","0.5320758","0.5313744","0.53105927","0.5296103","0.52955854","0.5294086","0.5283554","0.5281918","0.5269319","0.5264945","0.5264521","0.5263517","0.52576494","0.5242315","0.5241248","0.5229563","0.5227059","0.52119625","0.52030784","0.51997936","0.51960444","0.51950276","0.51831585","0.51681525","0.51622397","0.5161604","0.5158236","0.5156313","0.51529837","0.5149523","0.514835","0.5145893","0.5141617","0.51414967","0.51356626","0.51324487","0.5124319","0.5119482","0.51154184","0.5113382","0.5107726","0.5107612","0.51041776","0.510172","0.50950634","0.5089544","0.5086868","0.5083579","0.5079147","0.5071613","0.50688994","0.50587714","0.50583106","0.5048472","0.50465894","0.50439125","0.50425595","0.5029188","0.5029166","0.5024653","0.5023082","0.5016143","0.50149584","0.5010474","0.500944","0.50079805"],"string":"[\n \"0.66093767\",\n \"0.6576982\",\n \"0.6142489\",\n \"0.6118232\",\n \"0.6109181\",\n \"0.6053264\",\n \"0.5884454\",\n \"0.5807409\",\n \"0.57819825\",\n \"0.5768662\",\n \"0.5756515\",\n \"0.57349944\",\n \"0.56748885\",\n \"0.56665564\",\n \"0.5654893\",\n \"0.5644546\",\n \"0.5621295\",\n \"0.55847925\",\n \"0.55834424\",\n \"0.5561242\",\n \"0.5518169\",\n \"0.5516259\",\n \"0.5504033\",\n \"0.5492939\",\n \"0.5471564\",\n \"0.5444255\",\n \"0.54255176\",\n \"0.5425316\",\n \"0.54166967\",\n \"0.53698844\",\n \"0.536093\",\n \"0.53576416\",\n \"0.5352122\",\n \"0.53285515\",\n \"0.5320758\",\n \"0.5313744\",\n \"0.53105927\",\n \"0.5296103\",\n \"0.52955854\",\n \"0.5294086\",\n \"0.5283554\",\n \"0.5281918\",\n \"0.5269319\",\n \"0.5264945\",\n \"0.5264521\",\n \"0.5263517\",\n \"0.52576494\",\n \"0.5242315\",\n \"0.5241248\",\n \"0.5229563\",\n \"0.5227059\",\n \"0.52119625\",\n \"0.52030784\",\n \"0.51997936\",\n \"0.51960444\",\n \"0.51950276\",\n \"0.51831585\",\n \"0.51681525\",\n \"0.51622397\",\n \"0.5161604\",\n \"0.5158236\",\n \"0.5156313\",\n \"0.51529837\",\n \"0.5149523\",\n \"0.514835\",\n \"0.5145893\",\n \"0.5141617\",\n \"0.51414967\",\n \"0.51356626\",\n \"0.51324487\",\n \"0.5124319\",\n \"0.5119482\",\n \"0.51154184\",\n \"0.5113382\",\n \"0.5107726\",\n \"0.5107612\",\n \"0.51041776\",\n \"0.510172\",\n \"0.50950634\",\n \"0.5089544\",\n \"0.5086868\",\n \"0.5083579\",\n \"0.5079147\",\n \"0.5071613\",\n \"0.50688994\",\n \"0.50587714\",\n \"0.50583106\",\n \"0.5048472\",\n \"0.50465894\",\n \"0.50439125\",\n \"0.50425595\",\n \"0.5029188\",\n \"0.5029166\",\n \"0.5024653\",\n \"0.5023082\",\n \"0.5016143\",\n \"0.50149584\",\n \"0.5010474\",\n \"0.500944\",\n \"0.50079805\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5960294"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":17475,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_SKIPPED state"},"document":{"kind":"string","value":"def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def verify_skip(self, d_stmt, table): \n pass","def skip_dti_tests():\n return True","def IsSkipped(self):\n state = self.GetState()\n return state.status == TestState.SKIPPED","def test_table_false_positives(self):\n pass","def test_csv_skipped_status_report(self):\n skipped_rows = [row for row in csv_reader\n if row['Status'] == STATUS_SKIPPED and\n row['AVI Object'] == '']\n self.assertTrue(len(skipped_rows) == 0)","def test_get_unsuccessful_ta():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\n\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\n assert np.isnan(list_failed[0])","def test_no_timesteps_property(self):\n expected_values = {\n 'no_timesteps': 1000,\n 'no_sweeps': 10,\n 'no_channels': 4,\n }\n test_rec = rt.Recording(\n np.zeros(\n [\n expected_values['no_channels'],\n expected_values['no_timesteps'],\n expected_values['no_sweeps'],\n ]\n ),\n dt=0.1,\n )\n self.assertEqual(\n test_rec.no_timesteps,\n expected_values['no_timesteps'],\n 'Expected {} for `no_timesteps` property; got {} instead.'.format(\n expected_values['no_timesteps'], test_rec.no_timesteps\n ),\n )","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def tick_skipped(self):\n pass","def test_noTimedEventsInsertion(self):\n self.test_noTimedEventsExtraction()\n self._testInsertion()","def test_non_batch(self):\r\n uid = uuid4()\r\n tmp = TestTimestampModel.create(id=uid, count=1)\r\n\r\n TestTimestampModel.get(id=uid).should.be.ok\r\n\r\n tmp.timestamp(timedelta(seconds=5)).delete()\r\n\r\n with self.assertRaises(TestTimestampModel.DoesNotExist):\r\n TestTimestampModel.get(id=uid)\r\n\r\n tmp = TestTimestampModel.create(id=uid, count=1)\r\n\r\n with self.assertRaises(TestTimestampModel.DoesNotExist):\r\n TestTimestampModel.get(id=uid)\r\n\r\n # calling .timestamp sets the TS on the model\r\n tmp.timestamp(timedelta(seconds=5))\r\n tmp._timestamp.should.be.ok\r\n\r\n # calling save clears the set timestamp\r\n tmp.save()\r\n tmp._timestamp.shouldnt.be.ok\r\n\r\n tmp.timestamp(timedelta(seconds=5))\r\n tmp.update()\r\n tmp._timestamp.shouldnt.be.ok","def test_queryset_total_time_no_records(db):\n assert models.TimeRecord.objects.total_time() == datetime.timedelta(0)","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_ensure_not_ts_ts(self):\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')","def test_is_approved_no_approval(time_record_factory):\n record = time_record_factory()\n\n assert not record.is_approved","def skipped(self):\n return self._skipped","def test_no_spike_after_table(self):\n n = 5\n dt = 1.0\n t_max = 2*dt\n # make sure we have spikes at the end\n table = np.ones((1, n))\n\n G = TableSpikers(n)\n G.spike_table = table\n\n sim = simulation.Simulation(G, dt=dt)\n sim.run(t_max)\n \n self.assertFalse(np.any(G.spike))","def test_skip_list_run_skip(self):\n mock_sqr = SequenceRun()\n mock_sqr.instrument_run_id = TestConstant.instrument_run_id.value\n\n mock_workflow = Workflow()\n mock_workflow.wfr_id = f\"wfr.{_rand(32)}\"\n mock_workflow.type_name = WorkflowType.BCL_CONVERT.value\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\n mock_workflow.sequence_run = mock_sqr\n mock_workflow.output = \"\"\n\n when(fastq_update_step).perform(...).thenReturn(\"FASTQ_UPDATE_STEP\")\n when(google_lims_update_step).perform(...).thenReturn('GOOGLE_LIMS_UPDATE_STEP')\n when(dragen_wgs_qc_step).perform(...).thenReturn('DRAGEN_WGS_QC_STEP')\n when(dragen_tso_ctdna_step).perform(...).thenReturn('DRAGEN_TSO_CTDNA_STEP')\n when(dragen_wts_step).perform(...).thenReturn('DRAGEN_WTS_STEP')\n\n run_id = TestConstant.instrument_run_id.value\n skiplist = {\n 'global': [],\n 'by_run': {\n run_id: [\n \"DRAGEN_WGS_QC_STEP\"\n ]\n }\n }\n\n results = orchestrator.next_step(mock_workflow, skiplist, None)\n logger.info(results)\n\n self.assertFalse('DRAGEN_WGS_QC_STEP' in results)\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\n self.assertTrue('DRAGEN_WTS_STEP' in results)\n\n skiplist = {\n 'global': [\"DRAGEN_WGS_QC_STEP\"],\n 'by_run': {\n run_id: [\n \"DRAGEN_TSO_CTDNA_STEP\",\n \"DRAGEN_WTS_STEP\"\n ]\n }\n }\n\n results = orchestrator.next_step(mock_workflow, skiplist, None)\n logger.info(results)\n\n self.assertFalse('DRAGEN_WGS_QC_STEP' in results)\n self.assertFalse('DRAGEN_TSO_CTDNA_STEP' in results)\n self.assertFalse('DRAGEN_WTS_STEP' in results)","def test_skip_list_no_skip(self):\n mock_sqr = SequenceRun()\n mock_sqr.instrument_run_id = TestConstant.instrument_run_id.value\n\n mock_workflow = Workflow()\n mock_workflow.wfr_id = f\"wfr.{_rand(32)}\"\n mock_workflow.type_name = WorkflowType.BCL_CONVERT.value\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\n mock_workflow.sequence_run = mock_sqr\n mock_workflow.output = \"\"\n\n when(fastq_update_step).perform(...).thenReturn(\"FASTQ_UPDATE_STEP\")\n when(google_lims_update_step).perform(...).thenReturn('GOOGLE_LIMS_UPDATE_STEP')\n when(dragen_wgs_qc_step).perform(...).thenReturn('DRAGEN_WGS_QC_STEP')\n when(dragen_tso_ctdna_step).perform(...).thenReturn('DRAGEN_TSO_CTDNA_STEP')\n when(dragen_wts_step).perform(...).thenReturn('DRAGEN_WTS_STEP')\n\n skiplist = {\n 'global': [],\n 'by_run': {}\n }\n\n results = orchestrator.next_step(mock_workflow, skiplist, None)\n logger.info(results)\n\n self.assertTrue('DRAGEN_WGS_QC_STEP' in results)\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\n self.assertTrue('DRAGEN_WTS_STEP' in results)","def test_issue_tracked_times(self):\n pass","def isolate_self_reporting_cases(self, time: int):","def test_shift_nothing(self):\n records = map(\n to_integer,\n simulate_records(\n self.df_casesrecord,\n self.df_knotdateset,\n self.df_modeldaterange,\n self.df_possibledateset,\n (None, None),\n ),\n )\n record_fra = select(\"FRA\", date(2020, 4, 11), records)\n self.assertDictEqual(\n {\n \"date\": date(2020, 4, 11),\n \"iso_code\": \"FRA\",\n \"area\": 547557,\n \"population\": 67059887,\n \"weekly_avg_cases\": 5670,\n \"summed_avg_cases\": 18970,\n },\n record_fra,\n )\n record_gbr = select(\"GBR\", date(2020, 4, 10), records)\n self.assertDictEqual(\n {\n \"date\": date(2020, 4, 10),\n \"iso_code\": \"GBR\",\n \"area\": 241930,\n \"population\": 66834405,\n \"weekly_avg_cases\": 4781,\n \"summed_avg_cases\": 9581,\n },\n record_gbr,\n )","def get_skipped(self):\n return [result for result in self.values() if result.outcome == Result.SKIPPED]","def test_invalid_flag_record(self):\n log.info(\"START QUAL TEST INVALID FLAG RECORD\")\n\n # Made-up data with all flags except the first set to True.\n # First flag is not a zero or one.\n self.clear_sample_data()\n self.event_subscribers.clear_events()\n self.assert_initialize()\n self.create_sample_data('invalid_A0000003.DEC', \"A1000003.DEC\")\n\n # Verify an event was raised and we are in our retry state.\n self.verify_queue_empty()\n self.assert_event_received(ResourceAgentErrorEvent, 10)\n self.assert_state_change(ResourceAgentState.STREAMING, 10)\n\n log.info(\"END QUAL TEST INVALID FLAG RECORD\")","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def run_skip(self):\n pass","def test_time_type_state_is_evening(day):\n\n assert day_time_info(day.hours_0).is_evening is False\n assert day_time_info(day.hours_1).is_evening is False\n assert day_time_info(day.hours_2).is_evening is False\n assert day_time_info(day.hours_3).is_evening is False\n assert day_time_info(day.hours_4).is_evening is False\n assert day_time_info(day.hours_5).is_evening is False\n assert day_time_info(day.hours_6).is_evening is False\n assert day_time_info(day.hours_7).is_evening is False\n assert day_time_info(day.hours_8).is_evening is False\n assert day_time_info(day.hours_9).is_evening is False\n assert day_time_info(day.hours_10).is_evening is False\n assert day_time_info(day.hours_11).is_evening is False\n assert day_time_info(day.hours_12).is_evening is False\n assert day_time_info(day.hours_13).is_evening is False\n assert day_time_info(day.hours_14).is_evening is False\n assert day_time_info(day.hours_15).is_evening is False\n assert day_time_info(day.hours_16).is_evening is False\n assert day_time_info(day.hours_17).is_evening is False\n assert day_time_info(day.hours_18).is_evening is True\n assert day_time_info(day.hours_19).is_evening is True\n assert day_time_info(day.hours_20).is_evening is True\n assert day_time_info(day.hours_21).is_evening is True\n assert day_time_info(day.hours_22).is_evening is True\n assert day_time_info(day.hours_23).is_evening is False","def testNoFailureFlag(self):\n schema = lsst.afw.table.SourceTable.makeMinimalSchema()\n\n # This is a FlagDefinition structure like a plugin might have\n flagDefs = FlagDefinitionList()\n FIRST = flagDefs.add(\"1st error\", \"this is the first failure type\")\n SECOND = flagDefs.add(\"2nd error\", \"this is the second failure type\")\n fh = FlagHandler.addFields(schema, \"test\", flagDefs)\n # Check to be sure that the FlagHandler was correctly initialized\n for index in range(len(flagDefs)):\n self.assertEqual(flagDefs.getDefinition(index).name, fh.getFlagName(index))\n\n catalog = lsst.afw.table.SourceCatalog(schema)\n\n # Now check to be sure that all of the known failures set the bits correctly\n record = catalog.addNew()\n fh.handleFailure(record)\n self.assertFalse(fh.getValue(record, FIRST.number))\n self.assertFalse(fh.getValue(record, SECOND.number))\n record = catalog.addNew()\n\n record = catalog.addNew()\n error = MeasurementError(FIRST.doc, FIRST.number)\n fh.handleFailure(record, error.cpp)\n self.assertTrue(fh.getValue(record, FIRST.number))\n self.assertFalse(fh.getValue(record, SECOND.number))\n\n record = catalog.addNew()\n error = MeasurementError(SECOND.doc, SECOND.number)\n fh.handleFailure(record, error.cpp)\n self.assertFalse(fh.getValue(record, FIRST.number))\n self.assertTrue(fh.getValue(record, SECOND.number))","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_skip():\n # Create lex instance\n lexer = lex.lex()\n\n lexer.skip(\n r\"test\",\n dependencies=[lex_bases.rule(\"TEST_2\", r\"test_2\")],\n function=example_function,\n )\n\n # Create a rule using the rule constructor with the same inputs\n expected_rule = lex_bases.rule(\n [\"SKIPPED\", False],\n r\"test\",\n dependencies=convert_dependencies([lex_bases.rule(\"TEST_2\", r\"test_2\")]),\n function=example_function,\n )\n\n assert (\n lexer.rules[1]._complete_equal(expected_rule)\n and lexer._skip_rules[0]._complete_equal(expected_rule)\n and lexer.rules[1].get_if_accept_skips()\n )","def test_skipitem(self):\n empty_tuple = ()\n tuple_1 = (0,)\n dict_b = {'b':1}\n keywords = [\"a\", \"b\"]\n\n for i in range(32, 127):\n c = chr(i)\n\n # skip parentheses, the error reporting is inconsistent about them\n # skip 'e', it's always a two-character code\n # skip '|' and '$', they don't represent arguments anyway\n if c in '()e|$':\n continue\n\n # test the format unit when not skipped\n format = c + \"i\"\n try:\n _testcapi.parse_tuple_and_keywords(tuple_1, dict_b,\n format, keywords)\n when_not_skipped = False\n except SystemError as e:\n s = \"argument 1 (impossible<bad format char>)\"\n when_not_skipped = (str(e) == s)\n except TypeError:\n when_not_skipped = False\n\n # test the format unit when skipped\n optional_format = \"|\" + format\n try:\n _testcapi.parse_tuple_and_keywords(empty_tuple, dict_b,\n optional_format, keywords)\n when_skipped = False\n except SystemError as e:\n s = \"impossible<bad format char>: '{}'\".format(format)\n when_skipped = (str(e) == s)\n\n message = (\"test_skipitem_parity: \"\n \"detected mismatch between convertsimple and skipitem \"\n \"for format unit '{}' ({}), not skipped {}, skipped {}\".format(\n c, i, when_skipped, when_not_skipped))\n self.assertIs(when_skipped, when_not_skipped, message)","def test_skip_list_wrong_run_skip(self):\n mock_sqr = SequenceRun()\n mock_sqr.instrument_run_id = TestConstant.instrument_run_id.value\n\n mock_workflow = Workflow()\n mock_workflow.wfr_id = f\"wfr.{_rand(32)}\"\n mock_workflow.type_name = WorkflowType.BCL_CONVERT.value\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\n mock_workflow.sequence_run = mock_sqr\n mock_workflow.output = \"\"\n\n when(fastq_update_step).perform(...).thenReturn(\"FASTQ_UPDATE_STEP\")\n when(google_lims_update_step).perform(...).thenReturn('GOOGLE_LIMS_UPDATE_STEP')\n when(dragen_wgs_qc_step).perform(...).thenReturn('DRAGEN_WGS_QC_STEP')\n when(dragen_tso_ctdna_step).perform(...).thenReturn('DRAGEN_TSO_CTDNA_STEP')\n when(dragen_wts_step).perform(...).thenReturn('DRAGEN_WTS_STEP')\n\n run_id = str(TestConstant.instrument_run_id.value).replace(\"2\", \"1\")\n skiplist = {\n 'global': [],\n 'by_run': {\n run_id: [\n \"DRAGEN_WGS_QC_STEP\"\n ]\n }\n }\n\n results = orchestrator.next_step(mock_workflow, skiplist, None)\n logger.info(results)\n\n # by_run skip list should not apply, since run id mismatch, so all workflows should be listed\n self.assertTrue('DRAGEN_WGS_QC_STEP' in results)\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\n self.assertTrue('DRAGEN_WTS_STEP' in results)\n\n skiplist = {\n 'global': [\"DRAGEN_WGS_QC_STEP\"],\n 'by_run': {\n run_id: [\n \"DRAGEN_TSO_CTDNA_STEP\",\n \"DRAGEN_WTS_STEP\"\n ]\n }\n }\n\n results = orchestrator.next_step(mock_workflow, skiplist, None)\n logger.info(results)\n\n # only global skip list should apply, due to run ID mismatch\n self.assertFalse('DRAGEN_WGS_QC_STEP' in results)\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\n self.assertTrue('DRAGEN_WTS_STEP' in results)","def test_not_state(self):\n\n rule = ('alert(name:\"test1\"; match:\"AB\"; state:set,a; state:set,b;)\\n'\n 'alert(name:\"test2\"; match:\"CD\"; state:is,a; state:unset,b;)\\n'\n 'alert(name:\"test3\"; match:\"EF\"; state:is,a; state:not,b;)\\n')\n\n tests = {\n \"ABCDEF\": [\"proxying connection from\",\n \"INFO : filter matched: 'test1'\",\n \"INFO : filter matched: 'test2'\",\n \"INFO : filter matched: 'test3'\"],\n \"ABEFCD\": [\"proxying connection from\",\n \"INFO : filter matched: 'test1'\",\n \"INFO : filter matched: 'test2'\"],\n \"CDEF\": [\"proxying connection from\"],\n }\n\n self.run_rules(rule, tests)","def test_load_skip_list(self):\n step_skip_list_json = libssm.get_ssm_param(f\"{ICA_WORKFLOW_PREFIX}/step_skip_list\")\n step_skip_list = libjson.loads(step_skip_list_json)\n\n logger.info(step_skip_list)\n\n self.assertIn('global', step_skip_list)\n self.assertIn(\"DRAGEN_WTS_STEP\", step_skip_list['global'])\n self.assertIn(\"TUMOR_NORMAL_STEP\", step_skip_list['global'])\n self.assertIn(\"UMCCRISE_STEP\", step_skip_list['global'])","def testReplayNonState(t, env):\n c = env.c1\n c.maketree([t.code])\n ops = c.use_obj(c.homedir + [t.code, 'vapor'])\n _replay(c, ops, NFS4ERR_NOENT)","def test_rt_table(self) -> None:\n expected = Fixtures.next_table()\n expected.description = '\"hello!\" said no one'\n expected.tags.sort()\n\n self.get_proxy().put_table(table=expected)\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\n actual.last_updated_timestamp = None\n actual.tags.sort()\n\n self.assertEqual(expected, actual)","def test_bad_time_repeat(self):\n repeated = np.concatenate([np.repeat(self.times[0], 3),\n self.times[3:]])\n self.assertFalse(utils.check_timestamps(repeated))","def test_no_timestamp(self):\n self.assertRaises(PyntsError, extract_columns, self.data[['a', 'b']], ['a'], ['timestamp'])","def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_write_skips_invalid_rows(self):\n writer = BaseTSVWriter([\n ('Prop1', Mock(side_effect=InvalidTsvRowException)),\n ])\n\n row = NonCallableMock()\n\n valid, invalid = writer.write(self.tsv_file, [row])\n\n assert valid == []\n assert invalid == [row]\n assert self.tsv_value == \"Prop1\\r\\n\"","def skip ( nEvents ) :\n st = SUCCESS \n with DisabledAlgos() :\n st = run ( nEvents )\n \n return st","def skipped(self, cloud_obj, reason):\n\t\telog(\"skipped {} ({})\".format(cloud_obj, reason))","def test_dt_preservation(self):\n dt = 0.6767335\n test_rec = rt.Recording([[[0, 1, 2]]], dt=dt)\n self.assertEqual(\n test_rec.dt,\n dt,\n 'Assigned `dt` not equal to attribute `dt`; test code probably '\n 'broken.',\n )\n self.assertEqual(\n test_rec[..., 1:].dt,\n dt,\n '`Recording.dt` attribute altered by slicing.',\n )\n self.assertEqual(\n test_rec[..., 1].dt,\n dt,\n '`dt` attribute altered by retrieving single element of `Recording`.',\n )","def test_timestamps(self):\n test_particle = self.TestDataParticle(self.sample_raw_data,\n preferred_timestamp=DataParticleKey.PORT_TIMESTAMP,\n internal_timestamp=self.sample_internal_timestamp)\n\n self.assertRaises(SampleException, test_particle.generate_raw)","def test_long_run_case_that_we_want_to_skip():\n time.sleep(30)\n assert 0","def test_missing_timestep(\n self,\n setup_folder_structure,\n config_handler,\n get_faulty_scenario_data,\n scenario_spec,\n ):\n key = _write_scenario_csv(\n setup_folder_structure,\n get_faulty_scenario_data,\n (\"population_count\", \"county\", \"season\", \"year\"),\n )\n\n with raises(SmifDataMismatchError) as ex:\n config_handler.read_scenario_variant_data(key, scenario_spec, 2017)\n msg = (\n \"Data for 'population_count' expected a column called 'timestep', instead \"\n + \"got data columns ['population_count', 'county', 'season', 'year'] and \"\n + \"index names [None]\"\n )\n assert msg in str(ex.value)","async def test_ignore_transaction(self):\n self.set_source_parameter(\"transactions_to_ignore\", [self.API2])\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\n self.assert_measurement(response, value=\"123\", entities=[])","def test_noTimedEventsExtraction(self):\n userbase.extractUserStore(self.account, self.destdir)\n self.assertEqual(\n self.ls.accountByAddress(self.localpart, self.domain),\n None)\n\n self.assertFalse(list(self.store.query(SubStore, SubStore.storepath == self.origdir)))\n self.origdir.restat(False)\n self.assertFalse(self.origdir.exists())\n self.assertFalse(list(self.store.query(_SubSchedulerParentHook)))","def test_dont_save(self):\n self.assertFalse(State.objects.exists())","def test_13_not_indexed_by_time(self):\n parsed = timeseries.parse_timeseries(data=self.meas_13,\n index_by_time=False)\n\n # In this case, we should have 'time' as a column rather than\n # the index.\n self.assertIn('time', parsed.columns)\n\n # Ensure we just have a simple range index.\n self.assertEqual(parsed.index[0], 0)\n self.assertEqual(parsed.index[-1], parsed.shape[0] - 1)","def test_total_time_no_end_time(time_record_factory):\n d = datetime.datetime(2018, 10, 1, 15, 26)\n t = time_record_factory(time_start=d, time_end=None)\n expected = datetime.timedelta(0)\n assert t.total_time == expected","def test_unequality(self):\n self.assertFalse(Record(1, 2) != Record(1, 2))\n self.assertTrue(Record(1, 2) != Record(1, 3))\n self.assertTrue(Record(1, 2) != Record(2, 2))\n self.assertTrue(Record(1, 2) != Record(3, 4))","def test_import_is_skipped(self):\n self.database.reports.distinct.return_value = [\"id\"]\n self.import_report(self.report_json)\n self.database.reports.insert_one.assert_not_called()","def test_no_updated_traits(self):\n response = self.client.get(self.get_url(self.study.pk))\n context = response.context\n table = context['source_trait_table']\n for trait in self.source_traits_v3:\n self.assertNotIn(trait, table.data)","async def test_ignore_transaction(self):\n self.set_source_parameter(\"transactions_to_ignore\", [self.API2])\n response = await self.collect(get_request_json_return_value=self.GATLING_JSON)\n self.assert_measurement(response, value=\"1\", entities=self.expected_entities[:1])","def test_ReportingPeriodDetailView_add_unsubmitted_time(self):\n\n self.timecard = hours.models.Timecard.objects.create(\n user=self.regular_user,\n submitted=False,\n reporting_period=self.reporting_period\n )\n\n response = self.app.get(\n reverse(\n 'reports:ReportingPeriodDetailView',\n kwargs={'reporting_period': '2015-01-01'},\n )\n )\n\n tables = response.html.find_all('table')\n not_filed_time = tables[0]\n filed_time = tables[1]\n\n self.assertEqual(\n len(not_filed_time.find_all('tr', {'class': 'user'})), 1\n )\n self.assertEqual(\n len(filed_time.find_all('tr', {'class': 'user'})), 1\n )","def test_state_after_failure(self):\n pass","def teminated_before(ind, t):\n val = terimated_particle_ids.get(ind)\n return val is not None and val != t","def test_DataTransformationFailureMode_no_duplicates():\n\n dataset = smlb.TabularData(data=np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\n fails = []\n failmode = smlb.DataTransformationFailureMode((\"index\", fails), dataset.num_samples)\n failmode.handle_failure(1)\n failmode.handle_failure(5)\n failmode.handle_failure(6)\n failmode.handle_failure(5)\n dataset = failmode.finalize(dataset)\n\n assert dataset.num_samples == 10\n assert fails == [1, 5, 6]","def test_timestamp_spacing_too_frequent(times):\n assert_series_equal(\n time.spacing(times, '30min'),\n pd.Series([True] + [False] * (len(times) - 1), index=times)\n )","def test_time_type_state_is_night(day):\n\n assert day_time_info(day.hours_0).is_night is True\n assert day_time_info(day.hours_1).is_night is True\n assert day_time_info(day.hours_2).is_night is True\n assert day_time_info(day.hours_3).is_night is True\n assert day_time_info(day.hours_4).is_night is True\n assert day_time_info(day.hours_5).is_night is False\n assert day_time_info(day.hours_6).is_night is False\n assert day_time_info(day.hours_7).is_night is False\n assert day_time_info(day.hours_8).is_night is False\n assert day_time_info(day.hours_9).is_night is False\n assert day_time_info(day.hours_10).is_night is False\n assert day_time_info(day.hours_11).is_night is False\n assert day_time_info(day.hours_12).is_night is False\n assert day_time_info(day.hours_13).is_night is False\n assert day_time_info(day.hours_14).is_night is False\n assert day_time_info(day.hours_15).is_night is False\n assert day_time_info(day.hours_16).is_night is False\n assert day_time_info(day.hours_17).is_night is False\n assert day_time_info(day.hours_18).is_night is False\n assert day_time_info(day.hours_19).is_night is False\n assert day_time_info(day.hours_20).is_night is False\n assert day_time_info(day.hours_21).is_night is False\n assert day_time_info(day.hours_22).is_night is False\n assert day_time_info(day.hours_23).is_night is True","def test_record_without_ttl_should_fail(self):\n zone = Zone('test.example.com')\n with self.assertRaises(InvalidRecordData) as e:\n Record(zone, 'test-record', {'type': 'A'})\n self.assertEqual('missing key \\'ttl\\' in Record data',\n str(e.exception))","def test_exclude_from_report(self):\n data = [\n {\n 'name': 'page 1',\n 'item_num': 1,\n },\n {\n 'name': 'page 2',\n 'item_num': 2,\n },\n ]\n\n class TableWithExclude(TableReportForTesting):\n class Meta:\n exclude_from_report = ('item_num',)\n\n table = TableWithExclude(data)\n table.exclude = ('name', )\n self.assertEqual(table.exclude_from_report, ('item_num',))\n\n response = table.as_csv(HttpRequest())\n self.assertEqual(response.status_code, 200)\n content = response.content\n if PY3:\n content = content.decode(settings.DEFAULT_CHARSET).replace('\\x00', '')\n\n self.assertEqual(table.exclude, ('name',)) # Attribute 'exclude_from_report' shouldn't overwrite 'exclude'\n self.assertEqual(\n content,\n ('Name\\r\\n'\n 'page 1\\r\\n'\n 'page 2\\r\\n')\n )","def skip(*, reason):\n return unittest.skip(reason)","def test_time_type_state_is_morning(day):\n\n assert day_time_info(day.hours_0).is_morning is False\n assert day_time_info(day.hours_1).is_morning is False\n assert day_time_info(day.hours_2).is_morning is False\n assert day_time_info(day.hours_3).is_morning is False\n assert day_time_info(day.hours_4).is_morning is False\n assert day_time_info(day.hours_5).is_morning is True\n assert day_time_info(day.hours_6).is_morning is True\n assert day_time_info(day.hours_7).is_morning is True\n assert day_time_info(day.hours_8).is_morning is True\n assert day_time_info(day.hours_9).is_morning is True\n assert day_time_info(day.hours_10).is_morning is False\n assert day_time_info(day.hours_11).is_morning is False\n assert day_time_info(day.hours_12).is_morning is False\n assert day_time_info(day.hours_13).is_morning is False\n assert day_time_info(day.hours_14).is_morning is False\n assert day_time_info(day.hours_15).is_morning is False\n assert day_time_info(day.hours_16).is_morning is False\n assert day_time_info(day.hours_17).is_morning is False\n assert day_time_info(day.hours_18).is_morning is False\n assert day_time_info(day.hours_19).is_morning is False\n assert day_time_info(day.hours_20).is_morning is False\n assert day_time_info(day.hours_21).is_morning is False\n assert day_time_info(day.hours_22).is_morning is False\n assert day_time_info(day.hours_23).is_morning is False","def test_invalid_flag_record(self):\n log.info(\"START INTEG TEST INVALID\")\n\n self.clear_sample_data()\n self.clear_async_data()\n\n # Made-up data with all flags except the first set to True.\n # First flag is not a zero or one.\n filename = \"A1000003.DEC\"\n self.create_sample_data('invalid_A0000003.DEC', filename)\n\n # Start sampling.\n self.driver.start_sampling()\n\n # an event catches the sample exception\n self.assert_event('ResourceAgentErrorEvent')\n\n # Verify that the entire file has been read.\n self.assert_file_ingested(filename)\n log.info(\"END INTEG TEST INVALID\")","def test_fkey_nav_stops_on_skip(dumper, db):\n dumper.reader.load_db(\n db.create_sample(\n 5,\n fkeys=[\n (\"table1\", \"t2id\", \"table2\", \"id\"),\n (\"table2\", \"t3id\", \"table3\", \"id\"),\n (\"table3\", \"t4id\", \"table4\", \"id\"),\n ],\n )\n )\n dumper.add_config(\n {\n \"db_objects\": [\n {\"name\": \"table1\"},\n {\"name\": \"table3\", \"action\": \"skip\"},\n ]\n }\n )\n dumper.perform_dump()\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\n assert len(objs) == 2","def test_step_negative_indices(self):\n _, backend = _collect_episode_data(num_episodes=6)\n data_reader = in_memory_backend.InMemoryBackendReader(backend)\n np.testing.assert_equal(data_reader.steps[-1],\n data_reader.steps[len(data_reader.steps) - 1])\n np.testing.assert_equal(data_reader.steps[-len(data_reader.steps)],\n data_reader.steps[0])","def test_skip_list_handler(self):\n mock_workflow = WorkflowFactory()\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\n mock_workflow.output = \"\"\n mock_workflow.save()\n\n # stub at after bcl_convert workflow succeeded state\n when(fastq_update_step).perform(...).thenRaise(ValueError(\"FASTQ_UPDATE_STEP should not be called\"))\n when(google_lims_update_step).perform(...).thenRaise(ValueError(\"GOOGLE_LIMS_UPDATE_STEP should not be called\"))\n when(dragen_wgs_qc_step).perform(...).thenRaise(ValueError(\"DRAGEN_WGS_QC_STEP should not be called\"))\n when(dragen_tso_ctdna_step).perform(...).thenRaise(ValueError(\"DRAGEN_TSO_CTDNA_STEP should not be called\"))\n when(dragen_wts_step).perform(...).thenRaise(ValueError(\"DRAGEN_WTS_STEP should not be called\"))\n when(orchestrator).update_step(...).thenRaise(ValueError(\"UPDATE_STEP should not be called\"))\n\n skiplist = {\n 'global': [\n \"UPDATE_STEP\",\n \"FASTQ_UPDATE_STEP\",\n \"GOOGLE_LIMS_UPDATE_STEP\",\n \"DRAGEN_WGS_QC_STEP\",\n \"DRAGEN_TSO_CTDNA_STEP\",\n \"DRAGEN_WTS_STEP\",\n ],\n 'by_run': {}\n }\n\n event = {\n 'wfr_id': mock_workflow.wfr_id,\n 'wfv_id': mock_workflow.wfv_id,\n 'skip': skiplist,\n }\n\n results = orchestrator.handler(event, None)\n logger.info(results)\n\n self.assertEqual(len(results), 0) # should skip all","def test_incomplete_dataset():\n train = ((\"Lorem ipsum dolor sit amet\", 3, 4.5),\n (\"Sed ut perspiciatis unde\", 4, 5.5))\n t = TabularDataset(train)\n\n assert len(t.val) == 0\n assert len(t.test) == 0","def test_course_unexpired_runs():\n course = CourseFactory.create()\n now = now_in_utc()\n start_dates = [now, now + timedelta(days=-3)]\n end_dates = [now + timedelta(hours=1), now + timedelta(days=-2)]\n CourseRunFactory.create_batch(\n 2,\n course=course,\n start_date=factory.Iterator(start_dates),\n end_date=factory.Iterator(end_dates),\n live=True,\n )\n\n # Add a run that is not live and shouldn't show up in unexpired list\n CourseRunFactory.create(\n course=course, start_date=start_dates[0], end_date=end_dates[0], live=False\n )\n\n assert len(course.unexpired_runs) == 1\n course_run = course.unexpired_runs[0]\n assert course_run.start_date == start_dates[0]\n assert course_run.end_date == end_dates[0]","def test_003_not_enough_datetimes() -> None:\n df = generate_test_data()\n df = df.head(2)\n skim(df)","def addSkip(self, test):\n test.status = \"skipped\"","def skip(t, n):\n pu(t)\n fd(t, n)\n pd(t)","def skip(t, n):\n pu(t)\n fd(t, n)\n pd(t)","def test_time_type_state_types(day):\n\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}","def test_all_repetition_frequency_have_timedelta(self):\n for value in EventRepetitionFrequency:\n if value is EventRepetitionFrequency.not_repeated:\n self.assertIsNone(value.to_timedelta())\n else:\n self.assertIsNotNone(value.to_timedelta())","def test_timestamp_not_found(self, l):\n extract_columns(data=self.data, columns=['a'], timestamps=['timestamp'])\n l.check(\n ('pynts.util', 'WARNING', \"Couldn't find timestamps '['timestamp']' in data, using 'ts' instead\"),\n )","def test_record_without_values(self):\n zone = Zone('test.example.com')\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\n self.assertListEqual(record.values, [])","def test_forTable(self):\n self.assertIdentical(\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\n )","def test_skipif_false():\n pass","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def test_notBeforeBefore(self):\n dbpool, qpool, clock, performerChosen = self._setupPools()\n\n @transactionally(dbpool.pool.connection)\n def check(txn):\n return qpool.enqueueWork(\n txn, DummyWorkItem, a=3, b=9,\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\n )\n\n yield check\n\n clock.advance(1000)\n # Advance far beyond the given timestamp.\n self.assertEquals(performerChosen, [True])\n\n # Wait for job\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\n clock.advance(1)\n\n # Work item complete\n self.assertTrue(DummyWorkItem.results == {1: 12})","def test_shift_ruptures_no_shift(midday):\n shift_mask, shift_amounts = time.shifts_ruptures(\n midday, midday\n )\n assert not shift_mask.any()\n assert_series_equal(\n shift_amounts,\n pd.Series(0, index=midday.index, dtype='int64'),\n check_names=False\n )","def test_stop_resume_at_time_record(self):\n log.info(\"START QUAL TEST STOP RESUME AT TIME RECORD\")\n\n # Read the velocity records of a 4 velocity record file.\n self.create_sample_data('valid_A0000004.DEC', \"A0000004.DEC\")\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\n self.dataset_agent_client.set_resource(\n {DriverParameter.RECORDS_PER_SECOND: 1})\n self.assert_start_sampling()\n result = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 4)\n\n self.assert_stop_sampling()\n self.verify_queue_empty()\n\n # Get the time record and combine with previous records.\n self.assert_start_sampling()\n time_result = self.data_subscribers.get_samples(\n DataParticleType.TIME_PARTICLE, 1)\n result.extend(time_result)\n self.assert_data_values(result, 'valid_A0000004.yml')\n self.verify_queue_empty()\n\n log.info(\"END QUAL TEST STOP RESUME AT TIME RECORD\")","def test_not_sorted_stream(self):\n self._test_insertion(TicketMetrics)","def _skip_row(self, row):\n return False","def addSkip(self, test, reason):\r\n self.skipped.append((test, reason))","def v2_runner_on_skipped(self, result):\n super(Callback, self).v2_runner_on_skipped(result)\n self._store(result, STATUS_SKIPPED)","def irgen_skip(stmt, builder, table):\n pass","def _skipped_tests(self, metric_source_id: str) -> int:\n return self.__test_count(metric_source_id, 'skipped')","def testHealthAssessWorkMissed(self):\n attr = self.session.create_visit_attr()\n\n self.util.intTypeTest(self, attr, \"work_missed\")\n\n self.util.intPropertyTest(self, attr, \"work_missed\")","def test_issue_delete_time(self):\n pass","def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_reactorTimeCountSkips(self):\n times = []\n clock = task.Clock()\n def aCallback(numCalls):\n times.append((clock.seconds(), numCalls))\n\n # Start a LoopingCall that tracks the time passed, and the number of\n # skips, with a 0.5 second increment.\n call = task.LoopingCall.withCount(aCallback)\n call.clock = clock\n INTERVAL = 0.5\n REALISTIC_DELAY = 0.01\n call.start(INTERVAL)\n\n # Initially, no seconds should have passed, and one calls should have\n # been made.\n self.assertEqual(times, [(0, 1)])\n\n # After the interval (plus a small delay, to account for the time that\n # the reactor takes to wake up and process the LoopingCall), we should\n # still have only made one call.\n clock.advance(INTERVAL + REALISTIC_DELAY)\n self.assertEqual(times, [(0, 1), (INTERVAL + REALISTIC_DELAY, 1)])\n\n # After advancing the clock by three intervals (plus a small delay to\n # account for the reactor), we should have skipped two calls; one less\n # than the number of intervals which have completely elapsed. Along\n # with the call we did actually make, the final number of calls is 3.\n clock.advance((3 * INTERVAL) + REALISTIC_DELAY)\n self.assertEqual(times,\n [(0, 1), (INTERVAL + REALISTIC_DELAY, 1),\n ((4 * INTERVAL) + (2 * REALISTIC_DELAY), 3)])\n\n # Advancing the clock by 0 seconds should not cause any changes!\n clock.advance(0)\n self.assertEqual(times,\n [(0, 1), (INTERVAL + REALISTIC_DELAY, 1),\n ((4 * INTERVAL) + (2 * REALISTIC_DELAY), 3)])","def test_reading_pk_timestamps_with_counters(self):\n self.prepare()\n self.session.execute(\"\"\"\n CREATE TABLE test_pk_timestamps_with_counters\n (columnname text, day timestamp,\n israndom boolean, columnvalue text, counter counter,\n PRIMARY KEY ((columnname, day, israndom), columnvalue)\n )\"\"\")\n\n records = ['origins|2016-10-01 00:00:00+0000|False|ACTUAL|6\\n',\n 'origins|2016-10-01 00:00:00+0000|False|ADGMOB|4\\n',\n 'origins|2016-10-01 00:00:00+0000|False|ANONPM|4\\n',\n 'origins|2016-10-01 00:00:00+0000|False|CSRT2L|76\\n',\n 'origins|2016-10-01 00:00:00+0000|False|DIAGOP|18\\n',\n 'origins|2016-10-01 00:00:00+0000|False|E-SOFT|17\\n',\n 'origins|2016-10-01 00:00:00+0000|False|E-TASK|10\\n']\n\n tempfile = self.get_temp_file()\n with open(tempfile.name, 'w') as f:\n f.writelines(records)\n\n logger.debug('Importing from csv file: {name}'.format(name=tempfile.name))\n cmds = \"COPY ks.test_pk_timestamps_with_counters FROM '{name}' WITH delimiter = '|'\".format(name=tempfile.name)\n self.run_cqlsh(cmds=cmds)\n\n res = rows_to_list(self.session.execute(\"SELECT COUNT(*) FROM ks.test_pk_timestamps_with_counters\"))[0][0]\n assert len(records) == res, \"Failed to import one or more rows, expected {} but got {}\".format(len(records), res)","def testTrialNoSave(self):\n ray.init(num_cpus=3)\n tmpdir = tempfile.mkdtemp()\n\n runner = TrialRunner(local_checkpoint_dir=tmpdir, checkpoint_period=0)\n runner.add_trial(\n Trial(\n \"__fake\",\n trial_id=\"non_checkpoint\",\n stopping_criterion={\"training_iteration\": 2}))\n\n while not all(t.status == Trial.TERMINATED\n for t in runner.get_trials()):\n runner.step()\n\n runner.add_trial(\n Trial(\n \"__fake\",\n trial_id=\"checkpoint\",\n checkpoint_at_end=True,\n stopping_criterion={\"training_iteration\": 2}))\n\n while not all(t.status == Trial.TERMINATED\n for t in runner.get_trials()):\n runner.step()\n\n runner.add_trial(\n Trial(\n \"__fake\",\n trial_id=\"pending\",\n stopping_criterion={\"training_iteration\": 2}))\n\n runner.step()\n runner.step()\n\n runner2 = TrialRunner(resume=\"LOCAL\", local_checkpoint_dir=tmpdir)\n new_trials = runner2.get_trials()\n self.assertEquals(len(new_trials), 3)\n self.assertTrue(\n runner2.get_trial(\"non_checkpoint\").status == Trial.TERMINATED)\n self.assertTrue(\n runner2.get_trial(\"checkpoint\").status == Trial.TERMINATED)\n self.assertTrue(runner2.get_trial(\"pending\").status == Trial.PENDING)\n self.assertTrue(not runner2.get_trial(\"pending\").last_result)\n runner2.step()\n shutil.rmtree(tmpdir)","def test_time(self):\n conn = self.database.connection()\n cursor = conn.cursor()\n dialect = self.database.dialect()\n dbapi = self.database.dbapi()\n query = dialect.translate('DROP TABLE test_time')\n try:\n cursor.execute(query)\n except dbapi.Error:\n conn.rollback()\n query = dialect.translate('CREATE TABLE test_time ' \\\n '( value TIME NOT NULL )')\n cursor.execute(query)\n data = []\n rr = random.randrange\n query = 'INSERT INTO test_time VALUES (%s)'\n for i in range(100):\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\n data.append(item)\n cursor.execute(query, (item,))\n query = 'SELECT * FROM test_time'\n cursor.execute(query)\n result = cursor.fetchall()\n for row in result:\n item = row[0]\n assert isinstance(item, datetime.time)\n assert item in data\n data.remove(item)\n query = dialect.translate('DELETE FROM test_time')\n cursor.execute(query)\n query = dialect.translate('DROP TABLE test_time')\n cursor.execute(query)\n conn.commit()"],"string":"[\n \"def verify_skip(self, d_stmt, table): \\n pass\",\n \"def skip_dti_tests():\\n return True\",\n \"def IsSkipped(self):\\n state = self.GetState()\\n return state.status == TestState.SKIPPED\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def test_csv_skipped_status_report(self):\\n skipped_rows = [row for row in csv_reader\\n if row['Status'] == STATUS_SKIPPED and\\n row['AVI Object'] == '']\\n self.assertTrue(len(skipped_rows) == 0)\",\n \"def test_get_unsuccessful_ta():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n list_failed, list_else = ta.get_unsuccessful_ta('ta_status_bool')\\n\\n assert list_else[0] == ta.source.data['ta_status_bool'][0]\\n assert np.isnan(list_failed[0])\",\n \"def test_no_timesteps_property(self):\\n expected_values = {\\n 'no_timesteps': 1000,\\n 'no_sweeps': 10,\\n 'no_channels': 4,\\n }\\n test_rec = rt.Recording(\\n np.zeros(\\n [\\n expected_values['no_channels'],\\n expected_values['no_timesteps'],\\n expected_values['no_sweeps'],\\n ]\\n ),\\n dt=0.1,\\n )\\n self.assertEqual(\\n test_rec.no_timesteps,\\n expected_values['no_timesteps'],\\n 'Expected {} for `no_timesteps` property; got {} instead.'.format(\\n expected_values['no_timesteps'], test_rec.no_timesteps\\n ),\\n )\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def tick_skipped(self):\\n pass\",\n \"def test_noTimedEventsInsertion(self):\\n self.test_noTimedEventsExtraction()\\n self._testInsertion()\",\n \"def test_non_batch(self):\\r\\n uid = uuid4()\\r\\n tmp = TestTimestampModel.create(id=uid, count=1)\\r\\n\\r\\n TestTimestampModel.get(id=uid).should.be.ok\\r\\n\\r\\n tmp.timestamp(timedelta(seconds=5)).delete()\\r\\n\\r\\n with self.assertRaises(TestTimestampModel.DoesNotExist):\\r\\n TestTimestampModel.get(id=uid)\\r\\n\\r\\n tmp = TestTimestampModel.create(id=uid, count=1)\\r\\n\\r\\n with self.assertRaises(TestTimestampModel.DoesNotExist):\\r\\n TestTimestampModel.get(id=uid)\\r\\n\\r\\n # calling .timestamp sets the TS on the model\\r\\n tmp.timestamp(timedelta(seconds=5))\\r\\n tmp._timestamp.should.be.ok\\r\\n\\r\\n # calling save clears the set timestamp\\r\\n tmp.save()\\r\\n tmp._timestamp.shouldnt.be.ok\\r\\n\\r\\n tmp.timestamp(timedelta(seconds=5))\\r\\n tmp.update()\\r\\n tmp._timestamp.shouldnt.be.ok\",\n \"def test_queryset_total_time_no_records(db):\\n assert models.TimeRecord.objects.total_time() == datetime.timedelta(0)\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_ensure_not_ts_ts(self):\\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')\",\n \"def test_is_approved_no_approval(time_record_factory):\\n record = time_record_factory()\\n\\n assert not record.is_approved\",\n \"def skipped(self):\\n return self._skipped\",\n \"def test_no_spike_after_table(self):\\n n = 5\\n dt = 1.0\\n t_max = 2*dt\\n # make sure we have spikes at the end\\n table = np.ones((1, n))\\n\\n G = TableSpikers(n)\\n G.spike_table = table\\n\\n sim = simulation.Simulation(G, dt=dt)\\n sim.run(t_max)\\n \\n self.assertFalse(np.any(G.spike))\",\n \"def test_skip_list_run_skip(self):\\n mock_sqr = SequenceRun()\\n mock_sqr.instrument_run_id = TestConstant.instrument_run_id.value\\n\\n mock_workflow = Workflow()\\n mock_workflow.wfr_id = f\\\"wfr.{_rand(32)}\\\"\\n mock_workflow.type_name = WorkflowType.BCL_CONVERT.value\\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\\n mock_workflow.sequence_run = mock_sqr\\n mock_workflow.output = \\\"\\\"\\n\\n when(fastq_update_step).perform(...).thenReturn(\\\"FASTQ_UPDATE_STEP\\\")\\n when(google_lims_update_step).perform(...).thenReturn('GOOGLE_LIMS_UPDATE_STEP')\\n when(dragen_wgs_qc_step).perform(...).thenReturn('DRAGEN_WGS_QC_STEP')\\n when(dragen_tso_ctdna_step).perform(...).thenReturn('DRAGEN_TSO_CTDNA_STEP')\\n when(dragen_wts_step).perform(...).thenReturn('DRAGEN_WTS_STEP')\\n\\n run_id = TestConstant.instrument_run_id.value\\n skiplist = {\\n 'global': [],\\n 'by_run': {\\n run_id: [\\n \\\"DRAGEN_WGS_QC_STEP\\\"\\n ]\\n }\\n }\\n\\n results = orchestrator.next_step(mock_workflow, skiplist, None)\\n logger.info(results)\\n\\n self.assertFalse('DRAGEN_WGS_QC_STEP' in results)\\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\\n self.assertTrue('DRAGEN_WTS_STEP' in results)\\n\\n skiplist = {\\n 'global': [\\\"DRAGEN_WGS_QC_STEP\\\"],\\n 'by_run': {\\n run_id: [\\n \\\"DRAGEN_TSO_CTDNA_STEP\\\",\\n \\\"DRAGEN_WTS_STEP\\\"\\n ]\\n }\\n }\\n\\n results = orchestrator.next_step(mock_workflow, skiplist, None)\\n logger.info(results)\\n\\n self.assertFalse('DRAGEN_WGS_QC_STEP' in results)\\n self.assertFalse('DRAGEN_TSO_CTDNA_STEP' in results)\\n self.assertFalse('DRAGEN_WTS_STEP' in results)\",\n \"def test_skip_list_no_skip(self):\\n mock_sqr = SequenceRun()\\n mock_sqr.instrument_run_id = TestConstant.instrument_run_id.value\\n\\n mock_workflow = Workflow()\\n mock_workflow.wfr_id = f\\\"wfr.{_rand(32)}\\\"\\n mock_workflow.type_name = WorkflowType.BCL_CONVERT.value\\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\\n mock_workflow.sequence_run = mock_sqr\\n mock_workflow.output = \\\"\\\"\\n\\n when(fastq_update_step).perform(...).thenReturn(\\\"FASTQ_UPDATE_STEP\\\")\\n when(google_lims_update_step).perform(...).thenReturn('GOOGLE_LIMS_UPDATE_STEP')\\n when(dragen_wgs_qc_step).perform(...).thenReturn('DRAGEN_WGS_QC_STEP')\\n when(dragen_tso_ctdna_step).perform(...).thenReturn('DRAGEN_TSO_CTDNA_STEP')\\n when(dragen_wts_step).perform(...).thenReturn('DRAGEN_WTS_STEP')\\n\\n skiplist = {\\n 'global': [],\\n 'by_run': {}\\n }\\n\\n results = orchestrator.next_step(mock_workflow, skiplist, None)\\n logger.info(results)\\n\\n self.assertTrue('DRAGEN_WGS_QC_STEP' in results)\\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\\n self.assertTrue('DRAGEN_WTS_STEP' in results)\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def isolate_self_reporting_cases(self, time: int):\",\n \"def test_shift_nothing(self):\\n records = map(\\n to_integer,\\n simulate_records(\\n self.df_casesrecord,\\n self.df_knotdateset,\\n self.df_modeldaterange,\\n self.df_possibledateset,\\n (None, None),\\n ),\\n )\\n record_fra = select(\\\"FRA\\\", date(2020, 4, 11), records)\\n self.assertDictEqual(\\n {\\n \\\"date\\\": date(2020, 4, 11),\\n \\\"iso_code\\\": \\\"FRA\\\",\\n \\\"area\\\": 547557,\\n \\\"population\\\": 67059887,\\n \\\"weekly_avg_cases\\\": 5670,\\n \\\"summed_avg_cases\\\": 18970,\\n },\\n record_fra,\\n )\\n record_gbr = select(\\\"GBR\\\", date(2020, 4, 10), records)\\n self.assertDictEqual(\\n {\\n \\\"date\\\": date(2020, 4, 10),\\n \\\"iso_code\\\": \\\"GBR\\\",\\n \\\"area\\\": 241930,\\n \\\"population\\\": 66834405,\\n \\\"weekly_avg_cases\\\": 4781,\\n \\\"summed_avg_cases\\\": 9581,\\n },\\n record_gbr,\\n )\",\n \"def get_skipped(self):\\n return [result for result in self.values() if result.outcome == Result.SKIPPED]\",\n \"def test_invalid_flag_record(self):\\n log.info(\\\"START QUAL TEST INVALID FLAG RECORD\\\")\\n\\n # Made-up data with all flags except the first set to True.\\n # First flag is not a zero or one.\\n self.clear_sample_data()\\n self.event_subscribers.clear_events()\\n self.assert_initialize()\\n self.create_sample_data('invalid_A0000003.DEC', \\\"A1000003.DEC\\\")\\n\\n # Verify an event was raised and we are in our retry state.\\n self.verify_queue_empty()\\n self.assert_event_received(ResourceAgentErrorEvent, 10)\\n self.assert_state_change(ResourceAgentState.STREAMING, 10)\\n\\n log.info(\\\"END QUAL TEST INVALID FLAG RECORD\\\")\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def run_skip(self):\\n pass\",\n \"def test_time_type_state_is_evening(day):\\n\\n assert day_time_info(day.hours_0).is_evening is False\\n assert day_time_info(day.hours_1).is_evening is False\\n assert day_time_info(day.hours_2).is_evening is False\\n assert day_time_info(day.hours_3).is_evening is False\\n assert day_time_info(day.hours_4).is_evening is False\\n assert day_time_info(day.hours_5).is_evening is False\\n assert day_time_info(day.hours_6).is_evening is False\\n assert day_time_info(day.hours_7).is_evening is False\\n assert day_time_info(day.hours_8).is_evening is False\\n assert day_time_info(day.hours_9).is_evening is False\\n assert day_time_info(day.hours_10).is_evening is False\\n assert day_time_info(day.hours_11).is_evening is False\\n assert day_time_info(day.hours_12).is_evening is False\\n assert day_time_info(day.hours_13).is_evening is False\\n assert day_time_info(day.hours_14).is_evening is False\\n assert day_time_info(day.hours_15).is_evening is False\\n assert day_time_info(day.hours_16).is_evening is False\\n assert day_time_info(day.hours_17).is_evening is False\\n assert day_time_info(day.hours_18).is_evening is True\\n assert day_time_info(day.hours_19).is_evening is True\\n assert day_time_info(day.hours_20).is_evening is True\\n assert day_time_info(day.hours_21).is_evening is True\\n assert day_time_info(day.hours_22).is_evening is True\\n assert day_time_info(day.hours_23).is_evening is False\",\n \"def testNoFailureFlag(self):\\n schema = lsst.afw.table.SourceTable.makeMinimalSchema()\\n\\n # This is a FlagDefinition structure like a plugin might have\\n flagDefs = FlagDefinitionList()\\n FIRST = flagDefs.add(\\\"1st error\\\", \\\"this is the first failure type\\\")\\n SECOND = flagDefs.add(\\\"2nd error\\\", \\\"this is the second failure type\\\")\\n fh = FlagHandler.addFields(schema, \\\"test\\\", flagDefs)\\n # Check to be sure that the FlagHandler was correctly initialized\\n for index in range(len(flagDefs)):\\n self.assertEqual(flagDefs.getDefinition(index).name, fh.getFlagName(index))\\n\\n catalog = lsst.afw.table.SourceCatalog(schema)\\n\\n # Now check to be sure that all of the known failures set the bits correctly\\n record = catalog.addNew()\\n fh.handleFailure(record)\\n self.assertFalse(fh.getValue(record, FIRST.number))\\n self.assertFalse(fh.getValue(record, SECOND.number))\\n record = catalog.addNew()\\n\\n record = catalog.addNew()\\n error = MeasurementError(FIRST.doc, FIRST.number)\\n fh.handleFailure(record, error.cpp)\\n self.assertTrue(fh.getValue(record, FIRST.number))\\n self.assertFalse(fh.getValue(record, SECOND.number))\\n\\n record = catalog.addNew()\\n error = MeasurementError(SECOND.doc, SECOND.number)\\n fh.handleFailure(record, error.cpp)\\n self.assertFalse(fh.getValue(record, FIRST.number))\\n self.assertTrue(fh.getValue(record, SECOND.number))\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_skip():\\n # Create lex instance\\n lexer = lex.lex()\\n\\n lexer.skip(\\n r\\\"test\\\",\\n dependencies=[lex_bases.rule(\\\"TEST_2\\\", r\\\"test_2\\\")],\\n function=example_function,\\n )\\n\\n # Create a rule using the rule constructor with the same inputs\\n expected_rule = lex_bases.rule(\\n [\\\"SKIPPED\\\", False],\\n r\\\"test\\\",\\n dependencies=convert_dependencies([lex_bases.rule(\\\"TEST_2\\\", r\\\"test_2\\\")]),\\n function=example_function,\\n )\\n\\n assert (\\n lexer.rules[1]._complete_equal(expected_rule)\\n and lexer._skip_rules[0]._complete_equal(expected_rule)\\n and lexer.rules[1].get_if_accept_skips()\\n )\",\n \"def test_skipitem(self):\\n empty_tuple = ()\\n tuple_1 = (0,)\\n dict_b = {'b':1}\\n keywords = [\\\"a\\\", \\\"b\\\"]\\n\\n for i in range(32, 127):\\n c = chr(i)\\n\\n # skip parentheses, the error reporting is inconsistent about them\\n # skip 'e', it's always a two-character code\\n # skip '|' and '$', they don't represent arguments anyway\\n if c in '()e|$':\\n continue\\n\\n # test the format unit when not skipped\\n format = c + \\\"i\\\"\\n try:\\n _testcapi.parse_tuple_and_keywords(tuple_1, dict_b,\\n format, keywords)\\n when_not_skipped = False\\n except SystemError as e:\\n s = \\\"argument 1 (impossible<bad format char>)\\\"\\n when_not_skipped = (str(e) == s)\\n except TypeError:\\n when_not_skipped = False\\n\\n # test the format unit when skipped\\n optional_format = \\\"|\\\" + format\\n try:\\n _testcapi.parse_tuple_and_keywords(empty_tuple, dict_b,\\n optional_format, keywords)\\n when_skipped = False\\n except SystemError as e:\\n s = \\\"impossible<bad format char>: '{}'\\\".format(format)\\n when_skipped = (str(e) == s)\\n\\n message = (\\\"test_skipitem_parity: \\\"\\n \\\"detected mismatch between convertsimple and skipitem \\\"\\n \\\"for format unit '{}' ({}), not skipped {}, skipped {}\\\".format(\\n c, i, when_skipped, when_not_skipped))\\n self.assertIs(when_skipped, when_not_skipped, message)\",\n \"def test_skip_list_wrong_run_skip(self):\\n mock_sqr = SequenceRun()\\n mock_sqr.instrument_run_id = TestConstant.instrument_run_id.value\\n\\n mock_workflow = Workflow()\\n mock_workflow.wfr_id = f\\\"wfr.{_rand(32)}\\\"\\n mock_workflow.type_name = WorkflowType.BCL_CONVERT.value\\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\\n mock_workflow.sequence_run = mock_sqr\\n mock_workflow.output = \\\"\\\"\\n\\n when(fastq_update_step).perform(...).thenReturn(\\\"FASTQ_UPDATE_STEP\\\")\\n when(google_lims_update_step).perform(...).thenReturn('GOOGLE_LIMS_UPDATE_STEP')\\n when(dragen_wgs_qc_step).perform(...).thenReturn('DRAGEN_WGS_QC_STEP')\\n when(dragen_tso_ctdna_step).perform(...).thenReturn('DRAGEN_TSO_CTDNA_STEP')\\n when(dragen_wts_step).perform(...).thenReturn('DRAGEN_WTS_STEP')\\n\\n run_id = str(TestConstant.instrument_run_id.value).replace(\\\"2\\\", \\\"1\\\")\\n skiplist = {\\n 'global': [],\\n 'by_run': {\\n run_id: [\\n \\\"DRAGEN_WGS_QC_STEP\\\"\\n ]\\n }\\n }\\n\\n results = orchestrator.next_step(mock_workflow, skiplist, None)\\n logger.info(results)\\n\\n # by_run skip list should not apply, since run id mismatch, so all workflows should be listed\\n self.assertTrue('DRAGEN_WGS_QC_STEP' in results)\\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\\n self.assertTrue('DRAGEN_WTS_STEP' in results)\\n\\n skiplist = {\\n 'global': [\\\"DRAGEN_WGS_QC_STEP\\\"],\\n 'by_run': {\\n run_id: [\\n \\\"DRAGEN_TSO_CTDNA_STEP\\\",\\n \\\"DRAGEN_WTS_STEP\\\"\\n ]\\n }\\n }\\n\\n results = orchestrator.next_step(mock_workflow, skiplist, None)\\n logger.info(results)\\n\\n # only global skip list should apply, due to run ID mismatch\\n self.assertFalse('DRAGEN_WGS_QC_STEP' in results)\\n self.assertTrue('DRAGEN_TSO_CTDNA_STEP' in results)\\n self.assertTrue('DRAGEN_WTS_STEP' in results)\",\n \"def test_not_state(self):\\n\\n rule = ('alert(name:\\\"test1\\\"; match:\\\"AB\\\"; state:set,a; state:set,b;)\\\\n'\\n 'alert(name:\\\"test2\\\"; match:\\\"CD\\\"; state:is,a; state:unset,b;)\\\\n'\\n 'alert(name:\\\"test3\\\"; match:\\\"EF\\\"; state:is,a; state:not,b;)\\\\n')\\n\\n tests = {\\n \\\"ABCDEF\\\": [\\\"proxying connection from\\\",\\n \\\"INFO : filter matched: 'test1'\\\",\\n \\\"INFO : filter matched: 'test2'\\\",\\n \\\"INFO : filter matched: 'test3'\\\"],\\n \\\"ABEFCD\\\": [\\\"proxying connection from\\\",\\n \\\"INFO : filter matched: 'test1'\\\",\\n \\\"INFO : filter matched: 'test2'\\\"],\\n \\\"CDEF\\\": [\\\"proxying connection from\\\"],\\n }\\n\\n self.run_rules(rule, tests)\",\n \"def test_load_skip_list(self):\\n step_skip_list_json = libssm.get_ssm_param(f\\\"{ICA_WORKFLOW_PREFIX}/step_skip_list\\\")\\n step_skip_list = libjson.loads(step_skip_list_json)\\n\\n logger.info(step_skip_list)\\n\\n self.assertIn('global', step_skip_list)\\n self.assertIn(\\\"DRAGEN_WTS_STEP\\\", step_skip_list['global'])\\n self.assertIn(\\\"TUMOR_NORMAL_STEP\\\", step_skip_list['global'])\\n self.assertIn(\\\"UMCCRISE_STEP\\\", step_skip_list['global'])\",\n \"def testReplayNonState(t, env):\\n c = env.c1\\n c.maketree([t.code])\\n ops = c.use_obj(c.homedir + [t.code, 'vapor'])\\n _replay(c, ops, NFS4ERR_NOENT)\",\n \"def test_rt_table(self) -> None:\\n expected = Fixtures.next_table()\\n expected.description = '\\\"hello!\\\" said no one'\\n expected.tags.sort()\\n\\n self.get_proxy().put_table(table=expected)\\n actual: Table = self.get_proxy().get_table(table_uri=checkNotNone(expected.key))\\n actual.last_updated_timestamp = None\\n actual.tags.sort()\\n\\n self.assertEqual(expected, actual)\",\n \"def test_bad_time_repeat(self):\\n repeated = np.concatenate([np.repeat(self.times[0], 3),\\n self.times[3:]])\\n self.assertFalse(utils.check_timestamps(repeated))\",\n \"def test_no_timestamp(self):\\n self.assertRaises(PyntsError, extract_columns, self.data[['a', 'b']], ['a'], ['timestamp'])\",\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_write_skips_invalid_rows(self):\\n writer = BaseTSVWriter([\\n ('Prop1', Mock(side_effect=InvalidTsvRowException)),\\n ])\\n\\n row = NonCallableMock()\\n\\n valid, invalid = writer.write(self.tsv_file, [row])\\n\\n assert valid == []\\n assert invalid == [row]\\n assert self.tsv_value == \\\"Prop1\\\\r\\\\n\\\"\",\n \"def skip ( nEvents ) :\\n st = SUCCESS \\n with DisabledAlgos() :\\n st = run ( nEvents )\\n \\n return st\",\n \"def skipped(self, cloud_obj, reason):\\n\\t\\telog(\\\"skipped {} ({})\\\".format(cloud_obj, reason))\",\n \"def test_dt_preservation(self):\\n dt = 0.6767335\\n test_rec = rt.Recording([[[0, 1, 2]]], dt=dt)\\n self.assertEqual(\\n test_rec.dt,\\n dt,\\n 'Assigned `dt` not equal to attribute `dt`; test code probably '\\n 'broken.',\\n )\\n self.assertEqual(\\n test_rec[..., 1:].dt,\\n dt,\\n '`Recording.dt` attribute altered by slicing.',\\n )\\n self.assertEqual(\\n test_rec[..., 1].dt,\\n dt,\\n '`dt` attribute altered by retrieving single element of `Recording`.',\\n )\",\n \"def test_timestamps(self):\\n test_particle = self.TestDataParticle(self.sample_raw_data,\\n preferred_timestamp=DataParticleKey.PORT_TIMESTAMP,\\n internal_timestamp=self.sample_internal_timestamp)\\n\\n self.assertRaises(SampleException, test_particle.generate_raw)\",\n \"def test_long_run_case_that_we_want_to_skip():\\n time.sleep(30)\\n assert 0\",\n \"def test_missing_timestep(\\n self,\\n setup_folder_structure,\\n config_handler,\\n get_faulty_scenario_data,\\n scenario_spec,\\n ):\\n key = _write_scenario_csv(\\n setup_folder_structure,\\n get_faulty_scenario_data,\\n (\\\"population_count\\\", \\\"county\\\", \\\"season\\\", \\\"year\\\"),\\n )\\n\\n with raises(SmifDataMismatchError) as ex:\\n config_handler.read_scenario_variant_data(key, scenario_spec, 2017)\\n msg = (\\n \\\"Data for 'population_count' expected a column called 'timestep', instead \\\"\\n + \\\"got data columns ['population_count', 'county', 'season', 'year'] and \\\"\\n + \\\"index names [None]\\\"\\n )\\n assert msg in str(ex.value)\",\n \"async def test_ignore_transaction(self):\\n self.set_source_parameter(\\\"transactions_to_ignore\\\", [self.API2])\\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\\n self.assert_measurement(response, value=\\\"123\\\", entities=[])\",\n \"def test_noTimedEventsExtraction(self):\\n userbase.extractUserStore(self.account, self.destdir)\\n self.assertEqual(\\n self.ls.accountByAddress(self.localpart, self.domain),\\n None)\\n\\n self.assertFalse(list(self.store.query(SubStore, SubStore.storepath == self.origdir)))\\n self.origdir.restat(False)\\n self.assertFalse(self.origdir.exists())\\n self.assertFalse(list(self.store.query(_SubSchedulerParentHook)))\",\n \"def test_dont_save(self):\\n self.assertFalse(State.objects.exists())\",\n \"def test_13_not_indexed_by_time(self):\\n parsed = timeseries.parse_timeseries(data=self.meas_13,\\n index_by_time=False)\\n\\n # In this case, we should have 'time' as a column rather than\\n # the index.\\n self.assertIn('time', parsed.columns)\\n\\n # Ensure we just have a simple range index.\\n self.assertEqual(parsed.index[0], 0)\\n self.assertEqual(parsed.index[-1], parsed.shape[0] - 1)\",\n \"def test_total_time_no_end_time(time_record_factory):\\n d = datetime.datetime(2018, 10, 1, 15, 26)\\n t = time_record_factory(time_start=d, time_end=None)\\n expected = datetime.timedelta(0)\\n assert t.total_time == expected\",\n \"def test_unequality(self):\\n self.assertFalse(Record(1, 2) != Record(1, 2))\\n self.assertTrue(Record(1, 2) != Record(1, 3))\\n self.assertTrue(Record(1, 2) != Record(2, 2))\\n self.assertTrue(Record(1, 2) != Record(3, 4))\",\n \"def test_import_is_skipped(self):\\n self.database.reports.distinct.return_value = [\\\"id\\\"]\\n self.import_report(self.report_json)\\n self.database.reports.insert_one.assert_not_called()\",\n \"def test_no_updated_traits(self):\\n response = self.client.get(self.get_url(self.study.pk))\\n context = response.context\\n table = context['source_trait_table']\\n for trait in self.source_traits_v3:\\n self.assertNotIn(trait, table.data)\",\n \"async def test_ignore_transaction(self):\\n self.set_source_parameter(\\\"transactions_to_ignore\\\", [self.API2])\\n response = await self.collect(get_request_json_return_value=self.GATLING_JSON)\\n self.assert_measurement(response, value=\\\"1\\\", entities=self.expected_entities[:1])\",\n \"def test_ReportingPeriodDetailView_add_unsubmitted_time(self):\\n\\n self.timecard = hours.models.Timecard.objects.create(\\n user=self.regular_user,\\n submitted=False,\\n reporting_period=self.reporting_period\\n )\\n\\n response = self.app.get(\\n reverse(\\n 'reports:ReportingPeriodDetailView',\\n kwargs={'reporting_period': '2015-01-01'},\\n )\\n )\\n\\n tables = response.html.find_all('table')\\n not_filed_time = tables[0]\\n filed_time = tables[1]\\n\\n self.assertEqual(\\n len(not_filed_time.find_all('tr', {'class': 'user'})), 1\\n )\\n self.assertEqual(\\n len(filed_time.find_all('tr', {'class': 'user'})), 1\\n )\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def teminated_before(ind, t):\\n val = terimated_particle_ids.get(ind)\\n return val is not None and val != t\",\n \"def test_DataTransformationFailureMode_no_duplicates():\\n\\n dataset = smlb.TabularData(data=np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\\n fails = []\\n failmode = smlb.DataTransformationFailureMode((\\\"index\\\", fails), dataset.num_samples)\\n failmode.handle_failure(1)\\n failmode.handle_failure(5)\\n failmode.handle_failure(6)\\n failmode.handle_failure(5)\\n dataset = failmode.finalize(dataset)\\n\\n assert dataset.num_samples == 10\\n assert fails == [1, 5, 6]\",\n \"def test_timestamp_spacing_too_frequent(times):\\n assert_series_equal(\\n time.spacing(times, '30min'),\\n pd.Series([True] + [False] * (len(times) - 1), index=times)\\n )\",\n \"def test_time_type_state_is_night(day):\\n\\n assert day_time_info(day.hours_0).is_night is True\\n assert day_time_info(day.hours_1).is_night is True\\n assert day_time_info(day.hours_2).is_night is True\\n assert day_time_info(day.hours_3).is_night is True\\n assert day_time_info(day.hours_4).is_night is True\\n assert day_time_info(day.hours_5).is_night is False\\n assert day_time_info(day.hours_6).is_night is False\\n assert day_time_info(day.hours_7).is_night is False\\n assert day_time_info(day.hours_8).is_night is False\\n assert day_time_info(day.hours_9).is_night is False\\n assert day_time_info(day.hours_10).is_night is False\\n assert day_time_info(day.hours_11).is_night is False\\n assert day_time_info(day.hours_12).is_night is False\\n assert day_time_info(day.hours_13).is_night is False\\n assert day_time_info(day.hours_14).is_night is False\\n assert day_time_info(day.hours_15).is_night is False\\n assert day_time_info(day.hours_16).is_night is False\\n assert day_time_info(day.hours_17).is_night is False\\n assert day_time_info(day.hours_18).is_night is False\\n assert day_time_info(day.hours_19).is_night is False\\n assert day_time_info(day.hours_20).is_night is False\\n assert day_time_info(day.hours_21).is_night is False\\n assert day_time_info(day.hours_22).is_night is False\\n assert day_time_info(day.hours_23).is_night is True\",\n \"def test_record_without_ttl_should_fail(self):\\n zone = Zone('test.example.com')\\n with self.assertRaises(InvalidRecordData) as e:\\n Record(zone, 'test-record', {'type': 'A'})\\n self.assertEqual('missing key \\\\'ttl\\\\' in Record data',\\n str(e.exception))\",\n \"def test_exclude_from_report(self):\\n data = [\\n {\\n 'name': 'page 1',\\n 'item_num': 1,\\n },\\n {\\n 'name': 'page 2',\\n 'item_num': 2,\\n },\\n ]\\n\\n class TableWithExclude(TableReportForTesting):\\n class Meta:\\n exclude_from_report = ('item_num',)\\n\\n table = TableWithExclude(data)\\n table.exclude = ('name', )\\n self.assertEqual(table.exclude_from_report, ('item_num',))\\n\\n response = table.as_csv(HttpRequest())\\n self.assertEqual(response.status_code, 200)\\n content = response.content\\n if PY3:\\n content = content.decode(settings.DEFAULT_CHARSET).replace('\\\\x00', '')\\n\\n self.assertEqual(table.exclude, ('name',)) # Attribute 'exclude_from_report' shouldn't overwrite 'exclude'\\n self.assertEqual(\\n content,\\n ('Name\\\\r\\\\n'\\n 'page 1\\\\r\\\\n'\\n 'page 2\\\\r\\\\n')\\n )\",\n \"def skip(*, reason):\\n return unittest.skip(reason)\",\n \"def test_time_type_state_is_morning(day):\\n\\n assert day_time_info(day.hours_0).is_morning is False\\n assert day_time_info(day.hours_1).is_morning is False\\n assert day_time_info(day.hours_2).is_morning is False\\n assert day_time_info(day.hours_3).is_morning is False\\n assert day_time_info(day.hours_4).is_morning is False\\n assert day_time_info(day.hours_5).is_morning is True\\n assert day_time_info(day.hours_6).is_morning is True\\n assert day_time_info(day.hours_7).is_morning is True\\n assert day_time_info(day.hours_8).is_morning is True\\n assert day_time_info(day.hours_9).is_morning is True\\n assert day_time_info(day.hours_10).is_morning is False\\n assert day_time_info(day.hours_11).is_morning is False\\n assert day_time_info(day.hours_12).is_morning is False\\n assert day_time_info(day.hours_13).is_morning is False\\n assert day_time_info(day.hours_14).is_morning is False\\n assert day_time_info(day.hours_15).is_morning is False\\n assert day_time_info(day.hours_16).is_morning is False\\n assert day_time_info(day.hours_17).is_morning is False\\n assert day_time_info(day.hours_18).is_morning is False\\n assert day_time_info(day.hours_19).is_morning is False\\n assert day_time_info(day.hours_20).is_morning is False\\n assert day_time_info(day.hours_21).is_morning is False\\n assert day_time_info(day.hours_22).is_morning is False\\n assert day_time_info(day.hours_23).is_morning is False\",\n \"def test_invalid_flag_record(self):\\n log.info(\\\"START INTEG TEST INVALID\\\")\\n\\n self.clear_sample_data()\\n self.clear_async_data()\\n\\n # Made-up data with all flags except the first set to True.\\n # First flag is not a zero or one.\\n filename = \\\"A1000003.DEC\\\"\\n self.create_sample_data('invalid_A0000003.DEC', filename)\\n\\n # Start sampling.\\n self.driver.start_sampling()\\n\\n # an event catches the sample exception\\n self.assert_event('ResourceAgentErrorEvent')\\n\\n # Verify that the entire file has been read.\\n self.assert_file_ingested(filename)\\n log.info(\\\"END INTEG TEST INVALID\\\")\",\n \"def test_fkey_nav_stops_on_skip(dumper, db):\\n dumper.reader.load_db(\\n db.create_sample(\\n 5,\\n fkeys=[\\n (\\\"table1\\\", \\\"t2id\\\", \\\"table2\\\", \\\"id\\\"),\\n (\\\"table2\\\", \\\"t3id\\\", \\\"table3\\\", \\\"id\\\"),\\n (\\\"table3\\\", \\\"t4id\\\", \\\"table4\\\", \\\"id\\\"),\\n ],\\n )\\n )\\n dumper.add_config(\\n {\\n \\\"db_objects\\\": [\\n {\\\"name\\\": \\\"table1\\\"},\\n {\\\"name\\\": \\\"table3\\\", \\\"action\\\": \\\"skip\\\"},\\n ]\\n }\\n )\\n dumper.perform_dump()\\n objs = [obj for obj, match in dumper.writer.dumped if isinstance(obj, Table)]\\n assert len(objs) == 2\",\n \"def test_step_negative_indices(self):\\n _, backend = _collect_episode_data(num_episodes=6)\\n data_reader = in_memory_backend.InMemoryBackendReader(backend)\\n np.testing.assert_equal(data_reader.steps[-1],\\n data_reader.steps[len(data_reader.steps) - 1])\\n np.testing.assert_equal(data_reader.steps[-len(data_reader.steps)],\\n data_reader.steps[0])\",\n \"def test_skip_list_handler(self):\\n mock_workflow = WorkflowFactory()\\n mock_workflow.end_status = WorkflowStatus.SUCCEEDED.value\\n mock_workflow.output = \\\"\\\"\\n mock_workflow.save()\\n\\n # stub at after bcl_convert workflow succeeded state\\n when(fastq_update_step).perform(...).thenRaise(ValueError(\\\"FASTQ_UPDATE_STEP should not be called\\\"))\\n when(google_lims_update_step).perform(...).thenRaise(ValueError(\\\"GOOGLE_LIMS_UPDATE_STEP should not be called\\\"))\\n when(dragen_wgs_qc_step).perform(...).thenRaise(ValueError(\\\"DRAGEN_WGS_QC_STEP should not be called\\\"))\\n when(dragen_tso_ctdna_step).perform(...).thenRaise(ValueError(\\\"DRAGEN_TSO_CTDNA_STEP should not be called\\\"))\\n when(dragen_wts_step).perform(...).thenRaise(ValueError(\\\"DRAGEN_WTS_STEP should not be called\\\"))\\n when(orchestrator).update_step(...).thenRaise(ValueError(\\\"UPDATE_STEP should not be called\\\"))\\n\\n skiplist = {\\n 'global': [\\n \\\"UPDATE_STEP\\\",\\n \\\"FASTQ_UPDATE_STEP\\\",\\n \\\"GOOGLE_LIMS_UPDATE_STEP\\\",\\n \\\"DRAGEN_WGS_QC_STEP\\\",\\n \\\"DRAGEN_TSO_CTDNA_STEP\\\",\\n \\\"DRAGEN_WTS_STEP\\\",\\n ],\\n 'by_run': {}\\n }\\n\\n event = {\\n 'wfr_id': mock_workflow.wfr_id,\\n 'wfv_id': mock_workflow.wfv_id,\\n 'skip': skiplist,\\n }\\n\\n results = orchestrator.handler(event, None)\\n logger.info(results)\\n\\n self.assertEqual(len(results), 0) # should skip all\",\n \"def test_incomplete_dataset():\\n train = ((\\\"Lorem ipsum dolor sit amet\\\", 3, 4.5),\\n (\\\"Sed ut perspiciatis unde\\\", 4, 5.5))\\n t = TabularDataset(train)\\n\\n assert len(t.val) == 0\\n assert len(t.test) == 0\",\n \"def test_course_unexpired_runs():\\n course = CourseFactory.create()\\n now = now_in_utc()\\n start_dates = [now, now + timedelta(days=-3)]\\n end_dates = [now + timedelta(hours=1), now + timedelta(days=-2)]\\n CourseRunFactory.create_batch(\\n 2,\\n course=course,\\n start_date=factory.Iterator(start_dates),\\n end_date=factory.Iterator(end_dates),\\n live=True,\\n )\\n\\n # Add a run that is not live and shouldn't show up in unexpired list\\n CourseRunFactory.create(\\n course=course, start_date=start_dates[0], end_date=end_dates[0], live=False\\n )\\n\\n assert len(course.unexpired_runs) == 1\\n course_run = course.unexpired_runs[0]\\n assert course_run.start_date == start_dates[0]\\n assert course_run.end_date == end_dates[0]\",\n \"def test_003_not_enough_datetimes() -> None:\\n df = generate_test_data()\\n df = df.head(2)\\n skim(df)\",\n \"def addSkip(self, test):\\n test.status = \\\"skipped\\\"\",\n \"def skip(t, n):\\n pu(t)\\n fd(t, n)\\n pd(t)\",\n \"def skip(t, n):\\n pu(t)\\n fd(t, n)\\n pd(t)\",\n \"def test_time_type_state_types(day):\\n\\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}\",\n \"def test_all_repetition_frequency_have_timedelta(self):\\n for value in EventRepetitionFrequency:\\n if value is EventRepetitionFrequency.not_repeated:\\n self.assertIsNone(value.to_timedelta())\\n else:\\n self.assertIsNotNone(value.to_timedelta())\",\n \"def test_timestamp_not_found(self, l):\\n extract_columns(data=self.data, columns=['a'], timestamps=['timestamp'])\\n l.check(\\n ('pynts.util', 'WARNING', \\\"Couldn't find timestamps '['timestamp']' in data, using 'ts' instead\\\"),\\n )\",\n \"def test_record_without_values(self):\\n zone = Zone('test.example.com')\\n record = Record(zone, 'test-record', {'type': 'A', 'ttl': 300})\\n self.assertListEqual(record.values, [])\",\n \"def test_forTable(self):\\n self.assertIdentical(\\n WorkItem.forTable(schema.DUMMY_WORK_ITEM), DummyWorkItem\\n )\",\n \"def test_skipif_false():\\n pass\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def test_notBeforeBefore(self):\\n dbpool, qpool, clock, performerChosen = self._setupPools()\\n\\n @transactionally(dbpool.pool.connection)\\n def check(txn):\\n return qpool.enqueueWork(\\n txn, DummyWorkItem, a=3, b=9,\\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\\n )\\n\\n yield check\\n\\n clock.advance(1000)\\n # Advance far beyond the given timestamp.\\n self.assertEquals(performerChosen, [True])\\n\\n # Wait for job\\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\\n clock.advance(1)\\n\\n # Work item complete\\n self.assertTrue(DummyWorkItem.results == {1: 12})\",\n \"def test_shift_ruptures_no_shift(midday):\\n shift_mask, shift_amounts = time.shifts_ruptures(\\n midday, midday\\n )\\n assert not shift_mask.any()\\n assert_series_equal(\\n shift_amounts,\\n pd.Series(0, index=midday.index, dtype='int64'),\\n check_names=False\\n )\",\n \"def test_stop_resume_at_time_record(self):\\n log.info(\\\"START QUAL TEST STOP RESUME AT TIME RECORD\\\")\\n\\n # Read the velocity records of a 4 velocity record file.\\n self.create_sample_data('valid_A0000004.DEC', \\\"A0000004.DEC\\\")\\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\\n self.dataset_agent_client.set_resource(\\n {DriverParameter.RECORDS_PER_SECOND: 1})\\n self.assert_start_sampling()\\n result = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 4)\\n\\n self.assert_stop_sampling()\\n self.verify_queue_empty()\\n\\n # Get the time record and combine with previous records.\\n self.assert_start_sampling()\\n time_result = self.data_subscribers.get_samples(\\n DataParticleType.TIME_PARTICLE, 1)\\n result.extend(time_result)\\n self.assert_data_values(result, 'valid_A0000004.yml')\\n self.verify_queue_empty()\\n\\n log.info(\\\"END QUAL TEST STOP RESUME AT TIME RECORD\\\")\",\n \"def test_not_sorted_stream(self):\\n self._test_insertion(TicketMetrics)\",\n \"def _skip_row(self, row):\\n return False\",\n \"def addSkip(self, test, reason):\\r\\n self.skipped.append((test, reason))\",\n \"def v2_runner_on_skipped(self, result):\\n super(Callback, self).v2_runner_on_skipped(result)\\n self._store(result, STATUS_SKIPPED)\",\n \"def irgen_skip(stmt, builder, table):\\n pass\",\n \"def _skipped_tests(self, metric_source_id: str) -> int:\\n return self.__test_count(metric_source_id, 'skipped')\",\n \"def testHealthAssessWorkMissed(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.intTypeTest(self, attr, \\\"work_missed\\\")\\n\\n self.util.intPropertyTest(self, attr, \\\"work_missed\\\")\",\n \"def test_issue_delete_time(self):\\n pass\",\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_reactorTimeCountSkips(self):\\n times = []\\n clock = task.Clock()\\n def aCallback(numCalls):\\n times.append((clock.seconds(), numCalls))\\n\\n # Start a LoopingCall that tracks the time passed, and the number of\\n # skips, with a 0.5 second increment.\\n call = task.LoopingCall.withCount(aCallback)\\n call.clock = clock\\n INTERVAL = 0.5\\n REALISTIC_DELAY = 0.01\\n call.start(INTERVAL)\\n\\n # Initially, no seconds should have passed, and one calls should have\\n # been made.\\n self.assertEqual(times, [(0, 1)])\\n\\n # After the interval (plus a small delay, to account for the time that\\n # the reactor takes to wake up and process the LoopingCall), we should\\n # still have only made one call.\\n clock.advance(INTERVAL + REALISTIC_DELAY)\\n self.assertEqual(times, [(0, 1), (INTERVAL + REALISTIC_DELAY, 1)])\\n\\n # After advancing the clock by three intervals (plus a small delay to\\n # account for the reactor), we should have skipped two calls; one less\\n # than the number of intervals which have completely elapsed. Along\\n # with the call we did actually make, the final number of calls is 3.\\n clock.advance((3 * INTERVAL) + REALISTIC_DELAY)\\n self.assertEqual(times,\\n [(0, 1), (INTERVAL + REALISTIC_DELAY, 1),\\n ((4 * INTERVAL) + (2 * REALISTIC_DELAY), 3)])\\n\\n # Advancing the clock by 0 seconds should not cause any changes!\\n clock.advance(0)\\n self.assertEqual(times,\\n [(0, 1), (INTERVAL + REALISTIC_DELAY, 1),\\n ((4 * INTERVAL) + (2 * REALISTIC_DELAY), 3)])\",\n \"def test_reading_pk_timestamps_with_counters(self):\\n self.prepare()\\n self.session.execute(\\\"\\\"\\\"\\n CREATE TABLE test_pk_timestamps_with_counters\\n (columnname text, day timestamp,\\n israndom boolean, columnvalue text, counter counter,\\n PRIMARY KEY ((columnname, day, israndom), columnvalue)\\n )\\\"\\\"\\\")\\n\\n records = ['origins|2016-10-01 00:00:00+0000|False|ACTUAL|6\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|ADGMOB|4\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|ANONPM|4\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|CSRT2L|76\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|DIAGOP|18\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|E-SOFT|17\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|E-TASK|10\\\\n']\\n\\n tempfile = self.get_temp_file()\\n with open(tempfile.name, 'w') as f:\\n f.writelines(records)\\n\\n logger.debug('Importing from csv file: {name}'.format(name=tempfile.name))\\n cmds = \\\"COPY ks.test_pk_timestamps_with_counters FROM '{name}' WITH delimiter = '|'\\\".format(name=tempfile.name)\\n self.run_cqlsh(cmds=cmds)\\n\\n res = rows_to_list(self.session.execute(\\\"SELECT COUNT(*) FROM ks.test_pk_timestamps_with_counters\\\"))[0][0]\\n assert len(records) == res, \\\"Failed to import one or more rows, expected {} but got {}\\\".format(len(records), res)\",\n \"def testTrialNoSave(self):\\n ray.init(num_cpus=3)\\n tmpdir = tempfile.mkdtemp()\\n\\n runner = TrialRunner(local_checkpoint_dir=tmpdir, checkpoint_period=0)\\n runner.add_trial(\\n Trial(\\n \\\"__fake\\\",\\n trial_id=\\\"non_checkpoint\\\",\\n stopping_criterion={\\\"training_iteration\\\": 2}))\\n\\n while not all(t.status == Trial.TERMINATED\\n for t in runner.get_trials()):\\n runner.step()\\n\\n runner.add_trial(\\n Trial(\\n \\\"__fake\\\",\\n trial_id=\\\"checkpoint\\\",\\n checkpoint_at_end=True,\\n stopping_criterion={\\\"training_iteration\\\": 2}))\\n\\n while not all(t.status == Trial.TERMINATED\\n for t in runner.get_trials()):\\n runner.step()\\n\\n runner.add_trial(\\n Trial(\\n \\\"__fake\\\",\\n trial_id=\\\"pending\\\",\\n stopping_criterion={\\\"training_iteration\\\": 2}))\\n\\n runner.step()\\n runner.step()\\n\\n runner2 = TrialRunner(resume=\\\"LOCAL\\\", local_checkpoint_dir=tmpdir)\\n new_trials = runner2.get_trials()\\n self.assertEquals(len(new_trials), 3)\\n self.assertTrue(\\n runner2.get_trial(\\\"non_checkpoint\\\").status == Trial.TERMINATED)\\n self.assertTrue(\\n runner2.get_trial(\\\"checkpoint\\\").status == Trial.TERMINATED)\\n self.assertTrue(runner2.get_trial(\\\"pending\\\").status == Trial.PENDING)\\n self.assertTrue(not runner2.get_trial(\\\"pending\\\").last_result)\\n runner2.step()\\n shutil.rmtree(tmpdir)\",\n \"def test_time(self):\\n conn = self.database.connection()\\n cursor = conn.cursor()\\n dialect = self.database.dialect()\\n dbapi = self.database.dbapi()\\n query = dialect.translate('DROP TABLE test_time')\\n try:\\n cursor.execute(query)\\n except dbapi.Error:\\n conn.rollback()\\n query = dialect.translate('CREATE TABLE test_time ' \\\\\\n '( value TIME NOT NULL )')\\n cursor.execute(query)\\n data = []\\n rr = random.randrange\\n query = 'INSERT INTO test_time VALUES (%s)'\\n for i in range(100):\\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\\n data.append(item)\\n cursor.execute(query, (item,))\\n query = 'SELECT * FROM test_time'\\n cursor.execute(query)\\n result = cursor.fetchall()\\n for row in result:\\n item = row[0]\\n assert isinstance(item, datetime.time)\\n assert item in data\\n data.remove(item)\\n query = dialect.translate('DELETE FROM test_time')\\n cursor.execute(query)\\n query = dialect.translate('DROP TABLE test_time')\\n cursor.execute(query)\\n conn.commit()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64526933","0.62094676","0.61737317","0.6080123","0.6063041","0.59828675","0.59114105","0.5883286","0.5864295","0.5789228","0.578475","0.5716462","0.56791854","0.5611211","0.5603914","0.5570936","0.5559213","0.55535394","0.5551212","0.5548782","0.5545937","0.5541316","0.5520128","0.5500454","0.54754937","0.5475036","0.54674447","0.5443703","0.5439752","0.54363716","0.5399841","0.5384851","0.53697354","0.5353853","0.5338578","0.5330397","0.53300995","0.5328065","0.53269416","0.5319725","0.5309674","0.5299247","0.5299107","0.52831626","0.52820194","0.52652377","0.5264916","0.5263629","0.5259734","0.52547574","0.52410215","0.5240791","0.5238228","0.5229051","0.52240956","0.5223044","0.5215223","0.5213018","0.5205124","0.52006304","0.5200226","0.51924974","0.5190057","0.5185664","0.5176854","0.5175556","0.51663125","0.5160332","0.51552176","0.51294875","0.5125832","0.5124301","0.5120726","0.51193184","0.51153624","0.5112111","0.5112111","0.51121086","0.510781","0.51044106","0.5100057","0.5097884","0.50959504","0.50948447","0.50939","0.5088241","0.508583","0.507802","0.5075124","0.50732934","0.5070251","0.50694823","0.5056083","0.50524884","0.50463676","0.5044334","0.50421786","0.50421524","0.50393987","0.50240636"],"string":"[\n \"0.64526933\",\n \"0.62094676\",\n \"0.61737317\",\n \"0.6080123\",\n \"0.6063041\",\n \"0.59828675\",\n \"0.59114105\",\n \"0.5883286\",\n \"0.5864295\",\n \"0.5789228\",\n \"0.578475\",\n \"0.5716462\",\n \"0.56791854\",\n \"0.5611211\",\n \"0.5603914\",\n \"0.5570936\",\n \"0.5559213\",\n \"0.55535394\",\n \"0.5551212\",\n \"0.5548782\",\n \"0.5545937\",\n \"0.5541316\",\n \"0.5520128\",\n \"0.5500454\",\n \"0.54754937\",\n \"0.5475036\",\n \"0.54674447\",\n \"0.5443703\",\n \"0.5439752\",\n \"0.54363716\",\n \"0.5399841\",\n \"0.5384851\",\n \"0.53697354\",\n \"0.5353853\",\n \"0.5338578\",\n \"0.5330397\",\n \"0.53300995\",\n \"0.5328065\",\n \"0.53269416\",\n \"0.5319725\",\n \"0.5309674\",\n \"0.5299247\",\n \"0.5299107\",\n \"0.52831626\",\n \"0.52820194\",\n \"0.52652377\",\n \"0.5264916\",\n \"0.5263629\",\n \"0.5259734\",\n \"0.52547574\",\n \"0.52410215\",\n \"0.5240791\",\n \"0.5238228\",\n \"0.5229051\",\n \"0.52240956\",\n \"0.5223044\",\n \"0.5215223\",\n \"0.5213018\",\n \"0.5205124\",\n \"0.52006304\",\n \"0.5200226\",\n \"0.51924974\",\n \"0.5190057\",\n \"0.5185664\",\n \"0.5176854\",\n \"0.5175556\",\n \"0.51663125\",\n \"0.5160332\",\n \"0.51552176\",\n \"0.51294875\",\n \"0.5125832\",\n \"0.5124301\",\n \"0.5120726\",\n \"0.51193184\",\n \"0.51153624\",\n \"0.5112111\",\n \"0.5112111\",\n \"0.51121086\",\n \"0.510781\",\n \"0.51044106\",\n \"0.5100057\",\n \"0.5097884\",\n \"0.50959504\",\n \"0.50948447\",\n \"0.50939\",\n \"0.5088241\",\n \"0.508583\",\n \"0.507802\",\n \"0.5075124\",\n \"0.50732934\",\n \"0.5070251\",\n \"0.50694823\",\n \"0.5056083\",\n \"0.50524884\",\n \"0.50463676\",\n \"0.5044334\",\n \"0.50421786\",\n \"0.50421524\",\n \"0.50393987\",\n \"0.50240636\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6912011"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17476,"cells":{"query":{"kind":"string","value":"method tests timetable records in STATE_PROCESSED state"},"document":{"kind":"string","value":"def test_state_processed(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_PROCESSED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=0).\\\n # _process_state_final_run(any(str), any(Job))","def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_retry_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n self.pipeline_real.insert_uow = then_raise\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_IN_PROGRESS,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n # verify(pipeline, times=1).\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\n # verify(pipeline, times=1).\\\n # _process_state_final_run(any(str), any(Job))","def test_processed_state_final_run(self):\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_FINAL_RUN,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=1).get_tree(any(str))","def test_state_skipped(self):\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_SKIPPED,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def test_retrieve_instances_schedule_state(self):\n pass","def test_update_instances_schedule_state(self):\n pass","def test_issue_tracked_times(self):\n pass","def test_timeout_processing(self):\n # setup\n self.transaction_behaviour.processing_time = None\n\n # operation\n self.transaction_behaviour._timeout_processing()\n\n # after\n self.assert_quantity_in_outbox(0)","def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def phone_timezones_have_been_processed():\n if settings.UNIT_TESTING:\n override = getattr(\n settings, 'PHONE_TIMEZONES_HAVE_BEEN_PROCESSED', None)\n if override is not None:\n return override\n return (_get_migration_status_from_threadlocals()\n == MigrationStatus.COMPLETE)","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def saveNeventsByProcessingType(neventsByProcessingType, qtime):\n\n try:\n with transaction.atomic():\n for pt, data in neventsByProcessingType.items():\n row = ProdNeventsHistory(processingtype=pt,\n neventstotal=data['total'],\n neventsused=data['used'],\n neventswaiting=data['waiting'],\n neventsrunning=data['running'],\n timestamp=qtime)\n row.save()\n except DatabaseError as e:\n print (e.message)\n return False\n return True","def test_reading_pk_timestamps_with_counters(self):\n self.prepare()\n self.session.execute(\"\"\"\n CREATE TABLE test_pk_timestamps_with_counters\n (columnname text, day timestamp,\n israndom boolean, columnvalue text, counter counter,\n PRIMARY KEY ((columnname, day, israndom), columnvalue)\n )\"\"\")\n\n records = ['origins|2016-10-01 00:00:00+0000|False|ACTUAL|6\\n',\n 'origins|2016-10-01 00:00:00+0000|False|ADGMOB|4\\n',\n 'origins|2016-10-01 00:00:00+0000|False|ANONPM|4\\n',\n 'origins|2016-10-01 00:00:00+0000|False|CSRT2L|76\\n',\n 'origins|2016-10-01 00:00:00+0000|False|DIAGOP|18\\n',\n 'origins|2016-10-01 00:00:00+0000|False|E-SOFT|17\\n',\n 'origins|2016-10-01 00:00:00+0000|False|E-TASK|10\\n']\n\n tempfile = self.get_temp_file()\n with open(tempfile.name, 'w') as f:\n f.writelines(records)\n\n logger.debug('Importing from csv file: {name}'.format(name=tempfile.name))\n cmds = \"COPY ks.test_pk_timestamps_with_counters FROM '{name}' WITH delimiter = '|'\".format(name=tempfile.name)\n self.run_cqlsh(cmds=cmds)\n\n res = rows_to_list(self.session.execute(\"SELECT COUNT(*) FROM ks.test_pk_timestamps_with_counters\"))[0][0]\n assert len(records) == res, \"Failed to import one or more rows, expected {} but got {}\".format(len(records), res)","def test_ensure_ts_ts(self):\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')","def _compute_queue_state(self):\n for record in self:\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\n record.state = \"completed\"\n elif record.queue_line_draft_records == record.queue_line_total_records:\n record.state = \"draft\"\n elif record.queue_line_total_records == record.queue_line_fail_records:\n record.state = \"failed\"\n else:\n record.state = \"partially_completed\"","def test_unfinished_activity(self):\n with open(os.path.dirname(os.path.abspath(__file__)) + \"/time_unfinished.log\", 'r') as f:\n results_list = analysis.parse_time_log(f)\n\n for x in range(len(expected_activity_list)):\n assert results_list[x] == expected_activity_list[x]\n\n # the last event is ongoing so I can't test an exact end time\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\n and results_list[-1].activity_type == 'p' \\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)","def test_initial_timestamp_states(\n self, get_pipe_manager, retrospective, which_checkpoint_state\n ):\n\n # Create the manager and make the timestamp call.\n pm = get_pipe_manager(name=\"InitialTimestampState\")\n stage_name = \"quality_control\"\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\n\n # Form expectations.\n if retrospective:\n prev_exp = stage_name\n curr_exp = None\n else:\n prev_exp = None\n curr_exp = stage_name\n\n # Make the assertion on the specified checkpoint state.\n if which_checkpoint_state == \"curr_checkpoint\":\n assert curr_exp == getattr(pm, \"curr_checkpoint\")\n else:\n assert prev_exp == getattr(pm, \"prev_checkpoint\")","def test_run_now(curent_time,state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.run_now(curent_time) == state","def test_retrospective_the_prospective_checkpointed_timestamps(\n self, test_type, stage_pair, pm\n ):\n\n stage1, stage2 = stage_pair\n pm.timestamp(checkpoint=stage1, finished=True)\n assert stage1 == pm.prev_checkpoint\n assert pm.curr_checkpoint is None\n pm.timestamp(checkpoint=stage2, finished=False)\n\n if test_type == FILES_TEST:\n expected = [checkpoint_filepath(stage1, pm)]\n assert set(expected) == set(fetch_checkpoint_files(pm))\n else:\n assert pm.prev_checkpoint is None\n assert stage2 == pm.curr_checkpoint","def __checkInited(self, date=None, tkey=None, ttype=None):\n if ttype == 'crontab':\n return False\n if date is None:\n date = self.ddate\n\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\n if doc.get(\"status\") == \"waiting\":\n return True\n return False","def test_setting_continuous_processing(processor):\n processor.continuous_processing = False\n assert not processor._state.test('continuous_processing')\n processor.continuous_processing = True\n assert processor._state.test('continuous_processing')","def identify_exec_running(self, record):\n return [\"running\"]","def test_prospective_then_retrospective_checkpointed_timestamps(\n self, test_type, stage_pair, pm\n ):\n\n stage1, stage2 = stage_pair\n pm.timestamp(checkpoint=stage1, finished=False)\n assert stage1 == pm.curr_checkpoint\n pm.timestamp(checkpoint=stage2, finished=True)\n\n if test_type == FILES_TEST:\n checkpoint_files = fetch_checkpoint_files(pm)\n expected = [checkpoint_filepath(stage2, pm)]\n assert set(expected) == set(checkpoint_files)\n else:\n # Current checkpoint will be reset by second (retrospective)\n # timestamp call.\n assert stage2 == pm.prev_checkpoint\n assert pm.curr_checkpoint is None","def test_cancelled_state_final_run(self):\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_CANCELED))\n self.pipeline_real.uow_dao = uow_dao_mock\n\n pipeline = spy(self.pipeline_real)\n job_record = get_job_record(job.STATE_FINAL_RUN,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=1). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\n verify(self.time_table_mocked, times=0).get_tree(any(str))","def load_status_table():","def test_get_refresh_job_status(self):\n pass","def test_running_job(self):\n\n running_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"running\"))\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def process(rows):\n # current state\n current_guard = None\n sleeping = False\n start = None\n # handle time data as datetime\n # I saw the 23:58 begin time in test data and figured I wanted date classes\n from datetime import datetime\n dateformat = '[%Y-%m-%d %H:%M]'\n schedule = []\n for row in rows:\n parts = row.split()\n time = datetime.strptime(' '.join(parts[0:2]), dateformat)\n if parts[2] == 'Guard':\n if sleeping: # shouldn't happen\n print('Messy input?!')\n schedule.append({\n 'guard': current_guard,\n 'start': start,\n 'stop': time,\n })\n start = None\n current_guard = int(parts[3].strip('#'))\n elif parts[2] == 'falls':\n start = time\n sleeping = True\n elif parts[2] == 'wakes':\n if sleeping:\n schedule.append({\n 'guard': current_guard,\n 'start': start,\n 'stop': time,\n })\n sleeping = False\n else:\n print('Messy input!?')\n return schedule","def batch_uses_proper_timestamp_test(self):\n cursor = self.prepare()\n cursor.execute(\"\"\"\n BEGIN BATCH USING TIMESTAMP 1111111111111111\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\n APPLY BATCH\n \"\"\")\n cursor.execute(\"SELECT id, writetime(firstname), writetime(lastname) FROM users\")\n res = sorted(cursor.fetchall())\n assert res == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111111, 1111111111111111]], res","def test_getting_state_parallel(self):\n no_replicates = 25\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\n for i in range(no_replicates):\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)","def test_state(self):\n # blocks [0 3583] [3840 4058]\n test_file1 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-2-0.mdd')\n # blocks [0 1279] [1536 1791] [2048 2303] [2560 2815] [3072 4059]\n test_file2 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-3-0.mdd')\n\n # parse the two .mdd files into the node and instrument group files\n mdd.procall([test_file1, test_file2])\n\n file_state = self.get_file_state('node58p1.dat')\n # there is an unprocessed '/n' in between records\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[4059, 4060]],\n StateKey.FILE_SIZE: 4060,\n StateKey.OUTPUT_INDEX: 1}\n\n if file_state != expected_file_state:\n print file_state\n self.fail(\"Expected file state 1 does not match\")\n\n # blocks [0 2047] [2304 4095] [4096 7451]\n test_file3 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-6-0.mdd')\n\n # parse another .mdd file adding on to the node file, and making\n # another sequence of instrument group files\n mdd.procall([test_file3])\n\n file_state = self.get_file_state('node58p1.dat')\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[4059, 4060]],\n StateKey.FILE_SIZE: 7452,\n StateKey.OUTPUT_INDEX: 2}\n\n if file_state != expected_file_state:\n print \"file state: '%s'\" % file_state\n self.fail(\"Expected file state 2 does not match\")\n\n data_orig = self.read_full_file('node58p1.dat')\n\n # read the data from all generated files into one data string\n data_out = self.read_full_file('node58p1_0.status_1236801.dat')\n data_out += self.read_full_file('node58p1_0.wa_wfp_1236820.dat')\n data_out += self.read_full_file('node58p1_0.wc_wfp_1236820.dat')\n data_out += self.read_full_file('node58p1_0.we_wfp_1236820.dat')\n data_out += self.read_full_file('node58p1_1.status_1236801.dat')\n data_out += self.read_full_file('node58p1_1.wa_wfp_1236822.dat')\n data_out += self.read_full_file('node58p1_1.wc_wfp_1236822.dat')\n data_out += self.read_full_file('node58p1_1.we_wfp_1236822.dat')\n\n # confirm data in the node file matches those output in the instrument groups\n if not TestSioUnpack.compare_sio_matches(data_orig, data_out):\n self.fail(\"Failed sio block compare\")","def testProcess(self):\n plugin = macos_tcc.MacOSTCCPlugin()\n storage_writer = self._ParseDatabaseFileWithPlugin(\n ['TCC-test.db'], plugin)\n\n self.assertEqual(storage_writer.number_of_warnings, 0)\n self.assertEqual(storage_writer.number_of_events, 21)\n\n events = list(storage_writer.GetEvents())\n\n expected_event_values = {\n 'allowed': 1,\n 'client': 'com.apple.weather',\n 'service': 'kTCCServiceUbiquity',\n 'prompt_count': 1,\n 'timestamp': '2020-05-29 12:09:51.000000',\n 'timestamp_desc': definitions.TIME_DESCRIPTION_LAST_PROMPTED_USER}\n\n self.CheckEventValues(storage_writer, events[0], expected_event_values)\n\n expected_message = (\n 'Service: kTCCServiceUbiquity '\n 'Client: com.apple.weather '\n 'Allowed: True '\n 'Prompt count: 1')\n expected_short_message = 'kTCCServiceUbiquity: com.apple.weather'\n\n event_data = self._GetEventDataOfEvent(storage_writer, events[0])\n self._TestGetMessageStrings(\n event_data, expected_message, expected_short_message)","def test_clean_run(self):\n Historical_ROAs_Parsed_Table(clear=True)\n with Historical_ROAs_Table(clear=True) as t:\n Historical_ROAs_Parser().run()\n assert t.get_count() > 2000000","def test_run_is_next_run(curent_time, state):\n date = datetime(2020,5,5,12,0)\n duration_in_minutes = 65\n run = Run(date, duration_in_minutes/60)\n\n assert run.is_next_run(curent_time) == state","def test_meeting_live_stream_status_update(self):\n pass","def test_duplicatekeyerror_state_embryo(self):\n self.pipeline_real.insert_uow = then_raise\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\n verify(self.time_table_mocked, times=0). \\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))","def test_previous_state(self):\n self.report_start(self.whoami())\n\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\n\n # set a series of previous transitions for panda02\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\n\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\n\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\n self.panda_type.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=jobs[0].id)\n job_id = job.id\n self.assertEqual(job.status, TestJob.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(len(jobs), 1)\n job = TestJob.objects.get(id=job_id)\n self.assertEqual(job.status, TestJob.COMPLETE)\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').status,\n Device.IDLE\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda02').health_status,\n Device.HEALTH_PASS\n )\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n panda01 = Device.objects.get(hostname='panda01')\n panda01.status = Device.IDLE\n panda01.save()\n\n jobs = self.scheduler_tick()\n\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_UNKNOWN\n )\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\n\n for job in jobs:\n job_obj = TestJob.objects.get(pk=job.id) # reload\n job_obj.status = TestJob.COMPLETE\n self.job_finished(job_obj)\n\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n self.scheduler_tick()\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\n self.assertEqual(\n Device.objects.get(hostname='panda01').health_status,\n Device.HEALTH_PASS\n )\n\n self.cleanup(self.whoami())","def batch_uses_proper_timestamp_test(self):\n session = self.prepare()\n session.execute(\"\"\"\n BEGIN BATCH USING TIMESTAMP 1111111111111111\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\n APPLY BATCH\n \"\"\")\n rows = session.execute(\"SELECT id, writetime(firstname), writetime(lastname) FROM users\")\n res = sorted(rows)\n assert [list(res[0]), list(res[1])] == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111111, 1111111111111111]], res","def test_act_ii(self):\n # setup\n processing_time = 5.0\n self.transaction_behaviour.processing = \"some_dialogue\"\n self.transaction_behaviour.max_processing = 120\n self.transaction_behaviour.processing_time = processing_time\n\n # operation\n self.transaction_behaviour.act()\n\n # after\n self.assert_quantity_in_outbox(0)\n assert (\n self.transaction_behaviour.processing_time\n == processing_time + self.transaction_behaviour.tick_interval\n )","def emit(self, record):\n self.eng.dispose()\n trys = 3\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\"__\")}\n for t in range(trys):\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\n failed = False\n try:\n with self.eng.connect() as con:\n # formating values to convert from python or parsl to db standards\n info['task_fail_history'] = str(info.get('task_fail_history', None))\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\n run_id = info['run_id']\n\n # if workflow or task has completed, update their entries with the time.\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\n con.execute(up)\n return\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\n workflow = self.meta.tables['task']\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\n .where(workflow.c.run_id == run_id)\n con.execute(up)\n\n # create workflows table if this is a new database without one\n if 'workflows' not in self.meta.tables.keys():\n workflows = create_workflows_table(self.meta)\n self.meta.create_all(con)\n # if this is the first sight of the workflow, add it to the workflows table\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\n workflows = self.meta.tables['workflows']\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\n con.execute(ins)\n\n # if log has task counts, update the workflow entry in the workflows table\n if 'tasks_completed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n if 'tasks_failed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n\n # create task table if this is a new run without one\n if 'task' not in self.meta.tables.keys():\n workflow = create_task_table(self.meta)\n self.meta.create_all(con)\n\n # check to make sure it is a task log and not just a workflow overview log\n if info.get('task_id', None) is not None:\n # if this is the first sight of the task in the workflow, add it to the workflow table\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\n if 'psutil_process_pid' in info.keys():\n # this is the race condition that a resource log is before a status log so ignore this resource update\n return\n\n workflow = self.meta.tables['task']\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\n con.execute(ins)\n\n if 'task_status' in info.keys():\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\n if 'task_status' not in self.meta.tables.keys():\n task_status_table = create_task_status_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n # if this status table already exists, just insert the update\n else:\n task_status_table = self.meta.tables['task_status']\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n return\n\n if 'psutil_process_pid' in info.keys():\n # if this is a task resource update then handle that, if the resource table DNE then create it\n if 'task_resources' not in self.meta.tables.keys():\n task_resource_table = create_task_resource_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n # if this resource table already exists, just insert the update\n else:\n task_resource_table = self.meta.tables['task_resources']\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n return\n\n except Exception as e:\n logger.error(\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\".format(t, str(e)))\n failed = True\n time.sleep(5)\n if not failed:\n return","def test_cron_workflow_service_update_cron_workflow(self):\n pass","def test_non_batch(self):\r\n uid = uuid4()\r\n tmp = TestTimestampModel.create(id=uid, count=1)\r\n\r\n TestTimestampModel.get(id=uid).should.be.ok\r\n\r\n tmp.timestamp(timedelta(seconds=5)).delete()\r\n\r\n with self.assertRaises(TestTimestampModel.DoesNotExist):\r\n TestTimestampModel.get(id=uid)\r\n\r\n tmp = TestTimestampModel.create(id=uid, count=1)\r\n\r\n with self.assertRaises(TestTimestampModel.DoesNotExist):\r\n TestTimestampModel.get(id=uid)\r\n\r\n # calling .timestamp sets the TS on the model\r\n tmp.timestamp(timedelta(seconds=5))\r\n tmp._timestamp.should.be.ok\r\n\r\n # calling save clears the set timestamp\r\n tmp.save()\r\n tmp._timestamp.shouldnt.be.ok\r\n\r\n tmp.timestamp(timedelta(seconds=5))\r\n tmp.update()\r\n tmp._timestamp.shouldnt.be.ok","def test_setting_state_parallel(self):\n no_replicates = 25\n\n replicate(experiment, no_replicates, parallel=True, no_processes=2)\n for i in range(no_replicates):\n self.assertIn('result', state[SUBSTATE_KEY_PATTERN % i])\n self.assertEqual(state[SUBSTATE_KEY_PATTERN % i]['result'], \"bla\")","def test_time_status(self):\n result = self.test_client.time_status\n\n assert result == \"12312\"","def test_ensure_not_ts_ts(self):\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')","def test_post_processing_time():\r\n cmd = ShdlcCmdGetErrorState(clear=False)\r\n assert type(cmd.post_processing_time) is float\r\n assert cmd.post_processing_time == 0.0","def test_timeout_with_crud_failures(self):\n\n # Local methods to validate vb_seqno\n\n def compare_vb_stat(stat_1, stat_2, vb, comparison=\"!=\"):\n keys_to_check = [\"high_seqno\", \"high_completed_seqno\"]\n result = True\n for key in keys_to_check:\n if vb in stat_1.keys():\n if stat_1[vb][\"uuid\"] != stat_2[vb][\"uuid\"]:\n self.log_failure(\"Mismatch in vb-%s UUID. %s != %s\"\n % (vb, stat_1[vb][\"uuid\"],\n stat_2[vb][\"uuid\"]))\n if comparison == \"!=\":\n if stat_1[vb][key] != stat_2[vb][key]:\n result = False\n self.log.warning(\n \"Mismatch in vb-%s stat %s. %s != %s\"\n % (vb, key, stat_1[vb][key], stat_2[vb][key]))\n elif stat_1[vb][key] == stat_2[vb][key]:\n result = False\n self.log.warning(\"Stat not updated for vb-%s stat %s. \"\n \"%s == %s\"\n % (vb, key,\n stat_1[vb][key], stat_2[vb][key]))\n return result\n\n def validate_vb_seqno_stats():\n \"\"\"\n :return retry_validation: Boolean denoting to retry validation\n \"\"\"\n retry_validation = False\n vb_info[\"post_timeout\"][node.ip] = \\\n cbstat_obj[node.ip].vbucket_seqno(self.bucket.name)\n for tem_vb_num in range(self.cluster.vbuckets):\n tem_vb_num = str(tem_vb_num)\n if tem_vb_num not in affected_vbs:\n if compare_vb_stat(vb_info[\"init\"][node.ip],\n vb_info[\"post_timeout\"][node.ip],\n tem_vb_num) is False:\n self.log_failure(\"Unaffected vb-%s stat\" % tem_vb_num)\n elif int(tem_vb_num) in target_nodes_vbuckets[\"active\"]:\n if compare_vb_stat(vb_info[\"init\"][node.ip],\n vb_info[\"post_timeout\"][node.ip],\n tem_vb_num) is False:\n self.log.warning(\"%s - mismatch in %s vb-%s seq_no\"\n % (node.ip, \"active\", tem_vb_num))\n elif int(tem_vb_num) in target_nodes_vbuckets[\"replica\"]:\n if compare_vb_stat(vb_info[\"init\"][node.ip],\n vb_info[\"post_timeout\"][node.ip],\n tem_vb_num, comparison=\"==\") is False:\n retry_validation = True\n self.log.warning(\"%s - mismatch in %s vb-%s seq_no\"\n % (node.ip, \"replica\", tem_vb_num))\n return retry_validation\n\n shell_conn = dict()\n cbstat_obj = dict()\n error_sim = dict()\n target_nodes_vbuckets = dict()\n vb_info = dict()\n tasks = dict()\n doc_gen = dict()\n affected_vbs = list()\n\n target_nodes_vbuckets[\"active\"] = []\n target_nodes_vbuckets[\"replica\"] = []\n vb_info[\"init\"] = dict()\n vb_info[\"post_timeout\"] = dict()\n vb_info[\"afterCrud\"] = dict()\n\n # Override crud_batch_size to minimum value for testing\n self.crud_batch_size = 5\n self.key = \"test_collections\"\n self.sdk_timeout = 3\n\n # Select target vbucket type to load_docs\n target_vb_type = \"replica\"\n if self.simulate_error == CouchbaseError.STOP_PERSISTENCE \\\n and self.durability_level \\\n == Bucket.DurabilityLevel.MAJORITY_AND_PERSIST_TO_ACTIVE:\n target_vb_type = \"active\"\n\n # Create required scope/collection for successful CRUD operation\n if self.scope_name != CbServer.default_scope:\n self.scope_name = self.bucket_util.get_random_name()\n self.collection_name = self.bucket_util.get_random_name()\n self.log.info(\"Creating scope::collection %s::%s\"\n % (self.scope_name, self.collection_name))\n self.create_scope_collection()\n\n # Load docs into created collection\n self.log.info(\"Loading data into created collection\")\n load_gen = doc_generator(self.key, 0, self.num_items)\n task = self.task.async_load_gen_docs(\n self.cluster, self.bucket, load_gen, \"create\", 0,\n scope=self.scope_name,\n collection=self.collection_name,\n sdk_client_pool=self.sdk_client_pool,\n batch_size=200, process_concurrency=8,\n timeout_secs=60)\n self.task_manager.get_task_result(task)\n if self.subdoc_test:\n load_gen = sub_doc_generator(self.key, 0, self.num_items/2)\n task = self.task.async_load_gen_sub_docs(\n self.cluster, self.bucket,\n load_gen, Bucket_Op.SubDocOps.INSERT,\n timeout_secs=self.sdk_timeout,\n compression=self.sdk_compression,\n path_create=True,\n batch_size=100,\n process_concurrency=8,\n durability=self.durability_level,\n scope=self.scope_name, collection=self.collection_name,\n sdk_client_pool=self.sdk_client_pool)\n self.task_manager.get_task_result(task)\n\n self.bucket.scopes[self.scope_name].collections[\n self.collection_name].num_items = self.num_items\n\n target_nodes = DurabilityHelper.getTargetNodes(self.cluster,\n self.nodes_init,\n self.num_nodes_affected)\n for node in target_nodes:\n shell_conn[node.ip] = RemoteMachineShellConnection(node)\n cbstat_obj[node.ip] = Cbstats(node)\n target_nodes_vbuckets[\"active\"] += \\\n cbstat_obj[node.ip].vbucket_list(self.bucket.name,\n vbucket_type=\"active\")\n target_nodes_vbuckets[\"replica\"] += \\\n cbstat_obj[node.ip].vbucket_list(self.bucket.name,\n vbucket_type=\"replica\")\n vb_info[\"init\"][node.ip] = cbstat_obj[node.ip].vbucket_seqno(\n self.bucket.name)\n error_sim[node.ip] = CouchbaseError(self.log, shell_conn[node.ip])\n\n curr_time = int(time.time())\n expected_timeout = curr_time + self.sdk_timeout\n\n if target_vb_type == \"active\":\n target_vbs = list(\n set(target_nodes_vbuckets[target_vb_type])\n .difference(set(target_nodes_vbuckets[\"replica\"])))\n else:\n target_vbs = list(\n set(target_nodes_vbuckets[target_vb_type])\n .difference(set(target_nodes_vbuckets[\"active\"])))\n\n # Create required doc_generators\n doc_gen[\"create\"] = doc_generator(self.key, self.num_items,\n self.crud_batch_size,\n target_vbucket=target_vbs)\n doc_gen[\"delete\"] = doc_generator(self.key, 0,\n self.crud_batch_size,\n target_vbucket=target_vbs)\n doc_gen[\"read\"] = doc_generator(\n self.key, int(self.num_items/3),\n self.crud_batch_size,\n target_vbucket=target_vbs)\n doc_gen[\"update\"] = doc_generator(\n self.key, int(self.num_items/2),\n self.crud_batch_size,\n target_vbucket=target_vbs)\n\n # Create required subdoc generators\n doc_gen[\"insert\"] = sub_doc_generator(\n self.key, int(self.num_items/2), self.crud_batch_size,\n target_vbucket=target_vbs)\n doc_gen[\"upsert\"] = sub_doc_generator_for_edit(\n self.key, 0, self.crud_batch_size,\n template_index=1,\n target_vbucket=target_vbs)\n doc_gen[\"remove\"] = sub_doc_generator(\n self.key, 0, self.crud_batch_size,\n target_vbucket=target_vbs)\n\n # Perform specified action\n for node in target_nodes:\n error_sim[node.ip].create(self.simulate_error,\n bucket_name=self.bucket.name)\n self.sleep(5, \"Wait for error_simulation to take effect\")\n\n ops_to_perform = [Bucket_Op.DocOps.CREATE, Bucket_Op.DocOps.UPDATE,\n Bucket_Op.DocOps.READ, Bucket_Op.DocOps.DELETE]\n if self.subdoc_test:\n ops_to_perform = [Bucket_Op.SubDocOps.INSERT,\n Bucket_Op.SubDocOps.UPSERT,\n Bucket_Op.SubDocOps.REMOVE]\n\n for op_type in ops_to_perform:\n self.log.info(\"Starting doc op %s\" % op_type)\n if op_type in Bucket_Op.DOC_OPS:\n tasks[op_type] = self.task.async_load_gen_docs(\n self.cluster, self.bucket, doc_gen[op_type], op_type, 0,\n scope=self.scope_name,\n collection=self.collection_name,\n sdk_client_pool=self.sdk_client_pool,\n batch_size=1, process_concurrency=8,\n durability=self.durability_level,\n timeout_secs=self.sdk_timeout,\n suppress_error_table=True,\n print_ops_rate=False,\n skip_read_on_error=True)\n else:\n tasks[op_type] = self.task.async_load_gen_sub_docs(\n self.cluster, self.bucket, doc_gen[op_type], op_type, 0,\n scope=self.scope_name,\n collection=self.collection_name,\n sdk_client_pool=self.sdk_client_pool,\n path_create=True,\n batch_size=1, process_concurrency=8,\n durability=self.durability_level,\n timeout_secs=self.sdk_timeout,\n print_ops_rate=False)\n\n self.task.jython_task_manager.get_task_result(tasks[op_type])\n\n # Validate task failures\n if op_type == Bucket_Op.DocOps.READ:\n # Validation for read task\n if len(tasks[op_type].fail.keys()) != 0:\n self.log_failure(\"Read failed for few docs: %s\"\n % tasks[op_type].fail.keys())\n else:\n # Validation of CRUDs - Update / Create / Delete\n for doc_id, crud_result in tasks[op_type].fail.items():\n vb_num = self.bucket_util.get_vbucket_num_for_key(\n doc_id, self.cluster.vbuckets)\n if SDKException.DurabilityAmbiguousException \\\n not in str(crud_result[\"error\"]):\n self.log_failure(\n \"Invalid exception for doc %s, vb %s: %s\"\n % (doc_id, vb_num, crud_result))\n\n # Revert the specified error scenario\n for node in target_nodes:\n error_sim[node.ip].revert(self.simulate_error,\n bucket_name=self.bucket.name)\n\n # Check whether the timeout triggered properly\n if int(time.time()) < expected_timeout:\n self.log_failure(\"Timed-out before expected time\")\n\n for op_type in ops_to_perform:\n if op_type == Bucket_Op.DocOps.READ:\n continue\n while doc_gen[op_type].has_next():\n doc_id, _ = doc_gen[op_type].next()\n affected_vbs.append(\n str(self.bucket_util.get_vbucket_num_for_key(\n doc_id,\n self.cluster.vbuckets)))\n\n affected_vbs = list(set(affected_vbs))\n # Fetch latest stats and validate the seq_nos are not updated\n for node in target_nodes:\n retry_count = 0\n max_retry = 3\n while retry_count < max_retry:\n self.log.info(\"Trying to validate vbseq_no stats: %d\"\n % (retry_count+1))\n retry_count += 1\n retry_required = validate_vb_seqno_stats()\n if not retry_required:\n break\n self.sleep(5, \"Sleep for vbseq_no stats to update\")\n else:\n # This will be exited only if `break` condition is not met\n self.log_failure(\"validate_vb_seqno_stats verification failed\")\n\n self.validate_test_failure()\n\n # Get SDK Client from client_pool\n sdk_client = self.sdk_client_pool.get_client_for_bucket(\n self.bucket,\n self.scope_name,\n self.collection_name)\n\n # Doc error validation\n for op_type in ops_to_perform:\n task = tasks[op_type]\n\n if self.nodes_init == 1 \\\n and op_type != Bucket_Op.DocOps.READ \\\n and len(task.fail.keys()) != (doc_gen[op_type].end\n - doc_gen[op_type].start):\n self.log_failure(\"Failed keys %d are less than expected %d\"\n % (len(task.fail.keys()),\n (doc_gen[op_type].end\n - doc_gen[op_type].start)))\n\n # Create table objects for display\n table_view = TableView(self.log.error)\n ambiguous_table_view = TableView(self.log.info)\n table_view.set_headers([\"Key\", \"vBucket\", \"Exception\"])\n ambiguous_table_view.set_headers([\"Key\", \"vBucket\"])\n\n # Iterate failed keys for validation\n for doc_key, doc_info in task.fail.items():\n vb_for_key = self.bucket_util.get_vbucket_num_for_key(doc_key)\n\n if SDKException.DurabilityAmbiguousException \\\n not in str(doc_info[\"error\"]):\n table_view.add_row([doc_key, vb_for_key,\n doc_info[\"error\"]])\n\n ambiguous_table_view.add_row([doc_key, str(vb_for_key)])\n if op_type not in Bucket_Op.SUB_DOC_OPS:\n retry_success = \\\n self.durability_helper.retry_for_ambiguous_exception(\n sdk_client, op_type, doc_key, doc_info)\n if not retry_success:\n self.log_failure(\"%s failed in retry for %s\"\n % (op_type, doc_key))\n\n # Display the tables (if any errors)\n table_view.display(\"Unexpected exception during %s\" % op_type)\n ambiguous_table_view.display(\"D_Ambiguous exception during %s\"\n % op_type)\n\n # Release the acquired client\n self.sdk_client_pool.release_client(sdk_client)\n\n # Verify doc count after expected CRUD failure\n self.bucket_util._wait_for_stats_all_buckets(self.cluster,\n self.cluster.buckets)\n self.bucket_util.validate_docs_per_collections_all_buckets(\n self.cluster)\n\n # Fetch latest stats and validate the values are updated\n for node in target_nodes:\n vb_info[\"afterCrud\"][node.ip] = \\\n cbstat_obj[node.ip].vbucket_seqno(self.bucket.name)\n if vb_info[\"init\"][node.ip] == vb_info[\"afterCrud\"][node.ip]:\n self.log_failure(\"vBucket seq_no stats not updated\")\n\n # Disconnect the shell connection\n for node in target_nodes:\n shell_conn[node.ip].disconnect()\n\n self.validate_test_failure()","def test_two_prospective_checkpointed_timestamps(self, test_type, stage_pair, pm):\n\n stage1, stage2 = stage_pair\n pm.timestamp(checkpoint=stage1, finished=False)\n pm.timestamp(checkpoint=stage2, finished=False)\n\n if test_type == FILES_TEST:\n checkpoint_files = fetch_checkpoint_files(pm)\n expected = [checkpoint_filepath(stage1, pm)]\n assert set(expected) == set(checkpoint_files)\n else:\n assert stage1 == pm.prev_checkpoint\n assert stage2 == pm.curr_checkpoint","def pg_query_state(config, pid, verbose=False, costs=False, timing=False, \\\n\t\t\t\t\t\t\t\tbuffers=False, triggers=False, format='text'):\n\n\tconn = psycopg2.connect(**config)\n\tcurs = conn.cursor()\n\tcurs.callproc('pg_query_state', (pid, verbose, costs, timing, buffers, triggers, format))\n\tresult = curs.fetchall()\n\tnotices = conn.notices[:]\n\tconn.close()\n\n\treturn result, notices","def get_exist_phase_timestamp(self):\r\n csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\r\n if not os.path.isfile(csv_path):\r\n print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\r\n exit(-1)\r\n converter = {'benchmarkPhase': str, 'streamNumber': int, 'queryNumber': int,\r\n 'epochStartTimestamp': 'int64', 'epochEndTimestamp': 'int64'}\r\n df = pd.read_csv(csv_path, delimiter=';').loc[:,\r\n ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\r\n phase_ts = OrderedDict()\r\n is_exist = False\r\n for phase in self.phase_name:\r\n benchmark_phase = (df['benchmarkPhase'] == phase)\r\n if any(benchmark_phase): # whether this phase exist in the BB logs\r\n if phase == 'POWER_TEST': # power test overall and each query\r\n stream_num = ((df['streamNumber']) == 0)\r\n mask = benchmark_phase & stream_num\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 2] = 0 # fill 0 to blank value\r\n elif phase == 'THROUGHPUT_TEST_1': # throughput test overall and each stream\r\n query_num = (pd.isnull(df['queryNumber']))\r\n mask = benchmark_phase & query_num\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 1:3] = -1 # file -1 to overall throughput test\r\n phase_ts[phase].iloc[1:, 2] = 0 # fill 0 to blank value\r\n else: # other phases\r\n stream_num = (pd.isnull(df['streamNumber']))\r\n query_num = (pd.isnull(df['queryNumber']))\r\n mask = benchmark_phase & stream_num & query_num\r\n # phase_start = line['epochStartTimestamp'].values / 1000\r\n # phase_end = line['epochEndTimestamp'].values / 1000\r\n phase_ts[phase] = df[mask].reset_index(drop=True)\r\n phase_ts[phase].iloc[0, 1:3] = 0 # fill 0 to blank value\r\n phase_ts[phase] = phase_ts[phase].astype(converter)\r\n is_exist = True\r\n if is_exist:\r\n return phase_ts\r\n else:\r\n print('It seems BigBenchTimes.csv in {0} does not include any TPCx-BB phases, ' \\\r\n 'existing...'.format(self.bb_log_path))\r\n exit(-1)","def test_update_activity_occurrence_status(self):\n pass","def test_time(self):\n M = simulation.StateMonitor(self.G, 'v')\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))","def verify_event_timing(self, event, item):\n return True","def test_status(self):\n keys = [\n 'receiver time mark count', 'ant type base', 'solution status',\n ...\n ]\n\n for i in xrange(0, 100):\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)","def test_ensure_ts_not_ts(self):\n self.assertEqual(ensure_ts(self.jobset1), 'opt')","def filter_by_state(self, stream_state: Mapping[str, Any] = None, record: Mapping[str, Any] = None) -> Iterable:\n start_date = \"1900-01-01T00:00:00.0Z\"\n if pendulum.parse(record[self.cursor_field]) > pendulum.parse((stream_state or {}).get(self.cursor_field, start_date)):\n # Persist state.\n # There is a bug in state setter: because of self._cursor_value is not defined it raises Attribute error\n # which is ignored in airbyte_cdk/sources/abstract_source.py:320 and we have an empty state in return\n # See: https://github.com/airbytehq/oncall/issues/1317\n self.state = record\n yield record","def test_update_single_row_if_status_is_in_progress(self):\n first = generate_mock_result(status='IN_PROGRESS', success=False)\n self.db.insert_single_result(first)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'IN_PROGRESS')\n second = generate_mock_result(status='SUCCESS', success=True)\n self.db.insert_single_result(second)\n current = self.db.get_result_by_primary_key(first.get('id'))\n self.assertEqual(current.status, 'SUCCESS')","def testPostProcessResourceState(self):\n resource_id_1 = \"resource_id_1\"\n resource_id_2 = \"resource_id_2\"\n task_id_1 = \"task_id_1\"\n task_id_2 = \"task_id_2\"\n\n # Add test resource ids and task ids into state file\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n\n # Test that task id was removed from resource id\n json_out_1 = {resource_id_1: [task_id_2], resource_id_2: [task_id_1]}\n is_detachable = resource_manager.PostProcessResourceState(\n resource_id_1, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_1)\n self.assertEqual(is_detachable, False)\n\n # Test that resource id was removed from resource state\n json_out_2 = {resource_id_2: [task_id_1]}\n is_detachable = resource_manager.PostProcessResourceState(\n resource_id_1, task_id_2)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\n self.assertEqual(is_detachable, True)\n\n # Test that non existent task did not throw an error\n is_detachable = resource_manager.PostProcessResourceState(\n resource_id_2, task_id_2)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\n self.assertEqual(is_detachable, False)\n\n # Test removing all from resource state\n json_out_3 = {}\n is_detachable = resource_manager.PostProcessResourceState(\n resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\n self.assertEqual(is_detachable, True)","def test_running_job(self):\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_same_unique_crash_type_with_same_state(self):\n self.testcases[0].security_flag = False\n self.testcases[0].crash_type = 'Timeout'\n self.testcases[0].crash_state = 'abcde'\n self.testcases[0].timestamp = datetime.datetime.utcfromtimestamp(0)\n self.testcases[1].security_flag = False\n self.testcases[1].crash_type = 'Timeout'\n self.testcases[1].crash_state = 'abcde'\n self.testcases[1].timestamp = datetime.datetime.utcfromtimestamp(1)\n\n for t in self.testcases:\n t.put()\n\n grouper.group_testcases()\n\n testcases = []\n for testcase_id in data_handler.get_open_testcase_id_iterator():\n testcases.append(data_handler.get_testcase_by_id(testcase_id))\n\n self.assertEqual(len(testcases), 1)\n self.assertEqual(testcases[0].group_id, 0)\n self.assertTrue(testcases[0].is_leader)","def task_instance_pre_save_handler(instance, **_):\n if instance.state in (SUCCESSFUL, FAILED):\n instance.datetime_finished = timezone.now()","def assert_history(self, rows):\r\n self.assertEqual(self.parse_rows(rows), self.read_history())","def test_time_successful(self):\n\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\n '2016-01-10T00:00:00Z',\n '2016-01-13T00:00:00Z',\n 'source')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 2)\n for result in results:\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])","def _server_poll_expcompleted_(self):\n #print \"class Princeton_CCD function _server_poll_expcompleted_\" \n try:\n last_state = self.polled_running\n except (AttributeError,UnboundLocalError):\n self.polled_running = False\n last_state = False\n self.polled_running = self.query_running()\n if (not bool(last_state) and bool(self.polled_running)):\n self.begin_acq_time = time.time()\n #print self.query_running(), last_state\n #if ((last_state == True) and (self.polled_running == False)): CP\n if (bool(last_state) and not bool(self.polled_running)):\n self.end_acq_time = time.time()\n return True\n else:\n return False","def test_update_splits():\n assert new_log.iloc[0]['start'] == 0","def test_time_type_state_is_noon(day):\n\n assert day_time_info(day.hours_0).is_noon is False\n assert day_time_info(day.hours_1).is_noon is False\n assert day_time_info(day.hours_2).is_noon is False\n assert day_time_info(day.hours_3).is_noon is False\n assert day_time_info(day.hours_4).is_noon is False\n assert day_time_info(day.hours_5).is_noon is False\n assert day_time_info(day.hours_6).is_noon is False\n assert day_time_info(day.hours_7).is_noon is False\n assert day_time_info(day.hours_8).is_noon is False\n assert day_time_info(day.hours_9).is_noon is False\n assert day_time_info(day.hours_10).is_noon is False\n assert day_time_info(day.hours_11).is_noon is False\n assert day_time_info(day.hours_12).is_noon is True\n assert day_time_info(day.hours_13).is_noon is False\n assert day_time_info(day.hours_14).is_noon is False\n assert day_time_info(day.hours_15).is_noon is False\n assert day_time_info(day.hours_16).is_noon is False\n assert day_time_info(day.hours_17).is_noon is False\n assert day_time_info(day.hours_18).is_noon is False\n assert day_time_info(day.hours_19).is_noon is False\n assert day_time_info(day.hours_20).is_noon is False\n assert day_time_info(day.hours_21).is_noon is False\n assert day_time_info(day.hours_22).is_noon is False\n assert day_time_info(day.hours_23).is_noon is False","def test_time(self):\n conn = self.database.connection()\n cursor = conn.cursor()\n dialect = self.database.dialect()\n dbapi = self.database.dbapi()\n query = dialect.translate('DROP TABLE test_time')\n try:\n cursor.execute(query)\n except dbapi.Error:\n conn.rollback()\n query = dialect.translate('CREATE TABLE test_time ' \\\n '( value TIME NOT NULL )')\n cursor.execute(query)\n data = []\n rr = random.randrange\n query = 'INSERT INTO test_time VALUES (%s)'\n for i in range(100):\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\n data.append(item)\n cursor.execute(query, (item,))\n query = 'SELECT * FROM test_time'\n cursor.execute(query)\n result = cursor.fetchall()\n for row in result:\n item = row[0]\n assert isinstance(item, datetime.time)\n assert item in data\n data.remove(item)\n query = dialect.translate('DELETE FROM test_time')\n cursor.execute(query)\n query = dialect.translate('DROP TABLE test_time')\n cursor.execute(query)\n conn.commit()","def test_state(self, api, state):\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\n\n assert stream.state == {}\n\n stream.state = state\n actual_state = stream.state\n actual_state[\"slices\"] = sorted(actual_state.get(\"slices\", []))\n state[\"slices\"] = sorted(state.get(\"slices\", []))\n state[\"time_increment\"] = 1\n\n assert actual_state == state","def each_statement_in_batch_uses_proper_timestamp_test(self):\n cursor = self.prepare()\n cursor.execute(\"\"\"\n BEGIN BATCH\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow') USING TIMESTAMP 1111111111111111\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner') USING TIMESTAMP 1111111111111112\n APPLY BATCH\n \"\"\")\n cursor.execute(\"SELECT id, writetime(firstname), writetime(lastname) FROM users\")\n res = sorted(cursor.fetchall())\n assert res == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111112, 1111111111111112]], res","def testScanForProcessedTasks(self):\n redis_client = self._CreateRedisClient()\n\n session = sessions.Session()\n task = tasks.Task(session_identifier=session.identifier)\n\n # There should be no processed task identifiers initially.\n task_identifiers, _ = redis_store.RedisStore.ScanForProcessedTasks(\n session.identifier, redis_client=redis_client)\n self.assertEqual([], task_identifiers)\n\n # Opening and closing a writer for a task should cause the task to be marked\n # as complete.\n storage_writer = writer.RedisStorageWriter(\n storage_type=definitions.STORAGE_TYPE_TASK)\n storage_writer.Open(\n redis_client=redis_client, session_identifier=task.session_identifier,\n task_identifier=task.identifier)\n storage_writer.Close()\n\n # The task store is now marked as processed.\n task_identifiers, _ = redis_store.RedisStore.ScanForProcessedTasks(\n session.identifier, redis_client=redis_client)\n self.assertEqual([task.identifier], task_identifiers)","def test_ensure_not_ts_novib(self):\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')","def test_notBeforeBefore(self):\n dbpool, qpool, clock, performerChosen = self._setupPools()\n\n @transactionally(dbpool.pool.connection)\n def check(txn):\n return qpool.enqueueWork(\n txn, DummyWorkItem, a=3, b=9,\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\n )\n\n yield check\n\n clock.advance(1000)\n # Advance far beyond the given timestamp.\n self.assertEquals(performerChosen, [True])\n\n # Wait for job\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\n clock.advance(1)\n\n # Work item complete\n self.assertTrue(DummyWorkItem.results == {1: 12})","def test_source_time_successful(self):\n\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\n '2016-01-01T00:00:00Z',\n '2016-01-02T00:00:00Z')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 1)\n for result in results:\n self.assertTrue(result['id'] in [self.job1.id])","def test_meeting_status(self):\n pass","def each_statement_in_batch_uses_proper_timestamp_test(self):\n session = self.prepare()\n session.execute(\"\"\"\n BEGIN BATCH\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow') USING TIMESTAMP 1111111111111111\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner') USING TIMESTAMP 1111111111111112\n APPLY BATCH\n \"\"\")\n rows = session.execute(\"SELECT id, writetime(firstname), writetime(lastname) FROM users\")\n res = sorted(rows)\n assert [list(res[0]), list(res[1])] == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111112, 1111111111111112]], res","def test_set_state(self):\n self.stream_handle = open(os.path.join(RESOURCE_PATH, 'adcpt_20130929_091817.DAT'))\n self.parser = AdcpsJlnStcParser(self.config, self.start_state, self.stream_handle,\n self.state_callback, self.pub_callback, self.exception_callback)\n new_state = {StateKey.POSITION: 590}\n self.parser.set_state(new_state)\n result = self.parser.get_records(1)\n self.assert_result(result, self.particle_c, 1251)\n result = self.parser.get_records(1)\n self.assert_result(result, self.particle_d, 1622)","def test_exp_by_states(self):\n api = my_mock.api_mock({\"items\": [{'state': 'Waiting', 'id': 10134},\n {'state': 'Waiting', 'id': 10135},\n {'state': 'Running', 'id': 10130}]})\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\n self.assertEquals(\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\n my_mock.api_mock_stop()","def test_same_unique_crash_type_with_different_state(self):\n self.testcases[0].security_flag = False\n self.testcases[0].crash_type = 'Timeout'\n self.testcases[0].crash_state = 'abcdef'\n self.testcases[1].security_flag = False\n self.testcases[1].crash_type = 'Timeout'\n self.testcases[1].crash_state = 'abcde'\n\n for t in self.testcases:\n t.put()\n\n grouper.group_testcases()\n\n for index, t in enumerate(self.testcases):\n self.testcases[index] = data_handler.get_testcase_by_id(t.key.id())\n self.assertEqual(self.testcases[index].group_id, 0)\n self.assertTrue(self.testcases[index].is_leader)","def test_peformance(self):\n timedeltas = []\n for file in os.listdir(settings.ANALYSIS_REPORT_FOLDER):\n _file = open(os.path.join(settings.ANALYSIS_REPORT_FOLDER, file), \"r\")\n report = json.loads(_file.read())\n timedeltas.append(\n parse_datetime(report['finish']) - parse_datetime(report['start']))\n _file.close()\n\n # number of queue\n print('NUMBER OF QUEUE = {}'.format(len(timedeltas)))\n\n # get average time\n average_timedelta = sum(timedeltas, datetime.timedelta(0)) / len(timedeltas)\n print('AVERAGE = {}'.format(average_timedelta))\n self.assertTrue(average_timedelta < datetime.timedelta(minutes=3))\n\n # get total process time\n total = timedeltas[0]\n for delta in timedeltas[:1]:\n total += delta\n print('TOTAL = {}'.format(total))\n self.assertTrue(total < datetime.timedelta(minutes=3 * len(timedeltas)))","def queryTCS():\n timestamp = datetime.datetime.now().isoformat()\n values = []\n for statusPiece in statusPieces:\n TN.write(statusPiece+\"\\r\\n\")\n value = TN.read_until(\"\\n\", 0.5)\n value = value.strip()\n if not value:\n value = \"?\"\n values.append(value)\n if values[2] == \"?\" or int(values[2]) != 2:\n return # only record if in tracking state\n with open(filename, \"a\") as f:\n f.write(\"%s %s %s %s %s %s\\n\"%tuple([timestamp]+values))","def _assert_state(self, state_dict):\n instances = db.instance_get_all(self.context)\n self.assertEqual(len(instances), 1)\n\n if 'vm_state' in state_dict:\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\n if 'task_state' in state_dict:\n self.assertEqual(state_dict['task_state'],\n instances[0]['task_state'])\n if 'power_state' in state_dict:\n self.assertEqual(state_dict['power_state'],\n instances[0]['power_state'])","def test_time(self):\r\n pass","def test_matlab_response_migration_of_queuetime(self, time):\r\n state = {'input_state': {'queuestate': 'queued'}}\r\n elt = etree.fromstring(self.xml)\r\n\r\n the_input = self.input_class(test_capa_system(), elt, state)\r\n context = the_input._get_render_context()\r\n self.assertEqual(the_input.status, 'unsubmitted')","def test_immutable_results(env):\n def process(env):\n timeout = [env.timeout(delay, value=delay) for delay in range(3)]\n # The or condition in this expression will trigger immediately. The and\n # condition will trigger later on.\n condition = timeout[0] | (timeout[1] & timeout[2])\n\n results = yield condition\n assert results == {timeout[0]: 0}\n\n # Make sure that the results of condition were frozen. The results of\n # the nested and condition do not become visible afterwards.\n yield env.timeout(2)\n assert results == {timeout[0]: 0}\n\n env.process(process(env))\n env.run()","def test_commitsstate(self):\n\n m = mapper(Order, orders, properties = {\n 'userident':deferred(orders.c.user_id, group='primary'),\n 'description':deferred(orders.c.description, group='primary'),\n 'opened':deferred(orders.c.isopen, group='primary')\n })\n sess = create_session()\n q = sess.query(m)\n o2 = q.all()[2]\n # this will load the group of attributes\n assert o2.description == 'order 3'\n assert o2 not in sess.dirty\n # this will mark it as 'dirty', but nothing actually changed\n o2.description = 'order 3'\n def go():\n # therefore the flush() shouldnt actually issue any SQL\n sess.flush()\n self.assert_sql_count(testing.db, go, 0)","def testPreProcessResourceState(self):\n resource_id_1 = \"resource_id_1\"\n task_id_1 = \"task_id_1\"\n json_out = {resource_id_1: [task_id_1]}\n\n # Test that the resource id is properly added with associated task\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\n\n # Test that the additional task id is appended to resource id\n task_id_2 = \"task_id_2\"\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\n\n # Test that additional resource id is added into state file\n resource_id_2 = \"resource_id_2\"\n json_out_3 = {\n resource_id_1: [task_id_1, task_id_2],\n resource_id_2: [task_id_1]\n }\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\n\n # Test that same task id is not duplicated into state file\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)","def cancellation_active(state: 'JobState') -> bool:\n return state in [\n JobState.STAGING_IN_CR, JobState.WAITING_CR, JobState.RUNNING_CR,\n JobState.STAGING_OUT_CR\n ]","def update_status(cls):\n for job in cls.query.filter(cls.finished == False):\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\n dynamo = DynamoIngestionStatusClient()\n num_urls_left = 0\n for url in urls_left:\n dynamo_url = dynamo.get(url.url)\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\n # will never be processed, so ignore for our purposes\n url.processed = True\n else:\n num_urls_left += 1\n if num_hits_left+num_urls_left == 0:\n job.finished = True\n print '*** Job ID: %s is complete ***' % str(job.id)\n\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\n if not job.finished:\n print 'unprocessed URLs: %i' % num_urls_left\n print 'outstanding HITs: %i\\n' % num_hits_left\n session.flush()","def test_process(self, tmp_path):\n export_dir = tmp_path / 'export'\n export_dir.mkdir()\n\n process(['raw_data/small_raw_data_0.csv',\n 'raw_data/small_raw_data_1.csv',\n # File 2 does not exist.\n 'raw_data/small_raw_data_2.csv',\n 'raw_data/small_raw_data_3.csv'],\n SIGNALS,\n ['median_home_dwell_time',\n 'completely_home_prop_7dav'],\n ['state'],\n export_dir)\n\n expected = {\n 'wip_median_home_dwell_time': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [6, 3.5],\n 'se': [None, 0.5],\n 'sample_size': [1, 2]\n }),\n 'completely_home_prop': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [0.15, 0.055],\n 'se': [None, 0.005],\n 'sample_size': [1, 2]\n }),\n 'part_time_work_prop': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [0.35, 0.055],\n 'se': [None, 0.005],\n 'sample_size': [1, 2]\n }),\n 'full_time_work_prop': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [0.45, 0.055],\n 'se': [None, 0.005],\n 'sample_size': [1, 2]\n }),\n 'median_home_dwell_time_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [4.5, 3.5, 7.5],\n 'se': [1.5, 0.5, 0.5],\n 'sample_size': [2, 2, 2]\n }),\n 'wip_completely_home_prop_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [0.1, 0.055, 0.15],\n 'se': [0.05, 0.005, 0.05],\n 'sample_size': [2, 2, 2]\n }),\n 'part_time_work_prop_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [0.25, 0.055, 0.25],\n 'se': [0.1, 0.005, 0.05],\n 'sample_size': [2, 2, 2]\n }),\n 'full_time_work_prop_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [0.35, 0.055, 0.35],\n 'se': [0.1, 0.005, 0.05],\n 'sample_size': [2, 2, 2]\n })\n }\n actual = {signal: pd.read_csv(\n export_dir / f'20200612_state_{signal}.csv')\n for signal in expected}\n for signal in expected:\n pd.testing.assert_frame_equal(expected[signal], actual[signal])","def test_process_filter_value():\n now = dt.utcnow()\n now_ts = now.timestamp()\n filter_ = {'column': \"ts_created_at\", 'value': now_ts, type: 'leq'}\n assert process_filter_value(filter_) == now\n\n filter_ = {'column': \"created_at\", 'value': now_ts, type: 'leq'}\n assert process_filter_value(filter_) == now_ts","def test_order_update_status_function(self):\n order = OrderInfo.objects.create(user=self.create_test_user())\n self.assertIsInstance(order.ordered, datetime)\n self.assertIsNone(order.cooked)\n self.assertIsNone(order.delivered)\n\n order.update_current_state()\n self.assertIsInstance(order.cooked, datetime)\n self.assertIsNone(order.delivered)\n\n order.update_current_state()\n self.assertIsInstance(order.cooked, datetime)\n self.assertIsInstance(order.delivered, datetime)","def test_condition_value(env):\n timeouts = list([env.timeout(delay, value=delay) for delay in range(3)])\n\n def p(env, timeouts):\n results = yield env.all_of(timeouts)\n assert list(results) == timeouts\n assert list(results.keys()) == timeouts\n assert list(results.values()) == [0, 1, 2]\n assert list(results.items()) == list(zip(timeouts, [0, 1, 2]))\n assert timeouts[0] in results\n assert results[timeouts[0]] == 0\n assert results == results\n assert results == results.todict()\n\n env.process(p(env, timeouts))\n env.run()","def test_update_file_state(self):\n # blocks [0 4012], based on unit_362-2013-202-2-0.mdd\n test_file1 = os.path.join(INPUT_HYPM_PATH, 'first.mdd')\n\n # parse the first .mdd files into the node and instrument group files\n mdd.procall([test_file1])\n\n file_state = self.get_file_state('node60p1.dat')\n expected_file_state_1 = {StateKey.UNPROCESSED_DATA: [],\n StateKey.FILE_SIZE: 4012,\n StateKey.OUTPUT_INDEX: 1}\n\n if file_state != expected_file_state_1:\n print \"file state try 1: '%s'\" % file_state\n self.fail(\"Expected file state 1 does not match\")\n\n test_file2 = os.path.join(INPUT_HYPM_PATH, 'unit_362-2013-202-2-0.mdd')\n\n # parse the first .mdd files into the node and instrument group files\n mdd.procall([test_file2])\n\n file_state = self.get_file_state('node60p1.dat')\n expected_file_state_2 = {StateKey.UNPROCESSED_DATA: [[4736, 8192]],\n StateKey.FILE_SIZE: 8192,\n StateKey.OUTPUT_INDEX: 2}\n\n if file_state != expected_file_state_2:\n print \"file state try 2: '%s'\" % file_state\n self.fail(\"Expected file state 2 does not match\")\n\n # start second test, switch to node58\n # blocks [0 3583] [3840 4058]\n test_file1 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-2-0.mdd')\n mdd.procall([test_file1])\n\n file_state = self.get_file_state('node58p1.dat')\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[3189, 3945]],\n StateKey.FILE_SIZE: 4059,\n StateKey.OUTPUT_INDEX: 1}\n\n if file_state != expected_file_state:\n print file_state\n self.fail(\"Expected file state 3 does not match\")\n\n # blocks [0 1279] [1536 1791] [2048 2303] [2560 2815] [3072 4059]\n test_file2 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-3-0.mdd')\n\n # parse the two .mdd files into the node and instrument group files\n mdd.procall([test_file2])\n\n file_state = self.get_file_state('node58p1.dat')\n # there is an unprocessed '/n' in between records\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[4059, 4060]],\n StateKey.FILE_SIZE: 4060,\n StateKey.OUTPUT_INDEX: 2}\n\n if file_state != expected_file_state:\n print file_state\n self.fail(\"Expected file state 4 does not match\")","def testTimestamps(self):\n predicate = \"metadata:predicate\"\n subject = \"aff4:/metadata:8\"\n\n # Extend the range of valid timestamps returned from the table to account\n # for potential clock skew.\n start = long(time.time() - 60) * 1e6\n data_store.DB.Set(subject, predicate, \"1\", token=self.token)\n\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\n\n # Check the time is reasonable\n end = long(time.time() + 60) * 1e6\n\n self.assert_(ts >= start and ts <= end)\n self.assertEqual(stored, \"1\")","def timestep(self):\n self.survive()\n self.move()\n self.set_survival_probability(self.vital_var)\n self.reproduce()\n # self.save_frame()\n if self.time % self.save_state_frequency == 0:\n self.save_state(self.state_file.format(self.time))\n if self.time % self.save_stats_frequency == 0:\n self.write_stats()\n # self.connected_component()\n self.time += 1\n\n if self.pop_size > 0:\n return True\n else:\n return False","def test_data_handlers(timeseries, handler):\n handler = handler(timeseries, HISTORICAL_CONTEXT_NUMBER)\n updates = []\n i = 0\n while True:\n try:\n context, update = handler.next()\n except NoMoreData:\n break\n i += 1\n\n updates.append(update)\n # import pdb; pdb.set_trace()\n assert context[-1].datetime < update.datetime\n\n # total lenght of series(25) - context(5) which aren't provided as updates\n assert i == 20\n updates = [x.datetime.date().isoformat() for x in updates]\n assert updates[10] == \"2018-01-22\"","def test_get_field_state_comparisons_tiny(self):\r\n comparison_groupings = get_field_state_comparisons(\r\n self.tiny_dist_matrix_header, self.tiny_dist_matrix,\r\n self.tiny_mapping_header, self.tiny_mapping, self.tiny_field,\r\n ['SampleFieldState1'])\r\n self.assertEqual(comparison_groupings, {})","def valid(t):\n return float(t) > time.time()"],"string":"[\n \"def test_preset_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=0).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_future_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS, TEST_FUTURE_TIMEPERIOD, PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_transfer_to_final_timeperiod_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_retry_state_in_progress(self):\\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n self.pipeline_real.insert_uow = then_raise\\n self.pipeline_real.recover_from_duplicatekeyerror = override_recover_function\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_IN_PROGRESS,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n # verify(pipeline, times=1).\\\\\\n # _compute_and_transfer_to_final_run(any(str), any(str), any(str), any(Job))\\n # verify(pipeline, times=1).\\\\\\n # _process_state_final_run(any(str), any(Job))\",\n \"def test_processed_state_final_run(self):\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_FINAL_RUN,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=1).get_tree(any(str))\",\n \"def test_state_skipped(self):\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_SKIPPED,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def test_retrieve_instances_schedule_state(self):\\n pass\",\n \"def test_update_instances_schedule_state(self):\\n pass\",\n \"def test_issue_tracked_times(self):\\n pass\",\n \"def test_timeout_processing(self):\\n # setup\\n self.transaction_behaviour.processing_time = None\\n\\n # operation\\n self.transaction_behaviour._timeout_processing()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\",\n \"def test_state_embryo(self):\\n self.pipeline_real.insert_uow = then_return_uow\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def phone_timezones_have_been_processed():\\n if settings.UNIT_TESTING:\\n override = getattr(\\n settings, 'PHONE_TIMEZONES_HAVE_BEEN_PROCESSED', None)\\n if override is not None:\\n return override\\n return (_get_migration_status_from_threadlocals()\\n == MigrationStatus.COMPLETE)\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def saveNeventsByProcessingType(neventsByProcessingType, qtime):\\n\\n try:\\n with transaction.atomic():\\n for pt, data in neventsByProcessingType.items():\\n row = ProdNeventsHistory(processingtype=pt,\\n neventstotal=data['total'],\\n neventsused=data['used'],\\n neventswaiting=data['waiting'],\\n neventsrunning=data['running'],\\n timestamp=qtime)\\n row.save()\\n except DatabaseError as e:\\n print (e.message)\\n return False\\n return True\",\n \"def test_reading_pk_timestamps_with_counters(self):\\n self.prepare()\\n self.session.execute(\\\"\\\"\\\"\\n CREATE TABLE test_pk_timestamps_with_counters\\n (columnname text, day timestamp,\\n israndom boolean, columnvalue text, counter counter,\\n PRIMARY KEY ((columnname, day, israndom), columnvalue)\\n )\\\"\\\"\\\")\\n\\n records = ['origins|2016-10-01 00:00:00+0000|False|ACTUAL|6\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|ADGMOB|4\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|ANONPM|4\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|CSRT2L|76\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|DIAGOP|18\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|E-SOFT|17\\\\n',\\n 'origins|2016-10-01 00:00:00+0000|False|E-TASK|10\\\\n']\\n\\n tempfile = self.get_temp_file()\\n with open(tempfile.name, 'w') as f:\\n f.writelines(records)\\n\\n logger.debug('Importing from csv file: {name}'.format(name=tempfile.name))\\n cmds = \\\"COPY ks.test_pk_timestamps_with_counters FROM '{name}' WITH delimiter = '|'\\\".format(name=tempfile.name)\\n self.run_cqlsh(cmds=cmds)\\n\\n res = rows_to_list(self.session.execute(\\\"SELECT COUNT(*) FROM ks.test_pk_timestamps_with_counters\\\"))[0][0]\\n assert len(records) == res, \\\"Failed to import one or more rows, expected {} but got {}\\\".format(len(records), res)\",\n \"def test_ensure_ts_ts(self):\\n self.assertEqual(ensure_ts(self.jobset2), 'imaginary')\",\n \"def _compute_queue_state(self):\\n for record in self:\\n if record.queue_line_total_records == record.queue_line_done_records + record.queue_line_cancel_records:\\n record.state = \\\"completed\\\"\\n elif record.queue_line_draft_records == record.queue_line_total_records:\\n record.state = \\\"draft\\\"\\n elif record.queue_line_total_records == record.queue_line_fail_records:\\n record.state = \\\"failed\\\"\\n else:\\n record.state = \\\"partially_completed\\\"\",\n \"def test_unfinished_activity(self):\\n with open(os.path.dirname(os.path.abspath(__file__)) + \\\"/time_unfinished.log\\\", 'r') as f:\\n results_list = analysis.parse_time_log(f)\\n\\n for x in range(len(expected_activity_list)):\\n assert results_list[x] == expected_activity_list[x]\\n\\n # the last event is ongoing so I can't test an exact end time\\n expected_ongoing_dt_begin = datetime(2016, 8, 25, 18, 50, 53)\\n assert results_list[-1].dt_begin == expected_ongoing_dt_begin \\\\\\n and results_list[-1].activity_type == 'p' \\\\\\n and datetime.now() - results_list[-1].dt_end < timedelta(minutes=1)\",\n \"def test_initial_timestamp_states(\\n self, get_pipe_manager, retrospective, which_checkpoint_state\\n ):\\n\\n # Create the manager and make the timestamp call.\\n pm = get_pipe_manager(name=\\\"InitialTimestampState\\\")\\n stage_name = \\\"quality_control\\\"\\n pm.timestamp(checkpoint=stage_name, finished=retrospective)\\n\\n # Form expectations.\\n if retrospective:\\n prev_exp = stage_name\\n curr_exp = None\\n else:\\n prev_exp = None\\n curr_exp = stage_name\\n\\n # Make the assertion on the specified checkpoint state.\\n if which_checkpoint_state == \\\"curr_checkpoint\\\":\\n assert curr_exp == getattr(pm, \\\"curr_checkpoint\\\")\\n else:\\n assert prev_exp == getattr(pm, \\\"prev_checkpoint\\\")\",\n \"def test_run_now(curent_time,state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.run_now(curent_time) == state\",\n \"def test_retrospective_the_prospective_checkpointed_timestamps(\\n self, test_type, stage_pair, pm\\n ):\\n\\n stage1, stage2 = stage_pair\\n pm.timestamp(checkpoint=stage1, finished=True)\\n assert stage1 == pm.prev_checkpoint\\n assert pm.curr_checkpoint is None\\n pm.timestamp(checkpoint=stage2, finished=False)\\n\\n if test_type == FILES_TEST:\\n expected = [checkpoint_filepath(stage1, pm)]\\n assert set(expected) == set(fetch_checkpoint_files(pm))\\n else:\\n assert pm.prev_checkpoint is None\\n assert stage2 == pm.curr_checkpoint\",\n \"def __checkInited(self, date=None, tkey=None, ttype=None):\\n if ttype == 'crontab':\\n return False\\n if date is None:\\n date = self.ddate\\n\\n for doc in TaskHistory().search(task_day=date, task_key=tkey):\\n if doc.get(\\\"status\\\") == \\\"waiting\\\":\\n return True\\n return False\",\n \"def test_setting_continuous_processing(processor):\\n processor.continuous_processing = False\\n assert not processor._state.test('continuous_processing')\\n processor.continuous_processing = True\\n assert processor._state.test('continuous_processing')\",\n \"def identify_exec_running(self, record):\\n return [\\\"running\\\"]\",\n \"def test_prospective_then_retrospective_checkpointed_timestamps(\\n self, test_type, stage_pair, pm\\n ):\\n\\n stage1, stage2 = stage_pair\\n pm.timestamp(checkpoint=stage1, finished=False)\\n assert stage1 == pm.curr_checkpoint\\n pm.timestamp(checkpoint=stage2, finished=True)\\n\\n if test_type == FILES_TEST:\\n checkpoint_files = fetch_checkpoint_files(pm)\\n expected = [checkpoint_filepath(stage2, pm)]\\n assert set(expected) == set(checkpoint_files)\\n else:\\n # Current checkpoint will be reset by second (retrospective)\\n # timestamp call.\\n assert stage2 == pm.prev_checkpoint\\n assert pm.curr_checkpoint is None\",\n \"def test_cancelled_state_final_run(self):\\n uow_dao_mock = mock(UnitOfWorkDao)\\n when(uow_dao_mock).get_one(any()).thenReturn(\\n create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_CANCELED))\\n self.pipeline_real.uow_dao = uow_dao_mock\\n\\n pipeline = spy(self.pipeline_real)\\n job_record = get_job_record(job.STATE_FINAL_RUN,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=1). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\\n verify(self.time_table_mocked, times=0).get_tree(any(str))\",\n \"def load_status_table():\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def test_running_job(self):\\n\\n running_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"running\\\"))\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def process(rows):\\n # current state\\n current_guard = None\\n sleeping = False\\n start = None\\n # handle time data as datetime\\n # I saw the 23:58 begin time in test data and figured I wanted date classes\\n from datetime import datetime\\n dateformat = '[%Y-%m-%d %H:%M]'\\n schedule = []\\n for row in rows:\\n parts = row.split()\\n time = datetime.strptime(' '.join(parts[0:2]), dateformat)\\n if parts[2] == 'Guard':\\n if sleeping: # shouldn't happen\\n print('Messy input?!')\\n schedule.append({\\n 'guard': current_guard,\\n 'start': start,\\n 'stop': time,\\n })\\n start = None\\n current_guard = int(parts[3].strip('#'))\\n elif parts[2] == 'falls':\\n start = time\\n sleeping = True\\n elif parts[2] == 'wakes':\\n if sleeping:\\n schedule.append({\\n 'guard': current_guard,\\n 'start': start,\\n 'stop': time,\\n })\\n sleeping = False\\n else:\\n print('Messy input!?')\\n return schedule\",\n \"def batch_uses_proper_timestamp_test(self):\\n cursor = self.prepare()\\n cursor.execute(\\\"\\\"\\\"\\n BEGIN BATCH USING TIMESTAMP 1111111111111111\\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\\n APPLY BATCH\\n \\\"\\\"\\\")\\n cursor.execute(\\\"SELECT id, writetime(firstname), writetime(lastname) FROM users\\\")\\n res = sorted(cursor.fetchall())\\n assert res == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111111, 1111111111111111]], res\",\n \"def test_getting_state_parallel(self):\\n no_replicates = 25\\n replicate(experiment2, no_replicates, parallel=True, no_processes=2)\\n for i in range(no_replicates):\\n self.assertNotIn(SUBSTATE_KEY_PATTERN % i + '.result', state)\",\n \"def test_state(self):\\n # blocks [0 3583] [3840 4058]\\n test_file1 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-2-0.mdd')\\n # blocks [0 1279] [1536 1791] [2048 2303] [2560 2815] [3072 4059]\\n test_file2 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-3-0.mdd')\\n\\n # parse the two .mdd files into the node and instrument group files\\n mdd.procall([test_file1, test_file2])\\n\\n file_state = self.get_file_state('node58p1.dat')\\n # there is an unprocessed '/n' in between records\\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[4059, 4060]],\\n StateKey.FILE_SIZE: 4060,\\n StateKey.OUTPUT_INDEX: 1}\\n\\n if file_state != expected_file_state:\\n print file_state\\n self.fail(\\\"Expected file state 1 does not match\\\")\\n\\n # blocks [0 2047] [2304 4095] [4096 7451]\\n test_file3 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-6-0.mdd')\\n\\n # parse another .mdd file adding on to the node file, and making\\n # another sequence of instrument group files\\n mdd.procall([test_file3])\\n\\n file_state = self.get_file_state('node58p1.dat')\\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[4059, 4060]],\\n StateKey.FILE_SIZE: 7452,\\n StateKey.OUTPUT_INDEX: 2}\\n\\n if file_state != expected_file_state:\\n print \\\"file state: '%s'\\\" % file_state\\n self.fail(\\\"Expected file state 2 does not match\\\")\\n\\n data_orig = self.read_full_file('node58p1.dat')\\n\\n # read the data from all generated files into one data string\\n data_out = self.read_full_file('node58p1_0.status_1236801.dat')\\n data_out += self.read_full_file('node58p1_0.wa_wfp_1236820.dat')\\n data_out += self.read_full_file('node58p1_0.wc_wfp_1236820.dat')\\n data_out += self.read_full_file('node58p1_0.we_wfp_1236820.dat')\\n data_out += self.read_full_file('node58p1_1.status_1236801.dat')\\n data_out += self.read_full_file('node58p1_1.wa_wfp_1236822.dat')\\n data_out += self.read_full_file('node58p1_1.wc_wfp_1236822.dat')\\n data_out += self.read_full_file('node58p1_1.we_wfp_1236822.dat')\\n\\n # confirm data in the node file matches those output in the instrument groups\\n if not TestSioUnpack.compare_sio_matches(data_orig, data_out):\\n self.fail(\\\"Failed sio block compare\\\")\",\n \"def testProcess(self):\\n plugin = macos_tcc.MacOSTCCPlugin()\\n storage_writer = self._ParseDatabaseFileWithPlugin(\\n ['TCC-test.db'], plugin)\\n\\n self.assertEqual(storage_writer.number_of_warnings, 0)\\n self.assertEqual(storage_writer.number_of_events, 21)\\n\\n events = list(storage_writer.GetEvents())\\n\\n expected_event_values = {\\n 'allowed': 1,\\n 'client': 'com.apple.weather',\\n 'service': 'kTCCServiceUbiquity',\\n 'prompt_count': 1,\\n 'timestamp': '2020-05-29 12:09:51.000000',\\n 'timestamp_desc': definitions.TIME_DESCRIPTION_LAST_PROMPTED_USER}\\n\\n self.CheckEventValues(storage_writer, events[0], expected_event_values)\\n\\n expected_message = (\\n 'Service: kTCCServiceUbiquity '\\n 'Client: com.apple.weather '\\n 'Allowed: True '\\n 'Prompt count: 1')\\n expected_short_message = 'kTCCServiceUbiquity: com.apple.weather'\\n\\n event_data = self._GetEventDataOfEvent(storage_writer, events[0])\\n self._TestGetMessageStrings(\\n event_data, expected_message, expected_short_message)\",\n \"def test_clean_run(self):\\n Historical_ROAs_Parsed_Table(clear=True)\\n with Historical_ROAs_Table(clear=True) as t:\\n Historical_ROAs_Parser().run()\\n assert t.get_count() > 2000000\",\n \"def test_run_is_next_run(curent_time, state):\\n date = datetime(2020,5,5,12,0)\\n duration_in_minutes = 65\\n run = Run(date, duration_in_minutes/60)\\n\\n assert run.is_next_run(curent_time) == state\",\n \"def test_meeting_live_stream_status_update(self):\\n pass\",\n \"def test_duplicatekeyerror_state_embryo(self):\\n self.pipeline_real.insert_uow = then_raise\\n pipeline = spy(self.pipeline_real)\\n\\n job_record = get_job_record(job.STATE_EMBRYO,\\n TEST_PRESET_TIMEPERIOD,\\n PROCESS_SITE_HOURLY)\\n\\n pipeline.manage_pipeline_for_process(job_record.process_name, job_record)\\n verify(self.time_table_mocked, times=0). \\\\\\n update_job_record(any(str), any(Job), any(UnitOfWork), any(str))\",\n \"def test_previous_state(self):\\n self.report_start(self.whoami())\\n\\n Device.objects.get(hostname='panda01').state_transition_to(Device.OFFLINE)\\n\\n # set a series of previous transitions for panda02\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.OFFLINE)\\n Device.objects.get(hostname='panda02').state_transition_to(Device.IDLE)\\n\\n Device.objects.get(hostname='panda03').state_transition_to(Device.RUNNING)\\n Device.objects.get(hostname='panda04').state_transition_to(Device.RESERVED)\\n Device.objects.get(hostname='panda05').state_transition_to(Device.RETIRED)\\n Device.objects.get(hostname='panda06').state_transition_to(Device.OFFLINING)\\n\\n self.panda_type.health_check_job = self.factory.make_job_json(health_check='true')\\n self.panda_type.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=jobs[0].id)\\n job_id = job.id\\n self.assertEqual(job.status, TestJob.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(len(jobs), 1)\\n job = TestJob.objects.get(id=job_id)\\n self.assertEqual(job.status, TestJob.COMPLETE)\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').status,\\n Device.IDLE\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda02').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n panda01 = Device.objects.get(hostname='panda01')\\n panda01.status = Device.IDLE\\n panda01.save()\\n\\n jobs = self.scheduler_tick()\\n\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_UNKNOWN\\n )\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.RUNNING)\\n\\n for job in jobs:\\n job_obj = TestJob.objects.get(pk=job.id) # reload\\n job_obj.status = TestJob.COMPLETE\\n self.job_finished(job_obj)\\n\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n self.scheduler_tick()\\n self.assertEqual(Device.objects.get(hostname='panda01').status, Device.IDLE)\\n self.assertIsNone(Device.objects.get(hostname='panda01').current_job)\\n self.assertEqual(\\n Device.objects.get(hostname='panda01').health_status,\\n Device.HEALTH_PASS\\n )\\n\\n self.cleanup(self.whoami())\",\n \"def batch_uses_proper_timestamp_test(self):\\n session = self.prepare()\\n session.execute(\\\"\\\"\\\"\\n BEGIN BATCH USING TIMESTAMP 1111111111111111\\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\\n APPLY BATCH\\n \\\"\\\"\\\")\\n rows = session.execute(\\\"SELECT id, writetime(firstname), writetime(lastname) FROM users\\\")\\n res = sorted(rows)\\n assert [list(res[0]), list(res[1])] == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111111, 1111111111111111]], res\",\n \"def test_act_ii(self):\\n # setup\\n processing_time = 5.0\\n self.transaction_behaviour.processing = \\\"some_dialogue\\\"\\n self.transaction_behaviour.max_processing = 120\\n self.transaction_behaviour.processing_time = processing_time\\n\\n # operation\\n self.transaction_behaviour.act()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\\n assert (\\n self.transaction_behaviour.processing_time\\n == processing_time + self.transaction_behaviour.tick_interval\\n )\",\n \"def emit(self, record):\\n self.eng.dispose()\\n trys = 3\\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\\\"__\\\")}\\n for t in range(trys):\\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\\n failed = False\\n try:\\n with self.eng.connect() as con:\\n # formating values to convert from python or parsl to db standards\\n info['task_fail_history'] = str(info.get('task_fail_history', None))\\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\\n run_id = info['run_id']\\n\\n # if workflow or task has completed, update their entries with the time.\\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n return\\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\\n workflow = self.meta.tables['task']\\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\\\\n .where(workflow.c.run_id == run_id)\\n con.execute(up)\\n\\n # create workflows table if this is a new database without one\\n if 'workflows' not in self.meta.tables.keys():\\n workflows = create_workflows_table(self.meta)\\n self.meta.create_all(con)\\n # if this is the first sight of the workflow, add it to the workflows table\\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\\n workflows = self.meta.tables['workflows']\\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\\n con.execute(ins)\\n\\n # if log has task counts, update the workflow entry in the workflows table\\n if 'tasks_completed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n if 'tasks_failed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n\\n # create task table if this is a new run without one\\n if 'task' not in self.meta.tables.keys():\\n workflow = create_task_table(self.meta)\\n self.meta.create_all(con)\\n\\n # check to make sure it is a task log and not just a workflow overview log\\n if info.get('task_id', None) is not None:\\n # if this is the first sight of the task in the workflow, add it to the workflow table\\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\\n if 'psutil_process_pid' in info.keys():\\n # this is the race condition that a resource log is before a status log so ignore this resource update\\n return\\n\\n workflow = self.meta.tables['task']\\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\\n con.execute(ins)\\n\\n if 'task_status' in info.keys():\\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\\n if 'task_status' not in self.meta.tables.keys():\\n task_status_table = create_task_status_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n # if this status table already exists, just insert the update\\n else:\\n task_status_table = self.meta.tables['task_status']\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n return\\n\\n if 'psutil_process_pid' in info.keys():\\n # if this is a task resource update then handle that, if the resource table DNE then create it\\n if 'task_resources' not in self.meta.tables.keys():\\n task_resource_table = create_task_resource_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n # if this resource table already exists, just insert the update\\n else:\\n task_resource_table = self.meta.tables['task_resources']\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n return\\n\\n except Exception as e:\\n logger.error(\\\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\\\".format(t, str(e)))\\n failed = True\\n time.sleep(5)\\n if not failed:\\n return\",\n \"def test_cron_workflow_service_update_cron_workflow(self):\\n pass\",\n \"def test_non_batch(self):\\r\\n uid = uuid4()\\r\\n tmp = TestTimestampModel.create(id=uid, count=1)\\r\\n\\r\\n TestTimestampModel.get(id=uid).should.be.ok\\r\\n\\r\\n tmp.timestamp(timedelta(seconds=5)).delete()\\r\\n\\r\\n with self.assertRaises(TestTimestampModel.DoesNotExist):\\r\\n TestTimestampModel.get(id=uid)\\r\\n\\r\\n tmp = TestTimestampModel.create(id=uid, count=1)\\r\\n\\r\\n with self.assertRaises(TestTimestampModel.DoesNotExist):\\r\\n TestTimestampModel.get(id=uid)\\r\\n\\r\\n # calling .timestamp sets the TS on the model\\r\\n tmp.timestamp(timedelta(seconds=5))\\r\\n tmp._timestamp.should.be.ok\\r\\n\\r\\n # calling save clears the set timestamp\\r\\n tmp.save()\\r\\n tmp._timestamp.shouldnt.be.ok\\r\\n\\r\\n tmp.timestamp(timedelta(seconds=5))\\r\\n tmp.update()\\r\\n tmp._timestamp.shouldnt.be.ok\",\n \"def test_setting_state_parallel(self):\\n no_replicates = 25\\n\\n replicate(experiment, no_replicates, parallel=True, no_processes=2)\\n for i in range(no_replicates):\\n self.assertIn('result', state[SUBSTATE_KEY_PATTERN % i])\\n self.assertEqual(state[SUBSTATE_KEY_PATTERN % i]['result'], \\\"bla\\\")\",\n \"def test_time_status(self):\\n result = self.test_client.time_status\\n\\n assert result == \\\"12312\\\"\",\n \"def test_ensure_not_ts_ts(self):\\n self.assertEqual(ensure_not_ts(self.jobset3), 'error')\",\n \"def test_post_processing_time():\\r\\n cmd = ShdlcCmdGetErrorState(clear=False)\\r\\n assert type(cmd.post_processing_time) is float\\r\\n assert cmd.post_processing_time == 0.0\",\n \"def test_timeout_with_crud_failures(self):\\n\\n # Local methods to validate vb_seqno\\n\\n def compare_vb_stat(stat_1, stat_2, vb, comparison=\\\"!=\\\"):\\n keys_to_check = [\\\"high_seqno\\\", \\\"high_completed_seqno\\\"]\\n result = True\\n for key in keys_to_check:\\n if vb in stat_1.keys():\\n if stat_1[vb][\\\"uuid\\\"] != stat_2[vb][\\\"uuid\\\"]:\\n self.log_failure(\\\"Mismatch in vb-%s UUID. %s != %s\\\"\\n % (vb, stat_1[vb][\\\"uuid\\\"],\\n stat_2[vb][\\\"uuid\\\"]))\\n if comparison == \\\"!=\\\":\\n if stat_1[vb][key] != stat_2[vb][key]:\\n result = False\\n self.log.warning(\\n \\\"Mismatch in vb-%s stat %s. %s != %s\\\"\\n % (vb, key, stat_1[vb][key], stat_2[vb][key]))\\n elif stat_1[vb][key] == stat_2[vb][key]:\\n result = False\\n self.log.warning(\\\"Stat not updated for vb-%s stat %s. \\\"\\n \\\"%s == %s\\\"\\n % (vb, key,\\n stat_1[vb][key], stat_2[vb][key]))\\n return result\\n\\n def validate_vb_seqno_stats():\\n \\\"\\\"\\\"\\n :return retry_validation: Boolean denoting to retry validation\\n \\\"\\\"\\\"\\n retry_validation = False\\n vb_info[\\\"post_timeout\\\"][node.ip] = \\\\\\n cbstat_obj[node.ip].vbucket_seqno(self.bucket.name)\\n for tem_vb_num in range(self.cluster.vbuckets):\\n tem_vb_num = str(tem_vb_num)\\n if tem_vb_num not in affected_vbs:\\n if compare_vb_stat(vb_info[\\\"init\\\"][node.ip],\\n vb_info[\\\"post_timeout\\\"][node.ip],\\n tem_vb_num) is False:\\n self.log_failure(\\\"Unaffected vb-%s stat\\\" % tem_vb_num)\\n elif int(tem_vb_num) in target_nodes_vbuckets[\\\"active\\\"]:\\n if compare_vb_stat(vb_info[\\\"init\\\"][node.ip],\\n vb_info[\\\"post_timeout\\\"][node.ip],\\n tem_vb_num) is False:\\n self.log.warning(\\\"%s - mismatch in %s vb-%s seq_no\\\"\\n % (node.ip, \\\"active\\\", tem_vb_num))\\n elif int(tem_vb_num) in target_nodes_vbuckets[\\\"replica\\\"]:\\n if compare_vb_stat(vb_info[\\\"init\\\"][node.ip],\\n vb_info[\\\"post_timeout\\\"][node.ip],\\n tem_vb_num, comparison=\\\"==\\\") is False:\\n retry_validation = True\\n self.log.warning(\\\"%s - mismatch in %s vb-%s seq_no\\\"\\n % (node.ip, \\\"replica\\\", tem_vb_num))\\n return retry_validation\\n\\n shell_conn = dict()\\n cbstat_obj = dict()\\n error_sim = dict()\\n target_nodes_vbuckets = dict()\\n vb_info = dict()\\n tasks = dict()\\n doc_gen = dict()\\n affected_vbs = list()\\n\\n target_nodes_vbuckets[\\\"active\\\"] = []\\n target_nodes_vbuckets[\\\"replica\\\"] = []\\n vb_info[\\\"init\\\"] = dict()\\n vb_info[\\\"post_timeout\\\"] = dict()\\n vb_info[\\\"afterCrud\\\"] = dict()\\n\\n # Override crud_batch_size to minimum value for testing\\n self.crud_batch_size = 5\\n self.key = \\\"test_collections\\\"\\n self.sdk_timeout = 3\\n\\n # Select target vbucket type to load_docs\\n target_vb_type = \\\"replica\\\"\\n if self.simulate_error == CouchbaseError.STOP_PERSISTENCE \\\\\\n and self.durability_level \\\\\\n == Bucket.DurabilityLevel.MAJORITY_AND_PERSIST_TO_ACTIVE:\\n target_vb_type = \\\"active\\\"\\n\\n # Create required scope/collection for successful CRUD operation\\n if self.scope_name != CbServer.default_scope:\\n self.scope_name = self.bucket_util.get_random_name()\\n self.collection_name = self.bucket_util.get_random_name()\\n self.log.info(\\\"Creating scope::collection %s::%s\\\"\\n % (self.scope_name, self.collection_name))\\n self.create_scope_collection()\\n\\n # Load docs into created collection\\n self.log.info(\\\"Loading data into created collection\\\")\\n load_gen = doc_generator(self.key, 0, self.num_items)\\n task = self.task.async_load_gen_docs(\\n self.cluster, self.bucket, load_gen, \\\"create\\\", 0,\\n scope=self.scope_name,\\n collection=self.collection_name,\\n sdk_client_pool=self.sdk_client_pool,\\n batch_size=200, process_concurrency=8,\\n timeout_secs=60)\\n self.task_manager.get_task_result(task)\\n if self.subdoc_test:\\n load_gen = sub_doc_generator(self.key, 0, self.num_items/2)\\n task = self.task.async_load_gen_sub_docs(\\n self.cluster, self.bucket,\\n load_gen, Bucket_Op.SubDocOps.INSERT,\\n timeout_secs=self.sdk_timeout,\\n compression=self.sdk_compression,\\n path_create=True,\\n batch_size=100,\\n process_concurrency=8,\\n durability=self.durability_level,\\n scope=self.scope_name, collection=self.collection_name,\\n sdk_client_pool=self.sdk_client_pool)\\n self.task_manager.get_task_result(task)\\n\\n self.bucket.scopes[self.scope_name].collections[\\n self.collection_name].num_items = self.num_items\\n\\n target_nodes = DurabilityHelper.getTargetNodes(self.cluster,\\n self.nodes_init,\\n self.num_nodes_affected)\\n for node in target_nodes:\\n shell_conn[node.ip] = RemoteMachineShellConnection(node)\\n cbstat_obj[node.ip] = Cbstats(node)\\n target_nodes_vbuckets[\\\"active\\\"] += \\\\\\n cbstat_obj[node.ip].vbucket_list(self.bucket.name,\\n vbucket_type=\\\"active\\\")\\n target_nodes_vbuckets[\\\"replica\\\"] += \\\\\\n cbstat_obj[node.ip].vbucket_list(self.bucket.name,\\n vbucket_type=\\\"replica\\\")\\n vb_info[\\\"init\\\"][node.ip] = cbstat_obj[node.ip].vbucket_seqno(\\n self.bucket.name)\\n error_sim[node.ip] = CouchbaseError(self.log, shell_conn[node.ip])\\n\\n curr_time = int(time.time())\\n expected_timeout = curr_time + self.sdk_timeout\\n\\n if target_vb_type == \\\"active\\\":\\n target_vbs = list(\\n set(target_nodes_vbuckets[target_vb_type])\\n .difference(set(target_nodes_vbuckets[\\\"replica\\\"])))\\n else:\\n target_vbs = list(\\n set(target_nodes_vbuckets[target_vb_type])\\n .difference(set(target_nodes_vbuckets[\\\"active\\\"])))\\n\\n # Create required doc_generators\\n doc_gen[\\\"create\\\"] = doc_generator(self.key, self.num_items,\\n self.crud_batch_size,\\n target_vbucket=target_vbs)\\n doc_gen[\\\"delete\\\"] = doc_generator(self.key, 0,\\n self.crud_batch_size,\\n target_vbucket=target_vbs)\\n doc_gen[\\\"read\\\"] = doc_generator(\\n self.key, int(self.num_items/3),\\n self.crud_batch_size,\\n target_vbucket=target_vbs)\\n doc_gen[\\\"update\\\"] = doc_generator(\\n self.key, int(self.num_items/2),\\n self.crud_batch_size,\\n target_vbucket=target_vbs)\\n\\n # Create required subdoc generators\\n doc_gen[\\\"insert\\\"] = sub_doc_generator(\\n self.key, int(self.num_items/2), self.crud_batch_size,\\n target_vbucket=target_vbs)\\n doc_gen[\\\"upsert\\\"] = sub_doc_generator_for_edit(\\n self.key, 0, self.crud_batch_size,\\n template_index=1,\\n target_vbucket=target_vbs)\\n doc_gen[\\\"remove\\\"] = sub_doc_generator(\\n self.key, 0, self.crud_batch_size,\\n target_vbucket=target_vbs)\\n\\n # Perform specified action\\n for node in target_nodes:\\n error_sim[node.ip].create(self.simulate_error,\\n bucket_name=self.bucket.name)\\n self.sleep(5, \\\"Wait for error_simulation to take effect\\\")\\n\\n ops_to_perform = [Bucket_Op.DocOps.CREATE, Bucket_Op.DocOps.UPDATE,\\n Bucket_Op.DocOps.READ, Bucket_Op.DocOps.DELETE]\\n if self.subdoc_test:\\n ops_to_perform = [Bucket_Op.SubDocOps.INSERT,\\n Bucket_Op.SubDocOps.UPSERT,\\n Bucket_Op.SubDocOps.REMOVE]\\n\\n for op_type in ops_to_perform:\\n self.log.info(\\\"Starting doc op %s\\\" % op_type)\\n if op_type in Bucket_Op.DOC_OPS:\\n tasks[op_type] = self.task.async_load_gen_docs(\\n self.cluster, self.bucket, doc_gen[op_type], op_type, 0,\\n scope=self.scope_name,\\n collection=self.collection_name,\\n sdk_client_pool=self.sdk_client_pool,\\n batch_size=1, process_concurrency=8,\\n durability=self.durability_level,\\n timeout_secs=self.sdk_timeout,\\n suppress_error_table=True,\\n print_ops_rate=False,\\n skip_read_on_error=True)\\n else:\\n tasks[op_type] = self.task.async_load_gen_sub_docs(\\n self.cluster, self.bucket, doc_gen[op_type], op_type, 0,\\n scope=self.scope_name,\\n collection=self.collection_name,\\n sdk_client_pool=self.sdk_client_pool,\\n path_create=True,\\n batch_size=1, process_concurrency=8,\\n durability=self.durability_level,\\n timeout_secs=self.sdk_timeout,\\n print_ops_rate=False)\\n\\n self.task.jython_task_manager.get_task_result(tasks[op_type])\\n\\n # Validate task failures\\n if op_type == Bucket_Op.DocOps.READ:\\n # Validation for read task\\n if len(tasks[op_type].fail.keys()) != 0:\\n self.log_failure(\\\"Read failed for few docs: %s\\\"\\n % tasks[op_type].fail.keys())\\n else:\\n # Validation of CRUDs - Update / Create / Delete\\n for doc_id, crud_result in tasks[op_type].fail.items():\\n vb_num = self.bucket_util.get_vbucket_num_for_key(\\n doc_id, self.cluster.vbuckets)\\n if SDKException.DurabilityAmbiguousException \\\\\\n not in str(crud_result[\\\"error\\\"]):\\n self.log_failure(\\n \\\"Invalid exception for doc %s, vb %s: %s\\\"\\n % (doc_id, vb_num, crud_result))\\n\\n # Revert the specified error scenario\\n for node in target_nodes:\\n error_sim[node.ip].revert(self.simulate_error,\\n bucket_name=self.bucket.name)\\n\\n # Check whether the timeout triggered properly\\n if int(time.time()) < expected_timeout:\\n self.log_failure(\\\"Timed-out before expected time\\\")\\n\\n for op_type in ops_to_perform:\\n if op_type == Bucket_Op.DocOps.READ:\\n continue\\n while doc_gen[op_type].has_next():\\n doc_id, _ = doc_gen[op_type].next()\\n affected_vbs.append(\\n str(self.bucket_util.get_vbucket_num_for_key(\\n doc_id,\\n self.cluster.vbuckets)))\\n\\n affected_vbs = list(set(affected_vbs))\\n # Fetch latest stats and validate the seq_nos are not updated\\n for node in target_nodes:\\n retry_count = 0\\n max_retry = 3\\n while retry_count < max_retry:\\n self.log.info(\\\"Trying to validate vbseq_no stats: %d\\\"\\n % (retry_count+1))\\n retry_count += 1\\n retry_required = validate_vb_seqno_stats()\\n if not retry_required:\\n break\\n self.sleep(5, \\\"Sleep for vbseq_no stats to update\\\")\\n else:\\n # This will be exited only if `break` condition is not met\\n self.log_failure(\\\"validate_vb_seqno_stats verification failed\\\")\\n\\n self.validate_test_failure()\\n\\n # Get SDK Client from client_pool\\n sdk_client = self.sdk_client_pool.get_client_for_bucket(\\n self.bucket,\\n self.scope_name,\\n self.collection_name)\\n\\n # Doc error validation\\n for op_type in ops_to_perform:\\n task = tasks[op_type]\\n\\n if self.nodes_init == 1 \\\\\\n and op_type != Bucket_Op.DocOps.READ \\\\\\n and len(task.fail.keys()) != (doc_gen[op_type].end\\n - doc_gen[op_type].start):\\n self.log_failure(\\\"Failed keys %d are less than expected %d\\\"\\n % (len(task.fail.keys()),\\n (doc_gen[op_type].end\\n - doc_gen[op_type].start)))\\n\\n # Create table objects for display\\n table_view = TableView(self.log.error)\\n ambiguous_table_view = TableView(self.log.info)\\n table_view.set_headers([\\\"Key\\\", \\\"vBucket\\\", \\\"Exception\\\"])\\n ambiguous_table_view.set_headers([\\\"Key\\\", \\\"vBucket\\\"])\\n\\n # Iterate failed keys for validation\\n for doc_key, doc_info in task.fail.items():\\n vb_for_key = self.bucket_util.get_vbucket_num_for_key(doc_key)\\n\\n if SDKException.DurabilityAmbiguousException \\\\\\n not in str(doc_info[\\\"error\\\"]):\\n table_view.add_row([doc_key, vb_for_key,\\n doc_info[\\\"error\\\"]])\\n\\n ambiguous_table_view.add_row([doc_key, str(vb_for_key)])\\n if op_type not in Bucket_Op.SUB_DOC_OPS:\\n retry_success = \\\\\\n self.durability_helper.retry_for_ambiguous_exception(\\n sdk_client, op_type, doc_key, doc_info)\\n if not retry_success:\\n self.log_failure(\\\"%s failed in retry for %s\\\"\\n % (op_type, doc_key))\\n\\n # Display the tables (if any errors)\\n table_view.display(\\\"Unexpected exception during %s\\\" % op_type)\\n ambiguous_table_view.display(\\\"D_Ambiguous exception during %s\\\"\\n % op_type)\\n\\n # Release the acquired client\\n self.sdk_client_pool.release_client(sdk_client)\\n\\n # Verify doc count after expected CRUD failure\\n self.bucket_util._wait_for_stats_all_buckets(self.cluster,\\n self.cluster.buckets)\\n self.bucket_util.validate_docs_per_collections_all_buckets(\\n self.cluster)\\n\\n # Fetch latest stats and validate the values are updated\\n for node in target_nodes:\\n vb_info[\\\"afterCrud\\\"][node.ip] = \\\\\\n cbstat_obj[node.ip].vbucket_seqno(self.bucket.name)\\n if vb_info[\\\"init\\\"][node.ip] == vb_info[\\\"afterCrud\\\"][node.ip]:\\n self.log_failure(\\\"vBucket seq_no stats not updated\\\")\\n\\n # Disconnect the shell connection\\n for node in target_nodes:\\n shell_conn[node.ip].disconnect()\\n\\n self.validate_test_failure()\",\n \"def test_two_prospective_checkpointed_timestamps(self, test_type, stage_pair, pm):\\n\\n stage1, stage2 = stage_pair\\n pm.timestamp(checkpoint=stage1, finished=False)\\n pm.timestamp(checkpoint=stage2, finished=False)\\n\\n if test_type == FILES_TEST:\\n checkpoint_files = fetch_checkpoint_files(pm)\\n expected = [checkpoint_filepath(stage1, pm)]\\n assert set(expected) == set(checkpoint_files)\\n else:\\n assert stage1 == pm.prev_checkpoint\\n assert stage2 == pm.curr_checkpoint\",\n \"def pg_query_state(config, pid, verbose=False, costs=False, timing=False, \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\tbuffers=False, triggers=False, format='text'):\\n\\n\\tconn = psycopg2.connect(**config)\\n\\tcurs = conn.cursor()\\n\\tcurs.callproc('pg_query_state', (pid, verbose, costs, timing, buffers, triggers, format))\\n\\tresult = curs.fetchall()\\n\\tnotices = conn.notices[:]\\n\\tconn.close()\\n\\n\\treturn result, notices\",\n \"def get_exist_phase_timestamp(self):\\r\\n csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\\r\\n if not os.path.isfile(csv_path):\\r\\n print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\\r\\n exit(-1)\\r\\n converter = {'benchmarkPhase': str, 'streamNumber': int, 'queryNumber': int,\\r\\n 'epochStartTimestamp': 'int64', 'epochEndTimestamp': 'int64'}\\r\\n df = pd.read_csv(csv_path, delimiter=';').loc[:,\\r\\n ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\\r\\n phase_ts = OrderedDict()\\r\\n is_exist = False\\r\\n for phase in self.phase_name:\\r\\n benchmark_phase = (df['benchmarkPhase'] == phase)\\r\\n if any(benchmark_phase): # whether this phase exist in the BB logs\\r\\n if phase == 'POWER_TEST': # power test overall and each query\\r\\n stream_num = ((df['streamNumber']) == 0)\\r\\n mask = benchmark_phase & stream_num\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 2] = 0 # fill 0 to blank value\\r\\n elif phase == 'THROUGHPUT_TEST_1': # throughput test overall and each stream\\r\\n query_num = (pd.isnull(df['queryNumber']))\\r\\n mask = benchmark_phase & query_num\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 1:3] = -1 # file -1 to overall throughput test\\r\\n phase_ts[phase].iloc[1:, 2] = 0 # fill 0 to blank value\\r\\n else: # other phases\\r\\n stream_num = (pd.isnull(df['streamNumber']))\\r\\n query_num = (pd.isnull(df['queryNumber']))\\r\\n mask = benchmark_phase & stream_num & query_num\\r\\n # phase_start = line['epochStartTimestamp'].values / 1000\\r\\n # phase_end = line['epochEndTimestamp'].values / 1000\\r\\n phase_ts[phase] = df[mask].reset_index(drop=True)\\r\\n phase_ts[phase].iloc[0, 1:3] = 0 # fill 0 to blank value\\r\\n phase_ts[phase] = phase_ts[phase].astype(converter)\\r\\n is_exist = True\\r\\n if is_exist:\\r\\n return phase_ts\\r\\n else:\\r\\n print('It seems BigBenchTimes.csv in {0} does not include any TPCx-BB phases, ' \\\\\\r\\n 'existing...'.format(self.bb_log_path))\\r\\n exit(-1)\",\n \"def test_update_activity_occurrence_status(self):\\n pass\",\n \"def test_time(self):\\n M = simulation.StateMonitor(self.G, 'v')\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n self.assertTrue(np.allclose(M.t, np.arange(0, self.t_max, self.dt)))\",\n \"def verify_event_timing(self, event, item):\\n return True\",\n \"def test_status(self):\\n keys = [\\n 'receiver time mark count', 'ant type base', 'solution status',\\n ...\\n ]\\n\\n for i in xrange(0, 100):\\n self.assertEqual(self.rtkrcv.get_status().keys(), keys)\",\n \"def test_ensure_ts_not_ts(self):\\n self.assertEqual(ensure_ts(self.jobset1), 'opt')\",\n \"def filter_by_state(self, stream_state: Mapping[str, Any] = None, record: Mapping[str, Any] = None) -> Iterable:\\n start_date = \\\"1900-01-01T00:00:00.0Z\\\"\\n if pendulum.parse(record[self.cursor_field]) > pendulum.parse((stream_state or {}).get(self.cursor_field, start_date)):\\n # Persist state.\\n # There is a bug in state setter: because of self._cursor_value is not defined it raises Attribute error\\n # which is ignored in airbyte_cdk/sources/abstract_source.py:320 and we have an empty state in return\\n # See: https://github.com/airbytehq/oncall/issues/1317\\n self.state = record\\n yield record\",\n \"def test_update_single_row_if_status_is_in_progress(self):\\n first = generate_mock_result(status='IN_PROGRESS', success=False)\\n self.db.insert_single_result(first)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'IN_PROGRESS')\\n second = generate_mock_result(status='SUCCESS', success=True)\\n self.db.insert_single_result(second)\\n current = self.db.get_result_by_primary_key(first.get('id'))\\n self.assertEqual(current.status, 'SUCCESS')\",\n \"def testPostProcessResourceState(self):\\n resource_id_1 = \\\"resource_id_1\\\"\\n resource_id_2 = \\\"resource_id_2\\\"\\n task_id_1 = \\\"task_id_1\\\"\\n task_id_2 = \\\"task_id_2\\\"\\n\\n # Add test resource ids and task ids into state file\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n\\n # Test that task id was removed from resource id\\n json_out_1 = {resource_id_1: [task_id_2], resource_id_2: [task_id_1]}\\n is_detachable = resource_manager.PostProcessResourceState(\\n resource_id_1, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_1)\\n self.assertEqual(is_detachable, False)\\n\\n # Test that resource id was removed from resource state\\n json_out_2 = {resource_id_2: [task_id_1]}\\n is_detachable = resource_manager.PostProcessResourceState(\\n resource_id_1, task_id_2)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\\n self.assertEqual(is_detachable, True)\\n\\n # Test that non existent task did not throw an error\\n is_detachable = resource_manager.PostProcessResourceState(\\n resource_id_2, task_id_2)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\\n self.assertEqual(is_detachable, False)\\n\\n # Test removing all from resource state\\n json_out_3 = {}\\n is_detachable = resource_manager.PostProcessResourceState(\\n resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\\n self.assertEqual(is_detachable, True)\",\n \"def test_running_job(self):\\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_same_unique_crash_type_with_same_state(self):\\n self.testcases[0].security_flag = False\\n self.testcases[0].crash_type = 'Timeout'\\n self.testcases[0].crash_state = 'abcde'\\n self.testcases[0].timestamp = datetime.datetime.utcfromtimestamp(0)\\n self.testcases[1].security_flag = False\\n self.testcases[1].crash_type = 'Timeout'\\n self.testcases[1].crash_state = 'abcde'\\n self.testcases[1].timestamp = datetime.datetime.utcfromtimestamp(1)\\n\\n for t in self.testcases:\\n t.put()\\n\\n grouper.group_testcases()\\n\\n testcases = []\\n for testcase_id in data_handler.get_open_testcase_id_iterator():\\n testcases.append(data_handler.get_testcase_by_id(testcase_id))\\n\\n self.assertEqual(len(testcases), 1)\\n self.assertEqual(testcases[0].group_id, 0)\\n self.assertTrue(testcases[0].is_leader)\",\n \"def task_instance_pre_save_handler(instance, **_):\\n if instance.state in (SUCCESSFUL, FAILED):\\n instance.datetime_finished = timezone.now()\",\n \"def assert_history(self, rows):\\r\\n self.assertEqual(self.parse_rows(rows), self.read_history())\",\n \"def test_time_successful(self):\\n\\n url = '/%s/jobs/%i/input_files/?started=%s&ended=%s&time_field=%s' % (self.api, self.job.id,\\n '2016-01-10T00:00:00Z',\\n '2016-01-13T00:00:00Z',\\n 'source')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 2)\\n for result in results:\\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])\",\n \"def _server_poll_expcompleted_(self):\\n #print \\\"class Princeton_CCD function _server_poll_expcompleted_\\\" \\n try:\\n last_state = self.polled_running\\n except (AttributeError,UnboundLocalError):\\n self.polled_running = False\\n last_state = False\\n self.polled_running = self.query_running()\\n if (not bool(last_state) and bool(self.polled_running)):\\n self.begin_acq_time = time.time()\\n #print self.query_running(), last_state\\n #if ((last_state == True) and (self.polled_running == False)): CP\\n if (bool(last_state) and not bool(self.polled_running)):\\n self.end_acq_time = time.time()\\n return True\\n else:\\n return False\",\n \"def test_update_splits():\\n assert new_log.iloc[0]['start'] == 0\",\n \"def test_time_type_state_is_noon(day):\\n\\n assert day_time_info(day.hours_0).is_noon is False\\n assert day_time_info(day.hours_1).is_noon is False\\n assert day_time_info(day.hours_2).is_noon is False\\n assert day_time_info(day.hours_3).is_noon is False\\n assert day_time_info(day.hours_4).is_noon is False\\n assert day_time_info(day.hours_5).is_noon is False\\n assert day_time_info(day.hours_6).is_noon is False\\n assert day_time_info(day.hours_7).is_noon is False\\n assert day_time_info(day.hours_8).is_noon is False\\n assert day_time_info(day.hours_9).is_noon is False\\n assert day_time_info(day.hours_10).is_noon is False\\n assert day_time_info(day.hours_11).is_noon is False\\n assert day_time_info(day.hours_12).is_noon is True\\n assert day_time_info(day.hours_13).is_noon is False\\n assert day_time_info(day.hours_14).is_noon is False\\n assert day_time_info(day.hours_15).is_noon is False\\n assert day_time_info(day.hours_16).is_noon is False\\n assert day_time_info(day.hours_17).is_noon is False\\n assert day_time_info(day.hours_18).is_noon is False\\n assert day_time_info(day.hours_19).is_noon is False\\n assert day_time_info(day.hours_20).is_noon is False\\n assert day_time_info(day.hours_21).is_noon is False\\n assert day_time_info(day.hours_22).is_noon is False\\n assert day_time_info(day.hours_23).is_noon is False\",\n \"def test_time(self):\\n conn = self.database.connection()\\n cursor = conn.cursor()\\n dialect = self.database.dialect()\\n dbapi = self.database.dbapi()\\n query = dialect.translate('DROP TABLE test_time')\\n try:\\n cursor.execute(query)\\n except dbapi.Error:\\n conn.rollback()\\n query = dialect.translate('CREATE TABLE test_time ' \\\\\\n '( value TIME NOT NULL )')\\n cursor.execute(query)\\n data = []\\n rr = random.randrange\\n query = 'INSERT INTO test_time VALUES (%s)'\\n for i in range(100):\\n item = datetime.time(rr(0,24), rr(0,60), rr(0,60))\\n data.append(item)\\n cursor.execute(query, (item,))\\n query = 'SELECT * FROM test_time'\\n cursor.execute(query)\\n result = cursor.fetchall()\\n for row in result:\\n item = row[0]\\n assert isinstance(item, datetime.time)\\n assert item in data\\n data.remove(item)\\n query = dialect.translate('DELETE FROM test_time')\\n cursor.execute(query)\\n query = dialect.translate('DROP TABLE test_time')\\n cursor.execute(query)\\n conn.commit()\",\n \"def test_state(self, api, state):\\n stream = AdsInsights(api=api, start_date=datetime(2010, 1, 1), end_date=datetime(2011, 1, 1), insights_lookback_window=28)\\n\\n assert stream.state == {}\\n\\n stream.state = state\\n actual_state = stream.state\\n actual_state[\\\"slices\\\"] = sorted(actual_state.get(\\\"slices\\\", []))\\n state[\\\"slices\\\"] = sorted(state.get(\\\"slices\\\", []))\\n state[\\\"time_increment\\\"] = 1\\n\\n assert actual_state == state\",\n \"def each_statement_in_batch_uses_proper_timestamp_test(self):\\n cursor = self.prepare()\\n cursor.execute(\\\"\\\"\\\"\\n BEGIN BATCH\\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow') USING TIMESTAMP 1111111111111111\\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner') USING TIMESTAMP 1111111111111112\\n APPLY BATCH\\n \\\"\\\"\\\")\\n cursor.execute(\\\"SELECT id, writetime(firstname), writetime(lastname) FROM users\\\")\\n res = sorted(cursor.fetchall())\\n assert res == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111112, 1111111111111112]], res\",\n \"def testScanForProcessedTasks(self):\\n redis_client = self._CreateRedisClient()\\n\\n session = sessions.Session()\\n task = tasks.Task(session_identifier=session.identifier)\\n\\n # There should be no processed task identifiers initially.\\n task_identifiers, _ = redis_store.RedisStore.ScanForProcessedTasks(\\n session.identifier, redis_client=redis_client)\\n self.assertEqual([], task_identifiers)\\n\\n # Opening and closing a writer for a task should cause the task to be marked\\n # as complete.\\n storage_writer = writer.RedisStorageWriter(\\n storage_type=definitions.STORAGE_TYPE_TASK)\\n storage_writer.Open(\\n redis_client=redis_client, session_identifier=task.session_identifier,\\n task_identifier=task.identifier)\\n storage_writer.Close()\\n\\n # The task store is now marked as processed.\\n task_identifiers, _ = redis_store.RedisStore.ScanForProcessedTasks(\\n session.identifier, redis_client=redis_client)\\n self.assertEqual([task.identifier], task_identifiers)\",\n \"def test_ensure_not_ts_novib(self):\\n self.assertEqual(ensure_not_ts(self.jobset2), 'error')\",\n \"def test_notBeforeBefore(self):\\n dbpool, qpool, clock, performerChosen = self._setupPools()\\n\\n @transactionally(dbpool.pool.connection)\\n def check(txn):\\n return qpool.enqueueWork(\\n txn, DummyWorkItem, a=3, b=9,\\n notBefore=datetime.datetime(2012, 12, 12, 12, 12, 0)\\n )\\n\\n yield check\\n\\n clock.advance(1000)\\n # Advance far beyond the given timestamp.\\n self.assertEquals(performerChosen, [True])\\n\\n # Wait for job\\n while (yield inTransaction(dbpool.pool.connection, lambda txn: JobItem.all(txn))):\\n clock.advance(1)\\n\\n # Work item complete\\n self.assertTrue(DummyWorkItem.results == {1: 12})\",\n \"def test_source_time_successful(self):\\n\\n url = '/%s/jobs/?source_started=%s&source_ended=%s' % ( self.api,\\n '2016-01-01T00:00:00Z',\\n '2016-01-02T00:00:00Z')\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 1)\\n for result in results:\\n self.assertTrue(result['id'] in [self.job1.id])\",\n \"def test_meeting_status(self):\\n pass\",\n \"def each_statement_in_batch_uses_proper_timestamp_test(self):\\n session = self.prepare()\\n session.execute(\\\"\\\"\\\"\\n BEGIN BATCH\\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow') USING TIMESTAMP 1111111111111111\\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner') USING TIMESTAMP 1111111111111112\\n APPLY BATCH\\n \\\"\\\"\\\")\\n rows = session.execute(\\\"SELECT id, writetime(firstname), writetime(lastname) FROM users\\\")\\n res = sorted(rows)\\n assert [list(res[0]), list(res[1])] == [[0, 1111111111111111, 1111111111111111], [1, 1111111111111112, 1111111111111112]], res\",\n \"def test_set_state(self):\\n self.stream_handle = open(os.path.join(RESOURCE_PATH, 'adcpt_20130929_091817.DAT'))\\n self.parser = AdcpsJlnStcParser(self.config, self.start_state, self.stream_handle,\\n self.state_callback, self.pub_callback, self.exception_callback)\\n new_state = {StateKey.POSITION: 590}\\n self.parser.set_state(new_state)\\n result = self.parser.get_records(1)\\n self.assert_result(result, self.particle_c, 1251)\\n result = self.parser.get_records(1)\\n self.assert_result(result, self.particle_d, 1622)\",\n \"def test_exp_by_states(self):\\n api = my_mock.api_mock({\\\"items\\\": [{'state': 'Waiting', 'id': 10134},\\n {'state': 'Waiting', 'id': 10135},\\n {'state': 'Running', 'id': 10130}]})\\n states_d = helpers.exps_by_states_dict(api, helpers.ACTIVE_STATES)\\n self.assertEquals(\\n {'Waiting': [10134, 10135], 'Running': [10130]}, states_d)\\n my_mock.api_mock_stop()\",\n \"def test_same_unique_crash_type_with_different_state(self):\\n self.testcases[0].security_flag = False\\n self.testcases[0].crash_type = 'Timeout'\\n self.testcases[0].crash_state = 'abcdef'\\n self.testcases[1].security_flag = False\\n self.testcases[1].crash_type = 'Timeout'\\n self.testcases[1].crash_state = 'abcde'\\n\\n for t in self.testcases:\\n t.put()\\n\\n grouper.group_testcases()\\n\\n for index, t in enumerate(self.testcases):\\n self.testcases[index] = data_handler.get_testcase_by_id(t.key.id())\\n self.assertEqual(self.testcases[index].group_id, 0)\\n self.assertTrue(self.testcases[index].is_leader)\",\n \"def test_peformance(self):\\n timedeltas = []\\n for file in os.listdir(settings.ANALYSIS_REPORT_FOLDER):\\n _file = open(os.path.join(settings.ANALYSIS_REPORT_FOLDER, file), \\\"r\\\")\\n report = json.loads(_file.read())\\n timedeltas.append(\\n parse_datetime(report['finish']) - parse_datetime(report['start']))\\n _file.close()\\n\\n # number of queue\\n print('NUMBER OF QUEUE = {}'.format(len(timedeltas)))\\n\\n # get average time\\n average_timedelta = sum(timedeltas, datetime.timedelta(0)) / len(timedeltas)\\n print('AVERAGE = {}'.format(average_timedelta))\\n self.assertTrue(average_timedelta < datetime.timedelta(minutes=3))\\n\\n # get total process time\\n total = timedeltas[0]\\n for delta in timedeltas[:1]:\\n total += delta\\n print('TOTAL = {}'.format(total))\\n self.assertTrue(total < datetime.timedelta(minutes=3 * len(timedeltas)))\",\n \"def queryTCS():\\n timestamp = datetime.datetime.now().isoformat()\\n values = []\\n for statusPiece in statusPieces:\\n TN.write(statusPiece+\\\"\\\\r\\\\n\\\")\\n value = TN.read_until(\\\"\\\\n\\\", 0.5)\\n value = value.strip()\\n if not value:\\n value = \\\"?\\\"\\n values.append(value)\\n if values[2] == \\\"?\\\" or int(values[2]) != 2:\\n return # only record if in tracking state\\n with open(filename, \\\"a\\\") as f:\\n f.write(\\\"%s %s %s %s %s %s\\\\n\\\"%tuple([timestamp]+values))\",\n \"def _assert_state(self, state_dict):\\n instances = db.instance_get_all(self.context)\\n self.assertEqual(len(instances), 1)\\n\\n if 'vm_state' in state_dict:\\n self.assertEqual(state_dict['vm_state'], instances[0]['vm_state'])\\n if 'task_state' in state_dict:\\n self.assertEqual(state_dict['task_state'],\\n instances[0]['task_state'])\\n if 'power_state' in state_dict:\\n self.assertEqual(state_dict['power_state'],\\n instances[0]['power_state'])\",\n \"def test_time(self):\\r\\n pass\",\n \"def test_matlab_response_migration_of_queuetime(self, time):\\r\\n state = {'input_state': {'queuestate': 'queued'}}\\r\\n elt = etree.fromstring(self.xml)\\r\\n\\r\\n the_input = self.input_class(test_capa_system(), elt, state)\\r\\n context = the_input._get_render_context()\\r\\n self.assertEqual(the_input.status, 'unsubmitted')\",\n \"def test_immutable_results(env):\\n def process(env):\\n timeout = [env.timeout(delay, value=delay) for delay in range(3)]\\n # The or condition in this expression will trigger immediately. The and\\n # condition will trigger later on.\\n condition = timeout[0] | (timeout[1] & timeout[2])\\n\\n results = yield condition\\n assert results == {timeout[0]: 0}\\n\\n # Make sure that the results of condition were frozen. The results of\\n # the nested and condition do not become visible afterwards.\\n yield env.timeout(2)\\n assert results == {timeout[0]: 0}\\n\\n env.process(process(env))\\n env.run()\",\n \"def test_commitsstate(self):\\n\\n m = mapper(Order, orders, properties = {\\n 'userident':deferred(orders.c.user_id, group='primary'),\\n 'description':deferred(orders.c.description, group='primary'),\\n 'opened':deferred(orders.c.isopen, group='primary')\\n })\\n sess = create_session()\\n q = sess.query(m)\\n o2 = q.all()[2]\\n # this will load the group of attributes\\n assert o2.description == 'order 3'\\n assert o2 not in sess.dirty\\n # this will mark it as 'dirty', but nothing actually changed\\n o2.description = 'order 3'\\n def go():\\n # therefore the flush() shouldnt actually issue any SQL\\n sess.flush()\\n self.assert_sql_count(testing.db, go, 0)\",\n \"def testPreProcessResourceState(self):\\n resource_id_1 = \\\"resource_id_1\\\"\\n task_id_1 = \\\"task_id_1\\\"\\n json_out = {resource_id_1: [task_id_1]}\\n\\n # Test that the resource id is properly added with associated task\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out)\\n\\n # Test that the additional task id is appended to resource id\\n task_id_2 = \\\"task_id_2\\\"\\n json_out_2 = {resource_id_1: [task_id_1, task_id_2]}\\n resource_manager.PreprocessResourceState(resource_id_1, task_id_2)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_2)\\n\\n # Test that additional resource id is added into state file\\n resource_id_2 = \\\"resource_id_2\\\"\\n json_out_3 = {\\n resource_id_1: [task_id_1, task_id_2],\\n resource_id_2: [task_id_1]\\n }\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\\n\\n # Test that same task id is not duplicated into state file\\n resource_manager.PreprocessResourceState(resource_id_2, task_id_1)\\n self.assertEqual(resource_manager.RetrieveResourceState(), json_out_3)\",\n \"def cancellation_active(state: 'JobState') -> bool:\\n return state in [\\n JobState.STAGING_IN_CR, JobState.WAITING_CR, JobState.RUNNING_CR,\\n JobState.STAGING_OUT_CR\\n ]\",\n \"def update_status(cls):\\n for job in cls.query.filter(cls.finished == False):\\n num_hits_left = session.query(BoxHit).filter_by(training_job_id = job.id, outstanding=True).count()\\n urls_left = session.query(VideoTrainingURL).filter_by(training_job_id=job.id, processed = False)\\n dynamo = DynamoIngestionStatusClient()\\n num_urls_left = 0\\n for url in urls_left:\\n dynamo_url = dynamo.get(url.url)\\n if dynamo_url is None or dynamo_url['status'] == 'Failed':\\n # will never be processed, so ignore for our purposes\\n url.processed = True\\n else:\\n num_urls_left += 1\\n if num_hits_left+num_urls_left == 0:\\n job.finished = True\\n print '*** Job ID: %s is complete ***' % str(job.id)\\n\\n print '------------- Stats for Job ID: %s -------------' % str(job.id)\\n print 'Total URLs : %i' % VideoTrainingURL.query.filter_by(training_job_id = job.id).count()\\n print 'Total HITs : %i' % BoxHit.query.filter_by(training_job_id = job.id).count()\\n if not job.finished:\\n print 'unprocessed URLs: %i' % num_urls_left\\n print 'outstanding HITs: %i\\\\n' % num_hits_left\\n session.flush()\",\n \"def test_process(self, tmp_path):\\n export_dir = tmp_path / 'export'\\n export_dir.mkdir()\\n\\n process(['raw_data/small_raw_data_0.csv',\\n 'raw_data/small_raw_data_1.csv',\\n # File 2 does not exist.\\n 'raw_data/small_raw_data_2.csv',\\n 'raw_data/small_raw_data_3.csv'],\\n SIGNALS,\\n ['median_home_dwell_time',\\n 'completely_home_prop_7dav'],\\n ['state'],\\n export_dir)\\n\\n expected = {\\n 'wip_median_home_dwell_time': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [6, 3.5],\\n 'se': [None, 0.5],\\n 'sample_size': [1, 2]\\n }),\\n 'completely_home_prop': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [0.15, 0.055],\\n 'se': [None, 0.005],\\n 'sample_size': [1, 2]\\n }),\\n 'part_time_work_prop': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [0.35, 0.055],\\n 'se': [None, 0.005],\\n 'sample_size': [1, 2]\\n }),\\n 'full_time_work_prop': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [0.45, 0.055],\\n 'se': [None, 0.005],\\n 'sample_size': [1, 2]\\n }),\\n 'median_home_dwell_time_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [4.5, 3.5, 7.5],\\n 'se': [1.5, 0.5, 0.5],\\n 'sample_size': [2, 2, 2]\\n }),\\n 'wip_completely_home_prop_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [0.1, 0.055, 0.15],\\n 'se': [0.05, 0.005, 0.05],\\n 'sample_size': [2, 2, 2]\\n }),\\n 'part_time_work_prop_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [0.25, 0.055, 0.25],\\n 'se': [0.1, 0.005, 0.05],\\n 'sample_size': [2, 2, 2]\\n }),\\n 'full_time_work_prop_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [0.35, 0.055, 0.35],\\n 'se': [0.1, 0.005, 0.05],\\n 'sample_size': [2, 2, 2]\\n })\\n }\\n actual = {signal: pd.read_csv(\\n export_dir / f'20200612_state_{signal}.csv')\\n for signal in expected}\\n for signal in expected:\\n pd.testing.assert_frame_equal(expected[signal], actual[signal])\",\n \"def test_process_filter_value():\\n now = dt.utcnow()\\n now_ts = now.timestamp()\\n filter_ = {'column': \\\"ts_created_at\\\", 'value': now_ts, type: 'leq'}\\n assert process_filter_value(filter_) == now\\n\\n filter_ = {'column': \\\"created_at\\\", 'value': now_ts, type: 'leq'}\\n assert process_filter_value(filter_) == now_ts\",\n \"def test_order_update_status_function(self):\\n order = OrderInfo.objects.create(user=self.create_test_user())\\n self.assertIsInstance(order.ordered, datetime)\\n self.assertIsNone(order.cooked)\\n self.assertIsNone(order.delivered)\\n\\n order.update_current_state()\\n self.assertIsInstance(order.cooked, datetime)\\n self.assertIsNone(order.delivered)\\n\\n order.update_current_state()\\n self.assertIsInstance(order.cooked, datetime)\\n self.assertIsInstance(order.delivered, datetime)\",\n \"def test_condition_value(env):\\n timeouts = list([env.timeout(delay, value=delay) for delay in range(3)])\\n\\n def p(env, timeouts):\\n results = yield env.all_of(timeouts)\\n assert list(results) == timeouts\\n assert list(results.keys()) == timeouts\\n assert list(results.values()) == [0, 1, 2]\\n assert list(results.items()) == list(zip(timeouts, [0, 1, 2]))\\n assert timeouts[0] in results\\n assert results[timeouts[0]] == 0\\n assert results == results\\n assert results == results.todict()\\n\\n env.process(p(env, timeouts))\\n env.run()\",\n \"def test_update_file_state(self):\\n # blocks [0 4012], based on unit_362-2013-202-2-0.mdd\\n test_file1 = os.path.join(INPUT_HYPM_PATH, 'first.mdd')\\n\\n # parse the first .mdd files into the node and instrument group files\\n mdd.procall([test_file1])\\n\\n file_state = self.get_file_state('node60p1.dat')\\n expected_file_state_1 = {StateKey.UNPROCESSED_DATA: [],\\n StateKey.FILE_SIZE: 4012,\\n StateKey.OUTPUT_INDEX: 1}\\n\\n if file_state != expected_file_state_1:\\n print \\\"file state try 1: '%s'\\\" % file_state\\n self.fail(\\\"Expected file state 1 does not match\\\")\\n\\n test_file2 = os.path.join(INPUT_HYPM_PATH, 'unit_362-2013-202-2-0.mdd')\\n\\n # parse the first .mdd files into the node and instrument group files\\n mdd.procall([test_file2])\\n\\n file_state = self.get_file_state('node60p1.dat')\\n expected_file_state_2 = {StateKey.UNPROCESSED_DATA: [[4736, 8192]],\\n StateKey.FILE_SIZE: 8192,\\n StateKey.OUTPUT_INDEX: 2}\\n\\n if file_state != expected_file_state_2:\\n print \\\"file state try 2: '%s'\\\" % file_state\\n self.fail(\\\"Expected file state 2 does not match\\\")\\n\\n # start second test, switch to node58\\n # blocks [0 3583] [3840 4058]\\n test_file1 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-2-0.mdd')\\n mdd.procall([test_file1])\\n\\n file_state = self.get_file_state('node58p1.dat')\\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[3189, 3945]],\\n StateKey.FILE_SIZE: 4059,\\n StateKey.OUTPUT_INDEX: 1}\\n\\n if file_state != expected_file_state:\\n print file_state\\n self.fail(\\\"Expected file state 3 does not match\\\")\\n\\n # blocks [0 1279] [1536 1791] [2048 2303] [2560 2815] [3072 4059]\\n test_file2 = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-206-3-0.mdd')\\n\\n # parse the two .mdd files into the node and instrument group files\\n mdd.procall([test_file2])\\n\\n file_state = self.get_file_state('node58p1.dat')\\n # there is an unprocessed '/n' in between records\\n expected_file_state = {StateKey.UNPROCESSED_DATA: [[4059, 4060]],\\n StateKey.FILE_SIZE: 4060,\\n StateKey.OUTPUT_INDEX: 2}\\n\\n if file_state != expected_file_state:\\n print file_state\\n self.fail(\\\"Expected file state 4 does not match\\\")\",\n \"def testTimestamps(self):\\n predicate = \\\"metadata:predicate\\\"\\n subject = \\\"aff4:/metadata:8\\\"\\n\\n # Extend the range of valid timestamps returned from the table to account\\n # for potential clock skew.\\n start = long(time.time() - 60) * 1e6\\n data_store.DB.Set(subject, predicate, \\\"1\\\", token=self.token)\\n\\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\\n\\n # Check the time is reasonable\\n end = long(time.time() + 60) * 1e6\\n\\n self.assert_(ts >= start and ts <= end)\\n self.assertEqual(stored, \\\"1\\\")\",\n \"def timestep(self):\\n self.survive()\\n self.move()\\n self.set_survival_probability(self.vital_var)\\n self.reproduce()\\n # self.save_frame()\\n if self.time % self.save_state_frequency == 0:\\n self.save_state(self.state_file.format(self.time))\\n if self.time % self.save_stats_frequency == 0:\\n self.write_stats()\\n # self.connected_component()\\n self.time += 1\\n\\n if self.pop_size > 0:\\n return True\\n else:\\n return False\",\n \"def test_data_handlers(timeseries, handler):\\n handler = handler(timeseries, HISTORICAL_CONTEXT_NUMBER)\\n updates = []\\n i = 0\\n while True:\\n try:\\n context, update = handler.next()\\n except NoMoreData:\\n break\\n i += 1\\n\\n updates.append(update)\\n # import pdb; pdb.set_trace()\\n assert context[-1].datetime < update.datetime\\n\\n # total lenght of series(25) - context(5) which aren't provided as updates\\n assert i == 20\\n updates = [x.datetime.date().isoformat() for x in updates]\\n assert updates[10] == \\\"2018-01-22\\\"\",\n \"def test_get_field_state_comparisons_tiny(self):\\r\\n comparison_groupings = get_field_state_comparisons(\\r\\n self.tiny_dist_matrix_header, self.tiny_dist_matrix,\\r\\n self.tiny_mapping_header, self.tiny_mapping, self.tiny_field,\\r\\n ['SampleFieldState1'])\\r\\n self.assertEqual(comparison_groupings, {})\",\n \"def valid(t):\\n return float(t) > time.time()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67496896","0.6644145","0.6472953","0.62294924","0.6101738","0.5977244","0.59124064","0.5691305","0.55319977","0.5511322","0.55038923","0.5429801","0.54225284","0.5324777","0.5302315","0.52370924","0.5232825","0.5190848","0.5188738","0.5178702","0.51469487","0.51411235","0.51252335","0.51232964","0.50751364","0.50745195","0.5074006","0.50698984","0.50698435","0.5052384","0.5035761","0.503217","0.5017193","0.50170237","0.50160325","0.5013755","0.50136334","0.4983446","0.49691173","0.4960085","0.49508885","0.49417767","0.49381986","0.49295262","0.49272388","0.491873","0.49167147","0.4916342","0.49144593","0.49077785","0.48939273","0.48927534","0.48907053","0.48901922","0.48876655","0.48818985","0.48781678","0.4877524","0.48683298","0.4868232","0.48584202","0.48565543","0.48561355","0.48553765","0.48535198","0.4844593","0.48377106","0.48372963","0.4835882","0.48357844","0.4822115","0.481957","0.48190284","0.48098415","0.48094347","0.48064548","0.48025495","0.4802225","0.48022202","0.48018336","0.48015642","0.47992337","0.4797087","0.4795351","0.47930127","0.47927746","0.47924003","0.47882956","0.47844613","0.47798073","0.4773192","0.4768172","0.4766683","0.47626954","0.47624332","0.47599944","0.4757389","0.4753122","0.4753053","0.47483718"],"string":"[\n \"0.67496896\",\n \"0.6644145\",\n \"0.6472953\",\n \"0.62294924\",\n \"0.6101738\",\n \"0.5977244\",\n \"0.59124064\",\n \"0.5691305\",\n \"0.55319977\",\n \"0.5511322\",\n \"0.55038923\",\n \"0.5429801\",\n \"0.54225284\",\n \"0.5324777\",\n \"0.5302315\",\n \"0.52370924\",\n \"0.5232825\",\n \"0.5190848\",\n \"0.5188738\",\n \"0.5178702\",\n \"0.51469487\",\n \"0.51411235\",\n \"0.51252335\",\n \"0.51232964\",\n \"0.50751364\",\n \"0.50745195\",\n \"0.5074006\",\n \"0.50698984\",\n \"0.50698435\",\n \"0.5052384\",\n \"0.5035761\",\n \"0.503217\",\n \"0.5017193\",\n \"0.50170237\",\n \"0.50160325\",\n \"0.5013755\",\n \"0.50136334\",\n \"0.4983446\",\n \"0.49691173\",\n \"0.4960085\",\n \"0.49508885\",\n \"0.49417767\",\n \"0.49381986\",\n \"0.49295262\",\n \"0.49272388\",\n \"0.491873\",\n \"0.49167147\",\n \"0.4916342\",\n \"0.49144593\",\n \"0.49077785\",\n \"0.48939273\",\n \"0.48927534\",\n \"0.48907053\",\n \"0.48901922\",\n \"0.48876655\",\n \"0.48818985\",\n \"0.48781678\",\n \"0.4877524\",\n \"0.48683298\",\n \"0.4868232\",\n \"0.48584202\",\n \"0.48565543\",\n \"0.48561355\",\n \"0.48553765\",\n \"0.48535198\",\n \"0.4844593\",\n \"0.48377106\",\n \"0.48372963\",\n \"0.4835882\",\n \"0.48357844\",\n \"0.4822115\",\n \"0.481957\",\n \"0.48190284\",\n \"0.48098415\",\n \"0.48094347\",\n \"0.48064548\",\n \"0.48025495\",\n \"0.4802225\",\n \"0.48022202\",\n \"0.48018336\",\n \"0.48015642\",\n \"0.47992337\",\n \"0.4797087\",\n \"0.4795351\",\n \"0.47930127\",\n \"0.47927746\",\n \"0.47924003\",\n \"0.47882956\",\n \"0.47844613\",\n \"0.47798073\",\n \"0.4773192\",\n \"0.4768172\",\n \"0.4766683\",\n \"0.47626954\",\n \"0.47624332\",\n \"0.47599944\",\n \"0.4757389\",\n \"0.4753122\",\n \"0.4753053\",\n \"0.47483718\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72442085"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17477,"cells":{"query":{"kind":"string","value":"This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`."},"document":{"kind":"string","value":"def plot_confusion_matrix(cm, classes=[],\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.figure()\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n #print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=90)\n plt.yticks(tick_marks, classes)\n\n fmt = '.1f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n if print_matrix:\n print(cm)","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',saveas='cm', cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n \n print(cm)\n\n plt.figure() \n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n \n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n \n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n \n foo_fig = plt.gcf() # 'get current figure'\n# foo_fig.savefig('confusion_matrix.eps', format='eps', dpi=1000) \n foo_fig.savefig(saveas, dpi=1000, bbox_inches='tight')\n plt.show()","def plot_confusion_matrix(cm, y_test, y_pred, class_names,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print('\\n')\n print(\"Normalized confusion matrix\")\n else:\n print('\\n')\n print('Confusion matrix, without normalization')\n print_cm(cm, class_names)\n text_labels = [['True Negative', 'False Positive'],\n ['False Negative', 'True Positive']]\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(class_names))\n plt.xticks(tick_marks, class_names, rotation=45)\n plt.yticks(tick_marks, class_names)\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i - 0.1, format(cm[i, j], fmt),\n verticalalignment='bottom',\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n plt.text(j, i + 0.1, text_labels[i][j],\n verticalalignment='top',\n horizontalalignment=\"center\",\n fontsize=12,\n color=\"white\" if cm[i, j] > thresh else \"black\")\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n\n # Print accuracy and precision\n print('Accuracy: ', accuracy_score(y_test, y_pred, normalize=True))\n print('Precision: ', precision_score(y_test, y_pred, average='macro'))\n print('Roc-Auc: ', roc_auc_score(y_test, y_pred))\n # Plot non-normalized confusion matrix","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n #cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n cm = cm.astype('float') / np.sum(cm.ravel())\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n fig=plt.figure\n plt.imshow(cm, interpolation='nearest', cmap=cmap )\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()\n return fig","def plot_confusion_matrix(\n y_true, y_pred, classes, normalize=True, title=\"Confusion matrix\", cmap=plt.cm.Blues\n):\n cm = confusion_matrix(y_true, y_pred)\n\n if normalize:\n cm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print(\"Confusion matrix, without normalization\")\n\n plt.imshow(cm, interpolation=\"nearest\", cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = \".2f\" if normalize else \"d\"\n thresh = cm.max() / 2.0\n for i, j in product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(\n j,\n i,\n format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\",\n )\n\n plt.tight_layout()\n plt.ylabel(\"True label\")\n plt.xlabel(\"Predicted label\")","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n #print(\"Normalized confusion matrix\")\n #else:\n\n #print('Confusion matrix, without normalization')\n\n# print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n# for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n# plt.text(j, i, format(cm[i, j], fmt),\n# horizontalalignment=\"center\",\n# color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n cm = confusion_matrix(y_test,predictions)\n plt.figure()\n plot_confusion_matrix(cm, classes=[0,1,2], normalize=True,\n title='Confusion Matrix')","def plot_confusion_matrix(y_test, y_pred, classes,\n normalize=True,\n title='Average accuracy \\n',\n cmap=plt.cm.Blues, verbose = 0, precision = 0):\n from sklearn.metrics import confusion_matrix\n import itertools\n \n cm = confusion_matrix(y_test, y_pred)\n accuracy = (np.sum(np.diag(cm)) / np.sum(cm)) * 100.0\n\n if normalize:\n cm = (cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]) * 100.0\n if verbose == 1:\n print(\"Normalized confusion matrix\")\n else:\n if verbose == 1:\n print('Confusion matrix, without normalization')\n \n if verbose == 1:\n print(cm)\n\n plt.figure(figsize=(18, 9))\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title.format_map({'acc':accuracy}), fontsize=25)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45, fontsize=20)\n plt.yticks(tick_marks, classes, fontsize=20)\n\n fmt = '{:.'+ '%d'%(precision) +'f} %' if normalize else '{:d}'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, fmt.format(cm[i, j]),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\", fontsize=16)\n plt.tight_layout()\n plt.ylabel('True label', fontsize=20)\n plt.xlabel('Predicted label', fontsize=20)","def plot_confusion_matrix(self, cm, classes, normalize, cmap=plt.cm.Blues, title='confusin Matrix'):\r\n if normalize:\r\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\r\n print(\"Normalized confusion matrix\")\r\n else:\r\n print('Confusion matrix, without normalization')\r\n\r\n print(cm)\r\n\r\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\r\n plt.title(title)\r\n plt.colorbar()\r\n tick_marks = np.arange(len(classes))\r\n plt.xticks(tick_marks, classes, rotation=45)\r\n plt.yticks(tick_marks, classes)\r\n\r\n fmt = '.2f' if normalize else 'd'\r\n thresh = cm.max() / 2.\r\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n plt.text(j, i, format(cm[i, j], fmt),\r\n horizontalalignment=\"center\",\r\n color=\"white\" if cm[i, j] > thresh else \"black\")\r\n\r\n tick_marks = np.arange(len(classes))\r\n\r\n self.subplt.set_xlabel(\"Predicted label\")\r\n self.subplt.set_ylabel(\"True Label\")\r\n self.subplt.set_title(\"Confusion Matrix\")\r\n self.subplt.set_xticks(tick_marks,classes)\r\n self.subplt.set_yticks(tick_marks,classes)\r\n\r\n self.canvas2.show()","def showConfusionMatrix(self): \r\n sn.heatmap(self.conf_matrix, annot=True)\r\n plt.plot( label=\"Accuracy\")\r\n plt.plot( label=\"Error\")\r\n plt.figtext(0,0,'Accuracy: {}\\nError: {}\\nRecall: {}\\nPrecision: {}'.format(self.accuracy,\r\n self.error,\r\n self.recall,\r\n self.precision))\r\n plt.title('Confusion Matrix')\r\n plt.show()\r\n return None","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n \n print(cm)\n \n \n plt.title(title)\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n \n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n \n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.colorbar()\n plt.show()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, \"%.2f\" % cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print('Normalized confusion matrix')\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment='center',\n color='white' if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n #based on http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n cmap=plt.cm.Blues\n \n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n np.set_printoptions(precision=2)\n \n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, '%1.2f' % cm[i, j],\n horizontalalignment=\"center\",\n fontsize =12,\n color=\"white\" if cm[i, j] > thresh else \"black\")\n #plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n \n print(cm)\n \n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes)) \n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n \n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n \n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.axis('auto')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n print('Confusion matrix')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\r\n if normalize:\r\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\r\n print(\"Normalized confusion matrix\")\r\n else:\r\n print('Confusion matrix, without normalization')\r\n # print(cm)\r\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\r\n plt.title(title)\r\n plt.colorbar()\r\n tick_marks = np.arange(len(classes))\r\n plt.xticks(tick_marks, classes, rotation=45)\r\n plt.yticks(tick_marks, classes)\r\n fmt = '.2f' if normalize else 'd'\r\n thresh = cm.max() / 2.\r\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n plt.text(j, i, format(cm[i, j], fmt),\r\n horizontalalignment=\"center\",\r\n color=\"white\" if cm[i, j] > thresh else \"black\")\r\n plt.tight_layout()\r\n plt.ylabel('True label')\r\n plt.xlabel('Predicted label')","def plot_confusion_matrix(confusion_matrix, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(confusion_matrix, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n confusion_matrix = confusion_matrix.astype(\n 'float') / confusion_matrix.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(confusion_matrix)\n\n thresh = confusion_matrix.max() / 2.\n for i, j in itertools.product(range(confusion_matrix.shape[0]), range(confusion_matrix.shape[1])):\n plt.text(j, i, confusion_matrix[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if confusion_matrix[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n confusion_matrix_dir = './confusion_matrix_plots'\n if not os.path.exists(confusion_matrix_dir):\n os.mkdir(confusion_matrix_dir)\n\n plt.cla()\n plt.figure()\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"#BFD1D4\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n\n if normalize:\n plt.savefig(os.path.join(confusion_matrix_dir, 'normalized.jpg'))\n else:\n plt.savefig(os.path.join(confusion_matrix_dir, 'without_normalization.jpg'))","def plot_confusion_matrix(cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):\n\n plt.figure(figsize=(10,10))\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n cm = np.around(cm, decimals=2)\n cm[np.isnan(cm)] = 0.0\n print(\"Normalized confusion matrix\")\n\n else:\n print('Confusion matrix, without normalization')\n\n thresh = cm.max() / 2.\n\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n t = \"(%.2f)\"%(cm[i, j])\n #print t\n# plt.text(j, i, t,\n# horizontalalignment=\"center\",\n# color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.savefig('IOB-Confusion-Matrix-SVM.png')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n if normalize:\n # 1. find out how many samples per class have received their correct label\n # 计算真正类别为k的样本被预测成各个类别的比例\n # e.g. 有25个样本的 true label 是 6，其中10个样本被预测为类别7，那么在混淆矩阵中 true label = 6 并且 predicted label = 7 的一个格子中的值为 0.4\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n # 2. get the precision (fraction of class-k predictions that have ground truth label k)\n # 计算预测的准确率\n # e.g. 预测为类别k的有12个，但其中只有9个的真正类别是k，那么准确率为 0.75\n # cm = cm.astype('float') / cm.sum(axis=0)[:, np.newaxis]\n \n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n # plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n # tick_marks = np.arange(len(classes))\n # plt.xticks(tick_marks, classes, rotation=45)\n # plt.yticks(tick_marks, classes)\n\n # fmt = '.2f' if normalize else 'd'\n # thresh = cm.max() / 2.\n # for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n # plt.text(j, i, format(cm[i, j], fmt),\n # horizontalalignment=\"center\",\n # color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n \n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n \n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n \n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.savefig('confusion_matrix.png')","def plotConfusionMatrix(self, cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()\n plt.savefig('confusion_matrix.png')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion Matrix', cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion Matrix, without normalization')\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title, weight='bold')\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n if i == 0:\n plt.text(j-0.1, i+0.3, format(cm[i, j], fmt), color=\"white\" if cm[i, j] > thresh else \"black\")\n if i == 1:\n plt.text(j-0.1, i-0.2, format(cm[i, j], fmt), color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True Label', weight='bold')\n plt.xlabel('Predicted Label', weight='bold')\n plt.show()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n #print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=90)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n #pdb.set_trace()\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def sam_plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n plots_dims = itertools.product(list(range(cm.shape[0])),\n list(range(cm.shape[1])))\n for i, j in plots_dims:\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \n print(a)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap, vmin=0.0, vmax=1.0)\n\n plt.title(title)\n\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.3f'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n # plt.title(title)\n # plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n # Tweak spacing to prevent clipping of tick-labels\n plt.subplots_adjust(bottom=0.2)","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n # print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.xlabel('Predicted label') \n plt.ylabel('True label') \n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label') \n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Purples):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n # plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n # plt.grid('off')\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=True,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n# print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()\n return plt.gcf()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title + \"Confusion matrix\")\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print(title + ' confusion matrix')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=None):\n if normalize:\n # cm = cm.T\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n # cm = cm.T\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.figure(figsize=(4, 4))\n plt.imshow(cm, interpolation='nearest', cmap=cmap or plt.cm.Blues)\n plt.title(('Normalized ' if normalize else '') + title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(list(range(cm.shape[0])), list(range(cm.shape[1]))):\n plt.text(\n j,\n i,\n format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=0)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n #print(\"Normalized confusion matrix\")\n else:\n 1#print('Confusion matrix, without normalization')\n\n #print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title, fontsize=14)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title,fontsize=20)\n# plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, fontsize=15)\n plt.yticks(tick_marks, classes,rotation=30,fontsize=15)\n\n fmt = '.2f'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\",fontsize=20)\n\n plt.tight_layout()\n plt.ylabel('True label',fontsize=20)\n plt.xlabel('Predicted label',fontsize=20)","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Confusion matrix\")\n else:\n print('Confusion matrix')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Greens):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, '%.02f'%cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"red\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.figure()\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n # plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title.split('/')[-1])\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n # plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n if title:\n plt.savefig(title+'.png')\n\n plt.close()","def plot_confusion_matrix(cm, classes,\r\n normalize=False,\r\n title='Confusion matrix',\r\n cmap=plt.cm.Blues):\r\n if normalize:\r\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\r\n print(\"Normalized confusion matrix\")\r\n else:\r\n print('Confusion matrix, without normalization')\r\n\r\n print(cm)\r\n\r\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\r\n plt.title(title)\r\n plt.colorbar()\r\n tick_marks = np.arange(len(classes))\r\n plt.xticks(tick_marks, classes, rotation=45)\r\n plt.yticks(tick_marks, classes)\r\n\r\n fmt = '.2f' if normalize else 'd'\r\n thresh = cm.max() / 2.\r\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n plt.text(j, i, format(cm[i, j], fmt),\r\n horizontalalignment=\"center\",\r\n color=\"white\" if cm[i, j] > thresh else \"black\")\r\n\r\n plt.tight_layout()\r\n plt.ylabel('True label')\r\n plt.xlabel('Predicted label')\r\n plt.savefig('Logistik.png')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='',\n cmap=plt.cm.Blues, file_name='cm_plot'):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.rcParams[\"font.family\"] = \"Times New Roman\"\n plt.rcParams[\"font.size\"] = FONT_SIZE\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=0)\n plt.yticks(tick_marks, classes)\n\n fmt = '.6f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label', fontsize=FONT_SIZE)\n plt.xlabel('Predicted label', fontsize=FONT_SIZE)\n plt.subplots_adjust(bottom=0.13)\n with PdfPages(file_name) as pdf:\n pdf.savefig()\n plt.close()","def plot_confusion_matrix(self):\r\n interp = ClassificationInterpretation.from_learner(self.learn)\r\n interp.plot_confusion_matrix()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm,\r\n normalize=False,\r\n title='Confusion matrix',\r\n cmap=plt.cm.Blues):\r\n if normalize:\r\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\r\n print(\"Normalized confusion matrix\")\r\n else:\r\n print('Confusion matrix, without normalization')\r\n\r\n# print(cm)\r\n\r\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\r\n plt.title(title)\r\n plt.colorbar()\r\n\r\n fmt = '.2f' if normalize else 'd'\r\n thresh = cm.max() / 2.\r\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n plt.text(j, i, format(cm[i, j], fmt),\r\n horizontalalignment=\"center\",\r\n color=\"white\" if cm[i, j] > thresh else \"black\")\r\n\r\n plt.tight_layout()\r\n plt.ylabel('True label')\r\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes=None, normalize=False,\n title='Confusion matrix', cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.figure()\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n\n if classes:\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()\n plt.show()","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n plt.figure()\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(cm)\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n plt.figure()\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(cm)\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')","def plot_confusion_matrix(y_true, y_pred, classes,\n normalize=False,\n title=None,\n cmap=plt.cm.Blues):\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n\n # Compute confusion matrix\n cm = confusion_matrix(y_true, y_pred)\n # Only use the labels that appear in the data\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n fig, ax = plt.subplots(figsize=(8, 8))\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n # We want to show all ticks...\n ax.set(xticks=np.arange(cm.shape[1]),\n yticks=np.arange(cm.shape[0]),\n # ... and label them with the respective list entries\n xticklabels=classes, yticklabels=classes)\n ax.set_title(title,size = 20)\n ax.set_ylabel('True label',size = 20)\n ax.set_xlabel('Predicted label',size = 20)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n rotation_mode=\"anchor\",size = 18)\n plt.setp(ax.get_yticklabels(),size = 18)\n\n # Loop over data dimensions and create text annotations.\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt),\n ha=\"center\", va=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n fig.tight_layout()\n name = OUTFOLDER + \"/confusion_matrix_batch%d_layers%d_epochs%d_f1%d\" % (BATCH_SIZE,LAYERS,EPOCHS,f1_mean_test*100)\n if normalize:\n name = name + \"_norm\"\n plt.savefig(name)\n plt.close()\n return ax","def plot_confusion_matrix(cm, classes,\n\t\t\t\t\t\t normalize=False,\n\t\t\t\t\t\t title='Confusion matrix',\n\t\t\t\t\t\t cmap=plt.cm.Blues):\n\tif normalize:\n\t\tcm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\t\tprint(\"Normalized confusion matrix\")\n\telse:\n\t\tprint('Confusion matrix, without normalization')\n\n\tprint(cm)\n\n\tplt.imshow(cm, interpolation='nearest', cmap=cmap)\n\tplt.title(title)\n\tplt.colorbar()\n\ttick_marks = np.arange(len(classes))\n\tplt.xticks(tick_marks, classes, rotation=45)\n\tplt.yticks(tick_marks, classes)\n\n\tfmt = '.2f' if normalize else 'd'\n\tthresh = cm.max() / 2.\n\tfor i, j in product(range(cm.shape[0]), range(cm.shape[1])):\n\t\tplt.text(j, i, format(cm[i, j], fmt),\n\t\t\t\t horizontalalignment=\"center\",\n\t\t\t\t color=\"white\" if cm[i, j] > thresh else \"black\")\n\n\tplt.tight_layout()\n\tplt.ylabel('True label')\n\tplt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes,\n\t\t\t\t\t\t\t normalize=False,\n\t\t\t\t\t\t\t title='Confusion matrix',\n\t\t\t\t\t\t\t cmap=plt.cm.Blues):\n\t\tif normalize:\n\t\t\tcm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\t\t\tprint(\"Normalized confusion matrix\")\n\t\telse:\n\t\t\tprint('Confusion matrix, without normalization')\n\n\t\tplt.imshow(cm, interpolation='nearest', cmap=cmap)\n\t\tplt.title(title)\n\t\tplt.colorbar()\n\t\ttick_marks = np.arange(len(classes))\n\t\tplt.xticks(tick_marks, classes, rotation=45)\n\t\tplt.yticks(tick_marks, classes)\n\n\t\tplt.tight_layout()\n\t\tplt.ylabel('True label')\n\t\tplt.xlabel('Predicted label')","def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, cm[i, j],\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.savefig('../results/conf_matr.png')\n\n return cm","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()","def plot(self):\n plt.imshow(self.cm, interpolation='nearest', cmap=self.cmap)\n plt.title(self.title)\n plt.colorbar()\n tick_marks = np.arange(len(self.classes))\n plt.xticks(tick_marks, self.classes, rotation=45)\n plt.yticks(tick_marks, self.classes)\n \n if self.normalize:\n self.cm = self.cm.astype('float') / self.cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n \n print(self.cm)\n \n thresh = self.cm.max() / 2.\n for i, j in itertools.product(range(self.cm.shape[0]), range(self.cm.shape[1])):\n plt.text(j, i, self.cm[i, j], horizontalalignment=\"center\", color=\"white\" if self.cm[i, j] > thresh else \"black\")\n plt.tight_layout()\n plt.ylabel('True Label')\n plt.xlabel('Predicted label')"],"string":"[\n \"def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n if print_matrix:\\n print(cm)\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',saveas='cm', cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n \\n print(cm)\\n\\n plt.figure() \\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n \\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n \\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n \\n foo_fig = plt.gcf() # 'get current figure'\\n# foo_fig.savefig('confusion_matrix.eps', format='eps', dpi=1000) \\n foo_fig.savefig(saveas, dpi=1000, bbox_inches='tight')\\n plt.show()\",\n \"def plot_confusion_matrix(cm, y_test, y_pred, class_names,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print('\\\\n')\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('\\\\n')\\n print('Confusion matrix, without normalization')\\n print_cm(cm, class_names)\\n text_labels = [['True Negative', 'False Positive'],\\n ['False Negative', 'True Positive']]\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(class_names))\\n plt.xticks(tick_marks, class_names, rotation=45)\\n plt.yticks(tick_marks, class_names)\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i - 0.1, format(cm[i, j], fmt),\\n verticalalignment='bottom',\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n plt.text(j, i + 0.1, text_labels[i][j],\\n verticalalignment='top',\\n horizontalalignment=\\\"center\\\",\\n fontsize=12,\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n\\n # Print accuracy and precision\\n print('Accuracy: ', accuracy_score(y_test, y_pred, normalize=True))\\n print('Precision: ', precision_score(y_test, y_pred, average='macro'))\\n print('Roc-Auc: ', roc_auc_score(y_test, y_pred))\\n # Plot non-normalized confusion matrix\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n #cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n cm = cm.astype('float') / np.sum(cm.ravel())\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n fig=plt.figure\\n plt.imshow(cm, interpolation='nearest', cmap=cmap )\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.show()\\n return fig\",\n \"def plot_confusion_matrix(\\n y_true, y_pred, classes, normalize=True, title=\\\"Confusion matrix\\\", cmap=plt.cm.Blues\\n):\\n cm = confusion_matrix(y_true, y_pred)\\n\\n if normalize:\\n cm = cm.astype(\\\"float\\\") / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print(\\\"Confusion matrix, without normalization\\\")\\n\\n plt.imshow(cm, interpolation=\\\"nearest\\\", cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = \\\".2f\\\" if normalize else \\\"d\\\"\\n thresh = cm.max() / 2.0\\n for i, j in product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(\\n j,\\n i,\\n format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\",\\n )\\n\\n plt.tight_layout()\\n plt.ylabel(\\\"True label\\\")\\n plt.xlabel(\\\"Predicted label\\\")\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n #print(\\\"Normalized confusion matrix\\\")\\n #else:\\n\\n #print('Confusion matrix, without normalization')\\n\\n# print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n# for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n# plt.text(j, i, format(cm[i, j], fmt),\\n# horizontalalignment=\\\"center\\\",\\n# color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n print(cm)\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n cm = confusion_matrix(y_test,predictions)\\n plt.figure()\\n plot_confusion_matrix(cm, classes=[0,1,2], normalize=True,\\n title='Confusion Matrix')\",\n \"def plot_confusion_matrix(y_test, y_pred, classes,\\n normalize=True,\\n title='Average accuracy \\\\n',\\n cmap=plt.cm.Blues, verbose = 0, precision = 0):\\n from sklearn.metrics import confusion_matrix\\n import itertools\\n \\n cm = confusion_matrix(y_test, y_pred)\\n accuracy = (np.sum(np.diag(cm)) / np.sum(cm)) * 100.0\\n\\n if normalize:\\n cm = (cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]) * 100.0\\n if verbose == 1:\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n if verbose == 1:\\n print('Confusion matrix, without normalization')\\n \\n if verbose == 1:\\n print(cm)\\n\\n plt.figure(figsize=(18, 9))\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title.format_map({'acc':accuracy}), fontsize=25)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45, fontsize=20)\\n plt.yticks(tick_marks, classes, fontsize=20)\\n\\n fmt = '{:.'+ '%d'%(precision) +'f} %' if normalize else '{:d}'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, fmt.format(cm[i, j]),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\", fontsize=16)\\n plt.tight_layout()\\n plt.ylabel('True label', fontsize=20)\\n plt.xlabel('Predicted label', fontsize=20)\",\n \"def plot_confusion_matrix(self, cm, classes, normalize, cmap=plt.cm.Blues, title='confusin Matrix'):\\r\\n if normalize:\\r\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\r\\n print(\\\"Normalized confusion matrix\\\")\\r\\n else:\\r\\n print('Confusion matrix, without normalization')\\r\\n\\r\\n print(cm)\\r\\n\\r\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\r\\n plt.title(title)\\r\\n plt.colorbar()\\r\\n tick_marks = np.arange(len(classes))\\r\\n plt.xticks(tick_marks, classes, rotation=45)\\r\\n plt.yticks(tick_marks, classes)\\r\\n\\r\\n fmt = '.2f' if normalize else 'd'\\r\\n thresh = cm.max() / 2.\\r\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\r\\n plt.text(j, i, format(cm[i, j], fmt),\\r\\n horizontalalignment=\\\"center\\\",\\r\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\r\\n\\r\\n tick_marks = np.arange(len(classes))\\r\\n\\r\\n self.subplt.set_xlabel(\\\"Predicted label\\\")\\r\\n self.subplt.set_ylabel(\\\"True Label\\\")\\r\\n self.subplt.set_title(\\\"Confusion Matrix\\\")\\r\\n self.subplt.set_xticks(tick_marks,classes)\\r\\n self.subplt.set_yticks(tick_marks,classes)\\r\\n\\r\\n self.canvas2.show()\",\n \"def showConfusionMatrix(self): \\r\\n sn.heatmap(self.conf_matrix, annot=True)\\r\\n plt.plot( label=\\\"Accuracy\\\")\\r\\n plt.plot( label=\\\"Error\\\")\\r\\n plt.figtext(0,0,'Accuracy: {}\\\\nError: {}\\\\nRecall: {}\\\\nPrecision: {}'.format(self.accuracy,\\r\\n self.error,\\r\\n self.recall,\\r\\n self.precision))\\r\\n plt.title('Confusion Matrix')\\r\\n plt.show()\\r\\n return None\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n \\n print(cm)\\n \\n \\n plt.title(title)\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n \\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n \\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.colorbar()\\n plt.show()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, \\\"%.2f\\\" % cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print('Normalized confusion matrix')\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment='center',\\n color='white' if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n #based on http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n cmap=plt.cm.Blues\\n \\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n np.set_printoptions(precision=2)\\n \\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, '%1.2f' % cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n fontsize =12,\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n #plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n \\n print(cm)\\n \\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes)) \\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n \\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n \\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.axis('auto')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n print('Confusion matrix')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\r\\n if normalize:\\r\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\r\\n print(\\\"Normalized confusion matrix\\\")\\r\\n else:\\r\\n print('Confusion matrix, without normalization')\\r\\n # print(cm)\\r\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\r\\n plt.title(title)\\r\\n plt.colorbar()\\r\\n tick_marks = np.arange(len(classes))\\r\\n plt.xticks(tick_marks, classes, rotation=45)\\r\\n plt.yticks(tick_marks, classes)\\r\\n fmt = '.2f' if normalize else 'd'\\r\\n thresh = cm.max() / 2.\\r\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\r\\n plt.text(j, i, format(cm[i, j], fmt),\\r\\n horizontalalignment=\\\"center\\\",\\r\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\r\\n plt.tight_layout()\\r\\n plt.ylabel('True label')\\r\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(confusion_matrix, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(confusion_matrix, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n confusion_matrix = confusion_matrix.astype(\\n 'float') / confusion_matrix.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(confusion_matrix)\\n\\n thresh = confusion_matrix.max() / 2.\\n for i, j in itertools.product(range(confusion_matrix.shape[0]), range(confusion_matrix.shape[1])):\\n plt.text(j, i, confusion_matrix[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if confusion_matrix[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n confusion_matrix_dir = './confusion_matrix_plots'\\n if not os.path.exists(confusion_matrix_dir):\\n os.mkdir(confusion_matrix_dir)\\n\\n plt.cla()\\n plt.figure()\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"#BFD1D4\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n\\n if normalize:\\n plt.savefig(os.path.join(confusion_matrix_dir, 'normalized.jpg'))\\n else:\\n plt.savefig(os.path.join(confusion_matrix_dir, 'without_normalization.jpg'))\",\n \"def plot_confusion_matrix(cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):\\n\\n plt.figure(figsize=(10,10))\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n cm = np.around(cm, decimals=2)\\n cm[np.isnan(cm)] = 0.0\\n print(\\\"Normalized confusion matrix\\\")\\n\\n else:\\n print('Confusion matrix, without normalization')\\n\\n thresh = cm.max() / 2.\\n\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.show()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n t = \\\"(%.2f)\\\"%(cm[i, j])\\n #print t\\n# plt.text(j, i, t,\\n# horizontalalignment=\\\"center\\\",\\n# color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.savefig('IOB-Confusion-Matrix-SVM.png')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n if normalize:\\n # 1. find out how many samples per class have received their correct label\\n # 计算真正类别为k的样本被预测成各个类别的比例\\n # e.g. 有25个样本的 true label 是 6，其中10个样本被预测为类别7，那么在混淆矩阵中 true label = 6 并且 predicted label = 7 的一个格子中的值为 0.4\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n\\n # 2. get the precision (fraction of class-k predictions that have ground truth label k)\\n # 计算预测的准确率\\n # e.g. 预测为类别k的有12个，但其中只有9个的真正类别是k，那么准确率为 0.75\\n # cm = cm.astype('float') / cm.sum(axis=0)[:, np.newaxis]\\n \\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n # plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n # tick_marks = np.arange(len(classes))\\n # plt.xticks(tick_marks, classes, rotation=45)\\n # plt.yticks(tick_marks, classes)\\n\\n # fmt = '.2f' if normalize else 'd'\\n # thresh = cm.max() / 2.\\n # for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n # plt.text(j, i, format(cm[i, j], fmt),\\n # horizontalalignment=\\\"center\\\",\\n # color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n print(cm)\\n \\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n \\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n \\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.savefig('confusion_matrix.png')\",\n \"def plotConfusionMatrix(self, cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\\n plt.savefig('confusion_matrix.png')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.show()\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion Matrix', cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion Matrix, without normalization')\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title, weight='bold')\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n if i == 0:\\n plt.text(j-0.1, i+0.3, format(cm[i, j], fmt), color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n if i == 1:\\n plt.text(j-0.1, i-0.2, format(cm[i, j], fmt), color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True Label', weight='bold')\\n plt.xlabel('Predicted Label', weight='bold')\\n plt.show()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n #print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=90)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n #pdb.set_trace()\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def sam_plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n plots_dims = itertools.product(list(range(cm.shape[0])),\\n list(range(cm.shape[1])))\\n for i, j in plots_dims:\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n \\n print(a)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap, vmin=0.0, vmax=1.0)\\n\\n plt.title(title)\\n\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.3f'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n # plt.title(title)\\n # plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n # Tweak spacing to prevent clipping of tick-labels\\n plt.subplots_adjust(bottom=0.2)\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n # print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.xlabel('Predicted label') \\n plt.ylabel('True label') \\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label') \\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Purples):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n # plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n # plt.grid('off')\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=True,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n# print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\\n return plt.gcf()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title + \\\"Confusion matrix\\\")\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print(title + ' confusion matrix')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=None):\\n if normalize:\\n # cm = cm.T\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n # cm = cm.T\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.figure(figsize=(4, 4))\\n plt.imshow(cm, interpolation='nearest', cmap=cmap or plt.cm.Blues)\\n plt.title(('Normalized ' if normalize else '') + title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(list(range(cm.shape[0])), list(range(cm.shape[1]))):\\n plt.text(\\n j,\\n i,\\n format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=0)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n #print(\\\"Normalized confusion matrix\\\")\\n else:\\n 1#print('Confusion matrix, without normalization')\\n\\n #print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.show()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n print(cm)\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title, fontsize=14)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title,fontsize=20)\\n# plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, fontsize=15)\\n plt.yticks(tick_marks, classes,rotation=30,fontsize=15)\\n\\n fmt = '.2f'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\",fontsize=20)\\n\\n plt.tight_layout()\\n plt.ylabel('True label',fontsize=20)\\n plt.xlabel('Predicted label',fontsize=20)\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Confusion matrix\\\")\\n else:\\n print('Confusion matrix')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Greens):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, '%.02f'%cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"red\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.figure()\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.show()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n # plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title.split('/')[-1])\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n # plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n if title:\\n plt.savefig(title+'.png')\\n\\n plt.close()\",\n \"def plot_confusion_matrix(cm, classes,\\r\\n normalize=False,\\r\\n title='Confusion matrix',\\r\\n cmap=plt.cm.Blues):\\r\\n if normalize:\\r\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\r\\n print(\\\"Normalized confusion matrix\\\")\\r\\n else:\\r\\n print('Confusion matrix, without normalization')\\r\\n\\r\\n print(cm)\\r\\n\\r\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\r\\n plt.title(title)\\r\\n plt.colorbar()\\r\\n tick_marks = np.arange(len(classes))\\r\\n plt.xticks(tick_marks, classes, rotation=45)\\r\\n plt.yticks(tick_marks, classes)\\r\\n\\r\\n fmt = '.2f' if normalize else 'd'\\r\\n thresh = cm.max() / 2.\\r\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\r\\n plt.text(j, i, format(cm[i, j], fmt),\\r\\n horizontalalignment=\\\"center\\\",\\r\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\r\\n\\r\\n plt.tight_layout()\\r\\n plt.ylabel('True label')\\r\\n plt.xlabel('Predicted label')\\r\\n plt.savefig('Logistik.png')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='',\\n cmap=plt.cm.Blues, file_name='cm_plot'):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.rcParams[\\\"font.family\\\"] = \\\"Times New Roman\\\"\\n plt.rcParams[\\\"font.size\\\"] = FONT_SIZE\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=0)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.6f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label', fontsize=FONT_SIZE)\\n plt.xlabel('Predicted label', fontsize=FONT_SIZE)\\n plt.subplots_adjust(bottom=0.13)\\n with PdfPages(file_name) as pdf:\\n pdf.savefig()\\n plt.close()\",\n \"def plot_confusion_matrix(self):\\r\\n interp = ClassificationInterpretation.from_learner(self.learn)\\r\\n interp.plot_confusion_matrix()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm,\\r\\n normalize=False,\\r\\n title='Confusion matrix',\\r\\n cmap=plt.cm.Blues):\\r\\n if normalize:\\r\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\r\\n print(\\\"Normalized confusion matrix\\\")\\r\\n else:\\r\\n print('Confusion matrix, without normalization')\\r\\n\\r\\n# print(cm)\\r\\n\\r\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\r\\n plt.title(title)\\r\\n plt.colorbar()\\r\\n\\r\\n fmt = '.2f' if normalize else 'd'\\r\\n thresh = cm.max() / 2.\\r\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\r\\n plt.text(j, i, format(cm[i, j], fmt),\\r\\n horizontalalignment=\\\"center\\\",\\r\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\r\\n\\r\\n plt.tight_layout()\\r\\n plt.ylabel('True label')\\r\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes=None, normalize=False,\\n title='Confusion matrix', cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.figure()\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n\\n if classes:\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\\n plt.show()\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n plt.figure()\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(cm)\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j], horizontalalignment=\\\"center\\\", color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n plt.figure()\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(cm)\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j], horizontalalignment=\\\"center\\\", color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(y_true, y_pred, classes,\\n normalize=False,\\n title=None,\\n cmap=plt.cm.Blues):\\n if not title:\\n if normalize:\\n title = 'Normalized confusion matrix'\\n else:\\n title = 'Confusion matrix, without normalization'\\n\\n # Compute confusion matrix\\n cm = confusion_matrix(y_true, y_pred)\\n # Only use the labels that appear in the data\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n fig, ax = plt.subplots(figsize=(8, 8))\\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\\n ax.figure.colorbar(im, ax=ax)\\n # We want to show all ticks...\\n ax.set(xticks=np.arange(cm.shape[1]),\\n yticks=np.arange(cm.shape[0]),\\n # ... and label them with the respective list entries\\n xticklabels=classes, yticklabels=classes)\\n ax.set_title(title,size = 20)\\n ax.set_ylabel('True label',size = 20)\\n ax.set_xlabel('Predicted label',size = 20)\\n\\n # Rotate the tick labels and set their alignment.\\n plt.setp(ax.get_xticklabels(), rotation=45, ha=\\\"right\\\",\\n rotation_mode=\\\"anchor\\\",size = 18)\\n plt.setp(ax.get_yticklabels(),size = 18)\\n\\n # Loop over data dimensions and create text annotations.\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i in range(cm.shape[0]):\\n for j in range(cm.shape[1]):\\n ax.text(j, i, format(cm[i, j], fmt),\\n ha=\\\"center\\\", va=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n fig.tight_layout()\\n name = OUTFOLDER + \\\"/confusion_matrix_batch%d_layers%d_epochs%d_f1%d\\\" % (BATCH_SIZE,LAYERS,EPOCHS,f1_mean_test*100)\\n if normalize:\\n name = name + \\\"_norm\\\"\\n plt.savefig(name)\\n plt.close()\\n return ax\",\n \"def plot_confusion_matrix(cm, classes,\\n\\t\\t\\t\\t\\t\\t normalize=False,\\n\\t\\t\\t\\t\\t\\t title='Confusion matrix',\\n\\t\\t\\t\\t\\t\\t cmap=plt.cm.Blues):\\n\\tif normalize:\\n\\t\\tcm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n\\t\\tprint(\\\"Normalized confusion matrix\\\")\\n\\telse:\\n\\t\\tprint('Confusion matrix, without normalization')\\n\\n\\tprint(cm)\\n\\n\\tplt.imshow(cm, interpolation='nearest', cmap=cmap)\\n\\tplt.title(title)\\n\\tplt.colorbar()\\n\\ttick_marks = np.arange(len(classes))\\n\\tplt.xticks(tick_marks, classes, rotation=45)\\n\\tplt.yticks(tick_marks, classes)\\n\\n\\tfmt = '.2f' if normalize else 'd'\\n\\tthresh = cm.max() / 2.\\n\\tfor i, j in product(range(cm.shape[0]), range(cm.shape[1])):\\n\\t\\tplt.text(j, i, format(cm[i, j], fmt),\\n\\t\\t\\t\\t horizontalalignment=\\\"center\\\",\\n\\t\\t\\t\\t color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n\\tplt.tight_layout()\\n\\tplt.ylabel('True label')\\n\\tplt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes,\\n\\t\\t\\t\\t\\t\\t\\t normalize=False,\\n\\t\\t\\t\\t\\t\\t\\t title='Confusion matrix',\\n\\t\\t\\t\\t\\t\\t\\t cmap=plt.cm.Blues):\\n\\t\\tif normalize:\\n\\t\\t\\tcm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n\\t\\t\\tprint(\\\"Normalized confusion matrix\\\")\\n\\t\\telse:\\n\\t\\t\\tprint('Confusion matrix, without normalization')\\n\\n\\t\\tplt.imshow(cm, interpolation='nearest', cmap=cmap)\\n\\t\\tplt.title(title)\\n\\t\\tplt.colorbar()\\n\\t\\ttick_marks = np.arange(len(classes))\\n\\t\\tplt.xticks(tick_marks, classes, rotation=45)\\n\\t\\tplt.yticks(tick_marks, classes)\\n\\n\\t\\tplt.tight_layout()\\n\\t\\tplt.ylabel('True label')\\n\\t\\tplt.xlabel('Predicted label')\",\n \"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, cm[i, j],\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.tight_layout()\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.savefig('../results/conf_matr.png')\\n\\n return cm\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes, rotation=45)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot_confusion_matrix(cm, classes,\\n normalize=False,\\n title='Confusion matrix',\\n cmap=plt.cm.Blues):\\n if normalize:\\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n\\n print(cm)\\n\\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n plt.title(title)\\n plt.colorbar()\\n tick_marks = np.arange(len(classes))\\n plt.xticks(tick_marks, classes)\\n plt.yticks(tick_marks, classes)\\n\\n fmt = '.2f' if normalize else 'd'\\n thresh = cm.max() / 2.\\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\\n plt.text(j, i, format(cm[i, j], fmt),\\n horizontalalignment=\\\"center\\\",\\n color=\\\"white\\\" if cm[i, j] > thresh else \\\"black\\\")\\n\\n plt.ylabel('True label')\\n plt.xlabel('Predicted label')\\n plt.tight_layout()\",\n \"def plot(self):\\n plt.imshow(self.cm, interpolation='nearest', cmap=self.cmap)\\n plt.title(self.title)\\n plt.colorbar()\\n tick_marks = np.arange(len(self.classes))\\n plt.xticks(tick_marks, self.classes, rotation=45)\\n plt.yticks(tick_marks, self.classes)\\n \\n if self.normalize:\\n self.cm = self.cm.astype('float') / self.cm.sum(axis=1)[:, np.newaxis]\\n print(\\\"Normalized confusion matrix\\\")\\n else:\\n print('Confusion matrix, without normalization')\\n \\n print(self.cm)\\n \\n thresh = self.cm.max() / 2.\\n for i, j in itertools.product(range(self.cm.shape[0]), range(self.cm.shape[1])):\\n plt.text(j, i, self.cm[i, j], horizontalalignment=\\\"center\\\", color=\\\"white\\\" if self.cm[i, j] > thresh else \\\"black\\\")\\n plt.tight_layout()\\n plt.ylabel('True Label')\\n plt.xlabel('Predicted label')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8194366","0.8095442","0.8029135","0.8019134","0.7993755","0.7991176","0.79808146","0.79755133","0.79609716","0.7956584","0.79375756","0.79346216","0.79343396","0.7931212","0.7926087","0.7924503","0.7923946","0.79228765","0.79227114","0.7921956","0.79178125","0.79158294","0.79082936","0.7907202","0.79068476","0.7904496","0.7900355","0.7900221","0.7896536","0.78944707","0.7893717","0.7891533","0.7890418","0.7889376","0.78865474","0.7884377","0.78840333","0.7876922","0.7874393","0.78698254","0.7860123","0.7857072","0.7856571","0.7853045","0.7852314","0.7849124","0.7847998","0.78474987","0.78455323","0.78434485","0.78434485","0.78434485","0.78434485","0.78434485","0.78434485","0.78434485","0.78434485","0.78426313","0.78368723","0.78368205","0.7836732","0.7832058","0.78312457","0.78289366","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.7828738","0.78223664","0.78221697","0.7820835","0.7820835","0.78193974","0.7817457","0.781585","0.7815741","0.7814427","0.7814427","0.7814427","0.7814427","0.7814427","0.7814427","0.7814427","0.7814427","0.78137684","0.7813203"],"string":"[\n \"0.8194366\",\n \"0.8095442\",\n \"0.8029135\",\n \"0.8019134\",\n \"0.7993755\",\n \"0.7991176\",\n \"0.79808146\",\n \"0.79755133\",\n \"0.79609716\",\n \"0.7956584\",\n \"0.79375756\",\n \"0.79346216\",\n \"0.79343396\",\n \"0.7931212\",\n \"0.7926087\",\n \"0.7924503\",\n \"0.7923946\",\n \"0.79228765\",\n \"0.79227114\",\n \"0.7921956\",\n \"0.79178125\",\n \"0.79158294\",\n \"0.79082936\",\n \"0.7907202\",\n \"0.79068476\",\n \"0.7904496\",\n \"0.7900355\",\n \"0.7900221\",\n \"0.7896536\",\n \"0.78944707\",\n \"0.7893717\",\n \"0.7891533\",\n \"0.7890418\",\n \"0.7889376\",\n \"0.78865474\",\n \"0.7884377\",\n \"0.78840333\",\n \"0.7876922\",\n \"0.7874393\",\n \"0.78698254\",\n \"0.7860123\",\n \"0.7857072\",\n \"0.7856571\",\n \"0.7853045\",\n \"0.7852314\",\n \"0.7849124\",\n \"0.7847998\",\n \"0.78474987\",\n \"0.78455323\",\n \"0.78434485\",\n \"0.78434485\",\n \"0.78434485\",\n \"0.78434485\",\n \"0.78434485\",\n \"0.78434485\",\n \"0.78434485\",\n \"0.78434485\",\n \"0.78426313\",\n \"0.78368723\",\n \"0.78368205\",\n \"0.7836732\",\n \"0.7832058\",\n \"0.78312457\",\n \"0.78289366\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.7828738\",\n \"0.78223664\",\n \"0.78221697\",\n \"0.7820835\",\n \"0.7820835\",\n \"0.78193974\",\n \"0.7817457\",\n \"0.781585\",\n \"0.7815741\",\n \"0.7814427\",\n \"0.7814427\",\n \"0.7814427\",\n \"0.7814427\",\n \"0.7814427\",\n \"0.7814427\",\n \"0.7814427\",\n \"0.7814427\",\n \"0.78137684\",\n \"0.7813203\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7835582"},"document_rank":{"kind":"string","value":"61"}}},{"rowIdx":17478,"cells":{"query":{"kind":"string","value":"function to create employee manager."},"document":{"kind":"string","value":"def create_manager(self, name, pos, dept):\n self.manager[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'senior': [],\n 'junior': [],\n 'trainee': []\n }\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n else:\n self.create_trainee(name, pos, dept)","def create_manager(self, username, tenancy):\n raise NotImplementedError","def create_employee(self):\n try:\n name = input(\"Enter name: \")\n if not name.isalpha():\n print(\"Invalid data format. Name should contain only alphabets. \")\n return False\n email = input(\"Enter email: \")\n if not InputValidations.validate_email(email):\n return False\n employee = EmployeeModel(name=name, email=email)\n self.admin_repository.create_employee(employee)\n print(\"Employee created successfully!\")\n return True\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def createManager(firstName, lastName, ssn, salary, title, yearBonus):\n manager = Manager(firstName, lastName, ssn, salary, title, yearBonus)\n if firstName != manager.firstName or \\\n lastName != manager.lastName or \\\n ssn != manager.ssn or \\\n salary != manager.salary or \\\n title != manager.title or \\\n yearBonus != manager.yearBonus:\n raise ValueError(\"Failed to initialize Manager\")\n return manager","def create_employee(self,personal_identity):\r\n new_emp = Employee(*personal_identity)\r\n registration_str = new_emp.get_registration_str()\r\n\r\n return_value = self.save_object_to_DB(\"employee\",registration_str)\r\n return return_value","def create(user):\n # if the user does not have an email manager yet, create it\n if not EmailManager.objects.filter(user=user).exists():\n\n # create a new email manager obj\n new = EmailManager(\n key=EmailManager.generate_key(),\n user=user\n )\n new.save()\n\n # Send confirmation email\n welcome_email(new)\n\n return user.emailmanager","def add_employee(self, first_name, last_name):\n self.switch_main_menu(\"PIM\")\n self.click_menu(\"Add Employee\")\n self.pim = AddEmployee(self.driver)\n self.pim.add_user_employee(first_name, last_name)","def test_create(self):\n\n responses.add(\n responses.POST,\n self.host + \"/manager\",\n json={\"path\": \"manager?project=ProjectTest\", \"action\": \"redirect\", \"status\": \"success\"},\n status=200\n )\n\n self.azk.create(self.project, self.description)","def setUp(self):\n\n self.user = self.make_user()\n self.employee = Employee.objects.create(\n cpf=\"974.220.200-16\",\n user=self.user,\n departament=Employee.ADMINISTRATION\n )","async def create_bot_manager(self, guild):\n role_settings = {\"name\": self.manager_role,\n \"permissions\": discord.Permissions.all(),\n \"hoist\": False,\n \"mentionable\": False,\n \"color\": discord.Colour.from_rgb(0, 0, 1)}\n await guild.create_role(**role_settings)","def test_employee_creation(self):\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\n\n # Returned result is an OrderedDict\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\n\n self.assertEqual(result['name'], helper.name)\n self.assertEqual(result['hiredOn'], helper.hired_on)\n self.assertEqual(result['salary'], helper.salary)\n self.assertEqual(result['departmentId'], helper.department_id)","def post(self):\n try:\n employee = self.service.add_employee(self.schema, request.json)\n except ValidationError as error:\n return error.messages, 400\n return self.schema.dump(employee), 201","def create_employee(request, company_id):\n\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n logUnauthorizedAccess(\"User tried to create_employee\", request)\n raise PermissionDenied()\n form = EmployeeForm(request, initial=dict(company=company))\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\n # Q(company=company) | Q(company__isnull=True))\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n\n return TemplateResponse(\n request,\n 'mus/create_employee_form.html',\n {\n 'employee_form': form,\n }\n )\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n # return JsonResponse(status=200, data=data)","def assign_store_manager(user_name: str, new_store_manager_name: str, store_name: str):\n\n user_name = auth.get_username_from_hash(user_name)\n permission_handler.is_permmited_to(user_name, Action.ADD_MANAGER.value, store_name)\n permission_handler.assign_store_employee(action.MANAGER_INITIAL_PERMISSIONS,\n new_store_manager_name,\n store_name)\n user_handler.assign_store_employee(user_name, new_store_manager_name, store_name)","def create_manager(\n pdb_hierarchy,\n geometry_restraints_manager,\n fmodel,\n wavelength,\n params,\n resolution_factor = 0.25,\n nproc = Auto,\n verbose = False,\n log = None,\n manager_class=None):\n connectivity = \\\n geometry_restraints_manager.shell_sym_tables[0].full_simple_connectivity()\n if (manager_class is None):\n manager_class = manager\n manager_obj = manager_class(\n fmodel = fmodel,\n pdb_hierarchy = pdb_hierarchy,\n xray_structure = fmodel.xray_structure,\n connectivity = connectivity,\n wavelength = wavelength,\n params = params,\n nproc = nproc,\n verbose = verbose,\n log = log)\n return manager_obj","def post(self):\n data = EmployeeRegister.parser.parse_args()\n new_employee_id = str(uuid.uuid4())\n\n while EmployeeModel.find_by_id(new_employee_id):\n # if this id is already in use\n new_employee_id = str(uuid.uuid4())\n\n employee = EmployeeModel(**data, employee_id=new_employee_id)\n employee.save_to_db()\n\n return {\"message\": \"Employee successfully added to the system\"}, 201 # 201 - Created","def test_add_team_manager_to_team(self):\n pass","def test_create(self):\n\n adminuser,adminpass = self.testdata.find_account_for('toolmanager')\n\n self.utils.account.login_as(adminuser,adminpass)\n\n self.contribtool.create(TOOLNAME)","def get_manager_employees(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\n if manager_employees:\n emp_list=[]\n for emp in manager_employees:\n emp_data={}\n emp_data[\"id\"] = emp.id\n emp_data[\"username\"] = emp.user.username\n emp_data[\"first_name\"] = emp.user.first_name\n emp_data[\"last_name\"] = emp.user.last_name\n emp_data[\"manager_id\"] = emp.manager.id\n # emp_data[\"status_questions\"] = emp.status_questions\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\n # name_role_list = []\n # for obj in employee_role:\n # name_role_list.append(obj.role.name)\n # emp_data[\"roles\"] = name_role_list\n emp_list.append(emp_data)\n data = {\"employees:\": emp_list}\n return JsonResponse(status=201, data=data)\n else:\n return JsonResponse(\"The user with id={} isn't a manager for any user\".format(current_employee.user.id),\n status=404)","def create_user_using_manager(username,password):\n manager = UserManager()\n return manager.create_user(username=username, password=password)","def create_podmanager(cls, values):\n return cls.dbdriver.create_podmanager(values)","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\n employee, created = Employee.objects.update_or_create(user=user,\n employee_id=validated_data.pop('employee_id'),\n location=validated_data.pop('location'),\n avail_start_time= str(validated_data.pop('avail_start_time')),\n avail_end_time= str(validated_data.pop('avail_end_time')))\n return employee","def setUp(self):\n self.employee = Employee('Lucas', 'Guerra', 45000)","def create_leader_model(request, company_id):\n\n errors = {'noactions': []}\n company = Company.objects.get(pk=company_id)\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\n \"\"\":type : Employee \"\"\"\n\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\n raise PermissionDenied()\n\n if currentEmpl.isCompanySuperUserOrHigher():\n employeeQS = Employee.objects.filter(\n company__pk=company_id\n )\n else:\n employeeQS = Employee.objects.filter(\n Q(manager=currentEmpl),\n company__pk=company_id\n )\n\n form = MultiLeaderModelForm(request.POST or None)\n form.fields['employees'].queryset = employeeQS\n\n if request.method == 'POST' and form.is_valid():\n\n employees = form.cleaned_data['employees']\n \"\"\":type : list[Employee] \"\"\"\n\n pdf_response = get_leader_model_pdf(currentEmpl, employees)\n\n if isinstance(pdf_response, HttpResponse):\n return pdf_response\n else:\n errors = pdf_response\n\n print(errors)\n\n return TemplateResponse(\n request,\n 'mus/create_leader_model.html', {\n 'form': form,\n 'company': company,\n 'errors': errors\n }\n )","def add_employee(self, empl):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\n empl.active, empl.promotor))\n cursor.execute('SELECT LASTVAL()')\n eid = cursor.fetchone()[0]\n empl.id = eid\n # get id and return updated object\n self.dbconnect.commit()\n except(Exception, self.dbconnect.get_error()) as error:\n self.dbconnect.rollback()\n raise Exception('\\nUnable to save Employee!\\n(%s)' % (error))","def createMentor(self, org):\n self.createProfile()\n self.profile.mentor_for = [org.key()]\n self.profile.put()","def add_employee(self, obj):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\n\n self.dbconnect.commit()\n return obj\n except:\n self.dbconnect.rollback()\n raise","def add_manager(self, info):\n self.cursor.execute(\"\"\"SELECT COUNT(*) FROM managerpersonal WHERE phone=%s\"\"\", (int(info['phone']),))\n if not self.cursor.fetchone()[0]:\n self.cursor.execute(\"\"\"INSERT INTO managerpersonal VALUES (%s,%s)\"\"\", (int(info['phone']), info['address']))\n self.cursor.execute(\"\"\"INSERT INTO managercredentials (loginID, firstName, lastName, salt, pass_key, phone)\n VALUES (%s,%s,%s,%s,%s,%s)\"\"\", (info['loginID'], info['firstName'], info['lastName'], info['salt'],\n info['key'], int(info['phone'])))\n\n self.db.commit()\n self.cursor.execute(\"\"\"SELECT COUNT(*) FROM customercredentials WHERE loginID=%s\"\"\", (info['loginID'],))\n result = self.cursor.fetchone()\n if result[0]:\n self.cursor.execute(\"\"\"DELETE FROM customerCredentials WHERE loginID=%s\"\"\", (info['loginID'],))\n self.db.commit()\n self.cursor.execute(\"\"\"SELECT COUNT(*) FROM customerCredentials WHERE phone=%s\"\"\", (int(info['phone']),))\n phone_count = self.cursor.fetchone()\n if not phone_count[0]:\n self.cursor.execute(\"\"\"DELETE FROM customerPersonal WHERE phone=%s\"\"\", (int(info['phone']),))\n self.db.commit()\n self.update_book_scores()\n self.update_comment_usefulness()","def create(self, request):\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\n\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n print(F\"Request employee Data: {serializer.data}\")\n\n try:\n new_employee = self.controller.create_employee(request_data=request_data)\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (domain_exceptions.EmployeeIDIsNotUnique,\n domain_exceptions.WorkArrangementPercentageOutOfRange,\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.WorkArrangementPercentageNull\n ) as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def add_employee():\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n form = SignUp_Form()\n \n\n if form.validate_on_submit():\n try: \n employee = Employee.register(\n username = form.username.data,\n password = form.password.data, \n email = form.email.data, \n first_name = form.first_name.data,\n last_name = form.last_name.data,\n hire_date = form.hire_date.data, \n is_admin = form.is_admin.data,\n )\n\n db.session.add(employee)\n\n db.session.commit()\n except IntegrityError:\n flash(\"Email already in use\", \"danger\")\n return render_template(\"/admin/add_user.html\", form = form)\n\n flash(\"Employee Added!\", \"success\")\n return redirect(\"/administrator\")\n else:\n\n return render_template(\"/admin/add_user.html\", form = form)","def setUp(self):\n self.my_employee = Employee('knight', 'lee', 10000)","def handle(self, *args, **kwargs):\n seeder = Seed.seeder()\n seeder.add_entity(User, 20)\n\n seeder.add_entity(EmployeeMptt, 20, {\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\n 'parent': lambda x: EmployeeMptt.objects.order_by(\"?\").first(),\n 'level': lambda x: random.randint(0, 4),\n })\n seeder.execute()","def generateEmployees(self):\r\n\r\n # Name\r\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\r\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\r\n\r\n for i in range(self.num_of_employees):\r\n\r\n # Clock in an hour before opening, 6 hours after, or 12 hours after\r\n clockIn = random.choice([7, 13, 19])\r\n\r\n # Clock out after 5 hours, 10 hours, or 15 hours\r\n clockOut = random.choice([13, 19, 23])\r\n while clockOut <= clockIn:\r\n clockOut = random.choice([13, 19, 23])\r\n\r\n # Hourly wage\r\n wage = random.choice([8, 9, 10, 12, 20])\r\n\r\n gender = random.choice(['M', 'F'])\r\n if gender == 'M':\r\n name = random.choice(maleNames)\r\n else:\r\n name = random.choice(femaleNames)\r\n\r\n self.c.execute(\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\", (name, clockIn, clockOut, wage))\r\n self.conn.commit()\r\n\r\n if self.print_employees:\r\n print(\"\\nName:\", name)\r\n print(\"Clock in:\", clockIn)\r\n print(\"Clock out:\", clockOut)\r\n print(\"Wage:\", wage)","def setUp(self):\n\t\tfirst_name = 'Gerson'\n\t\tlast_name = 'Santos'\n\t\tannual_salary = 5000\n\t\tself.gerson = Employee(first_name, last_name, annual_salary)","def create():","def create():","def test_with_self_manager(self):\n user_key = Key()\n user_id = user_key.public_key\n name = self.test.user.name()\n\n message = protobuf.user_transaction_pb2.CreateUser(\n user_id=user_id, name=name, metadata=None, manager_id=user_id\n )\n inputs, outputs = self.rbac.user.make_addresses(\n message=message, signer_keypair=user_key\n )\n payload = self.rbac.user.batch.make_payload(\n message=message,\n message_type=self.rbac.user.message_type,\n inputs=inputs,\n outputs=outputs,\n )\n _, status = self.rbac.user.send(signer_keypair=user_key, payload=payload)\n self.assertStatusInvalid(status)","def setUp(self):\n\tself.emp = Employee('Lin',10000)\n\tself.emp2 = Employee('Jun',20000)","def create_employee_from_applicant(self, cr, uid, ids, context=None):\n if context is None:\n context = {}\n hr_employee = self.pool.get('hr.employee')\n model_data = self.pool.get('ir.model.data')\n act_window = self.pool.get('ir.actions.act_window')\n emp_id = False\n for applicant in self.browse(cr, uid, ids, context=context):\n address_id = contact_name = False\n if applicant.partner_id:\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\n if applicant.job_id and (applicant.partner_name or contact_name):\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\n create_ctx = dict(context, mail_broadcast=True)\n\n pes=self.browse(cr,uid,ids)[0]\n coy=pes.partner_name\n\n ##### Susunan Keluarga ayah/ibu #####\n le=self.pool.get('hr_recruit.suskel1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context)\n prod_ids=[] \n for pr in lele:\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\n \n ###### Susunan Keluarga Suami/istri #####\n le=self.pool.get('hr_recruit.suskel2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids1=[] \n for pr in lele:\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \n \n ###### riwayat Pendidikan #######\n le=self.pool.get('hr_recruit.rwt_pend')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids2=[] \n for pr in lele:\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \n \n ###### bahasa ######\n le=self.pool.get('hr_recruit.bahasa')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids3=[] \n for pr in lele:\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \n \n ##### Riwayat Pekerjaan ####\n le=self.pool.get('hr_recruit.rwt_krj')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids4=[] \n for pr in lele:\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \n \n ###### Koneksi Internal #####\n le=self.pool.get('hr_recruit.kon1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids5=[] \n for pr in lele:\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \n \n ###### Koneksi Eksternal ####\n le=self.pool.get('hr_recruit.kon2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids6=[]\n for pr in lele: \n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \n\n ####### create Employee ######## \n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\n 'job_id': applicant.job_id.id,\n 'department_id' : applicant.department_id.id,\n 'address_id2' : applicant.job_id.address_id.id,\n #### informasi Probadi ####\n 'kelamin':applicant.jen_kel,\n 'blood' : applicant.blood,\n 'agama' : applicant.agama_id.id,\n 'birthday' : applicant.tgl_lahir,\n 'place_of_birth' : applicant.kota_id.name,\n 'marital':applicant.status,\n 'sjk_tanggal' : applicant.sjk_tanggal,\n 'mobile_phone':applicant.partner_phone,\n 'country_id' : applicant.country_id.id,\n\n #### Pendidikan ####\n 'type_id':applicant.type_id.id,\n 'bid_id':applicant.bidang_id.id,\n 'jurusan_id':applicant.jurusan_id.id,\n 'pt_id':applicant.pt_id.id,\n 'gelar_id':applicant.gelar_id.id,\n\n #### alamat DOmisili ####\n 'country_id1':applicant.country_id1.id,\n 'prov_id':applicant.prov_id.id,\n 'kab_id' : applicant.kab_id.id,\n 'kec_id':applicant.kec_id.id,\n 'alamat1' : applicant.alamat1,\n 'kodepos' :applicant.kode1,\n 'telp1' : applicant.telp1,\n\n #### kartu identitas ####\n 'jenis_id': applicant.jenis_id,\n 'ktp' : applicant.no_id,\n 'tgl_berlaku' : applicant.tgl_berlaku,\n # 'issued_id' : applicant.dikeluarkan.id,\n \n #### Alamat Sesuai KTP #### \n 'country_id2':applicant.country_id2.id,\n 'prov_id2':applicant.prov_id2.id,\n 'kab_id2':applicant.kab_id2.id,\n 'kec_id2':applicant.kec_id2.id,\n 'alamat2' : applicant.alamat2,\n 'kodepos1':applicant.kode2,\n 'telp2' : applicant.telp2,\n \n # 'status': applicant.status,\n #### IDS ####\n 'susunan_kel1_ids' : prod_ids,\n 'susunan_kel2_ids':prod_ids1,\n 'rwt_pend_ids':prod_ids2,\n 'bahasa_ids':prod_ids3,\n 'rwt_krj_ids':prod_ids4,\n 'koneksi1_ids':prod_ids5,\n 'koneksi2_ids':prod_ids6, \n })\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\n self.pool['hr.job'].message_post(\n cr, uid, [applicant.job_id.id],\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\n subtype=\"hr_recruitment.mt_job_applicant_hired\", context=context)\n else:\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\n\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\n if emp_id:\n dict_act_window['res_id'] = emp_id\n dict_act_window['view_mode'] = 'form,tree'\n return dict_act_window","def setUp(self):\n\t\tself.mason = Employee(\"mason\",\"karsevar\",10000)","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n respond_data = self.controller.add_team_employee(request_data=request_data)\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.EmployeeDoesNotExist,\n domain_exceptions.EmployeeIsATeamMember\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def main():\n name = input(\"Please enter in your name: \")\n\n \"\"\"Ask the user to enter a number if they are a Director, Manager or Staff.\"\"\"\n \"\"\"This will check and make sure the user only enters in 1,2, \n or 3 and a number greater than zero\"\"\"\n while True:\n try:\n designation_number = int(input(\"Please enter in \\n1 for Director \"\n \"\\n2 for Manager \\n3 for Staff\\n\"))\n if 0 < designation_number <= 3:\n break\n print(\"Invalid number entered.\")\n except Exception as e:\n print(e)\n \"\"\"Gets the user salary and makes sure is a number and greater than 0\"\"\"\n while True:\n try:\n salary = float(input(\"Please enter in your salary: \"))\n if salary <= 0:\n print(\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\")\n else:\n break\n except ValueError:\n print(\"Oops! That was not a valid number. Try again...\")\n\n \"\"\"Create Employee\"\"\"\n employee1 = employee.Employee()\n employee1.set_name(name)\n employee1.set_designation(designation_number)\n employee1.set_salary(salary)\n print(employee1)","def employees_manager(request):\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_list = Employee.objects.filter(manager=request.user.employee_user, is_manager=True)\n employee = Employee.objects.get(pk=request.user.employee_user.id)\n employee_dict = model_to_dict(employee)\n employee_dict['first_name'] = employee.user.first_name\n employee_dict['last_name'] = employee.user.last_name\n employee_dict['photo'] = employee.photo.url if employee.photo else ''\n print employee_dict\n if len(manager_list) > 0:\n result_list = list(manager_list)\n all_managers_list = found_all_managers(manager_list, result_list)\n else:\n data = {\"employee_managers\": employee_dict}\n return JsonResponse(data=data, content_type='application/json', safe=False)\n employees = list()\n for manager in all_managers_list:\n manager_dict = model_to_dict(manager)\n manager_dict['first_name'] = manager.user.first_name\n manager_dict['last_name'] = manager.user.last_name\n manager_dict['photo'] = manager.photo.url if manager.photo else ''\n employees.append(manager_dict)\n employees.append(employee_dict)\n\n data = {\"employee_managers\": employees}\n return JsonResponse(data=data, content_type='application/json', safe=False)","def createEmployee():\n form = CreateEmployeeForm()\n if form.validate_on_submit():\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \n email=form.email.data, role=form.role.data, location=form.location.data)\n db.session.add(staff)\n db.session.commit()\n flash(f'Employee Added To Database', category='Success')\n return redirect(url_for('login'))\n return render_template('new_employee.html', title=\"Register\", form=form)","def create_junior(self, name, pos, dept):\n self.junior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def fusion_api_create_hypervisor_manager(self, body, api=None, headers=None):\n return self.hypervisor_mgr.create(body=body, api=api, headers=headers)","def post(self, request):\n data = request.data\n skill_data = data.pop('skills')\n Department_name = data.pop('department')\n department = Department.objects.get(name=Department_name)\n manager_name = data.pop('manager')\n manager = Manager.objects.get(name=manager_name)\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\n Employee.save()\n for skill in skill_data:\n skill_add, create = Skill.objects.get_or_create(name=skill)\n Employee.skills.add(skill_add)\n return Response(\n data=request.data\n )","def manager():\n pass","def createMachine():\n cd('/')\n machine = create(machineName, 'UnixMachine')\n cd('Machines/'+machineName+'/NodeManager/'+machineName)\n cmo.setName(machineName)\n cmo.setListenAddress(hostname)","def createEmployee(firstName, lastName, ssn, salary):\n employee = Employee(firstName, lastName, ssn, salary)\n # verify\n if firstName != employee.firstName or \\\n lastName != employee.lastName or \\\n ssn != employee.ssn or \\\n salary != employee.salary:\n raise ValueError(\"Failed to initialize Employee\")\n return employee","def test_admin_can_create_a_employee(self):\n\n account_data = {\n \"username\": \"Mike\",\n \"email\": \"mioshine@g.com\",\n \"password\": \"1234567\",\n \"confirm_password\": \"1234567\"\n }\n response = self.client.post(\n reverse('accounts:create-user'),\n account_data,\n format=\"json\")\n \"\"\"Test the api has bucket creation capability.\"\"\"\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertTrue(\"data\" in json.loads(response.content))","def create(self, name, username, emailaddress, maxMemoryCapacity=-1.0, maxVDiskCapacity=-1, maxCPUCapacity=-1, maxNetworkPeerTransfer=-1, maxNumPublicIP=-1, sendAccessEmails=True, **kwargs):\n #put your code here to implement this method\n raise NotImplementedError (\"not implemented method create\")","def test_unit_with_self_manager(self):\n user_key = Key()\n user_id = user_key.public_key\n name = self.test.user.name()\n with self.assertRaises(ValueError):\n message = self.rbac.user.make(\n user_id=user_id, name=name, metadata=None, manager_id=user_id\n )\n\n message = protobuf.user_transaction_pb2.CreateUser(\n user_id=user_id, name=name, metadata=None, manager_id=user_id\n )\n with self.assertRaises(ValueError):\n payload = self.rbac.user.make_payload(message=message)\n\n with self.assertRaises(ValueError):\n payload = self.rbac.user.create(signer_keypair=user_key, message=message)","def create_manager(app, db):\n manager = Manager(app)\n\n manager.add_command(\"runserver\", Server())\n\n def make_shell_context():\n return dict(app=app, db=db)\n manager.add_command(\"shell\", Shell(make_context=make_shell_context))\n\n return manager","def create_admin():\n from pyceo import prompt\n from .manage import create_user\n\n u = User.by_login(u'admin')\n if not u:\n print 'Creating the `admin` user…'\n email = prompt('>>> `admin` email?\\n')\n create_user(u'admin', 'admin', fullname=u'Admin', email=email)\n u = User.by_login(u'admin')\n\n u.add_role(u'admin')\n db.commit()\n return u","def is_manager(self) -> bool:\n return self.role in EmployeeRole.manager_roles()","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.EmployeeDoesNotExist\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create(self, context, data, is_auto_import=False):\n db_session_event = 'create-resource_mgr'\n db_session = self.db_api.get_transactional_session(\n db_session_event)\n state_map = {eon_const.RESOURCE_MGR_STATE_KEY:\n eon_const.EON_RESOURCE_MANAGER_STATE_REGISTERED}\n property_list = []\n try:\n _validate_create(context, self.db_api, data,\n eon_const.EON_RESOURCE_MANAGER)\n resource_mgr_type = data.get('type')\n resource_mgr_driver = driver.load_resource_mgr_driver(\n resource_mgr_type)\n data = resource_mgr_driver.validate_create(context, data)\n\n LOG.info(\"Registering resource manager, context: %s\",\n logging.mask_password(data))\n db_resource_mgr_data = self.db_api.create_resource_manager(\n context, data, session=db_session)\n properties = resource_mgr_driver.get_properties(state_map)\n for property_key, property_value in properties.iteritems():\n property_list.append(\n self.db_api.create_resource_mgr_property(\n context, db_resource_mgr_data['id'],\n property_key, property_value, session=db_session)\n )\n resource_mgr_dict = _make_response(db_resource_mgr_data,\n property_list=property_list)\n resource_mgr_driver.update_vc_pass_through(context,\n resource_mgr_dict)\n self.db_api.commit_session(db_session_event, db_session)\n return resource_mgr_dict\n except (exception.AddressResolutionFailure,\n exception.InternalFailure,\n exception.ResourceExists,\n exception.UnsupportedVCenterVersion,\n exception.VCenterRegisterFailure\n ) as e:\n self.db_api.rollback_session('create-resource_mgr', db_session)\n msg = (_(\"Registering resource manager failed. Reason: '%s'\")\n % e.message)\n log_msg = ((\"Registering resource manager failed. Reason: '%s'\")\n % e.message)\n LOG.error(log_msg)\n raise exception.CreateException(msg=msg)\n except Exception as e:\n LOG.exception(e)\n self.db_api.rollback_session('create-resource_mgr', db_session)\n msg = (_(\"Registering resource manager failed. Reason: '%s'\")\n % e)\n log_msg = ((\"Registering resource manager failed. Reason: '%s'\")\n % e)\n LOG.error(log_msg)\n raise exception.CreateException(msg=msg)","def create_admin(\n name: str,\n email: str,\n password: str\n) -> None:\n db: Session = SessionLocal()\n\n hashed_password = get_password_hash(password)\n db_admin = models.Admin(\n name=name,\n email=email,\n password=hashed_password\n )\n db.add(db_admin)\n db.commit()\n db.close()\n print('Admin criado com sucesso!')","def create():\n pass","def create(self, creator: User = None, *args, **kwargs) -> Employee:\n # If the creator argument is not None and is a non-staff user, raise\n # ValueError\n if creator and not creator.is_staff:\n raise ValueError(\n 'Only staff members can add new employees, \"creator\" must be '\n 'a staff user.'\n )\n\n # Get the user property from the provided keyword arguments\n user: Optional[User] = kwargs.get('user', None)\n\n # If the user property is missing or is a non-staff user, raise\n # ValueError\n if not user:\n raise ValueError(\n 'You must provide the \"user\" property as a keyword argument'\n )\n elif not user.is_staff:\n raise ValueError('The \"user\" property must be a staff user.')\n\n return super().create(creator, *args, **kwargs)","def create_employee(attributes):\n neccessary_keys = [\"empid\", \"gender\", \"sales\", \"bmi\", \"salary\", \"birthday\",\n \"age\"]\n for key in neccessary_keys:\n if not key in attributes.keys():\n raise ValueError(\"employee could not be created: {} is missing\".format(key))\n return Employee(attributes[\"empid\"], attributes[\"gender\"],\n attributes[\"sales\"], attributes[\"bmi\"],\n attributes[\"salary\"], attributes[\"birthday\"],\n attributes[\"age\"])","def setUp(self):\n self.salary = 40000\n self.custom_rise = 7500\n self.employee = Employee(\"Carlos\", \"Zapata\", self.salary)","def create_meal():","def p_makeAdminUser(self):\n\n # If already in database, return\n if self.dbManager.userExists(C_ADMINISTRATOR_USERNAME):\n return\n # Store admin in database\n self.dbManager.createUser(C_ADMINISTRATOR_USERNAME, C_ADMINISTRATOR_PASSWORD, UserRole.ADMIN, defaultPacemakerParameterData)","def add_employee(schema, employee_json):\n employee = schema.load(employee_json, session=db.session)\n db.session.add(employee)\n db.session.commit()\n return employee","def hrm():\n\n settings = current.deployment_settings\n\n teams = settings.get_hrm_teams()\n use_teams = lambda i: teams\n\n return M(c=\"hrm\")(\n M(settings.get_hrm_staff_label(), f=\"staff\")(\n M(\"Create\", m=\"create\"),\n ),\n M(teams, f=\"group\", check=use_teams)(\n M(\"Create\", m=\"create\"),\n ),\n M(\"Job Titles\", f=\"job_title\")(\n M(\"Create\", m=\"create\"),\n ),\n )","def create_person(self):","def make_employee_dict(names, ID_numbers, salaries, email_addresses):\r\n d = dict()\r\n for i in range(len(names)):\r\n d[ID_numbers[i]] = Employee(names[i], ID_numbers[i], salaries[i], email_addresses[i])\r\n return d","def create_admin():\n db.session.add(User(email='ad@min.com', password='admin', admin=True))\n db.session.commit()","def create_admin():\n db.session.add(User(email='ad@min.com', password='admin', admin=True))\n db.session.commit()","def add_employee(self, employee):\n self.employees.add(employee)","def create_senior(self, name, pos, dept):\n self.senior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'junior': [],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['senior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def create_room(self, name, typ):\n if typ == \"O\":\n if not name in self.all_rooms.keys():\n new_room = Office(name)\n self.offices[name] = {'room': new_room, \"occupants\": []}\n\n return \"Room created succesfully\"\n else:\n return \"Room could not be created: Room exists!\"\n\n elif typ == \"L\":\n if not name in self.all_rooms.keys():\n new_room = LivingSpace(name)\n self.living_spaces[name] = {'room': new_room, \"occupants\": []}\n\n return \"Room created succesfully\"\n else:\n return \"Room could not be created: Room exists!\"\n\n else:\n return -1","def assign_employee(id):\r\n check_admin()\r\n\r\n employee = Employee.query.get_or_404(id)\r\n\r\n # prevent admin from being assigned a department or role\r\n if employee.is_admin:\r\n abort(403)\r\n\r\n form = EmployeeAssignForm(obj=employee)\r\n if form.validate_on_submit():\r\n employee.department = form.department.data\r\n employee.role = form.role.data\r\n db.session.add(employee)\r\n db.session.commit()\r\n flash('You have successfully assigned a department and role.')\r\n\r\n # redirect to the roles page\r\n return redirect(url_for('admin.list_employees'))\r\n\r\n return render_template('admin/employees/employee.html',\r\n employee=employee, form=form,\r\n title='Assign Employee')","def register_manager(self, update, context):\r\n new_manager_chat_id = update['message']['chat']['id']\r\n new_manager_name = update['message']['chat']['first_name']\r\n\r\n with open('managers.json') as obj:\r\n managers = json.load(obj)\r\n\r\n managers[new_manager_name] = new_manager_chat_id\r\n\r\n with open('managers.json', 'w') as obj:\r\n json.dump(managers, obj)\r\n\r\n context.bot.send_message(chat_id=update.message.chat_id, text=f'{new_manager_name} - {new_manager_chat_id}')","def setUp(self):\n self.employee = Employee('John', 'Doe', 50000)\n self.raise_amount = 20000","def setup_general():\n Role.insert_roles()\n admin_query = Role.query.filter_by(name='Administrator')\n if admin_query.first() is not None:\n if Employee.query.filter_by(email=Config.ADMIN_EMAIL).first() is None:\n user = Employee(first_name='Admin',\n last_name='Account',\n password=Config.ADMIN_PASSWORD,\n email=Config.ADMIN_EMAIL)\n db.session.add(user)\n db.session.commit()\n print('Added administrator {}'.format(user.full_name()))","def test_access_employee(self):\n # Employee can't see any SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).read()\n # Employee can't edit the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\n # Employee can't create the SO\n with self.assertRaises(AccessError):\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\n 'partner_id': self.partner_a.id,\n })\n # Employee can't delete the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).unlink()","def test_with_manager_not_in_state(self):\n user_key = Key()\n user_id = user_key.public_key\n manager_key = Key()\n manager_id = manager_key.public_key\n name = self.test.user.name()\n\n message = protobuf.user_transaction_pb2.CreateUser(\n user_id=user_id, name=name, metadata=None, manager_id=manager_id\n )\n inputs, outputs = self.rbac.user.make_addresses(\n message=message, signer_keypair=user_key\n )\n payload = self.rbac.user.batch.make_payload(\n message=message,\n message_type=self.rbac.user.message_type,\n inputs=inputs,\n outputs=outputs,\n )\n _, status = self.rbac.user.send(signer_keypair=user_key, payload=payload)\n self.assertStatusInvalid(status)","def administrator():\n\n administrator = Administrator.objects.create(name='Michał', surname='Paluch',\n login='Udfsr43', password='Password_3',\n password_repeat='Password_3')\n return administrator","def create_superuser(self, email, name, password):\n employee = self.create_user(email, name, password)\n employee.is_superuser = True\n employee.is_staff = True\n employee.save(using=self._db)\n\n return employee","def createGroup(listOfPerson):\n atk=Department()\n atk.members=listOfPerson\n return atk","def create(self, name, login, password, email, address=\"\", vat=\"\", jobguid=\"\", executionparams=None):","def create(self):\n ...","def register():\n add_employee = True\n form = RegistrationForm()\n if form.validate_on_submit():\n employee = Employee(email=form.email.data,\n username=form.username.data,\n glad_id=form.glad_id.data,\n tel_no=form.tel_no.data,\n role_id=2 , ##form.role_id.data,\n password=form.password.data)\n\n # add employee to the database\n db.session.add(employee)\n db.session.commit()\n flash('You have successfully registered! You may now login.')\n\n # redirect to the login page\n return redirect(url_for('auth.login'))\n\n # load registration template\n return render_template('auth/register.html', form=form, title='Register')","def create_admin():\n click.echo(db)\n # db.session.add(User(\n # email=\"ad@min.com\",\n # password=\"admin\",\n # username=\"admin\"\n # )\n # db.session.commit()","def create_job(self, employer_id, compensation, location, description, category_id, group_id):\n\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \n db.session.add(job)\n db.session.commit()","def users_create():","def create(self):\n pass","def create(self):\n pass","def create(self):\n pass","def create(self):","def add_manager(self, agent):\n with self.simulation_mutex:\n self.get(\"manager_agents\")[agent.name] = agent","def createManagedDomain():\n selectCustomTemplate(localTemplate)\n loadTemplates()\n # set the Node Manager listen address and listen port.\n cd('/')\n cd('NMProperties')\n set('ListenAddress', hostname)\n #create the domain\n writeDomain(domainPath)","def test_create_member(self):\r\n resource = 'member'\r\n cmd = member.CreateMember(test_cli20.MyApp(sys.stdout), None)\r\n address = '10.0.0.1'\r\n port = '8080'\r\n tenant_id = 'my-tenant'\r\n my_id = 'my-id'\r\n pool_id = 'pool-id'\r\n args = ['--address', address, '--protocol-port', port,\r\n '--tenant-id', tenant_id, pool_id]\r\n position_names = ['address', 'protocol_port', 'tenant_id', 'pool_id',\r\n 'admin_state_up']\r\n position_values = [address, port, tenant_id, pool_id, True]\r\n self._test_create_resource(resource, cmd, None, my_id, args,\r\n position_names, position_values,\r\n admin_state_up=None)","def create_new_project(self,\n customer_name,\n contract_date,\n project_info,\n project_datest,\n project_dateend,\n project_budget,\n project_actst=None,\n project_actend=None,\n project_cost=None):\n\n customer_info = self.query_customer(cus_name=customer_name)\n\n if customer_info:\n # Search for project manager in the same region as the customer.\n customer_region_id = customer_info[0][1]\n get_employee_query = \"select employee.emp_id, emp_lname, emp_fname from employee, \" \\\n \"empskill, skill, region where employee.emp_id = \" \\\n \"empskill.emp_id and empskill.skill_id = \" \\\n \"skill.skill_id and skill.skill_descrpt = \" \\\n \"'Project Manager' and region.region_id = \" \\\n \"employee.region_id and region.region_id = '{}' \"\n try:\n self.dbCursor.execute(\n get_employee_query.format(customer_region_id))\n employee_info = self.dbCursor.fetchall()\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n if len(employee_info) == 0:\n ErrorMessageWindow(\"No suitable project manager found!\")\n else:\n if customer_info and employee_info:\n if len(customer_info) > 1:\n MultiRowScreen(customer_info, \"project\")\n else:\n cus_id = customer_info[0][0]\n emp_id = employee_info[0][0]\n optional_inputs = [project_actst, project_actend,\n project_cost]\n\n query = \"insert into project(cus_id, emp_id, proj_date, \" \\\n \"proj_descrpt, proj_estdatest, proj_estdateend, \" \\\n \"proj_estbudget) values ('{}', '{}', '{}', '{}', \" \\\n \"'{}', '{}', '{}') \".format(cus_id,\n emp_id,\n contract_date,\n project_info,\n project_datest,\n project_dateend,\n project_budget)\n\n yes_options = False\n for item in optional_inputs:\n if item != \"\":\n yes_options = True\n\n if yes_options is False:\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n option_names = [\"proj_actdatest\",\n \"proj_actdateend\",\n \"proj_actcost\"]\n options_index = []\n filled_options = []\n\n index = 0\n for item in optional_inputs:\n if item != \"\":\n options_index.append(index)\n filled_options.append(item)\n index += 1\n update_query = \"update project set \"\n\n j = 0\n for i in options_index:\n if j < len(filled_options) - 1:\n update_query += \"{}='{}', \".format(\n option_names[i], filled_options[j]\n )\n else:\n update_query += \"{}='{}' \".format(\n option_names[i], filled_options[j]\n )\n j += 1\n\n try:\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n\n self.dbCursor.execute(update_query)\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n ErrorMessageWindow(\"Customer not found!\")","def create(self):\n\n pass","def get_create_employee_query(self):\n template = \"\"\"\n mutation createEmployee {{\n createEmployee(input: {{ {params} }}) {{\n employee {{\n name\n hiredOn\n salary\n departmentId\n }}\n }}\n }}\n \"\"\"\n # Add input parameters as needed\n input_params = 'name:\"{}\",'.format(self.name)\n\n if self.hired_on is not None:\n input_params += 'hiredOn: \"{}\", '.format(self.hired_on)\n\n if self.salary is not None:\n input_params += 'salary: {}, '.format(self.salary)\n\n if self.department_id is not None:\n input_params += 'departmentId: {}'.format(self.department_id)\n\n return template.format(params=input_params)","def create_educator(data):\n\n educator = Educator(\n name=data['name'],\n work_email=data['work_email'],\n organization_name=data['organization_name'],\n org_or_school=data['org_or_school'],\n address_line_1=data['address_line_1'],\n address_line_2=data['address_line_2'],\n city=data['city'],\n state=data['state'],\n zipcode=data['zipcode'],\n num_students=data['num_students']\n )\n educator.save()\n return educator"],"string":"[\n \"def create_emp(self, name, pos, dept):\\n if pos.upper() == 'MANAGER':\\n self.create_manager(name, pos, dept)\\n elif pos.upper() == 'SENIOR':\\n self.create_senior(name, pos, dept)\\n elif pos.upper() == 'JUNIOR':\\n self.create_junior(name, pos, dept)\\n else:\\n self.create_trainee(name, pos, dept)\",\n \"def create_manager(self, username, tenancy):\\n raise NotImplementedError\",\n \"def create_employee(self):\\n try:\\n name = input(\\\"Enter name: \\\")\\n if not name.isalpha():\\n print(\\\"Invalid data format. Name should contain only alphabets. \\\")\\n return False\\n email = input(\\\"Enter email: \\\")\\n if not InputValidations.validate_email(email):\\n return False\\n employee = EmployeeModel(name=name, email=email)\\n self.admin_repository.create_employee(employee)\\n print(\\\"Employee created successfully!\\\")\\n return True\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def createManager(firstName, lastName, ssn, salary, title, yearBonus):\\n manager = Manager(firstName, lastName, ssn, salary, title, yearBonus)\\n if firstName != manager.firstName or \\\\\\n lastName != manager.lastName or \\\\\\n ssn != manager.ssn or \\\\\\n salary != manager.salary or \\\\\\n title != manager.title or \\\\\\n yearBonus != manager.yearBonus:\\n raise ValueError(\\\"Failed to initialize Manager\\\")\\n return manager\",\n \"def create_employee(self,personal_identity):\\r\\n new_emp = Employee(*personal_identity)\\r\\n registration_str = new_emp.get_registration_str()\\r\\n\\r\\n return_value = self.save_object_to_DB(\\\"employee\\\",registration_str)\\r\\n return return_value\",\n \"def create(user):\\n # if the user does not have an email manager yet, create it\\n if not EmailManager.objects.filter(user=user).exists():\\n\\n # create a new email manager obj\\n new = EmailManager(\\n key=EmailManager.generate_key(),\\n user=user\\n )\\n new.save()\\n\\n # Send confirmation email\\n welcome_email(new)\\n\\n return user.emailmanager\",\n \"def add_employee(self, first_name, last_name):\\n self.switch_main_menu(\\\"PIM\\\")\\n self.click_menu(\\\"Add Employee\\\")\\n self.pim = AddEmployee(self.driver)\\n self.pim.add_user_employee(first_name, last_name)\",\n \"def test_create(self):\\n\\n responses.add(\\n responses.POST,\\n self.host + \\\"/manager\\\",\\n json={\\\"path\\\": \\\"manager?project=ProjectTest\\\", \\\"action\\\": \\\"redirect\\\", \\\"status\\\": \\\"success\\\"},\\n status=200\\n )\\n\\n self.azk.create(self.project, self.description)\",\n \"def setUp(self):\\n\\n self.user = self.make_user()\\n self.employee = Employee.objects.create(\\n cpf=\\\"974.220.200-16\\\",\\n user=self.user,\\n departament=Employee.ADMINISTRATION\\n )\",\n \"async def create_bot_manager(self, guild):\\n role_settings = {\\\"name\\\": self.manager_role,\\n \\\"permissions\\\": discord.Permissions.all(),\\n \\\"hoist\\\": False,\\n \\\"mentionable\\\": False,\\n \\\"color\\\": discord.Colour.from_rgb(0, 0, 1)}\\n await guild.create_role(**role_settings)\",\n \"def test_employee_creation(self):\\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\\n\\n # Returned result is an OrderedDict\\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\\n\\n self.assertEqual(result['name'], helper.name)\\n self.assertEqual(result['hiredOn'], helper.hired_on)\\n self.assertEqual(result['salary'], helper.salary)\\n self.assertEqual(result['departmentId'], helper.department_id)\",\n \"def post(self):\\n try:\\n employee = self.service.add_employee(self.schema, request.json)\\n except ValidationError as error:\\n return error.messages, 400\\n return self.schema.dump(employee), 201\",\n \"def create_employee(request, company_id):\\n\\n company = Company.objects.get(pk=company_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n logUnauthorizedAccess(\\\"User tried to create_employee\\\", request)\\n raise PermissionDenied()\\n form = EmployeeForm(request, initial=dict(company=company))\\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\\n # Q(company=company) | Q(company__isnull=True))\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_employee_form.html',\\n {\\n 'employee_form': form,\\n }\\n )\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n # return JsonResponse(status=200, data=data)\",\n \"def assign_store_manager(user_name: str, new_store_manager_name: str, store_name: str):\\n\\n user_name = auth.get_username_from_hash(user_name)\\n permission_handler.is_permmited_to(user_name, Action.ADD_MANAGER.value, store_name)\\n permission_handler.assign_store_employee(action.MANAGER_INITIAL_PERMISSIONS,\\n new_store_manager_name,\\n store_name)\\n user_handler.assign_store_employee(user_name, new_store_manager_name, store_name)\",\n \"def create_manager(\\n pdb_hierarchy,\\n geometry_restraints_manager,\\n fmodel,\\n wavelength,\\n params,\\n resolution_factor = 0.25,\\n nproc = Auto,\\n verbose = False,\\n log = None,\\n manager_class=None):\\n connectivity = \\\\\\n geometry_restraints_manager.shell_sym_tables[0].full_simple_connectivity()\\n if (manager_class is None):\\n manager_class = manager\\n manager_obj = manager_class(\\n fmodel = fmodel,\\n pdb_hierarchy = pdb_hierarchy,\\n xray_structure = fmodel.xray_structure,\\n connectivity = connectivity,\\n wavelength = wavelength,\\n params = params,\\n nproc = nproc,\\n verbose = verbose,\\n log = log)\\n return manager_obj\",\n \"def post(self):\\n data = EmployeeRegister.parser.parse_args()\\n new_employee_id = str(uuid.uuid4())\\n\\n while EmployeeModel.find_by_id(new_employee_id):\\n # if this id is already in use\\n new_employee_id = str(uuid.uuid4())\\n\\n employee = EmployeeModel(**data, employee_id=new_employee_id)\\n employee.save_to_db()\\n\\n return {\\\"message\\\": \\\"Employee successfully added to the system\\\"}, 201 # 201 - Created\",\n \"def test_add_team_manager_to_team(self):\\n pass\",\n \"def test_create(self):\\n\\n adminuser,adminpass = self.testdata.find_account_for('toolmanager')\\n\\n self.utils.account.login_as(adminuser,adminpass)\\n\\n self.contribtool.create(TOOLNAME)\",\n \"def get_manager_employees(request):\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\\n if manager_employees:\\n emp_list=[]\\n for emp in manager_employees:\\n emp_data={}\\n emp_data[\\\"id\\\"] = emp.id\\n emp_data[\\\"username\\\"] = emp.user.username\\n emp_data[\\\"first_name\\\"] = emp.user.first_name\\n emp_data[\\\"last_name\\\"] = emp.user.last_name\\n emp_data[\\\"manager_id\\\"] = emp.manager.id\\n # emp_data[\\\"status_questions\\\"] = emp.status_questions\\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\\n # name_role_list = []\\n # for obj in employee_role:\\n # name_role_list.append(obj.role.name)\\n # emp_data[\\\"roles\\\"] = name_role_list\\n emp_list.append(emp_data)\\n data = {\\\"employees:\\\": emp_list}\\n return JsonResponse(status=201, data=data)\\n else:\\n return JsonResponse(\\\"The user with id={} isn't a manager for any user\\\".format(current_employee.user.id),\\n status=404)\",\n \"def create_user_using_manager(username,password):\\n manager = UserManager()\\n return manager.create_user(username=username, password=password)\",\n \"def create_podmanager(cls, values):\\n return cls.dbdriver.create_podmanager(values)\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\\n employee, created = Employee.objects.update_or_create(user=user,\\n employee_id=validated_data.pop('employee_id'),\\n location=validated_data.pop('location'),\\n avail_start_time= str(validated_data.pop('avail_start_time')),\\n avail_end_time= str(validated_data.pop('avail_end_time')))\\n return employee\",\n \"def setUp(self):\\n self.employee = Employee('Lucas', 'Guerra', 45000)\",\n \"def create_leader_model(request, company_id):\\n\\n errors = {'noactions': []}\\n company = Company.objects.get(pk=company_id)\\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\\n \\\"\\\"\\\":type : Employee \\\"\\\"\\\"\\n\\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\\n raise PermissionDenied()\\n\\n if currentEmpl.isCompanySuperUserOrHigher():\\n employeeQS = Employee.objects.filter(\\n company__pk=company_id\\n )\\n else:\\n employeeQS = Employee.objects.filter(\\n Q(manager=currentEmpl),\\n company__pk=company_id\\n )\\n\\n form = MultiLeaderModelForm(request.POST or None)\\n form.fields['employees'].queryset = employeeQS\\n\\n if request.method == 'POST' and form.is_valid():\\n\\n employees = form.cleaned_data['employees']\\n \\\"\\\"\\\":type : list[Employee] \\\"\\\"\\\"\\n\\n pdf_response = get_leader_model_pdf(currentEmpl, employees)\\n\\n if isinstance(pdf_response, HttpResponse):\\n return pdf_response\\n else:\\n errors = pdf_response\\n\\n print(errors)\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_leader_model.html', {\\n 'form': form,\\n 'company': company,\\n 'errors': errors\\n }\\n )\",\n \"def add_employee(self, empl):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\\n empl.active, empl.promotor))\\n cursor.execute('SELECT LASTVAL()')\\n eid = cursor.fetchone()[0]\\n empl.id = eid\\n # get id and return updated object\\n self.dbconnect.commit()\\n except(Exception, self.dbconnect.get_error()) as error:\\n self.dbconnect.rollback()\\n raise Exception('\\\\nUnable to save Employee!\\\\n(%s)' % (error))\",\n \"def createMentor(self, org):\\n self.createProfile()\\n self.profile.mentor_for = [org.key()]\\n self.profile.put()\",\n \"def add_employee(self, obj):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\\n\\n self.dbconnect.commit()\\n return obj\\n except:\\n self.dbconnect.rollback()\\n raise\",\n \"def add_manager(self, info):\\n self.cursor.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM managerpersonal WHERE phone=%s\\\"\\\"\\\", (int(info['phone']),))\\n if not self.cursor.fetchone()[0]:\\n self.cursor.execute(\\\"\\\"\\\"INSERT INTO managerpersonal VALUES (%s,%s)\\\"\\\"\\\", (int(info['phone']), info['address']))\\n self.cursor.execute(\\\"\\\"\\\"INSERT INTO managercredentials (loginID, firstName, lastName, salt, pass_key, phone)\\n VALUES (%s,%s,%s,%s,%s,%s)\\\"\\\"\\\", (info['loginID'], info['firstName'], info['lastName'], info['salt'],\\n info['key'], int(info['phone'])))\\n\\n self.db.commit()\\n self.cursor.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM customercredentials WHERE loginID=%s\\\"\\\"\\\", (info['loginID'],))\\n result = self.cursor.fetchone()\\n if result[0]:\\n self.cursor.execute(\\\"\\\"\\\"DELETE FROM customerCredentials WHERE loginID=%s\\\"\\\"\\\", (info['loginID'],))\\n self.db.commit()\\n self.cursor.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM customerCredentials WHERE phone=%s\\\"\\\"\\\", (int(info['phone']),))\\n phone_count = self.cursor.fetchone()\\n if not phone_count[0]:\\n self.cursor.execute(\\\"\\\"\\\"DELETE FROM customerPersonal WHERE phone=%s\\\"\\\"\\\", (int(info['phone']),))\\n self.db.commit()\\n self.update_book_scores()\\n self.update_comment_usefulness()\",\n \"def create(self, request):\\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\\n\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n print(F\\\"Request employee Data: {serializer.data}\\\")\\n\\n try:\\n new_employee = self.controller.create_employee(request_data=request_data)\\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (domain_exceptions.EmployeeIDIsNotUnique,\\n domain_exceptions.WorkArrangementPercentageOutOfRange,\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.WorkArrangementPercentageNull\\n ) as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def add_employee():\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n form = SignUp_Form()\\n \\n\\n if form.validate_on_submit():\\n try: \\n employee = Employee.register(\\n username = form.username.data,\\n password = form.password.data, \\n email = form.email.data, \\n first_name = form.first_name.data,\\n last_name = form.last_name.data,\\n hire_date = form.hire_date.data, \\n is_admin = form.is_admin.data,\\n )\\n\\n db.session.add(employee)\\n\\n db.session.commit()\\n except IntegrityError:\\n flash(\\\"Email already in use\\\", \\\"danger\\\")\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\\n\\n flash(\\\"Employee Added!\\\", \\\"success\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\",\n \"def setUp(self):\\n self.my_employee = Employee('knight', 'lee', 10000)\",\n \"def handle(self, *args, **kwargs):\\n seeder = Seed.seeder()\\n seeder.add_entity(User, 20)\\n\\n seeder.add_entity(EmployeeMptt, 20, {\\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\\n 'parent': lambda x: EmployeeMptt.objects.order_by(\\\"?\\\").first(),\\n 'level': lambda x: random.randint(0, 4),\\n })\\n seeder.execute()\",\n \"def generateEmployees(self):\\r\\n\\r\\n # Name\\r\\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\\r\\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\\r\\n\\r\\n for i in range(self.num_of_employees):\\r\\n\\r\\n # Clock in an hour before opening, 6 hours after, or 12 hours after\\r\\n clockIn = random.choice([7, 13, 19])\\r\\n\\r\\n # Clock out after 5 hours, 10 hours, or 15 hours\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n while clockOut <= clockIn:\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n\\r\\n # Hourly wage\\r\\n wage = random.choice([8, 9, 10, 12, 20])\\r\\n\\r\\n gender = random.choice(['M', 'F'])\\r\\n if gender == 'M':\\r\\n name = random.choice(maleNames)\\r\\n else:\\r\\n name = random.choice(femaleNames)\\r\\n\\r\\n self.c.execute(\\\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\\\", (name, clockIn, clockOut, wage))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_employees:\\r\\n print(\\\"\\\\nName:\\\", name)\\r\\n print(\\\"Clock in:\\\", clockIn)\\r\\n print(\\\"Clock out:\\\", clockOut)\\r\\n print(\\\"Wage:\\\", wage)\",\n \"def setUp(self):\\n\\t\\tfirst_name = 'Gerson'\\n\\t\\tlast_name = 'Santos'\\n\\t\\tannual_salary = 5000\\n\\t\\tself.gerson = Employee(first_name, last_name, annual_salary)\",\n \"def create():\",\n \"def create():\",\n \"def test_with_self_manager(self):\\n user_key = Key()\\n user_id = user_key.public_key\\n name = self.test.user.name()\\n\\n message = protobuf.user_transaction_pb2.CreateUser(\\n user_id=user_id, name=name, metadata=None, manager_id=user_id\\n )\\n inputs, outputs = self.rbac.user.make_addresses(\\n message=message, signer_keypair=user_key\\n )\\n payload = self.rbac.user.batch.make_payload(\\n message=message,\\n message_type=self.rbac.user.message_type,\\n inputs=inputs,\\n outputs=outputs,\\n )\\n _, status = self.rbac.user.send(signer_keypair=user_key, payload=payload)\\n self.assertStatusInvalid(status)\",\n \"def setUp(self):\\n\\tself.emp = Employee('Lin',10000)\\n\\tself.emp2 = Employee('Jun',20000)\",\n \"def create_employee_from_applicant(self, cr, uid, ids, context=None):\\n if context is None:\\n context = {}\\n hr_employee = self.pool.get('hr.employee')\\n model_data = self.pool.get('ir.model.data')\\n act_window = self.pool.get('ir.actions.act_window')\\n emp_id = False\\n for applicant in self.browse(cr, uid, ids, context=context):\\n address_id = contact_name = False\\n if applicant.partner_id:\\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\\n if applicant.job_id and (applicant.partner_name or contact_name):\\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\\n create_ctx = dict(context, mail_broadcast=True)\\n\\n pes=self.browse(cr,uid,ids)[0]\\n coy=pes.partner_name\\n\\n ##### Susunan Keluarga ayah/ibu #####\\n le=self.pool.get('hr_recruit.suskel1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context)\\n prod_ids=[] \\n for pr in lele:\\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\\n \\n ###### Susunan Keluarga Suami/istri #####\\n le=self.pool.get('hr_recruit.suskel2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids1=[] \\n for pr in lele:\\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \\n \\n ###### riwayat Pendidikan #######\\n le=self.pool.get('hr_recruit.rwt_pend')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids2=[] \\n for pr in lele:\\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \\n \\n ###### bahasa ######\\n le=self.pool.get('hr_recruit.bahasa')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids3=[] \\n for pr in lele:\\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \\n \\n ##### Riwayat Pekerjaan ####\\n le=self.pool.get('hr_recruit.rwt_krj')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids4=[] \\n for pr in lele:\\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \\n \\n ###### Koneksi Internal #####\\n le=self.pool.get('hr_recruit.kon1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids5=[] \\n for pr in lele:\\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \\n \\n ###### Koneksi Eksternal ####\\n le=self.pool.get('hr_recruit.kon2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids6=[]\\n for pr in lele: \\n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \\n\\n ####### create Employee ######## \\n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\\n 'job_id': applicant.job_id.id,\\n 'department_id' : applicant.department_id.id,\\n 'address_id2' : applicant.job_id.address_id.id,\\n #### informasi Probadi ####\\n 'kelamin':applicant.jen_kel,\\n 'blood' : applicant.blood,\\n 'agama' : applicant.agama_id.id,\\n 'birthday' : applicant.tgl_lahir,\\n 'place_of_birth' : applicant.kota_id.name,\\n 'marital':applicant.status,\\n 'sjk_tanggal' : applicant.sjk_tanggal,\\n 'mobile_phone':applicant.partner_phone,\\n 'country_id' : applicant.country_id.id,\\n\\n #### Pendidikan ####\\n 'type_id':applicant.type_id.id,\\n 'bid_id':applicant.bidang_id.id,\\n 'jurusan_id':applicant.jurusan_id.id,\\n 'pt_id':applicant.pt_id.id,\\n 'gelar_id':applicant.gelar_id.id,\\n\\n #### alamat DOmisili ####\\n 'country_id1':applicant.country_id1.id,\\n 'prov_id':applicant.prov_id.id,\\n 'kab_id' : applicant.kab_id.id,\\n 'kec_id':applicant.kec_id.id,\\n 'alamat1' : applicant.alamat1,\\n 'kodepos' :applicant.kode1,\\n 'telp1' : applicant.telp1,\\n\\n #### kartu identitas ####\\n 'jenis_id': applicant.jenis_id,\\n 'ktp' : applicant.no_id,\\n 'tgl_berlaku' : applicant.tgl_berlaku,\\n # 'issued_id' : applicant.dikeluarkan.id,\\n \\n #### Alamat Sesuai KTP #### \\n 'country_id2':applicant.country_id2.id,\\n 'prov_id2':applicant.prov_id2.id,\\n 'kab_id2':applicant.kab_id2.id,\\n 'kec_id2':applicant.kec_id2.id,\\n 'alamat2' : applicant.alamat2,\\n 'kodepos1':applicant.kode2,\\n 'telp2' : applicant.telp2,\\n \\n # 'status': applicant.status,\\n #### IDS ####\\n 'susunan_kel1_ids' : prod_ids,\\n 'susunan_kel2_ids':prod_ids1,\\n 'rwt_pend_ids':prod_ids2,\\n 'bahasa_ids':prod_ids3,\\n 'rwt_krj_ids':prod_ids4,\\n 'koneksi1_ids':prod_ids5,\\n 'koneksi2_ids':prod_ids6, \\n })\\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\\n self.pool['hr.job'].message_post(\\n cr, uid, [applicant.job_id.id],\\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\\n subtype=\\\"hr_recruitment.mt_job_applicant_hired\\\", context=context)\\n else:\\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\\n\\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\\n if emp_id:\\n dict_act_window['res_id'] = emp_id\\n dict_act_window['view_mode'] = 'form,tree'\\n return dict_act_window\",\n \"def setUp(self):\\n\\t\\tself.mason = Employee(\\\"mason\\\",\\\"karsevar\\\",10000)\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n respond_data = self.controller.add_team_employee(request_data=request_data)\\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.EmployeeDoesNotExist,\\n domain_exceptions.EmployeeIsATeamMember\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def main():\\n name = input(\\\"Please enter in your name: \\\")\\n\\n \\\"\\\"\\\"Ask the user to enter a number if they are a Director, Manager or Staff.\\\"\\\"\\\"\\n \\\"\\\"\\\"This will check and make sure the user only enters in 1,2, \\n or 3 and a number greater than zero\\\"\\\"\\\"\\n while True:\\n try:\\n designation_number = int(input(\\\"Please enter in \\\\n1 for Director \\\"\\n \\\"\\\\n2 for Manager \\\\n3 for Staff\\\\n\\\"))\\n if 0 < designation_number <= 3:\\n break\\n print(\\\"Invalid number entered.\\\")\\n except Exception as e:\\n print(e)\\n \\\"\\\"\\\"Gets the user salary and makes sure is a number and greater than 0\\\"\\\"\\\"\\n while True:\\n try:\\n salary = float(input(\\\"Please enter in your salary: \\\"))\\n if salary <= 0:\\n print(\\\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\\\")\\n else:\\n break\\n except ValueError:\\n print(\\\"Oops! That was not a valid number. Try again...\\\")\\n\\n \\\"\\\"\\\"Create Employee\\\"\\\"\\\"\\n employee1 = employee.Employee()\\n employee1.set_name(name)\\n employee1.set_designation(designation_number)\\n employee1.set_salary(salary)\\n print(employee1)\",\n \"def employees_manager(request):\\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\\n manager_list = Employee.objects.filter(manager=request.user.employee_user, is_manager=True)\\n employee = Employee.objects.get(pk=request.user.employee_user.id)\\n employee_dict = model_to_dict(employee)\\n employee_dict['first_name'] = employee.user.first_name\\n employee_dict['last_name'] = employee.user.last_name\\n employee_dict['photo'] = employee.photo.url if employee.photo else ''\\n print employee_dict\\n if len(manager_list) > 0:\\n result_list = list(manager_list)\\n all_managers_list = found_all_managers(manager_list, result_list)\\n else:\\n data = {\\\"employee_managers\\\": employee_dict}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\\n employees = list()\\n for manager in all_managers_list:\\n manager_dict = model_to_dict(manager)\\n manager_dict['first_name'] = manager.user.first_name\\n manager_dict['last_name'] = manager.user.last_name\\n manager_dict['photo'] = manager.photo.url if manager.photo else ''\\n employees.append(manager_dict)\\n employees.append(employee_dict)\\n\\n data = {\\\"employee_managers\\\": employees}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\",\n \"def createEmployee():\\n form = CreateEmployeeForm()\\n if form.validate_on_submit():\\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \\n email=form.email.data, role=form.role.data, location=form.location.data)\\n db.session.add(staff)\\n db.session.commit()\\n flash(f'Employee Added To Database', category='Success')\\n return redirect(url_for('login'))\\n return render_template('new_employee.html', title=\\\"Register\\\", form=form)\",\n \"def create_junior(self, name, pos, dept):\\n self.junior[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'senior': self.senior[dept.upper()][0]['name'],\\n 'trainee': []\\n }\\n )\\n self.manager[dept.upper()][0]['junior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.senior[dept.upper()][0]['junior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def fusion_api_create_hypervisor_manager(self, body, api=None, headers=None):\\n return self.hypervisor_mgr.create(body=body, api=api, headers=headers)\",\n \"def post(self, request):\\n data = request.data\\n skill_data = data.pop('skills')\\n Department_name = data.pop('department')\\n department = Department.objects.get(name=Department_name)\\n manager_name = data.pop('manager')\\n manager = Manager.objects.get(name=manager_name)\\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\\n Employee.save()\\n for skill in skill_data:\\n skill_add, create = Skill.objects.get_or_create(name=skill)\\n Employee.skills.add(skill_add)\\n return Response(\\n data=request.data\\n )\",\n \"def manager():\\n pass\",\n \"def createMachine():\\n cd('/')\\n machine = create(machineName, 'UnixMachine')\\n cd('Machines/'+machineName+'/NodeManager/'+machineName)\\n cmo.setName(machineName)\\n cmo.setListenAddress(hostname)\",\n \"def createEmployee(firstName, lastName, ssn, salary):\\n employee = Employee(firstName, lastName, ssn, salary)\\n # verify\\n if firstName != employee.firstName or \\\\\\n lastName != employee.lastName or \\\\\\n ssn != employee.ssn or \\\\\\n salary != employee.salary:\\n raise ValueError(\\\"Failed to initialize Employee\\\")\\n return employee\",\n \"def test_admin_can_create_a_employee(self):\\n\\n account_data = {\\n \\\"username\\\": \\\"Mike\\\",\\n \\\"email\\\": \\\"mioshine@g.com\\\",\\n \\\"password\\\": \\\"1234567\\\",\\n \\\"confirm_password\\\": \\\"1234567\\\"\\n }\\n response = self.client.post(\\n reverse('accounts:create-user'),\\n account_data,\\n format=\\\"json\\\")\\n \\\"\\\"\\\"Test the api has bucket creation capability.\\\"\\\"\\\"\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertTrue(\\\"data\\\" in json.loads(response.content))\",\n \"def create(self, name, username, emailaddress, maxMemoryCapacity=-1.0, maxVDiskCapacity=-1, maxCPUCapacity=-1, maxNetworkPeerTransfer=-1, maxNumPublicIP=-1, sendAccessEmails=True, **kwargs):\\n #put your code here to implement this method\\n raise NotImplementedError (\\\"not implemented method create\\\")\",\n \"def test_unit_with_self_manager(self):\\n user_key = Key()\\n user_id = user_key.public_key\\n name = self.test.user.name()\\n with self.assertRaises(ValueError):\\n message = self.rbac.user.make(\\n user_id=user_id, name=name, metadata=None, manager_id=user_id\\n )\\n\\n message = protobuf.user_transaction_pb2.CreateUser(\\n user_id=user_id, name=name, metadata=None, manager_id=user_id\\n )\\n with self.assertRaises(ValueError):\\n payload = self.rbac.user.make_payload(message=message)\\n\\n with self.assertRaises(ValueError):\\n payload = self.rbac.user.create(signer_keypair=user_key, message=message)\",\n \"def create_manager(app, db):\\n manager = Manager(app)\\n\\n manager.add_command(\\\"runserver\\\", Server())\\n\\n def make_shell_context():\\n return dict(app=app, db=db)\\n manager.add_command(\\\"shell\\\", Shell(make_context=make_shell_context))\\n\\n return manager\",\n \"def create_admin():\\n from pyceo import prompt\\n from .manage import create_user\\n\\n u = User.by_login(u'admin')\\n if not u:\\n print 'Creating the `admin` user…'\\n email = prompt('>>> `admin` email?\\\\n')\\n create_user(u'admin', 'admin', fullname=u'Admin', email=email)\\n u = User.by_login(u'admin')\\n\\n u.add_role(u'admin')\\n db.commit()\\n return u\",\n \"def is_manager(self) -> bool:\\n return self.role in EmployeeRole.manager_roles()\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.EmployeeDoesNotExist\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create(self, context, data, is_auto_import=False):\\n db_session_event = 'create-resource_mgr'\\n db_session = self.db_api.get_transactional_session(\\n db_session_event)\\n state_map = {eon_const.RESOURCE_MGR_STATE_KEY:\\n eon_const.EON_RESOURCE_MANAGER_STATE_REGISTERED}\\n property_list = []\\n try:\\n _validate_create(context, self.db_api, data,\\n eon_const.EON_RESOURCE_MANAGER)\\n resource_mgr_type = data.get('type')\\n resource_mgr_driver = driver.load_resource_mgr_driver(\\n resource_mgr_type)\\n data = resource_mgr_driver.validate_create(context, data)\\n\\n LOG.info(\\\"Registering resource manager, context: %s\\\",\\n logging.mask_password(data))\\n db_resource_mgr_data = self.db_api.create_resource_manager(\\n context, data, session=db_session)\\n properties = resource_mgr_driver.get_properties(state_map)\\n for property_key, property_value in properties.iteritems():\\n property_list.append(\\n self.db_api.create_resource_mgr_property(\\n context, db_resource_mgr_data['id'],\\n property_key, property_value, session=db_session)\\n )\\n resource_mgr_dict = _make_response(db_resource_mgr_data,\\n property_list=property_list)\\n resource_mgr_driver.update_vc_pass_through(context,\\n resource_mgr_dict)\\n self.db_api.commit_session(db_session_event, db_session)\\n return resource_mgr_dict\\n except (exception.AddressResolutionFailure,\\n exception.InternalFailure,\\n exception.ResourceExists,\\n exception.UnsupportedVCenterVersion,\\n exception.VCenterRegisterFailure\\n ) as e:\\n self.db_api.rollback_session('create-resource_mgr', db_session)\\n msg = (_(\\\"Registering resource manager failed. Reason: '%s'\\\")\\n % e.message)\\n log_msg = ((\\\"Registering resource manager failed. Reason: '%s'\\\")\\n % e.message)\\n LOG.error(log_msg)\\n raise exception.CreateException(msg=msg)\\n except Exception as e:\\n LOG.exception(e)\\n self.db_api.rollback_session('create-resource_mgr', db_session)\\n msg = (_(\\\"Registering resource manager failed. Reason: '%s'\\\")\\n % e)\\n log_msg = ((\\\"Registering resource manager failed. Reason: '%s'\\\")\\n % e)\\n LOG.error(log_msg)\\n raise exception.CreateException(msg=msg)\",\n \"def create_admin(\\n name: str,\\n email: str,\\n password: str\\n) -> None:\\n db: Session = SessionLocal()\\n\\n hashed_password = get_password_hash(password)\\n db_admin = models.Admin(\\n name=name,\\n email=email,\\n password=hashed_password\\n )\\n db.add(db_admin)\\n db.commit()\\n db.close()\\n print('Admin criado com sucesso!')\",\n \"def create():\\n pass\",\n \"def create(self, creator: User = None, *args, **kwargs) -> Employee:\\n # If the creator argument is not None and is a non-staff user, raise\\n # ValueError\\n if creator and not creator.is_staff:\\n raise ValueError(\\n 'Only staff members can add new employees, \\\"creator\\\" must be '\\n 'a staff user.'\\n )\\n\\n # Get the user property from the provided keyword arguments\\n user: Optional[User] = kwargs.get('user', None)\\n\\n # If the user property is missing or is a non-staff user, raise\\n # ValueError\\n if not user:\\n raise ValueError(\\n 'You must provide the \\\"user\\\" property as a keyword argument'\\n )\\n elif not user.is_staff:\\n raise ValueError('The \\\"user\\\" property must be a staff user.')\\n\\n return super().create(creator, *args, **kwargs)\",\n \"def create_employee(attributes):\\n neccessary_keys = [\\\"empid\\\", \\\"gender\\\", \\\"sales\\\", \\\"bmi\\\", \\\"salary\\\", \\\"birthday\\\",\\n \\\"age\\\"]\\n for key in neccessary_keys:\\n if not key in attributes.keys():\\n raise ValueError(\\\"employee could not be created: {} is missing\\\".format(key))\\n return Employee(attributes[\\\"empid\\\"], attributes[\\\"gender\\\"],\\n attributes[\\\"sales\\\"], attributes[\\\"bmi\\\"],\\n attributes[\\\"salary\\\"], attributes[\\\"birthday\\\"],\\n attributes[\\\"age\\\"])\",\n \"def setUp(self):\\n self.salary = 40000\\n self.custom_rise = 7500\\n self.employee = Employee(\\\"Carlos\\\", \\\"Zapata\\\", self.salary)\",\n \"def create_meal():\",\n \"def p_makeAdminUser(self):\\n\\n # If already in database, return\\n if self.dbManager.userExists(C_ADMINISTRATOR_USERNAME):\\n return\\n # Store admin in database\\n self.dbManager.createUser(C_ADMINISTRATOR_USERNAME, C_ADMINISTRATOR_PASSWORD, UserRole.ADMIN, defaultPacemakerParameterData)\",\n \"def add_employee(schema, employee_json):\\n employee = schema.load(employee_json, session=db.session)\\n db.session.add(employee)\\n db.session.commit()\\n return employee\",\n \"def hrm():\\n\\n settings = current.deployment_settings\\n\\n teams = settings.get_hrm_teams()\\n use_teams = lambda i: teams\\n\\n return M(c=\\\"hrm\\\")(\\n M(settings.get_hrm_staff_label(), f=\\\"staff\\\")(\\n M(\\\"Create\\\", m=\\\"create\\\"),\\n ),\\n M(teams, f=\\\"group\\\", check=use_teams)(\\n M(\\\"Create\\\", m=\\\"create\\\"),\\n ),\\n M(\\\"Job Titles\\\", f=\\\"job_title\\\")(\\n M(\\\"Create\\\", m=\\\"create\\\"),\\n ),\\n )\",\n \"def create_person(self):\",\n \"def make_employee_dict(names, ID_numbers, salaries, email_addresses):\\r\\n d = dict()\\r\\n for i in range(len(names)):\\r\\n d[ID_numbers[i]] = Employee(names[i], ID_numbers[i], salaries[i], email_addresses[i])\\r\\n return d\",\n \"def create_admin():\\n db.session.add(User(email='ad@min.com', password='admin', admin=True))\\n db.session.commit()\",\n \"def create_admin():\\n db.session.add(User(email='ad@min.com', password='admin', admin=True))\\n db.session.commit()\",\n \"def add_employee(self, employee):\\n self.employees.add(employee)\",\n \"def create_senior(self, name, pos, dept):\\n self.senior[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'junior': [],\\n 'trainee': []\\n }\\n )\\n self.manager[dept.upper()][0]['senior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def create_room(self, name, typ):\\n if typ == \\\"O\\\":\\n if not name in self.all_rooms.keys():\\n new_room = Office(name)\\n self.offices[name] = {'room': new_room, \\\"occupants\\\": []}\\n\\n return \\\"Room created succesfully\\\"\\n else:\\n return \\\"Room could not be created: Room exists!\\\"\\n\\n elif typ == \\\"L\\\":\\n if not name in self.all_rooms.keys():\\n new_room = LivingSpace(name)\\n self.living_spaces[name] = {'room': new_room, \\\"occupants\\\": []}\\n\\n return \\\"Room created succesfully\\\"\\n else:\\n return \\\"Room could not be created: Room exists!\\\"\\n\\n else:\\n return -1\",\n \"def assign_employee(id):\\r\\n check_admin()\\r\\n\\r\\n employee = Employee.query.get_or_404(id)\\r\\n\\r\\n # prevent admin from being assigned a department or role\\r\\n if employee.is_admin:\\r\\n abort(403)\\r\\n\\r\\n form = EmployeeAssignForm(obj=employee)\\r\\n if form.validate_on_submit():\\r\\n employee.department = form.department.data\\r\\n employee.role = form.role.data\\r\\n db.session.add(employee)\\r\\n db.session.commit()\\r\\n flash('You have successfully assigned a department and role.')\\r\\n\\r\\n # redirect to the roles page\\r\\n return redirect(url_for('admin.list_employees'))\\r\\n\\r\\n return render_template('admin/employees/employee.html',\\r\\n employee=employee, form=form,\\r\\n title='Assign Employee')\",\n \"def register_manager(self, update, context):\\r\\n new_manager_chat_id = update['message']['chat']['id']\\r\\n new_manager_name = update['message']['chat']['first_name']\\r\\n\\r\\n with open('managers.json') as obj:\\r\\n managers = json.load(obj)\\r\\n\\r\\n managers[new_manager_name] = new_manager_chat_id\\r\\n\\r\\n with open('managers.json', 'w') as obj:\\r\\n json.dump(managers, obj)\\r\\n\\r\\n context.bot.send_message(chat_id=update.message.chat_id, text=f'{new_manager_name} - {new_manager_chat_id}')\",\n \"def setUp(self):\\n self.employee = Employee('John', 'Doe', 50000)\\n self.raise_amount = 20000\",\n \"def setup_general():\\n Role.insert_roles()\\n admin_query = Role.query.filter_by(name='Administrator')\\n if admin_query.first() is not None:\\n if Employee.query.filter_by(email=Config.ADMIN_EMAIL).first() is None:\\n user = Employee(first_name='Admin',\\n last_name='Account',\\n password=Config.ADMIN_PASSWORD,\\n email=Config.ADMIN_EMAIL)\\n db.session.add(user)\\n db.session.commit()\\n print('Added administrator {}'.format(user.full_name()))\",\n \"def test_access_employee(self):\\n # Employee can't see any SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).read()\\n # Employee can't edit the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\\n # Employee can't create the SO\\n with self.assertRaises(AccessError):\\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\\n 'partner_id': self.partner_a.id,\\n })\\n # Employee can't delete the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).unlink()\",\n \"def test_with_manager_not_in_state(self):\\n user_key = Key()\\n user_id = user_key.public_key\\n manager_key = Key()\\n manager_id = manager_key.public_key\\n name = self.test.user.name()\\n\\n message = protobuf.user_transaction_pb2.CreateUser(\\n user_id=user_id, name=name, metadata=None, manager_id=manager_id\\n )\\n inputs, outputs = self.rbac.user.make_addresses(\\n message=message, signer_keypair=user_key\\n )\\n payload = self.rbac.user.batch.make_payload(\\n message=message,\\n message_type=self.rbac.user.message_type,\\n inputs=inputs,\\n outputs=outputs,\\n )\\n _, status = self.rbac.user.send(signer_keypair=user_key, payload=payload)\\n self.assertStatusInvalid(status)\",\n \"def administrator():\\n\\n administrator = Administrator.objects.create(name='Michał', surname='Paluch',\\n login='Udfsr43', password='Password_3',\\n password_repeat='Password_3')\\n return administrator\",\n \"def create_superuser(self, email, name, password):\\n employee = self.create_user(email, name, password)\\n employee.is_superuser = True\\n employee.is_staff = True\\n employee.save(using=self._db)\\n\\n return employee\",\n \"def createGroup(listOfPerson):\\n atk=Department()\\n atk.members=listOfPerson\\n return atk\",\n \"def create(self, name, login, password, email, address=\\\"\\\", vat=\\\"\\\", jobguid=\\\"\\\", executionparams=None):\",\n \"def create(self):\\n ...\",\n \"def register():\\n add_employee = True\\n form = RegistrationForm()\\n if form.validate_on_submit():\\n employee = Employee(email=form.email.data,\\n username=form.username.data,\\n glad_id=form.glad_id.data,\\n tel_no=form.tel_no.data,\\n role_id=2 , ##form.role_id.data,\\n password=form.password.data)\\n\\n # add employee to the database\\n db.session.add(employee)\\n db.session.commit()\\n flash('You have successfully registered! You may now login.')\\n\\n # redirect to the login page\\n return redirect(url_for('auth.login'))\\n\\n # load registration template\\n return render_template('auth/register.html', form=form, title='Register')\",\n \"def create_admin():\\n click.echo(db)\\n # db.session.add(User(\\n # email=\\\"ad@min.com\\\",\\n # password=\\\"admin\\\",\\n # username=\\\"admin\\\"\\n # )\\n # db.session.commit()\",\n \"def create_job(self, employer_id, compensation, location, description, category_id, group_id):\\n\\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \\n db.session.add(job)\\n db.session.commit()\",\n \"def users_create():\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create(self):\",\n \"def add_manager(self, agent):\\n with self.simulation_mutex:\\n self.get(\\\"manager_agents\\\")[agent.name] = agent\",\n \"def createManagedDomain():\\n selectCustomTemplate(localTemplate)\\n loadTemplates()\\n # set the Node Manager listen address and listen port.\\n cd('/')\\n cd('NMProperties')\\n set('ListenAddress', hostname)\\n #create the domain\\n writeDomain(domainPath)\",\n \"def test_create_member(self):\\r\\n resource = 'member'\\r\\n cmd = member.CreateMember(test_cli20.MyApp(sys.stdout), None)\\r\\n address = '10.0.0.1'\\r\\n port = '8080'\\r\\n tenant_id = 'my-tenant'\\r\\n my_id = 'my-id'\\r\\n pool_id = 'pool-id'\\r\\n args = ['--address', address, '--protocol-port', port,\\r\\n '--tenant-id', tenant_id, pool_id]\\r\\n position_names = ['address', 'protocol_port', 'tenant_id', 'pool_id',\\r\\n 'admin_state_up']\\r\\n position_values = [address, port, tenant_id, pool_id, True]\\r\\n self._test_create_resource(resource, cmd, None, my_id, args,\\r\\n position_names, position_values,\\r\\n admin_state_up=None)\",\n \"def create_new_project(self,\\n customer_name,\\n contract_date,\\n project_info,\\n project_datest,\\n project_dateend,\\n project_budget,\\n project_actst=None,\\n project_actend=None,\\n project_cost=None):\\n\\n customer_info = self.query_customer(cus_name=customer_name)\\n\\n if customer_info:\\n # Search for project manager in the same region as the customer.\\n customer_region_id = customer_info[0][1]\\n get_employee_query = \\\"select employee.emp_id, emp_lname, emp_fname from employee, \\\" \\\\\\n \\\"empskill, skill, region where employee.emp_id = \\\" \\\\\\n \\\"empskill.emp_id and empskill.skill_id = \\\" \\\\\\n \\\"skill.skill_id and skill.skill_descrpt = \\\" \\\\\\n \\\"'Project Manager' and region.region_id = \\\" \\\\\\n \\\"employee.region_id and region.region_id = '{}' \\\"\\n try:\\n self.dbCursor.execute(\\n get_employee_query.format(customer_region_id))\\n employee_info = self.dbCursor.fetchall()\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n if len(employee_info) == 0:\\n ErrorMessageWindow(\\\"No suitable project manager found!\\\")\\n else:\\n if customer_info and employee_info:\\n if len(customer_info) > 1:\\n MultiRowScreen(customer_info, \\\"project\\\")\\n else:\\n cus_id = customer_info[0][0]\\n emp_id = employee_info[0][0]\\n optional_inputs = [project_actst, project_actend,\\n project_cost]\\n\\n query = \\\"insert into project(cus_id, emp_id, proj_date, \\\" \\\\\\n \\\"proj_descrpt, proj_estdatest, proj_estdateend, \\\" \\\\\\n \\\"proj_estbudget) values ('{}', '{}', '{}', '{}', \\\" \\\\\\n \\\"'{}', '{}', '{}') \\\".format(cus_id,\\n emp_id,\\n contract_date,\\n project_info,\\n project_datest,\\n project_dateend,\\n project_budget)\\n\\n yes_options = False\\n for item in optional_inputs:\\n if item != \\\"\\\":\\n yes_options = True\\n\\n if yes_options is False:\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n option_names = [\\\"proj_actdatest\\\",\\n \\\"proj_actdateend\\\",\\n \\\"proj_actcost\\\"]\\n options_index = []\\n filled_options = []\\n\\n index = 0\\n for item in optional_inputs:\\n if item != \\\"\\\":\\n options_index.append(index)\\n filled_options.append(item)\\n index += 1\\n update_query = \\\"update project set \\\"\\n\\n j = 0\\n for i in options_index:\\n if j < len(filled_options) - 1:\\n update_query += \\\"{}='{}', \\\".format(\\n option_names[i], filled_options[j]\\n )\\n else:\\n update_query += \\\"{}='{}' \\\".format(\\n option_names[i], filled_options[j]\\n )\\n j += 1\\n\\n try:\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n\\n self.dbCursor.execute(update_query)\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n ErrorMessageWindow(\\\"Customer not found!\\\")\",\n \"def create(self):\\n\\n pass\",\n \"def get_create_employee_query(self):\\n template = \\\"\\\"\\\"\\n mutation createEmployee {{\\n createEmployee(input: {{ {params} }}) {{\\n employee {{\\n name\\n hiredOn\\n salary\\n departmentId\\n }}\\n }}\\n }}\\n \\\"\\\"\\\"\\n # Add input parameters as needed\\n input_params = 'name:\\\"{}\\\",'.format(self.name)\\n\\n if self.hired_on is not None:\\n input_params += 'hiredOn: \\\"{}\\\", '.format(self.hired_on)\\n\\n if self.salary is not None:\\n input_params += 'salary: {}, '.format(self.salary)\\n\\n if self.department_id is not None:\\n input_params += 'departmentId: {}'.format(self.department_id)\\n\\n return template.format(params=input_params)\",\n \"def create_educator(data):\\n\\n educator = Educator(\\n name=data['name'],\\n work_email=data['work_email'],\\n organization_name=data['organization_name'],\\n org_or_school=data['org_or_school'],\\n address_line_1=data['address_line_1'],\\n address_line_2=data['address_line_2'],\\n city=data['city'],\\n state=data['state'],\\n zipcode=data['zipcode'],\\n num_students=data['num_students']\\n )\\n educator.save()\\n return educator\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70986205","0.70202345","0.6854372","0.6521865","0.63904655","0.62510014","0.6242744","0.6056396","0.60380864","0.6034902","0.6033909","0.6022222","0.60213274","0.5988317","0.59657365","0.591008","0.5900008","0.58407825","0.58102566","0.57738036","0.57492363","0.5748451","0.5742093","0.57378864","0.57374865","0.57279265","0.5721603","0.57109344","0.57106096","0.5704365","0.5697137","0.5694639","0.5681508","0.56460917","0.563321","0.563321","0.5627807","0.56048495","0.5599928","0.5591763","0.55791384","0.5563893","0.5560078","0.5532486","0.55272865","0.55189425","0.5505664","0.54999655","0.5496157","0.5488662","0.54822147","0.5472146","0.5463006","0.5442477","0.5439194","0.5436469","0.54355186","0.5406831","0.53906333","0.5388404","0.5382133","0.5372932","0.5365207","0.5364403","0.5329089","0.5316546","0.531157","0.5302978","0.5298915","0.52797437","0.52797437","0.52576447","0.52576405","0.5245006","0.5235704","0.523434","0.52278537","0.522119","0.5219739","0.52183324","0.52138555","0.52110034","0.520987","0.52063644","0.52001214","0.5197744","0.5194592","0.51903695","0.51897264","0.51896596","0.51896596","0.51896596","0.51796216","0.51786774","0.51726884","0.5171323","0.51682127","0.51672214","0.5167066","0.51550674"],"string":"[\n \"0.70986205\",\n \"0.70202345\",\n \"0.6854372\",\n \"0.6521865\",\n \"0.63904655\",\n \"0.62510014\",\n \"0.6242744\",\n \"0.6056396\",\n \"0.60380864\",\n \"0.6034902\",\n \"0.6033909\",\n \"0.6022222\",\n \"0.60213274\",\n \"0.5988317\",\n \"0.59657365\",\n \"0.591008\",\n \"0.5900008\",\n \"0.58407825\",\n \"0.58102566\",\n \"0.57738036\",\n \"0.57492363\",\n \"0.5748451\",\n \"0.5742093\",\n \"0.57378864\",\n \"0.57374865\",\n \"0.57279265\",\n \"0.5721603\",\n \"0.57109344\",\n \"0.57106096\",\n \"0.5704365\",\n \"0.5697137\",\n \"0.5694639\",\n \"0.5681508\",\n \"0.56460917\",\n \"0.563321\",\n \"0.563321\",\n \"0.5627807\",\n \"0.56048495\",\n \"0.5599928\",\n \"0.5591763\",\n \"0.55791384\",\n \"0.5563893\",\n \"0.5560078\",\n \"0.5532486\",\n \"0.55272865\",\n \"0.55189425\",\n \"0.5505664\",\n \"0.54999655\",\n \"0.5496157\",\n \"0.5488662\",\n \"0.54822147\",\n \"0.5472146\",\n \"0.5463006\",\n \"0.5442477\",\n \"0.5439194\",\n \"0.5436469\",\n \"0.54355186\",\n \"0.5406831\",\n \"0.53906333\",\n \"0.5388404\",\n \"0.5382133\",\n \"0.5372932\",\n \"0.5365207\",\n \"0.5364403\",\n \"0.5329089\",\n \"0.5316546\",\n \"0.531157\",\n \"0.5302978\",\n \"0.5298915\",\n \"0.52797437\",\n \"0.52797437\",\n \"0.52576447\",\n \"0.52576405\",\n \"0.5245006\",\n \"0.5235704\",\n \"0.523434\",\n \"0.52278537\",\n \"0.522119\",\n \"0.5219739\",\n \"0.52183324\",\n \"0.52138555\",\n \"0.52110034\",\n \"0.520987\",\n \"0.52063644\",\n \"0.52001214\",\n \"0.5197744\",\n \"0.5194592\",\n \"0.51903695\",\n \"0.51897264\",\n \"0.51896596\",\n \"0.51896596\",\n \"0.51896596\",\n \"0.51796216\",\n \"0.51786774\",\n \"0.51726884\",\n \"0.5171323\",\n \"0.51682127\",\n \"0.51672214\",\n \"0.5167066\",\n \"0.51550674\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74205023"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17479,"cells":{"query":{"kind":"string","value":"function to create employee senior."},"document":{"kind":"string","value":"def create_senior(self, name, pos, dept):\n self.senior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'junior': [],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['senior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n else:\n self.create_trainee(name, pos, dept)","def create_employee(self,personal_identity):\r\n new_emp = Employee(*personal_identity)\r\n registration_str = new_emp.get_registration_str()\r\n\r\n return_value = self.save_object_to_DB(\"employee\",registration_str)\r\n return return_value","def create_person(self):","def create_junior(self, name, pos, dept):\n self.junior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\n teacher_account_id = person.get_last()\n query = 'INSERT INTO teacher VALUES( %s,%s );'\n args = (teacher_account_id, availability)\n database.connection.save_data(query, args)","def create(self, values):\n if values.get('country_id', False):\n country = self.env['res.country'].browse(values['country_id'])\n if country.code == 'SA':\n values.update({'is_saudi': True})\n else:\n values.update({'is_saudi': False})\n\n res = super(HrEmployee, self).create(values)\n if values.get('user_id', False):\n self.user_id.write({'employee_id': res})\n return res","def create_employee(self):\n try:\n name = input(\"Enter name: \")\n if not name.isalpha():\n print(\"Invalid data format. Name should contain only alphabets. \")\n return False\n email = input(\"Enter email: \")\n if not InputValidations.validate_email(email):\n return False\n employee = EmployeeModel(name=name, email=email)\n self.admin_repository.create_employee(employee)\n print(\"Employee created successfully!\")\n return True\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def add_employee(self, first_name, last_name):\n self.switch_main_menu(\"PIM\")\n self.click_menu(\"Add Employee\")\n self.pim = AddEmployee(self.driver)\n self.pim.add_user_employee(first_name, last_name)","def users_create():","def createStudent(self):\n self.createProfile()\n from soc.modules.gsoc.models.profile import GSoCStudentInfo\n properties = {'key_name': self.profile.key().name(), 'parent': self.profile}\n self.profile.student_info = seeder_logic.seed(GSoCStudentInfo, properties)\n self.profile.put()","def create_student(self, username):\r\n return self._create_user(username, is_staff=False)","def createDeveloper(self):\n self.createUser()\n self.user.is_developer = True\n self.user.put()","def create_member(org_id, group_id, target_group_ids, sex, first_name, last_name, title_name, email):\n user = get_user_by_email(email)\n # --- falls e-mail schon existiert wird nichts unternommen\n if user != None:\n if org_id > 0: # nur bei Schulen wird die Schulnummer vorangestellt\n prefix = '%i_' % org_id\n else:\n prefix = ''\n user = User()\n username = get_username(prefix, first_name, last_name)\n user.username = username\n user.sex = sex\n user.first_name = first_name\n user.last_name = last_name\n user.email = email\n user.title = title_name\n user.is_staff = False\n user.is_active = True\n user.is_superuser = False\n user.date_joined = datetime.datetime.now()\n password = generate_passwd()\n user.set_password(password)\n user.save()\n set_user_org(org_id, user)\n send_password(email, username, password)\n set_user_group(user, get_group_by_id(group_id))\n for group in target_group_ids:\n set_user_group(user, get_group_by_id(group))\n transaction.commit()","def create_instructor(self, username):\r\n return self._create_user(username, is_staff=True)","def create_user(schools_dictionnary, domains_to_skills_dictionnary, companies, places, skills_oh, places_oh, domains_oh, rng, _id):\n\n age = rng.randint(20,60)\n schools = rng.choice(list(schools_dictionnary.keys()), rng.choice([1, 2], p = [0.95, 0.05]), replace = False) \n\n available_skills = list(set([skill for school in schools \\\n for domain in schools_dictionnary[school].domains \\\n for skill in domains_to_skills_dictionnary[domain]]))\n\n expo = np.round(rng.exponential(0.3) * len(schools)) + age // 17 + 1\n\n nb_skills_to_choose = min(int(expo), 5 + (len(schools) - 1) * 3)\n\n _skills = rng.choice(available_skills, nb_skills_to_choose, replace = False)\n\n company = rng.choice(companies)\n place = rng.choice(places)\n\n user = User(skills_oh, places_oh, domains_oh, schools_dictionnary, skills = _skills, age = age, place = place, company = company,\n schools = schools, _id = _id)\n\n return user","def perform_create(self, serializer):\n if self.request.data.get('user_type', None) == 'employee':\n serializer.save(is_staff=False)\n else:\n serializer.save()","def _create_nsem_user():\n users = User.objects.filter(username=settings.CWWED_NSEM_USER)\n if users.exists():\n user = users[0]\n else:\n user = User.objects.create_user(settings.CWWED_NSEM_USER, password=settings.CWWED_NSEM_PASSWORD)\n group, _ = Group.objects.get_or_create(name=settings.CWWED_NSEM_GROUP)\n perm_names = [\n 'add_{}'.format(NsemPsa._meta.model_name),\n 'add_{}'.format(NamedStormCoveredDataSnapshot._meta.model_name),\n ]\n perms = Permission.objects.filter(codename__in=perm_names)\n # set permission\n user.user_permissions.set(list(perms))\n group.permissions.set(list(perms))\n # add user to group\n group.user_set.add(user)","def create_individual(self):\n pass","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\n employee, created = Employee.objects.update_or_create(user=user,\n employee_id=validated_data.pop('employee_id'),\n location=validated_data.pop('location'),\n avail_start_time= str(validated_data.pop('avail_start_time')),\n avail_end_time= str(validated_data.pop('avail_end_time')))\n return employee","def create_educator(data):\n\n educator = Educator(\n name=data['name'],\n work_email=data['work_email'],\n organization_name=data['organization_name'],\n org_or_school=data['org_or_school'],\n address_line_1=data['address_line_1'],\n address_line_2=data['address_line_2'],\n city=data['city'],\n state=data['state'],\n zipcode=data['zipcode'],\n num_students=data['num_students']\n )\n educator.save()\n return educator","def createMentor(self, org):\n self.createProfile()\n self.profile.mentor_for = [org.key()]\n self.profile.put()","def create_employee(request, company_id):\n\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n logUnauthorizedAccess(\"User tried to create_employee\", request)\n raise PermissionDenied()\n form = EmployeeForm(request, initial=dict(company=company))\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\n # Q(company=company) | Q(company__isnull=True))\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n\n return TemplateResponse(\n request,\n 'mus/create_employee_form.html',\n {\n 'employee_form': form,\n }\n )\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n # return JsonResponse(status=200, data=data)","def create_employee_from_applicant(self, cr, uid, ids, context=None):\n if context is None:\n context = {}\n hr_employee = self.pool.get('hr.employee')\n model_data = self.pool.get('ir.model.data')\n act_window = self.pool.get('ir.actions.act_window')\n emp_id = False\n for applicant in self.browse(cr, uid, ids, context=context):\n address_id = contact_name = False\n if applicant.partner_id:\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\n if applicant.job_id and (applicant.partner_name or contact_name):\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\n create_ctx = dict(context, mail_broadcast=True)\n\n pes=self.browse(cr,uid,ids)[0]\n coy=pes.partner_name\n\n ##### Susunan Keluarga ayah/ibu #####\n le=self.pool.get('hr_recruit.suskel1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context)\n prod_ids=[] \n for pr in lele:\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\n \n ###### Susunan Keluarga Suami/istri #####\n le=self.pool.get('hr_recruit.suskel2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids1=[] \n for pr in lele:\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \n \n ###### riwayat Pendidikan #######\n le=self.pool.get('hr_recruit.rwt_pend')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids2=[] \n for pr in lele:\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \n \n ###### bahasa ######\n le=self.pool.get('hr_recruit.bahasa')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids3=[] \n for pr in lele:\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \n \n ##### Riwayat Pekerjaan ####\n le=self.pool.get('hr_recruit.rwt_krj')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids4=[] \n for pr in lele:\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \n \n ###### Koneksi Internal #####\n le=self.pool.get('hr_recruit.kon1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids5=[] \n for pr in lele:\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \n \n ###### Koneksi Eksternal ####\n le=self.pool.get('hr_recruit.kon2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids6=[]\n for pr in lele: \n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \n\n ####### create Employee ######## \n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\n 'job_id': applicant.job_id.id,\n 'department_id' : applicant.department_id.id,\n 'address_id2' : applicant.job_id.address_id.id,\n #### informasi Probadi ####\n 'kelamin':applicant.jen_kel,\n 'blood' : applicant.blood,\n 'agama' : applicant.agama_id.id,\n 'birthday' : applicant.tgl_lahir,\n 'place_of_birth' : applicant.kota_id.name,\n 'marital':applicant.status,\n 'sjk_tanggal' : applicant.sjk_tanggal,\n 'mobile_phone':applicant.partner_phone,\n 'country_id' : applicant.country_id.id,\n\n #### Pendidikan ####\n 'type_id':applicant.type_id.id,\n 'bid_id':applicant.bidang_id.id,\n 'jurusan_id':applicant.jurusan_id.id,\n 'pt_id':applicant.pt_id.id,\n 'gelar_id':applicant.gelar_id.id,\n\n #### alamat DOmisili ####\n 'country_id1':applicant.country_id1.id,\n 'prov_id':applicant.prov_id.id,\n 'kab_id' : applicant.kab_id.id,\n 'kec_id':applicant.kec_id.id,\n 'alamat1' : applicant.alamat1,\n 'kodepos' :applicant.kode1,\n 'telp1' : applicant.telp1,\n\n #### kartu identitas ####\n 'jenis_id': applicant.jenis_id,\n 'ktp' : applicant.no_id,\n 'tgl_berlaku' : applicant.tgl_berlaku,\n # 'issued_id' : applicant.dikeluarkan.id,\n \n #### Alamat Sesuai KTP #### \n 'country_id2':applicant.country_id2.id,\n 'prov_id2':applicant.prov_id2.id,\n 'kab_id2':applicant.kab_id2.id,\n 'kec_id2':applicant.kec_id2.id,\n 'alamat2' : applicant.alamat2,\n 'kodepos1':applicant.kode2,\n 'telp2' : applicant.telp2,\n \n # 'status': applicant.status,\n #### IDS ####\n 'susunan_kel1_ids' : prod_ids,\n 'susunan_kel2_ids':prod_ids1,\n 'rwt_pend_ids':prod_ids2,\n 'bahasa_ids':prod_ids3,\n 'rwt_krj_ids':prod_ids4,\n 'koneksi1_ids':prod_ids5,\n 'koneksi2_ids':prod_ids6, \n })\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\n self.pool['hr.job'].message_post(\n cr, uid, [applicant.job_id.id],\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\n subtype=\"hr_recruitment.mt_job_applicant_hired\", context=context)\n else:\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\n\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\n if emp_id:\n dict_act_window['res_id'] = emp_id\n dict_act_window['view_mode'] = 'form,tree'\n return dict_act_window","def create_student(faculty: str) -> None:\r\n global usernames, pointer, student_file_info\r\n username = usernames[pointer]\r\n password = username[:6][::-1]\r\n student_file_info.append([username, password, faculty])\r\n pointer += 1","def partner_create(self):\n try:\n mongo_module.mongo_insert(self.partner)\n output = 'sucesfully created'\n code = 201\n except Exception as err:\n output = str(err)\n code = 409\n return output, code","def setUp(self):\n\n self.user = self.make_user()\n self.employee = Employee.objects.create(\n cpf=\"974.220.200-16\",\n user=self.user,\n departament=Employee.ADMINISTRATION\n )","def create_user(email, password, f_name, l_name):\n pass","def createEmployee(firstName, lastName, ssn, salary):\n employee = Employee(firstName, lastName, ssn, salary)\n # verify\n if firstName != employee.firstName or \\\n lastName != employee.lastName or \\\n ssn != employee.ssn or \\\n salary != employee.salary:\n raise ValueError(\"Failed to initialize Employee\")\n return employee","def generateEmployees(self):\r\n\r\n # Name\r\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\r\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\r\n\r\n for i in range(self.num_of_employees):\r\n\r\n # Clock in an hour before opening, 6 hours after, or 12 hours after\r\n clockIn = random.choice([7, 13, 19])\r\n\r\n # Clock out after 5 hours, 10 hours, or 15 hours\r\n clockOut = random.choice([13, 19, 23])\r\n while clockOut <= clockIn:\r\n clockOut = random.choice([13, 19, 23])\r\n\r\n # Hourly wage\r\n wage = random.choice([8, 9, 10, 12, 20])\r\n\r\n gender = random.choice(['M', 'F'])\r\n if gender == 'M':\r\n name = random.choice(maleNames)\r\n else:\r\n name = random.choice(femaleNames)\r\n\r\n self.c.execute(\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\", (name, clockIn, clockOut, wage))\r\n self.conn.commit()\r\n\r\n if self.print_employees:\r\n print(\"\\nName:\", name)\r\n print(\"Clock in:\", clockIn)\r\n print(\"Clock out:\", clockOut)\r\n print(\"Wage:\", wage)","def createStudentWithProject(self):\n self.createStudentWithProposal()\n from soc.modules.gsoc.models.student_project import StudentProject\n properties = {'link_id': self.profile.link_id, 'scope': self.profile,\n 'student': self.profile, 'parent': self.profile}\n seeder_logic.seed(StudentProject, properties)","def createMentorWithProject(self, org):\n self.createMentor(org)\n from soc.modules.gsoc.models.student_project import StudentProject\n properties = {'mentor': self.profile}\n seeder_logic.seed(StudentProject, properties)","def _add_sponsor(data: dict) -> dict:\n user = create_user()\n user['name'] = data['agency']\n user['type'] = 32\n if 'agency_class' in data:\n user['tag'] = data['agency_class']\n return user","def add_employee():\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n form = SignUp_Form()\n \n\n if form.validate_on_submit():\n try: \n employee = Employee.register(\n username = form.username.data,\n password = form.password.data, \n email = form.email.data, \n first_name = form.first_name.data,\n last_name = form.last_name.data,\n hire_date = form.hire_date.data, \n is_admin = form.is_admin.data,\n )\n\n db.session.add(employee)\n\n db.session.commit()\n except IntegrityError:\n flash(\"Email already in use\", \"danger\")\n return render_template(\"/admin/add_user.html\", form = form)\n\n flash(\"Employee Added!\", \"success\")\n return redirect(\"/administrator\")\n else:\n\n return render_template(\"/admin/add_user.html\", form = form)","def create_investor(sender, **kwargs):\n u = kwargs[\"instance\"]\n try:\n \n if not InvestorProfile.objects.filter(username=u.username):\n inv = InvestorProfile(username=u.username,user=u)\n inv.save()\n g = DjangoGroup.objects.get(name='Investors') \n g.user_set.add(u)\n except Exception as e:\n print e","def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\n peronjson={}, notes='', tenantid=''):\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\n self.persontdetailscoll.insert(tenantobj, safe=True)","def create_user(change):\n return change()","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n respond_data = self.controller.add_team_employee(request_data=request_data)\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.EmployeeDoesNotExist,\n domain_exceptions.EmployeeIsATeamMember\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def add_employee(self, obj):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\n\n self.dbconnect.commit()\n return obj\n except:\n self.dbconnect.rollback()\n raise","def do_user_create():\n target = User(\n request.form['gender'],\n request.form['first_name'],\n request.form['name'],\n request.form['mail'],\n request.form['meter_id'],\n request.form['group_id'],\n secrets.token_hex(33))\n target.set_role(request.form['role'])\n target.nick = request.form['nick']\n db.session.add(target)\n db.session.commit()\n return user_list(\"Created user \" + target.name)","def create_partner(name):\n\n return Partner.objects.create(name=name)","def test_create_new_person_w_no_role(self, selenium):\n expected_person = entities_factory.PeopleFactory().create(\n system_wide_role=roles.NO_ROLE)\n actual_person = admin_webui_service.PeopleAdminWebUiService(\n selenium).create_new_person(expected_person)\n self.general_equal_assert(expected_person, actual_person)","def create_person(dbo, username, collationid):\n l = dbo.locale\n fields = get_onlineformincoming_detail(dbo, collationid)\n d = {}\n flags = None\n for f in fields:\n if flags is None: flags = f[\"FLAGS\"]\n if f[\"FIELDNAME\"] == \"title\": d[\"title\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"forenames\": d[\"forenames\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"firstname\": d[\"forenames\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"surname\": d[\"surname\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"lastname\": d[\"surname\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"address\": d[\"address\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"town\": d[\"town\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"city\": d[\"town\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"county\": d[\"county\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"state\": d[\"county\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"postcode\": d[\"postcode\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"zipcode\": d[\"postcode\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"hometelephone\": d[\"hometelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"worktelephone\": d[\"worktelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"mobiletelephone\": d[\"mobiletelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"celltelephone\": d[\"mobiletelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"emailaddress\": d[\"emailaddress\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"].startswith(\"reserveanimalname\"): d[f[\"FIELDNAME\"]] = f[\"VALUE\"]\n d[\"flags\"] = flags\n # Have we got enough info to create the person record? We just need a surname\n if not d.has_key(\"surname\"):\n raise utils.ASMValidationError(i18n._(\"There is not enough information in the form to create a person record (need a surname).\", l))\n # Does this person already exist?\n personid = 0\n if d.has_key(\"surname\") and d.has_key(\"forenames\") and d.has_key(\"address\"):\n similar = person.get_person_similar(dbo, d[\"surname\"], d[\"forenames\"], d[\"address\"])\n if len(similar) > 0:\n personid = similar[0][\"ID\"]\n # Create the person record if we didn't find one\n if personid == 0:\n personid = person.insert_person_from_form(dbo, utils.PostedData(d, dbo.locale), username)\n personname = person.get_person_name_code(dbo, personid)\n # Attach the form to the person\n formname = get_onlineformincoming_name(dbo, collationid)\n formhtml = get_onlineformincoming_html_print(dbo, [collationid,])\n media.create_document_media(dbo, username, media.PERSON, personid, formname, formhtml )\n # Was there a reserveanimalname field? If so, create reservation(s) to the person if possible\n for k, v in d.iteritems():\n if k.startswith(\"reserveanimalname\"):\n movement.insert_reserve_for_animal_name(dbo, username, personid, v)\n return (collationid, personid, personname)","def create_student_accounts(self):\r\n # Create student accounts and activate them.\r\n for i in range(len(self.STUDENT_INFO)):\r\n email, password = self.STUDENT_INFO[i]\r\n username = 'u{0}'.format(i)\r\n self.create_account(username, email, password)\r\n self.activate_user(email)","def create_leader_model(request, company_id):\n\n errors = {'noactions': []}\n company = Company.objects.get(pk=company_id)\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\n \"\"\":type : Employee \"\"\"\n\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\n raise PermissionDenied()\n\n if currentEmpl.isCompanySuperUserOrHigher():\n employeeQS = Employee.objects.filter(\n company__pk=company_id\n )\n else:\n employeeQS = Employee.objects.filter(\n Q(manager=currentEmpl),\n company__pk=company_id\n )\n\n form = MultiLeaderModelForm(request.POST or None)\n form.fields['employees'].queryset = employeeQS\n\n if request.method == 'POST' and form.is_valid():\n\n employees = form.cleaned_data['employees']\n \"\"\":type : list[Employee] \"\"\"\n\n pdf_response = get_leader_model_pdf(currentEmpl, employees)\n\n if isinstance(pdf_response, HttpResponse):\n return pdf_response\n else:\n errors = pdf_response\n\n print(errors)\n\n return TemplateResponse(\n request,\n 'mus/create_leader_model.html', {\n 'form': form,\n 'company': company,\n 'errors': errors\n }\n )","def create_user(user, first_name, last_name, major, bio):\n return userAccount.objects.create(user=user, first_name=first_name, last_name=last_name, major=major, bio=bio)","def createStudentWithProposal(self):\n self.createStudent()\n from soc.modules.gsoc.models.proposal import GSoCProposal\n properties = {'link_id': self.profile.link_id, 'scope': self.profile,\n 'parent': self.profile, 'status': 'new'}\n seeder_logic.seed(GSoCProposal, properties)","def test_create_owner(self):\n url = reverse(\n 'projectroles:api_role_create',\n kwargs={'project': self.project.sodar_uuid},\n )\n post_data = {\n 'role': PROJECT_ROLE_OWNER,\n 'user': str(self.assign_user.sodar_uuid),\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 400, msg=response.content)","def createUser(self, firstName, lastName, email, role):\n self.waitForElement(locator=self._createUserBtn, locatorType=\"xpath\")\n self.elementClick(locator=self._createUserBtn, locatorType=\"xpath\")\n self.waitForElement(locator=self._createUser_firstName)\n self.sendKeys(firstName, locator=self._createUser_firstName)\n self.sendKeys(lastName, locator=self._createUser_lastName)\n self.sendKeys(email, locator=self._createUser_email)\n self.selectBy(by=\"text\", data=\"User\", locator=self._createUser_role)\n pp.time.sleep(2)\n self.elementClick(locator=self._createUser_addUserBtn, locatorType=\"xpath\")\n # Bypassing error code\n self.driver.back()\n self.driver.refresh()","def post(self, request):\n data = request.data\n skill_data = data.pop('skills')\n Department_name = data.pop('department')\n department = Department.objects.get(name=Department_name)\n manager_name = data.pop('manager')\n manager = Manager.objects.get(name=manager_name)\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\n Employee.save()\n for skill in skill_data:\n skill_add, create = Skill.objects.get_or_create(name=skill)\n Employee.skills.add(skill_add)\n return Response(\n data=request.data\n )","def create(self, vals):\n if not vals.get('nik_number'):\n vals['nik_number'] = self.generate_nik(vals)\n return super(Employee, self).create(vals)","def createEmployee():\n form = CreateEmployeeForm()\n if form.validate_on_submit():\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \n email=form.email.data, role=form.role.data, location=form.location.data)\n db.session.add(staff)\n db.session.commit()\n flash(f'Employee Added To Database', category='Success')\n return redirect(url_for('login'))\n return render_template('new_employee.html', title=\"Register\", form=form)","def create(self, request):\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\n\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n print(F\"Request employee Data: {serializer.data}\")\n\n try:\n new_employee = self.controller.create_employee(request_data=request_data)\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (domain_exceptions.EmployeeIDIsNotUnique,\n domain_exceptions.WorkArrangementPercentageOutOfRange,\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.WorkArrangementPercentageNull\n ) as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def post(self):\n try:\n employee = self.service.add_employee(self.schema, request.json)\n except ValidationError as error:\n return error.messages, 400\n return self.schema.dump(employee), 201","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.EmployeeDoesNotExist\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def setUp(self):\n\t\tfirst_name = 'Gerson'\n\t\tlast_name = 'Santos'\n\t\tannual_salary = 5000\n\t\tself.gerson = Employee(first_name, last_name, annual_salary)","def create_superuser(self, email, name, password):\n employee = self.create_user(email, name, password)\n employee.is_superuser = True\n employee.is_staff = True\n employee.save(using=self._db)\n\n return employee","def createEMPStudy(self, user_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries):\n con = self.getMetadataDatabaseConnection()\n study_id = 0\n results = con.cursor().callproc('qiime_assets.emp_study_insert', \n [study_id, user_id, study_name,\n investigation_type, miens_compliant, submit_to_insdc, portal_type, \n study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries])\n return results[0]","def createGroup(listOfPerson):\n atk=Department()\n atk.members=listOfPerson\n return atk","def new_user():\n pass","def create(self, creator: User = None, *args, **kwargs) -> Employee:\n # If the creator argument is not None and is a non-staff user, raise\n # ValueError\n if creator and not creator.is_staff:\n raise ValueError(\n 'Only staff members can add new employees, \"creator\" must be '\n 'a staff user.'\n )\n\n # Get the user property from the provided keyword arguments\n user: Optional[User] = kwargs.get('user', None)\n\n # If the user property is missing or is a non-staff user, raise\n # ValueError\n if not user:\n raise ValueError(\n 'You must provide the \"user\" property as a keyword argument'\n )\n elif not user.is_staff:\n raise ValueError('The \"user\" property must be a staff user.')\n\n return super().create(creator, *args, **kwargs)","def create_user(self):\n unique_id = str(uuid.uuid4())\n new_user_properties = {\n \"name\": self.name,\n \"mission_statement\": self.mission_statement,\n \"unique_id\": unique_id,\n \"email\": self.email.lower(),\n \"is_mentor\": True,\n \"is_tutor\": True,\n \"is_visible\": True,\n \"is_available_for_in_person\": True,\n \"is_admin\": True}\n new_user_node = Node.cast(AgoraLabel.USER, new_user_properties)\n try:\n self.graph_db.create(new_user_node)\n except:\n pass\n return new_user_node","def create_person(data_dict, m2m_dict):\n email_address = data_dict['email_address']\n # check if the person already exists:\n try:\n exists = Person.objects.get(email_address=email_address)\n create_message = f'Skipping: Person with {email_address} already exists.'\n except:\n # try:\n person_obj, person_created = Person.objects.update_or_create(**data_dict)\n # populate MANY-TO-MANY fields\n if m2m_dict:\n # created_by (FK)\n created_by_email = m2m_dict['created_by']\n user, user_created = User.objects.get_or_create(email=created_by_email)\n person_qs = Person.objects.filter(email_address=person_obj.email_address)\n person_qs.update(created_by=user)\n # expertise (M2M)\n expertise_values = m2m_dict['expertise']\n if expertise_values:\n values_list = [value.strip() for value in expertise_values.split(',')]\n for value in values_list:\n expertise_obj, expertise_created = Expertise.objects.get_or_create(name=value)\n person_obj.expertise.add(expertise_obj)\n # industry (M2M)\n industry_values = m2m_dict['industries']\n if industry_values:\n values_list = [value.strip() for value in industry_values.split(',')]\n for value in values_list:\n industry_obj, industry_created = Industry.objects.get_or_create(name=value)\n person_obj.industries.add(industry_obj)\n # organization (M2M)\n organization_values = m2m_dict['organization']\n if organization_values:\n values_list = [value.strip() for value in organization_values.split(',')]\n for value in values_list:\n organization_obj, organization_created = Organization.objects.get_or_create(name=value)\n person_obj.organization.add(organization_obj)\n # owned by (M2M)\n exportable_by_values = m2m_dict['exportable_by']\n if exportable_by_values:\n values_list = [value.strip() for value in exportable_by_values.split(',')]\n for value in values_list:\n dive_obj, dive_created = Dive.objects.get_or_create(name=value)\n person_obj.exportable_by.add(dive_obj)\n # let us know how it went\n if person_created:\n create_message = f'Success: {person_obj}'\n else:\n create_message = f'Failed: {person_obj}'\n # except Exception as e:\n # create_message = f'Error for {email_address}: {e}\\n'\n print(create_message)\n # except:\n # failed_rows.append(counter)\n # try:\n # obj, created = Person.objects.create(**csv_to_model)\n # except:\n # message = 'Create person' + str(sys.exc_info())\n # print(message)","def post(self):\n data = EmployeeRegister.parser.parse_args()\n new_employee_id = str(uuid.uuid4())\n\n while EmployeeModel.find_by_id(new_employee_id):\n # if this id is already in use\n new_employee_id = str(uuid.uuid4())\n\n employee = EmployeeModel(**data, employee_id=new_employee_id)\n employee.save_to_db()\n\n return {\"message\": \"Employee successfully added to the system\"}, 201 # 201 - Created","def create_user(context, params):\n form_user = dict()\n # form_user['edited_by'] = context.user\n if params.get('username'):\n form_user['username'] = params.get('username')\n else:\n form_user['username'] = create_username(params) # 'email_user{}'.format(MISUser.objects.latest('id').id + 1\n form_user['first_name'] = params.get('first_name')\n form_user['last_name'] = params.get('last_name')\n form_person = create_person(params)\n form_user.update(form_person)\n user = User.objects.create(**form_user)\n user.set_password(params.get('password'))\n\n email = {'label': 'Work', 'val': params.get('email'), 'person': user, 'is_main': True}\n create_email(context, email)\n\n user.save()\n return user","def create_candidate(self, data, header):\n return self.client.post(\n path='/api/v2/office/1/register/', data=json.dumps(data), content_type='application/json', headers=header)","def is_student_employee():\n return _is_member('uw_affiliation_student-employee')","def add_user(first_name,last_name,email,password,typeOfUser):\n user=User.objects.create(first_name=first_name,last_name=last_name,email=email,password=password,role=typeOfUser)\n return user","def test_meeting_registrant_create(self):\n pass","def create_trainee(self, name, pos, dept):\n self.trainee[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'junior': self.junior[dept.upper()][0]['name'],\n }\n )\n self.manager[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.junior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def create():","def create():","def onUserCreation(event):\n\n client = getUtility(IAdminClient)\n xmpp_users = getUtility(IXMPPUsers)\n storage = getUtility(IPubSubStorage)\n principal = event.principal\n mtool = getToolByName(principal, 'portal_membership')\n\n principal_id = principal.getUserId()\n principal_jid = xmpp_users.getUserJID(principal_id)\n members_jids = [xmpp_users.getUserJID(member.getUserId())\n for member in mtool.listMembers()]\n pass_storage = getUtility(IXMPPPasswordStorage)\n principal_pass = pass_storage.set(principal_id)\n\n storage.leaf_nodes.append(principal_id)\n storage.node_items[principal_id] = []\n storage.collections['people'].append(principal_id)\n storage.publishers[principal_id] = [principal_id]\n\n d = setupPrincipal(client, principal_jid, principal_pass, members_jids)\n return d","def m_create_identity(DID, domain_name, website, commercial_name, parent_node_account, password, overwrite):\n\n error, didDoc = create_identity(\n DID, domain_name, website, commercial_name, parent_node_account, password, overwrite)\n if error is not None:\n print(error)\n\n print(f\"Created\")","def create_user(fname, phone_num):\n\n user = User(fname=fname, phone_num=phone_num)\n\n db.session.add(user)\n db.session.commit()\n\n return user","def test_admin_can_create_a_employee(self):\n\n account_data = {\n \"username\": \"Mike\",\n \"email\": \"mioshine@g.com\",\n \"password\": \"1234567\",\n \"confirm_password\": \"1234567\"\n }\n response = self.client.post(\n reverse('accounts:create-user'),\n account_data,\n format=\"json\")\n \"\"\"Test the api has bucket creation capability.\"\"\"\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertTrue(\"data\" in json.loads(response.content))","def main():\n name = input(\"Please enter in your name: \")\n\n \"\"\"Ask the user to enter a number if they are a Director, Manager or Staff.\"\"\"\n \"\"\"This will check and make sure the user only enters in 1,2, \n or 3 and a number greater than zero\"\"\"\n while True:\n try:\n designation_number = int(input(\"Please enter in \\n1 for Director \"\n \"\\n2 for Manager \\n3 for Staff\\n\"))\n if 0 < designation_number <= 3:\n break\n print(\"Invalid number entered.\")\n except Exception as e:\n print(e)\n \"\"\"Gets the user salary and makes sure is a number and greater than 0\"\"\"\n while True:\n try:\n salary = float(input(\"Please enter in your salary: \"))\n if salary <= 0:\n print(\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\")\n else:\n break\n except ValueError:\n print(\"Oops! That was not a valid number. Try again...\")\n\n \"\"\"Create Employee\"\"\"\n employee1 = employee.Employee()\n employee1.set_name(name)\n employee1.set_designation(designation_number)\n employee1.set_salary(salary)\n print(employee1)","def create(self, values):\n\t\temployee_id = values.get('employee_id', False)\n\t\tprint(\"the val in the dict\", values)\n\t\tif (values.get('date_from') and values.get('date_to')) == False:\n\t\t\tcurrent = datetime.strftime(datetime.today().date(),'%Y-%m-%d')\n\t\t\tvalues.update({'allocate_date': current})\n\t\t\tprint(values)\n\t\tif not values.get('department_id'):\n\t\t\tvalues.update({'department_id': self.env['hr.employee'].browse (employee_id).department_id.id})\n\n\t\tholiday = super (Holidays, self.with_context (mail_create_nolog=True, mail_create_nosubscribe=True)).create(values)\n\t\tholiday.add_follower (employee_id)\n\n\t\t# Trilok code for policies\n\t\tpolicy_id = holiday.env['leaves.policy'].search(\n\t\t\t[('leave_type', '=', holiday.holiday_status_id.id), ('company_id', '=', self.env.user.company_id.id)])\n\t\t# print (\"policy iddddddddddddddd\",policy_id)\n\t\temp_type = holiday.employee_id.employee_type.id\n\t\tfor val in policy_id:\n\t\t\tif val.employee_type.id == emp_type:\n\t\t\t\tfor employee in holiday.employee_id:\n\t\t\t\t\tif holiday.type == 'remove':\n\t\t\t\t\t\tquery = '''select count(*) from hr_holidays where upper(type) = upper('rEMove')and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') and now()::date and employee_id = %s''' % employee.id\n\t\t\t\t\t\tholiday.env.cr.execute(query)\n\t\t\t\t\t\tquery_result = holiday.env.cr.dictfetchone()\n\t\t\t\t\t\t# print(\"query_result\", query_result)\n\t\t\t\t\t\tif val.min_app_per_year > 0 and query_result[\"count\"] > val.min_app_per_year:\n\t\t\t\t\t\t\traise ValidationError(\"maximum number of applications per year is {} days\".format(val.min_app_per_year))\n\n\t\t\t\t\t\tquery1 = '''select create_date::date,date_to::date from hr_holidays where upper(type) = \n\t\t\t\t\t\tupper('rEMove') and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') \n\t\t and now()::date and employee_id = %s order by create_date desc limit 1'''\\\n\t\t\t\t\t\t\t\t % employee.id\n\t\t\t\t\t\tholiday.env.cr.execute(query1)\n\t\t\t\t\t\tquery_result1 = holiday.env.cr.fetchall()\n\t\t\t\t\t\tif query_result1 is not None:\n\t\t\t\t\t\t\t# print(\"query_resulttttttttttttttttttttttttttt\", query_result1)\n\t\t\t\t\t\t\t# print(\"query_resulttttttttttttttttttttttttttt\", query_result1[0][0], query_result1[0][1])\n\t\t\t\t\t\t\tcre_date = datetime.strptime(query_result1[0][0], '%Y-%m-%d')\n\t\t\t\t\t\t\tdate_to = datetime.strptime(query_result1[0][1], '%Y-%m-%d')\n\t\t\t\t\t\t\t# print(\"cre_date\", cre_date, type(cre_date))\n\t\t\t\t\t\t\tcurrent_dt = fields.Datetime.now()\n\t\t\t\t\t\t\t# cdate=datetime.strptime(current_dt,'%Y-%m-%d')\n\t\t\t\t\t\t\tcurrent_date = datetime.strptime(current_dt.split(\" \")[0], '%Y-%m-%d')\n\t\t\t\t\t\t\tdays = (current_date - date_to).days\n\t\t\t\t\t\t\tif val.min_leave_app_gap > 0 and days > val.min_leave_app_gap:\n\t\t\t\t\t\t\t\traise ValidationError(\n\t\t\t\t\t\t\t\t\t\"Minimum gap between two application should be atleast {} days\".format(\n\t\t\t\t\t\t\t\t\t\tval.min_leave_app_gap))\n\n\t\treturn holiday","def test_create_user(self):\n \n new_user = {\"username\": \"beny1976\", \"vocab_count\": 0, \"name\": \"beny rood\", \"sex\": \"male\", \"dob\": \"18/10/1979\"}\n msg = app.create_user(predefined_user=new_user)\n self.assertTrue(msg != \"\")","def create_job(self, employer_id, compensation, location, description, category_id, group_id):\n\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \n db.session.add(job)\n db.session.commit()","def make_salaried(self,salary,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"2\"\n print(\"{}{}\".format(name,\" was successfully changed to be a salaried employee\"))\n self.emp_dict[id][7] = salary\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def create_manager(self, name, pos, dept):\n self.manager[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'senior': [],\n 'junior': [],\n 'trainee': []\n }\n )","def create_user(UserName=None, MessageAction=None, FirstName=None, LastName=None, AuthenticationType=None):\n pass","def create(person):\n lname = person.get(\"lname\", None)\n fname = person.get(\"fname\", None)\n\n # Does the person exist already?\n if lname not in PEOPLE and lname is not None:\n PEOPLE[lname] = {\n \"lname\": lname,\n \"fname\": fname,\n \"timestamp\": get_timestamp(),\n }\n return PEOPLE[lname], 201\n\n # Otherwise, they exist, that's an error\n else:\n abort(\n 406,\n \"Person with last name {lname} already exists\".format(lname=lname),\n )","def add_employee(self, empl):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\n empl.active, empl.promotor))\n cursor.execute('SELECT LASTVAL()')\n eid = cursor.fetchone()[0]\n empl.id = eid\n # get id and return updated object\n self.dbconnect.commit()\n except(Exception, self.dbconnect.get_error()) as error:\n self.dbconnect.rollback()\n raise Exception('\\nUnable to save Employee!\\n(%s)' % (error))","def _create_user(self, new_user):\n new_user = User(user_name=new_user['user_name'], pin=new_user['pin'], user_type='customer')\n self.session.output(new_user.get_user_info(), '\\n[ New user created ]')","def _create_expense(initiator_id: str, recipient_id: str, amount: float, description: str):\n if amount <= 0:\n raise CommandException('Negative amounts not allowed')\n\n tso: User = User.objects.get(friendly_name='tso')\n initiator: User = User.objects.get(slack_id=initiator_id)\n recipient: User = User.objects.get(slack_id=recipient_id)\n users = []\n\n expense = Expense()\n expense.setGroupId(settings.SPLITWISE_GROUP_ID)\n expense.setDescription(description)\n expense.setCost(amount)\n\n initiator_user = ExpenseUser()\n initiator_user.setId(initiator.splitwise_id)\n initiator_user.setPaidShare(0)\n initiator_user.setOwedShare(amount)\n users.append(initiator_user)\n\n recipient_user = ExpenseUser()\n recipient_user.setId(recipient.splitwise_id)\n recipient_user.setPaidShare(amount)\n recipient_user.setOwedShare(0)\n users.append(recipient_user)\n\n # We do this because we're using my access token.\n if tso.id not in [initiator.id, recipient.id]:\n tso_user = ExpenseUser()\n tso_user.setId(tso.splitwise_id)\n tso_user.setPaidShare(0)\n tso_user.setOwedShare(0)\n users.append(tso_user)\n\n expense.setUsers(users)\n if not sObj.createExpense(expense):\n raise CommandException('Failed to create SW expense')","def create(ctx, name, company, mail, age):\n client = Client(name,company,mail,age)\n client_service = ClientService(ctx.obj['clients_table']) \n client_service.create_client(client)","def create_user_questionnaire_in_progress(self):\n username = 'pseudo'\n email = 'martine@tests.com'\n password = '00000000'\n user_created = self.user.objects.create_user(id=2, username=username,\n email=email, password=password)\n HistoryUser.objects.create(user=user_created)\n StatusUser.objects.create(user=user_created)\n list_advice_id = [1, 5, 10]\n self.add_advice_to_user_created(user_created, list_advice_id)\n\n return user_created","def createUser(self):\n if self.user:\n return self.user\n from soc.models.user import User\n from soc.modules.seeder.logic.providers.user import CurrentUserProvider\n properties = {'account': CurrentUserProvider(),\n 'status': 'valid', 'is_developer': self.dev_test}\n self.user = seeder_logic.seed(User, properties=properties)\n return self.user","def test_create_user(self):\n pass","def test_create_user(self):\n pass","def test_create_user(self):\n pass","def insert_employee(self,\n region_name,\n last_name,\n first_name,\n hire_date,\n mi=None):\n\n if self.check_input_type(region_name, \"Region\"):\n if self.check_input_type(hire_date, \"Date\"):\n region_info = self.query_region(region_name)\n region_id = region_info[0][0]\n\n if mi != \"\":\n query_format = \"insert into employee(Region_ID, \" \\\n \"Emp_Lname, Emp_Mi, Emp_Fname, Emp_Hiredate) \" \\\n \"values ((select region_id from region where \" \\\n \"region_id='{}'), '{}', '{}', '{}', '{}')\"\n query = query_format.format(\n region_id, last_name, mi, first_name, hire_date\n )\n else:\n query_format = \"insert into employee(Region_ID, \" \\\n \"Emp_Lname, Emp_Fname, Emp_Hiredate) \" \\\n \"values ((select region_id from region where \" \\\n \"region_id='{}'), '{}', '{}', '{}')\"\n query = query_format.format(\n region_id, last_name, first_name, hire_date\n )\n\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n ErrorMessageWindow(\"Date format not valid!\")\n else:\n ErrorMessageWindow(\"Region input not valid!\")","def createOrgAdmin(self, org):\n self.createProfile()\n self.profile.org_admin_for = [org.key()]\n self.profile.put()","def register():\n add_employee = True\n form = RegistrationForm()\n if form.validate_on_submit():\n employee = Employee(email=form.email.data,\n username=form.username.data,\n glad_id=form.glad_id.data,\n tel_no=form.tel_no.data,\n role_id=2 , ##form.role_id.data,\n password=form.password.data)\n\n # add employee to the database\n db.session.add(employee)\n db.session.commit()\n flash('You have successfully registered! You may now login.')\n\n # redirect to the login page\n return redirect(url_for('auth.login'))\n\n # load registration template\n return render_template('auth/register.html', form=form, title='Register')","def add_nurse(request):\n if request.POST:\n post = request.POST\n username = post.get(\"username\")\n first_name = post.get(\"first_name\")\n last_name = post.get(\"last_name\")\n email = post.get(\"email\")\n password = post.get(\"password\")\n chosen_hospitals = post.getlist(\"chosen_hospitals\")\n\n new_user = User.objects.create_user(\n username=username,\n password=password,\n first_name=first_name,\n last_name=last_name,\n email=email\n )\n\n new_user_profile = UserProfile.objects.create(\n user=new_user,\n status=UserStatus.objects.get(pk=2)\n )\n\n if new_user:\n for chosen_hospital in chosen_hospitals:\n HospitalStaff.objects.create(user_profile=new_user_profile, hospital=Hospital.objects.get(pk=chosen_hospital))\n\n return redirect('add_nurse')\n\n hospitals = Hospital.objects.all()\n return render(request, 'add_nurse.html', {'hospitals': hospitals})","def create_organization(user, name):\n organization = Organization.objects.create(name=name)\n new_user = OrganizationUser.objects.create(organization=organization,\n user=user, is_admin=True)\n OrganizationOwner.objects.create(organization=organization,\n organization_user=new_user)\n return organization","def add_student():\n\n\tprint('You must enter the student as is:\\n'\n\t\t\"'First name', 'middle name', 'Last name', 'major', 'major', 'gpa', id_number, 'minor'\"\n\t\t\" 'minor' graduation year, advisor number\\n For example: 'Kyle', 'Jacob', 'Ranney', 'Insurance'\"\n\t\t\", 'Chemistry', 3.0, 93988, 'Biology', 'NULL', 2016, 2234\\n\")\n\t# use sql insert statement\n\t# become familiar with this!\t","def post(self, orgname):\n permission = AdministerOrganizationPermission(orgname)\n if permission.can() or allow_if_superuser():\n try:\n org = model.organization.get_organization(orgname)\n except model.InvalidOrganizationException:\n raise NotFound()\n\n details = request.get_json()\n activating_username = None\n\n if (\n \"activating_user\" in details\n and details[\"activating_user\"]\n and \"name\" in details[\"activating_user\"]\n ):\n activating_username = details[\"activating_user\"][\"name\"]\n\n delegate = details[\"delegate\"] if \"delegate\" in details else {}\n delegate_kind = delegate.get(\"kind\", None)\n delegate_name = delegate.get(\"name\", None)\n\n delegate_username = delegate_name if delegate_kind == \"user\" else None\n delegate_teamname = delegate_name if delegate_kind == \"team\" else None\n\n activating_user = (\n model.user.get_user(activating_username) if activating_username else None\n )\n delegate_user = model.user.get_user(delegate_username) if delegate_username else None\n delegate_team = (\n model.team.get_organization_team(orgname, delegate_teamname)\n if delegate_teamname\n else None\n )\n\n if activating_username and not activating_user:\n raise request_error(message=\"Unknown activating user\")\n\n if not delegate_user and not delegate_team:\n raise request_error(message=\"Missing delegate user or team\")\n\n role_name = details[\"role\"]\n\n prototype = model.permission.add_prototype_permission(\n org, role_name, activating_user, delegate_user, delegate_team\n )\n log_prototype_action(\"create_prototype_permission\", orgname, prototype)\n\n users_filter = {prototype.activating_user, prototype.delegate_user}\n org_members = model.organization.get_organization_member_set(\n org, users_filter=users_filter\n )\n return prototype_view(prototype, org_members)\n\n raise Unauthorized()","def register_as_student(request):\n\n if request.method == \"POST\":\n # access the data send in the form\n first_name = request.POST['firstname']\n last_name = request.POST['lastname']\n user_name = request.POST['username']\n email = request.POST['email']\n password = request.POST['password']\n ssn = request.POST['ssn']\n university_id = request.POST['universityid']\n level = request.POST['level']\n\n # make array of error in eorm\n error = {}\n #check if first naem valid\n if first_name.isalpha() == False:\n error.update({\"firstname\" : \"First name is not valid\"})\n #check if first naem valid\n if last_name.isalpha() == False:\n error.update({\"lastname\" : \"Last name is not valid\"})\n # check if user exist \n if username_exists(user_name):\n error.update({\"username\" : \"User name already exist\"})\n # check if email is valid \n if validate_email(email) == False:\n error.update({\"email\" : \"It's not valid email\"})\n # check if ssn is less than 20 number\n if len(ssn) != 14:\n error.update({\"SSN\" : \"SSN is must be 14\"})\n # check if ssn is less than 20 number\n if len(university_id) > 10 and len(university_id) < 5:\n error.update({\"universityid\" : \"University id is greater than 10\"})\n #check level is correct pr not\n try:\n int(level)\n if int(level) > 7:\n error.update({\"levellen\" : \"level must be less than 7\"})\n except:\n error.update({\"levelint\" : \"level must be integer\"})\n\n # if found error in errors array return array of error \n if len(error) != 0:\n return JsonResponse({'errors': error}, status=400)\n\n # add data to User object \n user = User.objects.create_user(user_name, email, password)\n user.first_name = first_name\n user.last_name = last_name\n user.is_staff = False\n\n # add information to the user \n info = UserInfo(user = user, ssn = ssn, university_id = university_id, level = level)\n # commit change \n user.save()\n\n # now login the user\n info.login_user()\n info.save()\n\n my_user = user_serializer(user)\n success = {'success': True}\n\n # return success message \n return JsonResponse({ **my_user, **success }, status=200)\n\n else:\n return JsonResponse({'error': 'Method not allowed'}, status=405)"],"string":"[\n \"def create_emp(self, name, pos, dept):\\n if pos.upper() == 'MANAGER':\\n self.create_manager(name, pos, dept)\\n elif pos.upper() == 'SENIOR':\\n self.create_senior(name, pos, dept)\\n elif pos.upper() == 'JUNIOR':\\n self.create_junior(name, pos, dept)\\n else:\\n self.create_trainee(name, pos, dept)\",\n \"def create_employee(self,personal_identity):\\r\\n new_emp = Employee(*personal_identity)\\r\\n registration_str = new_emp.get_registration_str()\\r\\n\\r\\n return_value = self.save_object_to_DB(\\\"employee\\\",registration_str)\\r\\n return return_value\",\n \"def create_person(self):\",\n \"def create_junior(self, name, pos, dept):\\n self.junior[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'senior': self.senior[dept.upper()][0]['name'],\\n 'trainee': []\\n }\\n )\\n self.manager[dept.upper()][0]['junior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.senior[dept.upper()][0]['junior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\\n teacher_account_id = person.get_last()\\n query = 'INSERT INTO teacher VALUES( %s,%s );'\\n args = (teacher_account_id, availability)\\n database.connection.save_data(query, args)\",\n \"def create(self, values):\\n if values.get('country_id', False):\\n country = self.env['res.country'].browse(values['country_id'])\\n if country.code == 'SA':\\n values.update({'is_saudi': True})\\n else:\\n values.update({'is_saudi': False})\\n\\n res = super(HrEmployee, self).create(values)\\n if values.get('user_id', False):\\n self.user_id.write({'employee_id': res})\\n return res\",\n \"def create_employee(self):\\n try:\\n name = input(\\\"Enter name: \\\")\\n if not name.isalpha():\\n print(\\\"Invalid data format. Name should contain only alphabets. \\\")\\n return False\\n email = input(\\\"Enter email: \\\")\\n if not InputValidations.validate_email(email):\\n return False\\n employee = EmployeeModel(name=name, email=email)\\n self.admin_repository.create_employee(employee)\\n print(\\\"Employee created successfully!\\\")\\n return True\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def add_employee(self, first_name, last_name):\\n self.switch_main_menu(\\\"PIM\\\")\\n self.click_menu(\\\"Add Employee\\\")\\n self.pim = AddEmployee(self.driver)\\n self.pim.add_user_employee(first_name, last_name)\",\n \"def users_create():\",\n \"def createStudent(self):\\n self.createProfile()\\n from soc.modules.gsoc.models.profile import GSoCStudentInfo\\n properties = {'key_name': self.profile.key().name(), 'parent': self.profile}\\n self.profile.student_info = seeder_logic.seed(GSoCStudentInfo, properties)\\n self.profile.put()\",\n \"def create_student(self, username):\\r\\n return self._create_user(username, is_staff=False)\",\n \"def createDeveloper(self):\\n self.createUser()\\n self.user.is_developer = True\\n self.user.put()\",\n \"def create_member(org_id, group_id, target_group_ids, sex, first_name, last_name, title_name, email):\\n user = get_user_by_email(email)\\n # --- falls e-mail schon existiert wird nichts unternommen\\n if user != None:\\n if org_id > 0: # nur bei Schulen wird die Schulnummer vorangestellt\\n prefix = '%i_' % org_id\\n else:\\n prefix = ''\\n user = User()\\n username = get_username(prefix, first_name, last_name)\\n user.username = username\\n user.sex = sex\\n user.first_name = first_name\\n user.last_name = last_name\\n user.email = email\\n user.title = title_name\\n user.is_staff = False\\n user.is_active = True\\n user.is_superuser = False\\n user.date_joined = datetime.datetime.now()\\n password = generate_passwd()\\n user.set_password(password)\\n user.save()\\n set_user_org(org_id, user)\\n send_password(email, username, password)\\n set_user_group(user, get_group_by_id(group_id))\\n for group in target_group_ids:\\n set_user_group(user, get_group_by_id(group))\\n transaction.commit()\",\n \"def create_instructor(self, username):\\r\\n return self._create_user(username, is_staff=True)\",\n \"def create_user(schools_dictionnary, domains_to_skills_dictionnary, companies, places, skills_oh, places_oh, domains_oh, rng, _id):\\n\\n age = rng.randint(20,60)\\n schools = rng.choice(list(schools_dictionnary.keys()), rng.choice([1, 2], p = [0.95, 0.05]), replace = False) \\n\\n available_skills = list(set([skill for school in schools \\\\\\n for domain in schools_dictionnary[school].domains \\\\\\n for skill in domains_to_skills_dictionnary[domain]]))\\n\\n expo = np.round(rng.exponential(0.3) * len(schools)) + age // 17 + 1\\n\\n nb_skills_to_choose = min(int(expo), 5 + (len(schools) - 1) * 3)\\n\\n _skills = rng.choice(available_skills, nb_skills_to_choose, replace = False)\\n\\n company = rng.choice(companies)\\n place = rng.choice(places)\\n\\n user = User(skills_oh, places_oh, domains_oh, schools_dictionnary, skills = _skills, age = age, place = place, company = company,\\n schools = schools, _id = _id)\\n\\n return user\",\n \"def perform_create(self, serializer):\\n if self.request.data.get('user_type', None) == 'employee':\\n serializer.save(is_staff=False)\\n else:\\n serializer.save()\",\n \"def _create_nsem_user():\\n users = User.objects.filter(username=settings.CWWED_NSEM_USER)\\n if users.exists():\\n user = users[0]\\n else:\\n user = User.objects.create_user(settings.CWWED_NSEM_USER, password=settings.CWWED_NSEM_PASSWORD)\\n group, _ = Group.objects.get_or_create(name=settings.CWWED_NSEM_GROUP)\\n perm_names = [\\n 'add_{}'.format(NsemPsa._meta.model_name),\\n 'add_{}'.format(NamedStormCoveredDataSnapshot._meta.model_name),\\n ]\\n perms = Permission.objects.filter(codename__in=perm_names)\\n # set permission\\n user.user_permissions.set(list(perms))\\n group.permissions.set(list(perms))\\n # add user to group\\n group.user_set.add(user)\",\n \"def create_individual(self):\\n pass\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\\n employee, created = Employee.objects.update_or_create(user=user,\\n employee_id=validated_data.pop('employee_id'),\\n location=validated_data.pop('location'),\\n avail_start_time= str(validated_data.pop('avail_start_time')),\\n avail_end_time= str(validated_data.pop('avail_end_time')))\\n return employee\",\n \"def create_educator(data):\\n\\n educator = Educator(\\n name=data['name'],\\n work_email=data['work_email'],\\n organization_name=data['organization_name'],\\n org_or_school=data['org_or_school'],\\n address_line_1=data['address_line_1'],\\n address_line_2=data['address_line_2'],\\n city=data['city'],\\n state=data['state'],\\n zipcode=data['zipcode'],\\n num_students=data['num_students']\\n )\\n educator.save()\\n return educator\",\n \"def createMentor(self, org):\\n self.createProfile()\\n self.profile.mentor_for = [org.key()]\\n self.profile.put()\",\n \"def create_employee(request, company_id):\\n\\n company = Company.objects.get(pk=company_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n logUnauthorizedAccess(\\\"User tried to create_employee\\\", request)\\n raise PermissionDenied()\\n form = EmployeeForm(request, initial=dict(company=company))\\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\\n # Q(company=company) | Q(company__isnull=True))\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_employee_form.html',\\n {\\n 'employee_form': form,\\n }\\n )\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n # return JsonResponse(status=200, data=data)\",\n \"def create_employee_from_applicant(self, cr, uid, ids, context=None):\\n if context is None:\\n context = {}\\n hr_employee = self.pool.get('hr.employee')\\n model_data = self.pool.get('ir.model.data')\\n act_window = self.pool.get('ir.actions.act_window')\\n emp_id = False\\n for applicant in self.browse(cr, uid, ids, context=context):\\n address_id = contact_name = False\\n if applicant.partner_id:\\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\\n if applicant.job_id and (applicant.partner_name or contact_name):\\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\\n create_ctx = dict(context, mail_broadcast=True)\\n\\n pes=self.browse(cr,uid,ids)[0]\\n coy=pes.partner_name\\n\\n ##### Susunan Keluarga ayah/ibu #####\\n le=self.pool.get('hr_recruit.suskel1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context)\\n prod_ids=[] \\n for pr in lele:\\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\\n \\n ###### Susunan Keluarga Suami/istri #####\\n le=self.pool.get('hr_recruit.suskel2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids1=[] \\n for pr in lele:\\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \\n \\n ###### riwayat Pendidikan #######\\n le=self.pool.get('hr_recruit.rwt_pend')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids2=[] \\n for pr in lele:\\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \\n \\n ###### bahasa ######\\n le=self.pool.get('hr_recruit.bahasa')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids3=[] \\n for pr in lele:\\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \\n \\n ##### Riwayat Pekerjaan ####\\n le=self.pool.get('hr_recruit.rwt_krj')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids4=[] \\n for pr in lele:\\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \\n \\n ###### Koneksi Internal #####\\n le=self.pool.get('hr_recruit.kon1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids5=[] \\n for pr in lele:\\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \\n \\n ###### Koneksi Eksternal ####\\n le=self.pool.get('hr_recruit.kon2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids6=[]\\n for pr in lele: \\n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \\n\\n ####### create Employee ######## \\n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\\n 'job_id': applicant.job_id.id,\\n 'department_id' : applicant.department_id.id,\\n 'address_id2' : applicant.job_id.address_id.id,\\n #### informasi Probadi ####\\n 'kelamin':applicant.jen_kel,\\n 'blood' : applicant.blood,\\n 'agama' : applicant.agama_id.id,\\n 'birthday' : applicant.tgl_lahir,\\n 'place_of_birth' : applicant.kota_id.name,\\n 'marital':applicant.status,\\n 'sjk_tanggal' : applicant.sjk_tanggal,\\n 'mobile_phone':applicant.partner_phone,\\n 'country_id' : applicant.country_id.id,\\n\\n #### Pendidikan ####\\n 'type_id':applicant.type_id.id,\\n 'bid_id':applicant.bidang_id.id,\\n 'jurusan_id':applicant.jurusan_id.id,\\n 'pt_id':applicant.pt_id.id,\\n 'gelar_id':applicant.gelar_id.id,\\n\\n #### alamat DOmisili ####\\n 'country_id1':applicant.country_id1.id,\\n 'prov_id':applicant.prov_id.id,\\n 'kab_id' : applicant.kab_id.id,\\n 'kec_id':applicant.kec_id.id,\\n 'alamat1' : applicant.alamat1,\\n 'kodepos' :applicant.kode1,\\n 'telp1' : applicant.telp1,\\n\\n #### kartu identitas ####\\n 'jenis_id': applicant.jenis_id,\\n 'ktp' : applicant.no_id,\\n 'tgl_berlaku' : applicant.tgl_berlaku,\\n # 'issued_id' : applicant.dikeluarkan.id,\\n \\n #### Alamat Sesuai KTP #### \\n 'country_id2':applicant.country_id2.id,\\n 'prov_id2':applicant.prov_id2.id,\\n 'kab_id2':applicant.kab_id2.id,\\n 'kec_id2':applicant.kec_id2.id,\\n 'alamat2' : applicant.alamat2,\\n 'kodepos1':applicant.kode2,\\n 'telp2' : applicant.telp2,\\n \\n # 'status': applicant.status,\\n #### IDS ####\\n 'susunan_kel1_ids' : prod_ids,\\n 'susunan_kel2_ids':prod_ids1,\\n 'rwt_pend_ids':prod_ids2,\\n 'bahasa_ids':prod_ids3,\\n 'rwt_krj_ids':prod_ids4,\\n 'koneksi1_ids':prod_ids5,\\n 'koneksi2_ids':prod_ids6, \\n })\\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\\n self.pool['hr.job'].message_post(\\n cr, uid, [applicant.job_id.id],\\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\\n subtype=\\\"hr_recruitment.mt_job_applicant_hired\\\", context=context)\\n else:\\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\\n\\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\\n if emp_id:\\n dict_act_window['res_id'] = emp_id\\n dict_act_window['view_mode'] = 'form,tree'\\n return dict_act_window\",\n \"def create_student(faculty: str) -> None:\\r\\n global usernames, pointer, student_file_info\\r\\n username = usernames[pointer]\\r\\n password = username[:6][::-1]\\r\\n student_file_info.append([username, password, faculty])\\r\\n pointer += 1\",\n \"def partner_create(self):\\n try:\\n mongo_module.mongo_insert(self.partner)\\n output = 'sucesfully created'\\n code = 201\\n except Exception as err:\\n output = str(err)\\n code = 409\\n return output, code\",\n \"def setUp(self):\\n\\n self.user = self.make_user()\\n self.employee = Employee.objects.create(\\n cpf=\\\"974.220.200-16\\\",\\n user=self.user,\\n departament=Employee.ADMINISTRATION\\n )\",\n \"def create_user(email, password, f_name, l_name):\\n pass\",\n \"def createEmployee(firstName, lastName, ssn, salary):\\n employee = Employee(firstName, lastName, ssn, salary)\\n # verify\\n if firstName != employee.firstName or \\\\\\n lastName != employee.lastName or \\\\\\n ssn != employee.ssn or \\\\\\n salary != employee.salary:\\n raise ValueError(\\\"Failed to initialize Employee\\\")\\n return employee\",\n \"def generateEmployees(self):\\r\\n\\r\\n # Name\\r\\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\\r\\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\\r\\n\\r\\n for i in range(self.num_of_employees):\\r\\n\\r\\n # Clock in an hour before opening, 6 hours after, or 12 hours after\\r\\n clockIn = random.choice([7, 13, 19])\\r\\n\\r\\n # Clock out after 5 hours, 10 hours, or 15 hours\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n while clockOut <= clockIn:\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n\\r\\n # Hourly wage\\r\\n wage = random.choice([8, 9, 10, 12, 20])\\r\\n\\r\\n gender = random.choice(['M', 'F'])\\r\\n if gender == 'M':\\r\\n name = random.choice(maleNames)\\r\\n else:\\r\\n name = random.choice(femaleNames)\\r\\n\\r\\n self.c.execute(\\\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\\\", (name, clockIn, clockOut, wage))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_employees:\\r\\n print(\\\"\\\\nName:\\\", name)\\r\\n print(\\\"Clock in:\\\", clockIn)\\r\\n print(\\\"Clock out:\\\", clockOut)\\r\\n print(\\\"Wage:\\\", wage)\",\n \"def createStudentWithProject(self):\\n self.createStudentWithProposal()\\n from soc.modules.gsoc.models.student_project import StudentProject\\n properties = {'link_id': self.profile.link_id, 'scope': self.profile,\\n 'student': self.profile, 'parent': self.profile}\\n seeder_logic.seed(StudentProject, properties)\",\n \"def createMentorWithProject(self, org):\\n self.createMentor(org)\\n from soc.modules.gsoc.models.student_project import StudentProject\\n properties = {'mentor': self.profile}\\n seeder_logic.seed(StudentProject, properties)\",\n \"def _add_sponsor(data: dict) -> dict:\\n user = create_user()\\n user['name'] = data['agency']\\n user['type'] = 32\\n if 'agency_class' in data:\\n user['tag'] = data['agency_class']\\n return user\",\n \"def add_employee():\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n form = SignUp_Form()\\n \\n\\n if form.validate_on_submit():\\n try: \\n employee = Employee.register(\\n username = form.username.data,\\n password = form.password.data, \\n email = form.email.data, \\n first_name = form.first_name.data,\\n last_name = form.last_name.data,\\n hire_date = form.hire_date.data, \\n is_admin = form.is_admin.data,\\n )\\n\\n db.session.add(employee)\\n\\n db.session.commit()\\n except IntegrityError:\\n flash(\\\"Email already in use\\\", \\\"danger\\\")\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\\n\\n flash(\\\"Employee Added!\\\", \\\"success\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\",\n \"def create_investor(sender, **kwargs):\\n u = kwargs[\\\"instance\\\"]\\n try:\\n \\n if not InvestorProfile.objects.filter(username=u.username):\\n inv = InvestorProfile(username=u.username,user=u)\\n inv.save()\\n g = DjangoGroup.objects.get(name='Investors') \\n g.user_set.add(u)\\n except Exception as e:\\n print e\",\n \"def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\\n peronjson={}, notes='', tenantid=''):\\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\\n self.persontdetailscoll.insert(tenantobj, safe=True)\",\n \"def create_user(change):\\n return change()\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n respond_data = self.controller.add_team_employee(request_data=request_data)\\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.EmployeeDoesNotExist,\\n domain_exceptions.EmployeeIsATeamMember\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def add_employee(self, obj):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\\n\\n self.dbconnect.commit()\\n return obj\\n except:\\n self.dbconnect.rollback()\\n raise\",\n \"def do_user_create():\\n target = User(\\n request.form['gender'],\\n request.form['first_name'],\\n request.form['name'],\\n request.form['mail'],\\n request.form['meter_id'],\\n request.form['group_id'],\\n secrets.token_hex(33))\\n target.set_role(request.form['role'])\\n target.nick = request.form['nick']\\n db.session.add(target)\\n db.session.commit()\\n return user_list(\\\"Created user \\\" + target.name)\",\n \"def create_partner(name):\\n\\n return Partner.objects.create(name=name)\",\n \"def test_create_new_person_w_no_role(self, selenium):\\n expected_person = entities_factory.PeopleFactory().create(\\n system_wide_role=roles.NO_ROLE)\\n actual_person = admin_webui_service.PeopleAdminWebUiService(\\n selenium).create_new_person(expected_person)\\n self.general_equal_assert(expected_person, actual_person)\",\n \"def create_person(dbo, username, collationid):\\n l = dbo.locale\\n fields = get_onlineformincoming_detail(dbo, collationid)\\n d = {}\\n flags = None\\n for f in fields:\\n if flags is None: flags = f[\\\"FLAGS\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"title\\\": d[\\\"title\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"forenames\\\": d[\\\"forenames\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"firstname\\\": d[\\\"forenames\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"surname\\\": d[\\\"surname\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"lastname\\\": d[\\\"surname\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"address\\\": d[\\\"address\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"town\\\": d[\\\"town\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"city\\\": d[\\\"town\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"county\\\": d[\\\"county\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"state\\\": d[\\\"county\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"postcode\\\": d[\\\"postcode\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"zipcode\\\": d[\\\"postcode\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"hometelephone\\\": d[\\\"hometelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"worktelephone\\\": d[\\\"worktelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"mobiletelephone\\\": d[\\\"mobiletelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"celltelephone\\\": d[\\\"mobiletelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"emailaddress\\\": d[\\\"emailaddress\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"].startswith(\\\"reserveanimalname\\\"): d[f[\\\"FIELDNAME\\\"]] = f[\\\"VALUE\\\"]\\n d[\\\"flags\\\"] = flags\\n # Have we got enough info to create the person record? We just need a surname\\n if not d.has_key(\\\"surname\\\"):\\n raise utils.ASMValidationError(i18n._(\\\"There is not enough information in the form to create a person record (need a surname).\\\", l))\\n # Does this person already exist?\\n personid = 0\\n if d.has_key(\\\"surname\\\") and d.has_key(\\\"forenames\\\") and d.has_key(\\\"address\\\"):\\n similar = person.get_person_similar(dbo, d[\\\"surname\\\"], d[\\\"forenames\\\"], d[\\\"address\\\"])\\n if len(similar) > 0:\\n personid = similar[0][\\\"ID\\\"]\\n # Create the person record if we didn't find one\\n if personid == 0:\\n personid = person.insert_person_from_form(dbo, utils.PostedData(d, dbo.locale), username)\\n personname = person.get_person_name_code(dbo, personid)\\n # Attach the form to the person\\n formname = get_onlineformincoming_name(dbo, collationid)\\n formhtml = get_onlineformincoming_html_print(dbo, [collationid,])\\n media.create_document_media(dbo, username, media.PERSON, personid, formname, formhtml )\\n # Was there a reserveanimalname field? If so, create reservation(s) to the person if possible\\n for k, v in d.iteritems():\\n if k.startswith(\\\"reserveanimalname\\\"):\\n movement.insert_reserve_for_animal_name(dbo, username, personid, v)\\n return (collationid, personid, personname)\",\n \"def create_student_accounts(self):\\r\\n # Create student accounts and activate them.\\r\\n for i in range(len(self.STUDENT_INFO)):\\r\\n email, password = self.STUDENT_INFO[i]\\r\\n username = 'u{0}'.format(i)\\r\\n self.create_account(username, email, password)\\r\\n self.activate_user(email)\",\n \"def create_leader_model(request, company_id):\\n\\n errors = {'noactions': []}\\n company = Company.objects.get(pk=company_id)\\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\\n \\\"\\\"\\\":type : Employee \\\"\\\"\\\"\\n\\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\\n raise PermissionDenied()\\n\\n if currentEmpl.isCompanySuperUserOrHigher():\\n employeeQS = Employee.objects.filter(\\n company__pk=company_id\\n )\\n else:\\n employeeQS = Employee.objects.filter(\\n Q(manager=currentEmpl),\\n company__pk=company_id\\n )\\n\\n form = MultiLeaderModelForm(request.POST or None)\\n form.fields['employees'].queryset = employeeQS\\n\\n if request.method == 'POST' and form.is_valid():\\n\\n employees = form.cleaned_data['employees']\\n \\\"\\\"\\\":type : list[Employee] \\\"\\\"\\\"\\n\\n pdf_response = get_leader_model_pdf(currentEmpl, employees)\\n\\n if isinstance(pdf_response, HttpResponse):\\n return pdf_response\\n else:\\n errors = pdf_response\\n\\n print(errors)\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_leader_model.html', {\\n 'form': form,\\n 'company': company,\\n 'errors': errors\\n }\\n )\",\n \"def create_user(user, first_name, last_name, major, bio):\\n return userAccount.objects.create(user=user, first_name=first_name, last_name=last_name, major=major, bio=bio)\",\n \"def createStudentWithProposal(self):\\n self.createStudent()\\n from soc.modules.gsoc.models.proposal import GSoCProposal\\n properties = {'link_id': self.profile.link_id, 'scope': self.profile,\\n 'parent': self.profile, 'status': 'new'}\\n seeder_logic.seed(GSoCProposal, properties)\",\n \"def test_create_owner(self):\\n url = reverse(\\n 'projectroles:api_role_create',\\n kwargs={'project': self.project.sodar_uuid},\\n )\\n post_data = {\\n 'role': PROJECT_ROLE_OWNER,\\n 'user': str(self.assign_user.sodar_uuid),\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 400, msg=response.content)\",\n \"def createUser(self, firstName, lastName, email, role):\\n self.waitForElement(locator=self._createUserBtn, locatorType=\\\"xpath\\\")\\n self.elementClick(locator=self._createUserBtn, locatorType=\\\"xpath\\\")\\n self.waitForElement(locator=self._createUser_firstName)\\n self.sendKeys(firstName, locator=self._createUser_firstName)\\n self.sendKeys(lastName, locator=self._createUser_lastName)\\n self.sendKeys(email, locator=self._createUser_email)\\n self.selectBy(by=\\\"text\\\", data=\\\"User\\\", locator=self._createUser_role)\\n pp.time.sleep(2)\\n self.elementClick(locator=self._createUser_addUserBtn, locatorType=\\\"xpath\\\")\\n # Bypassing error code\\n self.driver.back()\\n self.driver.refresh()\",\n \"def post(self, request):\\n data = request.data\\n skill_data = data.pop('skills')\\n Department_name = data.pop('department')\\n department = Department.objects.get(name=Department_name)\\n manager_name = data.pop('manager')\\n manager = Manager.objects.get(name=manager_name)\\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\\n Employee.save()\\n for skill in skill_data:\\n skill_add, create = Skill.objects.get_or_create(name=skill)\\n Employee.skills.add(skill_add)\\n return Response(\\n data=request.data\\n )\",\n \"def create(self, vals):\\n if not vals.get('nik_number'):\\n vals['nik_number'] = self.generate_nik(vals)\\n return super(Employee, self).create(vals)\",\n \"def createEmployee():\\n form = CreateEmployeeForm()\\n if form.validate_on_submit():\\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \\n email=form.email.data, role=form.role.data, location=form.location.data)\\n db.session.add(staff)\\n db.session.commit()\\n flash(f'Employee Added To Database', category='Success')\\n return redirect(url_for('login'))\\n return render_template('new_employee.html', title=\\\"Register\\\", form=form)\",\n \"def create(self, request):\\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\\n\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n print(F\\\"Request employee Data: {serializer.data}\\\")\\n\\n try:\\n new_employee = self.controller.create_employee(request_data=request_data)\\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (domain_exceptions.EmployeeIDIsNotUnique,\\n domain_exceptions.WorkArrangementPercentageOutOfRange,\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.WorkArrangementPercentageNull\\n ) as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def post(self):\\n try:\\n employee = self.service.add_employee(self.schema, request.json)\\n except ValidationError as error:\\n return error.messages, 400\\n return self.schema.dump(employee), 201\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.EmployeeDoesNotExist\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def setUp(self):\\n\\t\\tfirst_name = 'Gerson'\\n\\t\\tlast_name = 'Santos'\\n\\t\\tannual_salary = 5000\\n\\t\\tself.gerson = Employee(first_name, last_name, annual_salary)\",\n \"def create_superuser(self, email, name, password):\\n employee = self.create_user(email, name, password)\\n employee.is_superuser = True\\n employee.is_staff = True\\n employee.save(using=self._db)\\n\\n return employee\",\n \"def createEMPStudy(self, user_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \\n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries):\\n con = self.getMetadataDatabaseConnection()\\n study_id = 0\\n results = con.cursor().callproc('qiime_assets.emp_study_insert', \\n [study_id, user_id, study_name,\\n investigation_type, miens_compliant, submit_to_insdc, portal_type, \\n study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries])\\n return results[0]\",\n \"def createGroup(listOfPerson):\\n atk=Department()\\n atk.members=listOfPerson\\n return atk\",\n \"def new_user():\\n pass\",\n \"def create(self, creator: User = None, *args, **kwargs) -> Employee:\\n # If the creator argument is not None and is a non-staff user, raise\\n # ValueError\\n if creator and not creator.is_staff:\\n raise ValueError(\\n 'Only staff members can add new employees, \\\"creator\\\" must be '\\n 'a staff user.'\\n )\\n\\n # Get the user property from the provided keyword arguments\\n user: Optional[User] = kwargs.get('user', None)\\n\\n # If the user property is missing or is a non-staff user, raise\\n # ValueError\\n if not user:\\n raise ValueError(\\n 'You must provide the \\\"user\\\" property as a keyword argument'\\n )\\n elif not user.is_staff:\\n raise ValueError('The \\\"user\\\" property must be a staff user.')\\n\\n return super().create(creator, *args, **kwargs)\",\n \"def create_user(self):\\n unique_id = str(uuid.uuid4())\\n new_user_properties = {\\n \\\"name\\\": self.name,\\n \\\"mission_statement\\\": self.mission_statement,\\n \\\"unique_id\\\": unique_id,\\n \\\"email\\\": self.email.lower(),\\n \\\"is_mentor\\\": True,\\n \\\"is_tutor\\\": True,\\n \\\"is_visible\\\": True,\\n \\\"is_available_for_in_person\\\": True,\\n \\\"is_admin\\\": True}\\n new_user_node = Node.cast(AgoraLabel.USER, new_user_properties)\\n try:\\n self.graph_db.create(new_user_node)\\n except:\\n pass\\n return new_user_node\",\n \"def create_person(data_dict, m2m_dict):\\n email_address = data_dict['email_address']\\n # check if the person already exists:\\n try:\\n exists = Person.objects.get(email_address=email_address)\\n create_message = f'Skipping: Person with {email_address} already exists.'\\n except:\\n # try:\\n person_obj, person_created = Person.objects.update_or_create(**data_dict)\\n # populate MANY-TO-MANY fields\\n if m2m_dict:\\n # created_by (FK)\\n created_by_email = m2m_dict['created_by']\\n user, user_created = User.objects.get_or_create(email=created_by_email)\\n person_qs = Person.objects.filter(email_address=person_obj.email_address)\\n person_qs.update(created_by=user)\\n # expertise (M2M)\\n expertise_values = m2m_dict['expertise']\\n if expertise_values:\\n values_list = [value.strip() for value in expertise_values.split(',')]\\n for value in values_list:\\n expertise_obj, expertise_created = Expertise.objects.get_or_create(name=value)\\n person_obj.expertise.add(expertise_obj)\\n # industry (M2M)\\n industry_values = m2m_dict['industries']\\n if industry_values:\\n values_list = [value.strip() for value in industry_values.split(',')]\\n for value in values_list:\\n industry_obj, industry_created = Industry.objects.get_or_create(name=value)\\n person_obj.industries.add(industry_obj)\\n # organization (M2M)\\n organization_values = m2m_dict['organization']\\n if organization_values:\\n values_list = [value.strip() for value in organization_values.split(',')]\\n for value in values_list:\\n organization_obj, organization_created = Organization.objects.get_or_create(name=value)\\n person_obj.organization.add(organization_obj)\\n # owned by (M2M)\\n exportable_by_values = m2m_dict['exportable_by']\\n if exportable_by_values:\\n values_list = [value.strip() for value in exportable_by_values.split(',')]\\n for value in values_list:\\n dive_obj, dive_created = Dive.objects.get_or_create(name=value)\\n person_obj.exportable_by.add(dive_obj)\\n # let us know how it went\\n if person_created:\\n create_message = f'Success: {person_obj}'\\n else:\\n create_message = f'Failed: {person_obj}'\\n # except Exception as e:\\n # create_message = f'Error for {email_address}: {e}\\\\n'\\n print(create_message)\\n # except:\\n # failed_rows.append(counter)\\n # try:\\n # obj, created = Person.objects.create(**csv_to_model)\\n # except:\\n # message = 'Create person' + str(sys.exc_info())\\n # print(message)\",\n \"def post(self):\\n data = EmployeeRegister.parser.parse_args()\\n new_employee_id = str(uuid.uuid4())\\n\\n while EmployeeModel.find_by_id(new_employee_id):\\n # if this id is already in use\\n new_employee_id = str(uuid.uuid4())\\n\\n employee = EmployeeModel(**data, employee_id=new_employee_id)\\n employee.save_to_db()\\n\\n return {\\\"message\\\": \\\"Employee successfully added to the system\\\"}, 201 # 201 - Created\",\n \"def create_user(context, params):\\n form_user = dict()\\n # form_user['edited_by'] = context.user\\n if params.get('username'):\\n form_user['username'] = params.get('username')\\n else:\\n form_user['username'] = create_username(params) # 'email_user{}'.format(MISUser.objects.latest('id').id + 1\\n form_user['first_name'] = params.get('first_name')\\n form_user['last_name'] = params.get('last_name')\\n form_person = create_person(params)\\n form_user.update(form_person)\\n user = User.objects.create(**form_user)\\n user.set_password(params.get('password'))\\n\\n email = {'label': 'Work', 'val': params.get('email'), 'person': user, 'is_main': True}\\n create_email(context, email)\\n\\n user.save()\\n return user\",\n \"def create_candidate(self, data, header):\\n return self.client.post(\\n path='/api/v2/office/1/register/', data=json.dumps(data), content_type='application/json', headers=header)\",\n \"def is_student_employee():\\n return _is_member('uw_affiliation_student-employee')\",\n \"def add_user(first_name,last_name,email,password,typeOfUser):\\n user=User.objects.create(first_name=first_name,last_name=last_name,email=email,password=password,role=typeOfUser)\\n return user\",\n \"def test_meeting_registrant_create(self):\\n pass\",\n \"def create_trainee(self, name, pos, dept):\\n self.trainee[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'senior': self.senior[dept.upper()][0]['name'],\\n 'junior': self.junior[dept.upper()][0]['name'],\\n }\\n )\\n self.manager[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.senior[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.junior[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def create():\",\n \"def create():\",\n \"def onUserCreation(event):\\n\\n client = getUtility(IAdminClient)\\n xmpp_users = getUtility(IXMPPUsers)\\n storage = getUtility(IPubSubStorage)\\n principal = event.principal\\n mtool = getToolByName(principal, 'portal_membership')\\n\\n principal_id = principal.getUserId()\\n principal_jid = xmpp_users.getUserJID(principal_id)\\n members_jids = [xmpp_users.getUserJID(member.getUserId())\\n for member in mtool.listMembers()]\\n pass_storage = getUtility(IXMPPPasswordStorage)\\n principal_pass = pass_storage.set(principal_id)\\n\\n storage.leaf_nodes.append(principal_id)\\n storage.node_items[principal_id] = []\\n storage.collections['people'].append(principal_id)\\n storage.publishers[principal_id] = [principal_id]\\n\\n d = setupPrincipal(client, principal_jid, principal_pass, members_jids)\\n return d\",\n \"def m_create_identity(DID, domain_name, website, commercial_name, parent_node_account, password, overwrite):\\n\\n error, didDoc = create_identity(\\n DID, domain_name, website, commercial_name, parent_node_account, password, overwrite)\\n if error is not None:\\n print(error)\\n\\n print(f\\\"Created\\\")\",\n \"def create_user(fname, phone_num):\\n\\n user = User(fname=fname, phone_num=phone_num)\\n\\n db.session.add(user)\\n db.session.commit()\\n\\n return user\",\n \"def test_admin_can_create_a_employee(self):\\n\\n account_data = {\\n \\\"username\\\": \\\"Mike\\\",\\n \\\"email\\\": \\\"mioshine@g.com\\\",\\n \\\"password\\\": \\\"1234567\\\",\\n \\\"confirm_password\\\": \\\"1234567\\\"\\n }\\n response = self.client.post(\\n reverse('accounts:create-user'),\\n account_data,\\n format=\\\"json\\\")\\n \\\"\\\"\\\"Test the api has bucket creation capability.\\\"\\\"\\\"\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertTrue(\\\"data\\\" in json.loads(response.content))\",\n \"def main():\\n name = input(\\\"Please enter in your name: \\\")\\n\\n \\\"\\\"\\\"Ask the user to enter a number if they are a Director, Manager or Staff.\\\"\\\"\\\"\\n \\\"\\\"\\\"This will check and make sure the user only enters in 1,2, \\n or 3 and a number greater than zero\\\"\\\"\\\"\\n while True:\\n try:\\n designation_number = int(input(\\\"Please enter in \\\\n1 for Director \\\"\\n \\\"\\\\n2 for Manager \\\\n3 for Staff\\\\n\\\"))\\n if 0 < designation_number <= 3:\\n break\\n print(\\\"Invalid number entered.\\\")\\n except Exception as e:\\n print(e)\\n \\\"\\\"\\\"Gets the user salary and makes sure is a number and greater than 0\\\"\\\"\\\"\\n while True:\\n try:\\n salary = float(input(\\\"Please enter in your salary: \\\"))\\n if salary <= 0:\\n print(\\\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\\\")\\n else:\\n break\\n except ValueError:\\n print(\\\"Oops! That was not a valid number. Try again...\\\")\\n\\n \\\"\\\"\\\"Create Employee\\\"\\\"\\\"\\n employee1 = employee.Employee()\\n employee1.set_name(name)\\n employee1.set_designation(designation_number)\\n employee1.set_salary(salary)\\n print(employee1)\",\n \"def create(self, values):\\n\\t\\temployee_id = values.get('employee_id', False)\\n\\t\\tprint(\\\"the val in the dict\\\", values)\\n\\t\\tif (values.get('date_from') and values.get('date_to')) == False:\\n\\t\\t\\tcurrent = datetime.strftime(datetime.today().date(),'%Y-%m-%d')\\n\\t\\t\\tvalues.update({'allocate_date': current})\\n\\t\\t\\tprint(values)\\n\\t\\tif not values.get('department_id'):\\n\\t\\t\\tvalues.update({'department_id': self.env['hr.employee'].browse (employee_id).department_id.id})\\n\\n\\t\\tholiday = super (Holidays, self.with_context (mail_create_nolog=True, mail_create_nosubscribe=True)).create(values)\\n\\t\\tholiday.add_follower (employee_id)\\n\\n\\t\\t# Trilok code for policies\\n\\t\\tpolicy_id = holiday.env['leaves.policy'].search(\\n\\t\\t\\t[('leave_type', '=', holiday.holiday_status_id.id), ('company_id', '=', self.env.user.company_id.id)])\\n\\t\\t# print (\\\"policy iddddddddddddddd\\\",policy_id)\\n\\t\\temp_type = holiday.employee_id.employee_type.id\\n\\t\\tfor val in policy_id:\\n\\t\\t\\tif val.employee_type.id == emp_type:\\n\\t\\t\\t\\tfor employee in holiday.employee_id:\\n\\t\\t\\t\\t\\tif holiday.type == 'remove':\\n\\t\\t\\t\\t\\t\\tquery = '''select count(*) from hr_holidays where upper(type) = upper('rEMove')and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') and now()::date and employee_id = %s''' % employee.id\\n\\t\\t\\t\\t\\t\\tholiday.env.cr.execute(query)\\n\\t\\t\\t\\t\\t\\tquery_result = holiday.env.cr.dictfetchone()\\n\\t\\t\\t\\t\\t\\t# print(\\\"query_result\\\", query_result)\\n\\t\\t\\t\\t\\t\\tif val.min_app_per_year > 0 and query_result[\\\"count\\\"] > val.min_app_per_year:\\n\\t\\t\\t\\t\\t\\t\\traise ValidationError(\\\"maximum number of applications per year is {} days\\\".format(val.min_app_per_year))\\n\\n\\t\\t\\t\\t\\t\\tquery1 = '''select create_date::date,date_to::date from hr_holidays where upper(type) = \\n\\t\\t\\t\\t\\t\\tupper('rEMove') and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') \\n\\t\\t and now()::date and employee_id = %s order by create_date desc limit 1'''\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t % employee.id\\n\\t\\t\\t\\t\\t\\tholiday.env.cr.execute(query1)\\n\\t\\t\\t\\t\\t\\tquery_result1 = holiday.env.cr.fetchall()\\n\\t\\t\\t\\t\\t\\tif query_result1 is not None:\\n\\t\\t\\t\\t\\t\\t\\t# print(\\\"query_resulttttttttttttttttttttttttttt\\\", query_result1)\\n\\t\\t\\t\\t\\t\\t\\t# print(\\\"query_resulttttttttttttttttttttttttttt\\\", query_result1[0][0], query_result1[0][1])\\n\\t\\t\\t\\t\\t\\t\\tcre_date = datetime.strptime(query_result1[0][0], '%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\tdate_to = datetime.strptime(query_result1[0][1], '%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\t# print(\\\"cre_date\\\", cre_date, type(cre_date))\\n\\t\\t\\t\\t\\t\\t\\tcurrent_dt = fields.Datetime.now()\\n\\t\\t\\t\\t\\t\\t\\t# cdate=datetime.strptime(current_dt,'%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\tcurrent_date = datetime.strptime(current_dt.split(\\\" \\\")[0], '%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\tdays = (current_date - date_to).days\\n\\t\\t\\t\\t\\t\\t\\tif val.min_leave_app_gap > 0 and days > val.min_leave_app_gap:\\n\\t\\t\\t\\t\\t\\t\\t\\traise ValidationError(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\\"Minimum gap between two application should be atleast {} days\\\".format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tval.min_leave_app_gap))\\n\\n\\t\\treturn holiday\",\n \"def test_create_user(self):\\n \\n new_user = {\\\"username\\\": \\\"beny1976\\\", \\\"vocab_count\\\": 0, \\\"name\\\": \\\"beny rood\\\", \\\"sex\\\": \\\"male\\\", \\\"dob\\\": \\\"18/10/1979\\\"}\\n msg = app.create_user(predefined_user=new_user)\\n self.assertTrue(msg != \\\"\\\")\",\n \"def create_job(self, employer_id, compensation, location, description, category_id, group_id):\\n\\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \\n db.session.add(job)\\n db.session.commit()\",\n \"def make_salaried(self,salary,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"2\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be a salaried employee\\\"))\\n self.emp_dict[id][7] = salary\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def create_manager(self, name, pos, dept):\\n self.manager[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'senior': [],\\n 'junior': [],\\n 'trainee': []\\n }\\n )\",\n \"def create_user(UserName=None, MessageAction=None, FirstName=None, LastName=None, AuthenticationType=None):\\n pass\",\n \"def create(person):\\n lname = person.get(\\\"lname\\\", None)\\n fname = person.get(\\\"fname\\\", None)\\n\\n # Does the person exist already?\\n if lname not in PEOPLE and lname is not None:\\n PEOPLE[lname] = {\\n \\\"lname\\\": lname,\\n \\\"fname\\\": fname,\\n \\\"timestamp\\\": get_timestamp(),\\n }\\n return PEOPLE[lname], 201\\n\\n # Otherwise, they exist, that's an error\\n else:\\n abort(\\n 406,\\n \\\"Person with last name {lname} already exists\\\".format(lname=lname),\\n )\",\n \"def add_employee(self, empl):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\\n empl.active, empl.promotor))\\n cursor.execute('SELECT LASTVAL()')\\n eid = cursor.fetchone()[0]\\n empl.id = eid\\n # get id and return updated object\\n self.dbconnect.commit()\\n except(Exception, self.dbconnect.get_error()) as error:\\n self.dbconnect.rollback()\\n raise Exception('\\\\nUnable to save Employee!\\\\n(%s)' % (error))\",\n \"def _create_user(self, new_user):\\n new_user = User(user_name=new_user['user_name'], pin=new_user['pin'], user_type='customer')\\n self.session.output(new_user.get_user_info(), '\\\\n[ New user created ]')\",\n \"def _create_expense(initiator_id: str, recipient_id: str, amount: float, description: str):\\n if amount <= 0:\\n raise CommandException('Negative amounts not allowed')\\n\\n tso: User = User.objects.get(friendly_name='tso')\\n initiator: User = User.objects.get(slack_id=initiator_id)\\n recipient: User = User.objects.get(slack_id=recipient_id)\\n users = []\\n\\n expense = Expense()\\n expense.setGroupId(settings.SPLITWISE_GROUP_ID)\\n expense.setDescription(description)\\n expense.setCost(amount)\\n\\n initiator_user = ExpenseUser()\\n initiator_user.setId(initiator.splitwise_id)\\n initiator_user.setPaidShare(0)\\n initiator_user.setOwedShare(amount)\\n users.append(initiator_user)\\n\\n recipient_user = ExpenseUser()\\n recipient_user.setId(recipient.splitwise_id)\\n recipient_user.setPaidShare(amount)\\n recipient_user.setOwedShare(0)\\n users.append(recipient_user)\\n\\n # We do this because we're using my access token.\\n if tso.id not in [initiator.id, recipient.id]:\\n tso_user = ExpenseUser()\\n tso_user.setId(tso.splitwise_id)\\n tso_user.setPaidShare(0)\\n tso_user.setOwedShare(0)\\n users.append(tso_user)\\n\\n expense.setUsers(users)\\n if not sObj.createExpense(expense):\\n raise CommandException('Failed to create SW expense')\",\n \"def create(ctx, name, company, mail, age):\\n client = Client(name,company,mail,age)\\n client_service = ClientService(ctx.obj['clients_table']) \\n client_service.create_client(client)\",\n \"def create_user_questionnaire_in_progress(self):\\n username = 'pseudo'\\n email = 'martine@tests.com'\\n password = '00000000'\\n user_created = self.user.objects.create_user(id=2, username=username,\\n email=email, password=password)\\n HistoryUser.objects.create(user=user_created)\\n StatusUser.objects.create(user=user_created)\\n list_advice_id = [1, 5, 10]\\n self.add_advice_to_user_created(user_created, list_advice_id)\\n\\n return user_created\",\n \"def createUser(self):\\n if self.user:\\n return self.user\\n from soc.models.user import User\\n from soc.modules.seeder.logic.providers.user import CurrentUserProvider\\n properties = {'account': CurrentUserProvider(),\\n 'status': 'valid', 'is_developer': self.dev_test}\\n self.user = seeder_logic.seed(User, properties=properties)\\n return self.user\",\n \"def test_create_user(self):\\n pass\",\n \"def test_create_user(self):\\n pass\",\n \"def test_create_user(self):\\n pass\",\n \"def insert_employee(self,\\n region_name,\\n last_name,\\n first_name,\\n hire_date,\\n mi=None):\\n\\n if self.check_input_type(region_name, \\\"Region\\\"):\\n if self.check_input_type(hire_date, \\\"Date\\\"):\\n region_info = self.query_region(region_name)\\n region_id = region_info[0][0]\\n\\n if mi != \\\"\\\":\\n query_format = \\\"insert into employee(Region_ID, \\\" \\\\\\n \\\"Emp_Lname, Emp_Mi, Emp_Fname, Emp_Hiredate) \\\" \\\\\\n \\\"values ((select region_id from region where \\\" \\\\\\n \\\"region_id='{}'), '{}', '{}', '{}', '{}')\\\"\\n query = query_format.format(\\n region_id, last_name, mi, first_name, hire_date\\n )\\n else:\\n query_format = \\\"insert into employee(Region_ID, \\\" \\\\\\n \\\"Emp_Lname, Emp_Fname, Emp_Hiredate) \\\" \\\\\\n \\\"values ((select region_id from region where \\\" \\\\\\n \\\"region_id='{}'), '{}', '{}', '{}')\\\"\\n query = query_format.format(\\n region_id, last_name, first_name, hire_date\\n )\\n\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n ErrorMessageWindow(\\\"Date format not valid!\\\")\\n else:\\n ErrorMessageWindow(\\\"Region input not valid!\\\")\",\n \"def createOrgAdmin(self, org):\\n self.createProfile()\\n self.profile.org_admin_for = [org.key()]\\n self.profile.put()\",\n \"def register():\\n add_employee = True\\n form = RegistrationForm()\\n if form.validate_on_submit():\\n employee = Employee(email=form.email.data,\\n username=form.username.data,\\n glad_id=form.glad_id.data,\\n tel_no=form.tel_no.data,\\n role_id=2 , ##form.role_id.data,\\n password=form.password.data)\\n\\n # add employee to the database\\n db.session.add(employee)\\n db.session.commit()\\n flash('You have successfully registered! You may now login.')\\n\\n # redirect to the login page\\n return redirect(url_for('auth.login'))\\n\\n # load registration template\\n return render_template('auth/register.html', form=form, title='Register')\",\n \"def add_nurse(request):\\n if request.POST:\\n post = request.POST\\n username = post.get(\\\"username\\\")\\n first_name = post.get(\\\"first_name\\\")\\n last_name = post.get(\\\"last_name\\\")\\n email = post.get(\\\"email\\\")\\n password = post.get(\\\"password\\\")\\n chosen_hospitals = post.getlist(\\\"chosen_hospitals\\\")\\n\\n new_user = User.objects.create_user(\\n username=username,\\n password=password,\\n first_name=first_name,\\n last_name=last_name,\\n email=email\\n )\\n\\n new_user_profile = UserProfile.objects.create(\\n user=new_user,\\n status=UserStatus.objects.get(pk=2)\\n )\\n\\n if new_user:\\n for chosen_hospital in chosen_hospitals:\\n HospitalStaff.objects.create(user_profile=new_user_profile, hospital=Hospital.objects.get(pk=chosen_hospital))\\n\\n return redirect('add_nurse')\\n\\n hospitals = Hospital.objects.all()\\n return render(request, 'add_nurse.html', {'hospitals': hospitals})\",\n \"def create_organization(user, name):\\n organization = Organization.objects.create(name=name)\\n new_user = OrganizationUser.objects.create(organization=organization,\\n user=user, is_admin=True)\\n OrganizationOwner.objects.create(organization=organization,\\n organization_user=new_user)\\n return organization\",\n \"def add_student():\\n\\n\\tprint('You must enter the student as is:\\\\n'\\n\\t\\t\\\"'First name', 'middle name', 'Last name', 'major', 'major', 'gpa', id_number, 'minor'\\\"\\n\\t\\t\\\" 'minor' graduation year, advisor number\\\\n For example: 'Kyle', 'Jacob', 'Ranney', 'Insurance'\\\"\\n\\t\\t\\\", 'Chemistry', 3.0, 93988, 'Biology', 'NULL', 2016, 2234\\\\n\\\")\\n\\t# use sql insert statement\\n\\t# become familiar with this!\\t\",\n \"def post(self, orgname):\\n permission = AdministerOrganizationPermission(orgname)\\n if permission.can() or allow_if_superuser():\\n try:\\n org = model.organization.get_organization(orgname)\\n except model.InvalidOrganizationException:\\n raise NotFound()\\n\\n details = request.get_json()\\n activating_username = None\\n\\n if (\\n \\\"activating_user\\\" in details\\n and details[\\\"activating_user\\\"]\\n and \\\"name\\\" in details[\\\"activating_user\\\"]\\n ):\\n activating_username = details[\\\"activating_user\\\"][\\\"name\\\"]\\n\\n delegate = details[\\\"delegate\\\"] if \\\"delegate\\\" in details else {}\\n delegate_kind = delegate.get(\\\"kind\\\", None)\\n delegate_name = delegate.get(\\\"name\\\", None)\\n\\n delegate_username = delegate_name if delegate_kind == \\\"user\\\" else None\\n delegate_teamname = delegate_name if delegate_kind == \\\"team\\\" else None\\n\\n activating_user = (\\n model.user.get_user(activating_username) if activating_username else None\\n )\\n delegate_user = model.user.get_user(delegate_username) if delegate_username else None\\n delegate_team = (\\n model.team.get_organization_team(orgname, delegate_teamname)\\n if delegate_teamname\\n else None\\n )\\n\\n if activating_username and not activating_user:\\n raise request_error(message=\\\"Unknown activating user\\\")\\n\\n if not delegate_user and not delegate_team:\\n raise request_error(message=\\\"Missing delegate user or team\\\")\\n\\n role_name = details[\\\"role\\\"]\\n\\n prototype = model.permission.add_prototype_permission(\\n org, role_name, activating_user, delegate_user, delegate_team\\n )\\n log_prototype_action(\\\"create_prototype_permission\\\", orgname, prototype)\\n\\n users_filter = {prototype.activating_user, prototype.delegate_user}\\n org_members = model.organization.get_organization_member_set(\\n org, users_filter=users_filter\\n )\\n return prototype_view(prototype, org_members)\\n\\n raise Unauthorized()\",\n \"def register_as_student(request):\\n\\n if request.method == \\\"POST\\\":\\n # access the data send in the form\\n first_name = request.POST['firstname']\\n last_name = request.POST['lastname']\\n user_name = request.POST['username']\\n email = request.POST['email']\\n password = request.POST['password']\\n ssn = request.POST['ssn']\\n university_id = request.POST['universityid']\\n level = request.POST['level']\\n\\n # make array of error in eorm\\n error = {}\\n #check if first naem valid\\n if first_name.isalpha() == False:\\n error.update({\\\"firstname\\\" : \\\"First name is not valid\\\"})\\n #check if first naem valid\\n if last_name.isalpha() == False:\\n error.update({\\\"lastname\\\" : \\\"Last name is not valid\\\"})\\n # check if user exist \\n if username_exists(user_name):\\n error.update({\\\"username\\\" : \\\"User name already exist\\\"})\\n # check if email is valid \\n if validate_email(email) == False:\\n error.update({\\\"email\\\" : \\\"It's not valid email\\\"})\\n # check if ssn is less than 20 number\\n if len(ssn) != 14:\\n error.update({\\\"SSN\\\" : \\\"SSN is must be 14\\\"})\\n # check if ssn is less than 20 number\\n if len(university_id) > 10 and len(university_id) < 5:\\n error.update({\\\"universityid\\\" : \\\"University id is greater than 10\\\"})\\n #check level is correct pr not\\n try:\\n int(level)\\n if int(level) > 7:\\n error.update({\\\"levellen\\\" : \\\"level must be less than 7\\\"})\\n except:\\n error.update({\\\"levelint\\\" : \\\"level must be integer\\\"})\\n\\n # if found error in errors array return array of error \\n if len(error) != 0:\\n return JsonResponse({'errors': error}, status=400)\\n\\n # add data to User object \\n user = User.objects.create_user(user_name, email, password)\\n user.first_name = first_name\\n user.last_name = last_name\\n user.is_staff = False\\n\\n # add information to the user \\n info = UserInfo(user = user, ssn = ssn, university_id = university_id, level = level)\\n # commit change \\n user.save()\\n\\n # now login the user\\n info.login_user()\\n info.save()\\n\\n my_user = user_serializer(user)\\n success = {'success': True}\\n\\n # return success message \\n return JsonResponse({ **my_user, **success }, status=200)\\n\\n else:\\n return JsonResponse({'error': 'Method not allowed'}, status=405)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7395388","0.6873678","0.6652067","0.6417113","0.63125676","0.6297487","0.6267351","0.62289935","0.6222155","0.6122551","0.6114099","0.6063726","0.6037256","0.60092777","0.60071415","0.59452486","0.5924796","0.5911677","0.58706707","0.58596087","0.5855955","0.5855521","0.58510476","0.58228815","0.57597584","0.57444173","0.57441914","0.5718857","0.5710078","0.56747264","0.5664169","0.56607646","0.56564194","0.5648625","0.56472635","0.5625796","0.56197894","0.56163836","0.56033844","0.5589279","0.5560751","0.55527395","0.55389255","0.5518759","0.5517931","0.5516793","0.551431","0.551414","0.5513403","0.5508567","0.5506041","0.550423","0.54939973","0.548214","0.5481652","0.5470395","0.5466684","0.54606104","0.546018","0.5450932","0.54501915","0.54478824","0.5432248","0.54081416","0.5403277","0.5398719","0.53868","0.5386149","0.5385197","0.53766525","0.53766525","0.53766215","0.5364703","0.5360403","0.5358758","0.53585577","0.5357338","0.53558576","0.5348447","0.5345371","0.5344279","0.5336539","0.5335731","0.5331947","0.53306407","0.53226376","0.5322278","0.53197795","0.5313193","0.53049266","0.53049266","0.53049266","0.5297546","0.5293932","0.52919126","0.52890253","0.5280869","0.52790743","0.5277633","0.52758926"],"string":"[\n \"0.7395388\",\n \"0.6873678\",\n \"0.6652067\",\n \"0.6417113\",\n \"0.63125676\",\n \"0.6297487\",\n \"0.6267351\",\n \"0.62289935\",\n \"0.6222155\",\n \"0.6122551\",\n \"0.6114099\",\n \"0.6063726\",\n \"0.6037256\",\n \"0.60092777\",\n \"0.60071415\",\n \"0.59452486\",\n \"0.5924796\",\n \"0.5911677\",\n \"0.58706707\",\n \"0.58596087\",\n \"0.5855955\",\n \"0.5855521\",\n \"0.58510476\",\n \"0.58228815\",\n \"0.57597584\",\n \"0.57444173\",\n \"0.57441914\",\n \"0.5718857\",\n \"0.5710078\",\n \"0.56747264\",\n \"0.5664169\",\n \"0.56607646\",\n \"0.56564194\",\n \"0.5648625\",\n \"0.56472635\",\n \"0.5625796\",\n \"0.56197894\",\n \"0.56163836\",\n \"0.56033844\",\n \"0.5589279\",\n \"0.5560751\",\n \"0.55527395\",\n \"0.55389255\",\n \"0.5518759\",\n \"0.5517931\",\n \"0.5516793\",\n \"0.551431\",\n \"0.551414\",\n \"0.5513403\",\n \"0.5508567\",\n \"0.5506041\",\n \"0.550423\",\n \"0.54939973\",\n \"0.548214\",\n \"0.5481652\",\n \"0.5470395\",\n \"0.5466684\",\n \"0.54606104\",\n \"0.546018\",\n \"0.5450932\",\n \"0.54501915\",\n \"0.54478824\",\n \"0.5432248\",\n \"0.54081416\",\n \"0.5403277\",\n \"0.5398719\",\n \"0.53868\",\n \"0.5386149\",\n \"0.5385197\",\n \"0.53766525\",\n \"0.53766525\",\n \"0.53766215\",\n \"0.5364703\",\n \"0.5360403\",\n \"0.5358758\",\n \"0.53585577\",\n \"0.5357338\",\n \"0.53558576\",\n \"0.5348447\",\n \"0.5345371\",\n \"0.5344279\",\n \"0.5336539\",\n \"0.5335731\",\n \"0.5331947\",\n \"0.53306407\",\n \"0.53226376\",\n \"0.5322278\",\n \"0.53197795\",\n \"0.5313193\",\n \"0.53049266\",\n \"0.53049266\",\n \"0.53049266\",\n \"0.5297546\",\n \"0.5293932\",\n \"0.52919126\",\n \"0.52890253\",\n \"0.5280869\",\n \"0.52790743\",\n \"0.5277633\",\n \"0.52758926\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72263277"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17480,"cells":{"query":{"kind":"string","value":"functions to create employee junior."},"document":{"kind":"string","value":"def create_junior(self, name, pos, dept):\n self.junior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n else:\n self.create_trainee(name, pos, dept)","def create_senior(self, name, pos, dept):\n self.senior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'junior': [],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['senior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def create_person(self):","def create_user(schools_dictionnary, domains_to_skills_dictionnary, companies, places, skills_oh, places_oh, domains_oh, rng, _id):\n\n age = rng.randint(20,60)\n schools = rng.choice(list(schools_dictionnary.keys()), rng.choice([1, 2], p = [0.95, 0.05]), replace = False) \n\n available_skills = list(set([skill for school in schools \\\n for domain in schools_dictionnary[school].domains \\\n for skill in domains_to_skills_dictionnary[domain]]))\n\n expo = np.round(rng.exponential(0.3) * len(schools)) + age // 17 + 1\n\n nb_skills_to_choose = min(int(expo), 5 + (len(schools) - 1) * 3)\n\n _skills = rng.choice(available_skills, nb_skills_to_choose, replace = False)\n\n company = rng.choice(companies)\n place = rng.choice(places)\n\n user = User(skills_oh, places_oh, domains_oh, schools_dictionnary, skills = _skills, age = age, place = place, company = company,\n schools = schools, _id = _id)\n\n return user","def create_employee(self,personal_identity):\r\n new_emp = Employee(*personal_identity)\r\n registration_str = new_emp.get_registration_str()\r\n\r\n return_value = self.save_object_to_DB(\"employee\",registration_str)\r\n return return_value","def create_employee(request, company_id):\n\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n logUnauthorizedAccess(\"User tried to create_employee\", request)\n raise PermissionDenied()\n form = EmployeeForm(request, initial=dict(company=company))\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\n # Q(company=company) | Q(company__isnull=True))\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n\n return TemplateResponse(\n request,\n 'mus/create_employee_form.html',\n {\n 'employee_form': form,\n }\n )\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n # return JsonResponse(status=200, data=data)","def generateEmployees(self):\r\n\r\n # Name\r\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\r\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\r\n\r\n for i in range(self.num_of_employees):\r\n\r\n # Clock in an hour before opening, 6 hours after, or 12 hours after\r\n clockIn = random.choice([7, 13, 19])\r\n\r\n # Clock out after 5 hours, 10 hours, or 15 hours\r\n clockOut = random.choice([13, 19, 23])\r\n while clockOut <= clockIn:\r\n clockOut = random.choice([13, 19, 23])\r\n\r\n # Hourly wage\r\n wage = random.choice([8, 9, 10, 12, 20])\r\n\r\n gender = random.choice(['M', 'F'])\r\n if gender == 'M':\r\n name = random.choice(maleNames)\r\n else:\r\n name = random.choice(femaleNames)\r\n\r\n self.c.execute(\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\", (name, clockIn, clockOut, wage))\r\n self.conn.commit()\r\n\r\n if self.print_employees:\r\n print(\"\\nName:\", name)\r\n print(\"Clock in:\", clockIn)\r\n print(\"Clock out:\", clockOut)\r\n print(\"Wage:\", wage)","def create_employee(self):\n try:\n name = input(\"Enter name: \")\n if not name.isalpha():\n print(\"Invalid data format. Name should contain only alphabets. \")\n return False\n email = input(\"Enter email: \")\n if not InputValidations.validate_email(email):\n return False\n employee = EmployeeModel(name=name, email=email)\n self.admin_repository.create_employee(employee)\n print(\"Employee created successfully!\")\n return True\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\n employee, created = Employee.objects.update_or_create(user=user,\n employee_id=validated_data.pop('employee_id'),\n location=validated_data.pop('location'),\n avail_start_time= str(validated_data.pop('avail_start_time')),\n avail_end_time= str(validated_data.pop('avail_end_time')))\n return employee","def add_employee(self, first_name, last_name):\n self.switch_main_menu(\"PIM\")\n self.click_menu(\"Add Employee\")\n self.pim = AddEmployee(self.driver)\n self.pim.add_user_employee(first_name, last_name)","def users_create():","def create_member(org_id, group_id, target_group_ids, sex, first_name, last_name, title_name, email):\n user = get_user_by_email(email)\n # --- falls e-mail schon existiert wird nichts unternommen\n if user != None:\n if org_id > 0: # nur bei Schulen wird die Schulnummer vorangestellt\n prefix = '%i_' % org_id\n else:\n prefix = ''\n user = User()\n username = get_username(prefix, first_name, last_name)\n user.username = username\n user.sex = sex\n user.first_name = first_name\n user.last_name = last_name\n user.email = email\n user.title = title_name\n user.is_staff = False\n user.is_active = True\n user.is_superuser = False\n user.date_joined = datetime.datetime.now()\n password = generate_passwd()\n user.set_password(password)\n user.save()\n set_user_org(org_id, user)\n send_password(email, username, password)\n set_user_group(user, get_group_by_id(group_id))\n for group in target_group_ids:\n set_user_group(user, get_group_by_id(group))\n transaction.commit()","def create(self, values):\n if values.get('country_id', False):\n country = self.env['res.country'].browse(values['country_id'])\n if country.code == 'SA':\n values.update({'is_saudi': True})\n else:\n values.update({'is_saudi': False})\n\n res = super(HrEmployee, self).create(values)\n if values.get('user_id', False):\n self.user_id.write({'employee_id': res})\n return res","def test_create_owner(self):\n url = reverse(\n 'projectroles:api_role_create',\n kwargs={'project': self.project.sodar_uuid},\n )\n post_data = {\n 'role': PROJECT_ROLE_OWNER,\n 'user': str(self.assign_user.sodar_uuid),\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 400, msg=response.content)","def is_employee():\n return _is_member('uw_employee')","def add_employee():\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n form = SignUp_Form()\n \n\n if form.validate_on_submit():\n try: \n employee = Employee.register(\n username = form.username.data,\n password = form.password.data, \n email = form.email.data, \n first_name = form.first_name.data,\n last_name = form.last_name.data,\n hire_date = form.hire_date.data, \n is_admin = form.is_admin.data,\n )\n\n db.session.add(employee)\n\n db.session.commit()\n except IntegrityError:\n flash(\"Email already in use\", \"danger\")\n return render_template(\"/admin/add_user.html\", form = form)\n\n flash(\"Employee Added!\", \"success\")\n return redirect(\"/administrator\")\n else:\n\n return render_template(\"/admin/add_user.html\", form = form)","def insert_employee(self,\n region_name,\n last_name,\n first_name,\n hire_date,\n mi=None):\n\n if self.check_input_type(region_name, \"Region\"):\n if self.check_input_type(hire_date, \"Date\"):\n region_info = self.query_region(region_name)\n region_id = region_info[0][0]\n\n if mi != \"\":\n query_format = \"insert into employee(Region_ID, \" \\\n \"Emp_Lname, Emp_Mi, Emp_Fname, Emp_Hiredate) \" \\\n \"values ((select region_id from region where \" \\\n \"region_id='{}'), '{}', '{}', '{}', '{}')\"\n query = query_format.format(\n region_id, last_name, mi, first_name, hire_date\n )\n else:\n query_format = \"insert into employee(Region_ID, \" \\\n \"Emp_Lname, Emp_Fname, Emp_Hiredate) \" \\\n \"values ((select region_id from region where \" \\\n \"region_id='{}'), '{}', '{}', '{}')\"\n query = query_format.format(\n region_id, last_name, first_name, hire_date\n )\n\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n ErrorMessageWindow(\"Date format not valid!\")\n else:\n ErrorMessageWindow(\"Region input not valid!\")","def create_user(change):\n return change()","def setUp(self):\n\n self.user = self.make_user()\n self.employee = Employee.objects.create(\n cpf=\"974.220.200-16\",\n user=self.user,\n departament=Employee.ADMINISTRATION\n )","def create_leader_model(request, company_id):\n\n errors = {'noactions': []}\n company = Company.objects.get(pk=company_id)\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\n \"\"\":type : Employee \"\"\"\n\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\n raise PermissionDenied()\n\n if currentEmpl.isCompanySuperUserOrHigher():\n employeeQS = Employee.objects.filter(\n company__pk=company_id\n )\n else:\n employeeQS = Employee.objects.filter(\n Q(manager=currentEmpl),\n company__pk=company_id\n )\n\n form = MultiLeaderModelForm(request.POST or None)\n form.fields['employees'].queryset = employeeQS\n\n if request.method == 'POST' and form.is_valid():\n\n employees = form.cleaned_data['employees']\n \"\"\":type : list[Employee] \"\"\"\n\n pdf_response = get_leader_model_pdf(currentEmpl, employees)\n\n if isinstance(pdf_response, HttpResponse):\n return pdf_response\n else:\n errors = pdf_response\n\n print(errors)\n\n return TemplateResponse(\n request,\n 'mus/create_leader_model.html', {\n 'form': form,\n 'company': company,\n 'errors': errors\n }\n )","def create_manager(self, name, pos, dept):\n self.manager[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'senior': [],\n 'junior': [],\n 'trainee': []\n }\n )","def create_employee_from_applicant(self, cr, uid, ids, context=None):\n if context is None:\n context = {}\n hr_employee = self.pool.get('hr.employee')\n model_data = self.pool.get('ir.model.data')\n act_window = self.pool.get('ir.actions.act_window')\n emp_id = False\n for applicant in self.browse(cr, uid, ids, context=context):\n address_id = contact_name = False\n if applicant.partner_id:\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\n if applicant.job_id and (applicant.partner_name or contact_name):\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\n create_ctx = dict(context, mail_broadcast=True)\n\n pes=self.browse(cr,uid,ids)[0]\n coy=pes.partner_name\n\n ##### Susunan Keluarga ayah/ibu #####\n le=self.pool.get('hr_recruit.suskel1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context)\n prod_ids=[] \n for pr in lele:\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\n \n ###### Susunan Keluarga Suami/istri #####\n le=self.pool.get('hr_recruit.suskel2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids1=[] \n for pr in lele:\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \n \n ###### riwayat Pendidikan #######\n le=self.pool.get('hr_recruit.rwt_pend')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids2=[] \n for pr in lele:\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \n \n ###### bahasa ######\n le=self.pool.get('hr_recruit.bahasa')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids3=[] \n for pr in lele:\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \n \n ##### Riwayat Pekerjaan ####\n le=self.pool.get('hr_recruit.rwt_krj')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids4=[] \n for pr in lele:\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \n \n ###### Koneksi Internal #####\n le=self.pool.get('hr_recruit.kon1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids5=[] \n for pr in lele:\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \n \n ###### Koneksi Eksternal ####\n le=self.pool.get('hr_recruit.kon2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids6=[]\n for pr in lele: \n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \n\n ####### create Employee ######## \n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\n 'job_id': applicant.job_id.id,\n 'department_id' : applicant.department_id.id,\n 'address_id2' : applicant.job_id.address_id.id,\n #### informasi Probadi ####\n 'kelamin':applicant.jen_kel,\n 'blood' : applicant.blood,\n 'agama' : applicant.agama_id.id,\n 'birthday' : applicant.tgl_lahir,\n 'place_of_birth' : applicant.kota_id.name,\n 'marital':applicant.status,\n 'sjk_tanggal' : applicant.sjk_tanggal,\n 'mobile_phone':applicant.partner_phone,\n 'country_id' : applicant.country_id.id,\n\n #### Pendidikan ####\n 'type_id':applicant.type_id.id,\n 'bid_id':applicant.bidang_id.id,\n 'jurusan_id':applicant.jurusan_id.id,\n 'pt_id':applicant.pt_id.id,\n 'gelar_id':applicant.gelar_id.id,\n\n #### alamat DOmisili ####\n 'country_id1':applicant.country_id1.id,\n 'prov_id':applicant.prov_id.id,\n 'kab_id' : applicant.kab_id.id,\n 'kec_id':applicant.kec_id.id,\n 'alamat1' : applicant.alamat1,\n 'kodepos' :applicant.kode1,\n 'telp1' : applicant.telp1,\n\n #### kartu identitas ####\n 'jenis_id': applicant.jenis_id,\n 'ktp' : applicant.no_id,\n 'tgl_berlaku' : applicant.tgl_berlaku,\n # 'issued_id' : applicant.dikeluarkan.id,\n \n #### Alamat Sesuai KTP #### \n 'country_id2':applicant.country_id2.id,\n 'prov_id2':applicant.prov_id2.id,\n 'kab_id2':applicant.kab_id2.id,\n 'kec_id2':applicant.kec_id2.id,\n 'alamat2' : applicant.alamat2,\n 'kodepos1':applicant.kode2,\n 'telp2' : applicant.telp2,\n \n # 'status': applicant.status,\n #### IDS ####\n 'susunan_kel1_ids' : prod_ids,\n 'susunan_kel2_ids':prod_ids1,\n 'rwt_pend_ids':prod_ids2,\n 'bahasa_ids':prod_ids3,\n 'rwt_krj_ids':prod_ids4,\n 'koneksi1_ids':prod_ids5,\n 'koneksi2_ids':prod_ids6, \n })\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\n self.pool['hr.job'].message_post(\n cr, uid, [applicant.job_id.id],\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\n subtype=\"hr_recruitment.mt_job_applicant_hired\", context=context)\n else:\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\n\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\n if emp_id:\n dict_act_window['res_id'] = emp_id\n dict_act_window['view_mode'] = 'form,tree'\n return dict_act_window","def setUp(self):\n\t\tfirst_name = 'Gerson'\n\t\tlast_name = 'Santos'\n\t\tannual_salary = 5000\n\t\tself.gerson = Employee(first_name, last_name, annual_salary)","def setUp(self):\n self.salary = 40000\n self.custom_rise = 7500\n self.employee = Employee(\"Carlos\", \"Zapata\", self.salary)","def main():\n name = input(\"Please enter in your name: \")\n\n \"\"\"Ask the user to enter a number if they are a Director, Manager or Staff.\"\"\"\n \"\"\"This will check and make sure the user only enters in 1,2, \n or 3 and a number greater than zero\"\"\"\n while True:\n try:\n designation_number = int(input(\"Please enter in \\n1 for Director \"\n \"\\n2 for Manager \\n3 for Staff\\n\"))\n if 0 < designation_number <= 3:\n break\n print(\"Invalid number entered.\")\n except Exception as e:\n print(e)\n \"\"\"Gets the user salary and makes sure is a number and greater than 0\"\"\"\n while True:\n try:\n salary = float(input(\"Please enter in your salary: \"))\n if salary <= 0:\n print(\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\")\n else:\n break\n except ValueError:\n print(\"Oops! That was not a valid number. Try again...\")\n\n \"\"\"Create Employee\"\"\"\n employee1 = employee.Employee()\n employee1.set_name(name)\n employee1.set_designation(designation_number)\n employee1.set_salary(salary)\n print(employee1)","def setUp(self):\n\tself.emp = Employee('Lin',10000)\n\tself.emp2 = Employee('Jun',20000)","def createMentor(self, org):\n self.createProfile()\n self.profile.mentor_for = [org.key()]\n self.profile.put()","def onUserCreation(event):\n\n client = getUtility(IAdminClient)\n xmpp_users = getUtility(IXMPPUsers)\n storage = getUtility(IPubSubStorage)\n principal = event.principal\n mtool = getToolByName(principal, 'portal_membership')\n\n principal_id = principal.getUserId()\n principal_jid = xmpp_users.getUserJID(principal_id)\n members_jids = [xmpp_users.getUserJID(member.getUserId())\n for member in mtool.listMembers()]\n pass_storage = getUtility(IXMPPPasswordStorage)\n principal_pass = pass_storage.set(principal_id)\n\n storage.leaf_nodes.append(principal_id)\n storage.node_items[principal_id] = []\n storage.collections['people'].append(principal_id)\n storage.publishers[principal_id] = [principal_id]\n\n d = setupPrincipal(client, principal_jid, principal_pass, members_jids)\n return d","def createEmployee(firstName, lastName, ssn, salary):\n employee = Employee(firstName, lastName, ssn, salary)\n # verify\n if firstName != employee.firstName or \\\n lastName != employee.lastName or \\\n ssn != employee.ssn or \\\n salary != employee.salary:\n raise ValueError(\"Failed to initialize Employee\")\n return employee","def make_commissioned(self,salary,commission,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"3\"\n print(\"{}{}\".format(name,\" was successfully changed to be a commissioned employee\"))\n self.emp_dict[id][7] = salary\n self.emp_dict[id][9] = commission\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\n teacher_account_id = person.get_last()\n query = 'INSERT INTO teacher VALUES( %s,%s );'\n args = (teacher_account_id, availability)\n database.connection.save_data(query, args)","def _test_pre_fill_and_assign_humor(self):\n for i in range(1, 50):\n User.objects.create_user(username=\"u{0}\".format(i))\n pre_fill.main([\"--managers\", \"--workshift\"])\n utils.make_workshift_pool_hours(semester=self.semester)\n # Assign manager shifts beforehand\n manager_shifts = RegularWorkshift.objects.filter(\n pool=self.p1, workshift_type__auto_assign=False,\n )\n profiles = WorkshiftProfile.objects.all()\n for profile, shift in zip(profiles, manager_shifts):\n shift.current_assignees.add(profile)\n shift.save()\n unfinished = utils.auto_assign_shifts(\n self.semester, pool=WorkshiftPool.objects.get(title=\"Humor Shift\")\n )\n self.assertEqual([], unfinished)","def create_job(self, employer_id, compensation, location, description, category_id, group_id):\n\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \n db.session.add(job)\n db.session.commit()","def test_employee_creation(self):\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\n\n # Returned result is an OrderedDict\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\n\n self.assertEqual(result['name'], helper.name)\n self.assertEqual(result['hiredOn'], helper.hired_on)\n self.assertEqual(result['salary'], helper.salary)\n self.assertEqual(result['departmentId'], helper.department_id)","def create(self, creator: User = None, *args, **kwargs) -> Employee:\n # If the creator argument is not None and is a non-staff user, raise\n # ValueError\n if creator and not creator.is_staff:\n raise ValueError(\n 'Only staff members can add new employees, \"creator\" must be '\n 'a staff user.'\n )\n\n # Get the user property from the provided keyword arguments\n user: Optional[User] = kwargs.get('user', None)\n\n # If the user property is missing or is a non-staff user, raise\n # ValueError\n if not user:\n raise ValueError(\n 'You must provide the \"user\" property as a keyword argument'\n )\n elif not user.is_staff:\n raise ValueError('The \"user\" property must be a staff user.')\n\n return super().create(creator, *args, **kwargs)","def create(person):\n lname = person.get(\"lname\", None)\n fname = person.get(\"fname\", None)\n\n # Does the person exist already?\n if lname not in PEOPLE and lname is not None:\n PEOPLE[lname] = {\n \"lname\": lname,\n \"fname\": fname,\n \"timestamp\": get_timestamp(),\n }\n return PEOPLE[lname], 201\n\n # Otherwise, they exist, that's an error\n else:\n abort(\n 406,\n \"Person with last name {lname} already exists\".format(lname=lname),\n )","def _add_sponsor(data: dict) -> dict:\n user = create_user()\n user['name'] = data['agency']\n user['type'] = 32\n if 'agency_class' in data:\n user['tag'] = data['agency_class']\n return user","def create_person(dbo, username, collationid):\n l = dbo.locale\n fields = get_onlineformincoming_detail(dbo, collationid)\n d = {}\n flags = None\n for f in fields:\n if flags is None: flags = f[\"FLAGS\"]\n if f[\"FIELDNAME\"] == \"title\": d[\"title\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"forenames\": d[\"forenames\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"firstname\": d[\"forenames\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"surname\": d[\"surname\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"lastname\": d[\"surname\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"address\": d[\"address\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"town\": d[\"town\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"city\": d[\"town\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"county\": d[\"county\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"state\": d[\"county\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"postcode\": d[\"postcode\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"zipcode\": d[\"postcode\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"hometelephone\": d[\"hometelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"worktelephone\": d[\"worktelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"mobiletelephone\": d[\"mobiletelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"celltelephone\": d[\"mobiletelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"emailaddress\": d[\"emailaddress\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"].startswith(\"reserveanimalname\"): d[f[\"FIELDNAME\"]] = f[\"VALUE\"]\n d[\"flags\"] = flags\n # Have we got enough info to create the person record? We just need a surname\n if not d.has_key(\"surname\"):\n raise utils.ASMValidationError(i18n._(\"There is not enough information in the form to create a person record (need a surname).\", l))\n # Does this person already exist?\n personid = 0\n if d.has_key(\"surname\") and d.has_key(\"forenames\") and d.has_key(\"address\"):\n similar = person.get_person_similar(dbo, d[\"surname\"], d[\"forenames\"], d[\"address\"])\n if len(similar) > 0:\n personid = similar[0][\"ID\"]\n # Create the person record if we didn't find one\n if personid == 0:\n personid = person.insert_person_from_form(dbo, utils.PostedData(d, dbo.locale), username)\n personname = person.get_person_name_code(dbo, personid)\n # Attach the form to the person\n formname = get_onlineformincoming_name(dbo, collationid)\n formhtml = get_onlineformincoming_html_print(dbo, [collationid,])\n media.create_document_media(dbo, username, media.PERSON, personid, formname, formhtml )\n # Was there a reserveanimalname field? If so, create reservation(s) to the person if possible\n for k, v in d.iteritems():\n if k.startswith(\"reserveanimalname\"):\n movement.insert_reserve_for_animal_name(dbo, username, personid, v)\n return (collationid, personid, personname)","def __init__(self, employee_id, name, supervisor_id, lft, rgt):\n self.employee_id = employee_id\n self.name = name\n self.supervisor_id = supervisor_id\n self.lft = lft\n self.rgt = rgt","def add_employee(self, obj):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\n\n self.dbconnect.commit()\n return obj\n except:\n self.dbconnect.rollback()\n raise","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.EmployeeDoesNotExist\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create_employee_structure(employees):\n employees_dict = {}\n for employee in position_sort(employees):\n if not employee.is_secretary:\n adder(employees_dict, employee.prosecutors_office, {'employees': [], 'departments': {}, 'divisions': {}})\n if employee.prosecutors_office and employee.department and employee.division:\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'divisions', {})\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'], employee.division, [])\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'][employee.division].append(employee)\n elif employee.prosecutors_office and employee.department:\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'employees', [])\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['employees'].append(employee)\n elif employee.prosecutors_office and employee.division:\n adder(employees_dict[employee.prosecutors_office]['divisions'], employee.division, [])\n employees_dict[employee.prosecutors_office]['divisions'][employee.division].append(employee)\n elif employee.prosecutors_office:\n employees_dict[employee.prosecutors_office]['employees'].append(employee)\n return employees_dict","def create_instructor(self, username):\r\n return self._create_user(username, is_staff=True)","def create(self, vals):\n record = super(KaHrPayrollRapelEmployeePromote, self).create(vals)\n if not 'name' in vals or not vals.get('name'):\n record.name = \"Rapel {0}\".format(record.new_employee_promote_id.name)\n return record","def create(self, vals):\n if not vals.get('nik_number'):\n vals['nik_number'] = self.generate_nik(vals)\n return super(Employee, self).create(vals)","def post(self, request):\n data = request.data\n skill_data = data.pop('skills')\n Department_name = data.pop('department')\n department = Department.objects.get(name=Department_name)\n manager_name = data.pop('manager')\n manager = Manager.objects.get(name=manager_name)\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\n Employee.save()\n for skill in skill_data:\n skill_add, create = Skill.objects.get_or_create(name=skill)\n Employee.skills.add(skill_add)\n return Response(\n data=request.data\n )","def createManager(firstName, lastName, ssn, salary, title, yearBonus):\n manager = Manager(firstName, lastName, ssn, salary, title, yearBonus)\n if firstName != manager.firstName or \\\n lastName != manager.lastName or \\\n ssn != manager.ssn or \\\n salary != manager.salary or \\\n title != manager.title or \\\n yearBonus != manager.yearBonus:\n raise ValueError(\"Failed to initialize Manager\")\n return manager","def handle(self, *args, **kwargs):\n seeder = Seed.seeder()\n seeder.add_entity(User, 20)\n\n seeder.add_entity(EmployeeMptt, 20, {\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\n 'parent': lambda x: EmployeeMptt.objects.order_by(\"?\").first(),\n 'level': lambda x: random.randint(0, 4),\n })\n seeder.execute()","def create_advisors(_advisors_data):\n\n for advisor_data in _advisors_data:\n occupations = advisor_data.pop('occupations', '').strip()\n specialties = advisor_data.pop('specialties', '').strip()\n subspecialties = advisor_data.pop('subspecialties', '').strip()\n prev_firms = advisor_data.pop('previous_firms', '').strip()\n curr_firm_name = advisor_data.pop('current_firm_name', '').strip()\n location = advisor_data.pop('location', '').strip()\n\n city, state_abbr = location.split(', ') if location.count(',') == 1 else (None, '')\n\n advisor_data['city'] = city\n advisor_data['state'] = ABBR_TO_STATE.get(state_abbr, 'Other')\n\n advisor_data['status'] = 'Active'\n\n success, result = create_advisor(advisor_data)\n validate(success, result)\n\n advisor = result\n\n occupation_objs = []\n\n if occupations:\n for occupation_name in occupations.split(', '):\n if occupation_name not in OCCUPATIONS:\n continue\n\n success, result = create_occupation({'name': occupation_name.strip().title()})\n validate(success, result)\n\n occupation = result\n occupation_objs.append(occupation)\n\n advisor.occupations.append(occupation)\n\n db.session.commit()\n\n if specialties:\n for specialty_name in specialties.split(', '):\n\n spec_occ = None\n for occupation in occupation_objs:\n if specialty_name in OCCUPATIONS[occupation.name]:\n spec_occ = occupation\n break\n\n if not spec_occ:\n continue\n\n success, result = create_specialty({\n 'name': specialty_name.strip().title(),\n 'occupation_id': spec_occ.pk_id,\n 'occupation': spec_occ\n })\n\n validate(success, result)\n\n specialty = result\n advisor.specialties.append(specialty)\n\n db.session.commit()\n\n if subspecialties:\n advisor.subspecialty_text = subspecialties\n\n db.session.commit()\n\n if prev_firms:\n for prev_firm_name in prev_firms.split(', '):\n success, result = create_bigfirm({'name': prev_firm_name.strip()})\n validate(success, result)\n\n big_firm = result\n advisor.previous_firms.append(big_firm)\n\n db.session.commit()\n\n if curr_firm_name:\n success, result = create_bigfirm({'name': curr_firm_name.strip()})\n validate(success, result)\n\n big_firm = result\n advisor.current_firm = big_firm\n advisor.current_firm_id = big_firm.pk_id\n\n db.session.commit()\n\n db.session.commit()","def createGroup(listOfPerson):\n atk=Department()\n atk.members=listOfPerson\n return atk","def create_investor(sender, **kwargs):\n u = kwargs[\"instance\"]\n try:\n \n if not InvestorProfile.objects.filter(username=u.username):\n inv = InvestorProfile(username=u.username,user=u)\n inv.save()\n g = DjangoGroup.objects.get(name='Investors') \n g.user_set.add(u)\n except Exception as e:\n print e","def create_person(data_dict, m2m_dict):\n email_address = data_dict['email_address']\n # check if the person already exists:\n try:\n exists = Person.objects.get(email_address=email_address)\n create_message = f'Skipping: Person with {email_address} already exists.'\n except:\n # try:\n person_obj, person_created = Person.objects.update_or_create(**data_dict)\n # populate MANY-TO-MANY fields\n if m2m_dict:\n # created_by (FK)\n created_by_email = m2m_dict['created_by']\n user, user_created = User.objects.get_or_create(email=created_by_email)\n person_qs = Person.objects.filter(email_address=person_obj.email_address)\n person_qs.update(created_by=user)\n # expertise (M2M)\n expertise_values = m2m_dict['expertise']\n if expertise_values:\n values_list = [value.strip() for value in expertise_values.split(',')]\n for value in values_list:\n expertise_obj, expertise_created = Expertise.objects.get_or_create(name=value)\n person_obj.expertise.add(expertise_obj)\n # industry (M2M)\n industry_values = m2m_dict['industries']\n if industry_values:\n values_list = [value.strip() for value in industry_values.split(',')]\n for value in values_list:\n industry_obj, industry_created = Industry.objects.get_or_create(name=value)\n person_obj.industries.add(industry_obj)\n # organization (M2M)\n organization_values = m2m_dict['organization']\n if organization_values:\n values_list = [value.strip() for value in organization_values.split(',')]\n for value in values_list:\n organization_obj, organization_created = Organization.objects.get_or_create(name=value)\n person_obj.organization.add(organization_obj)\n # owned by (M2M)\n exportable_by_values = m2m_dict['exportable_by']\n if exportable_by_values:\n values_list = [value.strip() for value in exportable_by_values.split(',')]\n for value in values_list:\n dive_obj, dive_created = Dive.objects.get_or_create(name=value)\n person_obj.exportable_by.add(dive_obj)\n # let us know how it went\n if person_created:\n create_message = f'Success: {person_obj}'\n else:\n create_message = f'Failed: {person_obj}'\n # except Exception as e:\n # create_message = f'Error for {email_address}: {e}\\n'\n print(create_message)\n # except:\n # failed_rows.append(counter)\n # try:\n # obj, created = Person.objects.create(**csv_to_model)\n # except:\n # message = 'Create person' + str(sys.exc_info())\n # print(message)","def make_salaried(self,salary,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"2\"\n print(\"{}{}\".format(name,\" was successfully changed to be a salaried employee\"))\n self.emp_dict[id][7] = salary\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n respond_data = self.controller.add_team_employee(request_data=request_data)\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.EmployeeDoesNotExist,\n domain_exceptions.EmployeeIsATeamMember\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create_user(self):\n unique_id = str(uuid.uuid4())\n new_user_properties = {\n \"name\": self.name,\n \"mission_statement\": self.mission_statement,\n \"unique_id\": unique_id,\n \"email\": self.email.lower(),\n \"is_mentor\": True,\n \"is_tutor\": True,\n \"is_visible\": True,\n \"is_available_for_in_person\": True,\n \"is_admin\": True}\n new_user_node = Node.cast(AgoraLabel.USER, new_user_properties)\n try:\n self.graph_db.create(new_user_node)\n except:\n pass\n return new_user_node","def create_player():\n\n\t#TODO : Ajout d'une BDD des différents joueurs avec des scores et vérifier la présence des joueurs choisis dans cette BDD pour charger les scores\n\n\tactivator = ''\n\tinhibitor = ''\n\n\tprint(\"\\nEntrez le pseudo du joueur\",colors.GREEN + \"'Activator' : \" + colors.STOP, end = \"\")\n\tactivator = input()\n\n\tprint(\"\\nEntrez le pseudo du joueur\", colors.RED + \"'Inhibitor' : \"+colors.STOP, end = \"\")\n\tinhibitor = input()\n\n\t# Default usernames if not defined by users\n\tif len(activator) == 0:\n\t\tactivator = 'Activator'\n\n\tif len(inhibitor) == 0:\n\t\tinhibitor = 'Inhibitor'\n\n\t# Attribute to each player the status he chose\n\tData.current_player['Activator'] = activator\n\tData.current_player['Inhibitor'] = inhibitor\n\n\treturn activator, inhibitor","def create(self, request):\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\n\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n print(F\"Request employee Data: {serializer.data}\")\n\n try:\n new_employee = self.controller.create_employee(request_data=request_data)\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (domain_exceptions.EmployeeIDIsNotUnique,\n domain_exceptions.WorkArrangementPercentageOutOfRange,\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.WorkArrangementPercentageNull\n ) as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create_individual(self):\n pass","def perform_create(self, serializer):\n if self.request.data.get('user_type', None) == 'employee':\n serializer.save(is_staff=False)\n else:\n serializer.save()","def create_user(email, password, f_name, l_name):\n pass","def get_manager_employees(request):\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\n if manager_employees:\n emp_list=[]\n for emp in manager_employees:\n emp_data={}\n emp_data[\"id\"] = emp.id\n emp_data[\"username\"] = emp.user.username\n emp_data[\"first_name\"] = emp.user.first_name\n emp_data[\"last_name\"] = emp.user.last_name\n emp_data[\"manager_id\"] = emp.manager.id\n # emp_data[\"status_questions\"] = emp.status_questions\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\n # name_role_list = []\n # for obj in employee_role:\n # name_role_list.append(obj.role.name)\n # emp_data[\"roles\"] = name_role_list\n emp_list.append(emp_data)\n data = {\"employees:\": emp_list}\n return JsonResponse(status=201, data=data)\n else:\n return JsonResponse(\"The user with id={} isn't a manager for any user\".format(current_employee.user.id),\n status=404)","def setUp(self):\n self.employee = Employee('Lucas', 'Guerra', 45000)","def _create_nsem_user():\n users = User.objects.filter(username=settings.CWWED_NSEM_USER)\n if users.exists():\n user = users[0]\n else:\n user = User.objects.create_user(settings.CWWED_NSEM_USER, password=settings.CWWED_NSEM_PASSWORD)\n group, _ = Group.objects.get_or_create(name=settings.CWWED_NSEM_GROUP)\n perm_names = [\n 'add_{}'.format(NsemPsa._meta.model_name),\n 'add_{}'.format(NamedStormCoveredDataSnapshot._meta.model_name),\n ]\n perms = Permission.objects.filter(codename__in=perm_names)\n # set permission\n user.user_permissions.set(list(perms))\n group.permissions.set(list(perms))\n # add user to group\n group.user_set.add(user)","def __init__(self,name,empid,designation,experience):\n self.name = name\n self.empid = empid\n self.designation = designation\n self.experience = experience\n self.salary = self.cal_sal()","def build_person(first_name, last_name, middle_name='', age=None): \n person = {'first': first_name, 'middle': middle_name, 'last': last_name}\n if age:\n person['age'] = age\n return person","def add_employee(self, empl):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\n empl.active, empl.promotor))\n cursor.execute('SELECT LASTVAL()')\n eid = cursor.fetchone()[0]\n empl.id = eid\n # get id and return updated object\n self.dbconnect.commit()\n except(Exception, self.dbconnect.get_error()) as error:\n self.dbconnect.rollback()\n raise Exception('\\nUnable to save Employee!\\n(%s)' % (error))","def create_implied_role(self, prior_role_id, implied_role_id):\n raise exception.NotImplemented() # pragma: no cover","def createDeveloper(self):\n self.createUser()\n self.user.is_developer = True\n self.user.put()","def get_create_employee_query(self):\n template = \"\"\"\n mutation createEmployee {{\n createEmployee(input: {{ {params} }}) {{\n employee {{\n name\n hiredOn\n salary\n departmentId\n }}\n }}\n }}\n \"\"\"\n # Add input parameters as needed\n input_params = 'name:\"{}\",'.format(self.name)\n\n if self.hired_on is not None:\n input_params += 'hiredOn: \"{}\", '.format(self.hired_on)\n\n if self.salary is not None:\n input_params += 'salary: {}, '.format(self.salary)\n\n if self.department_id is not None:\n input_params += 'departmentId: {}'.format(self.department_id)\n\n return template.format(params=input_params)","def create_candidate(self, username: str) -> int:","def test_create_new_person_w_no_role(self, selenium):\n expected_person = entities_factory.PeopleFactory().create(\n system_wide_role=roles.NO_ROLE)\n actual_person = admin_webui_service.PeopleAdminWebUiService(\n selenium).create_new_person(expected_person)\n self.general_equal_assert(expected_person, actual_person)","def create_user_questionnaire_in_progress(self):\n username = 'pseudo'\n email = 'martine@tests.com'\n password = '00000000'\n user_created = self.user.objects.create_user(id=2, username=username,\n email=email, password=password)\n HistoryUser.objects.create(user=user_created)\n StatusUser.objects.create(user=user_created)\n list_advice_id = [1, 5, 10]\n self.add_advice_to_user_created(user_created, list_advice_id)\n\n return user_created","def onUserCreation(event):\n request = getRequest()\n if not IProductLayer.providedBy(request):\n return\n client = getUtility(IAdminClient)\n xmpp_users = getUtility(IXMPPUsers)\n principal = event.principal\n principal_id = principal.getUserId()\n principal_jid = xmpp_users.getUserJID(principal_id)\n pass_storage = getUtility(IXMPPPasswordStorage)\n principal_pass = pass_storage.set(principal_id)\n users.setupPrincipal(client, principal_jid, principal_pass)","def test_admin_can_create_a_employee(self):\n\n account_data = {\n \"username\": \"Mike\",\n \"email\": \"mioshine@g.com\",\n \"password\": \"1234567\",\n \"confirm_password\": \"1234567\"\n }\n response = self.client.post(\n reverse('accounts:create-user'),\n account_data,\n format=\"json\")\n \"\"\"Test the api has bucket creation capability.\"\"\"\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertTrue(\"data\" in json.loads(response.content))","def event_register_collaborator(request, event_id, employee_id):\n if request.method == 'PUT':\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\n collaborator = get_object_or_404(Employee, pk=employee_id)\n event.collaborators.add(collaborator)\n event.save()\n serializer = EventSimpleRegistrationSerializer(event)\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)","def new_user():\n pass","def __init__(self, first_name, last_name, age, sex, city):\n\t\tsuper().__init__(first_name, last_name, age, sex, city)\n\t\tself.privileges = Privileges()","def create_employee(attributes):\n neccessary_keys = [\"empid\", \"gender\", \"sales\", \"bmi\", \"salary\", \"birthday\",\n \"age\"]\n for key in neccessary_keys:\n if not key in attributes.keys():\n raise ValueError(\"employee could not be created: {} is missing\".format(key))\n return Employee(attributes[\"empid\"], attributes[\"gender\"],\n attributes[\"sales\"], attributes[\"bmi\"],\n attributes[\"salary\"], attributes[\"birthday\"],\n attributes[\"age\"])","def __init__(self, name: str, professors: list[Professor]):\n self.professors = professors","def create_user(context, params):\n form_user = dict()\n # form_user['edited_by'] = context.user\n if params.get('username'):\n form_user['username'] = params.get('username')\n else:\n form_user['username'] = create_username(params) # 'email_user{}'.format(MISUser.objects.latest('id').id + 1\n form_user['first_name'] = params.get('first_name')\n form_user['last_name'] = params.get('last_name')\n form_person = create_person(params)\n form_user.update(form_person)\n user = User.objects.create(**form_user)\n user.set_password(params.get('password'))\n\n email = {'label': 'Work', 'val': params.get('email'), 'person': user, 'is_main': True}\n create_email(context, email)\n\n user.save()\n return user","def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\n peronjson={}, notes='', tenantid=''):\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\n self.persontdetailscoll.insert(tenantobj, safe=True)","def create_organization(user, name):\n organization = Organization.objects.create(name=name)\n new_user = OrganizationUser.objects.create(organization=organization,\n user=user, is_admin=True)\n OrganizationOwner.objects.create(organization=organization,\n organization_user=new_user)\n return organization","def create_educator(data):\n\n educator = Educator(\n name=data['name'],\n work_email=data['work_email'],\n organization_name=data['organization_name'],\n org_or_school=data['org_or_school'],\n address_line_1=data['address_line_1'],\n address_line_2=data['address_line_2'],\n city=data['city'],\n state=data['state'],\n zipcode=data['zipcode'],\n num_students=data['num_students']\n )\n educator.save()\n return educator","def do_user_create():\n target = User(\n request.form['gender'],\n request.form['first_name'],\n request.form['name'],\n request.form['mail'],\n request.form['meter_id'],\n request.form['group_id'],\n secrets.token_hex(33))\n target.set_role(request.form['role'])\n target.nick = request.form['nick']\n db.session.add(target)\n db.session.commit()\n return user_list(\"Created user \" + target.name)","def createMentorWithProject(self, org):\n self.createMentor(org)\n from soc.modules.gsoc.models.student_project import StudentProject\n properties = {'mentor': self.profile}\n seeder_logic.seed(StudentProject, properties)","def create_new_project(self,\n customer_name,\n contract_date,\n project_info,\n project_datest,\n project_dateend,\n project_budget,\n project_actst=None,\n project_actend=None,\n project_cost=None):\n\n customer_info = self.query_customer(cus_name=customer_name)\n\n if customer_info:\n # Search for project manager in the same region as the customer.\n customer_region_id = customer_info[0][1]\n get_employee_query = \"select employee.emp_id, emp_lname, emp_fname from employee, \" \\\n \"empskill, skill, region where employee.emp_id = \" \\\n \"empskill.emp_id and empskill.skill_id = \" \\\n \"skill.skill_id and skill.skill_descrpt = \" \\\n \"'Project Manager' and region.region_id = \" \\\n \"employee.region_id and region.region_id = '{}' \"\n try:\n self.dbCursor.execute(\n get_employee_query.format(customer_region_id))\n employee_info = self.dbCursor.fetchall()\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n if len(employee_info) == 0:\n ErrorMessageWindow(\"No suitable project manager found!\")\n else:\n if customer_info and employee_info:\n if len(customer_info) > 1:\n MultiRowScreen(customer_info, \"project\")\n else:\n cus_id = customer_info[0][0]\n emp_id = employee_info[0][0]\n optional_inputs = [project_actst, project_actend,\n project_cost]\n\n query = \"insert into project(cus_id, emp_id, proj_date, \" \\\n \"proj_descrpt, proj_estdatest, proj_estdateend, \" \\\n \"proj_estbudget) values ('{}', '{}', '{}', '{}', \" \\\n \"'{}', '{}', '{}') \".format(cus_id,\n emp_id,\n contract_date,\n project_info,\n project_datest,\n project_dateend,\n project_budget)\n\n yes_options = False\n for item in optional_inputs:\n if item != \"\":\n yes_options = True\n\n if yes_options is False:\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n option_names = [\"proj_actdatest\",\n \"proj_actdateend\",\n \"proj_actcost\"]\n options_index = []\n filled_options = []\n\n index = 0\n for item in optional_inputs:\n if item != \"\":\n options_index.append(index)\n filled_options.append(item)\n index += 1\n update_query = \"update project set \"\n\n j = 0\n for i in options_index:\n if j < len(filled_options) - 1:\n update_query += \"{}='{}', \".format(\n option_names[i], filled_options[j]\n )\n else:\n update_query += \"{}='{}' \".format(\n option_names[i], filled_options[j]\n )\n j += 1\n\n try:\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n\n self.dbCursor.execute(update_query)\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n ErrorMessageWindow(\"Customer not found!\")","def test_new_employer_crud_methods(self):\n response = self.client.post(\n '/employers/', self.new_employer_data, format='json')\n self.assertEqual(response.status_code, 201)\n self.assertEqual(len(Employer.objects.all()), 2)\n\n # test one employer retrieve\n response = self.client.get('/employers/1/')\n self.assertEqual(response.status_code, 200)\n self.assertIn('Andela', response.data['name'])\n\n # test one employer update\n response = self.client.put('/employers/1/',\n {'name': 'New Employer'})\n self.assertEqual(response.status_code, 200)\n self.assertIn('New Employer', response.data['name'])","def create_many_employees(request, company_id=None):\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n raise PermissionDenied()\n if \"upload\" in request.POST:\n form = UploadEmployeesForm(request.POST, request.FILES)\n if form.is_valid():\n data = csv_to_dict(request.FILES['file'])\n request.session['upload_employees'] = data\n return JsonResponse(status=201, data=form.cleaned_data)\n # return TemplateResponse(\n # request,\n # 'mus/create_many_employees_uploaded.html',\n # dict(data=data, company=company)\n # )\n elif \"next\" in request.POST:\n data = request.session['upload_employees']\n marked_data = list()\n fields = request.POST.getlist('field[]')\n for row in data:\n new_row = dict(is_manager=False)\n for i, item in enumerate(row):\n field_id = int(fields[i])\n if field_id == 1:\n new_row['first_name'] = item\n elif field_id == 2:\n new_row['last_name'] = item\n elif field_id == 3:\n p = item.partition(\" \")\n new_row['first_name'] = p[0]\n new_row['last_name'] = p[2]\n elif field_id == 4:\n new_row['email'] = item\n elif field_id == 5:\n new_row['username'] = item\n marked_data.append(new_row)\n formset = EmployeeRowFormSet(initial=marked_data)\n # TypeQS = DevelopmentPlanType.objects.filter(Q(company=company) | Q(company__isnull=True))\n # for form in formset:\n # form.fields['development_plan_type'].queryset = TypeQS\n return TemplateResponse(\n request,\n 'mus/create_many_employees_form.html',\n dict(formset=formset, company=company)\n )\n elif \"next2\" in request.POST:\n formset = EmployeeRowFormSet(request.POST)\n if formset.is_valid():\n data = list()","def add_employee(worksheet, row, employee, num_in_list, employee_format, employee_format_b):\n # Get number of row for employee\n if employee.work_telephone:\n work_telephone_list = regular_telephone(employee.work_telephone.split(';'))\n else:\n work_telephone_list = []\n if employee.private_telephone:\n private_telephone_list = regular_telephone(employee.private_telephone.split(';'))\n else:\n private_telephone_list = []\n rows_for_emp = max(len(work_telephone_list), len(private_telephone_list), 2)\n # Add name and position\n worksheet.merge_range(row, 0, row+rows_for_emp-1, 0, num_in_list, cell_format=employee_format)\n worksheet.merge_range(row, 1, row+rows_for_emp-1, 1, str(employee), cell_format=employee_format_b)\n worksheet.merge_range(row, 2, row+rows_for_emp-1, 2, str(employee.position), cell_format=employee_format)\n # Add work telephone numbers\n for num, work_telephone in enumerate(work_telephone_list):\n if (len(work_telephone_list) < rows_for_emp) and ((num + 1) == len(work_telephone_list)):\n worksheet.merge_range(row+num, 3, row+rows_for_emp-1, 3, work_telephone, cell_format=employee_format)\n else:\n worksheet.write(row+num, 3, work_telephone, employee_format)\n if len(work_telephone_list) == 0:\n worksheet.merge_range(row, 3, row+rows_for_emp-1, 3, '', cell_format=employee_format)\n # Add private telephone numbers\n for num, private_telephone in enumerate(private_telephone_list):\n if (len(private_telephone_list) < rows_for_emp) and ((num + 1) == len(private_telephone_list)):\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, private_telephone, cell_format=employee_format)\n else:\n worksheet.write(row+num, 4, private_telephone, employee_format)\n if (len(private_telephone_list) < rows_for_emp) and ((num - 1) == len(private_telephone_list)):\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\n if len(private_telephone_list) == 0:\n worksheet.merge_range(row, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\n # If secretary exist\n if employee.secretary:\n row = row + rows_for_emp\n add_employee(worksheet, row, employee.secretary, '', employee_format, employee_format_b)\n return row+rows_for_emp","def post(self, orgname):\n permission = AdministerOrganizationPermission(orgname)\n if permission.can() or allow_if_superuser():\n try:\n org = model.organization.get_organization(orgname)\n except model.InvalidOrganizationException:\n raise NotFound()\n\n details = request.get_json()\n activating_username = None\n\n if (\n \"activating_user\" in details\n and details[\"activating_user\"]\n and \"name\" in details[\"activating_user\"]\n ):\n activating_username = details[\"activating_user\"][\"name\"]\n\n delegate = details[\"delegate\"] if \"delegate\" in details else {}\n delegate_kind = delegate.get(\"kind\", None)\n delegate_name = delegate.get(\"name\", None)\n\n delegate_username = delegate_name if delegate_kind == \"user\" else None\n delegate_teamname = delegate_name if delegate_kind == \"team\" else None\n\n activating_user = (\n model.user.get_user(activating_username) if activating_username else None\n )\n delegate_user = model.user.get_user(delegate_username) if delegate_username else None\n delegate_team = (\n model.team.get_organization_team(orgname, delegate_teamname)\n if delegate_teamname\n else None\n )\n\n if activating_username and not activating_user:\n raise request_error(message=\"Unknown activating user\")\n\n if not delegate_user and not delegate_team:\n raise request_error(message=\"Missing delegate user or team\")\n\n role_name = details[\"role\"]\n\n prototype = model.permission.add_prototype_permission(\n org, role_name, activating_user, delegate_user, delegate_team\n )\n log_prototype_action(\"create_prototype_permission\", orgname, prototype)\n\n users_filter = {prototype.activating_user, prototype.delegate_user}\n org_members = model.organization.get_organization_member_set(\n org, users_filter=users_filter\n )\n return prototype_view(prototype, org_members)\n\n raise Unauthorized()","def assign_employee(id):\r\n check_admin()\r\n\r\n employee = Employee.query.get_or_404(id)\r\n\r\n # prevent admin from being assigned a department or role\r\n if employee.is_admin:\r\n abort(403)\r\n\r\n form = EmployeeAssignForm(obj=employee)\r\n if form.validate_on_submit():\r\n employee.department = form.department.data\r\n employee.role = form.role.data\r\n db.session.add(employee)\r\n db.session.commit()\r\n flash('You have successfully assigned a department and role.')\r\n\r\n # redirect to the roles page\r\n return redirect(url_for('admin.list_employees'))\r\n\r\n return render_template('admin/employees/employee.html',\r\n employee=employee, form=form,\r\n title='Assign Employee')","def employees_json(request):\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\n employee_list = Employee.objects.filter(manager=request.user.employee_user)\n employees = list()\n for employee in employee_list:\n manager_dict = model_to_dict(employee)\n manager_dict['first_name'] = employee.user.first_name\n manager_dict['last_name'] = employee.user.last_name\n employees.append(manager_dict)\n data = {\"employees\": employees}\n return JsonResponse(data=data, content_type='application/json', safe=False)","def main_role_create(\n client: CitusCloudMgmt,\n **opts: tp.Any\n) -> None:\n\n role_id = client.create_role(opts[\"formation\"], opts[\"name\"])\n logger.info(f\"Created new role \\\"name\\\" with id=\\\"{role_id}\\\"\")\n click.echo(role_id)","def testOwnershipAfterCreate(self):\n self.simulateATGUIInteraction(task='create')\n self.failUnlessEqual(self.person.getOwnerTuple()[1], 'abc123')","def create_student(self, username):\r\n return self._create_user(username, is_staff=False)","def create_trainee(self, name, pos, dept):\n self.trainee[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'junior': self.junior[dept.upper()][0]['name'],\n }\n )\n self.manager[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.junior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def handle(self, *args, **options):\r\n username = 'populate_creators_command'\r\n email = 'grant+creator+access@edx.org'\r\n try:\r\n admin = User.objects.create_user(username, email, 'foo')\r\n admin.is_staff = True\r\n admin.save()\r\n except IntegrityError:\r\n # If the script did not complete the last time it was run,\r\n # the admin user will already exist.\r\n admin = User.objects.get(username=username, email=email)\r\n\r\n for user in get_users_with_role(CourseInstructorRole.ROLE):\r\n add_user_with_status_granted(admin, user)\r\n\r\n # Some users will be both staff and instructors. Those folks have been\r\n # added with status granted above, and add_user_with_status_unrequested\r\n # will not try to add them again if they already exist in the course creator database.\r\n for user in get_users_with_role(CourseStaffRole.ROLE):\r\n add_user_with_status_unrequested(user)\r\n\r\n # There could be users who are not in either staff or instructor (they've\r\n # never actually done anything in Studio). I plan to add those as unrequested\r\n # when they first go to their dashboard.\r\n\r\n admin.delete()","def add_employee(schema, employee_json):\n employee = schema.load(employee_json, session=db.session)\n db.session.add(employee)\n db.session.commit()\n return employee","def insert_creator(self, creator_id, creator_username, rules, rewards, min_participants, max_participants):\n\n self.execute(TABELLE['constest_creator']['insert'],\n (creator_id, creator_username, rules, rewards, min_participants, max_participants,))","def org_organisation_create_onaccept(form):\n\n db = current.db\n s3db = current.s3db\n ftable = s3db.pr_forum\n\n # Lookup the Reserves Forum\n forum = db(ftable.name == \"Reserves\").select(ftable.pe_id,\n limitby = (0, 1)\n ).first()\n try:\n reserves_pe_id = forum.pe_id\n except AttributeError:\n current.log.error(\"Unable to link Org Forum to Reserves Forum: Forum not Found\")\n return\n\n form_vars_get = form.vars.get\n organisation_id = form_vars_get(\"id\")\n\n # Lookup the Organisation\n otable = s3db.org_organisation\n org = db(otable.id == organisation_id).select(otable.pe_id,\n limitby = (0, 1)\n ).first()\n org_pe_id = org.pe_id\n\n # Create Forum\n record = {\"organisation_id\": organisation_id,\n \"name\": \"%s Reserves\" % form_vars_get(\"name\"),\n }\n forum_id = ftable.insert(**record)\n record[\"id\"] = forum_id\n s3db.update_super(ftable, record)\n forum_pe_id = record[\"pe_id\"]\n\n # Add the Hierarchy links\n s3db.pr_add_affiliation(org_pe_id, forum_pe_id, role=\"Realm Hierarchy\")\n s3db.pr_add_affiliation(reserves_pe_id, forum_pe_id, role=\"Realm Hierarchy\")"],"string":"[\n \"def create_emp(self, name, pos, dept):\\n if pos.upper() == 'MANAGER':\\n self.create_manager(name, pos, dept)\\n elif pos.upper() == 'SENIOR':\\n self.create_senior(name, pos, dept)\\n elif pos.upper() == 'JUNIOR':\\n self.create_junior(name, pos, dept)\\n else:\\n self.create_trainee(name, pos, dept)\",\n \"def create_senior(self, name, pos, dept):\\n self.senior[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'junior': [],\\n 'trainee': []\\n }\\n )\\n self.manager[dept.upper()][0]['senior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def create_person(self):\",\n \"def create_user(schools_dictionnary, domains_to_skills_dictionnary, companies, places, skills_oh, places_oh, domains_oh, rng, _id):\\n\\n age = rng.randint(20,60)\\n schools = rng.choice(list(schools_dictionnary.keys()), rng.choice([1, 2], p = [0.95, 0.05]), replace = False) \\n\\n available_skills = list(set([skill for school in schools \\\\\\n for domain in schools_dictionnary[school].domains \\\\\\n for skill in domains_to_skills_dictionnary[domain]]))\\n\\n expo = np.round(rng.exponential(0.3) * len(schools)) + age // 17 + 1\\n\\n nb_skills_to_choose = min(int(expo), 5 + (len(schools) - 1) * 3)\\n\\n _skills = rng.choice(available_skills, nb_skills_to_choose, replace = False)\\n\\n company = rng.choice(companies)\\n place = rng.choice(places)\\n\\n user = User(skills_oh, places_oh, domains_oh, schools_dictionnary, skills = _skills, age = age, place = place, company = company,\\n schools = schools, _id = _id)\\n\\n return user\",\n \"def create_employee(self,personal_identity):\\r\\n new_emp = Employee(*personal_identity)\\r\\n registration_str = new_emp.get_registration_str()\\r\\n\\r\\n return_value = self.save_object_to_DB(\\\"employee\\\",registration_str)\\r\\n return return_value\",\n \"def create_employee(request, company_id):\\n\\n company = Company.objects.get(pk=company_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n logUnauthorizedAccess(\\\"User tried to create_employee\\\", request)\\n raise PermissionDenied()\\n form = EmployeeForm(request, initial=dict(company=company))\\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\\n # Q(company=company) | Q(company__isnull=True))\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_employee_form.html',\\n {\\n 'employee_form': form,\\n }\\n )\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n # return JsonResponse(status=200, data=data)\",\n \"def generateEmployees(self):\\r\\n\\r\\n # Name\\r\\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\\r\\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\\r\\n\\r\\n for i in range(self.num_of_employees):\\r\\n\\r\\n # Clock in an hour before opening, 6 hours after, or 12 hours after\\r\\n clockIn = random.choice([7, 13, 19])\\r\\n\\r\\n # Clock out after 5 hours, 10 hours, or 15 hours\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n while clockOut <= clockIn:\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n\\r\\n # Hourly wage\\r\\n wage = random.choice([8, 9, 10, 12, 20])\\r\\n\\r\\n gender = random.choice(['M', 'F'])\\r\\n if gender == 'M':\\r\\n name = random.choice(maleNames)\\r\\n else:\\r\\n name = random.choice(femaleNames)\\r\\n\\r\\n self.c.execute(\\\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\\\", (name, clockIn, clockOut, wage))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_employees:\\r\\n print(\\\"\\\\nName:\\\", name)\\r\\n print(\\\"Clock in:\\\", clockIn)\\r\\n print(\\\"Clock out:\\\", clockOut)\\r\\n print(\\\"Wage:\\\", wage)\",\n \"def create_employee(self):\\n try:\\n name = input(\\\"Enter name: \\\")\\n if not name.isalpha():\\n print(\\\"Invalid data format. Name should contain only alphabets. \\\")\\n return False\\n email = input(\\\"Enter email: \\\")\\n if not InputValidations.validate_email(email):\\n return False\\n employee = EmployeeModel(name=name, email=email)\\n self.admin_repository.create_employee(employee)\\n print(\\\"Employee created successfully!\\\")\\n return True\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\\n employee, created = Employee.objects.update_or_create(user=user,\\n employee_id=validated_data.pop('employee_id'),\\n location=validated_data.pop('location'),\\n avail_start_time= str(validated_data.pop('avail_start_time')),\\n avail_end_time= str(validated_data.pop('avail_end_time')))\\n return employee\",\n \"def add_employee(self, first_name, last_name):\\n self.switch_main_menu(\\\"PIM\\\")\\n self.click_menu(\\\"Add Employee\\\")\\n self.pim = AddEmployee(self.driver)\\n self.pim.add_user_employee(first_name, last_name)\",\n \"def users_create():\",\n \"def create_member(org_id, group_id, target_group_ids, sex, first_name, last_name, title_name, email):\\n user = get_user_by_email(email)\\n # --- falls e-mail schon existiert wird nichts unternommen\\n if user != None:\\n if org_id > 0: # nur bei Schulen wird die Schulnummer vorangestellt\\n prefix = '%i_' % org_id\\n else:\\n prefix = ''\\n user = User()\\n username = get_username(prefix, first_name, last_name)\\n user.username = username\\n user.sex = sex\\n user.first_name = first_name\\n user.last_name = last_name\\n user.email = email\\n user.title = title_name\\n user.is_staff = False\\n user.is_active = True\\n user.is_superuser = False\\n user.date_joined = datetime.datetime.now()\\n password = generate_passwd()\\n user.set_password(password)\\n user.save()\\n set_user_org(org_id, user)\\n send_password(email, username, password)\\n set_user_group(user, get_group_by_id(group_id))\\n for group in target_group_ids:\\n set_user_group(user, get_group_by_id(group))\\n transaction.commit()\",\n \"def create(self, values):\\n if values.get('country_id', False):\\n country = self.env['res.country'].browse(values['country_id'])\\n if country.code == 'SA':\\n values.update({'is_saudi': True})\\n else:\\n values.update({'is_saudi': False})\\n\\n res = super(HrEmployee, self).create(values)\\n if values.get('user_id', False):\\n self.user_id.write({'employee_id': res})\\n return res\",\n \"def test_create_owner(self):\\n url = reverse(\\n 'projectroles:api_role_create',\\n kwargs={'project': self.project.sodar_uuid},\\n )\\n post_data = {\\n 'role': PROJECT_ROLE_OWNER,\\n 'user': str(self.assign_user.sodar_uuid),\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 400, msg=response.content)\",\n \"def is_employee():\\n return _is_member('uw_employee')\",\n \"def add_employee():\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n form = SignUp_Form()\\n \\n\\n if form.validate_on_submit():\\n try: \\n employee = Employee.register(\\n username = form.username.data,\\n password = form.password.data, \\n email = form.email.data, \\n first_name = form.first_name.data,\\n last_name = form.last_name.data,\\n hire_date = form.hire_date.data, \\n is_admin = form.is_admin.data,\\n )\\n\\n db.session.add(employee)\\n\\n db.session.commit()\\n except IntegrityError:\\n flash(\\\"Email already in use\\\", \\\"danger\\\")\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\\n\\n flash(\\\"Employee Added!\\\", \\\"success\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\",\n \"def insert_employee(self,\\n region_name,\\n last_name,\\n first_name,\\n hire_date,\\n mi=None):\\n\\n if self.check_input_type(region_name, \\\"Region\\\"):\\n if self.check_input_type(hire_date, \\\"Date\\\"):\\n region_info = self.query_region(region_name)\\n region_id = region_info[0][0]\\n\\n if mi != \\\"\\\":\\n query_format = \\\"insert into employee(Region_ID, \\\" \\\\\\n \\\"Emp_Lname, Emp_Mi, Emp_Fname, Emp_Hiredate) \\\" \\\\\\n \\\"values ((select region_id from region where \\\" \\\\\\n \\\"region_id='{}'), '{}', '{}', '{}', '{}')\\\"\\n query = query_format.format(\\n region_id, last_name, mi, first_name, hire_date\\n )\\n else:\\n query_format = \\\"insert into employee(Region_ID, \\\" \\\\\\n \\\"Emp_Lname, Emp_Fname, Emp_Hiredate) \\\" \\\\\\n \\\"values ((select region_id from region where \\\" \\\\\\n \\\"region_id='{}'), '{}', '{}', '{}')\\\"\\n query = query_format.format(\\n region_id, last_name, first_name, hire_date\\n )\\n\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n ErrorMessageWindow(\\\"Date format not valid!\\\")\\n else:\\n ErrorMessageWindow(\\\"Region input not valid!\\\")\",\n \"def create_user(change):\\n return change()\",\n \"def setUp(self):\\n\\n self.user = self.make_user()\\n self.employee = Employee.objects.create(\\n cpf=\\\"974.220.200-16\\\",\\n user=self.user,\\n departament=Employee.ADMINISTRATION\\n )\",\n \"def create_leader_model(request, company_id):\\n\\n errors = {'noactions': []}\\n company = Company.objects.get(pk=company_id)\\n currentEmpl = Employee.objects.get(user__pk=request.user.pk)\\n \\\"\\\"\\\":type : Employee \\\"\\\"\\\"\\n\\n if not currentEmpl.isEnsoUser() and currentEmpl.company.pk != company.pk:\\n raise PermissionDenied()\\n\\n if currentEmpl.isCompanySuperUserOrHigher():\\n employeeQS = Employee.objects.filter(\\n company__pk=company_id\\n )\\n else:\\n employeeQS = Employee.objects.filter(\\n Q(manager=currentEmpl),\\n company__pk=company_id\\n )\\n\\n form = MultiLeaderModelForm(request.POST or None)\\n form.fields['employees'].queryset = employeeQS\\n\\n if request.method == 'POST' and form.is_valid():\\n\\n employees = form.cleaned_data['employees']\\n \\\"\\\"\\\":type : list[Employee] \\\"\\\"\\\"\\n\\n pdf_response = get_leader_model_pdf(currentEmpl, employees)\\n\\n if isinstance(pdf_response, HttpResponse):\\n return pdf_response\\n else:\\n errors = pdf_response\\n\\n print(errors)\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_leader_model.html', {\\n 'form': form,\\n 'company': company,\\n 'errors': errors\\n }\\n )\",\n \"def create_manager(self, name, pos, dept):\\n self.manager[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'senior': [],\\n 'junior': [],\\n 'trainee': []\\n }\\n )\",\n \"def create_employee_from_applicant(self, cr, uid, ids, context=None):\\n if context is None:\\n context = {}\\n hr_employee = self.pool.get('hr.employee')\\n model_data = self.pool.get('ir.model.data')\\n act_window = self.pool.get('ir.actions.act_window')\\n emp_id = False\\n for applicant in self.browse(cr, uid, ids, context=context):\\n address_id = contact_name = False\\n if applicant.partner_id:\\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\\n if applicant.job_id and (applicant.partner_name or contact_name):\\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\\n create_ctx = dict(context, mail_broadcast=True)\\n\\n pes=self.browse(cr,uid,ids)[0]\\n coy=pes.partner_name\\n\\n ##### Susunan Keluarga ayah/ibu #####\\n le=self.pool.get('hr_recruit.suskel1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context)\\n prod_ids=[] \\n for pr in lele:\\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\\n \\n ###### Susunan Keluarga Suami/istri #####\\n le=self.pool.get('hr_recruit.suskel2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids1=[] \\n for pr in lele:\\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \\n \\n ###### riwayat Pendidikan #######\\n le=self.pool.get('hr_recruit.rwt_pend')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids2=[] \\n for pr in lele:\\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \\n \\n ###### bahasa ######\\n le=self.pool.get('hr_recruit.bahasa')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids3=[] \\n for pr in lele:\\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \\n \\n ##### Riwayat Pekerjaan ####\\n le=self.pool.get('hr_recruit.rwt_krj')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids4=[] \\n for pr in lele:\\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \\n \\n ###### Koneksi Internal #####\\n le=self.pool.get('hr_recruit.kon1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids5=[] \\n for pr in lele:\\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \\n \\n ###### Koneksi Eksternal ####\\n le=self.pool.get('hr_recruit.kon2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids6=[]\\n for pr in lele: \\n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \\n\\n ####### create Employee ######## \\n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\\n 'job_id': applicant.job_id.id,\\n 'department_id' : applicant.department_id.id,\\n 'address_id2' : applicant.job_id.address_id.id,\\n #### informasi Probadi ####\\n 'kelamin':applicant.jen_kel,\\n 'blood' : applicant.blood,\\n 'agama' : applicant.agama_id.id,\\n 'birthday' : applicant.tgl_lahir,\\n 'place_of_birth' : applicant.kota_id.name,\\n 'marital':applicant.status,\\n 'sjk_tanggal' : applicant.sjk_tanggal,\\n 'mobile_phone':applicant.partner_phone,\\n 'country_id' : applicant.country_id.id,\\n\\n #### Pendidikan ####\\n 'type_id':applicant.type_id.id,\\n 'bid_id':applicant.bidang_id.id,\\n 'jurusan_id':applicant.jurusan_id.id,\\n 'pt_id':applicant.pt_id.id,\\n 'gelar_id':applicant.gelar_id.id,\\n\\n #### alamat DOmisili ####\\n 'country_id1':applicant.country_id1.id,\\n 'prov_id':applicant.prov_id.id,\\n 'kab_id' : applicant.kab_id.id,\\n 'kec_id':applicant.kec_id.id,\\n 'alamat1' : applicant.alamat1,\\n 'kodepos' :applicant.kode1,\\n 'telp1' : applicant.telp1,\\n\\n #### kartu identitas ####\\n 'jenis_id': applicant.jenis_id,\\n 'ktp' : applicant.no_id,\\n 'tgl_berlaku' : applicant.tgl_berlaku,\\n # 'issued_id' : applicant.dikeluarkan.id,\\n \\n #### Alamat Sesuai KTP #### \\n 'country_id2':applicant.country_id2.id,\\n 'prov_id2':applicant.prov_id2.id,\\n 'kab_id2':applicant.kab_id2.id,\\n 'kec_id2':applicant.kec_id2.id,\\n 'alamat2' : applicant.alamat2,\\n 'kodepos1':applicant.kode2,\\n 'telp2' : applicant.telp2,\\n \\n # 'status': applicant.status,\\n #### IDS ####\\n 'susunan_kel1_ids' : prod_ids,\\n 'susunan_kel2_ids':prod_ids1,\\n 'rwt_pend_ids':prod_ids2,\\n 'bahasa_ids':prod_ids3,\\n 'rwt_krj_ids':prod_ids4,\\n 'koneksi1_ids':prod_ids5,\\n 'koneksi2_ids':prod_ids6, \\n })\\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\\n self.pool['hr.job'].message_post(\\n cr, uid, [applicant.job_id.id],\\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\\n subtype=\\\"hr_recruitment.mt_job_applicant_hired\\\", context=context)\\n else:\\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\\n\\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\\n if emp_id:\\n dict_act_window['res_id'] = emp_id\\n dict_act_window['view_mode'] = 'form,tree'\\n return dict_act_window\",\n \"def setUp(self):\\n\\t\\tfirst_name = 'Gerson'\\n\\t\\tlast_name = 'Santos'\\n\\t\\tannual_salary = 5000\\n\\t\\tself.gerson = Employee(first_name, last_name, annual_salary)\",\n \"def setUp(self):\\n self.salary = 40000\\n self.custom_rise = 7500\\n self.employee = Employee(\\\"Carlos\\\", \\\"Zapata\\\", self.salary)\",\n \"def main():\\n name = input(\\\"Please enter in your name: \\\")\\n\\n \\\"\\\"\\\"Ask the user to enter a number if they are a Director, Manager or Staff.\\\"\\\"\\\"\\n \\\"\\\"\\\"This will check and make sure the user only enters in 1,2, \\n or 3 and a number greater than zero\\\"\\\"\\\"\\n while True:\\n try:\\n designation_number = int(input(\\\"Please enter in \\\\n1 for Director \\\"\\n \\\"\\\\n2 for Manager \\\\n3 for Staff\\\\n\\\"))\\n if 0 < designation_number <= 3:\\n break\\n print(\\\"Invalid number entered.\\\")\\n except Exception as e:\\n print(e)\\n \\\"\\\"\\\"Gets the user salary and makes sure is a number and greater than 0\\\"\\\"\\\"\\n while True:\\n try:\\n salary = float(input(\\\"Please enter in your salary: \\\"))\\n if salary <= 0:\\n print(\\\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\\\")\\n else:\\n break\\n except ValueError:\\n print(\\\"Oops! That was not a valid number. Try again...\\\")\\n\\n \\\"\\\"\\\"Create Employee\\\"\\\"\\\"\\n employee1 = employee.Employee()\\n employee1.set_name(name)\\n employee1.set_designation(designation_number)\\n employee1.set_salary(salary)\\n print(employee1)\",\n \"def setUp(self):\\n\\tself.emp = Employee('Lin',10000)\\n\\tself.emp2 = Employee('Jun',20000)\",\n \"def createMentor(self, org):\\n self.createProfile()\\n self.profile.mentor_for = [org.key()]\\n self.profile.put()\",\n \"def onUserCreation(event):\\n\\n client = getUtility(IAdminClient)\\n xmpp_users = getUtility(IXMPPUsers)\\n storage = getUtility(IPubSubStorage)\\n principal = event.principal\\n mtool = getToolByName(principal, 'portal_membership')\\n\\n principal_id = principal.getUserId()\\n principal_jid = xmpp_users.getUserJID(principal_id)\\n members_jids = [xmpp_users.getUserJID(member.getUserId())\\n for member in mtool.listMembers()]\\n pass_storage = getUtility(IXMPPPasswordStorage)\\n principal_pass = pass_storage.set(principal_id)\\n\\n storage.leaf_nodes.append(principal_id)\\n storage.node_items[principal_id] = []\\n storage.collections['people'].append(principal_id)\\n storage.publishers[principal_id] = [principal_id]\\n\\n d = setupPrincipal(client, principal_jid, principal_pass, members_jids)\\n return d\",\n \"def createEmployee(firstName, lastName, ssn, salary):\\n employee = Employee(firstName, lastName, ssn, salary)\\n # verify\\n if firstName != employee.firstName or \\\\\\n lastName != employee.lastName or \\\\\\n ssn != employee.ssn or \\\\\\n salary != employee.salary:\\n raise ValueError(\\\"Failed to initialize Employee\\\")\\n return employee\",\n \"def make_commissioned(self,salary,commission,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"3\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be a commissioned employee\\\"))\\n self.emp_dict[id][7] = salary\\n self.emp_dict[id][9] = commission\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\\n teacher_account_id = person.get_last()\\n query = 'INSERT INTO teacher VALUES( %s,%s );'\\n args = (teacher_account_id, availability)\\n database.connection.save_data(query, args)\",\n \"def _test_pre_fill_and_assign_humor(self):\\n for i in range(1, 50):\\n User.objects.create_user(username=\\\"u{0}\\\".format(i))\\n pre_fill.main([\\\"--managers\\\", \\\"--workshift\\\"])\\n utils.make_workshift_pool_hours(semester=self.semester)\\n # Assign manager shifts beforehand\\n manager_shifts = RegularWorkshift.objects.filter(\\n pool=self.p1, workshift_type__auto_assign=False,\\n )\\n profiles = WorkshiftProfile.objects.all()\\n for profile, shift in zip(profiles, manager_shifts):\\n shift.current_assignees.add(profile)\\n shift.save()\\n unfinished = utils.auto_assign_shifts(\\n self.semester, pool=WorkshiftPool.objects.get(title=\\\"Humor Shift\\\")\\n )\\n self.assertEqual([], unfinished)\",\n \"def create_job(self, employer_id, compensation, location, description, category_id, group_id):\\n\\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \\n db.session.add(job)\\n db.session.commit()\",\n \"def test_employee_creation(self):\\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\\n\\n # Returned result is an OrderedDict\\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\\n\\n self.assertEqual(result['name'], helper.name)\\n self.assertEqual(result['hiredOn'], helper.hired_on)\\n self.assertEqual(result['salary'], helper.salary)\\n self.assertEqual(result['departmentId'], helper.department_id)\",\n \"def create(self, creator: User = None, *args, **kwargs) -> Employee:\\n # If the creator argument is not None and is a non-staff user, raise\\n # ValueError\\n if creator and not creator.is_staff:\\n raise ValueError(\\n 'Only staff members can add new employees, \\\"creator\\\" must be '\\n 'a staff user.'\\n )\\n\\n # Get the user property from the provided keyword arguments\\n user: Optional[User] = kwargs.get('user', None)\\n\\n # If the user property is missing or is a non-staff user, raise\\n # ValueError\\n if not user:\\n raise ValueError(\\n 'You must provide the \\\"user\\\" property as a keyword argument'\\n )\\n elif not user.is_staff:\\n raise ValueError('The \\\"user\\\" property must be a staff user.')\\n\\n return super().create(creator, *args, **kwargs)\",\n \"def create(person):\\n lname = person.get(\\\"lname\\\", None)\\n fname = person.get(\\\"fname\\\", None)\\n\\n # Does the person exist already?\\n if lname not in PEOPLE and lname is not None:\\n PEOPLE[lname] = {\\n \\\"lname\\\": lname,\\n \\\"fname\\\": fname,\\n \\\"timestamp\\\": get_timestamp(),\\n }\\n return PEOPLE[lname], 201\\n\\n # Otherwise, they exist, that's an error\\n else:\\n abort(\\n 406,\\n \\\"Person with last name {lname} already exists\\\".format(lname=lname),\\n )\",\n \"def _add_sponsor(data: dict) -> dict:\\n user = create_user()\\n user['name'] = data['agency']\\n user['type'] = 32\\n if 'agency_class' in data:\\n user['tag'] = data['agency_class']\\n return user\",\n \"def create_person(dbo, username, collationid):\\n l = dbo.locale\\n fields = get_onlineformincoming_detail(dbo, collationid)\\n d = {}\\n flags = None\\n for f in fields:\\n if flags is None: flags = f[\\\"FLAGS\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"title\\\": d[\\\"title\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"forenames\\\": d[\\\"forenames\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"firstname\\\": d[\\\"forenames\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"surname\\\": d[\\\"surname\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"lastname\\\": d[\\\"surname\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"address\\\": d[\\\"address\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"town\\\": d[\\\"town\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"city\\\": d[\\\"town\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"county\\\": d[\\\"county\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"state\\\": d[\\\"county\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"postcode\\\": d[\\\"postcode\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"zipcode\\\": d[\\\"postcode\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"hometelephone\\\": d[\\\"hometelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"worktelephone\\\": d[\\\"worktelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"mobiletelephone\\\": d[\\\"mobiletelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"celltelephone\\\": d[\\\"mobiletelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"emailaddress\\\": d[\\\"emailaddress\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"].startswith(\\\"reserveanimalname\\\"): d[f[\\\"FIELDNAME\\\"]] = f[\\\"VALUE\\\"]\\n d[\\\"flags\\\"] = flags\\n # Have we got enough info to create the person record? We just need a surname\\n if not d.has_key(\\\"surname\\\"):\\n raise utils.ASMValidationError(i18n._(\\\"There is not enough information in the form to create a person record (need a surname).\\\", l))\\n # Does this person already exist?\\n personid = 0\\n if d.has_key(\\\"surname\\\") and d.has_key(\\\"forenames\\\") and d.has_key(\\\"address\\\"):\\n similar = person.get_person_similar(dbo, d[\\\"surname\\\"], d[\\\"forenames\\\"], d[\\\"address\\\"])\\n if len(similar) > 0:\\n personid = similar[0][\\\"ID\\\"]\\n # Create the person record if we didn't find one\\n if personid == 0:\\n personid = person.insert_person_from_form(dbo, utils.PostedData(d, dbo.locale), username)\\n personname = person.get_person_name_code(dbo, personid)\\n # Attach the form to the person\\n formname = get_onlineformincoming_name(dbo, collationid)\\n formhtml = get_onlineformincoming_html_print(dbo, [collationid,])\\n media.create_document_media(dbo, username, media.PERSON, personid, formname, formhtml )\\n # Was there a reserveanimalname field? If so, create reservation(s) to the person if possible\\n for k, v in d.iteritems():\\n if k.startswith(\\\"reserveanimalname\\\"):\\n movement.insert_reserve_for_animal_name(dbo, username, personid, v)\\n return (collationid, personid, personname)\",\n \"def __init__(self, employee_id, name, supervisor_id, lft, rgt):\\n self.employee_id = employee_id\\n self.name = name\\n self.supervisor_id = supervisor_id\\n self.lft = lft\\n self.rgt = rgt\",\n \"def add_employee(self, obj):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\\n\\n self.dbconnect.commit()\\n return obj\\n except:\\n self.dbconnect.rollback()\\n raise\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.EmployeeDoesNotExist\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create_employee_structure(employees):\\n employees_dict = {}\\n for employee in position_sort(employees):\\n if not employee.is_secretary:\\n adder(employees_dict, employee.prosecutors_office, {'employees': [], 'departments': {}, 'divisions': {}})\\n if employee.prosecutors_office and employee.department and employee.division:\\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'divisions', {})\\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'], employee.division, [])\\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'][employee.division].append(employee)\\n elif employee.prosecutors_office and employee.department:\\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'employees', [])\\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['employees'].append(employee)\\n elif employee.prosecutors_office and employee.division:\\n adder(employees_dict[employee.prosecutors_office]['divisions'], employee.division, [])\\n employees_dict[employee.prosecutors_office]['divisions'][employee.division].append(employee)\\n elif employee.prosecutors_office:\\n employees_dict[employee.prosecutors_office]['employees'].append(employee)\\n return employees_dict\",\n \"def create_instructor(self, username):\\r\\n return self._create_user(username, is_staff=True)\",\n \"def create(self, vals):\\n record = super(KaHrPayrollRapelEmployeePromote, self).create(vals)\\n if not 'name' in vals or not vals.get('name'):\\n record.name = \\\"Rapel {0}\\\".format(record.new_employee_promote_id.name)\\n return record\",\n \"def create(self, vals):\\n if not vals.get('nik_number'):\\n vals['nik_number'] = self.generate_nik(vals)\\n return super(Employee, self).create(vals)\",\n \"def post(self, request):\\n data = request.data\\n skill_data = data.pop('skills')\\n Department_name = data.pop('department')\\n department = Department.objects.get(name=Department_name)\\n manager_name = data.pop('manager')\\n manager = Manager.objects.get(name=manager_name)\\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\\n Employee.save()\\n for skill in skill_data:\\n skill_add, create = Skill.objects.get_or_create(name=skill)\\n Employee.skills.add(skill_add)\\n return Response(\\n data=request.data\\n )\",\n \"def createManager(firstName, lastName, ssn, salary, title, yearBonus):\\n manager = Manager(firstName, lastName, ssn, salary, title, yearBonus)\\n if firstName != manager.firstName or \\\\\\n lastName != manager.lastName or \\\\\\n ssn != manager.ssn or \\\\\\n salary != manager.salary or \\\\\\n title != manager.title or \\\\\\n yearBonus != manager.yearBonus:\\n raise ValueError(\\\"Failed to initialize Manager\\\")\\n return manager\",\n \"def handle(self, *args, **kwargs):\\n seeder = Seed.seeder()\\n seeder.add_entity(User, 20)\\n\\n seeder.add_entity(EmployeeMptt, 20, {\\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\\n 'parent': lambda x: EmployeeMptt.objects.order_by(\\\"?\\\").first(),\\n 'level': lambda x: random.randint(0, 4),\\n })\\n seeder.execute()\",\n \"def create_advisors(_advisors_data):\\n\\n for advisor_data in _advisors_data:\\n occupations = advisor_data.pop('occupations', '').strip()\\n specialties = advisor_data.pop('specialties', '').strip()\\n subspecialties = advisor_data.pop('subspecialties', '').strip()\\n prev_firms = advisor_data.pop('previous_firms', '').strip()\\n curr_firm_name = advisor_data.pop('current_firm_name', '').strip()\\n location = advisor_data.pop('location', '').strip()\\n\\n city, state_abbr = location.split(', ') if location.count(',') == 1 else (None, '')\\n\\n advisor_data['city'] = city\\n advisor_data['state'] = ABBR_TO_STATE.get(state_abbr, 'Other')\\n\\n advisor_data['status'] = 'Active'\\n\\n success, result = create_advisor(advisor_data)\\n validate(success, result)\\n\\n advisor = result\\n\\n occupation_objs = []\\n\\n if occupations:\\n for occupation_name in occupations.split(', '):\\n if occupation_name not in OCCUPATIONS:\\n continue\\n\\n success, result = create_occupation({'name': occupation_name.strip().title()})\\n validate(success, result)\\n\\n occupation = result\\n occupation_objs.append(occupation)\\n\\n advisor.occupations.append(occupation)\\n\\n db.session.commit()\\n\\n if specialties:\\n for specialty_name in specialties.split(', '):\\n\\n spec_occ = None\\n for occupation in occupation_objs:\\n if specialty_name in OCCUPATIONS[occupation.name]:\\n spec_occ = occupation\\n break\\n\\n if not spec_occ:\\n continue\\n\\n success, result = create_specialty({\\n 'name': specialty_name.strip().title(),\\n 'occupation_id': spec_occ.pk_id,\\n 'occupation': spec_occ\\n })\\n\\n validate(success, result)\\n\\n specialty = result\\n advisor.specialties.append(specialty)\\n\\n db.session.commit()\\n\\n if subspecialties:\\n advisor.subspecialty_text = subspecialties\\n\\n db.session.commit()\\n\\n if prev_firms:\\n for prev_firm_name in prev_firms.split(', '):\\n success, result = create_bigfirm({'name': prev_firm_name.strip()})\\n validate(success, result)\\n\\n big_firm = result\\n advisor.previous_firms.append(big_firm)\\n\\n db.session.commit()\\n\\n if curr_firm_name:\\n success, result = create_bigfirm({'name': curr_firm_name.strip()})\\n validate(success, result)\\n\\n big_firm = result\\n advisor.current_firm = big_firm\\n advisor.current_firm_id = big_firm.pk_id\\n\\n db.session.commit()\\n\\n db.session.commit()\",\n \"def createGroup(listOfPerson):\\n atk=Department()\\n atk.members=listOfPerson\\n return atk\",\n \"def create_investor(sender, **kwargs):\\n u = kwargs[\\\"instance\\\"]\\n try:\\n \\n if not InvestorProfile.objects.filter(username=u.username):\\n inv = InvestorProfile(username=u.username,user=u)\\n inv.save()\\n g = DjangoGroup.objects.get(name='Investors') \\n g.user_set.add(u)\\n except Exception as e:\\n print e\",\n \"def create_person(data_dict, m2m_dict):\\n email_address = data_dict['email_address']\\n # check if the person already exists:\\n try:\\n exists = Person.objects.get(email_address=email_address)\\n create_message = f'Skipping: Person with {email_address} already exists.'\\n except:\\n # try:\\n person_obj, person_created = Person.objects.update_or_create(**data_dict)\\n # populate MANY-TO-MANY fields\\n if m2m_dict:\\n # created_by (FK)\\n created_by_email = m2m_dict['created_by']\\n user, user_created = User.objects.get_or_create(email=created_by_email)\\n person_qs = Person.objects.filter(email_address=person_obj.email_address)\\n person_qs.update(created_by=user)\\n # expertise (M2M)\\n expertise_values = m2m_dict['expertise']\\n if expertise_values:\\n values_list = [value.strip() for value in expertise_values.split(',')]\\n for value in values_list:\\n expertise_obj, expertise_created = Expertise.objects.get_or_create(name=value)\\n person_obj.expertise.add(expertise_obj)\\n # industry (M2M)\\n industry_values = m2m_dict['industries']\\n if industry_values:\\n values_list = [value.strip() for value in industry_values.split(',')]\\n for value in values_list:\\n industry_obj, industry_created = Industry.objects.get_or_create(name=value)\\n person_obj.industries.add(industry_obj)\\n # organization (M2M)\\n organization_values = m2m_dict['organization']\\n if organization_values:\\n values_list = [value.strip() for value in organization_values.split(',')]\\n for value in values_list:\\n organization_obj, organization_created = Organization.objects.get_or_create(name=value)\\n person_obj.organization.add(organization_obj)\\n # owned by (M2M)\\n exportable_by_values = m2m_dict['exportable_by']\\n if exportable_by_values:\\n values_list = [value.strip() for value in exportable_by_values.split(',')]\\n for value in values_list:\\n dive_obj, dive_created = Dive.objects.get_or_create(name=value)\\n person_obj.exportable_by.add(dive_obj)\\n # let us know how it went\\n if person_created:\\n create_message = f'Success: {person_obj}'\\n else:\\n create_message = f'Failed: {person_obj}'\\n # except Exception as e:\\n # create_message = f'Error for {email_address}: {e}\\\\n'\\n print(create_message)\\n # except:\\n # failed_rows.append(counter)\\n # try:\\n # obj, created = Person.objects.create(**csv_to_model)\\n # except:\\n # message = 'Create person' + str(sys.exc_info())\\n # print(message)\",\n \"def make_salaried(self,salary,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"2\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be a salaried employee\\\"))\\n self.emp_dict[id][7] = salary\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n respond_data = self.controller.add_team_employee(request_data=request_data)\\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.EmployeeDoesNotExist,\\n domain_exceptions.EmployeeIsATeamMember\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create_user(self):\\n unique_id = str(uuid.uuid4())\\n new_user_properties = {\\n \\\"name\\\": self.name,\\n \\\"mission_statement\\\": self.mission_statement,\\n \\\"unique_id\\\": unique_id,\\n \\\"email\\\": self.email.lower(),\\n \\\"is_mentor\\\": True,\\n \\\"is_tutor\\\": True,\\n \\\"is_visible\\\": True,\\n \\\"is_available_for_in_person\\\": True,\\n \\\"is_admin\\\": True}\\n new_user_node = Node.cast(AgoraLabel.USER, new_user_properties)\\n try:\\n self.graph_db.create(new_user_node)\\n except:\\n pass\\n return new_user_node\",\n \"def create_player():\\n\\n\\t#TODO : Ajout d'une BDD des différents joueurs avec des scores et vérifier la présence des joueurs choisis dans cette BDD pour charger les scores\\n\\n\\tactivator = ''\\n\\tinhibitor = ''\\n\\n\\tprint(\\\"\\\\nEntrez le pseudo du joueur\\\",colors.GREEN + \\\"'Activator' : \\\" + colors.STOP, end = \\\"\\\")\\n\\tactivator = input()\\n\\n\\tprint(\\\"\\\\nEntrez le pseudo du joueur\\\", colors.RED + \\\"'Inhibitor' : \\\"+colors.STOP, end = \\\"\\\")\\n\\tinhibitor = input()\\n\\n\\t# Default usernames if not defined by users\\n\\tif len(activator) == 0:\\n\\t\\tactivator = 'Activator'\\n\\n\\tif len(inhibitor) == 0:\\n\\t\\tinhibitor = 'Inhibitor'\\n\\n\\t# Attribute to each player the status he chose\\n\\tData.current_player['Activator'] = activator\\n\\tData.current_player['Inhibitor'] = inhibitor\\n\\n\\treturn activator, inhibitor\",\n \"def create(self, request):\\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\\n\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n print(F\\\"Request employee Data: {serializer.data}\\\")\\n\\n try:\\n new_employee = self.controller.create_employee(request_data=request_data)\\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (domain_exceptions.EmployeeIDIsNotUnique,\\n domain_exceptions.WorkArrangementPercentageOutOfRange,\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.WorkArrangementPercentageNull\\n ) as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create_individual(self):\\n pass\",\n \"def perform_create(self, serializer):\\n if self.request.data.get('user_type', None) == 'employee':\\n serializer.save(is_staff=False)\\n else:\\n serializer.save()\",\n \"def create_user(email, password, f_name, l_name):\\n pass\",\n \"def get_manager_employees(request):\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n manager_employees = Employee.objects.filter(manager=current_employee, development_plan_type=None).all()\\n if manager_employees:\\n emp_list=[]\\n for emp in manager_employees:\\n emp_data={}\\n emp_data[\\\"id\\\"] = emp.id\\n emp_data[\\\"username\\\"] = emp.user.username\\n emp_data[\\\"first_name\\\"] = emp.user.first_name\\n emp_data[\\\"last_name\\\"] = emp.user.last_name\\n emp_data[\\\"manager_id\\\"] = emp.manager.id\\n # emp_data[\\\"status_questions\\\"] = emp.status_questions\\n # employee_role = EmployeeRole.objects.filter(employee=emp).all()\\n # name_role_list = []\\n # for obj in employee_role:\\n # name_role_list.append(obj.role.name)\\n # emp_data[\\\"roles\\\"] = name_role_list\\n emp_list.append(emp_data)\\n data = {\\\"employees:\\\": emp_list}\\n return JsonResponse(status=201, data=data)\\n else:\\n return JsonResponse(\\\"The user with id={} isn't a manager for any user\\\".format(current_employee.user.id),\\n status=404)\",\n \"def setUp(self):\\n self.employee = Employee('Lucas', 'Guerra', 45000)\",\n \"def _create_nsem_user():\\n users = User.objects.filter(username=settings.CWWED_NSEM_USER)\\n if users.exists():\\n user = users[0]\\n else:\\n user = User.objects.create_user(settings.CWWED_NSEM_USER, password=settings.CWWED_NSEM_PASSWORD)\\n group, _ = Group.objects.get_or_create(name=settings.CWWED_NSEM_GROUP)\\n perm_names = [\\n 'add_{}'.format(NsemPsa._meta.model_name),\\n 'add_{}'.format(NamedStormCoveredDataSnapshot._meta.model_name),\\n ]\\n perms = Permission.objects.filter(codename__in=perm_names)\\n # set permission\\n user.user_permissions.set(list(perms))\\n group.permissions.set(list(perms))\\n # add user to group\\n group.user_set.add(user)\",\n \"def __init__(self,name,empid,designation,experience):\\n self.name = name\\n self.empid = empid\\n self.designation = designation\\n self.experience = experience\\n self.salary = self.cal_sal()\",\n \"def build_person(first_name, last_name, middle_name='', age=None): \\n person = {'first': first_name, 'middle': middle_name, 'last': last_name}\\n if age:\\n person['age'] = age\\n return person\",\n \"def add_employee(self, empl):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\\n empl.active, empl.promotor))\\n cursor.execute('SELECT LASTVAL()')\\n eid = cursor.fetchone()[0]\\n empl.id = eid\\n # get id and return updated object\\n self.dbconnect.commit()\\n except(Exception, self.dbconnect.get_error()) as error:\\n self.dbconnect.rollback()\\n raise Exception('\\\\nUnable to save Employee!\\\\n(%s)' % (error))\",\n \"def create_implied_role(self, prior_role_id, implied_role_id):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def createDeveloper(self):\\n self.createUser()\\n self.user.is_developer = True\\n self.user.put()\",\n \"def get_create_employee_query(self):\\n template = \\\"\\\"\\\"\\n mutation createEmployee {{\\n createEmployee(input: {{ {params} }}) {{\\n employee {{\\n name\\n hiredOn\\n salary\\n departmentId\\n }}\\n }}\\n }}\\n \\\"\\\"\\\"\\n # Add input parameters as needed\\n input_params = 'name:\\\"{}\\\",'.format(self.name)\\n\\n if self.hired_on is not None:\\n input_params += 'hiredOn: \\\"{}\\\", '.format(self.hired_on)\\n\\n if self.salary is not None:\\n input_params += 'salary: {}, '.format(self.salary)\\n\\n if self.department_id is not None:\\n input_params += 'departmentId: {}'.format(self.department_id)\\n\\n return template.format(params=input_params)\",\n \"def create_candidate(self, username: str) -> int:\",\n \"def test_create_new_person_w_no_role(self, selenium):\\n expected_person = entities_factory.PeopleFactory().create(\\n system_wide_role=roles.NO_ROLE)\\n actual_person = admin_webui_service.PeopleAdminWebUiService(\\n selenium).create_new_person(expected_person)\\n self.general_equal_assert(expected_person, actual_person)\",\n \"def create_user_questionnaire_in_progress(self):\\n username = 'pseudo'\\n email = 'martine@tests.com'\\n password = '00000000'\\n user_created = self.user.objects.create_user(id=2, username=username,\\n email=email, password=password)\\n HistoryUser.objects.create(user=user_created)\\n StatusUser.objects.create(user=user_created)\\n list_advice_id = [1, 5, 10]\\n self.add_advice_to_user_created(user_created, list_advice_id)\\n\\n return user_created\",\n \"def onUserCreation(event):\\n request = getRequest()\\n if not IProductLayer.providedBy(request):\\n return\\n client = getUtility(IAdminClient)\\n xmpp_users = getUtility(IXMPPUsers)\\n principal = event.principal\\n principal_id = principal.getUserId()\\n principal_jid = xmpp_users.getUserJID(principal_id)\\n pass_storage = getUtility(IXMPPPasswordStorage)\\n principal_pass = pass_storage.set(principal_id)\\n users.setupPrincipal(client, principal_jid, principal_pass)\",\n \"def test_admin_can_create_a_employee(self):\\n\\n account_data = {\\n \\\"username\\\": \\\"Mike\\\",\\n \\\"email\\\": \\\"mioshine@g.com\\\",\\n \\\"password\\\": \\\"1234567\\\",\\n \\\"confirm_password\\\": \\\"1234567\\\"\\n }\\n response = self.client.post(\\n reverse('accounts:create-user'),\\n account_data,\\n format=\\\"json\\\")\\n \\\"\\\"\\\"Test the api has bucket creation capability.\\\"\\\"\\\"\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertTrue(\\\"data\\\" in json.loads(response.content))\",\n \"def event_register_collaborator(request, event_id, employee_id):\\n if request.method == 'PUT':\\n event = get_object_or_404(Event, pk=event_id, is_registration_open=True)\\n collaborator = get_object_or_404(Employee, pk=employee_id)\\n event.collaborators.add(collaborator)\\n event.save()\\n serializer = EventSimpleRegistrationSerializer(event)\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\",\n \"def new_user():\\n pass\",\n \"def __init__(self, first_name, last_name, age, sex, city):\\n\\t\\tsuper().__init__(first_name, last_name, age, sex, city)\\n\\t\\tself.privileges = Privileges()\",\n \"def create_employee(attributes):\\n neccessary_keys = [\\\"empid\\\", \\\"gender\\\", \\\"sales\\\", \\\"bmi\\\", \\\"salary\\\", \\\"birthday\\\",\\n \\\"age\\\"]\\n for key in neccessary_keys:\\n if not key in attributes.keys():\\n raise ValueError(\\\"employee could not be created: {} is missing\\\".format(key))\\n return Employee(attributes[\\\"empid\\\"], attributes[\\\"gender\\\"],\\n attributes[\\\"sales\\\"], attributes[\\\"bmi\\\"],\\n attributes[\\\"salary\\\"], attributes[\\\"birthday\\\"],\\n attributes[\\\"age\\\"])\",\n \"def __init__(self, name: str, professors: list[Professor]):\\n self.professors = professors\",\n \"def create_user(context, params):\\n form_user = dict()\\n # form_user['edited_by'] = context.user\\n if params.get('username'):\\n form_user['username'] = params.get('username')\\n else:\\n form_user['username'] = create_username(params) # 'email_user{}'.format(MISUser.objects.latest('id').id + 1\\n form_user['first_name'] = params.get('first_name')\\n form_user['last_name'] = params.get('last_name')\\n form_person = create_person(params)\\n form_user.update(form_person)\\n user = User.objects.create(**form_user)\\n user.set_password(params.get('password'))\\n\\n email = {'label': 'Work', 'val': params.get('email'), 'person': user, 'is_main': True}\\n create_email(context, email)\\n\\n user.save()\\n return user\",\n \"def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\\n peronjson={}, notes='', tenantid=''):\\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\\n self.persontdetailscoll.insert(tenantobj, safe=True)\",\n \"def create_organization(user, name):\\n organization = Organization.objects.create(name=name)\\n new_user = OrganizationUser.objects.create(organization=organization,\\n user=user, is_admin=True)\\n OrganizationOwner.objects.create(organization=organization,\\n organization_user=new_user)\\n return organization\",\n \"def create_educator(data):\\n\\n educator = Educator(\\n name=data['name'],\\n work_email=data['work_email'],\\n organization_name=data['organization_name'],\\n org_or_school=data['org_or_school'],\\n address_line_1=data['address_line_1'],\\n address_line_2=data['address_line_2'],\\n city=data['city'],\\n state=data['state'],\\n zipcode=data['zipcode'],\\n num_students=data['num_students']\\n )\\n educator.save()\\n return educator\",\n \"def do_user_create():\\n target = User(\\n request.form['gender'],\\n request.form['first_name'],\\n request.form['name'],\\n request.form['mail'],\\n request.form['meter_id'],\\n request.form['group_id'],\\n secrets.token_hex(33))\\n target.set_role(request.form['role'])\\n target.nick = request.form['nick']\\n db.session.add(target)\\n db.session.commit()\\n return user_list(\\\"Created user \\\" + target.name)\",\n \"def createMentorWithProject(self, org):\\n self.createMentor(org)\\n from soc.modules.gsoc.models.student_project import StudentProject\\n properties = {'mentor': self.profile}\\n seeder_logic.seed(StudentProject, properties)\",\n \"def create_new_project(self,\\n customer_name,\\n contract_date,\\n project_info,\\n project_datest,\\n project_dateend,\\n project_budget,\\n project_actst=None,\\n project_actend=None,\\n project_cost=None):\\n\\n customer_info = self.query_customer(cus_name=customer_name)\\n\\n if customer_info:\\n # Search for project manager in the same region as the customer.\\n customer_region_id = customer_info[0][1]\\n get_employee_query = \\\"select employee.emp_id, emp_lname, emp_fname from employee, \\\" \\\\\\n \\\"empskill, skill, region where employee.emp_id = \\\" \\\\\\n \\\"empskill.emp_id and empskill.skill_id = \\\" \\\\\\n \\\"skill.skill_id and skill.skill_descrpt = \\\" \\\\\\n \\\"'Project Manager' and region.region_id = \\\" \\\\\\n \\\"employee.region_id and region.region_id = '{}' \\\"\\n try:\\n self.dbCursor.execute(\\n get_employee_query.format(customer_region_id))\\n employee_info = self.dbCursor.fetchall()\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n if len(employee_info) == 0:\\n ErrorMessageWindow(\\\"No suitable project manager found!\\\")\\n else:\\n if customer_info and employee_info:\\n if len(customer_info) > 1:\\n MultiRowScreen(customer_info, \\\"project\\\")\\n else:\\n cus_id = customer_info[0][0]\\n emp_id = employee_info[0][0]\\n optional_inputs = [project_actst, project_actend,\\n project_cost]\\n\\n query = \\\"insert into project(cus_id, emp_id, proj_date, \\\" \\\\\\n \\\"proj_descrpt, proj_estdatest, proj_estdateend, \\\" \\\\\\n \\\"proj_estbudget) values ('{}', '{}', '{}', '{}', \\\" \\\\\\n \\\"'{}', '{}', '{}') \\\".format(cus_id,\\n emp_id,\\n contract_date,\\n project_info,\\n project_datest,\\n project_dateend,\\n project_budget)\\n\\n yes_options = False\\n for item in optional_inputs:\\n if item != \\\"\\\":\\n yes_options = True\\n\\n if yes_options is False:\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n option_names = [\\\"proj_actdatest\\\",\\n \\\"proj_actdateend\\\",\\n \\\"proj_actcost\\\"]\\n options_index = []\\n filled_options = []\\n\\n index = 0\\n for item in optional_inputs:\\n if item != \\\"\\\":\\n options_index.append(index)\\n filled_options.append(item)\\n index += 1\\n update_query = \\\"update project set \\\"\\n\\n j = 0\\n for i in options_index:\\n if j < len(filled_options) - 1:\\n update_query += \\\"{}='{}', \\\".format(\\n option_names[i], filled_options[j]\\n )\\n else:\\n update_query += \\\"{}='{}' \\\".format(\\n option_names[i], filled_options[j]\\n )\\n j += 1\\n\\n try:\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n\\n self.dbCursor.execute(update_query)\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n ErrorMessageWindow(\\\"Customer not found!\\\")\",\n \"def test_new_employer_crud_methods(self):\\n response = self.client.post(\\n '/employers/', self.new_employer_data, format='json')\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(len(Employer.objects.all()), 2)\\n\\n # test one employer retrieve\\n response = self.client.get('/employers/1/')\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('Andela', response.data['name'])\\n\\n # test one employer update\\n response = self.client.put('/employers/1/',\\n {'name': 'New Employer'})\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('New Employer', response.data['name'])\",\n \"def create_many_employees(request, company_id=None):\\n company = Company.objects.get(pk=company_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n raise PermissionDenied()\\n if \\\"upload\\\" in request.POST:\\n form = UploadEmployeesForm(request.POST, request.FILES)\\n if form.is_valid():\\n data = csv_to_dict(request.FILES['file'])\\n request.session['upload_employees'] = data\\n return JsonResponse(status=201, data=form.cleaned_data)\\n # return TemplateResponse(\\n # request,\\n # 'mus/create_many_employees_uploaded.html',\\n # dict(data=data, company=company)\\n # )\\n elif \\\"next\\\" in request.POST:\\n data = request.session['upload_employees']\\n marked_data = list()\\n fields = request.POST.getlist('field[]')\\n for row in data:\\n new_row = dict(is_manager=False)\\n for i, item in enumerate(row):\\n field_id = int(fields[i])\\n if field_id == 1:\\n new_row['first_name'] = item\\n elif field_id == 2:\\n new_row['last_name'] = item\\n elif field_id == 3:\\n p = item.partition(\\\" \\\")\\n new_row['first_name'] = p[0]\\n new_row['last_name'] = p[2]\\n elif field_id == 4:\\n new_row['email'] = item\\n elif field_id == 5:\\n new_row['username'] = item\\n marked_data.append(new_row)\\n formset = EmployeeRowFormSet(initial=marked_data)\\n # TypeQS = DevelopmentPlanType.objects.filter(Q(company=company) | Q(company__isnull=True))\\n # for form in formset:\\n # form.fields['development_plan_type'].queryset = TypeQS\\n return TemplateResponse(\\n request,\\n 'mus/create_many_employees_form.html',\\n dict(formset=formset, company=company)\\n )\\n elif \\\"next2\\\" in request.POST:\\n formset = EmployeeRowFormSet(request.POST)\\n if formset.is_valid():\\n data = list()\",\n \"def add_employee(worksheet, row, employee, num_in_list, employee_format, employee_format_b):\\n # Get number of row for employee\\n if employee.work_telephone:\\n work_telephone_list = regular_telephone(employee.work_telephone.split(';'))\\n else:\\n work_telephone_list = []\\n if employee.private_telephone:\\n private_telephone_list = regular_telephone(employee.private_telephone.split(';'))\\n else:\\n private_telephone_list = []\\n rows_for_emp = max(len(work_telephone_list), len(private_telephone_list), 2)\\n # Add name and position\\n worksheet.merge_range(row, 0, row+rows_for_emp-1, 0, num_in_list, cell_format=employee_format)\\n worksheet.merge_range(row, 1, row+rows_for_emp-1, 1, str(employee), cell_format=employee_format_b)\\n worksheet.merge_range(row, 2, row+rows_for_emp-1, 2, str(employee.position), cell_format=employee_format)\\n # Add work telephone numbers\\n for num, work_telephone in enumerate(work_telephone_list):\\n if (len(work_telephone_list) < rows_for_emp) and ((num + 1) == len(work_telephone_list)):\\n worksheet.merge_range(row+num, 3, row+rows_for_emp-1, 3, work_telephone, cell_format=employee_format)\\n else:\\n worksheet.write(row+num, 3, work_telephone, employee_format)\\n if len(work_telephone_list) == 0:\\n worksheet.merge_range(row, 3, row+rows_for_emp-1, 3, '', cell_format=employee_format)\\n # Add private telephone numbers\\n for num, private_telephone in enumerate(private_telephone_list):\\n if (len(private_telephone_list) < rows_for_emp) and ((num + 1) == len(private_telephone_list)):\\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, private_telephone, cell_format=employee_format)\\n else:\\n worksheet.write(row+num, 4, private_telephone, employee_format)\\n if (len(private_telephone_list) < rows_for_emp) and ((num - 1) == len(private_telephone_list)):\\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\\n if len(private_telephone_list) == 0:\\n worksheet.merge_range(row, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\\n # If secretary exist\\n if employee.secretary:\\n row = row + rows_for_emp\\n add_employee(worksheet, row, employee.secretary, '', employee_format, employee_format_b)\\n return row+rows_for_emp\",\n \"def post(self, orgname):\\n permission = AdministerOrganizationPermission(orgname)\\n if permission.can() or allow_if_superuser():\\n try:\\n org = model.organization.get_organization(orgname)\\n except model.InvalidOrganizationException:\\n raise NotFound()\\n\\n details = request.get_json()\\n activating_username = None\\n\\n if (\\n \\\"activating_user\\\" in details\\n and details[\\\"activating_user\\\"]\\n and \\\"name\\\" in details[\\\"activating_user\\\"]\\n ):\\n activating_username = details[\\\"activating_user\\\"][\\\"name\\\"]\\n\\n delegate = details[\\\"delegate\\\"] if \\\"delegate\\\" in details else {}\\n delegate_kind = delegate.get(\\\"kind\\\", None)\\n delegate_name = delegate.get(\\\"name\\\", None)\\n\\n delegate_username = delegate_name if delegate_kind == \\\"user\\\" else None\\n delegate_teamname = delegate_name if delegate_kind == \\\"team\\\" else None\\n\\n activating_user = (\\n model.user.get_user(activating_username) if activating_username else None\\n )\\n delegate_user = model.user.get_user(delegate_username) if delegate_username else None\\n delegate_team = (\\n model.team.get_organization_team(orgname, delegate_teamname)\\n if delegate_teamname\\n else None\\n )\\n\\n if activating_username and not activating_user:\\n raise request_error(message=\\\"Unknown activating user\\\")\\n\\n if not delegate_user and not delegate_team:\\n raise request_error(message=\\\"Missing delegate user or team\\\")\\n\\n role_name = details[\\\"role\\\"]\\n\\n prototype = model.permission.add_prototype_permission(\\n org, role_name, activating_user, delegate_user, delegate_team\\n )\\n log_prototype_action(\\\"create_prototype_permission\\\", orgname, prototype)\\n\\n users_filter = {prototype.activating_user, prototype.delegate_user}\\n org_members = model.organization.get_organization_member_set(\\n org, users_filter=users_filter\\n )\\n return prototype_view(prototype, org_members)\\n\\n raise Unauthorized()\",\n \"def assign_employee(id):\\r\\n check_admin()\\r\\n\\r\\n employee = Employee.query.get_or_404(id)\\r\\n\\r\\n # prevent admin from being assigned a department or role\\r\\n if employee.is_admin:\\r\\n abort(403)\\r\\n\\r\\n form = EmployeeAssignForm(obj=employee)\\r\\n if form.validate_on_submit():\\r\\n employee.department = form.department.data\\r\\n employee.role = form.role.data\\r\\n db.session.add(employee)\\r\\n db.session.commit()\\r\\n flash('You have successfully assigned a department and role.')\\r\\n\\r\\n # redirect to the roles page\\r\\n return redirect(url_for('admin.list_employees'))\\r\\n\\r\\n return render_template('admin/employees/employee.html',\\r\\n employee=employee, form=form,\\r\\n title='Assign Employee')\",\n \"def employees_json(request):\\n # current_employee = Employee.objects.get(user__pk=request.user.pk)\\n employee_list = Employee.objects.filter(manager=request.user.employee_user)\\n employees = list()\\n for employee in employee_list:\\n manager_dict = model_to_dict(employee)\\n manager_dict['first_name'] = employee.user.first_name\\n manager_dict['last_name'] = employee.user.last_name\\n employees.append(manager_dict)\\n data = {\\\"employees\\\": employees}\\n return JsonResponse(data=data, content_type='application/json', safe=False)\",\n \"def main_role_create(\\n client: CitusCloudMgmt,\\n **opts: tp.Any\\n) -> None:\\n\\n role_id = client.create_role(opts[\\\"formation\\\"], opts[\\\"name\\\"])\\n logger.info(f\\\"Created new role \\\\\\\"name\\\\\\\" with id=\\\\\\\"{role_id}\\\\\\\"\\\")\\n click.echo(role_id)\",\n \"def testOwnershipAfterCreate(self):\\n self.simulateATGUIInteraction(task='create')\\n self.failUnlessEqual(self.person.getOwnerTuple()[1], 'abc123')\",\n \"def create_student(self, username):\\r\\n return self._create_user(username, is_staff=False)\",\n \"def create_trainee(self, name, pos, dept):\\n self.trainee[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'senior': self.senior[dept.upper()][0]['name'],\\n 'junior': self.junior[dept.upper()][0]['name'],\\n }\\n )\\n self.manager[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.senior[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.junior[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def handle(self, *args, **options):\\r\\n username = 'populate_creators_command'\\r\\n email = 'grant+creator+access@edx.org'\\r\\n try:\\r\\n admin = User.objects.create_user(username, email, 'foo')\\r\\n admin.is_staff = True\\r\\n admin.save()\\r\\n except IntegrityError:\\r\\n # If the script did not complete the last time it was run,\\r\\n # the admin user will already exist.\\r\\n admin = User.objects.get(username=username, email=email)\\r\\n\\r\\n for user in get_users_with_role(CourseInstructorRole.ROLE):\\r\\n add_user_with_status_granted(admin, user)\\r\\n\\r\\n # Some users will be both staff and instructors. Those folks have been\\r\\n # added with status granted above, and add_user_with_status_unrequested\\r\\n # will not try to add them again if they already exist in the course creator database.\\r\\n for user in get_users_with_role(CourseStaffRole.ROLE):\\r\\n add_user_with_status_unrequested(user)\\r\\n\\r\\n # There could be users who are not in either staff or instructor (they've\\r\\n # never actually done anything in Studio). I plan to add those as unrequested\\r\\n # when they first go to their dashboard.\\r\\n\\r\\n admin.delete()\",\n \"def add_employee(schema, employee_json):\\n employee = schema.load(employee_json, session=db.session)\\n db.session.add(employee)\\n db.session.commit()\\n return employee\",\n \"def insert_creator(self, creator_id, creator_username, rules, rewards, min_participants, max_participants):\\n\\n self.execute(TABELLE['constest_creator']['insert'],\\n (creator_id, creator_username, rules, rewards, min_participants, max_participants,))\",\n \"def org_organisation_create_onaccept(form):\\n\\n db = current.db\\n s3db = current.s3db\\n ftable = s3db.pr_forum\\n\\n # Lookup the Reserves Forum\\n forum = db(ftable.name == \\\"Reserves\\\").select(ftable.pe_id,\\n limitby = (0, 1)\\n ).first()\\n try:\\n reserves_pe_id = forum.pe_id\\n except AttributeError:\\n current.log.error(\\\"Unable to link Org Forum to Reserves Forum: Forum not Found\\\")\\n return\\n\\n form_vars_get = form.vars.get\\n organisation_id = form_vars_get(\\\"id\\\")\\n\\n # Lookup the Organisation\\n otable = s3db.org_organisation\\n org = db(otable.id == organisation_id).select(otable.pe_id,\\n limitby = (0, 1)\\n ).first()\\n org_pe_id = org.pe_id\\n\\n # Create Forum\\n record = {\\\"organisation_id\\\": organisation_id,\\n \\\"name\\\": \\\"%s Reserves\\\" % form_vars_get(\\\"name\\\"),\\n }\\n forum_id = ftable.insert(**record)\\n record[\\\"id\\\"] = forum_id\\n s3db.update_super(ftable, record)\\n forum_pe_id = record[\\\"pe_id\\\"]\\n\\n # Add the Hierarchy links\\n s3db.pr_add_affiliation(org_pe_id, forum_pe_id, role=\\\"Realm Hierarchy\\\")\\n s3db.pr_add_affiliation(reserves_pe_id, forum_pe_id, role=\\\"Realm Hierarchy\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6703623","0.6629079","0.58912194","0.5825029","0.57799065","0.557808","0.5561385","0.55040497","0.5481859","0.54713845","0.5368414","0.5327975","0.52699995","0.52693903","0.52427757","0.52367204","0.52185136","0.52170134","0.5204411","0.5148115","0.51351565","0.5132368","0.51252276","0.512357","0.5113103","0.5106386","0.5105611","0.5081404","0.5071633","0.5069231","0.50649154","0.5046944","0.50369966","0.50347453","0.50112444","0.5003188","0.5003052","0.50003994","0.4982371","0.4960052","0.49580863","0.49546158","0.49537212","0.49445936","0.49354464","0.49348632","0.49312893","0.49298772","0.49285316","0.49258918","0.4925266","0.49094293","0.49077252","0.4906656","0.48952118","0.4894956","0.4889702","0.48886684","0.48823512","0.4877747","0.4861346","0.4856938","0.4845609","0.48343918","0.48307005","0.48224336","0.48120427","0.4811941","0.48009777","0.47961563","0.47954753","0.4792434","0.47906315","0.47766036","0.4776027","0.4774271","0.4762457","0.47569475","0.47543168","0.47504637","0.47489324","0.4746841","0.47458005","0.4743053","0.4741038","0.47396654","0.47201896","0.47166815","0.47125915","0.47105667","0.47058547","0.47019553","0.46986783","0.4694803","0.46940172","0.46938962","0.4693233","0.4693099","0.4692603","0.46909136"],"string":"[\n \"0.6703623\",\n \"0.6629079\",\n \"0.58912194\",\n \"0.5825029\",\n \"0.57799065\",\n \"0.557808\",\n \"0.5561385\",\n \"0.55040497\",\n \"0.5481859\",\n \"0.54713845\",\n \"0.5368414\",\n \"0.5327975\",\n \"0.52699995\",\n \"0.52693903\",\n \"0.52427757\",\n \"0.52367204\",\n \"0.52185136\",\n \"0.52170134\",\n \"0.5204411\",\n \"0.5148115\",\n \"0.51351565\",\n \"0.5132368\",\n \"0.51252276\",\n \"0.512357\",\n \"0.5113103\",\n \"0.5106386\",\n \"0.5105611\",\n \"0.5081404\",\n \"0.5071633\",\n \"0.5069231\",\n \"0.50649154\",\n \"0.5046944\",\n \"0.50369966\",\n \"0.50347453\",\n \"0.50112444\",\n \"0.5003188\",\n \"0.5003052\",\n \"0.50003994\",\n \"0.4982371\",\n \"0.4960052\",\n \"0.49580863\",\n \"0.49546158\",\n \"0.49537212\",\n \"0.49445936\",\n \"0.49354464\",\n \"0.49348632\",\n \"0.49312893\",\n \"0.49298772\",\n \"0.49285316\",\n \"0.49258918\",\n \"0.4925266\",\n \"0.49094293\",\n \"0.49077252\",\n \"0.4906656\",\n \"0.48952118\",\n \"0.4894956\",\n \"0.4889702\",\n \"0.48886684\",\n \"0.48823512\",\n \"0.4877747\",\n \"0.4861346\",\n \"0.4856938\",\n \"0.4845609\",\n \"0.48343918\",\n \"0.48307005\",\n \"0.48224336\",\n \"0.48120427\",\n \"0.4811941\",\n \"0.48009777\",\n \"0.47961563\",\n \"0.47954753\",\n \"0.4792434\",\n \"0.47906315\",\n \"0.47766036\",\n \"0.4776027\",\n \"0.4774271\",\n \"0.4762457\",\n \"0.47569475\",\n \"0.47543168\",\n \"0.47504637\",\n \"0.47489324\",\n \"0.4746841\",\n \"0.47458005\",\n \"0.4743053\",\n \"0.4741038\",\n \"0.47396654\",\n \"0.47201896\",\n \"0.47166815\",\n \"0.47125915\",\n \"0.47105667\",\n \"0.47058547\",\n \"0.47019553\",\n \"0.46986783\",\n \"0.4694803\",\n \"0.46940172\",\n \"0.46938962\",\n \"0.4693233\",\n \"0.4693099\",\n \"0.4692603\",\n \"0.46909136\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6939935"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17481,"cells":{"query":{"kind":"string","value":"function to create employee trainee."},"document":{"kind":"string","value":"def create_trainee(self, name, pos, dept):\n self.trainee[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'junior': self.junior[dept.upper()][0]['name'],\n }\n )\n self.manager[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.junior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n else:\n self.create_trainee(name, pos, dept)","def create_employee(self,personal_identity):\r\n new_emp = Employee(*personal_identity)\r\n registration_str = new_emp.get_registration_str()\r\n\r\n return_value = self.save_object_to_DB(\"employee\",registration_str)\r\n return return_value","def create_employee(self):\n try:\n name = input(\"Enter name: \")\n if not name.isalpha():\n print(\"Invalid data format. Name should contain only alphabets. \")\n return False\n email = input(\"Enter email: \")\n if not InputValidations.validate_email(email):\n return False\n employee = EmployeeModel(name=name, email=email)\n self.admin_repository.create_employee(employee)\n print(\"Employee created successfully!\")\n return True\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def test_employee_creation(self):\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\n\n # Returned result is an OrderedDict\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\n\n self.assertEqual(result['name'], helper.name)\n self.assertEqual(result['hiredOn'], helper.hired_on)\n self.assertEqual(result['salary'], helper.salary)\n self.assertEqual(result['departmentId'], helper.department_id)","def post(self):\n try:\n employee = self.service.add_employee(self.schema, request.json)\n except ValidationError as error:\n return error.messages, 400\n return self.schema.dump(employee), 201","def add_employee(self, obj):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\n\n self.dbconnect.commit()\n return obj\n except:\n self.dbconnect.rollback()\n raise","def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\n teacher_account_id = person.get_last()\n query = 'INSERT INTO teacher VALUES( %s,%s );'\n args = (teacher_account_id, availability)\n database.connection.save_data(query, args)","def setUp(self):\n\n self.user = self.make_user()\n self.employee = Employee.objects.create(\n cpf=\"974.220.200-16\",\n user=self.user,\n departament=Employee.ADMINISTRATION\n )","def create_educator(data):\n\n educator = Educator(\n name=data['name'],\n work_email=data['work_email'],\n organization_name=data['organization_name'],\n org_or_school=data['org_or_school'],\n address_line_1=data['address_line_1'],\n address_line_2=data['address_line_2'],\n city=data['city'],\n state=data['state'],\n zipcode=data['zipcode'],\n num_students=data['num_students']\n )\n educator.save()\n return educator","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\n employee, created = Employee.objects.update_or_create(user=user,\n employee_id=validated_data.pop('employee_id'),\n location=validated_data.pop('location'),\n avail_start_time= str(validated_data.pop('avail_start_time')),\n avail_end_time= str(validated_data.pop('avail_end_time')))\n return employee","def create(self, request):\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\n\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n print(F\"Request employee Data: {serializer.data}\")\n\n try:\n new_employee = self.controller.create_employee(request_data=request_data)\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (domain_exceptions.EmployeeIDIsNotUnique,\n domain_exceptions.WorkArrangementPercentageOutOfRange,\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.WorkArrangementPercentageNull\n ) as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create_tea():\n # Bypass if user is logged in\n\n form = TeaAddForm()\n # Validate login attempt\n if form.validate_on_submit():\n tea = Tea(\n name=form.name.data,\n price_per_gram=form.price_per_gram.data,\n )\n db.session.add(tea)\n db.session.commit() # Create new tea\n return redirect(url_for(\"main_bp.dashboard\"))\n return render_template(\n \"add_tea.jinja2\",\n title=\"Add a Tea\",\n form=form,\n template=\"add_tea-page\",\n body=\"Add a Tea\",\n )","def setUp(self):\n self.salary = 40000\n self.custom_rise = 7500\n self.employee = Employee(\"Carlos\", \"Zapata\", self.salary)","def create_test_run(\n self, assign_user_id, project_id, suite_id, testrun_name, tr_keys):\n data = {\n 'suite_id': suite_id,\n 'name': testrun_name,\n 'assignedto_id': assign_user_id,\n 'include_all': False,\n 'case_ids': tr_keys,\n }\n\n response = self.client.send_post(\n ADD_TESTRUN_URL.format(project_id),\n data,\n cert_check=self.cert_check\n )\n for key, _ in response.items():\n if key == 'error':\n print('Failed to create testrun: {}'.format(response))\n else:\n self.testrun_id = response['id']","def create_employee(attributes):\n neccessary_keys = [\"empid\", \"gender\", \"sales\", \"bmi\", \"salary\", \"birthday\",\n \"age\"]\n for key in neccessary_keys:\n if not key in attributes.keys():\n raise ValueError(\"employee could not be created: {} is missing\".format(key))\n return Employee(attributes[\"empid\"], attributes[\"gender\"],\n attributes[\"sales\"], attributes[\"bmi\"],\n attributes[\"salary\"], attributes[\"birthday\"],\n attributes[\"age\"])","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n respond_data = self.controller.add_team_employee(request_data=request_data)\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.EmployeeDoesNotExist,\n domain_exceptions.EmployeeIsATeamMember\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def setUp(self):\n self.my_employee = Employee('knight', 'lee', 10000)","def create_employee_from_applicant(self, cr, uid, ids, context=None):\n if context is None:\n context = {}\n hr_employee = self.pool.get('hr.employee')\n model_data = self.pool.get('ir.model.data')\n act_window = self.pool.get('ir.actions.act_window')\n emp_id = False\n for applicant in self.browse(cr, uid, ids, context=context):\n address_id = contact_name = False\n if applicant.partner_id:\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\n if applicant.job_id and (applicant.partner_name or contact_name):\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\n create_ctx = dict(context, mail_broadcast=True)\n\n pes=self.browse(cr,uid,ids)[0]\n coy=pes.partner_name\n\n ##### Susunan Keluarga ayah/ibu #####\n le=self.pool.get('hr_recruit.suskel1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context)\n prod_ids=[] \n for pr in lele:\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\n \n ###### Susunan Keluarga Suami/istri #####\n le=self.pool.get('hr_recruit.suskel2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids1=[] \n for pr in lele:\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \n \n ###### riwayat Pendidikan #######\n le=self.pool.get('hr_recruit.rwt_pend')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids2=[] \n for pr in lele:\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \n \n ###### bahasa ######\n le=self.pool.get('hr_recruit.bahasa')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids3=[] \n for pr in lele:\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \n \n ##### Riwayat Pekerjaan ####\n le=self.pool.get('hr_recruit.rwt_krj')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids4=[] \n for pr in lele:\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \n \n ###### Koneksi Internal #####\n le=self.pool.get('hr_recruit.kon1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids5=[] \n for pr in lele:\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \n \n ###### Koneksi Eksternal ####\n le=self.pool.get('hr_recruit.kon2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids6=[]\n for pr in lele: \n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \n\n ####### create Employee ######## \n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\n 'job_id': applicant.job_id.id,\n 'department_id' : applicant.department_id.id,\n 'address_id2' : applicant.job_id.address_id.id,\n #### informasi Probadi ####\n 'kelamin':applicant.jen_kel,\n 'blood' : applicant.blood,\n 'agama' : applicant.agama_id.id,\n 'birthday' : applicant.tgl_lahir,\n 'place_of_birth' : applicant.kota_id.name,\n 'marital':applicant.status,\n 'sjk_tanggal' : applicant.sjk_tanggal,\n 'mobile_phone':applicant.partner_phone,\n 'country_id' : applicant.country_id.id,\n\n #### Pendidikan ####\n 'type_id':applicant.type_id.id,\n 'bid_id':applicant.bidang_id.id,\n 'jurusan_id':applicant.jurusan_id.id,\n 'pt_id':applicant.pt_id.id,\n 'gelar_id':applicant.gelar_id.id,\n\n #### alamat DOmisili ####\n 'country_id1':applicant.country_id1.id,\n 'prov_id':applicant.prov_id.id,\n 'kab_id' : applicant.kab_id.id,\n 'kec_id':applicant.kec_id.id,\n 'alamat1' : applicant.alamat1,\n 'kodepos' :applicant.kode1,\n 'telp1' : applicant.telp1,\n\n #### kartu identitas ####\n 'jenis_id': applicant.jenis_id,\n 'ktp' : applicant.no_id,\n 'tgl_berlaku' : applicant.tgl_berlaku,\n # 'issued_id' : applicant.dikeluarkan.id,\n \n #### Alamat Sesuai KTP #### \n 'country_id2':applicant.country_id2.id,\n 'prov_id2':applicant.prov_id2.id,\n 'kab_id2':applicant.kab_id2.id,\n 'kec_id2':applicant.kec_id2.id,\n 'alamat2' : applicant.alamat2,\n 'kodepos1':applicant.kode2,\n 'telp2' : applicant.telp2,\n \n # 'status': applicant.status,\n #### IDS ####\n 'susunan_kel1_ids' : prod_ids,\n 'susunan_kel2_ids':prod_ids1,\n 'rwt_pend_ids':prod_ids2,\n 'bahasa_ids':prod_ids3,\n 'rwt_krj_ids':prod_ids4,\n 'koneksi1_ids':prod_ids5,\n 'koneksi2_ids':prod_ids6, \n })\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\n self.pool['hr.job'].message_post(\n cr, uid, [applicant.job_id.id],\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\n subtype=\"hr_recruitment.mt_job_applicant_hired\", context=context)\n else:\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\n\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\n if emp_id:\n dict_act_window['res_id'] = emp_id\n dict_act_window['view_mode'] = 'form,tree'\n return dict_act_window","def setUp(self):\n self.employee = Employee('Lucas', 'Guerra', 45000)","def add_employee(schema, employee_json):\n employee = schema.load(employee_json, session=db.session)\n db.session.add(employee)\n db.session.commit()\n return employee","def create():","def create():","def setUp(self):\n\tself.emp = Employee('Lin',10000)\n\tself.emp2 = Employee('Jun',20000)","def post(self, request):\n data = request.data\n skill_data = data.pop('skills')\n Department_name = data.pop('department')\n department = Department.objects.get(name=Department_name)\n manager_name = data.pop('manager')\n manager = Manager.objects.get(name=manager_name)\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\n Employee.save()\n for skill in skill_data:\n skill_add, create = Skill.objects.get_or_create(name=skill)\n Employee.skills.add(skill_add)\n return Response(\n data=request.data\n )","def handle(self, *args, **kwargs):\n seeder = Seed.seeder()\n seeder.add_entity(User, 20)\n\n seeder.add_entity(EmployeeMptt, 20, {\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\n 'parent': lambda x: EmployeeMptt.objects.order_by(\"?\").first(),\n 'level': lambda x: random.randint(0, 4),\n })\n seeder.execute()","def setUp(self):\n self.employee = Employee('John', 'Doe', 50000)\n self.raise_amount = 20000","def create(tesserae, title):\n try:\n return tesserae.create(title)\n except TesseraError, e:\n sys.stderr.write(\"Error: %s\\n\" % str(e))\n return False","def post(self):\n data = EmployeeRegister.parser.parse_args()\n new_employee_id = str(uuid.uuid4())\n\n while EmployeeModel.find_by_id(new_employee_id):\n # if this id is already in use\n new_employee_id = str(uuid.uuid4())\n\n employee = EmployeeModel(**data, employee_id=new_employee_id)\n employee.save_to_db()\n\n return {\"message\": \"Employee successfully added to the system\"}, 201 # 201 - Created","async def create(self):\n cur = self.sql.cur\n\n user = Client().get_server(self.server_id).get_member(self.user_id)\n\n self.nickname = user.nick if user.nick else user.name\n\n nickname = self.nickname\n trainer_id = str(uuid.uuid4())\n self.trainer_id = trainer_id\n now = datetime.datetime.now()\n user_id = self.user_id\n server_id = self.server_id\n\n self.current_zone_id = '86'\n self.current_building_id = None\n self.current_region_id = None\n\n cmd = \"\"\"INSERT INTO trainers\n (trainer_id,\n user_id,\n server_id,\n nickname,\n created_on)\n VALUES\n (:trainer_id,\n :user_id,\n :server_id,\n :nickname,\n :now)\"\"\"\n cur.execute(cmd, locals())\n\n cmd = \"\"\"INSERT INTO trainer_stats\n (trainer_id)\n VALUES\n (:trainer_id)\"\"\"\n cur.execute(cmd, locals())\n\n cmd = \"\"\"INSERT INTO trainer_data\n (trainer_id,\n current_region_id,\n current_zone_id,\n current_building_id)\n VALUES\n (:trainer_id,\n :current_region_id,\n :current_zone_id,\n :current_building_id)\"\"\"\n cur.execute(cmd, self.__dict__)\n\n cmd = \"\"\"INSERT INTO trainer_party\n (trainer_id)\n VALUES\n (:trainer_id)\"\"\"\n cur.execute(cmd, locals())\n\n await self.sql.commit(now=True)\n self.log.info(f\"New trainer has been born! Welcome {trainer_id}\")","def test_admin_can_create_a_employee(self):\n\n account_data = {\n \"username\": \"Mike\",\n \"email\": \"mioshine@g.com\",\n \"password\": \"1234567\",\n \"confirm_password\": \"1234567\"\n }\n response = self.client.post(\n reverse('accounts:create-user'),\n account_data,\n format=\"json\")\n \"\"\"Test the api has bucket creation capability.\"\"\"\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertTrue(\"data\" in json.loads(response.content))","def create_event(self, name, date):\n user = User.objects.create(username='userdemo')\n user.set_password('calnote24')\n user.save()\n Event.objects.create(name=name, date=date, user_id=user.id)","def generateEmployees(self):\r\n\r\n # Name\r\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\r\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\r\n\r\n for i in range(self.num_of_employees):\r\n\r\n # Clock in an hour before opening, 6 hours after, or 12 hours after\r\n clockIn = random.choice([7, 13, 19])\r\n\r\n # Clock out after 5 hours, 10 hours, or 15 hours\r\n clockOut = random.choice([13, 19, 23])\r\n while clockOut <= clockIn:\r\n clockOut = random.choice([13, 19, 23])\r\n\r\n # Hourly wage\r\n wage = random.choice([8, 9, 10, 12, 20])\r\n\r\n gender = random.choice(['M', 'F'])\r\n if gender == 'M':\r\n name = random.choice(maleNames)\r\n else:\r\n name = random.choice(femaleNames)\r\n\r\n self.c.execute(\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\", (name, clockIn, clockOut, wage))\r\n self.conn.commit()\r\n\r\n if self.print_employees:\r\n print(\"\\nName:\", name)\r\n print(\"Clock in:\", clockIn)\r\n print(\"Clock out:\", clockOut)\r\n print(\"Wage:\", wage)","def test_createItinerary(self):\n rv = self.json_post('/createItinerary/alex', dict(\n name = 'New Day',\n date = '2015-08-21T00:00:00.000Z'\n ))\n itinHash = str('alex' + \"_\" + '2015-08-21T00:00:00.000Z')\n assert itinHash in str(rv.data)\n\n rv = self.json_post('/createItinerary/alex', dict(\n name = 'New Day',\n date= '2015-08-21T00:00:00.000Z'\n ))\n assert 'Itinerary date already in use' in str(rv.data)\n\n rv = self.json_post('/createItinerary/bbbb', dict(\n name = 'New Day',\n date= '2015-08-21T00:00:00.000Z'\n ))\n assert 'Invalid username' in str(rv.data)","def add_employee(self, first_name, last_name):\n self.switch_main_menu(\"PIM\")\n self.click_menu(\"Add Employee\")\n self.pim = AddEmployee(self.driver)\n self.pim.add_user_employee(first_name, last_name)","def add_employee(self, empl):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\n empl.active, empl.promotor))\n cursor.execute('SELECT LASTVAL()')\n eid = cursor.fetchone()[0]\n empl.id = eid\n # get id and return updated object\n self.dbconnect.commit()\n except(Exception, self.dbconnect.get_error()) as error:\n self.dbconnect.rollback()\n raise Exception('\\nUnable to save Employee!\\n(%s)' % (error))","def do_create(service,summary,description,startday,\\\n starttime,endtime,username,email):\n event = {\n 'summary': 'Code Clinic: {}'.format(summary),\n 'description': '{}.'.format(description),\n 'start': {\n 'dateTime': '{}T{}:00'.format(startday, starttime),\n 'timeZone': 'GMT+02',\n },\n 'end': {\n 'dateTime': '{}T{}:00'.format(startday,endtime),\n 'timeZone': 'GMT+02',\n },\n 'recurrence': [\n 'RRULE:FREQ=DAILY;COUNT=1'\n ],\n 'attendees': [\n {\n 'displayName': username,\n 'email': email,\n 'optional': True,\n 'comment': 'Creator',\n 'responseStatus': 'accepted',\n },\n ],\n 'anyoneCanAddSelf': True,\n\n 'reminders': {\n 'useDefault': False,\n 'overrides': [\n {'method': 'email', 'minutes': 24 * 60},\n {'method': 'popup', 'minutes': 10},\n ],\n },\n }\n\n event = service.events().insert(calendarId='primary', body=event,\\\n sendUpdates='all').execute()\n\n return event","def create(ctx, name, company, mail, age):\n client = Client(name,company,mail,age)\n client_service = ClientService(ctx.obj['clients_table']) \n client_service.create_client(client)","def setUp(self):\n\t\tfirst_name = 'Gerson'\n\t\tlast_name = 'Santos'\n\t\tannual_salary = 5000\n\t\tself.gerson = Employee(first_name, last_name, annual_salary)","def create(self, values):\n if values.get('country_id', False):\n country = self.env['res.country'].browse(values['country_id'])\n if country.code == 'SA':\n values.update({'is_saudi': True})\n else:\n values.update({'is_saudi': False})\n\n res = super(HrEmployee, self).create(values)\n if values.get('user_id', False):\n self.user_id.write({'employee_id': res})\n return res","def create(args):\n print('Creates an HPC fleet with given name \"{}\"'.format(args.fleet_name))","def create():\n pass","def create_individual(self):\n pass","def post(self):\n try:\n body = json.loads(request.data.decode(\"utf-8\"))\n trainer = Trainer(body[\"firstName\"], body[\"lastName\"],\n body[\"email\"])\n trainer = TrainerService.create_trainer(trainer)\n return jsonify(trainer.json()), 201\n except ValueError:\n return INVALID_ID_ERROR, 400 # Bad Request\n except ResourceNotFound as r:\n return r.message, 404","def add_employee(self, employee):\n self.employees.add(employee)","def create_epic():\n client = RequestManager()\n client.set_method(\"POST\")\n client.set_endpoint(\"/projects/{0}/epics\".format(STORED_ID['project_id']))\n name = \"\".join(choices(string.ascii_letters, k=6))\n body = {\"name\": name}\n client.set_body(json.dumps(body))\n response = client.execute_request()\n STORED_ID['epic_id'] = response.json()['id']","def _create_td(self):\n raise NotImplementedError","def createMakingTest(tx, query, personId, testId, date, hour, result):\n tx.run(query, personId=personId, testId=testId, date=date, hour=hour, result=result)","def setUp(self):\n\t\tself.mason = Employee(\"mason\",\"karsevar\",10000)","def pl_create_order(self):\n\tprint()\n\tprint('Pl - Create Order')\n\n\n\tpartner = self.env['res.partner'].search([\n\t\t\t\t\t\t\t\t\t\t\t\t\t('name', '=', self.patient.name),\n\t\t\t\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t\t\t\t#order='appointment_date desc',\n\t\t\t\t\t\t\t\t\t\t\t\tlimit=1,)\n\n\n\t# Create Order\n\torder = self.env['sale.order'].create({\n\t\t\t\t\t\t\t\t\t\t\t\t\t'state':'draft',\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_doctor': self.physician.id,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t#'partner_id': self.partner_id.id,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'partner_id': partner.id,\n\t\t\t\t\t\t\t\t\t\t\t\t\t#'x_ruc': self.partner_id.x_ruc,\n\t\t\t\t\t\t\t\t\t\t\t\t\t#'x_dni': self.partner_id.x_dni,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t'patient': self.patient.id,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_id_doc': self.patient.x_id_doc,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_id_doc_type': self.patient.x_id_doc_type,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_family': 'procedure',\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t'treatment': self.id,\n\t\t\t\t\t\t\t\t\t\t\t\t})\n\t#print(order)\n\n\n\n\t# Create Order Lines\n\tfor cart_line in self.shopping_cart_ids:\n\n\t\tproduct = cart_line.product\n\n\t\t#print(product)\n\t\t#print(product.name)\n\n\t\t# Create Order Line\n\t\tol = order.order_line.create({\n\t\t\t\t\t\t\t\t\t\t'name': \t\tproduct.name,\n\t\t\t\t\t\t\t\t\t\t'product_id': \tproduct.id,\n\t\t\t\t\t\t\t\t\t\t'price_unit': \tcart_line.price,\n\t\t\t\t\t\t\t\t\t\t'product_uom_qty': cart_line.qty,\n\t\t\t\t\t\t\t\t\t\t'order_id': \torder.id,\n\t\t\t\t\t\t\t\t\t})\n\treturn order\n\n\t# pl_create_order","def create():\n data = request.get_json()\n print(\"DATA: \", data)\n db_helper.insert_new_record(data['first_name'], data['last_name'], data['class_period'], data['current_belt'], data['student_teacher_id'])\n result = {'success': True, 'response': 'Done'}\n return jsonify(result)","def get_create_employee_query(self):\n template = \"\"\"\n mutation createEmployee {{\n createEmployee(input: {{ {params} }}) {{\n employee {{\n name\n hiredOn\n salary\n departmentId\n }}\n }}\n }}\n \"\"\"\n # Add input parameters as needed\n input_params = 'name:\"{}\",'.format(self.name)\n\n if self.hired_on is not None:\n input_params += 'hiredOn: \"{}\", '.format(self.hired_on)\n\n if self.salary is not None:\n input_params += 'salary: {}, '.format(self.salary)\n\n if self.department_id is not None:\n input_params += 'departmentId: {}'.format(self.department_id)\n\n return template.format(params=input_params)","def create(self, vals):\n if not vals.get('nik_number'):\n vals['nik_number'] = self.generate_nik(vals)\n return super(Employee, self).create(vals)","def create_person(self):","def test_access_employee(self):\n # Employee can't see any SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).read()\n # Employee can't edit the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\n # Employee can't create the SO\n with self.assertRaises(AccessError):\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\n 'partner_id': self.partner_a.id,\n })\n # Employee can't delete the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).unlink()","def create(self, name, login, password, email, address=\"\", vat=\"\", jobguid=\"\", executionparams=None):","def create_employee(request, company_id):\n\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n logUnauthorizedAccess(\"User tried to create_employee\", request)\n raise PermissionDenied()\n form = EmployeeForm(request, initial=dict(company=company))\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\n # Q(company=company) | Q(company__isnull=True))\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n\n return TemplateResponse(\n request,\n 'mus/create_employee_form.html',\n {\n 'employee_form': form,\n }\n )\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n # return JsonResponse(status=200, data=data)","def create(self):","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.EmployeeDoesNotExist\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create_meal():","def _create_fleet(self):\n # make an alien\n alien = Alien(self)\n self.aliens.add(alien)","def create_ticket(self, user):\n return Ticket.objects.create_ticket('test', user)","def create_partner(name):\n\n return Partner.objects.create(name=name)","def create(*args):","def create_order(self):\n\tprint()\n\tprint('OH - pl_create_order')\n\n\t# Search Partner\n\tprint()\n\tprint('Search partner')\n\tpartner = self.env['res.partner'].search([\n\t\t\t\t\t\t\t\t\t\t\t\t\t('name', '=', self.patient.name),\n\t\t\t\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t\t\t\t#order='appointment_date desc',\n\t\t\t\t\t\t\t\t\t\t\t\tlimit=1,)\n\n\t# Search Pl\n\tprint()\n\tprint('Search pricelist')\n\tpricelist = self.env['product.pricelist'].search([\n\t\t\t\t\t\t\t\t\t\t\t#('active', 'in', [True]),\n\t\t\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t\t\t#order='x_serial_nr asc',\n\t\t\t\t\t\t\t\t\t\t\tlimit=1,\n\t\t\t\t\t\t\t\t\t\t)\n\tprint(pricelist)\n\n\t# Create Order\n\torder = self.env['sale.order'].create({\n\t\t\t\t\t\t\t\t\t\t\t\t\t'state':'draft',\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_doctor': self.physician.id,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t#'partner_id': self.partner_id.id,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'partner_id': partner.id,\n\t\t\t\t\t\t\t\t\t\t\t\t\t#'x_ruc': self.partner_id.x_ruc,\n\t\t\t\t\t\t\t\t\t\t\t\t\t#'x_dni': self.partner_id.x_dni,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t'patient': self.patient.id,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_id_doc': self.patient.x_id_doc,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_id_doc_type': self.patient.x_id_doc_type,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'x_family': 'procedure',\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t'treatment': self.id,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t'pricelist_id': pricelist.id,\n\t\t\t\t\t\t\t\t\t\t\t\t})\n\t#print(order)\n\n\n\n\t# Create Order Lines\n\tfor cart_line in self.shopping_cart_ids:\n\n\t\tproduct = cart_line.product\n\n\t\t#print(product)\n\t\t#print(product.name)\n\n\t\t# Create Order Line\n\t\tol = order.order_line.create({\n\t\t\t\t\t\t\t\t\t\t'name': \t\tproduct.name,\n\t\t\t\t\t\t\t\t\t\t'product_id': \tproduct.id,\n\t\t\t\t\t\t\t\t\t\t'price_unit': \tcart_line.price,\n\t\t\t\t\t\t\t\t\t\t'product_uom_qty': cart_line.qty,\n\t\t\t\t\t\t\t\t\t\t'order_id': \torder.id,\n\t\t\t\t\t\t\t\t\t})\n\treturn order","def main():\n name = input(\"Please enter in your name: \")\n\n \"\"\"Ask the user to enter a number if they are a Director, Manager or Staff.\"\"\"\n \"\"\"This will check and make sure the user only enters in 1,2, \n or 3 and a number greater than zero\"\"\"\n while True:\n try:\n designation_number = int(input(\"Please enter in \\n1 for Director \"\n \"\\n2 for Manager \\n3 for Staff\\n\"))\n if 0 < designation_number <= 3:\n break\n print(\"Invalid number entered.\")\n except Exception as e:\n print(e)\n \"\"\"Gets the user salary and makes sure is a number and greater than 0\"\"\"\n while True:\n try:\n salary = float(input(\"Please enter in your salary: \"))\n if salary <= 0:\n print(\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\")\n else:\n break\n except ValueError:\n print(\"Oops! That was not a valid number. Try again...\")\n\n \"\"\"Create Employee\"\"\"\n employee1 = employee.Employee()\n employee1.set_name(name)\n employee1.set_designation(designation_number)\n employee1.set_salary(salary)\n print(employee1)","def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\n peronjson={}, notes='', tenantid=''):\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\n self.persontdetailscoll.insert(tenantobj, safe=True)","def make_electrode_table(self):\n self.table = get_electrode_table()\n self.dev1 = Device(name='dev1')\n self.group = ElectrodeGroup(name='tetrode1',\n description='tetrode description',\n location='tetrode location',\n device=self.dev1)\n for i in range(4):\n self.table.add_row(location='CA1', group=self.group, group_name='tetrode1')","def make_electrode_table(self):\n self.table = get_electrode_table()\n self.dev1 = Device(name='dev1')\n self.group = ElectrodeGroup(name='tetrode1',\n description='tetrode description',\n location='tetrode location',\n device=self.dev1)\n for i in range(4):\n self.table.add_row(location='CA1', group=self.group, group_name='tetrode1')","def _create_new_attende(name, email, gdpr, marketing):\n\n new_attendee = Attendee.objects.create(\n name=name,\n email=email,\n gdpr=gdpr,\n marketing=marketing,\n token=uuid.uuid1(),\n date_signed=datetime.date.today()\n )\n new_attendee.save()\n return new_attendee","def create_new_test_run():\n if debug:\n print('[DEBUG] Func: create_new_test_run...')\n\n new_test_run_url = \"https://eei.testrail.com/index.php?/api/v2/add_run/{0}=\".format(project_id)\n\n new_test_run_json = {\n \"suite_id\": suite_id,\n \"name\": suite_name,\n \"assignedto_id\": assignedto_id,\n \"include_all\": False,\n \"case_ids\": [testcase_id]\n }\n\n new_test_run = requests.post(new_test_run_url, auth=authorization, json=new_test_run_json)\n\n if str(new_test_run.status_code) != '200':\n print('[ERROR] new_test_run: non 200 status code... ' + str(new_test_run.status_code))\n print(str(new_test_run.json()))\n sys.exit(1)\n\n global new_test_run_id\n new_test_run_id = str(new_test_run.json()[\"id\"])","def createEmployee(firstName, lastName, ssn, salary):\n employee = Employee(firstName, lastName, ssn, salary)\n # verify\n if firstName != employee.firstName or \\\n lastName != employee.lastName or \\\n ssn != employee.ssn or \\\n salary != employee.salary:\n raise ValueError(\"Failed to initialize Employee\")\n return employee","def create(self):\n ...","def create_trail(user_id, hike_id):\n\n trail = Trail(user=user_id, hike=hike_id)\n\n db.session.add(trail)\n db.session.commit()\n\n return trail","def add_trainee():\n\n add_trainee_form = AddTraineeForm()\n if add_trainee_form.validate_on_submit():\n role = Role(role_id=RoleIds.Trainee)\n add_trainee_form.populate_obj(role)\n\n supervised_activity_ids = [\n a.id for a in current_user.get_supervised_activities()\n ]\n if role.activity_id not in supervised_activity_ids:\n flash(\"Activité invalide\", \"error\")\n return redirect(url_for(\".leader_list\"))\n\n user = User.query.get(role.user_id)\n if user is None:\n flash(\"Utilisateur invalide\", \"error\")\n return redirect(url_for(\".leader_list\"))\n\n if user.has_role_for_activity(\n [RoleIds.Trainee, RoleIds.EventLeader],\n role.activity_id,\n ):\n flash(\n \"L'utilisateur est déjà encadrant ou encadrant en formation pour cette activité\",\n \"error\",\n )\n return redirect(url_for(\".leader_list\"))\n\n db.session.add(role)\n db.session.commit()\n add_trainee_form = AddTraineeForm(formdata=None)\n\n return render_template(\n \"leaders_list.html\",\n add_trainee_form=add_trainee_form,\n title=\"Encadrants en formation\",\n )","def test_create_team(self):\n pass","def setUp(self):\n response = self.client.post('/trainer/create/',\n {\"name\": \"Blue\",\n \"las_name\": \"Oak\"})\n self.trainer_id = response.json()[\"id\"]","def create(self, *args, **kwargs):\n pass","def test_new_employee_crud_methods(self):\n response = self.client.get(\n '/employees/', kwargs={'employer_id': self.employee.id})\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(Employee.objects.all()), 1)\n\n # Test that a new employee can be added\n response = self.client.post(\n '/employees/',\n {'name': 'MAdtraxx!!', 'employer': self.employer.id},\n kwargs={'pk': self.employer.id})\n self.assertEqual(response.status_code, 201)\n self.assertEqual(Employee.objects.count(), 2)\n\n # Test that employee info may be edited\n response = self.client.put('/employees/1/',\n {'name': 'Ashley',\n 'employer': self.employer.id},\n kwargs={'employer_id': self.employee.id,\n 'pk': self.employee.id})\n self.assertEqual(response.status_code, 200)","def create_task(self, name, date, isComplete):\n user = User.objects.create(username='userdemo')\n user.set_password('calnote24')\n user.save()\n Task.objects.create(task=name, dueDate=date, isComplete=isComplete, user_id=user.id)","def setUpTestData(cls):\n countries = [\"MX\", \"CHL\", \"USA\", \"PER\", \"COL\"]\n slack_user_ids = [\"UP0918MAV\", \"UP0918MAV\", \"UP0918MAV\", None, None]\n cls.menu = Menu.objects.create(available_on=date.today())\n for count in range(5):\n user = User.objects.create(username=f\"johny.doe {count}\")\n Employee.objects.create(\n user=user, country=countries[count], slack_user_id=slack_user_ids[count]\n )","def add_employee():\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n form = SignUp_Form()\n \n\n if form.validate_on_submit():\n try: \n employee = Employee.register(\n username = form.username.data,\n password = form.password.data, \n email = form.email.data, \n first_name = form.first_name.data,\n last_name = form.last_name.data,\n hire_date = form.hire_date.data, \n is_admin = form.is_admin.data,\n )\n\n db.session.add(employee)\n\n db.session.commit()\n except IntegrityError:\n flash(\"Email already in use\", \"danger\")\n return render_template(\"/admin/add_user.html\", form = form)\n\n flash(\"Employee Added!\", \"success\")\n return redirect(\"/administrator\")\n else:\n\n return render_template(\"/admin/add_user.html\", form = form)","def createEmployee():\n form = CreateEmployeeForm()\n if form.validate_on_submit():\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \n email=form.email.data, role=form.role.data, location=form.location.data)\n db.session.add(staff)\n db.session.commit()\n flash(f'Employee Added To Database', category='Success')\n return redirect(url_for('login'))\n return render_template('new_employee.html', title=\"Register\", form=form)","def create(self, vals):\n res = super(HelpdeskTicket, self).create(vals)\n content = \"\"\n if vals.get('timesheet_ids', False):\n content += \"Parte de Horas: <br/>\"\n for ts in vals.get('timesheet_ids'):\n if content != \"Parte de Horas: <br/>\":\n content += \"------------------------------------------------------------------------------<br/>\"\n for field_name in ts[2]:\n new_val = ts[2][field_name]\n if field_name == 'date':\n content += \" \\u2022 Fecha: \" + new_val + \"<br/>\"\n elif field_name == 'employee_id':\n new_employee = self.env['hr.employee'].browse(new_val).name\n content += \" \\u2022 Empleado: \" + new_employee + \"<br/>\"\n elif field_name == 'unit_amount':\n content += \" \\u2022 Duración: \" + '{0:02.0f}:{1:02.0f}'.format(\n *divmod(new_val * 60, 60)) + \"<br/>\"\n elif field_name == 'name' and new_val:\n content += \" \\u2022 Descripción: \" + new_val + \"<br/>\"\n res.message_post(body=content)\n\n return res","def test_create(self):\n\n adminuser,adminpass = self.testdata.find_account_for('toolmanager')\n\n self.utils.account.login_as(adminuser,adminpass)\n\n self.contribtool.create(TOOLNAME)","def amazonEc2_create(amazonEc2):\n\treturn amazonEc2","def ht_meeting_create(values, uid):\n\n\tprint 'ht_meeting_create: enter()'\n\t#\tStripe fields should be validated.\n\t#\tv1 - end time is reasonable (after start time)\n\t#\tv2 - hero exists.\n\t#\tv3 - place doesn't matter as much..\n\t#\tv4 - cost..\n\ttry:\n\t\tstripe_tokn = values.get('stripe_tokn')\t\t# used to charge\n\t\tstripe_card = values.get('stripe_card')\t\t# card to add to insprite_customer.\n\t\tstripe_cust = values.get('stripe_cust')\n\t\t#stripe_fngr = values.get('stripe_fngr')\t#card_fingerprint\n\n\t\tprop_mentor = values.get('prop_mentor')\n\t\tprop_cost = values.get('prop_cost')\n\t\tprop_desc = values.get('prop_desc')\n\t\tprop_location = values.get('prop_location')\n\t\tprop_lesson = values.get('prop_lesson')\n\t\tprop_groupsize = values.get('prop_groupsize')\n\n\t\tprint \"ht_meeting_create: mentor:\", prop_mentor\n\t\tprint \"ht_meeting_create: cost:\", prop_cost\n\t\tprint \"ht_meeting_create: desc:\", prop_desc\n\t\tprint \"ht_meeting_create: location:\", prop_location\n\t\tprint \"ht_meeting_create: lesson:\", prop_lesson\n\t\tprint \"ht_meeting_create: groupsize:\", prop_groupsize\n\n\t\t# validate start/end times via conversion.\n\t\tprop_s_date = values.get('prop_s_date')\n\t\tprop_s_time = values.get('prop_s_time')\n\t\tprop_f_date = values.get('prop_f_date')\n\t\tprop_f_time = values.get('prop_f_time')\n\t\tdt_start = dt.strptime(prop_s_date + \" \" + prop_s_time, '%A, %b %d, %Y %H:%M')\n\t\tdt_finsh = dt.strptime(prop_f_date + \" \" + prop_f_time, '%A, %b %d, %Y %H:%M')\n\n\t\t# convert to user's local TimeZone.\n\t\tdt_start_pacific = timezone('US/Pacific').localize(dt_start)\n\t\tdt_finsh_pacific = timezone('US/Pacific').localize(dt_finsh)\n\n\t\tprint 'ht_meeting_create: (from stripe) token =', stripe_tokn, 'card =', stripe_card\n\t\thp\t= Profile.get_by_prof_id(prop_mentor)\n\t\tbp\t= Profile.get_by_uid(uid)\n\t\tba = Account.get_by_uid(uid)\n\t\tha = Account.get_by_uid(hp.account)\n\t\tstripe_cust = ht_get_stripe_customer(ba, cc_token=stripe_tokn, cc_card=stripe_card, cust=stripe_cust)\n\t\tprint 'ht_meeting_create: Mentee (' + str(bp.prof_name) + ', ' + str(stripe_cust) + ')'\n\t\tprint 'ht_meeting_create: Mentor (' + str(hp.prof_name) + ')'\n\n\t\tmeeting = Meeting(hp.prof_id, bp.prof_id, dt_start_pacific, dt_finsh_pacific, (int(prop_cost)/100), str(prop_location), str(prop_desc), str(prop_lesson), prop_groupsize, token=stripe_tokn, customer=stripe_cust, card=stripe_card)\n\n\t\tdb_session.add(meeting)\n\t\tdb_session.commit()\n\t\tprint \"ht_meeting_create: successfully committed meeting\"\n\texcept Exception as e:\n\t\t# IntegrityError, from commit()\n\t\t# SanitizedException(None), from Meeting.init()\n\t\tprint type(e), e\n\t\tdb_session.rollback()\n\t\tht_sanitize_error(e)\n\tprint \"ht_meeting_create: sending notifications\"\n\tht_send_meeting_proposed_notifications(meeting, ha, hp, ba, bp)","def create(self, name, username, emailaddress, maxMemoryCapacity=-1.0, maxVDiskCapacity=-1, maxCPUCapacity=-1, maxNetworkPeerTransfer=-1, maxNumPublicIP=-1, sendAccessEmails=True, **kwargs):\n #put your code here to implement this method\n raise NotImplementedError (\"not implemented method create\")","def createOrder(self, name, email, table, items):\n customerID = self.__customer.locateCustomer(email)\n # Create customer if not exist\n if not customerID:\n customerID = self.__customer.createCustomer(name, email)\n # Create order\n orderID = self.__insertOrder(customerID, table)\n # Add items\n for item in items:\n self.__orderAddItem(orderID, item)\n return orderID","def test_create_record(self):\n pass","def create_course_to_teacher():\n try:\n #get teacher list\n teacher_list = Teacher.get_all_obj_list()\n for k,obj in enumerate(teacher_list):\n print(k,obj.name)\n tid = int(input('请输入老师：'))\n teacher_obj = teacher_list[tid]\n\n #get course list\n course_list = Course.get_all_obj_list()\n for k, obj in enumerate(course_list):\n print(k,obj.name)\n cid = int(input('请输入课程：'))\n course_obj = course_list[cid]\n print(course_obj.nid.get_obj_by_uuid().name)\n #course_list = [(obj.name, obj.school_nid.uuid) for obj in Course.get_all_obj_list()] # uuid???\n course_to_teacher_list = [(obj.course_nid,obj.teacher_nid) for obj in Course_to_teacher.get_all_obj_list()]\n # print(course_to_teacher_list)\n for (course_obj.nid,teacher_obj.nid) in course_to_teacher_list:\n raise Exception('\\033[33;1m[%s] [%s]已经存在，不可重复创建\\033[0m' % (obj.course_nid.get_obj_by_uuid().name, \\\n obj.teacher_nid.get_obj_by_uuid().name))\n obj = Course_to_teacher(course_obj.nid,teacher_obj.nid)\n obj.save()\n status=True\n error = ''\n data = '\\033[33;1m课程[%s] 老师[%s] 创建成功\\033[0m' % (course_obj.name, teacher_obj.name)\n\n except Exception as e:\n status = False\n error = str(e)\n data = ''\n return {'status': status, 'error': error, 'data': data}","def create(self):\n pass","def create(self):\n pass","def create(self):\n pass","def create_email(user):\n if 'research' in user.get_domains():\n domain = 'research'\n else: domain = 'academic'\n subject = \"ECE/CIS Account Created\"\n helprequest = \"https://www.eecis.udel.edu/service\"\n \n message = \"Your ECE/CIS %s account has been created with the username: %s\\n\\n\" % (domain, user.username)\n message += \"Please do not reply to this message. If you need assistance with your account, please visit:\\n\"\n message += \"%s\\n\\n\" % helprequest\n message += \"-- EE/CIS Labstaff\\n\"\n\n send('account@eecis.udel.edu', 'ECE/CIS Account System', \\\n [user.email], subject, message, MAILHOST)","def create(self):\n\n pass","def make_hourly(self,rate,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"1\"\n print(\"{}{}\".format(name,\" was successfully changed to be an hourly employee\"))\n self.emp_dict[id][8] = rate\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def create_task(self, ticket, user):\n return Task.objects.create_task('test', 'low', ticket, user, user)","def createTOFin(En):\n ftemplate = open(\"TOFtemplate.in\", \"r\")\n lines = ftemplate.readlines()\n ftofin = open(\"TOF.in\", \"w\") \n energyline = lines[12].split()\n lines[12] = \"%s %g %s\\n\"%(energyline[0], En, energyline[2])\n ftofin.writelines(lines)\n ftemplate.close()\n ftofin.close()","def create_instructor(self, username):\r\n return self._create_user(username, is_staff=True)","def createOrcaTenant(self,payload):\n response = None\n # Check if tenant with that name already exists\n systemObj = self.getSystemByUid(payload[\"system\"])\n try:\n # Systemname and tenant description always determine a specific tenant\n response = self.getTenantByName(systemObj[\"name\"],payload[\"description\"].upper())\n except KeyError as e:\n if e.args[1] == \"CIC_TENANT_LOOKUP_ERROR\":\n response = None\n pass\n else:\n raise\n try:\n # TMS delivers always a non-empty body if something was found\n if response:\n if response[\"description\"] == payload[\"description\"].upper():\n raise RuntimeError(\"*** INFO *** Tenant already exists\",\"CIC_CREATE_TENANT_ERROR\")\n # TMS delivers an empty body if nothing is found\n elif response is None:\n print \"*** INFO *** Starting tenant creation\"\n response = self.httpHandler.sendHttpRequest(CIC_TENANT_ENDPOINT,payload,\"POST\")\n status = response.getcode()\n if status == 202:\n print \"*** INFO *** Tenant creation successfully triggered\"\n\n except RuntimeError as e:\n print e.args[0]\n except AttributeError as e:\n print \"*** INFO *** Discarding request.Please wait until tenant creation finishes before sending another request\""],"string":"[\n \"def create_emp(self, name, pos, dept):\\n if pos.upper() == 'MANAGER':\\n self.create_manager(name, pos, dept)\\n elif pos.upper() == 'SENIOR':\\n self.create_senior(name, pos, dept)\\n elif pos.upper() == 'JUNIOR':\\n self.create_junior(name, pos, dept)\\n else:\\n self.create_trainee(name, pos, dept)\",\n \"def create_employee(self,personal_identity):\\r\\n new_emp = Employee(*personal_identity)\\r\\n registration_str = new_emp.get_registration_str()\\r\\n\\r\\n return_value = self.save_object_to_DB(\\\"employee\\\",registration_str)\\r\\n return return_value\",\n \"def create_employee(self):\\n try:\\n name = input(\\\"Enter name: \\\")\\n if not name.isalpha():\\n print(\\\"Invalid data format. Name should contain only alphabets. \\\")\\n return False\\n email = input(\\\"Enter email: \\\")\\n if not InputValidations.validate_email(email):\\n return False\\n employee = EmployeeModel(name=name, email=email)\\n self.admin_repository.create_employee(employee)\\n print(\\\"Employee created successfully!\\\")\\n return True\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def test_employee_creation(self):\\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\\n\\n # Returned result is an OrderedDict\\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\\n\\n self.assertEqual(result['name'], helper.name)\\n self.assertEqual(result['hiredOn'], helper.hired_on)\\n self.assertEqual(result['salary'], helper.salary)\\n self.assertEqual(result['departmentId'], helper.department_id)\",\n \"def post(self):\\n try:\\n employee = self.service.add_employee(self.schema, request.json)\\n except ValidationError as error:\\n return error.messages, 400\\n return self.schema.dump(employee), 201\",\n \"def add_employee(self, obj):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\\n\\n self.dbconnect.commit()\\n return obj\\n except:\\n self.dbconnect.rollback()\\n raise\",\n \"def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\\n teacher_account_id = person.get_last()\\n query = 'INSERT INTO teacher VALUES( %s,%s );'\\n args = (teacher_account_id, availability)\\n database.connection.save_data(query, args)\",\n \"def setUp(self):\\n\\n self.user = self.make_user()\\n self.employee = Employee.objects.create(\\n cpf=\\\"974.220.200-16\\\",\\n user=self.user,\\n departament=Employee.ADMINISTRATION\\n )\",\n \"def create_educator(data):\\n\\n educator = Educator(\\n name=data['name'],\\n work_email=data['work_email'],\\n organization_name=data['organization_name'],\\n org_or_school=data['org_or_school'],\\n address_line_1=data['address_line_1'],\\n address_line_2=data['address_line_2'],\\n city=data['city'],\\n state=data['state'],\\n zipcode=data['zipcode'],\\n num_students=data['num_students']\\n )\\n educator.save()\\n return educator\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\\n employee, created = Employee.objects.update_or_create(user=user,\\n employee_id=validated_data.pop('employee_id'),\\n location=validated_data.pop('location'),\\n avail_start_time= str(validated_data.pop('avail_start_time')),\\n avail_end_time= str(validated_data.pop('avail_end_time')))\\n return employee\",\n \"def create(self, request):\\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\\n\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n print(F\\\"Request employee Data: {serializer.data}\\\")\\n\\n try:\\n new_employee = self.controller.create_employee(request_data=request_data)\\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (domain_exceptions.EmployeeIDIsNotUnique,\\n domain_exceptions.WorkArrangementPercentageOutOfRange,\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.WorkArrangementPercentageNull\\n ) as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create_tea():\\n # Bypass if user is logged in\\n\\n form = TeaAddForm()\\n # Validate login attempt\\n if form.validate_on_submit():\\n tea = Tea(\\n name=form.name.data,\\n price_per_gram=form.price_per_gram.data,\\n )\\n db.session.add(tea)\\n db.session.commit() # Create new tea\\n return redirect(url_for(\\\"main_bp.dashboard\\\"))\\n return render_template(\\n \\\"add_tea.jinja2\\\",\\n title=\\\"Add a Tea\\\",\\n form=form,\\n template=\\\"add_tea-page\\\",\\n body=\\\"Add a Tea\\\",\\n )\",\n \"def setUp(self):\\n self.salary = 40000\\n self.custom_rise = 7500\\n self.employee = Employee(\\\"Carlos\\\", \\\"Zapata\\\", self.salary)\",\n \"def create_test_run(\\n self, assign_user_id, project_id, suite_id, testrun_name, tr_keys):\\n data = {\\n 'suite_id': suite_id,\\n 'name': testrun_name,\\n 'assignedto_id': assign_user_id,\\n 'include_all': False,\\n 'case_ids': tr_keys,\\n }\\n\\n response = self.client.send_post(\\n ADD_TESTRUN_URL.format(project_id),\\n data,\\n cert_check=self.cert_check\\n )\\n for key, _ in response.items():\\n if key == 'error':\\n print('Failed to create testrun: {}'.format(response))\\n else:\\n self.testrun_id = response['id']\",\n \"def create_employee(attributes):\\n neccessary_keys = [\\\"empid\\\", \\\"gender\\\", \\\"sales\\\", \\\"bmi\\\", \\\"salary\\\", \\\"birthday\\\",\\n \\\"age\\\"]\\n for key in neccessary_keys:\\n if not key in attributes.keys():\\n raise ValueError(\\\"employee could not be created: {} is missing\\\".format(key))\\n return Employee(attributes[\\\"empid\\\"], attributes[\\\"gender\\\"],\\n attributes[\\\"sales\\\"], attributes[\\\"bmi\\\"],\\n attributes[\\\"salary\\\"], attributes[\\\"birthday\\\"],\\n attributes[\\\"age\\\"])\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n respond_data = self.controller.add_team_employee(request_data=request_data)\\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.EmployeeDoesNotExist,\\n domain_exceptions.EmployeeIsATeamMember\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def setUp(self):\\n self.my_employee = Employee('knight', 'lee', 10000)\",\n \"def create_employee_from_applicant(self, cr, uid, ids, context=None):\\n if context is None:\\n context = {}\\n hr_employee = self.pool.get('hr.employee')\\n model_data = self.pool.get('ir.model.data')\\n act_window = self.pool.get('ir.actions.act_window')\\n emp_id = False\\n for applicant in self.browse(cr, uid, ids, context=context):\\n address_id = contact_name = False\\n if applicant.partner_id:\\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\\n if applicant.job_id and (applicant.partner_name or contact_name):\\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\\n create_ctx = dict(context, mail_broadcast=True)\\n\\n pes=self.browse(cr,uid,ids)[0]\\n coy=pes.partner_name\\n\\n ##### Susunan Keluarga ayah/ibu #####\\n le=self.pool.get('hr_recruit.suskel1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context)\\n prod_ids=[] \\n for pr in lele:\\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\\n \\n ###### Susunan Keluarga Suami/istri #####\\n le=self.pool.get('hr_recruit.suskel2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids1=[] \\n for pr in lele:\\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \\n \\n ###### riwayat Pendidikan #######\\n le=self.pool.get('hr_recruit.rwt_pend')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids2=[] \\n for pr in lele:\\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \\n \\n ###### bahasa ######\\n le=self.pool.get('hr_recruit.bahasa')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids3=[] \\n for pr in lele:\\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \\n \\n ##### Riwayat Pekerjaan ####\\n le=self.pool.get('hr_recruit.rwt_krj')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids4=[] \\n for pr in lele:\\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \\n \\n ###### Koneksi Internal #####\\n le=self.pool.get('hr_recruit.kon1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids5=[] \\n for pr in lele:\\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \\n \\n ###### Koneksi Eksternal ####\\n le=self.pool.get('hr_recruit.kon2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids6=[]\\n for pr in lele: \\n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \\n\\n ####### create Employee ######## \\n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\\n 'job_id': applicant.job_id.id,\\n 'department_id' : applicant.department_id.id,\\n 'address_id2' : applicant.job_id.address_id.id,\\n #### informasi Probadi ####\\n 'kelamin':applicant.jen_kel,\\n 'blood' : applicant.blood,\\n 'agama' : applicant.agama_id.id,\\n 'birthday' : applicant.tgl_lahir,\\n 'place_of_birth' : applicant.kota_id.name,\\n 'marital':applicant.status,\\n 'sjk_tanggal' : applicant.sjk_tanggal,\\n 'mobile_phone':applicant.partner_phone,\\n 'country_id' : applicant.country_id.id,\\n\\n #### Pendidikan ####\\n 'type_id':applicant.type_id.id,\\n 'bid_id':applicant.bidang_id.id,\\n 'jurusan_id':applicant.jurusan_id.id,\\n 'pt_id':applicant.pt_id.id,\\n 'gelar_id':applicant.gelar_id.id,\\n\\n #### alamat DOmisili ####\\n 'country_id1':applicant.country_id1.id,\\n 'prov_id':applicant.prov_id.id,\\n 'kab_id' : applicant.kab_id.id,\\n 'kec_id':applicant.kec_id.id,\\n 'alamat1' : applicant.alamat1,\\n 'kodepos' :applicant.kode1,\\n 'telp1' : applicant.telp1,\\n\\n #### kartu identitas ####\\n 'jenis_id': applicant.jenis_id,\\n 'ktp' : applicant.no_id,\\n 'tgl_berlaku' : applicant.tgl_berlaku,\\n # 'issued_id' : applicant.dikeluarkan.id,\\n \\n #### Alamat Sesuai KTP #### \\n 'country_id2':applicant.country_id2.id,\\n 'prov_id2':applicant.prov_id2.id,\\n 'kab_id2':applicant.kab_id2.id,\\n 'kec_id2':applicant.kec_id2.id,\\n 'alamat2' : applicant.alamat2,\\n 'kodepos1':applicant.kode2,\\n 'telp2' : applicant.telp2,\\n \\n # 'status': applicant.status,\\n #### IDS ####\\n 'susunan_kel1_ids' : prod_ids,\\n 'susunan_kel2_ids':prod_ids1,\\n 'rwt_pend_ids':prod_ids2,\\n 'bahasa_ids':prod_ids3,\\n 'rwt_krj_ids':prod_ids4,\\n 'koneksi1_ids':prod_ids5,\\n 'koneksi2_ids':prod_ids6, \\n })\\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\\n self.pool['hr.job'].message_post(\\n cr, uid, [applicant.job_id.id],\\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\\n subtype=\\\"hr_recruitment.mt_job_applicant_hired\\\", context=context)\\n else:\\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\\n\\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\\n if emp_id:\\n dict_act_window['res_id'] = emp_id\\n dict_act_window['view_mode'] = 'form,tree'\\n return dict_act_window\",\n \"def setUp(self):\\n self.employee = Employee('Lucas', 'Guerra', 45000)\",\n \"def add_employee(schema, employee_json):\\n employee = schema.load(employee_json, session=db.session)\\n db.session.add(employee)\\n db.session.commit()\\n return employee\",\n \"def create():\",\n \"def create():\",\n \"def setUp(self):\\n\\tself.emp = Employee('Lin',10000)\\n\\tself.emp2 = Employee('Jun',20000)\",\n \"def post(self, request):\\n data = request.data\\n skill_data = data.pop('skills')\\n Department_name = data.pop('department')\\n department = Department.objects.get(name=Department_name)\\n manager_name = data.pop('manager')\\n manager = Manager.objects.get(name=manager_name)\\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\\n Employee.save()\\n for skill in skill_data:\\n skill_add, create = Skill.objects.get_or_create(name=skill)\\n Employee.skills.add(skill_add)\\n return Response(\\n data=request.data\\n )\",\n \"def handle(self, *args, **kwargs):\\n seeder = Seed.seeder()\\n seeder.add_entity(User, 20)\\n\\n seeder.add_entity(EmployeeMptt, 20, {\\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\\n 'parent': lambda x: EmployeeMptt.objects.order_by(\\\"?\\\").first(),\\n 'level': lambda x: random.randint(0, 4),\\n })\\n seeder.execute()\",\n \"def setUp(self):\\n self.employee = Employee('John', 'Doe', 50000)\\n self.raise_amount = 20000\",\n \"def create(tesserae, title):\\n try:\\n return tesserae.create(title)\\n except TesseraError, e:\\n sys.stderr.write(\\\"Error: %s\\\\n\\\" % str(e))\\n return False\",\n \"def post(self):\\n data = EmployeeRegister.parser.parse_args()\\n new_employee_id = str(uuid.uuid4())\\n\\n while EmployeeModel.find_by_id(new_employee_id):\\n # if this id is already in use\\n new_employee_id = str(uuid.uuid4())\\n\\n employee = EmployeeModel(**data, employee_id=new_employee_id)\\n employee.save_to_db()\\n\\n return {\\\"message\\\": \\\"Employee successfully added to the system\\\"}, 201 # 201 - Created\",\n \"async def create(self):\\n cur = self.sql.cur\\n\\n user = Client().get_server(self.server_id).get_member(self.user_id)\\n\\n self.nickname = user.nick if user.nick else user.name\\n\\n nickname = self.nickname\\n trainer_id = str(uuid.uuid4())\\n self.trainer_id = trainer_id\\n now = datetime.datetime.now()\\n user_id = self.user_id\\n server_id = self.server_id\\n\\n self.current_zone_id = '86'\\n self.current_building_id = None\\n self.current_region_id = None\\n\\n cmd = \\\"\\\"\\\"INSERT INTO trainers\\n (trainer_id,\\n user_id,\\n server_id,\\n nickname,\\n created_on)\\n VALUES\\n (:trainer_id,\\n :user_id,\\n :server_id,\\n :nickname,\\n :now)\\\"\\\"\\\"\\n cur.execute(cmd, locals())\\n\\n cmd = \\\"\\\"\\\"INSERT INTO trainer_stats\\n (trainer_id)\\n VALUES\\n (:trainer_id)\\\"\\\"\\\"\\n cur.execute(cmd, locals())\\n\\n cmd = \\\"\\\"\\\"INSERT INTO trainer_data\\n (trainer_id,\\n current_region_id,\\n current_zone_id,\\n current_building_id)\\n VALUES\\n (:trainer_id,\\n :current_region_id,\\n :current_zone_id,\\n :current_building_id)\\\"\\\"\\\"\\n cur.execute(cmd, self.__dict__)\\n\\n cmd = \\\"\\\"\\\"INSERT INTO trainer_party\\n (trainer_id)\\n VALUES\\n (:trainer_id)\\\"\\\"\\\"\\n cur.execute(cmd, locals())\\n\\n await self.sql.commit(now=True)\\n self.log.info(f\\\"New trainer has been born! Welcome {trainer_id}\\\")\",\n \"def test_admin_can_create_a_employee(self):\\n\\n account_data = {\\n \\\"username\\\": \\\"Mike\\\",\\n \\\"email\\\": \\\"mioshine@g.com\\\",\\n \\\"password\\\": \\\"1234567\\\",\\n \\\"confirm_password\\\": \\\"1234567\\\"\\n }\\n response = self.client.post(\\n reverse('accounts:create-user'),\\n account_data,\\n format=\\\"json\\\")\\n \\\"\\\"\\\"Test the api has bucket creation capability.\\\"\\\"\\\"\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertTrue(\\\"data\\\" in json.loads(response.content))\",\n \"def create_event(self, name, date):\\n user = User.objects.create(username='userdemo')\\n user.set_password('calnote24')\\n user.save()\\n Event.objects.create(name=name, date=date, user_id=user.id)\",\n \"def generateEmployees(self):\\r\\n\\r\\n # Name\\r\\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\\r\\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\\r\\n\\r\\n for i in range(self.num_of_employees):\\r\\n\\r\\n # Clock in an hour before opening, 6 hours after, or 12 hours after\\r\\n clockIn = random.choice([7, 13, 19])\\r\\n\\r\\n # Clock out after 5 hours, 10 hours, or 15 hours\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n while clockOut <= clockIn:\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n\\r\\n # Hourly wage\\r\\n wage = random.choice([8, 9, 10, 12, 20])\\r\\n\\r\\n gender = random.choice(['M', 'F'])\\r\\n if gender == 'M':\\r\\n name = random.choice(maleNames)\\r\\n else:\\r\\n name = random.choice(femaleNames)\\r\\n\\r\\n self.c.execute(\\\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\\\", (name, clockIn, clockOut, wage))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_employees:\\r\\n print(\\\"\\\\nName:\\\", name)\\r\\n print(\\\"Clock in:\\\", clockIn)\\r\\n print(\\\"Clock out:\\\", clockOut)\\r\\n print(\\\"Wage:\\\", wage)\",\n \"def test_createItinerary(self):\\n rv = self.json_post('/createItinerary/alex', dict(\\n name = 'New Day',\\n date = '2015-08-21T00:00:00.000Z'\\n ))\\n itinHash = str('alex' + \\\"_\\\" + '2015-08-21T00:00:00.000Z')\\n assert itinHash in str(rv.data)\\n\\n rv = self.json_post('/createItinerary/alex', dict(\\n name = 'New Day',\\n date= '2015-08-21T00:00:00.000Z'\\n ))\\n assert 'Itinerary date already in use' in str(rv.data)\\n\\n rv = self.json_post('/createItinerary/bbbb', dict(\\n name = 'New Day',\\n date= '2015-08-21T00:00:00.000Z'\\n ))\\n assert 'Invalid username' in str(rv.data)\",\n \"def add_employee(self, first_name, last_name):\\n self.switch_main_menu(\\\"PIM\\\")\\n self.click_menu(\\\"Add Employee\\\")\\n self.pim = AddEmployee(self.driver)\\n self.pim.add_user_employee(first_name, last_name)\",\n \"def add_employee(self, empl):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\\n empl.active, empl.promotor))\\n cursor.execute('SELECT LASTVAL()')\\n eid = cursor.fetchone()[0]\\n empl.id = eid\\n # get id and return updated object\\n self.dbconnect.commit()\\n except(Exception, self.dbconnect.get_error()) as error:\\n self.dbconnect.rollback()\\n raise Exception('\\\\nUnable to save Employee!\\\\n(%s)' % (error))\",\n \"def do_create(service,summary,description,startday,\\\\\\n starttime,endtime,username,email):\\n event = {\\n 'summary': 'Code Clinic: {}'.format(summary),\\n 'description': '{}.'.format(description),\\n 'start': {\\n 'dateTime': '{}T{}:00'.format(startday, starttime),\\n 'timeZone': 'GMT+02',\\n },\\n 'end': {\\n 'dateTime': '{}T{}:00'.format(startday,endtime),\\n 'timeZone': 'GMT+02',\\n },\\n 'recurrence': [\\n 'RRULE:FREQ=DAILY;COUNT=1'\\n ],\\n 'attendees': [\\n {\\n 'displayName': username,\\n 'email': email,\\n 'optional': True,\\n 'comment': 'Creator',\\n 'responseStatus': 'accepted',\\n },\\n ],\\n 'anyoneCanAddSelf': True,\\n\\n 'reminders': {\\n 'useDefault': False,\\n 'overrides': [\\n {'method': 'email', 'minutes': 24 * 60},\\n {'method': 'popup', 'minutes': 10},\\n ],\\n },\\n }\\n\\n event = service.events().insert(calendarId='primary', body=event,\\\\\\n sendUpdates='all').execute()\\n\\n return event\",\n \"def create(ctx, name, company, mail, age):\\n client = Client(name,company,mail,age)\\n client_service = ClientService(ctx.obj['clients_table']) \\n client_service.create_client(client)\",\n \"def setUp(self):\\n\\t\\tfirst_name = 'Gerson'\\n\\t\\tlast_name = 'Santos'\\n\\t\\tannual_salary = 5000\\n\\t\\tself.gerson = Employee(first_name, last_name, annual_salary)\",\n \"def create(self, values):\\n if values.get('country_id', False):\\n country = self.env['res.country'].browse(values['country_id'])\\n if country.code == 'SA':\\n values.update({'is_saudi': True})\\n else:\\n values.update({'is_saudi': False})\\n\\n res = super(HrEmployee, self).create(values)\\n if values.get('user_id', False):\\n self.user_id.write({'employee_id': res})\\n return res\",\n \"def create(args):\\n print('Creates an HPC fleet with given name \\\"{}\\\"'.format(args.fleet_name))\",\n \"def create():\\n pass\",\n \"def create_individual(self):\\n pass\",\n \"def post(self):\\n try:\\n body = json.loads(request.data.decode(\\\"utf-8\\\"))\\n trainer = Trainer(body[\\\"firstName\\\"], body[\\\"lastName\\\"],\\n body[\\\"email\\\"])\\n trainer = TrainerService.create_trainer(trainer)\\n return jsonify(trainer.json()), 201\\n except ValueError:\\n return INVALID_ID_ERROR, 400 # Bad Request\\n except ResourceNotFound as r:\\n return r.message, 404\",\n \"def add_employee(self, employee):\\n self.employees.add(employee)\",\n \"def create_epic():\\n client = RequestManager()\\n client.set_method(\\\"POST\\\")\\n client.set_endpoint(\\\"/projects/{0}/epics\\\".format(STORED_ID['project_id']))\\n name = \\\"\\\".join(choices(string.ascii_letters, k=6))\\n body = {\\\"name\\\": name}\\n client.set_body(json.dumps(body))\\n response = client.execute_request()\\n STORED_ID['epic_id'] = response.json()['id']\",\n \"def _create_td(self):\\n raise NotImplementedError\",\n \"def createMakingTest(tx, query, personId, testId, date, hour, result):\\n tx.run(query, personId=personId, testId=testId, date=date, hour=hour, result=result)\",\n \"def setUp(self):\\n\\t\\tself.mason = Employee(\\\"mason\\\",\\\"karsevar\\\",10000)\",\n \"def pl_create_order(self):\\n\\tprint()\\n\\tprint('Pl - Create Order')\\n\\n\\n\\tpartner = self.env['res.partner'].search([\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t('name', '=', self.patient.name),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#order='appointment_date desc',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tlimit=1,)\\n\\n\\n\\t# Create Order\\n\\torder = self.env['sale.order'].create({\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'state':'draft',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_doctor': self.physician.id,\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#'partner_id': self.partner_id.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'partner_id': partner.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#'x_ruc': self.partner_id.x_ruc,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#'x_dni': self.partner_id.x_dni,\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'patient': self.patient.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_id_doc': self.patient.x_id_doc,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_id_doc_type': self.patient.x_id_doc_type,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_family': 'procedure',\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'treatment': self.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t})\\n\\t#print(order)\\n\\n\\n\\n\\t# Create Order Lines\\n\\tfor cart_line in self.shopping_cart_ids:\\n\\n\\t\\tproduct = cart_line.product\\n\\n\\t\\t#print(product)\\n\\t\\t#print(product.name)\\n\\n\\t\\t# Create Order Line\\n\\t\\tol = order.order_line.create({\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'name': \\t\\tproduct.name,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'product_id': \\tproduct.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'price_unit': \\tcart_line.price,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'product_uom_qty': cart_line.qty,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'order_id': \\torder.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t})\\n\\treturn order\\n\\n\\t# pl_create_order\",\n \"def create():\\n data = request.get_json()\\n print(\\\"DATA: \\\", data)\\n db_helper.insert_new_record(data['first_name'], data['last_name'], data['class_period'], data['current_belt'], data['student_teacher_id'])\\n result = {'success': True, 'response': 'Done'}\\n return jsonify(result)\",\n \"def get_create_employee_query(self):\\n template = \\\"\\\"\\\"\\n mutation createEmployee {{\\n createEmployee(input: {{ {params} }}) {{\\n employee {{\\n name\\n hiredOn\\n salary\\n departmentId\\n }}\\n }}\\n }}\\n \\\"\\\"\\\"\\n # Add input parameters as needed\\n input_params = 'name:\\\"{}\\\",'.format(self.name)\\n\\n if self.hired_on is not None:\\n input_params += 'hiredOn: \\\"{}\\\", '.format(self.hired_on)\\n\\n if self.salary is not None:\\n input_params += 'salary: {}, '.format(self.salary)\\n\\n if self.department_id is not None:\\n input_params += 'departmentId: {}'.format(self.department_id)\\n\\n return template.format(params=input_params)\",\n \"def create(self, vals):\\n if not vals.get('nik_number'):\\n vals['nik_number'] = self.generate_nik(vals)\\n return super(Employee, self).create(vals)\",\n \"def create_person(self):\",\n \"def test_access_employee(self):\\n # Employee can't see any SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).read()\\n # Employee can't edit the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\\n # Employee can't create the SO\\n with self.assertRaises(AccessError):\\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\\n 'partner_id': self.partner_a.id,\\n })\\n # Employee can't delete the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).unlink()\",\n \"def create(self, name, login, password, email, address=\\\"\\\", vat=\\\"\\\", jobguid=\\\"\\\", executionparams=None):\",\n \"def create_employee(request, company_id):\\n\\n company = Company.objects.get(pk=company_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n logUnauthorizedAccess(\\\"User tried to create_employee\\\", request)\\n raise PermissionDenied()\\n form = EmployeeForm(request, initial=dict(company=company))\\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\\n # Q(company=company) | Q(company__isnull=True))\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_employee_form.html',\\n {\\n 'employee_form': form,\\n }\\n )\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n # return JsonResponse(status=200, data=data)\",\n \"def create(self):\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.EmployeeDoesNotExist\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create_meal():\",\n \"def _create_fleet(self):\\n # make an alien\\n alien = Alien(self)\\n self.aliens.add(alien)\",\n \"def create_ticket(self, user):\\n return Ticket.objects.create_ticket('test', user)\",\n \"def create_partner(name):\\n\\n return Partner.objects.create(name=name)\",\n \"def create(*args):\",\n \"def create_order(self):\\n\\tprint()\\n\\tprint('OH - pl_create_order')\\n\\n\\t# Search Partner\\n\\tprint()\\n\\tprint('Search partner')\\n\\tpartner = self.env['res.partner'].search([\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t('name', '=', self.patient.name),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#order='appointment_date desc',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tlimit=1,)\\n\\n\\t# Search Pl\\n\\tprint()\\n\\tprint('Search pricelist')\\n\\tpricelist = self.env['product.pricelist'].search([\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#('active', 'in', [True]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#order='x_serial_nr asc',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tlimit=1,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t)\\n\\tprint(pricelist)\\n\\n\\t# Create Order\\n\\torder = self.env['sale.order'].create({\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'state':'draft',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_doctor': self.physician.id,\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#'partner_id': self.partner_id.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'partner_id': partner.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#'x_ruc': self.partner_id.x_ruc,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#'x_dni': self.partner_id.x_dni,\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'patient': self.patient.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_id_doc': self.patient.x_id_doc,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_id_doc_type': self.patient.x_id_doc_type,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'x_family': 'procedure',\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'treatment': self.id,\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'pricelist_id': pricelist.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t})\\n\\t#print(order)\\n\\n\\n\\n\\t# Create Order Lines\\n\\tfor cart_line in self.shopping_cart_ids:\\n\\n\\t\\tproduct = cart_line.product\\n\\n\\t\\t#print(product)\\n\\t\\t#print(product.name)\\n\\n\\t\\t# Create Order Line\\n\\t\\tol = order.order_line.create({\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'name': \\t\\tproduct.name,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'product_id': \\tproduct.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'price_unit': \\tcart_line.price,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'product_uom_qty': cart_line.qty,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'order_id': \\torder.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t})\\n\\treturn order\",\n \"def main():\\n name = input(\\\"Please enter in your name: \\\")\\n\\n \\\"\\\"\\\"Ask the user to enter a number if they are a Director, Manager or Staff.\\\"\\\"\\\"\\n \\\"\\\"\\\"This will check and make sure the user only enters in 1,2, \\n or 3 and a number greater than zero\\\"\\\"\\\"\\n while True:\\n try:\\n designation_number = int(input(\\\"Please enter in \\\\n1 for Director \\\"\\n \\\"\\\\n2 for Manager \\\\n3 for Staff\\\\n\\\"))\\n if 0 < designation_number <= 3:\\n break\\n print(\\\"Invalid number entered.\\\")\\n except Exception as e:\\n print(e)\\n \\\"\\\"\\\"Gets the user salary and makes sure is a number and greater than 0\\\"\\\"\\\"\\n while True:\\n try:\\n salary = float(input(\\\"Please enter in your salary: \\\"))\\n if salary <= 0:\\n print(\\\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\\\")\\n else:\\n break\\n except ValueError:\\n print(\\\"Oops! That was not a valid number. Try again...\\\")\\n\\n \\\"\\\"\\\"Create Employee\\\"\\\"\\\"\\n employee1 = employee.Employee()\\n employee1.set_name(name)\\n employee1.set_designation(designation_number)\\n employee1.set_salary(salary)\\n print(employee1)\",\n \"def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\\n peronjson={}, notes='', tenantid=''):\\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\\n self.persontdetailscoll.insert(tenantobj, safe=True)\",\n \"def make_electrode_table(self):\\n self.table = get_electrode_table()\\n self.dev1 = Device(name='dev1')\\n self.group = ElectrodeGroup(name='tetrode1',\\n description='tetrode description',\\n location='tetrode location',\\n device=self.dev1)\\n for i in range(4):\\n self.table.add_row(location='CA1', group=self.group, group_name='tetrode1')\",\n \"def make_electrode_table(self):\\n self.table = get_electrode_table()\\n self.dev1 = Device(name='dev1')\\n self.group = ElectrodeGroup(name='tetrode1',\\n description='tetrode description',\\n location='tetrode location',\\n device=self.dev1)\\n for i in range(4):\\n self.table.add_row(location='CA1', group=self.group, group_name='tetrode1')\",\n \"def _create_new_attende(name, email, gdpr, marketing):\\n\\n new_attendee = Attendee.objects.create(\\n name=name,\\n email=email,\\n gdpr=gdpr,\\n marketing=marketing,\\n token=uuid.uuid1(),\\n date_signed=datetime.date.today()\\n )\\n new_attendee.save()\\n return new_attendee\",\n \"def create_new_test_run():\\n if debug:\\n print('[DEBUG] Func: create_new_test_run...')\\n\\n new_test_run_url = \\\"https://eei.testrail.com/index.php?/api/v2/add_run/{0}=\\\".format(project_id)\\n\\n new_test_run_json = {\\n \\\"suite_id\\\": suite_id,\\n \\\"name\\\": suite_name,\\n \\\"assignedto_id\\\": assignedto_id,\\n \\\"include_all\\\": False,\\n \\\"case_ids\\\": [testcase_id]\\n }\\n\\n new_test_run = requests.post(new_test_run_url, auth=authorization, json=new_test_run_json)\\n\\n if str(new_test_run.status_code) != '200':\\n print('[ERROR] new_test_run: non 200 status code... ' + str(new_test_run.status_code))\\n print(str(new_test_run.json()))\\n sys.exit(1)\\n\\n global new_test_run_id\\n new_test_run_id = str(new_test_run.json()[\\\"id\\\"])\",\n \"def createEmployee(firstName, lastName, ssn, salary):\\n employee = Employee(firstName, lastName, ssn, salary)\\n # verify\\n if firstName != employee.firstName or \\\\\\n lastName != employee.lastName or \\\\\\n ssn != employee.ssn or \\\\\\n salary != employee.salary:\\n raise ValueError(\\\"Failed to initialize Employee\\\")\\n return employee\",\n \"def create(self):\\n ...\",\n \"def create_trail(user_id, hike_id):\\n\\n trail = Trail(user=user_id, hike=hike_id)\\n\\n db.session.add(trail)\\n db.session.commit()\\n\\n return trail\",\n \"def add_trainee():\\n\\n add_trainee_form = AddTraineeForm()\\n if add_trainee_form.validate_on_submit():\\n role = Role(role_id=RoleIds.Trainee)\\n add_trainee_form.populate_obj(role)\\n\\n supervised_activity_ids = [\\n a.id for a in current_user.get_supervised_activities()\\n ]\\n if role.activity_id not in supervised_activity_ids:\\n flash(\\\"Activité invalide\\\", \\\"error\\\")\\n return redirect(url_for(\\\".leader_list\\\"))\\n\\n user = User.query.get(role.user_id)\\n if user is None:\\n flash(\\\"Utilisateur invalide\\\", \\\"error\\\")\\n return redirect(url_for(\\\".leader_list\\\"))\\n\\n if user.has_role_for_activity(\\n [RoleIds.Trainee, RoleIds.EventLeader],\\n role.activity_id,\\n ):\\n flash(\\n \\\"L'utilisateur est déjà encadrant ou encadrant en formation pour cette activité\\\",\\n \\\"error\\\",\\n )\\n return redirect(url_for(\\\".leader_list\\\"))\\n\\n db.session.add(role)\\n db.session.commit()\\n add_trainee_form = AddTraineeForm(formdata=None)\\n\\n return render_template(\\n \\\"leaders_list.html\\\",\\n add_trainee_form=add_trainee_form,\\n title=\\\"Encadrants en formation\\\",\\n )\",\n \"def test_create_team(self):\\n pass\",\n \"def setUp(self):\\n response = self.client.post('/trainer/create/',\\n {\\\"name\\\": \\\"Blue\\\",\\n \\\"las_name\\\": \\\"Oak\\\"})\\n self.trainer_id = response.json()[\\\"id\\\"]\",\n \"def create(self, *args, **kwargs):\\n pass\",\n \"def test_new_employee_crud_methods(self):\\n response = self.client.get(\\n '/employees/', kwargs={'employer_id': self.employee.id})\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(len(Employee.objects.all()), 1)\\n\\n # Test that a new employee can be added\\n response = self.client.post(\\n '/employees/',\\n {'name': 'MAdtraxx!!', 'employer': self.employer.id},\\n kwargs={'pk': self.employer.id})\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(Employee.objects.count(), 2)\\n\\n # Test that employee info may be edited\\n response = self.client.put('/employees/1/',\\n {'name': 'Ashley',\\n 'employer': self.employer.id},\\n kwargs={'employer_id': self.employee.id,\\n 'pk': self.employee.id})\\n self.assertEqual(response.status_code, 200)\",\n \"def create_task(self, name, date, isComplete):\\n user = User.objects.create(username='userdemo')\\n user.set_password('calnote24')\\n user.save()\\n Task.objects.create(task=name, dueDate=date, isComplete=isComplete, user_id=user.id)\",\n \"def setUpTestData(cls):\\n countries = [\\\"MX\\\", \\\"CHL\\\", \\\"USA\\\", \\\"PER\\\", \\\"COL\\\"]\\n slack_user_ids = [\\\"UP0918MAV\\\", \\\"UP0918MAV\\\", \\\"UP0918MAV\\\", None, None]\\n cls.menu = Menu.objects.create(available_on=date.today())\\n for count in range(5):\\n user = User.objects.create(username=f\\\"johny.doe {count}\\\")\\n Employee.objects.create(\\n user=user, country=countries[count], slack_user_id=slack_user_ids[count]\\n )\",\n \"def add_employee():\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n form = SignUp_Form()\\n \\n\\n if form.validate_on_submit():\\n try: \\n employee = Employee.register(\\n username = form.username.data,\\n password = form.password.data, \\n email = form.email.data, \\n first_name = form.first_name.data,\\n last_name = form.last_name.data,\\n hire_date = form.hire_date.data, \\n is_admin = form.is_admin.data,\\n )\\n\\n db.session.add(employee)\\n\\n db.session.commit()\\n except IntegrityError:\\n flash(\\\"Email already in use\\\", \\\"danger\\\")\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\\n\\n flash(\\\"Employee Added!\\\", \\\"success\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\",\n \"def createEmployee():\\n form = CreateEmployeeForm()\\n if form.validate_on_submit():\\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \\n email=form.email.data, role=form.role.data, location=form.location.data)\\n db.session.add(staff)\\n db.session.commit()\\n flash(f'Employee Added To Database', category='Success')\\n return redirect(url_for('login'))\\n return render_template('new_employee.html', title=\\\"Register\\\", form=form)\",\n \"def create(self, vals):\\n res = super(HelpdeskTicket, self).create(vals)\\n content = \\\"\\\"\\n if vals.get('timesheet_ids', False):\\n content += \\\"Parte de Horas: <br/>\\\"\\n for ts in vals.get('timesheet_ids'):\\n if content != \\\"Parte de Horas: <br/>\\\":\\n content += \\\"------------------------------------------------------------------------------<br/>\\\"\\n for field_name in ts[2]:\\n new_val = ts[2][field_name]\\n if field_name == 'date':\\n content += \\\" \\\\u2022 Fecha: \\\" + new_val + \\\"<br/>\\\"\\n elif field_name == 'employee_id':\\n new_employee = self.env['hr.employee'].browse(new_val).name\\n content += \\\" \\\\u2022 Empleado: \\\" + new_employee + \\\"<br/>\\\"\\n elif field_name == 'unit_amount':\\n content += \\\" \\\\u2022 Duración: \\\" + '{0:02.0f}:{1:02.0f}'.format(\\n *divmod(new_val * 60, 60)) + \\\"<br/>\\\"\\n elif field_name == 'name' and new_val:\\n content += \\\" \\\\u2022 Descripción: \\\" + new_val + \\\"<br/>\\\"\\n res.message_post(body=content)\\n\\n return res\",\n \"def test_create(self):\\n\\n adminuser,adminpass = self.testdata.find_account_for('toolmanager')\\n\\n self.utils.account.login_as(adminuser,adminpass)\\n\\n self.contribtool.create(TOOLNAME)\",\n \"def amazonEc2_create(amazonEc2):\\n\\treturn amazonEc2\",\n \"def ht_meeting_create(values, uid):\\n\\n\\tprint 'ht_meeting_create: enter()'\\n\\t#\\tStripe fields should be validated.\\n\\t#\\tv1 - end time is reasonable (after start time)\\n\\t#\\tv2 - hero exists.\\n\\t#\\tv3 - place doesn't matter as much..\\n\\t#\\tv4 - cost..\\n\\ttry:\\n\\t\\tstripe_tokn = values.get('stripe_tokn')\\t\\t# used to charge\\n\\t\\tstripe_card = values.get('stripe_card')\\t\\t# card to add to insprite_customer.\\n\\t\\tstripe_cust = values.get('stripe_cust')\\n\\t\\t#stripe_fngr = values.get('stripe_fngr')\\t#card_fingerprint\\n\\n\\t\\tprop_mentor = values.get('prop_mentor')\\n\\t\\tprop_cost = values.get('prop_cost')\\n\\t\\tprop_desc = values.get('prop_desc')\\n\\t\\tprop_location = values.get('prop_location')\\n\\t\\tprop_lesson = values.get('prop_lesson')\\n\\t\\tprop_groupsize = values.get('prop_groupsize')\\n\\n\\t\\tprint \\\"ht_meeting_create: mentor:\\\", prop_mentor\\n\\t\\tprint \\\"ht_meeting_create: cost:\\\", prop_cost\\n\\t\\tprint \\\"ht_meeting_create: desc:\\\", prop_desc\\n\\t\\tprint \\\"ht_meeting_create: location:\\\", prop_location\\n\\t\\tprint \\\"ht_meeting_create: lesson:\\\", prop_lesson\\n\\t\\tprint \\\"ht_meeting_create: groupsize:\\\", prop_groupsize\\n\\n\\t\\t# validate start/end times via conversion.\\n\\t\\tprop_s_date = values.get('prop_s_date')\\n\\t\\tprop_s_time = values.get('prop_s_time')\\n\\t\\tprop_f_date = values.get('prop_f_date')\\n\\t\\tprop_f_time = values.get('prop_f_time')\\n\\t\\tdt_start = dt.strptime(prop_s_date + \\\" \\\" + prop_s_time, '%A, %b %d, %Y %H:%M')\\n\\t\\tdt_finsh = dt.strptime(prop_f_date + \\\" \\\" + prop_f_time, '%A, %b %d, %Y %H:%M')\\n\\n\\t\\t# convert to user's local TimeZone.\\n\\t\\tdt_start_pacific = timezone('US/Pacific').localize(dt_start)\\n\\t\\tdt_finsh_pacific = timezone('US/Pacific').localize(dt_finsh)\\n\\n\\t\\tprint 'ht_meeting_create: (from stripe) token =', stripe_tokn, 'card =', stripe_card\\n\\t\\thp\\t= Profile.get_by_prof_id(prop_mentor)\\n\\t\\tbp\\t= Profile.get_by_uid(uid)\\n\\t\\tba = Account.get_by_uid(uid)\\n\\t\\tha = Account.get_by_uid(hp.account)\\n\\t\\tstripe_cust = ht_get_stripe_customer(ba, cc_token=stripe_tokn, cc_card=stripe_card, cust=stripe_cust)\\n\\t\\tprint 'ht_meeting_create: Mentee (' + str(bp.prof_name) + ', ' + str(stripe_cust) + ')'\\n\\t\\tprint 'ht_meeting_create: Mentor (' + str(hp.prof_name) + ')'\\n\\n\\t\\tmeeting = Meeting(hp.prof_id, bp.prof_id, dt_start_pacific, dt_finsh_pacific, (int(prop_cost)/100), str(prop_location), str(prop_desc), str(prop_lesson), prop_groupsize, token=stripe_tokn, customer=stripe_cust, card=stripe_card)\\n\\n\\t\\tdb_session.add(meeting)\\n\\t\\tdb_session.commit()\\n\\t\\tprint \\\"ht_meeting_create: successfully committed meeting\\\"\\n\\texcept Exception as e:\\n\\t\\t# IntegrityError, from commit()\\n\\t\\t# SanitizedException(None), from Meeting.init()\\n\\t\\tprint type(e), e\\n\\t\\tdb_session.rollback()\\n\\t\\tht_sanitize_error(e)\\n\\tprint \\\"ht_meeting_create: sending notifications\\\"\\n\\tht_send_meeting_proposed_notifications(meeting, ha, hp, ba, bp)\",\n \"def create(self, name, username, emailaddress, maxMemoryCapacity=-1.0, maxVDiskCapacity=-1, maxCPUCapacity=-1, maxNetworkPeerTransfer=-1, maxNumPublicIP=-1, sendAccessEmails=True, **kwargs):\\n #put your code here to implement this method\\n raise NotImplementedError (\\\"not implemented method create\\\")\",\n \"def createOrder(self, name, email, table, items):\\n customerID = self.__customer.locateCustomer(email)\\n # Create customer if not exist\\n if not customerID:\\n customerID = self.__customer.createCustomer(name, email)\\n # Create order\\n orderID = self.__insertOrder(customerID, table)\\n # Add items\\n for item in items:\\n self.__orderAddItem(orderID, item)\\n return orderID\",\n \"def test_create_record(self):\\n pass\",\n \"def create_course_to_teacher():\\n try:\\n #get teacher list\\n teacher_list = Teacher.get_all_obj_list()\\n for k,obj in enumerate(teacher_list):\\n print(k,obj.name)\\n tid = int(input('请输入老师：'))\\n teacher_obj = teacher_list[tid]\\n\\n #get course list\\n course_list = Course.get_all_obj_list()\\n for k, obj in enumerate(course_list):\\n print(k,obj.name)\\n cid = int(input('请输入课程：'))\\n course_obj = course_list[cid]\\n print(course_obj.nid.get_obj_by_uuid().name)\\n #course_list = [(obj.name, obj.school_nid.uuid) for obj in Course.get_all_obj_list()] # uuid???\\n course_to_teacher_list = [(obj.course_nid,obj.teacher_nid) for obj in Course_to_teacher.get_all_obj_list()]\\n # print(course_to_teacher_list)\\n for (course_obj.nid,teacher_obj.nid) in course_to_teacher_list:\\n raise Exception('\\\\033[33;1m[%s] [%s]已经存在，不可重复创建\\\\033[0m' % (obj.course_nid.get_obj_by_uuid().name, \\\\\\n obj.teacher_nid.get_obj_by_uuid().name))\\n obj = Course_to_teacher(course_obj.nid,teacher_obj.nid)\\n obj.save()\\n status=True\\n error = ''\\n data = '\\\\033[33;1m课程[%s] 老师[%s] 创建成功\\\\033[0m' % (course_obj.name, teacher_obj.name)\\n\\n except Exception as e:\\n status = False\\n error = str(e)\\n data = ''\\n return {'status': status, 'error': error, 'data': data}\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create_email(user):\\n if 'research' in user.get_domains():\\n domain = 'research'\\n else: domain = 'academic'\\n subject = \\\"ECE/CIS Account Created\\\"\\n helprequest = \\\"https://www.eecis.udel.edu/service\\\"\\n \\n message = \\\"Your ECE/CIS %s account has been created with the username: %s\\\\n\\\\n\\\" % (domain, user.username)\\n message += \\\"Please do not reply to this message. If you need assistance with your account, please visit:\\\\n\\\"\\n message += \\\"%s\\\\n\\\\n\\\" % helprequest\\n message += \\\"-- EE/CIS Labstaff\\\\n\\\"\\n\\n send('account@eecis.udel.edu', 'ECE/CIS Account System', \\\\\\n [user.email], subject, message, MAILHOST)\",\n \"def create(self):\\n\\n pass\",\n \"def make_hourly(self,rate,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"1\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be an hourly employee\\\"))\\n self.emp_dict[id][8] = rate\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def create_task(self, ticket, user):\\n return Task.objects.create_task('test', 'low', ticket, user, user)\",\n \"def createTOFin(En):\\n ftemplate = open(\\\"TOFtemplate.in\\\", \\\"r\\\")\\n lines = ftemplate.readlines()\\n ftofin = open(\\\"TOF.in\\\", \\\"w\\\") \\n energyline = lines[12].split()\\n lines[12] = \\\"%s %g %s\\\\n\\\"%(energyline[0], En, energyline[2])\\n ftofin.writelines(lines)\\n ftemplate.close()\\n ftofin.close()\",\n \"def create_instructor(self, username):\\r\\n return self._create_user(username, is_staff=True)\",\n \"def createOrcaTenant(self,payload):\\n response = None\\n # Check if tenant with that name already exists\\n systemObj = self.getSystemByUid(payload[\\\"system\\\"])\\n try:\\n # Systemname and tenant description always determine a specific tenant\\n response = self.getTenantByName(systemObj[\\\"name\\\"],payload[\\\"description\\\"].upper())\\n except KeyError as e:\\n if e.args[1] == \\\"CIC_TENANT_LOOKUP_ERROR\\\":\\n response = None\\n pass\\n else:\\n raise\\n try:\\n # TMS delivers always a non-empty body if something was found\\n if response:\\n if response[\\\"description\\\"] == payload[\\\"description\\\"].upper():\\n raise RuntimeError(\\\"*** INFO *** Tenant already exists\\\",\\\"CIC_CREATE_TENANT_ERROR\\\")\\n # TMS delivers an empty body if nothing is found\\n elif response is None:\\n print \\\"*** INFO *** Starting tenant creation\\\"\\n response = self.httpHandler.sendHttpRequest(CIC_TENANT_ENDPOINT,payload,\\\"POST\\\")\\n status = response.getcode()\\n if status == 202:\\n print \\\"*** INFO *** Tenant creation successfully triggered\\\"\\n\\n except RuntimeError as e:\\n print e.args[0]\\n except AttributeError as e:\\n print \\\"*** INFO *** Discarding request.Please wait until tenant creation finishes before sending another request\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71946114","0.64039135","0.6397927","0.60852325","0.60668916","0.5989232","0.59618133","0.5926233","0.59155416","0.584691","0.5840692","0.58354217","0.57622826","0.5757782","0.57444525","0.57208085","0.5719296","0.56969446","0.56930053","0.56875426","0.5683447","0.5683447","0.566679","0.56505567","0.5648289","0.5639002","0.5594797","0.5589957","0.55689746","0.5568751","0.55661595","0.5558385","0.55153275","0.55070263","0.5493737","0.54914534","0.5490442","0.5479768","0.5469869","0.5449478","0.5441794","0.5406746","0.5404841","0.5404173","0.5373631","0.5360738","0.535724","0.53557414","0.5346829","0.5342015","0.53388196","0.5335895","0.53357065","0.53170794","0.5316215","0.5315655","0.5314175","0.5309421","0.5280494","0.5275202","0.5268677","0.5264249","0.52602917","0.5255037","0.5237357","0.5235951","0.5234087","0.5234087","0.5233435","0.5229068","0.5223271","0.52164745","0.52140886","0.51993227","0.519775","0.5193167","0.5181733","0.51703125","0.51594615","0.51451665","0.5143428","0.51383233","0.5136122","0.51347506","0.51314044","0.5129582","0.51266766","0.51255107","0.5124651","0.5115196","0.5113948","0.5113948","0.5113948","0.5113205","0.51007545","0.5090346","0.5088334","0.508721","0.5085655","0.5083728"],"string":"[\n \"0.71946114\",\n \"0.64039135\",\n \"0.6397927\",\n \"0.60852325\",\n \"0.60668916\",\n \"0.5989232\",\n \"0.59618133\",\n \"0.5926233\",\n \"0.59155416\",\n \"0.584691\",\n \"0.5840692\",\n \"0.58354217\",\n \"0.57622826\",\n \"0.5757782\",\n \"0.57444525\",\n \"0.57208085\",\n \"0.5719296\",\n \"0.56969446\",\n \"0.56930053\",\n \"0.56875426\",\n \"0.5683447\",\n \"0.5683447\",\n \"0.566679\",\n \"0.56505567\",\n \"0.5648289\",\n \"0.5639002\",\n \"0.5594797\",\n \"0.5589957\",\n \"0.55689746\",\n \"0.5568751\",\n \"0.55661595\",\n \"0.5558385\",\n \"0.55153275\",\n \"0.55070263\",\n \"0.5493737\",\n \"0.54914534\",\n \"0.5490442\",\n \"0.5479768\",\n \"0.5469869\",\n \"0.5449478\",\n \"0.5441794\",\n \"0.5406746\",\n \"0.5404841\",\n \"0.5404173\",\n \"0.5373631\",\n \"0.5360738\",\n \"0.535724\",\n \"0.53557414\",\n \"0.5346829\",\n \"0.5342015\",\n \"0.53388196\",\n \"0.5335895\",\n \"0.53357065\",\n \"0.53170794\",\n \"0.5316215\",\n \"0.5315655\",\n \"0.5314175\",\n \"0.5309421\",\n \"0.5280494\",\n \"0.5275202\",\n \"0.5268677\",\n \"0.5264249\",\n \"0.52602917\",\n \"0.5255037\",\n \"0.5237357\",\n \"0.5235951\",\n \"0.5234087\",\n \"0.5234087\",\n \"0.5233435\",\n \"0.5229068\",\n \"0.5223271\",\n \"0.52164745\",\n \"0.52140886\",\n \"0.51993227\",\n \"0.519775\",\n \"0.5193167\",\n \"0.5181733\",\n \"0.51703125\",\n \"0.51594615\",\n \"0.51451665\",\n \"0.5143428\",\n \"0.51383233\",\n \"0.5136122\",\n \"0.51347506\",\n \"0.51314044\",\n \"0.5129582\",\n \"0.51266766\",\n \"0.51255107\",\n \"0.5124651\",\n \"0.5115196\",\n \"0.5113948\",\n \"0.5113948\",\n \"0.5113948\",\n \"0.5113205\",\n \"0.51007545\",\n \"0.5090346\",\n \"0.5088334\",\n \"0.508721\",\n \"0.5085655\",\n \"0.5083728\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.696836"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17482,"cells":{"query":{"kind":"string","value":"function to create employee based on postion."},"document":{"kind":"string","value":"def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n else:\n self.create_trainee(name, pos, dept)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_employee(self,personal_identity):\r\n new_emp = Employee(*personal_identity)\r\n registration_str = new_emp.get_registration_str()\r\n\r\n return_value = self.save_object_to_DB(\"employee\",registration_str)\r\n return return_value","def create_employee(self):\n try:\n name = input(\"Enter name: \")\n if not name.isalpha():\n print(\"Invalid data format. Name should contain only alphabets. \")\n return False\n email = input(\"Enter email: \")\n if not InputValidations.validate_email(email):\n return False\n employee = EmployeeModel(name=name, email=email)\n self.admin_repository.create_employee(employee)\n print(\"Employee created successfully!\")\n return True\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def test_employee_creation(self):\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\n\n # Returned result is an OrderedDict\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\n\n self.assertEqual(result['name'], helper.name)\n self.assertEqual(result['hiredOn'], helper.hired_on)\n self.assertEqual(result['salary'], helper.salary)\n self.assertEqual(result['departmentId'], helper.department_id)","def post(self):\n try:\n employee = self.service.add_employee(self.schema, request.json)\n except ValidationError as error:\n return error.messages, 400\n return self.schema.dump(employee), 201","def add_employee(self, obj):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\n\n self.dbconnect.commit()\n return obj\n except:\n self.dbconnect.rollback()\n raise","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\n employee, created = Employee.objects.update_or_create(user=user,\n employee_id=validated_data.pop('employee_id'),\n location=validated_data.pop('location'),\n avail_start_time= str(validated_data.pop('avail_start_time')),\n avail_end_time= str(validated_data.pop('avail_end_time')))\n return employee","def create_employee_from_applicant(self, cr, uid, ids, context=None):\n if context is None:\n context = {}\n hr_employee = self.pool.get('hr.employee')\n model_data = self.pool.get('ir.model.data')\n act_window = self.pool.get('ir.actions.act_window')\n emp_id = False\n for applicant in self.browse(cr, uid, ids, context=context):\n address_id = contact_name = False\n if applicant.partner_id:\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\n if applicant.job_id and (applicant.partner_name or contact_name):\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\n create_ctx = dict(context, mail_broadcast=True)\n\n pes=self.browse(cr,uid,ids)[0]\n coy=pes.partner_name\n\n ##### Susunan Keluarga ayah/ibu #####\n le=self.pool.get('hr_recruit.suskel1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context)\n prod_ids=[] \n for pr in lele:\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\n \n ###### Susunan Keluarga Suami/istri #####\n le=self.pool.get('hr_recruit.suskel2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids1=[] \n for pr in lele:\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \n \n ###### riwayat Pendidikan #######\n le=self.pool.get('hr_recruit.rwt_pend')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids2=[] \n for pr in lele:\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \n \n ###### bahasa ######\n le=self.pool.get('hr_recruit.bahasa')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids3=[] \n for pr in lele:\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \n \n ##### Riwayat Pekerjaan ####\n le=self.pool.get('hr_recruit.rwt_krj')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids4=[] \n for pr in lele:\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \n \n ###### Koneksi Internal #####\n le=self.pool.get('hr_recruit.kon1')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids5=[] \n for pr in lele:\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \n \n ###### Koneksi Eksternal ####\n le=self.pool.get('hr_recruit.kon2')\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\n lele=le.browse(cr,uid,lel,context=context) \n prod_ids6=[]\n for pr in lele: \n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \n\n ####### create Employee ######## \n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\n 'job_id': applicant.job_id.id,\n 'department_id' : applicant.department_id.id,\n 'address_id2' : applicant.job_id.address_id.id,\n #### informasi Probadi ####\n 'kelamin':applicant.jen_kel,\n 'blood' : applicant.blood,\n 'agama' : applicant.agama_id.id,\n 'birthday' : applicant.tgl_lahir,\n 'place_of_birth' : applicant.kota_id.name,\n 'marital':applicant.status,\n 'sjk_tanggal' : applicant.sjk_tanggal,\n 'mobile_phone':applicant.partner_phone,\n 'country_id' : applicant.country_id.id,\n\n #### Pendidikan ####\n 'type_id':applicant.type_id.id,\n 'bid_id':applicant.bidang_id.id,\n 'jurusan_id':applicant.jurusan_id.id,\n 'pt_id':applicant.pt_id.id,\n 'gelar_id':applicant.gelar_id.id,\n\n #### alamat DOmisili ####\n 'country_id1':applicant.country_id1.id,\n 'prov_id':applicant.prov_id.id,\n 'kab_id' : applicant.kab_id.id,\n 'kec_id':applicant.kec_id.id,\n 'alamat1' : applicant.alamat1,\n 'kodepos' :applicant.kode1,\n 'telp1' : applicant.telp1,\n\n #### kartu identitas ####\n 'jenis_id': applicant.jenis_id,\n 'ktp' : applicant.no_id,\n 'tgl_berlaku' : applicant.tgl_berlaku,\n # 'issued_id' : applicant.dikeluarkan.id,\n \n #### Alamat Sesuai KTP #### \n 'country_id2':applicant.country_id2.id,\n 'prov_id2':applicant.prov_id2.id,\n 'kab_id2':applicant.kab_id2.id,\n 'kec_id2':applicant.kec_id2.id,\n 'alamat2' : applicant.alamat2,\n 'kodepos1':applicant.kode2,\n 'telp2' : applicant.telp2,\n \n # 'status': applicant.status,\n #### IDS ####\n 'susunan_kel1_ids' : prod_ids,\n 'susunan_kel2_ids':prod_ids1,\n 'rwt_pend_ids':prod_ids2,\n 'bahasa_ids':prod_ids3,\n 'rwt_krj_ids':prod_ids4,\n 'koneksi1_ids':prod_ids5,\n 'koneksi2_ids':prod_ids6, \n })\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\n self.pool['hr.job'].message_post(\n cr, uid, [applicant.job_id.id],\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\n subtype=\"hr_recruitment.mt_job_applicant_hired\", context=context)\n else:\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\n\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\n if emp_id:\n dict_act_window['res_id'] = emp_id\n dict_act_window['view_mode'] = 'form,tree'\n return dict_act_window","def create(self, request):\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\n\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n print(F\"Request employee Data: {serializer.data}\")\n\n try:\n new_employee = self.controller.create_employee(request_data=request_data)\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (domain_exceptions.EmployeeIDIsNotUnique,\n domain_exceptions.WorkArrangementPercentageOutOfRange,\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.WorkArrangementPercentageNull\n ) as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create(self, vals):\n if not vals.get('nik_number'):\n vals['nik_number'] = self.generate_nik(vals)\n return super(Employee, self).create(vals)","def createEmployee(firstName, lastName, ssn, salary):\n employee = Employee(firstName, lastName, ssn, salary)\n # verify\n if firstName != employee.firstName or \\\n lastName != employee.lastName or \\\n ssn != employee.ssn or \\\n salary != employee.salary:\n raise ValueError(\"Failed to initialize Employee\")\n return employee","def get_create_employee_query(self):\n template = \"\"\"\n mutation createEmployee {{\n createEmployee(input: {{ {params} }}) {{\n employee {{\n name\n hiredOn\n salary\n departmentId\n }}\n }}\n }}\n \"\"\"\n # Add input parameters as needed\n input_params = 'name:\"{}\",'.format(self.name)\n\n if self.hired_on is not None:\n input_params += 'hiredOn: \"{}\", '.format(self.hired_on)\n\n if self.salary is not None:\n input_params += 'salary: {}, '.format(self.salary)\n\n if self.department_id is not None:\n input_params += 'departmentId: {}'.format(self.department_id)\n\n return template.format(params=input_params)","def generateEmployees(self):\r\n\r\n # Name\r\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\r\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\r\n\r\n for i in range(self.num_of_employees):\r\n\r\n # Clock in an hour before opening, 6 hours after, or 12 hours after\r\n clockIn = random.choice([7, 13, 19])\r\n\r\n # Clock out after 5 hours, 10 hours, or 15 hours\r\n clockOut = random.choice([13, 19, 23])\r\n while clockOut <= clockIn:\r\n clockOut = random.choice([13, 19, 23])\r\n\r\n # Hourly wage\r\n wage = random.choice([8, 9, 10, 12, 20])\r\n\r\n gender = random.choice(['M', 'F'])\r\n if gender == 'M':\r\n name = random.choice(maleNames)\r\n else:\r\n name = random.choice(femaleNames)\r\n\r\n self.c.execute(\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\", (name, clockIn, clockOut, wage))\r\n self.conn.commit()\r\n\r\n if self.print_employees:\r\n print(\"\\nName:\", name)\r\n print(\"Clock in:\", clockIn)\r\n print(\"Clock out:\", clockOut)\r\n print(\"Wage:\", wage)","def create_employee(attributes):\n neccessary_keys = [\"empid\", \"gender\", \"sales\", \"bmi\", \"salary\", \"birthday\",\n \"age\"]\n for key in neccessary_keys:\n if not key in attributes.keys():\n raise ValueError(\"employee could not be created: {} is missing\".format(key))\n return Employee(attributes[\"empid\"], attributes[\"gender\"],\n attributes[\"sales\"], attributes[\"bmi\"],\n attributes[\"salary\"], attributes[\"birthday\"],\n attributes[\"age\"])","def post(self):\n data = EmployeeRegister.parser.parse_args()\n new_employee_id = str(uuid.uuid4())\n\n while EmployeeModel.find_by_id(new_employee_id):\n # if this id is already in use\n new_employee_id = str(uuid.uuid4())\n\n employee = EmployeeModel(**data, employee_id=new_employee_id)\n employee.save_to_db()\n\n return {\"message\": \"Employee successfully added to the system\"}, 201 # 201 - Created","def add_employee(self, empl):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\n empl.active, empl.promotor))\n cursor.execute('SELECT LASTVAL()')\n eid = cursor.fetchone()[0]\n empl.id = eid\n # get id and return updated object\n self.dbconnect.commit()\n except(Exception, self.dbconnect.get_error()) as error:\n self.dbconnect.rollback()\n raise Exception('\\nUnable to save Employee!\\n(%s)' % (error))","def create(self, values):\n if values.get('country_id', False):\n country = self.env['res.country'].browse(values['country_id'])\n if country.code == 'SA':\n values.update({'is_saudi': True})\n else:\n values.update({'is_saudi': False})\n\n res = super(HrEmployee, self).create(values)\n if values.get('user_id', False):\n self.user_id.write({'employee_id': res})\n return res","def create_person(self):","def add_employee(schema, employee_json):\n employee = schema.load(employee_json, session=db.session)\n db.session.add(employee)\n db.session.commit()\n return employee","def setUp(self):\n\n self.user = self.make_user()\n self.employee = Employee.objects.create(\n cpf=\"974.220.200-16\",\n user=self.user,\n departament=Employee.ADMINISTRATION\n )","def create_employee(request, company_id):\n\n company = Company.objects.get(pk=company_id)\n current_employee = Employee.objects.get(user__pk=request.user.pk)\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\n logUnauthorizedAccess(\"User tried to create_employee\", request)\n raise PermissionDenied()\n form = EmployeeForm(request, initial=dict(company=company))\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\n # Q(company=company) | Q(company__isnull=True))\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n\n return TemplateResponse(\n request,\n 'mus/create_employee_form.html',\n {\n 'employee_form': form,\n }\n )\n # data = {\n # 'employee_form': form.cleaned_data,\n # 'company': company.cleaned_data[\"name\"]\n # }\n # return JsonResponse(status=200, data=data)","def post(self, request):\n data = request.data\n skill_data = data.pop('skills')\n Department_name = data.pop('department')\n department = Department.objects.get(name=Department_name)\n manager_name = data.pop('manager')\n manager = Manager.objects.get(name=manager_name)\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\n Employee.save()\n for skill in skill_data:\n skill_add, create = Skill.objects.get_or_create(name=skill)\n Employee.skills.add(skill_add)\n return Response(\n data=request.data\n )","def add_employee(self, first_name, last_name):\n self.switch_main_menu(\"PIM\")\n self.click_menu(\"Add Employee\")\n self.pim = AddEmployee(self.driver)\n self.pim.add_user_employee(first_name, last_name)","def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\n peronjson={}, notes='', tenantid=''):\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\n self.persontdetailscoll.insert(tenantobj, safe=True)","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n respond_data = self.controller.add_team_employee(request_data=request_data)\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.EmployeeDoesNotExist,\n domain_exceptions.EmployeeIsATeamMember\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create_individual(self):\n pass","def main():\n name = input(\"Please enter in your name: \")\n\n \"\"\"Ask the user to enter a number if they are a Director, Manager or Staff.\"\"\"\n \"\"\"This will check and make sure the user only enters in 1,2, \n or 3 and a number greater than zero\"\"\"\n while True:\n try:\n designation_number = int(input(\"Please enter in \\n1 for Director \"\n \"\\n2 for Manager \\n3 for Staff\\n\"))\n if 0 < designation_number <= 3:\n break\n print(\"Invalid number entered.\")\n except Exception as e:\n print(e)\n \"\"\"Gets the user salary and makes sure is a number and greater than 0\"\"\"\n while True:\n try:\n salary = float(input(\"Please enter in your salary: \"))\n if salary <= 0:\n print(\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\")\n else:\n break\n except ValueError:\n print(\"Oops! That was not a valid number. Try again...\")\n\n \"\"\"Create Employee\"\"\"\n employee1 = employee.Employee()\n employee1.set_name(name)\n employee1.set_designation(designation_number)\n employee1.set_salary(salary)\n print(employee1)","def create():","def create():","def create_employee_structure(employees):\n employees_dict = {}\n for employee in position_sort(employees):\n if not employee.is_secretary:\n adder(employees_dict, employee.prosecutors_office, {'employees': [], 'departments': {}, 'divisions': {}})\n if employee.prosecutors_office and employee.department and employee.division:\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'divisions', {})\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'], employee.division, [])\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'][employee.division].append(employee)\n elif employee.prosecutors_office and employee.department:\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'employees', [])\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['employees'].append(employee)\n elif employee.prosecutors_office and employee.division:\n adder(employees_dict[employee.prosecutors_office]['divisions'], employee.division, [])\n employees_dict[employee.prosecutors_office]['divisions'][employee.division].append(employee)\n elif employee.prosecutors_office:\n employees_dict[employee.prosecutors_office]['employees'].append(employee)\n return employees_dict","def make_employee_dict(names, ID_numbers, salaries, email_addresses):\r\n d = dict()\r\n for i in range(len(names)):\r\n d[ID_numbers[i]] = Employee(names[i], ID_numbers[i], salaries[i], email_addresses[i])\r\n return d","def setUp(self):\n\tself.emp = Employee('Lin',10000)\n\tself.emp2 = Employee('Jun',20000)","def test_access_employee(self):\n # Employee can't see any SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).read()\n # Employee can't edit the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\n # Employee can't create the SO\n with self.assertRaises(AccessError):\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\n 'partner_id': self.partner_a.id,\n })\n # Employee can't delete the SO\n with self.assertRaises(AccessError):\n self.order.with_user(self.company_data['default_user_employee']).unlink()","def test_employee_creation_bad_fields(self):\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=None, department_id=None)\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\n\n self.assertEqual(result['name'], helper.name)\n self.assertEqual(result['hiredOn'], helper.hired_on)\n self.assertEqual(result['salary'], 0)\n self.assertIsNone(result['departmentId'])","def create(self, values):\n\t\temployee_id = values.get('employee_id', False)\n\t\tprint(\"the val in the dict\", values)\n\t\tif (values.get('date_from') and values.get('date_to')) == False:\n\t\t\tcurrent = datetime.strftime(datetime.today().date(),'%Y-%m-%d')\n\t\t\tvalues.update({'allocate_date': current})\n\t\t\tprint(values)\n\t\tif not values.get('department_id'):\n\t\t\tvalues.update({'department_id': self.env['hr.employee'].browse (employee_id).department_id.id})\n\n\t\tholiday = super (Holidays, self.with_context (mail_create_nolog=True, mail_create_nosubscribe=True)).create(values)\n\t\tholiday.add_follower (employee_id)\n\n\t\t# Trilok code for policies\n\t\tpolicy_id = holiday.env['leaves.policy'].search(\n\t\t\t[('leave_type', '=', holiday.holiday_status_id.id), ('company_id', '=', self.env.user.company_id.id)])\n\t\t# print (\"policy iddddddddddddddd\",policy_id)\n\t\temp_type = holiday.employee_id.employee_type.id\n\t\tfor val in policy_id:\n\t\t\tif val.employee_type.id == emp_type:\n\t\t\t\tfor employee in holiday.employee_id:\n\t\t\t\t\tif holiday.type == 'remove':\n\t\t\t\t\t\tquery = '''select count(*) from hr_holidays where upper(type) = upper('rEMove')and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') and now()::date and employee_id = %s''' % employee.id\n\t\t\t\t\t\tholiday.env.cr.execute(query)\n\t\t\t\t\t\tquery_result = holiday.env.cr.dictfetchone()\n\t\t\t\t\t\t# print(\"query_result\", query_result)\n\t\t\t\t\t\tif val.min_app_per_year > 0 and query_result[\"count\"] > val.min_app_per_year:\n\t\t\t\t\t\t\traise ValidationError(\"maximum number of applications per year is {} days\".format(val.min_app_per_year))\n\n\t\t\t\t\t\tquery1 = '''select create_date::date,date_to::date from hr_holidays where upper(type) = \n\t\t\t\t\t\tupper('rEMove') and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') \n\t\t and now()::date and employee_id = %s order by create_date desc limit 1'''\\\n\t\t\t\t\t\t\t\t % employee.id\n\t\t\t\t\t\tholiday.env.cr.execute(query1)\n\t\t\t\t\t\tquery_result1 = holiday.env.cr.fetchall()\n\t\t\t\t\t\tif query_result1 is not None:\n\t\t\t\t\t\t\t# print(\"query_resulttttttttttttttttttttttttttt\", query_result1)\n\t\t\t\t\t\t\t# print(\"query_resulttttttttttttttttttttttttttt\", query_result1[0][0], query_result1[0][1])\n\t\t\t\t\t\t\tcre_date = datetime.strptime(query_result1[0][0], '%Y-%m-%d')\n\t\t\t\t\t\t\tdate_to = datetime.strptime(query_result1[0][1], '%Y-%m-%d')\n\t\t\t\t\t\t\t# print(\"cre_date\", cre_date, type(cre_date))\n\t\t\t\t\t\t\tcurrent_dt = fields.Datetime.now()\n\t\t\t\t\t\t\t# cdate=datetime.strptime(current_dt,'%Y-%m-%d')\n\t\t\t\t\t\t\tcurrent_date = datetime.strptime(current_dt.split(\" \")[0], '%Y-%m-%d')\n\t\t\t\t\t\t\tdays = (current_date - date_to).days\n\t\t\t\t\t\t\tif val.min_leave_app_gap > 0 and days > val.min_leave_app_gap:\n\t\t\t\t\t\t\t\traise ValidationError(\n\t\t\t\t\t\t\t\t\t\"Minimum gap between two application should be atleast {} days\".format(\n\t\t\t\t\t\t\t\t\t\tval.min_leave_app_gap))\n\n\t\treturn holiday","def add_employee(worksheet, row, employee, num_in_list, employee_format, employee_format_b):\n # Get number of row for employee\n if employee.work_telephone:\n work_telephone_list = regular_telephone(employee.work_telephone.split(';'))\n else:\n work_telephone_list = []\n if employee.private_telephone:\n private_telephone_list = regular_telephone(employee.private_telephone.split(';'))\n else:\n private_telephone_list = []\n rows_for_emp = max(len(work_telephone_list), len(private_telephone_list), 2)\n # Add name and position\n worksheet.merge_range(row, 0, row+rows_for_emp-1, 0, num_in_list, cell_format=employee_format)\n worksheet.merge_range(row, 1, row+rows_for_emp-1, 1, str(employee), cell_format=employee_format_b)\n worksheet.merge_range(row, 2, row+rows_for_emp-1, 2, str(employee.position), cell_format=employee_format)\n # Add work telephone numbers\n for num, work_telephone in enumerate(work_telephone_list):\n if (len(work_telephone_list) < rows_for_emp) and ((num + 1) == len(work_telephone_list)):\n worksheet.merge_range(row+num, 3, row+rows_for_emp-1, 3, work_telephone, cell_format=employee_format)\n else:\n worksheet.write(row+num, 3, work_telephone, employee_format)\n if len(work_telephone_list) == 0:\n worksheet.merge_range(row, 3, row+rows_for_emp-1, 3, '', cell_format=employee_format)\n # Add private telephone numbers\n for num, private_telephone in enumerate(private_telephone_list):\n if (len(private_telephone_list) < rows_for_emp) and ((num + 1) == len(private_telephone_list)):\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, private_telephone, cell_format=employee_format)\n else:\n worksheet.write(row+num, 4, private_telephone, employee_format)\n if (len(private_telephone_list) < rows_for_emp) and ((num - 1) == len(private_telephone_list)):\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\n if len(private_telephone_list) == 0:\n worksheet.merge_range(row, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\n # If secretary exist\n if employee.secretary:\n row = row + rows_for_emp\n add_employee(worksheet, row, employee.secretary, '', employee_format, employee_format_b)\n return row+rows_for_emp","def create_educator(data):\n\n educator = Educator(\n name=data['name'],\n work_email=data['work_email'],\n organization_name=data['organization_name'],\n org_or_school=data['org_or_school'],\n address_line_1=data['address_line_1'],\n address_line_2=data['address_line_2'],\n city=data['city'],\n state=data['state'],\n zipcode=data['zipcode'],\n num_students=data['num_students']\n )\n educator.save()\n return educator","def post(self):\n employee = Employee(**self.data)\n _dict = Employee.encode(employee)\n\n _id = DatabaseManager.insert(Collection.EMPLOYEES, _dict)\n employee_dict = DatabaseManager.find_document_by_id(\n Collection.EMPLOYEES, _id, True\n )\n return employee_dict","def setUp(self):\n self.salary = 40000\n self.custom_rise = 7500\n self.employee = Employee(\"Carlos\", \"Zapata\", self.salary)","def create(self, vals):\n record = super(KaHrPayrollRapelEmployeePromote, self).create(vals)\n if not 'name' in vals or not vals.get('name'):\n record.name = \"Rapel {0}\".format(record.new_employee_promote_id.name)\n return record","def add_employee():\n\n if not g.user:\n flash(\"Please login to access\", \"danger\")\n return redirect(\"/\")\n \n if g.user.is_admin == False:\n flash (\"Unauthorized\", \"danger\")\n return redirect(\"/login\")\n\n form = SignUp_Form()\n \n\n if form.validate_on_submit():\n try: \n employee = Employee.register(\n username = form.username.data,\n password = form.password.data, \n email = form.email.data, \n first_name = form.first_name.data,\n last_name = form.last_name.data,\n hire_date = form.hire_date.data, \n is_admin = form.is_admin.data,\n )\n\n db.session.add(employee)\n\n db.session.commit()\n except IntegrityError:\n flash(\"Email already in use\", \"danger\")\n return render_template(\"/admin/add_user.html\", form = form)\n\n flash(\"Employee Added!\", \"success\")\n return redirect(\"/administrator\")\n else:\n\n return render_template(\"/admin/add_user.html\", form = form)","def insert_employee(self,\n region_name,\n last_name,\n first_name,\n hire_date,\n mi=None):\n\n if self.check_input_type(region_name, \"Region\"):\n if self.check_input_type(hire_date, \"Date\"):\n region_info = self.query_region(region_name)\n region_id = region_info[0][0]\n\n if mi != \"\":\n query_format = \"insert into employee(Region_ID, \" \\\n \"Emp_Lname, Emp_Mi, Emp_Fname, Emp_Hiredate) \" \\\n \"values ((select region_id from region where \" \\\n \"region_id='{}'), '{}', '{}', '{}', '{}')\"\n query = query_format.format(\n region_id, last_name, mi, first_name, hire_date\n )\n else:\n query_format = \"insert into employee(Region_ID, \" \\\n \"Emp_Lname, Emp_Fname, Emp_Hiredate) \" \\\n \"values ((select region_id from region where \" \\\n \"region_id='{}'), '{}', '{}', '{}')\"\n query = query_format.format(\n region_id, last_name, first_name, hire_date\n )\n\n try:\n self.dbCursor.execute(query)\n SuccessMessageWindow(\"Insert success!\")\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)\n finally:\n self.dbConnection.commit()\n else:\n ErrorMessageWindow(\"Date format not valid!\")\n else:\n ErrorMessageWindow(\"Region input not valid!\")","def create_trainee(self, name, pos, dept):\n self.trainee[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'junior': self.junior[dept.upper()][0]['name'],\n }\n )\n self.manager[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.junior[dept.upper()][0]['trainee'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def create_epic():\n client = RequestManager()\n client.set_method(\"POST\")\n client.set_endpoint(\"/projects/{0}/epics\".format(STORED_ID['project_id']))\n name = \"\".join(choices(string.ascii_letters, k=6))\n body = {\"name\": name}\n client.set_body(json.dumps(body))\n response = client.execute_request()\n STORED_ID['epic_id'] = response.json()['id']","def setUp(self):\n self.employee = Employee('Lucas', 'Guerra', 45000)","def setUp(self):\n\t\tfirst_name = 'Gerson'\n\t\tlast_name = 'Santos'\n\t\tannual_salary = 5000\n\t\tself.gerson = Employee(first_name, last_name, annual_salary)","def setUp(self):\n self.my_employee = Employee('knight', 'lee', 10000)","def create_junior(self, name, pos, dept):\n self.junior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'senior': self.senior[dept.upper()][0]['name'],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )\n self.senior[dept.upper()][0]['junior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def test_admin_can_create_a_employee(self):\n\n account_data = {\n \"username\": \"Mike\",\n \"email\": \"mioshine@g.com\",\n \"password\": \"1234567\",\n \"confirm_password\": \"1234567\"\n }\n response = self.client.post(\n reverse('accounts:create-user'),\n account_data,\n format=\"json\")\n \"\"\"Test the api has bucket creation capability.\"\"\"\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertTrue(\"data\" in json.loads(response.content))","def createanastep(body):\n pilotpoint = body.get(\"ppname\", None)\n steps = body.get(\"steps\", None)\n print('lol',pilotpoint, steps)\n\n # Does the person exist already?\n if pilotpoint not in anastep_dict and pilotpoint is not None:\n anastep_dict[pilotpoint] = {\n \"pilotpoint\": pilotpoint,\n \"steps\": steps,\n }\n return anastep_dict[pilotpoint], 201\n\n # Otherwise, they exist, that's an error\n else:\n abort(\n 406,\n \"Person with last name {pilotpoint} already exists\".format(pilotpoint=pilotpoint),\n )","def create_manager(self, name, pos, dept):\n self.manager[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'senior': [],\n 'junior': [],\n 'trainee': []\n }\n )","def create_entity(self):\n \n if self.ORION_CB.get_entity(self.params['name']) is None:\n \n print('[INFO]: Create new PID entity')\n \n entity_dict = {\"id\":self.params['name'], \"type\":'PID_controller'}\n for attr in ['Kp', 'Ti', 'Td', 'lim_low', 'lim_high', 'setpoint']:\n entity_dict.update({attr:{'value':self.params[attr],'type':'Number'}})\n\n entity_dict.update({'reverse_act':{'value':self.params['reverse_act'],'type':'Text'}})\n \n entity = filip.orion.Entity(entity_dict)#, attrs)\n\n self.ORION_CB.post_entity(entity)\n \n else:\n print('Entity name already assigned')","def create():\n pass","def create_senior(self, name, pos, dept):\n self.senior[dept.upper()].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept,\n 'manager': self.manager[dept.upper()][0]['name'],\n 'junior': [],\n 'trainee': []\n }\n )\n self.manager[dept.upper()][0]['senior'].append(\n {\n 'name': name,\n 'pos': pos,\n 'dept': dept\n }\n )","def createEMPStudy(self, user_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries):\n con = self.getMetadataDatabaseConnection()\n study_id = 0\n results = con.cursor().callproc('qiime_assets.emp_study_insert', \n [study_id, user_id, study_name,\n investigation_type, miens_compliant, submit_to_insdc, portal_type, \n study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries])\n return results[0]","def make_salaried(self,salary,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"2\"\n print(\"{}{}\".format(name,\" was successfully changed to be a salaried employee\"))\n self.emp_dict[id][7] = salary\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def add_employee(self, emp):\n if emp not in self.employees: \n self.employees.append(emp)","def handle(self, *args, **kwargs):\n seeder = Seed.seeder()\n seeder.add_entity(User, 20)\n\n seeder.add_entity(EmployeeMptt, 20, {\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\n 'parent': lambda x: EmployeeMptt.objects.order_by(\"?\").first(),\n 'level': lambda x: random.randint(0, 4),\n })\n seeder.execute()","def make_commissioned(self,salary,commission,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"3\"\n print(\"{}{}\".format(name,\" was successfully changed to be a commissioned employee\"))\n self.emp_dict[id][7] = salary\n self.emp_dict[id][9] = commission\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def create(self, request):\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n request_data = serializer.save()\n try:\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n except (\n domain_exceptions.TeamDoesNotExist,\n domain_exceptions.TeamHasALeader,\n domain_exceptions.EmployeeDoesNotExist\n )as e:\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def create_person(data_dict, m2m_dict):\n email_address = data_dict['email_address']\n # check if the person already exists:\n try:\n exists = Person.objects.get(email_address=email_address)\n create_message = f'Skipping: Person with {email_address} already exists.'\n except:\n # try:\n person_obj, person_created = Person.objects.update_or_create(**data_dict)\n # populate MANY-TO-MANY fields\n if m2m_dict:\n # created_by (FK)\n created_by_email = m2m_dict['created_by']\n user, user_created = User.objects.get_or_create(email=created_by_email)\n person_qs = Person.objects.filter(email_address=person_obj.email_address)\n person_qs.update(created_by=user)\n # expertise (M2M)\n expertise_values = m2m_dict['expertise']\n if expertise_values:\n values_list = [value.strip() for value in expertise_values.split(',')]\n for value in values_list:\n expertise_obj, expertise_created = Expertise.objects.get_or_create(name=value)\n person_obj.expertise.add(expertise_obj)\n # industry (M2M)\n industry_values = m2m_dict['industries']\n if industry_values:\n values_list = [value.strip() for value in industry_values.split(',')]\n for value in values_list:\n industry_obj, industry_created = Industry.objects.get_or_create(name=value)\n person_obj.industries.add(industry_obj)\n # organization (M2M)\n organization_values = m2m_dict['organization']\n if organization_values:\n values_list = [value.strip() for value in organization_values.split(',')]\n for value in values_list:\n organization_obj, organization_created = Organization.objects.get_or_create(name=value)\n person_obj.organization.add(organization_obj)\n # owned by (M2M)\n exportable_by_values = m2m_dict['exportable_by']\n if exportable_by_values:\n values_list = [value.strip() for value in exportable_by_values.split(',')]\n for value in values_list:\n dive_obj, dive_created = Dive.objects.get_or_create(name=value)\n person_obj.exportable_by.add(dive_obj)\n # let us know how it went\n if person_created:\n create_message = f'Success: {person_obj}'\n else:\n create_message = f'Failed: {person_obj}'\n # except Exception as e:\n # create_message = f'Error for {email_address}: {e}\\n'\n print(create_message)\n # except:\n # failed_rows.append(counter)\n # try:\n # obj, created = Person.objects.create(**csv_to_model)\n # except:\n # message = 'Create person' + str(sys.exc_info())\n # print(message)","def create_job_detail(company_name, job_title, application_deadline, job_listing_url, state, city, application_listed, salary):\n\n job_detail = JobDetail(company_name = company_name, job_title = job_title, application_deadline = application_deadline, job_listing_url = job_listing_url, state = state , city = city, application_listed = application_listed, salary = salary)\n db.session.add(job_detail)\n db.session.commit()\n\n return job_detail","def create(task):\n tname = task.get(\"tname\")\n # user cannot create task without name\n\n # Does the new task have a name? If no we can't insert it.\n # Can we insert it?\n if tname is not None:\n\n # Create a person instance using the schema and the passed in person\n schema = TaskListSchema()\n print(task)\n new_task = schema.load(task, session=db.session).data\n\n # Add the person to the database\n db.session.add(new_task)\n db.session.commit()\n\n # Serialize and return the newly created person in the response\n data = schema.dump(new_task).data\n\n return data, 201\n\n # Otherwise, nope, person exists already\n else:\n abort(409, \"Task needs a name\".format(tname=tname),)","def createEmployee():\n form = CreateEmployeeForm()\n if form.validate_on_submit():\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \n email=form.email.data, role=form.role.data, location=form.location.data)\n db.session.add(staff)\n db.session.commit()\n flash(f'Employee Added To Database', category='Success')\n return redirect(url_for('login'))\n return render_template('new_employee.html', title=\"Register\", form=form)","def create(self):","def create_job(self, employer_id, compensation, location, description, category_id, group_id):\n\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \n db.session.add(job)\n db.session.commit()","def create(self, name, login, password, email, address=\"\", vat=\"\", jobguid=\"\", executionparams=None):","def create_meal():","def create_event(self, name, date):\n user = User.objects.create(username='userdemo')\n user.set_password('calnote24')\n user.save()\n Event.objects.create(name=name, date=date, user_id=user.id)","def setUp(self):\n\t\tself.mason = Employee(\"mason\",\"karsevar\",10000)","def users_create():","def create():\n\tnombre = input(\"Ingrese el nombre de pokemon: \")\n\testado = input(\"Ingrese el estado. Si el pokemon no tiene estado, ingrese X: \")\n\n\tif estado.upper() == \"X\":\n\t\testado = None\n\n\tif estado not in estados_permitidos:\n\t\tprint(\"Estado de pokemon no permitido. Registro no fue insertado.\")\n\t\tprint(\"Devolviendo al menu principal...\")\n\t\treturn\n\n\thp_actual = int(input(\"Ingrese HP actual de pokemon: \"))\n\tfecha = input(\"Ingrese la fecha en formato DD/MM/YY HH24:MM (ej 06/09/20 14:20): \")\n\tinsertar_pokemon(nombre, hp_actual, estado, fecha)","def add_employee(self, employee):\n self.employees.add(employee)","def createGroup(listOfPerson):\n atk=Department()\n atk.members=listOfPerson\n return atk","def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\n teacher_account_id = person.get_last()\n query = 'INSERT INTO teacher VALUES( %s,%s );'\n args = (teacher_account_id, availability)\n database.connection.save_data(query, args)","def test_office_creation(self):\n url = '/api/v1/consultorios/'\n data = {\n \"hospital\": \"Angeles Roma\",\n \"office\": \"306\"\n }\n request = self.client.post(url, data)\n\n self.assertEqual(request.status_code, status.HTTP_201_CREATED)","def create_master(conn,task):\r\n sql = ''' INSERT OR IGNORE INTO emp(emp_id,name)\r\n VALUES(?,?) '''\r\n c = conn.cursor()\r\n c.execute(sql,task)\r\n conn.commit()","def test_new_employee_crud_methods(self):\n response = self.client.get(\n '/employees/', kwargs={'employer_id': self.employee.id})\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(Employee.objects.all()), 1)\n\n # Test that a new employee can be added\n response = self.client.post(\n '/employees/',\n {'name': 'MAdtraxx!!', 'employer': self.employer.id},\n kwargs={'pk': self.employer.id})\n self.assertEqual(response.status_code, 201)\n self.assertEqual(Employee.objects.count(), 2)\n\n # Test that employee info may be edited\n response = self.client.put('/employees/1/',\n {'name': 'Ashley',\n 'employer': self.employer.id},\n kwargs={'employer_id': self.employee.id,\n 'pk': self.employee.id})\n self.assertEqual(response.status_code, 200)","def create():\n data = request.get_json()\n print(\"DATA: \", data)\n db_helper.insert_new_record(data['first_name'], data['last_name'], data['class_period'], data['current_belt'], data['student_teacher_id'])\n result = {'success': True, 'response': 'Done'}\n return jsonify(result)","def create(self):\n pass","def create(self):\n pass","def create(self):\n pass","def create(self, creator: User = None, *args, **kwargs) -> Employee:\n # If the creator argument is not None and is a non-staff user, raise\n # ValueError\n if creator and not creator.is_staff:\n raise ValueError(\n 'Only staff members can add new employees, \"creator\" must be '\n 'a staff user.'\n )\n\n # Get the user property from the provided keyword arguments\n user: Optional[User] = kwargs.get('user', None)\n\n # If the user property is missing or is a non-staff user, raise\n # ValueError\n if not user:\n raise ValueError(\n 'You must provide the \"user\" property as a keyword argument'\n )\n elif not user.is_staff:\n raise ValueError('The \"user\" property must be a staff user.')\n\n return super().create(creator, *args, **kwargs)","def get_employee():\n\n employee_id = get_employee_input_int('Enter employee ID to get the data ')\n employee = db.get_employee(employee_id)\n if not employee:\n print(\"No employee found with id \", employee_id)\n else:\n payscale = db.get_payScale(employee.grade)\n print('DATA:-> {} {} has grade = {} which gives {} per hours\\n'\n .format(employee.first_name, employee.last_name, employee.grade, payscale.salary))","def create(self, person):\n current_id = self._generate_person_id()\n self._timeline.add_event({\n 'type': EventTimeLine.PERSON_CREATION,\n 'personId': current_id,\n 'status': person.status,\n 'address': person.address.to_dict(),\n 'name': person.name.to_dict()\n })\n return current_id","def create(self):\n\n pass","def perform_create(self, serializer):\n if self.request.data.get('user_type', None) == 'employee':\n serializer.save(is_staff=False)\n else:\n serializer.save()","def make_hourly(self,rate,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"1\"\n print(\"{}{}\".format(name,\" was successfully changed to be an hourly employee\"))\n self.emp_dict[id][8] = rate\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def post(self):\r\n piso=self.request.get('piso')\r\n numext=self.request.get('numext')\r\n numint=self.request.get('numint')\r\n piso=self.validonumero(piso)\r\n numext=self.validonumero(numext)\r\n numint=self.validonumero(numint)\r\n \r\n empresa=empresas()\r\n empresa.nombre=self.request.get('desc')\r\n empresa.calle=self.request.get('calle')\r\n empresa.numeroExterior=int(numext)\r\n empresa.numeroInterior=int(numint)\r\n empresa.colonia=self.request.get('colonia')\r\n empresa.piso=int(piso)\r\n empresa.andador=self.request.get('andador')\r\n empresa.codigo_postal=int(self.request.get('cp'))\r\n empresa.sitioweb=self.request.get('web')\r\n empresa.correo=self.request.get('mail')\r\n empresa.nombreContacto=\"\"\r\n empresa.paternoContacto=\"\"\r\n empresa.maternoContacto=\"\"\r\n #### \r\n ciudad=self.request.get('ciudad')\r\n query=\"where ciudad='%s'\"%ciudad\r\n cd=ciudades.gql(query)\r\n city=cd.fetch(1)\r\n for lstcd in city:\r\n empresa.id_Ciudad=lstcd.key().id()\r\n empresa.put()\r\n jsondic={}\r\n jsondata=[]\r\n jsondata+=[self.addKey(jsondic,\"Dato\", empresa.key().id())]\r\n self.response.out.write(simplejson.dumps(jsondata))\r\n return False","def create(self):\n ...","def create_person(dbo, username, collationid):\n l = dbo.locale\n fields = get_onlineformincoming_detail(dbo, collationid)\n d = {}\n flags = None\n for f in fields:\n if flags is None: flags = f[\"FLAGS\"]\n if f[\"FIELDNAME\"] == \"title\": d[\"title\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"forenames\": d[\"forenames\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"firstname\": d[\"forenames\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"surname\": d[\"surname\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"lastname\": d[\"surname\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"address\": d[\"address\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"town\": d[\"town\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"city\": d[\"town\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"county\": d[\"county\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"state\": d[\"county\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"postcode\": d[\"postcode\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"zipcode\": d[\"postcode\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"hometelephone\": d[\"hometelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"worktelephone\": d[\"worktelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"mobiletelephone\": d[\"mobiletelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"celltelephone\": d[\"mobiletelephone\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"] == \"emailaddress\": d[\"emailaddress\"] = f[\"VALUE\"]\n if f[\"FIELDNAME\"].startswith(\"reserveanimalname\"): d[f[\"FIELDNAME\"]] = f[\"VALUE\"]\n d[\"flags\"] = flags\n # Have we got enough info to create the person record? We just need a surname\n if not d.has_key(\"surname\"):\n raise utils.ASMValidationError(i18n._(\"There is not enough information in the form to create a person record (need a surname).\", l))\n # Does this person already exist?\n personid = 0\n if d.has_key(\"surname\") and d.has_key(\"forenames\") and d.has_key(\"address\"):\n similar = person.get_person_similar(dbo, d[\"surname\"], d[\"forenames\"], d[\"address\"])\n if len(similar) > 0:\n personid = similar[0][\"ID\"]\n # Create the person record if we didn't find one\n if personid == 0:\n personid = person.insert_person_from_form(dbo, utils.PostedData(d, dbo.locale), username)\n personname = person.get_person_name_code(dbo, personid)\n # Attach the form to the person\n formname = get_onlineformincoming_name(dbo, collationid)\n formhtml = get_onlineformincoming_html_print(dbo, [collationid,])\n media.create_document_media(dbo, username, media.PERSON, personid, formname, formhtml )\n # Was there a reserveanimalname field? If so, create reservation(s) to the person if possible\n for k, v in d.iteritems():\n if k.startswith(\"reserveanimalname\"):\n movement.insert_reserve_for_animal_name(dbo, username, personid, v)\n return (collationid, personid, personname)","def employee_add(request, user_id):\n\n if request.method == \"GET\":\n employee = User.objects.get(pk=user_id)\n orgs = Organization.objects.filter(administrator=request.user)\n\n #desig_list = orgs.values_list('designations', flat=True)\n #designations = Designation.objects.filter(pk__in=desig_list)\n\n return render_to_response('organization/employee_add1.html',\n {'employee':employee, 'orgs': orgs},\n context_instance=RequestContext(request))\n\n user_id = request.POST.get('employee_id')\n org = request.POST.get('add2orgs') # id of selected org\n designations_list = request.POST.getlist('designations2add')#list of ids of selected designation\n\n try:\n user = User.objects.get(pk=user_id)# emp obj to add\n org = Organization.objects.get(pk=org)#selected org objects\n designations = Designation.objects.filter(pk__in=designations_list)#convert desig id in obj\n except Exception, e:\n # log this error\n print str(e)\n messages.error(request, str(e))\n user = org = designations = None\n\n if not (user and designations.count()):\n # add error message.\n messages.error(request, \"Select atleast one Organization and its Designation\")\n # redirect to the same page.\n return HttpResponseRedirect(request.path)\n\n #def send_email():\n # template = get_template('organization/confirmation_email.txt')\n # context = Context({'user':user,'org':o.organization,'desig':desig})\n # subject = u'confirmation email'\n # message = template.render(context)\n # send_mail(subject,message,settings.DEFAULT_FROM_EMAIL,['ravirajmca12@gmail.com'])\n\n # create visiting card(s) for the employee\n try:\n l=[]\n for desig in designations:\n if desig in org.designations.all():\n o,c = VisitingCards.objects.get_or_create(organization=org,\n designation=desig, user=user)\n\n if c:\n #send_email()\n template = get_template('organization/confirmation_email.txt')\n context = Context({'user':user,'org':o.organization,'desig':desig})\n subject = u'confirmation email'\n message = template.render(context)\n msg = (subject,message,settings.DEFAULT_FROM_EMAIL, ['ravirajmca12@gmail.com'])\n l.append(msg)\n #t=threading.Thread(target=send_email)\n #t.start()\n messages.success(request,\"Employee Added Succefully..!\")\n else:\n messages.info(request,\" %s Already Added ..!\" % user.username)\n else:\n messages.error(request,\"%s Doesnt have selected designation %s .!\" %(orgs, desig))\n raise Exception()\n if c:\n tupl=tuple(l)\n threading.Thread(send_mass_mail(tupl, fail_silently=False)).start()\n messages.success(request,\"Employee Added Succefully..!\")\n\n\n\n except:\n messages.error(request,\"something went wroung \")\n return HttpResponseRedirect(request.path)\n\n return HttpResponseRedirect(\"/org/user/list\")","def create(person):\n lname = person.get(\"lname\", None)\n fname = person.get(\"fname\", None)\n\n # Does the person exist already?\n if lname not in PEOPLE and lname is not None:\n PEOPLE[lname] = {\n \"lname\": lname,\n \"fname\": fname,\n \"timestamp\": get_timestamp(),\n }\n return PEOPLE[lname], 201\n\n # Otherwise, they exist, that's an error\n else:\n abort(\n 406,\n \"Person with last name {lname} already exists\".format(lname=lname),\n )","def loadEmployees(testList):\n # define an empty employee List\n employeeList = []\n\n for item in testList:\n itemToAdd = None\n if item['type'] == 'employee':\n try:\n itemToAdd = createEmployee(item['firstName'],\n item['lastName'],\n item['SSN'],\n item['salary'])\n except ValueError:\n continue\n\n elif item['type'] == 'manager':\n try:\n itemToAdd = createManager(item['firstName'],\n item['lastName'],\n item['SSN'],\n item['salary'],\n item['title'],\n item['yearBonus'])\n except ValueError:\n continue\n # Add Employee/Manager Object to List\n if itemToAdd != None: # Note : this line will call Employee __eq__ to verify that it is not equal to None\n employeeList.append(itemToAdd)\n\n return employeeList","def add_employee():\n\n while True:\n first_name = get_user_string(\"Enter your first name\")\n last_name = get_user_string(\"Enter your last name\")\n grade = get_employee_input_int(\"Enter your grade\")\n db.add_employee(first_name, last_name, grade)\n print(\"New employee \" + first_name + \" \" + last_name + \" has been added to the employee table\")\n user_input = input(\"Do you want to add more employees to the table ? (Y/N)\")\n if(str(user_input).upper()) == 'Y':\n continue\n elif (str(user_input).upper()) == 'N':\n break\n else:\n print(\"Invalid Input\\nReturning to the main menu\")\n break","def test_skip_creation_of_sd_let_go_employments(employment_status: str) -> None:\n # Arrange\n sd = get_sd_importer()\n sd.importer = MagicMock()\n\n person = {\n \"PersonCivilRegistrationIdentifier\": \"1234567890\",\n \"Employment\": [\n {\"EmploymentStatus\": {\"EmploymentStatusCode\": employment_status}}\n ],\n }\n\n # Act\n sd.create_employee(person)\n\n # Assert\n sd.importer.add_engagement.assert_not_called()","def post(self, data: Dict):\n data = {\n 'create_expensereport': data\n }\n return self.format_and_send_request(data)","def amazonEc2_create(amazonEc2):\n\treturn amazonEc2","def employee_id(self):\n for i in self.emp_dict:\n self.emp_id[i] = self.emp_dict[i][0]\n #print(self.emp_id)\n return self.emp_id","def create(self, vals):\n number_id = self.env['educa.establishment'].search([('id', '=', vals['establishment_id'])]).number_id\n code = self.search([('establishment_id', '=', vals['establishment_id'])], order=\"name desc\", limit=1).name\n if code:\n num = str(code).split('/')\n start_num =(int(num[0]))\n end_num = int(num[1])\n final_name=str(start_num) +'/' +str(end_num+1)\n vals['name'] = final_name\n else:\n vals['name'] = str(number_id) +'/' +'1'\n return super(EducaClassroom, self).create(vals)","def create_data_structure(self, records):\n col_title = [[] , []]\n all_exp = {}\n\n # Get all employee info, used for obtain employee account info\n emp_info = self.env['hr.employee'].sudo().search([])\n\n def append_data_list():\n # {a: {b: 1}}\n for rec in records:\n current_emp_acc = ''\n for emp in emp_info:\n if emp.name == rec.expense_id.employee_name:\n current_emp_acc = emp.bank_account_id.acc_number\n print(\"this employee: \" + rec.expense_id.employee_name + \"'s Acc No. is: \" + current_emp_acc)\n cur = rec.expense_line_currency.currency\n if not cur == \"RMB\":\n cur = \"HKD\"\n\n cat_key = (str(rec.expense_cate_id.acc_no), str(rec.expense_debit_acc_name))\n emp_key = (\n str(rec.expense_id.employee_id.rmb_account_no) if cur == \"RMB\" else current_emp_acc,\n rec.employee_name\n )\n exp_key = (\n str(rec.expense_id.expense_create_date),\n str(rec.expense_id.expense_num),\n str(rec.expense_id.name)\n )\n amt = float(rec.expense_line_cost) if cur == \"RMB\" else float(rec.expense_line_calculate)\n\n # -----\n if cur not in all_exp:\n all_exp[cur] = {}\n if emp_key not in all_exp[cur]:\n all_exp[cur][emp_key] = {}\n if exp_key not in all_exp[cur][emp_key]:\n all_exp[cur][emp_key][exp_key] = {}\n\n all_exp[cur][emp_key][exp_key][cat_key] = all_exp[cur][emp_key][exp_key].get(cat_key, 0.0) + amt\n\n if cur == \"HKD\" and cat_key not in col_title[0]:\n col_title[0].append(cat_key)\n elif cur == \"RMB\" and cat_key not in col_title[1]:\n col_title[1].append(cat_key)\n for titles in col_title:\n titles.sort()\n\n return\n\n append_data_list()\n\n # for emp_dict in all_exp:\n # all_exp[emp_dict] = collections.OrderedDict(sorted(all_exp[emp_dict].items())) # sort exp data keys\n # all_exp = collections.OrderedDict(sorted(all_exp.items())) # sort all_exp emp data keys by ID\n\n # get the selected exp line id for later use\n with open('./select_rec.txt', 'w') as sr:\n for rec in records:\n id_str = str(rec.id) + '\\n'\n sr.write(id_str)\n rev_name = records[0].expense_id.rec_reviewer_name\n\n for cur in all_exp:\n self.create_exl_report(col_title[0] if cur == \"HKD\" else col_title[1], all_exp[cur], rev_name, cur)\n self.create_HSBC_txt(all_exp[cur], cur)\n\n\n # return\n\n #\n # self.create_HSBC_txt(all_exp)\n\n return {\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'hhexpense.anticipated.date',\n 'type': 'ir.actions.act_window',\n }"],"string":"[\n \"def create_employee(self,personal_identity):\\r\\n new_emp = Employee(*personal_identity)\\r\\n registration_str = new_emp.get_registration_str()\\r\\n\\r\\n return_value = self.save_object_to_DB(\\\"employee\\\",registration_str)\\r\\n return return_value\",\n \"def create_employee(self):\\n try:\\n name = input(\\\"Enter name: \\\")\\n if not name.isalpha():\\n print(\\\"Invalid data format. Name should contain only alphabets. \\\")\\n return False\\n email = input(\\\"Enter email: \\\")\\n if not InputValidations.validate_email(email):\\n return False\\n employee = EmployeeModel(name=name, email=email)\\n self.admin_repository.create_employee(employee)\\n print(\\\"Employee created successfully!\\\")\\n return True\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def test_employee_creation(self):\\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=50000, department_id=1)\\n\\n # Returned result is an OrderedDict\\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\\n\\n self.assertEqual(result['name'], helper.name)\\n self.assertEqual(result['hiredOn'], helper.hired_on)\\n self.assertEqual(result['salary'], helper.salary)\\n self.assertEqual(result['departmentId'], helper.department_id)\",\n \"def post(self):\\n try:\\n employee = self.service.add_employee(self.schema, request.json)\\n except ValidationError as error:\\n return error.messages, 400\\n return self.schema.dump(employee), 201\",\n \"def add_employee(self, obj):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee(id, name, email, office, extra_info, picture_location, research_group, '\\n 'title, is_external, is_admin, is_active) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);',\\n (obj.e_id, obj.name, obj.email, obj.office, obj.extra_info, obj.picture_location, obj.research_group,\\n obj.title, obj.is_external, obj.is_admin, obj.is_active))\\n\\n self.dbconnect.commit()\\n return obj\\n except:\\n self.dbconnect.rollback()\\n raise\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\\n employee, created = Employee.objects.update_or_create(user=user,\\n employee_id=validated_data.pop('employee_id'),\\n location=validated_data.pop('location'),\\n avail_start_time= str(validated_data.pop('avail_start_time')),\\n avail_end_time= str(validated_data.pop('avail_end_time')))\\n return employee\",\n \"def create_employee_from_applicant(self, cr, uid, ids, context=None):\\n if context is None:\\n context = {}\\n hr_employee = self.pool.get('hr.employee')\\n model_data = self.pool.get('ir.model.data')\\n act_window = self.pool.get('ir.actions.act_window')\\n emp_id = False\\n for applicant in self.browse(cr, uid, ids, context=context):\\n address_id = contact_name = False\\n if applicant.partner_id:\\n address_id = self.pool.get('res.partner').address_get(cr, uid, [applicant.partner_id.id], ['contact'])['contact']\\n contact_name = self.pool.get('res.partner').name_get(cr, uid, [applicant.partner_id.id])[0][1]\\n if applicant.job_id and (applicant.partner_name or contact_name):\\n applicant.job_id.write({'no_of_hired_employee': applicant.job_id.no_of_hired_employee + 1})\\n create_ctx = dict(context, mail_broadcast=True)\\n\\n pes=self.browse(cr,uid,ids)[0]\\n coy=pes.partner_name\\n\\n ##### Susunan Keluarga ayah/ibu #####\\n le=self.pool.get('hr_recruit.suskel1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context)\\n prod_ids=[] \\n for pr in lele:\\n prod_ids.append((0,0, {'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan,'susunan':pr.susunan}))\\n \\n ###### Susunan Keluarga Suami/istri #####\\n le=self.pool.get('hr_recruit.suskel2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids1=[] \\n for pr in lele:\\n prod_ids1.append((0,0, {'susunan':pr.susunan,'name':pr.name,'kelamin':pr.kelamin,'kota_id':pr.kota_id.id,'tgl_lahir':pr.tgl_lahir,'type_id':pr.type_id.id,'pekerjaan':pr.pekerjaan})) \\n \\n ###### riwayat Pendidikan #######\\n le=self.pool.get('hr_recruit.rwt_pend')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids2=[] \\n for pr in lele:\\n prod_ids2.append((0,0, {'name':pr.name,'jurusan':pr.jurusan.id,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'ijazah':pr.ijazah.id})) \\n \\n ###### bahasa ######\\n le=self.pool.get('hr_recruit.bahasa')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids3=[] \\n for pr in lele:\\n prod_ids3.append((0,0, {'name':pr.name.id,'tulis':pr.tulis.id,'lisan':pr.lisan.id})) \\n \\n ##### Riwayat Pekerjaan ####\\n le=self.pool.get('hr_recruit.rwt_krj')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids4=[] \\n for pr in lele:\\n prod_ids4.append((0,0, {'no':pr.no,'name':pr.name,'tempat':pr.tempat,'tahun_msk':pr.tahun_msk,'tahun_klr':pr.tahun_klr,'jabatan':pr.jabatan,'gaji':pr.gaji,'alasan':pr.alasan})) \\n \\n ###### Koneksi Internal #####\\n le=self.pool.get('hr_recruit.kon1')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids5=[] \\n for pr in lele:\\n prod_ids5.append((0,0, {'employee_id':pr.employee_id.name,'alamat':pr.alamat,'job_id':pr.job_id.id,'telepon':pr.telepon})) \\n \\n ###### Koneksi Eksternal ####\\n le=self.pool.get('hr_recruit.kon2')\\n lel=le.search(cr,uid,[('applicant_id','=',coy)])\\n lele=le.browse(cr,uid,lel,context=context) \\n prod_ids6=[]\\n for pr in lele: \\n prod_ids6.append((0,0, {'name':pr.name,'alamat':pr.alamat,'jabatan':pr.jabatan,'telepon':pr.telepon})) \\n\\n ####### create Employee ######## \\n emp_id = hr_employee.create(cr, uid, {'name': applicant.partner_name or applicant.name,\\n 'job_id': applicant.job_id.id,\\n 'department_id' : applicant.department_id.id,\\n 'address_id2' : applicant.job_id.address_id.id,\\n #### informasi Probadi ####\\n 'kelamin':applicant.jen_kel,\\n 'blood' : applicant.blood,\\n 'agama' : applicant.agama_id.id,\\n 'birthday' : applicant.tgl_lahir,\\n 'place_of_birth' : applicant.kota_id.name,\\n 'marital':applicant.status,\\n 'sjk_tanggal' : applicant.sjk_tanggal,\\n 'mobile_phone':applicant.partner_phone,\\n 'country_id' : applicant.country_id.id,\\n\\n #### Pendidikan ####\\n 'type_id':applicant.type_id.id,\\n 'bid_id':applicant.bidang_id.id,\\n 'jurusan_id':applicant.jurusan_id.id,\\n 'pt_id':applicant.pt_id.id,\\n 'gelar_id':applicant.gelar_id.id,\\n\\n #### alamat DOmisili ####\\n 'country_id1':applicant.country_id1.id,\\n 'prov_id':applicant.prov_id.id,\\n 'kab_id' : applicant.kab_id.id,\\n 'kec_id':applicant.kec_id.id,\\n 'alamat1' : applicant.alamat1,\\n 'kodepos' :applicant.kode1,\\n 'telp1' : applicant.telp1,\\n\\n #### kartu identitas ####\\n 'jenis_id': applicant.jenis_id,\\n 'ktp' : applicant.no_id,\\n 'tgl_berlaku' : applicant.tgl_berlaku,\\n # 'issued_id' : applicant.dikeluarkan.id,\\n \\n #### Alamat Sesuai KTP #### \\n 'country_id2':applicant.country_id2.id,\\n 'prov_id2':applicant.prov_id2.id,\\n 'kab_id2':applicant.kab_id2.id,\\n 'kec_id2':applicant.kec_id2.id,\\n 'alamat2' : applicant.alamat2,\\n 'kodepos1':applicant.kode2,\\n 'telp2' : applicant.telp2,\\n \\n # 'status': applicant.status,\\n #### IDS ####\\n 'susunan_kel1_ids' : prod_ids,\\n 'susunan_kel2_ids':prod_ids1,\\n 'rwt_pend_ids':prod_ids2,\\n 'bahasa_ids':prod_ids3,\\n 'rwt_krj_ids':prod_ids4,\\n 'koneksi1_ids':prod_ids5,\\n 'koneksi2_ids':prod_ids6, \\n })\\n self.write(cr, uid, [applicant.id], {'emp_id': emp_id}, context=context)\\n self.pool['hr.job'].message_post(\\n cr, uid, [applicant.job_id.id],\\n body=_('New Employee %s Hired') % applicant.partner_name if applicant.partner_name else applicant.name,\\n subtype=\\\"hr_recruitment.mt_job_applicant_hired\\\", context=context)\\n else:\\n raise osv.except_osv(_('Warning!'), _('You must define an Applied Job and a Contact Name for this applicant.'))\\n\\n action_model, action_id = model_data.get_object_reference(cr, uid, 'hr', 'open_view_employee_list')\\n dict_act_window = act_window.read(cr, uid, [action_id], [])[0]\\n if emp_id:\\n dict_act_window['res_id'] = emp_id\\n dict_act_window['view_mode'] = 'form,tree'\\n return dict_act_window\",\n \"def create(self, request):\\n serializer = data_serializers.CreateEmployeeSerializer(data=request.data)\\n\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n print(F\\\"Request employee Data: {serializer.data}\\\")\\n\\n try:\\n new_employee = self.controller.create_employee(request_data=request_data)\\n serializer = data_serializers.PresentEmployeeDataSerializer(new_employee)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (domain_exceptions.EmployeeIDIsNotUnique,\\n domain_exceptions.WorkArrangementPercentageOutOfRange,\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.WorkArrangementPercentageNull\\n ) as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create(self, vals):\\n if not vals.get('nik_number'):\\n vals['nik_number'] = self.generate_nik(vals)\\n return super(Employee, self).create(vals)\",\n \"def createEmployee(firstName, lastName, ssn, salary):\\n employee = Employee(firstName, lastName, ssn, salary)\\n # verify\\n if firstName != employee.firstName or \\\\\\n lastName != employee.lastName or \\\\\\n ssn != employee.ssn or \\\\\\n salary != employee.salary:\\n raise ValueError(\\\"Failed to initialize Employee\\\")\\n return employee\",\n \"def get_create_employee_query(self):\\n template = \\\"\\\"\\\"\\n mutation createEmployee {{\\n createEmployee(input: {{ {params} }}) {{\\n employee {{\\n name\\n hiredOn\\n salary\\n departmentId\\n }}\\n }}\\n }}\\n \\\"\\\"\\\"\\n # Add input parameters as needed\\n input_params = 'name:\\\"{}\\\",'.format(self.name)\\n\\n if self.hired_on is not None:\\n input_params += 'hiredOn: \\\"{}\\\", '.format(self.hired_on)\\n\\n if self.salary is not None:\\n input_params += 'salary: {}, '.format(self.salary)\\n\\n if self.department_id is not None:\\n input_params += 'departmentId: {}'.format(self.department_id)\\n\\n return template.format(params=input_params)\",\n \"def generateEmployees(self):\\r\\n\\r\\n # Name\\r\\n maleNames = ['Perry Lovan', 'Horacio Arvidson', 'Gale Skipworth', 'Joshua Lodge', 'Noble Shutter', 'Kristopher Talor', 'Jarod Harrop', 'Joan Henrichs', 'Wilber Vitiello', 'Clayton Brannum', 'Joel Sennett', 'Wiley Maffei', 'Clemente Flore', 'Cliff Saari', 'Miquel Plamondon', 'Erwin Broadus', 'Elvin Defibaugh', 'Ramon Vaquera', 'Roberto Koval', 'Micah Sumter', 'Wyatt Cambareri', 'Jamal Delarosa', 'Franklyn Hayles', 'Riley Haslett', 'Robt Fincher', 'Abraham Denzer', 'Darius Jude', 'Phillip Sunderman', 'August Kindel', 'Jospeh Mawson', 'Damion Postma', 'Gregorio Pasco', 'Rosendo Downing', 'Chance Plascencia', 'Jewell Pankratz', 'Jerrell Tarrance', 'Michal Bliss', 'Josue Larocque', 'Aaron Harpster', 'Zack Hildebrant', 'Frank Souders', 'Lindsay Bechard', 'Agustin Marks', 'Mathew Fredericksen', 'Ivan Hanline', 'Michael Otto', 'Max Oberlander', 'Ricky Mckellar', 'Bernard Friedt', 'King Lorentzen']\\r\\n femaleNames = ['Lorretta Vansickle', 'Loura Steimle', 'Neomi Fritz', 'Vernie Vanderveen', 'Dede Poehler', 'Margarete Espinoza', 'Leda Leonardo', 'Fae Strand', 'Nichol Winford', 'Danika Ridgeway', 'Elvira Balentine', 'Sharell Xie', 'Sheree Booker', 'Emely Conine', 'Justina Kleve', 'Pia Maxton', 'Sophia Lark', 'Nilsa Albee', 'Felipa Seman', 'Jeraldine Watkins', 'Susann Sowards', 'Asha Irion', 'Shay Koran', 'Rosio Jahn', 'Rachal Slaven', 'Beryl Byron', 'Jona Lira', 'Margert Strite', 'Talia Beauregard', 'Jacqueline Vella', 'Rolande Mccready', 'Margret Hickerson', 'Precious Confer', 'Evita Nicolai', 'Fredda Groner', 'Laquanda Bracken', 'Alana Saddler', 'Melania Harring', 'Shae Everette', 'Marlyn Mcfalls', 'Madeline Nicols', 'Fonda Webster', 'Fumiko Steffy', 'Virginia Sprinkle', 'Lula Frisch', 'Mari Mulherin', 'Alecia Remillard', 'Jeanna Halderman', 'Ocie Waldrep', 'Theresa Knouse']\\r\\n\\r\\n for i in range(self.num_of_employees):\\r\\n\\r\\n # Clock in an hour before opening, 6 hours after, or 12 hours after\\r\\n clockIn = random.choice([7, 13, 19])\\r\\n\\r\\n # Clock out after 5 hours, 10 hours, or 15 hours\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n while clockOut <= clockIn:\\r\\n clockOut = random.choice([13, 19, 23])\\r\\n\\r\\n # Hourly wage\\r\\n wage = random.choice([8, 9, 10, 12, 20])\\r\\n\\r\\n gender = random.choice(['M', 'F'])\\r\\n if gender == 'M':\\r\\n name = random.choice(maleNames)\\r\\n else:\\r\\n name = random.choice(femaleNames)\\r\\n\\r\\n self.c.execute(\\\"INSERT INTO Employee (Name, ClockIn, ClockOut, Wage) VALUES (?, ?, ?, ?)\\\", (name, clockIn, clockOut, wage))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_employees:\\r\\n print(\\\"\\\\nName:\\\", name)\\r\\n print(\\\"Clock in:\\\", clockIn)\\r\\n print(\\\"Clock out:\\\", clockOut)\\r\\n print(\\\"Wage:\\\", wage)\",\n \"def create_employee(attributes):\\n neccessary_keys = [\\\"empid\\\", \\\"gender\\\", \\\"sales\\\", \\\"bmi\\\", \\\"salary\\\", \\\"birthday\\\",\\n \\\"age\\\"]\\n for key in neccessary_keys:\\n if not key in attributes.keys():\\n raise ValueError(\\\"employee could not be created: {} is missing\\\".format(key))\\n return Employee(attributes[\\\"empid\\\"], attributes[\\\"gender\\\"],\\n attributes[\\\"sales\\\"], attributes[\\\"bmi\\\"],\\n attributes[\\\"salary\\\"], attributes[\\\"birthday\\\"],\\n attributes[\\\"age\\\"])\",\n \"def post(self):\\n data = EmployeeRegister.parser.parse_args()\\n new_employee_id = str(uuid.uuid4())\\n\\n while EmployeeModel.find_by_id(new_employee_id):\\n # if this id is already in use\\n new_employee_id = str(uuid.uuid4())\\n\\n employee = EmployeeModel(**data, employee_id=new_employee_id)\\n employee.save_to_db()\\n\\n return {\\\"message\\\": \\\"Employee successfully added to the system\\\"}, 201 # 201 - Created\",\n \"def add_employee(self, empl):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employee values(default,%s,%s,%s,%s,%s,%s,%s,%s)',\\n (empl.name, empl.email, empl.office, empl.research_group, empl.title, empl.internOrExtern,\\n empl.active, empl.promotor))\\n cursor.execute('SELECT LASTVAL()')\\n eid = cursor.fetchone()[0]\\n empl.id = eid\\n # get id and return updated object\\n self.dbconnect.commit()\\n except(Exception, self.dbconnect.get_error()) as error:\\n self.dbconnect.rollback()\\n raise Exception('\\\\nUnable to save Employee!\\\\n(%s)' % (error))\",\n \"def create(self, values):\\n if values.get('country_id', False):\\n country = self.env['res.country'].browse(values['country_id'])\\n if country.code == 'SA':\\n values.update({'is_saudi': True})\\n else:\\n values.update({'is_saudi': False})\\n\\n res = super(HrEmployee, self).create(values)\\n if values.get('user_id', False):\\n self.user_id.write({'employee_id': res})\\n return res\",\n \"def create_person(self):\",\n \"def add_employee(schema, employee_json):\\n employee = schema.load(employee_json, session=db.session)\\n db.session.add(employee)\\n db.session.commit()\\n return employee\",\n \"def setUp(self):\\n\\n self.user = self.make_user()\\n self.employee = Employee.objects.create(\\n cpf=\\\"974.220.200-16\\\",\\n user=self.user,\\n departament=Employee.ADMINISTRATION\\n )\",\n \"def create_employee(request, company_id):\\n\\n company = Company.objects.get(pk=company_id)\\n current_employee = Employee.objects.get(user__pk=request.user.pk)\\n if not current_employee.isEnsoUser() and current_employee.company.pk != company.pk:\\n logUnauthorizedAccess(\\\"User tried to create_employee\\\", request)\\n raise PermissionDenied()\\n form = EmployeeForm(request, initial=dict(company=company))\\n form.fields['manager'].queryset = Employee.objects.filter(is_manager=True, company=company)\\n # form.fields['development_plan_type'].queryset = DevelopmentPlanType.objects.filter(\\n # Q(company=company) | Q(company__isnull=True))\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n\\n return TemplateResponse(\\n request,\\n 'mus/create_employee_form.html',\\n {\\n 'employee_form': form,\\n }\\n )\\n # data = {\\n # 'employee_form': form.cleaned_data,\\n # 'company': company.cleaned_data[\\\"name\\\"]\\n # }\\n # return JsonResponse(status=200, data=data)\",\n \"def post(self, request):\\n data = request.data\\n skill_data = data.pop('skills')\\n Department_name = data.pop('department')\\n department = Department.objects.get(name=Department_name)\\n manager_name = data.pop('manager')\\n manager = Manager.objects.get(name=manager_name)\\n Employee = EmployeeDetail.objects.create(department=department, manager=manager, **data)\\n Employee.save()\\n for skill in skill_data:\\n skill_add, create = Skill.objects.get_or_create(name=skill)\\n Employee.skills.add(skill_add)\\n return Response(\\n data=request.data\\n )\",\n \"def add_employee(self, first_name, last_name):\\n self.switch_main_menu(\\\"PIM\\\")\\n self.click_menu(\\\"Add Employee\\\")\\n self.pim = AddEmployee(self.driver)\\n self.pim.add_user_employee(first_name, last_name)\",\n \"def createobj(self, firstname='', middlename='', lasttname='', email='', phno='', gender='', address='',\\n peronjson={}, notes='', tenantid=''):\\n personobj = {'firstname': firstname, 'middlename': middlename, 'lasttname': lasttname, 'email': email, 'phno':\\n phno, 'gender': gender, 'address': address, 'peronjson': peronjson, 'notes': notes, 'tenantid': tenantid}\\n self.persontdetailscoll.insert(tenantobj, safe=True)\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n respond_data = self.controller.add_team_employee(request_data=request_data)\\n serializer = data_serializers.PresentTeamEmployeeDataSerializer(respond_data)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.EmployeeDoesNotExist,\\n domain_exceptions.EmployeeIsATeamMember\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create_individual(self):\\n pass\",\n \"def main():\\n name = input(\\\"Please enter in your name: \\\")\\n\\n \\\"\\\"\\\"Ask the user to enter a number if they are a Director, Manager or Staff.\\\"\\\"\\\"\\n \\\"\\\"\\\"This will check and make sure the user only enters in 1,2, \\n or 3 and a number greater than zero\\\"\\\"\\\"\\n while True:\\n try:\\n designation_number = int(input(\\\"Please enter in \\\\n1 for Director \\\"\\n \\\"\\\\n2 for Manager \\\\n3 for Staff\\\\n\\\"))\\n if 0 < designation_number <= 3:\\n break\\n print(\\\"Invalid number entered.\\\")\\n except Exception as e:\\n print(e)\\n \\\"\\\"\\\"Gets the user salary and makes sure is a number and greater than 0\\\"\\\"\\\"\\n while True:\\n try:\\n salary = float(input(\\\"Please enter in your salary: \\\"))\\n if salary <= 0:\\n print(\\\"Your salary must be at least 1 dollar. Please enter a number greater than zero.\\\")\\n else:\\n break\\n except ValueError:\\n print(\\\"Oops! That was not a valid number. Try again...\\\")\\n\\n \\\"\\\"\\\"Create Employee\\\"\\\"\\\"\\n employee1 = employee.Employee()\\n employee1.set_name(name)\\n employee1.set_designation(designation_number)\\n employee1.set_salary(salary)\\n print(employee1)\",\n \"def create():\",\n \"def create():\",\n \"def create_employee_structure(employees):\\n employees_dict = {}\\n for employee in position_sort(employees):\\n if not employee.is_secretary:\\n adder(employees_dict, employee.prosecutors_office, {'employees': [], 'departments': {}, 'divisions': {}})\\n if employee.prosecutors_office and employee.department and employee.division:\\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'divisions', {})\\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'], employee.division, [])\\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['divisions'][employee.division].append(employee)\\n elif employee.prosecutors_office and employee.department:\\n adder(employees_dict[employee.prosecutors_office]['departments'], employee.department, {})\\n adder(employees_dict[employee.prosecutors_office]['departments'][employee.department], 'employees', [])\\n employees_dict[employee.prosecutors_office]['departments'][employee.department]['employees'].append(employee)\\n elif employee.prosecutors_office and employee.division:\\n adder(employees_dict[employee.prosecutors_office]['divisions'], employee.division, [])\\n employees_dict[employee.prosecutors_office]['divisions'][employee.division].append(employee)\\n elif employee.prosecutors_office:\\n employees_dict[employee.prosecutors_office]['employees'].append(employee)\\n return employees_dict\",\n \"def make_employee_dict(names, ID_numbers, salaries, email_addresses):\\r\\n d = dict()\\r\\n for i in range(len(names)):\\r\\n d[ID_numbers[i]] = Employee(names[i], ID_numbers[i], salaries[i], email_addresses[i])\\r\\n return d\",\n \"def setUp(self):\\n\\tself.emp = Employee('Lin',10000)\\n\\tself.emp2 = Employee('Jun',20000)\",\n \"def test_access_employee(self):\\n # Employee can't see any SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).read()\\n # Employee can't edit the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).write({'team_id': self.company_data['default_sale_team'].id})\\n # Employee can't create the SO\\n with self.assertRaises(AccessError):\\n self.env['sale.order'].with_user(self.company_data['default_user_employee']).create({\\n 'partner_id': self.partner_a.id,\\n })\\n # Employee can't delete the SO\\n with self.assertRaises(AccessError):\\n self.order.with_user(self.company_data['default_user_employee']).unlink()\",\n \"def test_employee_creation_bad_fields(self):\\n helper = EmployeeHelper(name='Andrew', hired_on='2019-10-01T00:00:00', salary=None, department_id=None)\\n result = self.client.execute(helper.get_create_employee_query())['data']['createEmployee']['employee']\\n\\n self.assertEqual(result['name'], helper.name)\\n self.assertEqual(result['hiredOn'], helper.hired_on)\\n self.assertEqual(result['salary'], 0)\\n self.assertIsNone(result['departmentId'])\",\n \"def create(self, values):\\n\\t\\temployee_id = values.get('employee_id', False)\\n\\t\\tprint(\\\"the val in the dict\\\", values)\\n\\t\\tif (values.get('date_from') and values.get('date_to')) == False:\\n\\t\\t\\tcurrent = datetime.strftime(datetime.today().date(),'%Y-%m-%d')\\n\\t\\t\\tvalues.update({'allocate_date': current})\\n\\t\\t\\tprint(values)\\n\\t\\tif not values.get('department_id'):\\n\\t\\t\\tvalues.update({'department_id': self.env['hr.employee'].browse (employee_id).department_id.id})\\n\\n\\t\\tholiday = super (Holidays, self.with_context (mail_create_nolog=True, mail_create_nosubscribe=True)).create(values)\\n\\t\\tholiday.add_follower (employee_id)\\n\\n\\t\\t# Trilok code for policies\\n\\t\\tpolicy_id = holiday.env['leaves.policy'].search(\\n\\t\\t\\t[('leave_type', '=', holiday.holiday_status_id.id), ('company_id', '=', self.env.user.company_id.id)])\\n\\t\\t# print (\\\"policy iddddddddddddddd\\\",policy_id)\\n\\t\\temp_type = holiday.employee_id.employee_type.id\\n\\t\\tfor val in policy_id:\\n\\t\\t\\tif val.employee_type.id == emp_type:\\n\\t\\t\\t\\tfor employee in holiday.employee_id:\\n\\t\\t\\t\\t\\tif holiday.type == 'remove':\\n\\t\\t\\t\\t\\t\\tquery = '''select count(*) from hr_holidays where upper(type) = upper('rEMove')and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') and now()::date and employee_id = %s''' % employee.id\\n\\t\\t\\t\\t\\t\\tholiday.env.cr.execute(query)\\n\\t\\t\\t\\t\\t\\tquery_result = holiday.env.cr.dictfetchone()\\n\\t\\t\\t\\t\\t\\t# print(\\\"query_result\\\", query_result)\\n\\t\\t\\t\\t\\t\\tif val.min_app_per_year > 0 and query_result[\\\"count\\\"] > val.min_app_per_year:\\n\\t\\t\\t\\t\\t\\t\\traise ValidationError(\\\"maximum number of applications per year is {} days\\\".format(val.min_app_per_year))\\n\\n\\t\\t\\t\\t\\t\\tquery1 = '''select create_date::date,date_to::date from hr_holidays where upper(type) = \\n\\t\\t\\t\\t\\t\\tupper('rEMove') and upper(state) = upper('Validate') and create_date::date between to_date(concat(date_part('Year',now()::date),'-01-01'),'yyyy-mm-dd') \\n\\t\\t and now()::date and employee_id = %s order by create_date desc limit 1'''\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t % employee.id\\n\\t\\t\\t\\t\\t\\tholiday.env.cr.execute(query1)\\n\\t\\t\\t\\t\\t\\tquery_result1 = holiday.env.cr.fetchall()\\n\\t\\t\\t\\t\\t\\tif query_result1 is not None:\\n\\t\\t\\t\\t\\t\\t\\t# print(\\\"query_resulttttttttttttttttttttttttttt\\\", query_result1)\\n\\t\\t\\t\\t\\t\\t\\t# print(\\\"query_resulttttttttttttttttttttttttttt\\\", query_result1[0][0], query_result1[0][1])\\n\\t\\t\\t\\t\\t\\t\\tcre_date = datetime.strptime(query_result1[0][0], '%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\tdate_to = datetime.strptime(query_result1[0][1], '%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\t# print(\\\"cre_date\\\", cre_date, type(cre_date))\\n\\t\\t\\t\\t\\t\\t\\tcurrent_dt = fields.Datetime.now()\\n\\t\\t\\t\\t\\t\\t\\t# cdate=datetime.strptime(current_dt,'%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\tcurrent_date = datetime.strptime(current_dt.split(\\\" \\\")[0], '%Y-%m-%d')\\n\\t\\t\\t\\t\\t\\t\\tdays = (current_date - date_to).days\\n\\t\\t\\t\\t\\t\\t\\tif val.min_leave_app_gap > 0 and days > val.min_leave_app_gap:\\n\\t\\t\\t\\t\\t\\t\\t\\traise ValidationError(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\\"Minimum gap between two application should be atleast {} days\\\".format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tval.min_leave_app_gap))\\n\\n\\t\\treturn holiday\",\n \"def add_employee(worksheet, row, employee, num_in_list, employee_format, employee_format_b):\\n # Get number of row for employee\\n if employee.work_telephone:\\n work_telephone_list = regular_telephone(employee.work_telephone.split(';'))\\n else:\\n work_telephone_list = []\\n if employee.private_telephone:\\n private_telephone_list = regular_telephone(employee.private_telephone.split(';'))\\n else:\\n private_telephone_list = []\\n rows_for_emp = max(len(work_telephone_list), len(private_telephone_list), 2)\\n # Add name and position\\n worksheet.merge_range(row, 0, row+rows_for_emp-1, 0, num_in_list, cell_format=employee_format)\\n worksheet.merge_range(row, 1, row+rows_for_emp-1, 1, str(employee), cell_format=employee_format_b)\\n worksheet.merge_range(row, 2, row+rows_for_emp-1, 2, str(employee.position), cell_format=employee_format)\\n # Add work telephone numbers\\n for num, work_telephone in enumerate(work_telephone_list):\\n if (len(work_telephone_list) < rows_for_emp) and ((num + 1) == len(work_telephone_list)):\\n worksheet.merge_range(row+num, 3, row+rows_for_emp-1, 3, work_telephone, cell_format=employee_format)\\n else:\\n worksheet.write(row+num, 3, work_telephone, employee_format)\\n if len(work_telephone_list) == 0:\\n worksheet.merge_range(row, 3, row+rows_for_emp-1, 3, '', cell_format=employee_format)\\n # Add private telephone numbers\\n for num, private_telephone in enumerate(private_telephone_list):\\n if (len(private_telephone_list) < rows_for_emp) and ((num + 1) == len(private_telephone_list)):\\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, private_telephone, cell_format=employee_format)\\n else:\\n worksheet.write(row+num, 4, private_telephone, employee_format)\\n if (len(private_telephone_list) < rows_for_emp) and ((num - 1) == len(private_telephone_list)):\\n worksheet.merge_range(row+num, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\\n if len(private_telephone_list) == 0:\\n worksheet.merge_range(row, 4, row+rows_for_emp-1, 4, '', cell_format=employee_format)\\n # If secretary exist\\n if employee.secretary:\\n row = row + rows_for_emp\\n add_employee(worksheet, row, employee.secretary, '', employee_format, employee_format_b)\\n return row+rows_for_emp\",\n \"def create_educator(data):\\n\\n educator = Educator(\\n name=data['name'],\\n work_email=data['work_email'],\\n organization_name=data['organization_name'],\\n org_or_school=data['org_or_school'],\\n address_line_1=data['address_line_1'],\\n address_line_2=data['address_line_2'],\\n city=data['city'],\\n state=data['state'],\\n zipcode=data['zipcode'],\\n num_students=data['num_students']\\n )\\n educator.save()\\n return educator\",\n \"def post(self):\\n employee = Employee(**self.data)\\n _dict = Employee.encode(employee)\\n\\n _id = DatabaseManager.insert(Collection.EMPLOYEES, _dict)\\n employee_dict = DatabaseManager.find_document_by_id(\\n Collection.EMPLOYEES, _id, True\\n )\\n return employee_dict\",\n \"def setUp(self):\\n self.salary = 40000\\n self.custom_rise = 7500\\n self.employee = Employee(\\\"Carlos\\\", \\\"Zapata\\\", self.salary)\",\n \"def create(self, vals):\\n record = super(KaHrPayrollRapelEmployeePromote, self).create(vals)\\n if not 'name' in vals or not vals.get('name'):\\n record.name = \\\"Rapel {0}\\\".format(record.new_employee_promote_id.name)\\n return record\",\n \"def add_employee():\\n\\n if not g.user:\\n flash(\\\"Please login to access\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n if g.user.is_admin == False:\\n flash (\\\"Unauthorized\\\", \\\"danger\\\")\\n return redirect(\\\"/login\\\")\\n\\n form = SignUp_Form()\\n \\n\\n if form.validate_on_submit():\\n try: \\n employee = Employee.register(\\n username = form.username.data,\\n password = form.password.data, \\n email = form.email.data, \\n first_name = form.first_name.data,\\n last_name = form.last_name.data,\\n hire_date = form.hire_date.data, \\n is_admin = form.is_admin.data,\\n )\\n\\n db.session.add(employee)\\n\\n db.session.commit()\\n except IntegrityError:\\n flash(\\\"Email already in use\\\", \\\"danger\\\")\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\\n\\n flash(\\\"Employee Added!\\\", \\\"success\\\")\\n return redirect(\\\"/administrator\\\")\\n else:\\n\\n return render_template(\\\"/admin/add_user.html\\\", form = form)\",\n \"def insert_employee(self,\\n region_name,\\n last_name,\\n first_name,\\n hire_date,\\n mi=None):\\n\\n if self.check_input_type(region_name, \\\"Region\\\"):\\n if self.check_input_type(hire_date, \\\"Date\\\"):\\n region_info = self.query_region(region_name)\\n region_id = region_info[0][0]\\n\\n if mi != \\\"\\\":\\n query_format = \\\"insert into employee(Region_ID, \\\" \\\\\\n \\\"Emp_Lname, Emp_Mi, Emp_Fname, Emp_Hiredate) \\\" \\\\\\n \\\"values ((select region_id from region where \\\" \\\\\\n \\\"region_id='{}'), '{}', '{}', '{}', '{}')\\\"\\n query = query_format.format(\\n region_id, last_name, mi, first_name, hire_date\\n )\\n else:\\n query_format = \\\"insert into employee(Region_ID, \\\" \\\\\\n \\\"Emp_Lname, Emp_Fname, Emp_Hiredate) \\\" \\\\\\n \\\"values ((select region_id from region where \\\" \\\\\\n \\\"region_id='{}'), '{}', '{}', '{}')\\\"\\n query = query_format.format(\\n region_id, last_name, first_name, hire_date\\n )\\n\\n try:\\n self.dbCursor.execute(query)\\n SuccessMessageWindow(\\\"Insert success!\\\")\\n except mysql.connector.Error as err:\\n ErrorMessageWindow(err)\\n finally:\\n self.dbConnection.commit()\\n else:\\n ErrorMessageWindow(\\\"Date format not valid!\\\")\\n else:\\n ErrorMessageWindow(\\\"Region input not valid!\\\")\",\n \"def create_trainee(self, name, pos, dept):\\n self.trainee[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'senior': self.senior[dept.upper()][0]['name'],\\n 'junior': self.junior[dept.upper()][0]['name'],\\n }\\n )\\n self.manager[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.senior[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.junior[dept.upper()][0]['trainee'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def create_epic():\\n client = RequestManager()\\n client.set_method(\\\"POST\\\")\\n client.set_endpoint(\\\"/projects/{0}/epics\\\".format(STORED_ID['project_id']))\\n name = \\\"\\\".join(choices(string.ascii_letters, k=6))\\n body = {\\\"name\\\": name}\\n client.set_body(json.dumps(body))\\n response = client.execute_request()\\n STORED_ID['epic_id'] = response.json()['id']\",\n \"def setUp(self):\\n self.employee = Employee('Lucas', 'Guerra', 45000)\",\n \"def setUp(self):\\n\\t\\tfirst_name = 'Gerson'\\n\\t\\tlast_name = 'Santos'\\n\\t\\tannual_salary = 5000\\n\\t\\tself.gerson = Employee(first_name, last_name, annual_salary)\",\n \"def setUp(self):\\n self.my_employee = Employee('knight', 'lee', 10000)\",\n \"def create_junior(self, name, pos, dept):\\n self.junior[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'senior': self.senior[dept.upper()][0]['name'],\\n 'trainee': []\\n }\\n )\\n self.manager[dept.upper()][0]['junior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\\n self.senior[dept.upper()][0]['junior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def test_admin_can_create_a_employee(self):\\n\\n account_data = {\\n \\\"username\\\": \\\"Mike\\\",\\n \\\"email\\\": \\\"mioshine@g.com\\\",\\n \\\"password\\\": \\\"1234567\\\",\\n \\\"confirm_password\\\": \\\"1234567\\\"\\n }\\n response = self.client.post(\\n reverse('accounts:create-user'),\\n account_data,\\n format=\\\"json\\\")\\n \\\"\\\"\\\"Test the api has bucket creation capability.\\\"\\\"\\\"\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertTrue(\\\"data\\\" in json.loads(response.content))\",\n \"def createanastep(body):\\n pilotpoint = body.get(\\\"ppname\\\", None)\\n steps = body.get(\\\"steps\\\", None)\\n print('lol',pilotpoint, steps)\\n\\n # Does the person exist already?\\n if pilotpoint not in anastep_dict and pilotpoint is not None:\\n anastep_dict[pilotpoint] = {\\n \\\"pilotpoint\\\": pilotpoint,\\n \\\"steps\\\": steps,\\n }\\n return anastep_dict[pilotpoint], 201\\n\\n # Otherwise, they exist, that's an error\\n else:\\n abort(\\n 406,\\n \\\"Person with last name {pilotpoint} already exists\\\".format(pilotpoint=pilotpoint),\\n )\",\n \"def create_manager(self, name, pos, dept):\\n self.manager[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'senior': [],\\n 'junior': [],\\n 'trainee': []\\n }\\n )\",\n \"def create_entity(self):\\n \\n if self.ORION_CB.get_entity(self.params['name']) is None:\\n \\n print('[INFO]: Create new PID entity')\\n \\n entity_dict = {\\\"id\\\":self.params['name'], \\\"type\\\":'PID_controller'}\\n for attr in ['Kp', 'Ti', 'Td', 'lim_low', 'lim_high', 'setpoint']:\\n entity_dict.update({attr:{'value':self.params[attr],'type':'Number'}})\\n\\n entity_dict.update({'reverse_act':{'value':self.params['reverse_act'],'type':'Text'}})\\n \\n entity = filip.orion.Entity(entity_dict)#, attrs)\\n\\n self.ORION_CB.post_entity(entity)\\n \\n else:\\n print('Entity name already assigned')\",\n \"def create():\\n pass\",\n \"def create_senior(self, name, pos, dept):\\n self.senior[dept.upper()].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept,\\n 'manager': self.manager[dept.upper()][0]['name'],\\n 'junior': [],\\n 'trainee': []\\n }\\n )\\n self.manager[dept.upper()][0]['senior'].append(\\n {\\n 'name': name,\\n 'pos': pos,\\n 'dept': dept\\n }\\n )\",\n \"def createEMPStudy(self, user_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \\n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries):\\n con = self.getMetadataDatabaseConnection()\\n study_id = 0\\n results = con.cursor().callproc('qiime_assets.emp_study_insert', \\n [study_id, user_id, study_name,\\n investigation_type, miens_compliant, submit_to_insdc, portal_type, \\n study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries])\\n return results[0]\",\n \"def make_salaried(self,salary,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"2\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be a salaried employee\\\"))\\n self.emp_dict[id][7] = salary\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def add_employee(self, emp):\\n if emp not in self.employees: \\n self.employees.append(emp)\",\n \"def handle(self, *args, **kwargs):\\n seeder = Seed.seeder()\\n seeder.add_entity(User, 20)\\n\\n seeder.add_entity(EmployeeMptt, 20, {\\n 'user': lambda x: User.objects.filter(employeemptt=None).first(),\\n 'parent': lambda x: EmployeeMptt.objects.order_by(\\\"?\\\").first(),\\n 'level': lambda x: random.randint(0, 4),\\n })\\n seeder.execute()\",\n \"def make_commissioned(self,salary,commission,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"3\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be a commissioned employee\\\"))\\n self.emp_dict[id][7] = salary\\n self.emp_dict[id][9] = commission\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def create(self, request):\\n serializer = data_serializers.TeamLeaderOrEmployeeRequestDataSerializer(data=request.data)\\n if serializer.is_valid(raise_exception=True):\\n request_data = serializer.save()\\n try:\\n new_team_entity = self.controller.assign_team_leader(request_data=request_data)\\n serializer = data_serializers.TeamLeaderPresenterSerializer(new_team_entity)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n except (\\n domain_exceptions.TeamDoesNotExist,\\n domain_exceptions.TeamHasALeader,\\n domain_exceptions.EmployeeDoesNotExist\\n )as e:\\n return Response(e.message, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def create_person(data_dict, m2m_dict):\\n email_address = data_dict['email_address']\\n # check if the person already exists:\\n try:\\n exists = Person.objects.get(email_address=email_address)\\n create_message = f'Skipping: Person with {email_address} already exists.'\\n except:\\n # try:\\n person_obj, person_created = Person.objects.update_or_create(**data_dict)\\n # populate MANY-TO-MANY fields\\n if m2m_dict:\\n # created_by (FK)\\n created_by_email = m2m_dict['created_by']\\n user, user_created = User.objects.get_or_create(email=created_by_email)\\n person_qs = Person.objects.filter(email_address=person_obj.email_address)\\n person_qs.update(created_by=user)\\n # expertise (M2M)\\n expertise_values = m2m_dict['expertise']\\n if expertise_values:\\n values_list = [value.strip() for value in expertise_values.split(',')]\\n for value in values_list:\\n expertise_obj, expertise_created = Expertise.objects.get_or_create(name=value)\\n person_obj.expertise.add(expertise_obj)\\n # industry (M2M)\\n industry_values = m2m_dict['industries']\\n if industry_values:\\n values_list = [value.strip() for value in industry_values.split(',')]\\n for value in values_list:\\n industry_obj, industry_created = Industry.objects.get_or_create(name=value)\\n person_obj.industries.add(industry_obj)\\n # organization (M2M)\\n organization_values = m2m_dict['organization']\\n if organization_values:\\n values_list = [value.strip() for value in organization_values.split(',')]\\n for value in values_list:\\n organization_obj, organization_created = Organization.objects.get_or_create(name=value)\\n person_obj.organization.add(organization_obj)\\n # owned by (M2M)\\n exportable_by_values = m2m_dict['exportable_by']\\n if exportable_by_values:\\n values_list = [value.strip() for value in exportable_by_values.split(',')]\\n for value in values_list:\\n dive_obj, dive_created = Dive.objects.get_or_create(name=value)\\n person_obj.exportable_by.add(dive_obj)\\n # let us know how it went\\n if person_created:\\n create_message = f'Success: {person_obj}'\\n else:\\n create_message = f'Failed: {person_obj}'\\n # except Exception as e:\\n # create_message = f'Error for {email_address}: {e}\\\\n'\\n print(create_message)\\n # except:\\n # failed_rows.append(counter)\\n # try:\\n # obj, created = Person.objects.create(**csv_to_model)\\n # except:\\n # message = 'Create person' + str(sys.exc_info())\\n # print(message)\",\n \"def create_job_detail(company_name, job_title, application_deadline, job_listing_url, state, city, application_listed, salary):\\n\\n job_detail = JobDetail(company_name = company_name, job_title = job_title, application_deadline = application_deadline, job_listing_url = job_listing_url, state = state , city = city, application_listed = application_listed, salary = salary)\\n db.session.add(job_detail)\\n db.session.commit()\\n\\n return job_detail\",\n \"def create(task):\\n tname = task.get(\\\"tname\\\")\\n # user cannot create task without name\\n\\n # Does the new task have a name? If no we can't insert it.\\n # Can we insert it?\\n if tname is not None:\\n\\n # Create a person instance using the schema and the passed in person\\n schema = TaskListSchema()\\n print(task)\\n new_task = schema.load(task, session=db.session).data\\n\\n # Add the person to the database\\n db.session.add(new_task)\\n db.session.commit()\\n\\n # Serialize and return the newly created person in the response\\n data = schema.dump(new_task).data\\n\\n return data, 201\\n\\n # Otherwise, nope, person exists already\\n else:\\n abort(409, \\\"Task needs a name\\\".format(tname=tname),)\",\n \"def createEmployee():\\n form = CreateEmployeeForm()\\n if form.validate_on_submit():\\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\\n staff = Staff(first_name=form.first_name.data, last_name=form.last_name.data, password=hashed_password, \\n email=form.email.data, role=form.role.data, location=form.location.data)\\n db.session.add(staff)\\n db.session.commit()\\n flash(f'Employee Added To Database', category='Success')\\n return redirect(url_for('login'))\\n return render_template('new_employee.html', title=\\\"Register\\\", form=form)\",\n \"def create(self):\",\n \"def create_job(self, employer_id, compensation, location, description, category_id, group_id):\\n\\n job = Job(employer_id=employer_id, group_id=group_id, compensation=compensation, location=location, category_id=category_id, description=description) \\n db.session.add(job)\\n db.session.commit()\",\n \"def create(self, name, login, password, email, address=\\\"\\\", vat=\\\"\\\", jobguid=\\\"\\\", executionparams=None):\",\n \"def create_meal():\",\n \"def create_event(self, name, date):\\n user = User.objects.create(username='userdemo')\\n user.set_password('calnote24')\\n user.save()\\n Event.objects.create(name=name, date=date, user_id=user.id)\",\n \"def setUp(self):\\n\\t\\tself.mason = Employee(\\\"mason\\\",\\\"karsevar\\\",10000)\",\n \"def users_create():\",\n \"def create():\\n\\tnombre = input(\\\"Ingrese el nombre de pokemon: \\\")\\n\\testado = input(\\\"Ingrese el estado. Si el pokemon no tiene estado, ingrese X: \\\")\\n\\n\\tif estado.upper() == \\\"X\\\":\\n\\t\\testado = None\\n\\n\\tif estado not in estados_permitidos:\\n\\t\\tprint(\\\"Estado de pokemon no permitido. Registro no fue insertado.\\\")\\n\\t\\tprint(\\\"Devolviendo al menu principal...\\\")\\n\\t\\treturn\\n\\n\\thp_actual = int(input(\\\"Ingrese HP actual de pokemon: \\\"))\\n\\tfecha = input(\\\"Ingrese la fecha en formato DD/MM/YY HH24:MM (ej 06/09/20 14:20): \\\")\\n\\tinsertar_pokemon(nombre, hp_actual, estado, fecha)\",\n \"def add_employee(self, employee):\\n self.employees.add(employee)\",\n \"def createGroup(listOfPerson):\\n atk=Department()\\n atk.members=listOfPerson\\n return atk\",\n \"def create_teacher(username, password, email, preferred_language,skype_id,name, phone_number, country,availability):\\n person.create_person(username,password,email,preferred_language,skype_id,name,phone_number,country)\\n teacher_account_id = person.get_last()\\n query = 'INSERT INTO teacher VALUES( %s,%s );'\\n args = (teacher_account_id, availability)\\n database.connection.save_data(query, args)\",\n \"def test_office_creation(self):\\n url = '/api/v1/consultorios/'\\n data = {\\n \\\"hospital\\\": \\\"Angeles Roma\\\",\\n \\\"office\\\": \\\"306\\\"\\n }\\n request = self.client.post(url, data)\\n\\n self.assertEqual(request.status_code, status.HTTP_201_CREATED)\",\n \"def create_master(conn,task):\\r\\n sql = ''' INSERT OR IGNORE INTO emp(emp_id,name)\\r\\n VALUES(?,?) '''\\r\\n c = conn.cursor()\\r\\n c.execute(sql,task)\\r\\n conn.commit()\",\n \"def test_new_employee_crud_methods(self):\\n response = self.client.get(\\n '/employees/', kwargs={'employer_id': self.employee.id})\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(len(Employee.objects.all()), 1)\\n\\n # Test that a new employee can be added\\n response = self.client.post(\\n '/employees/',\\n {'name': 'MAdtraxx!!', 'employer': self.employer.id},\\n kwargs={'pk': self.employer.id})\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(Employee.objects.count(), 2)\\n\\n # Test that employee info may be edited\\n response = self.client.put('/employees/1/',\\n {'name': 'Ashley',\\n 'employer': self.employer.id},\\n kwargs={'employer_id': self.employee.id,\\n 'pk': self.employee.id})\\n self.assertEqual(response.status_code, 200)\",\n \"def create():\\n data = request.get_json()\\n print(\\\"DATA: \\\", data)\\n db_helper.insert_new_record(data['first_name'], data['last_name'], data['class_period'], data['current_belt'], data['student_teacher_id'])\\n result = {'success': True, 'response': 'Done'}\\n return jsonify(result)\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create(self, creator: User = None, *args, **kwargs) -> Employee:\\n # If the creator argument is not None and is a non-staff user, raise\\n # ValueError\\n if creator and not creator.is_staff:\\n raise ValueError(\\n 'Only staff members can add new employees, \\\"creator\\\" must be '\\n 'a staff user.'\\n )\\n\\n # Get the user property from the provided keyword arguments\\n user: Optional[User] = kwargs.get('user', None)\\n\\n # If the user property is missing or is a non-staff user, raise\\n # ValueError\\n if not user:\\n raise ValueError(\\n 'You must provide the \\\"user\\\" property as a keyword argument'\\n )\\n elif not user.is_staff:\\n raise ValueError('The \\\"user\\\" property must be a staff user.')\\n\\n return super().create(creator, *args, **kwargs)\",\n \"def get_employee():\\n\\n employee_id = get_employee_input_int('Enter employee ID to get the data ')\\n employee = db.get_employee(employee_id)\\n if not employee:\\n print(\\\"No employee found with id \\\", employee_id)\\n else:\\n payscale = db.get_payScale(employee.grade)\\n print('DATA:-> {} {} has grade = {} which gives {} per hours\\\\n'\\n .format(employee.first_name, employee.last_name, employee.grade, payscale.salary))\",\n \"def create(self, person):\\n current_id = self._generate_person_id()\\n self._timeline.add_event({\\n 'type': EventTimeLine.PERSON_CREATION,\\n 'personId': current_id,\\n 'status': person.status,\\n 'address': person.address.to_dict(),\\n 'name': person.name.to_dict()\\n })\\n return current_id\",\n \"def create(self):\\n\\n pass\",\n \"def perform_create(self, serializer):\\n if self.request.data.get('user_type', None) == 'employee':\\n serializer.save(is_staff=False)\\n else:\\n serializer.save()\",\n \"def make_hourly(self,rate,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"1\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be an hourly employee\\\"))\\n self.emp_dict[id][8] = rate\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def post(self):\\r\\n piso=self.request.get('piso')\\r\\n numext=self.request.get('numext')\\r\\n numint=self.request.get('numint')\\r\\n piso=self.validonumero(piso)\\r\\n numext=self.validonumero(numext)\\r\\n numint=self.validonumero(numint)\\r\\n \\r\\n empresa=empresas()\\r\\n empresa.nombre=self.request.get('desc')\\r\\n empresa.calle=self.request.get('calle')\\r\\n empresa.numeroExterior=int(numext)\\r\\n empresa.numeroInterior=int(numint)\\r\\n empresa.colonia=self.request.get('colonia')\\r\\n empresa.piso=int(piso)\\r\\n empresa.andador=self.request.get('andador')\\r\\n empresa.codigo_postal=int(self.request.get('cp'))\\r\\n empresa.sitioweb=self.request.get('web')\\r\\n empresa.correo=self.request.get('mail')\\r\\n empresa.nombreContacto=\\\"\\\"\\r\\n empresa.paternoContacto=\\\"\\\"\\r\\n empresa.maternoContacto=\\\"\\\"\\r\\n #### \\r\\n ciudad=self.request.get('ciudad')\\r\\n query=\\\"where ciudad='%s'\\\"%ciudad\\r\\n cd=ciudades.gql(query)\\r\\n city=cd.fetch(1)\\r\\n for lstcd in city:\\r\\n empresa.id_Ciudad=lstcd.key().id()\\r\\n empresa.put()\\r\\n jsondic={}\\r\\n jsondata=[]\\r\\n jsondata+=[self.addKey(jsondic,\\\"Dato\\\", empresa.key().id())]\\r\\n self.response.out.write(simplejson.dumps(jsondata))\\r\\n return False\",\n \"def create(self):\\n ...\",\n \"def create_person(dbo, username, collationid):\\n l = dbo.locale\\n fields = get_onlineformincoming_detail(dbo, collationid)\\n d = {}\\n flags = None\\n for f in fields:\\n if flags is None: flags = f[\\\"FLAGS\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"title\\\": d[\\\"title\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"forenames\\\": d[\\\"forenames\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"firstname\\\": d[\\\"forenames\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"surname\\\": d[\\\"surname\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"lastname\\\": d[\\\"surname\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"address\\\": d[\\\"address\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"town\\\": d[\\\"town\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"city\\\": d[\\\"town\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"county\\\": d[\\\"county\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"state\\\": d[\\\"county\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"postcode\\\": d[\\\"postcode\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"zipcode\\\": d[\\\"postcode\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"hometelephone\\\": d[\\\"hometelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"worktelephone\\\": d[\\\"worktelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"mobiletelephone\\\": d[\\\"mobiletelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"celltelephone\\\": d[\\\"mobiletelephone\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"] == \\\"emailaddress\\\": d[\\\"emailaddress\\\"] = f[\\\"VALUE\\\"]\\n if f[\\\"FIELDNAME\\\"].startswith(\\\"reserveanimalname\\\"): d[f[\\\"FIELDNAME\\\"]] = f[\\\"VALUE\\\"]\\n d[\\\"flags\\\"] = flags\\n # Have we got enough info to create the person record? We just need a surname\\n if not d.has_key(\\\"surname\\\"):\\n raise utils.ASMValidationError(i18n._(\\\"There is not enough information in the form to create a person record (need a surname).\\\", l))\\n # Does this person already exist?\\n personid = 0\\n if d.has_key(\\\"surname\\\") and d.has_key(\\\"forenames\\\") and d.has_key(\\\"address\\\"):\\n similar = person.get_person_similar(dbo, d[\\\"surname\\\"], d[\\\"forenames\\\"], d[\\\"address\\\"])\\n if len(similar) > 0:\\n personid = similar[0][\\\"ID\\\"]\\n # Create the person record if we didn't find one\\n if personid == 0:\\n personid = person.insert_person_from_form(dbo, utils.PostedData(d, dbo.locale), username)\\n personname = person.get_person_name_code(dbo, personid)\\n # Attach the form to the person\\n formname = get_onlineformincoming_name(dbo, collationid)\\n formhtml = get_onlineformincoming_html_print(dbo, [collationid,])\\n media.create_document_media(dbo, username, media.PERSON, personid, formname, formhtml )\\n # Was there a reserveanimalname field? If so, create reservation(s) to the person if possible\\n for k, v in d.iteritems():\\n if k.startswith(\\\"reserveanimalname\\\"):\\n movement.insert_reserve_for_animal_name(dbo, username, personid, v)\\n return (collationid, personid, personname)\",\n \"def employee_add(request, user_id):\\n\\n if request.method == \\\"GET\\\":\\n employee = User.objects.get(pk=user_id)\\n orgs = Organization.objects.filter(administrator=request.user)\\n\\n #desig_list = orgs.values_list('designations', flat=True)\\n #designations = Designation.objects.filter(pk__in=desig_list)\\n\\n return render_to_response('organization/employee_add1.html',\\n {'employee':employee, 'orgs': orgs},\\n context_instance=RequestContext(request))\\n\\n user_id = request.POST.get('employee_id')\\n org = request.POST.get('add2orgs') # id of selected org\\n designations_list = request.POST.getlist('designations2add')#list of ids of selected designation\\n\\n try:\\n user = User.objects.get(pk=user_id)# emp obj to add\\n org = Organization.objects.get(pk=org)#selected org objects\\n designations = Designation.objects.filter(pk__in=designations_list)#convert desig id in obj\\n except Exception, e:\\n # log this error\\n print str(e)\\n messages.error(request, str(e))\\n user = org = designations = None\\n\\n if not (user and designations.count()):\\n # add error message.\\n messages.error(request, \\\"Select atleast one Organization and its Designation\\\")\\n # redirect to the same page.\\n return HttpResponseRedirect(request.path)\\n\\n #def send_email():\\n # template = get_template('organization/confirmation_email.txt')\\n # context = Context({'user':user,'org':o.organization,'desig':desig})\\n # subject = u'confirmation email'\\n # message = template.render(context)\\n # send_mail(subject,message,settings.DEFAULT_FROM_EMAIL,['ravirajmca12@gmail.com'])\\n\\n # create visiting card(s) for the employee\\n try:\\n l=[]\\n for desig in designations:\\n if desig in org.designations.all():\\n o,c = VisitingCards.objects.get_or_create(organization=org,\\n designation=desig, user=user)\\n\\n if c:\\n #send_email()\\n template = get_template('organization/confirmation_email.txt')\\n context = Context({'user':user,'org':o.organization,'desig':desig})\\n subject = u'confirmation email'\\n message = template.render(context)\\n msg = (subject,message,settings.DEFAULT_FROM_EMAIL, ['ravirajmca12@gmail.com'])\\n l.append(msg)\\n #t=threading.Thread(target=send_email)\\n #t.start()\\n messages.success(request,\\\"Employee Added Succefully..!\\\")\\n else:\\n messages.info(request,\\\" %s Already Added ..!\\\" % user.username)\\n else:\\n messages.error(request,\\\"%s Doesnt have selected designation %s .!\\\" %(orgs, desig))\\n raise Exception()\\n if c:\\n tupl=tuple(l)\\n threading.Thread(send_mass_mail(tupl, fail_silently=False)).start()\\n messages.success(request,\\\"Employee Added Succefully..!\\\")\\n\\n\\n\\n except:\\n messages.error(request,\\\"something went wroung \\\")\\n return HttpResponseRedirect(request.path)\\n\\n return HttpResponseRedirect(\\\"/org/user/list\\\")\",\n \"def create(person):\\n lname = person.get(\\\"lname\\\", None)\\n fname = person.get(\\\"fname\\\", None)\\n\\n # Does the person exist already?\\n if lname not in PEOPLE and lname is not None:\\n PEOPLE[lname] = {\\n \\\"lname\\\": lname,\\n \\\"fname\\\": fname,\\n \\\"timestamp\\\": get_timestamp(),\\n }\\n return PEOPLE[lname], 201\\n\\n # Otherwise, they exist, that's an error\\n else:\\n abort(\\n 406,\\n \\\"Person with last name {lname} already exists\\\".format(lname=lname),\\n )\",\n \"def loadEmployees(testList):\\n # define an empty employee List\\n employeeList = []\\n\\n for item in testList:\\n itemToAdd = None\\n if item['type'] == 'employee':\\n try:\\n itemToAdd = createEmployee(item['firstName'],\\n item['lastName'],\\n item['SSN'],\\n item['salary'])\\n except ValueError:\\n continue\\n\\n elif item['type'] == 'manager':\\n try:\\n itemToAdd = createManager(item['firstName'],\\n item['lastName'],\\n item['SSN'],\\n item['salary'],\\n item['title'],\\n item['yearBonus'])\\n except ValueError:\\n continue\\n # Add Employee/Manager Object to List\\n if itemToAdd != None: # Note : this line will call Employee __eq__ to verify that it is not equal to None\\n employeeList.append(itemToAdd)\\n\\n return employeeList\",\n \"def add_employee():\\n\\n while True:\\n first_name = get_user_string(\\\"Enter your first name\\\")\\n last_name = get_user_string(\\\"Enter your last name\\\")\\n grade = get_employee_input_int(\\\"Enter your grade\\\")\\n db.add_employee(first_name, last_name, grade)\\n print(\\\"New employee \\\" + first_name + \\\" \\\" + last_name + \\\" has been added to the employee table\\\")\\n user_input = input(\\\"Do you want to add more employees to the table ? (Y/N)\\\")\\n if(str(user_input).upper()) == 'Y':\\n continue\\n elif (str(user_input).upper()) == 'N':\\n break\\n else:\\n print(\\\"Invalid Input\\\\nReturning to the main menu\\\")\\n break\",\n \"def test_skip_creation_of_sd_let_go_employments(employment_status: str) -> None:\\n # Arrange\\n sd = get_sd_importer()\\n sd.importer = MagicMock()\\n\\n person = {\\n \\\"PersonCivilRegistrationIdentifier\\\": \\\"1234567890\\\",\\n \\\"Employment\\\": [\\n {\\\"EmploymentStatus\\\": {\\\"EmploymentStatusCode\\\": employment_status}}\\n ],\\n }\\n\\n # Act\\n sd.create_employee(person)\\n\\n # Assert\\n sd.importer.add_engagement.assert_not_called()\",\n \"def post(self, data: Dict):\\n data = {\\n 'create_expensereport': data\\n }\\n return self.format_and_send_request(data)\",\n \"def amazonEc2_create(amazonEc2):\\n\\treturn amazonEc2\",\n \"def employee_id(self):\\n for i in self.emp_dict:\\n self.emp_id[i] = self.emp_dict[i][0]\\n #print(self.emp_id)\\n return self.emp_id\",\n \"def create(self, vals):\\n number_id = self.env['educa.establishment'].search([('id', '=', vals['establishment_id'])]).number_id\\n code = self.search([('establishment_id', '=', vals['establishment_id'])], order=\\\"name desc\\\", limit=1).name\\n if code:\\n num = str(code).split('/')\\n start_num =(int(num[0]))\\n end_num = int(num[1])\\n final_name=str(start_num) +'/' +str(end_num+1)\\n vals['name'] = final_name\\n else:\\n vals['name'] = str(number_id) +'/' +'1'\\n return super(EducaClassroom, self).create(vals)\",\n \"def create_data_structure(self, records):\\n col_title = [[] , []]\\n all_exp = {}\\n\\n # Get all employee info, used for obtain employee account info\\n emp_info = self.env['hr.employee'].sudo().search([])\\n\\n def append_data_list():\\n # {a: {b: 1}}\\n for rec in records:\\n current_emp_acc = ''\\n for emp in emp_info:\\n if emp.name == rec.expense_id.employee_name:\\n current_emp_acc = emp.bank_account_id.acc_number\\n print(\\\"this employee: \\\" + rec.expense_id.employee_name + \\\"'s Acc No. is: \\\" + current_emp_acc)\\n cur = rec.expense_line_currency.currency\\n if not cur == \\\"RMB\\\":\\n cur = \\\"HKD\\\"\\n\\n cat_key = (str(rec.expense_cate_id.acc_no), str(rec.expense_debit_acc_name))\\n emp_key = (\\n str(rec.expense_id.employee_id.rmb_account_no) if cur == \\\"RMB\\\" else current_emp_acc,\\n rec.employee_name\\n )\\n exp_key = (\\n str(rec.expense_id.expense_create_date),\\n str(rec.expense_id.expense_num),\\n str(rec.expense_id.name)\\n )\\n amt = float(rec.expense_line_cost) if cur == \\\"RMB\\\" else float(rec.expense_line_calculate)\\n\\n # -----\\n if cur not in all_exp:\\n all_exp[cur] = {}\\n if emp_key not in all_exp[cur]:\\n all_exp[cur][emp_key] = {}\\n if exp_key not in all_exp[cur][emp_key]:\\n all_exp[cur][emp_key][exp_key] = {}\\n\\n all_exp[cur][emp_key][exp_key][cat_key] = all_exp[cur][emp_key][exp_key].get(cat_key, 0.0) + amt\\n\\n if cur == \\\"HKD\\\" and cat_key not in col_title[0]:\\n col_title[0].append(cat_key)\\n elif cur == \\\"RMB\\\" and cat_key not in col_title[1]:\\n col_title[1].append(cat_key)\\n for titles in col_title:\\n titles.sort()\\n\\n return\\n\\n append_data_list()\\n\\n # for emp_dict in all_exp:\\n # all_exp[emp_dict] = collections.OrderedDict(sorted(all_exp[emp_dict].items())) # sort exp data keys\\n # all_exp = collections.OrderedDict(sorted(all_exp.items())) # sort all_exp emp data keys by ID\\n\\n # get the selected exp line id for later use\\n with open('./select_rec.txt', 'w') as sr:\\n for rec in records:\\n id_str = str(rec.id) + '\\\\n'\\n sr.write(id_str)\\n rev_name = records[0].expense_id.rec_reviewer_name\\n\\n for cur in all_exp:\\n self.create_exl_report(col_title[0] if cur == \\\"HKD\\\" else col_title[1], all_exp[cur], rev_name, cur)\\n self.create_HSBC_txt(all_exp[cur], cur)\\n\\n\\n # return\\n\\n #\\n # self.create_HSBC_txt(all_exp)\\n\\n return {\\n 'view_type': 'form',\\n 'view_mode': 'form',\\n 'res_model': 'hhexpense.anticipated.date',\\n 'type': 'ir.actions.act_window',\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7340392","0.7060474","0.6856134","0.66949964","0.66828686","0.6662708","0.6607132","0.66030127","0.6332911","0.6295497","0.6276587","0.62654424","0.626206","0.62513125","0.62459964","0.6240261","0.6239734","0.62260616","0.6169861","0.61182666","0.60744625","0.60650706","0.60377353","0.60375273","0.60265577","0.5954808","0.59057224","0.59057224","0.5867052","0.5863818","0.58319575","0.57900244","0.578959","0.5788105","0.5781969","0.57793546","0.5774725","0.57465214","0.57397497","0.5735295","0.5729593","0.5728667","0.57283515","0.5728156","0.5726278","0.57184726","0.5718251","0.57173157","0.5700344","0.5680688","0.56411314","0.5638549","0.5608662","0.55993783","0.5570657","0.55486506","0.5548614","0.55129206","0.5510394","0.5508749","0.550179","0.54967314","0.5479535","0.5473986","0.5460189","0.54462093","0.54285336","0.5426772","0.54229","0.5421863","0.54216087","0.5421329","0.54135406","0.541132","0.5394489","0.53875333","0.53867567","0.5376567","0.5356509","0.5356509","0.5356509","0.5356388","0.53390175","0.53385884","0.53377384","0.5333656","0.53282917","0.5323332","0.53190446","0.53024983","0.52985096","0.529582","0.5295183","0.526414","0.5260106","0.52586085","0.52555954","0.52537036","0.52403325","0.52373904"],"string":"[\n \"0.7340392\",\n \"0.7060474\",\n \"0.6856134\",\n \"0.66949964\",\n \"0.66828686\",\n \"0.6662708\",\n \"0.6607132\",\n \"0.66030127\",\n \"0.6332911\",\n \"0.6295497\",\n \"0.6276587\",\n \"0.62654424\",\n \"0.626206\",\n \"0.62513125\",\n \"0.62459964\",\n \"0.6240261\",\n \"0.6239734\",\n \"0.62260616\",\n \"0.6169861\",\n \"0.61182666\",\n \"0.60744625\",\n \"0.60650706\",\n \"0.60377353\",\n \"0.60375273\",\n \"0.60265577\",\n \"0.5954808\",\n \"0.59057224\",\n \"0.59057224\",\n \"0.5867052\",\n \"0.5863818\",\n \"0.58319575\",\n \"0.57900244\",\n \"0.578959\",\n \"0.5788105\",\n \"0.5781969\",\n \"0.57793546\",\n \"0.5774725\",\n \"0.57465214\",\n \"0.57397497\",\n \"0.5735295\",\n \"0.5729593\",\n \"0.5728667\",\n \"0.57283515\",\n \"0.5728156\",\n \"0.5726278\",\n \"0.57184726\",\n \"0.5718251\",\n \"0.57173157\",\n \"0.5700344\",\n \"0.5680688\",\n \"0.56411314\",\n \"0.5638549\",\n \"0.5608662\",\n \"0.55993783\",\n \"0.5570657\",\n \"0.55486506\",\n \"0.5548614\",\n \"0.55129206\",\n \"0.5510394\",\n \"0.5508749\",\n \"0.550179\",\n \"0.54967314\",\n \"0.5479535\",\n \"0.5473986\",\n \"0.5460189\",\n \"0.54462093\",\n \"0.54285336\",\n \"0.5426772\",\n \"0.54229\",\n \"0.5421863\",\n \"0.54216087\",\n \"0.5421329\",\n \"0.54135406\",\n \"0.541132\",\n \"0.5394489\",\n \"0.53875333\",\n \"0.53867567\",\n \"0.5376567\",\n \"0.5356509\",\n \"0.5356509\",\n \"0.5356509\",\n \"0.5356388\",\n \"0.53390175\",\n \"0.53385884\",\n \"0.53377384\",\n \"0.5333656\",\n \"0.53282917\",\n \"0.5323332\",\n \"0.53190446\",\n \"0.53024983\",\n \"0.52985096\",\n \"0.529582\",\n \"0.5295183\",\n \"0.526414\",\n \"0.5260106\",\n \"0.52586085\",\n \"0.52555954\",\n \"0.52537036\",\n \"0.52403325\",\n \"0.52373904\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7889773"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17483,"cells":{"query":{"kind":"string","value":"test attrs of City when created"},"document":{"kind":"string","value":"def test_attrs(self):\n city = City()\n self.assertEqual(city.name, \"\")\n self.assertEqual(City.name, \"\")\n self.assertEqual(city.state_id, \"\")\n self.assertEqual(City.state_id, \"\")\n self.assertIn(\"id\", city.__dict__)\n self.assertIn(\"created_at\", city.to_dict())\n self.assertIn(\"updated_at\", city.to_dict())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_attributes(self):\n self.assertTrue(hasattr(self.city, 'name'))\n self.assertTrue(hasattr(self.city, 'state_id'))","def test_set_attrs(self):\n city2 = City()\n city2.name = \"Hawaii\"\n self.assertEqual(city2.name, \"Hawaii\")\n city2.state_id = \"<3\"\n self.assertEqual(city2.state_id, \"<3\")\n self.assertEqual(City.name, \"\")\n self.assertEqual(City.state_id, \"\")","def test_city(self):\n my_city = City()\n self.assertTrue(hasattr(mycity, \"name\"))\n self.assertEqual(my_city.name, \"\")\n self.assertTrue(hasattr(mycity, \"state_id\"))\n self.assertEqual(my_city.state_id, \"\")","def test_city(self):\n c = City()\n self.assertEqual(c.name, \"\")\n self.assertEqual(c.state_id, \"\")\n c.name = \"San Francisco\"\n c.state_id = \"98\"\n self.assertEqual(c.name, \"San Francisco\")\n self.assertEqual(c.state_id, \"98\")\n self.assertEqual(type(c.state_id), str)","def testCityId(self):\n place = Place()\n self.assertTrue(hasattr(place, \"city_id\"))\n self.assertEqual(place.city_id, \"\")","def test_attr_type(self):\n self.my_city.state_id = \"1c5dd90a-a3df-4516-b1ac-32a8715e5539\"\n self.my_city.name = \"New York\"\n self.assertIsInstance(self.my_city.name, str)\n self.assertIsInstance(self.my_city.state_id, str)","def test_type_of_attributes(self):\n self.assertIsInstance(self.city.name, str)\n self.assertIsInstance(self.city.state_id, str)","def test_city(self):\n my_city = City()\n my_city.name = \"Medellin\"\n self.assertEqual(my_city.name, 'Medellin')","def test_city_id(self):\n place = Place()\n self.assertTrue(hasattr(place, \"city_id\"))\n self.assertEqual(type(place.city_id), str)\n self.assertEqual(place.city_id, \"\")","def test_class_attributes(self):\n self.assertTrue('__tablename__' in City.__dict__)\n self.assertTrue('name' in City.__dict__)\n self.assertTrue('state_id' in City.__dict__)\n self.assertTrue('places' in City.__dict__)","def test_state_id(self):\n c = City()\n self.assertTrue(hasattr(c, \"state_id\"))\n self.assertEqual(c.state_id, \"\")","def test_set_attr(self):\n self.my_city.name = \"Denver\"\n self.assertEqual(self.my_city.name, \"Denver\")","def test_name(self):\n c = City()\n self.assertTrue(hasattr(c, \"name\"))\n self.assertEqual(c.name, \"\")","def test_city(self):\n self.assertIsInstance(self.address.city, str)\n self.assertEqual(self.address.city, \"Paris\")","def setUp(self):\n self.my_city = City()","def test_has_attr(self):\n\n self.assertTrue(hasattr(City, \"save\"))","def test_has_attr(self):\n self.assertTrue(hasattr(City, \"save\"))","def test_init_without_kwargs(self):\n c = City()\n self.assertTrue('id' in c.__dict__)\n self.assertTrue('created_at' in c.__dict__)\n self.assertTrue('updated_at' in c.__dict__)\n self.assertIsInstance(c, BaseModel)","def test_create_hotel(self):\n amsterdam = City.objects.get(name=\"Amsterdam\")\n ibis = Hotel.objects.get(name=\"Ibis\")\n\n self.assertEqual(ibis.city, amsterdam)\n self.assertEqual(ibis.code, \"AMS01\")\n self.assertEqual(ibis.name, \"Ibis\")","def test_init(self, fixture_environment):\n\n # Generate city object\n city_object = cit.City(environment=fixture_environment)\n\n # Check inheritance from citydistrict object of pycity\n assert city_object._kind == 'citydistrict'","def test_create_state(self):\n state_2 = State('name=\"California\"')\n self.assertEqual(state_2.name, 'California')","def test_attr(self):\n self.assertTrue(hasattr(self.amenity, \"created_at\"))\n self.assertTrue(hasattr(self.amenity, \"id\"))\n self.assertTrue(hasattr(self.amenity, \"updated_at\"))\n self.assertFalse(hasattr(self.amenity, \"random_attr\"))\n self.assertTrue(hasattr(self.amenity, \"name\"))\n self.assertEqual(self.amenity.__class__.__name__, \"Amenity\")\n self.assertEqual(self.amenity.name, \"\")","def test_name(self):\n self.assertEqual(type(City.name), str)","def test_instance(self):\n self.assertIsInstance(self.my_city, City)","def test_required_city(self):\r\n self.url_params['city'] = 'New York'\r\n response = self.client.post(self.url, self.url_params)\r\n self.assertEqual(response.status_code, 200)\r\n obj = json.loads(response.content)\r\n self.assertTrue(obj['success'])","def test_state_id(self):\n\n self.assertEqual(type(City.state_id), str)","def test_subclass(self):\n self.assertIsInstance(self.city, BaseModel)\n self.assertTrue(hasattr(self.city, \"id\"))\n self.assertTrue(hasattr(self.city, \"created_at\"))\n self.assertTrue(hasattr(self.city, \"updated_at\"))","def __init__(self, city):\r\n self.city = city","def test_Amenity_attributes(self):\n new_amenity = Amenity()\n self.assertTrue(hasattr(new_amenity, \"name\"))\n self.assertEqual(new_amenity.name, \"\")","def create_city(state_id=None):\n state = storage.get(State, state_id)\n\n if state is None:\n abort(404)\n\n if not request.json:\n abort(400, description=\"Not a JSON\")\n\n if \"name\" not in request.get_json().keys():\n abort(400, description=\"Missing name\")\n\n data = request.get_json()\n obj = City(**data)\n setattr(obj, 'state_id', state_id)\n obj.save()\n\n return jsonify(obj.to_dict()), 201","def test_insert(self):\n c = city.City(name=\"Freiburg\")\n p1 = city.Citizen(name=\"Peter\")\n p2 = city.Citizen(name=\"Georg\")\n c.add(p1, p2, rel=city.hasInhabitant)\n\n with DataspaceSession(URI) as session:\n wrapper = city.CityWrapper(session=session)\n wrapper.add(c)\n session.commit()\n\n check_state(self, c, p1, p2, db=DB)","def test_create_city(self):\n n = City.objects.count()\n log_n = LogEntry.objects.count()\n country = CountryFactory()\n u = UserFactory(role=User.MODERATOR)\n u.set_password('123')\n u.save()\n\n url = prepare_url('admin-cities-list')\n\n city_data = {\n 'name': 'NewCity name',\n 'country_id': str(country.id),\n }\n\n auth_url = prepare_url('login')\n data = {\n 'username': u.username,\n 'password': '123'\n }\n\n # restricted for moderator\n response = self.client.post(auth_url, data=data, format='json')\n token = response.data['token']\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\n response = self.client.post(url, data=city_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(City.objects.count(), n)\n self.assertEqual(LogEntry.objects.count(), log_n)\n\n # restricted for administrator\n u.role = User.ADMINISTRATOR\n u.save()\n response = self.client.post(auth_url, data=data, format='json')\n token = response.data['token']\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\n response = self.client.post(url, data=city_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(City.objects.count(), n)\n self.assertEqual(LogEntry.objects.count(), log_n)\n\n # ok for root\n u.role = User.ROOT\n u.save()\n response = self.client.post(auth_url, data=data, format='json')\n token = response.data['token']\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\n response = self.client.post(url, data=city_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(City.objects.count(), n+1)\n self.assertEqual(response.data['country_name'], country.name)\n self.assertEqual(LogEntry.objects.count(), log_n+1)","def city_create(state_id):\n if not storage.get(State, state_id):\n abort(404)\n if not request.get_json():\n return jsonify({\"error\": \"Not a JSON\"}), 400\n if \"name\" not in request.get_json():\n return jsonify({\"error\": \"Missing name\"}), 400\n city = City(state_id=state_id, **request.get_json())\n storage.new(city)\n city.save()\n return jsonify(city.to_dict()), 201","def test_member_created_if_valid_fields(self):\n member = Member.objects.create(**self.data)\n\n member_dict = model_to_dict(member)\n self.data[\"ministry\"] = self.data[\"ministry\"].pk\n for key in self.data.keys():\n self.assertEqual(member_dict.get(key), self.data.get(key))\n\n self.assertTrue(member.is_active)","def expected_city_names_fixture():\n return {'b', 'a', 'c'}","def setUpClass(cls):\n cls.city = City()","def test_geography_unique(self):\n htown = City.objects.get(name=\"Houston\")\n CityUnique.objects.create(point=htown.point)\n duplicate = CityUnique(point=htown.point)\n msg = \"City unique with this Point already exists.\"\n with self.assertRaisesMessage(ValidationError, msg):\n duplicate.validate_unique()","def test_required_city_missing(self):\r\n self.url_params['city'] = ''\r\n response = self.client.post(self.url, self.url_params)\r\n self.assertEqual(response.status_code, 400)\r\n obj = json.loads(response.content)\r\n self.assertEqual(\r\n obj['value'],\r\n u'A city is required',\r\n )","def test_is_subclass(self):\n c = City()\n self.assertIsInstance(c, BaseModel)\n self.assertTrue(hasattr(c, \"id\"))\n self.assertTrue(hasattr(c, \"created_at\"))\n self.assertTrue(hasattr(c, \"updated_at\"))","def create_city():\n city = {}\n city['biysk'] = {}\n city['biysk']['barnaul'] = 9\n city['biysk']['novosibirsk'] = 11\n city['biysk']['belokurikha'] = 8\n city['barnaul'] = {}\n city['barnaul']['tomsk'] = 4\n city['belokurikha'] = {}\n city['belokurikha']['novosibirsk'] = 2\n city['novosibirsk'] = {}\n city['novosibirsk']['barnaul'] = 2\n city['novosibirsk']['tomsk'] = 5\n city['novosibirsk']['omsk'] = 20\n city['tomsk'] = {}\n city['tomsk']['krasnoyarsk'] = 6\n city['krasnoyarsk'] = {}\n city['krasnoyarsk']['omsk'] = 7\n city['omsk'] = {}\n return city","def test_creation(site):\n attr = models.Attribute.objects.create(\n resource_name='Device',\n site=site, name='test_attribute'\n )\n dev = models.Device.objects.create(\n hostname='foo-bar1', site=site\n )\n\n # Explicitly create a Value without providing site_id\n val = models.Value.objects.create(\n obj=dev, attribute=attr, value='foo'\n )\n\n # Value site should match attribute\n assert val.site == attr.site\n\n # Device attributes should match a simple dict\n dev.clean_attributes()\n dev.save()\n assert dev.get_attributes() == {'test_attribute': 'foo'}","def test_initial_attribute_values(self, create_controller: Controller) -> None:\n assert create_controller.coffee_machine.is_on\n assert create_controller.coffee_machine.water_level > 0\n assert create_controller.coffee_machine.milk_level > 0\n assert create_controller.coffee_machine.coffee_beans_level > 0\n assert create_controller.view\n assert not create_controller.play","def test_client_nationality_create(self):\n pass","def setUp(self):\n self.test_dict = {\"created_at\": \"2021-02-15T16:05:47.103874\",\n \"latitude\": 0.0, \"longitude\": 0.0,\n \"updated_at\": \"2021-02-15T16:05:47.103874\",\n \"amenity_ids\": [], \"city_id\": \"\", \"number_rooms\": 0,\n \"id\": \"52cf288d-f402-4564-a91f-06c71004c6b7\",\n \"name\": \"\", \"number_bathrooms\": 0, \"description\": \"\",\n \"price_by_night\": 0, \"user_id\": \"\", \"max_guest\": 0,\n \"__class__\": \"Place\"}\n self.obj = Place(**self.test_dict)","def test_update_city(self):\n country = CountryFactory()\n city = CityFactory()\n u = UserFactory(role=User.MODERATOR)\n u.set_password('123')\n u.save()\n log_n = LogEntry.objects.count()\n\n auth_url = prepare_url('login')\n data = {\n 'username': u.username,\n 'password': '123'\n }\n response = self.client.post(auth_url, data=data, format='json')\n token = response.data['token']\n\n url = prepare_url('admin-cities-detail', kwargs={'id': str(city.id)})\n data = {\n 'name': 'NewCity name',\n 'country_id': str(country.id),\n 'is_active': False\n }\n n = City.objects.count()\n\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\n response = self.client.patch(url, data=data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(City.objects.count(), n)\n self.assertEqual(response.data['name'], 'NewCity name')\n self.assertEqual(response.data['is_active'], False)\n self.assertEqual(response.data['country_name'], country.name)\n self.assertEqual(LogEntry.objects.count(), log_n+1)","def test_update(self):\n c = city.City(name=\"Paris\")\n p1 = city.Citizen(name=\"Peter\")\n c.add(p1, rel=city.hasInhabitant)\n\n with DataspaceSession(URI) as session:\n wrapper = city.CityWrapper(session=session)\n cw = wrapper.add(c)\n session.commit()\n\n p2 = city.Citizen(name=\"Georg\")\n cw.add(p2, rel=city.hasInhabitant)\n cw.name = \"Freiburg\"\n session.commit()\n\n check_state(self, c, p1, p2, db=DB)","def testNormalCreate(self):\n\n canada = self.Country(\n {\"name\": \"Canada\", \"abbreviation\": \"CA\", \"languages\": [\"english\", \"french\"]}\n )\n\n canada.save()\n\n self.assertEqual(\"Canada\", canada.name)\n self.assertEqual(\"CA\", canada.abbreviation)\n self.assertEqual(2, len(canada.languages))\n self.assertTrue(\"english\" in canada.languages)\n self.assertTrue(\"french\" in canada.languages)","def test_attribute(self):\n r1 = Review()\n self.assertEqual(r1.place_id, \"\")","def test_verify_attrs_customer(self):\n self.assertEqual(self.customer.name, 'Customer Test')\n self.assertEqual(self.customer.document, '000.000.000-00')","def __init__(self, city, state, country):\n self.city = city\n self.state = state\n self.country = country","def test_empty_string(self):\n self.assertEqual(self.place.city_id, \"\")\n self.assertEqual(self.place.user_id, \"\")\n self.assertEqual(self.place.name, \"\")","def test_create_company_props_using_post(self):\n pass","def test_customer_creation():\n agent = AgentFactory()\n customer = CustomerFactory(agent=agent)\n assert agent == customer.agent\n\n customer.name = 'customer test name 1'\n customer.customer_type = 'hom'\n customer.save()\n assert customer.name == 'customer test name 1'\n\n customer.name = 'customer test name 2'\n customer.customer_type = 'oth'\n customer.save()\n assert customer.name == 'customer test name 2'","def test_attr(self):\n new_review = Review()\n self.assertTrue(hasattr(new_review, \"id\"))\n self.assertTrue(hasattr(new_review, \"created_at\"))\n self.assertTrue(hasattr(new_review, \"updated_at\"))\n self.assertTrue(hasattr(new_review, \"place_id\"))\n self.assertTrue(hasattr(new_review, \"user_id\"))\n self.assertTrue(hasattr(new_review, \"text\"))","def test_method(self):\n self.amenity.save()\n self.assertTrue(hasattr(self.amenity, \"updated_at\"))","def test_capital_unicity(self):\n\n # Get Bangkok\n bangkok = Country.objects.get(iso3=\"THA\").capital\n\n # Get United States\n united_states = Country.objects.get(iso3=\"USA\")\n\n # Initialize assertRaises block\n with self.assertRaises(IntegrityError):\n\n # Set the capital of United States to Bangkok\n united_states.capital = bangkok\n\n # Try to save United States object to database\n united_states.save()","def test_customer_create(self):\n self._create_model(\"customer\", self.customer_data, [\"name\", \"email\", \"phone\"])","def put_city(city_id):\n\n if not request.get_json():\n return make_response(jsonify({\"error\": \"Not a JSON\"}), 400)\n if storage.get(\"City\", city_id) is not None:\n obj = storage.get(\"City\", city_id)\n obj_dict = obj.to_dict()\n dict1 = request.get_json()\n ignored_keys = ['id', 'created_at', 'updated_at', 'state_id']\n for key, value in obj_dict.items():\n if key in ignored_keys:\n pass\n else:\n for k, v in dict1.items():\n if key == k:\n setattr(obj, k, v)\n else:\n pass\n obj.save()\n return make_response(obj.to_dict(), 200)\n else:\n abort(404)","def create_city(states_id):\n state = storage.get(\"State\", state_id)\n if state is None:\n abort(404)\n if not request.json:\n abort(400, \"Not a JSON\")\n if 'name' not in request.json:\n abort(400, \"Missing name\")\n city = models.city.City(name=request.json['name'], state_id=states_id)\n city.save()\n return jsonify(city.to_dict()), 201","def test_CovidCase_creation(self):\n new_Covid = self.create_CovidCase()\n\n self.assertTrue(isinstance(new_Covid, CovidCase))\n self.assertEqual(new_Covid.country_id, \"TE\")","def test_creates_dict(self):\n c = City()\n dic = c.to_dict()\n self.assertEqual(type(dic), dict)\n for att in c.__dict__:\n self.assertTrue(att in dic)\n self.assertTrue(\"__class__\" in dic)","def check_attributes(self):\n self.assertEqual(type(self.amenity_1.name), str)","def test_city_country(self):\n santiago_chile = get_city_name('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n dublin_ireland = city_country('dublin', 'ireland')\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')","def test_addr_city_good_values(self):\n for input_val, output_val in self.known_values:\n self.line._parse_addr_city(input_val)\n self.assertEqual(output_val, self.line.addr_city)","def test_get_attributes(self):\n pass","def test_init_attributes(self):\n t = self.Test({'id': 1, 'poop': 'abc'})\n\n self.assertEqual(t.id, 1)\n self.assertEqual(t.name, None)\n self.assertRaises(AttributeError, t.__getattribute__, 'poop')","def post_city(state_id=None):\n jreq = request.get_json(silent=True)\n state = storage.get(State, state_id)\n\n if state is None:\n abort(404)\n if jreq is None:\n abort(400, 'Not a JSON')\n if 'name' not in jreq:\n abort(400, 'Missing name')\n new_city = City(**jreq)\n new_city.state_id = state_id\n storage.save()\n return jsonify(new_city.to_dict()), 201","def test_city_country(self):\n\t\tformatted_address = city_country('santiago', 'chile')\n\t\tself.assertEqual(formatted_address, 'Santiago, Chile')","def test_legal_address_is_complete(empty_field):\n address = LegalAddressFactory.create(country=\"US\")\n if empty_field:\n setattr(address, empty_field, \"\")\n assert address.is_complete is False\n else:\n assert address.is_complete is True","def test_client_tax_information_create(self):\n pass","def city_put(city_id):\n city_json = request.get_json(silent=True)\n if city_json is None:\n abort(400, 'Not a JSON')\n fetched_obj = storage.get(\"City\", str(city_id))\n if fetched_obj is None:\n abort(404)\n for key, val in city_json.items():\n if key not in [\"id\", \"created_at\", \"updated_at\", \"state_id\"]:\n setattr(fetched_obj, key, val)\n fetched_obj.save()\n return jsonify(fetched_obj.to_json())","def test_init(self):\n self.assertIsInstance(self.obj, Place)\n self.assertEqual(self.obj.id, self.test_dict[\"id\"])\n self.assertEqual(self.obj.created_at,\n strptime(self.test_dict[\"created_at\"],\n '%Y-%m-%dT%H:%M:%S.%f'))\n self.assertEqual(self.obj.updated_at,\n strptime(self.test_dict[\"updated_at\"],\n '%Y-%m-%dT%H:%M:%S.%f'))\n self.assertEqual(self.obj.created_at, self.obj.updated_at)\n self.assertEqual(self.obj.latitude, 0.0)\n self.assertEqual(self.obj.longitude, 0.0)\n self.assertEqual(self.obj.city_id, \"\")\n self.assertEqual(self.obj.user_id, \"\")\n self.assertEqual(self.obj.name, \"\")\n self.assertEqual(self.obj.description, \"\")\n self.assertEqual(self.obj.number_rooms, 0)\n self.assertEqual(self.obj.number_bathrooms, 0)\n self.assertEqual(self.obj.price_by_night, 0)\n self.assertEqual(self.obj.max_guest, 0)\n self.assertEqual(self.obj.amenity_ids, [])\n self.assertIsInstance(self.obj.created_at, datetime.datetime)\n self.assertIsInstance(self.obj.updated_at, datetime.datetime)\n self.assertIsInstance(self.obj.id, str)\n self.assertIsInstance(self.obj.latitude, float)\n self.assertIsInstance(self.obj.longitude, float)\n self.assertIsInstance(self.obj.city_id, str)\n self.assertIsInstance(self.obj.user_id, str)\n self.assertIsInstance(self.obj.name, str)\n self.assertIsInstance(self.obj.description, str)\n self.assertIsInstance(self.obj.number_rooms, int)\n self.assertIsInstance(self.obj.number_bathrooms, int)\n self.assertIsInstance(self.obj.price_by_night, int)\n self.assertIsInstance(self.obj.max_guest, int)\n self.assertIsInstance(self.obj.amenity_ids, list)\n\n 'This is test for else statment'\n self.obj = Place()\n\n self.assertIsInstance(self.obj, Place)\n self.assertNotEqual(self.obj.id, \"\")\n self.assertEqual(self.obj.created_at, self.obj.updated_at)\n self.assertEqual(self.obj.latitude, 0.0)\n self.assertEqual(self.obj.longitude, 0.0)\n self.assertEqual(self.obj.city_id, \"\")\n self.assertEqual(self.obj.user_id, \"\")\n self.assertEqual(self.obj.name, \"\")\n self.assertEqual(self.obj.description, \"\")\n self.assertEqual(self.obj.number_rooms, 0)\n self.assertEqual(self.obj.number_bathrooms, 0)\n self.assertEqual(self.obj.price_by_night, 0)\n self.assertEqual(self.obj.max_guest, 0)\n self.assertEqual(self.obj.amenity_ids, [])\n self.assertIsInstance(self.obj.created_at, datetime.datetime)\n self.assertIsInstance(self.obj.updated_at, datetime.datetime)\n self.assertIsInstance(self.obj.created_at, datetime.datetime)\n self.assertIsInstance(self.obj.updated_at, datetime.datetime)\n self.assertIs(self.obj, storage.objects[type(self.obj).__name__ + \".\" +\n str(self.obj.id)])\n self.assertIsInstance(self.obj.id, str)\n self.assertIsInstance(self.obj.id, str)\n self.assertIsInstance(self.obj.latitude, float)\n self.assertIsInstance(self.obj.longitude, float)\n self.assertIsInstance(self.obj.city_id, str)\n self.assertIsInstance(self.obj.user_id, str)\n self.assertIsInstance(self.obj.name, str)\n self.assertIsInstance(self.obj.description, str)\n self.assertIsInstance(self.obj.number_rooms, int)\n self.assertIsInstance(self.obj.number_bathrooms, int)\n self.assertIsInstance(self.obj.price_by_night, int)\n self.assertIsInstance(self.obj.max_guest, int)\n self.assertIsInstance(self.obj.amenity_ids, list)","def test_city_country(self):\n formatted_city = get_full_city(\"santiago\", \"chile\")\n self.assertEqual(formatted_city, \"Santiago, Chile\")","def city(self, city):\n self._city = city","def test_city_country(self):\n formatted_name = make_formatted_name('santiago', 'chile')\n self.assertEqual(formatted_name, 'Santiago, Chile')","def test_first_name_attr(self):\n user = User()\n user_details = {\"student_id\": user.id, \"first_name\": \"Joe\"}\n student = Student(**user_details)\n self.assertTrue(hasattr(student, \"first_name\"))\n if models.storage_t == 'db':\n self.assertEqual(student.first_name, \"Joe\")\n else:\n self.assertEqual(student.first_name, \"Joe\")","def test_person_create(self):\n User.objects.create(\n first_name='Pepe',\n last_name='Argento',\n email='pepe@argento.com.ar',\n timezone=TIMEZONE_CHOICES[0][0]\n )\n self.assertEqual(User.objects.count(), 1)","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_basic_instance_creation(self):\n first = self.constituencies[0]\n self.assertEqual(first.slug, 'my-place')\n self.assertEqual(first.get_absolute_url(),\n u\"/constituency/%s/\" % first.slug)\n count = 0\n for user in self.users:\n self.assertEqual(user.postcode, USERS[count]['postcode'])\n count += 1","def test_after_to_dict(self):\n my_model = City()\n new_model = City()\n test_dict = my_model.to_dict()\n self.assertIsInstance(my_model, City)\n self.assertEqual(type(my_model).__name__, \"City\")\n self.assertEqual(test_dict['__class__'], \"City\")\n self.assertTrue(type(test_dict['__class__']), 'str')\n self.assertTrue(type(test_dict['created_at']), 'str')\n self.assertTrue(type(test_dict['updated_at']), 'str')\n self.assertTrue(type(test_dict['id']), 'str')\n self.assertNotEqual(my_model.id, new_model.id)","def test_openmrs_location_uuid_set(self):\n cape_town = self.locations['Cape Town']\n case_id = uuid.uuid4().hex\n form, (case, ) = _create_case(domain=self.domain, case_id=case_id, owner_id=cape_town.location_id)\n\n self.assertEqual(\n get_ancestor_location_openmrs_uuid(case),\n self.openmrs_capetown_uuid\n )","def test_attributes(self):\n self.assertTrue(hasattr(User()), \"email\")\n self.assertTrue(hasattr(User()), \"password\")\n self.assertTrue(hasattr(User()), \"first_name\")\n self.assertTrue(hasattr(User()), \"last_name\")\n self.assertTrue(hasattr(User()), \"__init__\")","def test_amenity(self):\n my_amenity = Amenity()\n my_amenity.name = \"Wi-Fi\"\n self.assertEqual(my_amenity.name, 'Wi-Fi')","def test_create_restaurant_no_name(self):\n headers = {'Content-Type': 'application/json'}\n headers.update(auth_header_cru_restaurants)\n info = {'creator': 'nobody@gmail.com', 'city': 'Chicago'}\n resp = self.test_client.post(self.API_BASE + '/create', headers=headers, data=json.dumps(info))\n self.assertEqual(resp.status_code, 400)","def test_create(self):\n retreat = Retreat.objects.create(\n name=\"random_retreat\",\n details=\"This is a description of the retreat.\",\n seats=40,\n address_line1=\"123 random street\",\n postal_code=\"123 456\",\n state_province=\"Random state\",\n country=\"Random country\",\n timezone=\"America/Montreal\",\n price=3,\n start_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 15, 8)),\n end_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 17, 12)),\n min_day_refund=7,\n min_day_exchange=7,\n refund_rate=100,\n is_active=True,\n accessibility=True,\n form_url=\"example.com\",\n carpool_url='example2.com',\n review_url='example3.com',\n has_shared_rooms=True,\n room_type=Retreat.DOUBLE_OCCUPATION,\n toilet_gendered=True,\n )\n\n self.assertEqual(retreat.__str__(), \"random_retreat\")","def test_website_companies_create(self):\n pass","def test_attributes(self):\n user = User()\n self.assertTrue(hasattr(user, \"email\"))\n self.assertTrue(hasattr(user, \"password\"))\n self.assertTrue(hasattr(user, \"first_name\"))\n self.assertTrue(hasattr(user, \"last_name\"))","def test_State_attr_types(self):\n U = State()\n U.name = \"State\"\n self.assertEqual(type(U.id), str)\n self.assertTrue(type(U.created_at) is datetime.datetime)\n self.assertTrue(type(U.updated_at) is datetime.datetime)\n self.assertEqual(type(U.name), str)","def test_createperson(self):\n p = model.Person(firstname=\"Tobias\", lastname=\"Thelen\",\n email=\"tthelen@uos.de\", hobbies=[\"singen\",\"springen\",\"fröhlichsein\"])\n id = p.store()\n\n p2 = model.Person(id=id)\n self.assertEqual(p.id, p2.id)\n self.assertEqual(p.firstname, p2.firstname)\n self.assertEqual(p.lastname, p2.lastname)\n self.assertEqual(p.email, p2.email)\n self.assertEqual(p.hobbies, p2.hobbies)","def set_City(self, value):\n super(AddressValidationInputSet, self)._set_input('City', value)","def load_random_cities(data):\n cities = list(set([elem['name'] for elem in data]))\n city_objects = [City(data=city) for city in cities]\n City.objects.bulk_create(city_objects)","def test_city(self):\n with patch('sys.stdout', new=StringIO()) as f:\n HBNBCommand().onecmd(\"create City\")\n id = f.getvalue().strip()\n self.assertTrue(type(f), str)\n self.assertEqual(len(id), 36)\n\n with patch('sys.stdout', new=StringIO()) as f:\n HBNBCommand().onecmd(\"update City \" + str(id) + \" name Manga\")\n self.assertTrue(type(f), str)\n self.assertEqual(f.getvalue().strip(), \"\")\n\n with patch('sys.stdout', new=StringIO()) as f:\n HBNBCommand().onecmd(\"show City \" + str(id))\n self.assertTrue(\"name\" in f.getvalue().strip())\n self.assertTrue(\"Manga\" in f.getvalue().strip())","def type_city(self, city):\n\n\t\twith allure.step(\"Type payee city\"):\n\t\t\telement = Element(driver=self.driver,\n\t\t\t explicit_wait_time=self.explicit_wait_time,\n\t\t\t locator=BillPayPageLocator.CITY_INPUT)\n\t\t\telement.write(city)\n\t\t\treturn None","def test_place_for_type(self):\n self.assertEqual(type(self.place.city_id), str)\n self.assertEqual(type(self.place.user_id), str)\n self.assertEqual(type(self.place.name), str)\n self.assertEqual(type(self.place.description), str)\n self.assertEqual(type(self.place.number_rooms), int)\n self.assertEqual(type(self.place.number_bathrooms), int)\n self.assertEqual(type(self.place.max_guest), int)\n self.assertEqual(type(self.place.price_by_night), int)\n self.assertEqual(type(self.place.latitude), float)\n self.assertEqual(type(self.place.longitude), float)\n self.assertEqual(type(self.place.amenity_ids), list)","def test_create_new_address_with_permission(self):\n data = dict(\n address_line1='random address 2',\n postal_code='RAN DOM',\n city='random city',\n state_province=self.random_state_province.iso_code,\n country=self.random_country.iso_code,\n )\n\n self.client.force_authenticate(user=self.admin)\n\n response = self.client.post(\n reverse('location:addresses'),\n data,\n format='json',\n )\n\n data['address_line2'] = ''\n data['state_province'] = dict(\n name=self.random_state_province.name,\n iso_code=self.random_state_province.iso_code\n )\n data['country'] = dict(\n name=self.random_country.name,\n iso_code=self.random_country.iso_code\n )\n\n res = json.loads(response.content)\n del res['id']\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(res, data)","def test_city_country(self):\n formatted_name = city_country('santiago', 'chile')\n self.assertEqual(formatted_name, 'Santiago, Chile')","def test_init(self):\n self.assertEqual(self.new_newssource.id,'id')\n self.assertEqual(self.new_newssource.name,'name')\n self.assertEqual(self.new_newssource.description,'description')\n self.assertEqual(self.new_newssource.url,'www.abcnews.go.com')\n self.assertEqual(self.new_newssource.category,'category')\n self.assertEqual(self.new_newssource.language,'language')\n self.assertEqual(self.new_newssource.country,'country')"],"string":"[\n \"def test_attributes(self):\\n self.assertTrue(hasattr(self.city, 'name'))\\n self.assertTrue(hasattr(self.city, 'state_id'))\",\n \"def test_set_attrs(self):\\n city2 = City()\\n city2.name = \\\"Hawaii\\\"\\n self.assertEqual(city2.name, \\\"Hawaii\\\")\\n city2.state_id = \\\"<3\\\"\\n self.assertEqual(city2.state_id, \\\"<3\\\")\\n self.assertEqual(City.name, \\\"\\\")\\n self.assertEqual(City.state_id, \\\"\\\")\",\n \"def test_city(self):\\n my_city = City()\\n self.assertTrue(hasattr(mycity, \\\"name\\\"))\\n self.assertEqual(my_city.name, \\\"\\\")\\n self.assertTrue(hasattr(mycity, \\\"state_id\\\"))\\n self.assertEqual(my_city.state_id, \\\"\\\")\",\n \"def test_city(self):\\n c = City()\\n self.assertEqual(c.name, \\\"\\\")\\n self.assertEqual(c.state_id, \\\"\\\")\\n c.name = \\\"San Francisco\\\"\\n c.state_id = \\\"98\\\"\\n self.assertEqual(c.name, \\\"San Francisco\\\")\\n self.assertEqual(c.state_id, \\\"98\\\")\\n self.assertEqual(type(c.state_id), str)\",\n \"def testCityId(self):\\n place = Place()\\n self.assertTrue(hasattr(place, \\\"city_id\\\"))\\n self.assertEqual(place.city_id, \\\"\\\")\",\n \"def test_attr_type(self):\\n self.my_city.state_id = \\\"1c5dd90a-a3df-4516-b1ac-32a8715e5539\\\"\\n self.my_city.name = \\\"New York\\\"\\n self.assertIsInstance(self.my_city.name, str)\\n self.assertIsInstance(self.my_city.state_id, str)\",\n \"def test_type_of_attributes(self):\\n self.assertIsInstance(self.city.name, str)\\n self.assertIsInstance(self.city.state_id, str)\",\n \"def test_city(self):\\n my_city = City()\\n my_city.name = \\\"Medellin\\\"\\n self.assertEqual(my_city.name, 'Medellin')\",\n \"def test_city_id(self):\\n place = Place()\\n self.assertTrue(hasattr(place, \\\"city_id\\\"))\\n self.assertEqual(type(place.city_id), str)\\n self.assertEqual(place.city_id, \\\"\\\")\",\n \"def test_class_attributes(self):\\n self.assertTrue('__tablename__' in City.__dict__)\\n self.assertTrue('name' in City.__dict__)\\n self.assertTrue('state_id' in City.__dict__)\\n self.assertTrue('places' in City.__dict__)\",\n \"def test_state_id(self):\\n c = City()\\n self.assertTrue(hasattr(c, \\\"state_id\\\"))\\n self.assertEqual(c.state_id, \\\"\\\")\",\n \"def test_set_attr(self):\\n self.my_city.name = \\\"Denver\\\"\\n self.assertEqual(self.my_city.name, \\\"Denver\\\")\",\n \"def test_name(self):\\n c = City()\\n self.assertTrue(hasattr(c, \\\"name\\\"))\\n self.assertEqual(c.name, \\\"\\\")\",\n \"def test_city(self):\\n self.assertIsInstance(self.address.city, str)\\n self.assertEqual(self.address.city, \\\"Paris\\\")\",\n \"def setUp(self):\\n self.my_city = City()\",\n \"def test_has_attr(self):\\n\\n self.assertTrue(hasattr(City, \\\"save\\\"))\",\n \"def test_has_attr(self):\\n self.assertTrue(hasattr(City, \\\"save\\\"))\",\n \"def test_init_without_kwargs(self):\\n c = City()\\n self.assertTrue('id' in c.__dict__)\\n self.assertTrue('created_at' in c.__dict__)\\n self.assertTrue('updated_at' in c.__dict__)\\n self.assertIsInstance(c, BaseModel)\",\n \"def test_create_hotel(self):\\n amsterdam = City.objects.get(name=\\\"Amsterdam\\\")\\n ibis = Hotel.objects.get(name=\\\"Ibis\\\")\\n\\n self.assertEqual(ibis.city, amsterdam)\\n self.assertEqual(ibis.code, \\\"AMS01\\\")\\n self.assertEqual(ibis.name, \\\"Ibis\\\")\",\n \"def test_init(self, fixture_environment):\\n\\n # Generate city object\\n city_object = cit.City(environment=fixture_environment)\\n\\n # Check inheritance from citydistrict object of pycity\\n assert city_object._kind == 'citydistrict'\",\n \"def test_create_state(self):\\n state_2 = State('name=\\\"California\\\"')\\n self.assertEqual(state_2.name, 'California')\",\n \"def test_attr(self):\\n self.assertTrue(hasattr(self.amenity, \\\"created_at\\\"))\\n self.assertTrue(hasattr(self.amenity, \\\"id\\\"))\\n self.assertTrue(hasattr(self.amenity, \\\"updated_at\\\"))\\n self.assertFalse(hasattr(self.amenity, \\\"random_attr\\\"))\\n self.assertTrue(hasattr(self.amenity, \\\"name\\\"))\\n self.assertEqual(self.amenity.__class__.__name__, \\\"Amenity\\\")\\n self.assertEqual(self.amenity.name, \\\"\\\")\",\n \"def test_name(self):\\n self.assertEqual(type(City.name), str)\",\n \"def test_instance(self):\\n self.assertIsInstance(self.my_city, City)\",\n \"def test_required_city(self):\\r\\n self.url_params['city'] = 'New York'\\r\\n response = self.client.post(self.url, self.url_params)\\r\\n self.assertEqual(response.status_code, 200)\\r\\n obj = json.loads(response.content)\\r\\n self.assertTrue(obj['success'])\",\n \"def test_state_id(self):\\n\\n self.assertEqual(type(City.state_id), str)\",\n \"def test_subclass(self):\\n self.assertIsInstance(self.city, BaseModel)\\n self.assertTrue(hasattr(self.city, \\\"id\\\"))\\n self.assertTrue(hasattr(self.city, \\\"created_at\\\"))\\n self.assertTrue(hasattr(self.city, \\\"updated_at\\\"))\",\n \"def __init__(self, city):\\r\\n self.city = city\",\n \"def test_Amenity_attributes(self):\\n new_amenity = Amenity()\\n self.assertTrue(hasattr(new_amenity, \\\"name\\\"))\\n self.assertEqual(new_amenity.name, \\\"\\\")\",\n \"def create_city(state_id=None):\\n state = storage.get(State, state_id)\\n\\n if state is None:\\n abort(404)\\n\\n if not request.json:\\n abort(400, description=\\\"Not a JSON\\\")\\n\\n if \\\"name\\\" not in request.get_json().keys():\\n abort(400, description=\\\"Missing name\\\")\\n\\n data = request.get_json()\\n obj = City(**data)\\n setattr(obj, 'state_id', state_id)\\n obj.save()\\n\\n return jsonify(obj.to_dict()), 201\",\n \"def test_insert(self):\\n c = city.City(name=\\\"Freiburg\\\")\\n p1 = city.Citizen(name=\\\"Peter\\\")\\n p2 = city.Citizen(name=\\\"Georg\\\")\\n c.add(p1, p2, rel=city.hasInhabitant)\\n\\n with DataspaceSession(URI) as session:\\n wrapper = city.CityWrapper(session=session)\\n wrapper.add(c)\\n session.commit()\\n\\n check_state(self, c, p1, p2, db=DB)\",\n \"def test_create_city(self):\\n n = City.objects.count()\\n log_n = LogEntry.objects.count()\\n country = CountryFactory()\\n u = UserFactory(role=User.MODERATOR)\\n u.set_password('123')\\n u.save()\\n\\n url = prepare_url('admin-cities-list')\\n\\n city_data = {\\n 'name': 'NewCity name',\\n 'country_id': str(country.id),\\n }\\n\\n auth_url = prepare_url('login')\\n data = {\\n 'username': u.username,\\n 'password': '123'\\n }\\n\\n # restricted for moderator\\n response = self.client.post(auth_url, data=data, format='json')\\n token = response.data['token']\\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\\n response = self.client.post(url, data=city_data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertEqual(City.objects.count(), n)\\n self.assertEqual(LogEntry.objects.count(), log_n)\\n\\n # restricted for administrator\\n u.role = User.ADMINISTRATOR\\n u.save()\\n response = self.client.post(auth_url, data=data, format='json')\\n token = response.data['token']\\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\\n response = self.client.post(url, data=city_data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertEqual(City.objects.count(), n)\\n self.assertEqual(LogEntry.objects.count(), log_n)\\n\\n # ok for root\\n u.role = User.ROOT\\n u.save()\\n response = self.client.post(auth_url, data=data, format='json')\\n token = response.data['token']\\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\\n response = self.client.post(url, data=city_data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertEqual(City.objects.count(), n+1)\\n self.assertEqual(response.data['country_name'], country.name)\\n self.assertEqual(LogEntry.objects.count(), log_n+1)\",\n \"def city_create(state_id):\\n if not storage.get(State, state_id):\\n abort(404)\\n if not request.get_json():\\n return jsonify({\\\"error\\\": \\\"Not a JSON\\\"}), 400\\n if \\\"name\\\" not in request.get_json():\\n return jsonify({\\\"error\\\": \\\"Missing name\\\"}), 400\\n city = City(state_id=state_id, **request.get_json())\\n storage.new(city)\\n city.save()\\n return jsonify(city.to_dict()), 201\",\n \"def test_member_created_if_valid_fields(self):\\n member = Member.objects.create(**self.data)\\n\\n member_dict = model_to_dict(member)\\n self.data[\\\"ministry\\\"] = self.data[\\\"ministry\\\"].pk\\n for key in self.data.keys():\\n self.assertEqual(member_dict.get(key), self.data.get(key))\\n\\n self.assertTrue(member.is_active)\",\n \"def expected_city_names_fixture():\\n return {'b', 'a', 'c'}\",\n \"def setUpClass(cls):\\n cls.city = City()\",\n \"def test_geography_unique(self):\\n htown = City.objects.get(name=\\\"Houston\\\")\\n CityUnique.objects.create(point=htown.point)\\n duplicate = CityUnique(point=htown.point)\\n msg = \\\"City unique with this Point already exists.\\\"\\n with self.assertRaisesMessage(ValidationError, msg):\\n duplicate.validate_unique()\",\n \"def test_required_city_missing(self):\\r\\n self.url_params['city'] = ''\\r\\n response = self.client.post(self.url, self.url_params)\\r\\n self.assertEqual(response.status_code, 400)\\r\\n obj = json.loads(response.content)\\r\\n self.assertEqual(\\r\\n obj['value'],\\r\\n u'A city is required',\\r\\n )\",\n \"def test_is_subclass(self):\\n c = City()\\n self.assertIsInstance(c, BaseModel)\\n self.assertTrue(hasattr(c, \\\"id\\\"))\\n self.assertTrue(hasattr(c, \\\"created_at\\\"))\\n self.assertTrue(hasattr(c, \\\"updated_at\\\"))\",\n \"def create_city():\\n city = {}\\n city['biysk'] = {}\\n city['biysk']['barnaul'] = 9\\n city['biysk']['novosibirsk'] = 11\\n city['biysk']['belokurikha'] = 8\\n city['barnaul'] = {}\\n city['barnaul']['tomsk'] = 4\\n city['belokurikha'] = {}\\n city['belokurikha']['novosibirsk'] = 2\\n city['novosibirsk'] = {}\\n city['novosibirsk']['barnaul'] = 2\\n city['novosibirsk']['tomsk'] = 5\\n city['novosibirsk']['omsk'] = 20\\n city['tomsk'] = {}\\n city['tomsk']['krasnoyarsk'] = 6\\n city['krasnoyarsk'] = {}\\n city['krasnoyarsk']['omsk'] = 7\\n city['omsk'] = {}\\n return city\",\n \"def test_creation(site):\\n attr = models.Attribute.objects.create(\\n resource_name='Device',\\n site=site, name='test_attribute'\\n )\\n dev = models.Device.objects.create(\\n hostname='foo-bar1', site=site\\n )\\n\\n # Explicitly create a Value without providing site_id\\n val = models.Value.objects.create(\\n obj=dev, attribute=attr, value='foo'\\n )\\n\\n # Value site should match attribute\\n assert val.site == attr.site\\n\\n # Device attributes should match a simple dict\\n dev.clean_attributes()\\n dev.save()\\n assert dev.get_attributes() == {'test_attribute': 'foo'}\",\n \"def test_initial_attribute_values(self, create_controller: Controller) -> None:\\n assert create_controller.coffee_machine.is_on\\n assert create_controller.coffee_machine.water_level > 0\\n assert create_controller.coffee_machine.milk_level > 0\\n assert create_controller.coffee_machine.coffee_beans_level > 0\\n assert create_controller.view\\n assert not create_controller.play\",\n \"def test_client_nationality_create(self):\\n pass\",\n \"def setUp(self):\\n self.test_dict = {\\\"created_at\\\": \\\"2021-02-15T16:05:47.103874\\\",\\n \\\"latitude\\\": 0.0, \\\"longitude\\\": 0.0,\\n \\\"updated_at\\\": \\\"2021-02-15T16:05:47.103874\\\",\\n \\\"amenity_ids\\\": [], \\\"city_id\\\": \\\"\\\", \\\"number_rooms\\\": 0,\\n \\\"id\\\": \\\"52cf288d-f402-4564-a91f-06c71004c6b7\\\",\\n \\\"name\\\": \\\"\\\", \\\"number_bathrooms\\\": 0, \\\"description\\\": \\\"\\\",\\n \\\"price_by_night\\\": 0, \\\"user_id\\\": \\\"\\\", \\\"max_guest\\\": 0,\\n \\\"__class__\\\": \\\"Place\\\"}\\n self.obj = Place(**self.test_dict)\",\n \"def test_update_city(self):\\n country = CountryFactory()\\n city = CityFactory()\\n u = UserFactory(role=User.MODERATOR)\\n u.set_password('123')\\n u.save()\\n log_n = LogEntry.objects.count()\\n\\n auth_url = prepare_url('login')\\n data = {\\n 'username': u.username,\\n 'password': '123'\\n }\\n response = self.client.post(auth_url, data=data, format='json')\\n token = response.data['token']\\n\\n url = prepare_url('admin-cities-detail', kwargs={'id': str(city.id)})\\n data = {\\n 'name': 'NewCity name',\\n 'country_id': str(country.id),\\n 'is_active': False\\n }\\n n = City.objects.count()\\n\\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\\n response = self.client.patch(url, data=data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(City.objects.count(), n)\\n self.assertEqual(response.data['name'], 'NewCity name')\\n self.assertEqual(response.data['is_active'], False)\\n self.assertEqual(response.data['country_name'], country.name)\\n self.assertEqual(LogEntry.objects.count(), log_n+1)\",\n \"def test_update(self):\\n c = city.City(name=\\\"Paris\\\")\\n p1 = city.Citizen(name=\\\"Peter\\\")\\n c.add(p1, rel=city.hasInhabitant)\\n\\n with DataspaceSession(URI) as session:\\n wrapper = city.CityWrapper(session=session)\\n cw = wrapper.add(c)\\n session.commit()\\n\\n p2 = city.Citizen(name=\\\"Georg\\\")\\n cw.add(p2, rel=city.hasInhabitant)\\n cw.name = \\\"Freiburg\\\"\\n session.commit()\\n\\n check_state(self, c, p1, p2, db=DB)\",\n \"def testNormalCreate(self):\\n\\n canada = self.Country(\\n {\\\"name\\\": \\\"Canada\\\", \\\"abbreviation\\\": \\\"CA\\\", \\\"languages\\\": [\\\"english\\\", \\\"french\\\"]}\\n )\\n\\n canada.save()\\n\\n self.assertEqual(\\\"Canada\\\", canada.name)\\n self.assertEqual(\\\"CA\\\", canada.abbreviation)\\n self.assertEqual(2, len(canada.languages))\\n self.assertTrue(\\\"english\\\" in canada.languages)\\n self.assertTrue(\\\"french\\\" in canada.languages)\",\n \"def test_attribute(self):\\n r1 = Review()\\n self.assertEqual(r1.place_id, \\\"\\\")\",\n \"def test_verify_attrs_customer(self):\\n self.assertEqual(self.customer.name, 'Customer Test')\\n self.assertEqual(self.customer.document, '000.000.000-00')\",\n \"def __init__(self, city, state, country):\\n self.city = city\\n self.state = state\\n self.country = country\",\n \"def test_empty_string(self):\\n self.assertEqual(self.place.city_id, \\\"\\\")\\n self.assertEqual(self.place.user_id, \\\"\\\")\\n self.assertEqual(self.place.name, \\\"\\\")\",\n \"def test_create_company_props_using_post(self):\\n pass\",\n \"def test_customer_creation():\\n agent = AgentFactory()\\n customer = CustomerFactory(agent=agent)\\n assert agent == customer.agent\\n\\n customer.name = 'customer test name 1'\\n customer.customer_type = 'hom'\\n customer.save()\\n assert customer.name == 'customer test name 1'\\n\\n customer.name = 'customer test name 2'\\n customer.customer_type = 'oth'\\n customer.save()\\n assert customer.name == 'customer test name 2'\",\n \"def test_attr(self):\\n new_review = Review()\\n self.assertTrue(hasattr(new_review, \\\"id\\\"))\\n self.assertTrue(hasattr(new_review, \\\"created_at\\\"))\\n self.assertTrue(hasattr(new_review, \\\"updated_at\\\"))\\n self.assertTrue(hasattr(new_review, \\\"place_id\\\"))\\n self.assertTrue(hasattr(new_review, \\\"user_id\\\"))\\n self.assertTrue(hasattr(new_review, \\\"text\\\"))\",\n \"def test_method(self):\\n self.amenity.save()\\n self.assertTrue(hasattr(self.amenity, \\\"updated_at\\\"))\",\n \"def test_capital_unicity(self):\\n\\n # Get Bangkok\\n bangkok = Country.objects.get(iso3=\\\"THA\\\").capital\\n\\n # Get United States\\n united_states = Country.objects.get(iso3=\\\"USA\\\")\\n\\n # Initialize assertRaises block\\n with self.assertRaises(IntegrityError):\\n\\n # Set the capital of United States to Bangkok\\n united_states.capital = bangkok\\n\\n # Try to save United States object to database\\n united_states.save()\",\n \"def test_customer_create(self):\\n self._create_model(\\\"customer\\\", self.customer_data, [\\\"name\\\", \\\"email\\\", \\\"phone\\\"])\",\n \"def put_city(city_id):\\n\\n if not request.get_json():\\n return make_response(jsonify({\\\"error\\\": \\\"Not a JSON\\\"}), 400)\\n if storage.get(\\\"City\\\", city_id) is not None:\\n obj = storage.get(\\\"City\\\", city_id)\\n obj_dict = obj.to_dict()\\n dict1 = request.get_json()\\n ignored_keys = ['id', 'created_at', 'updated_at', 'state_id']\\n for key, value in obj_dict.items():\\n if key in ignored_keys:\\n pass\\n else:\\n for k, v in dict1.items():\\n if key == k:\\n setattr(obj, k, v)\\n else:\\n pass\\n obj.save()\\n return make_response(obj.to_dict(), 200)\\n else:\\n abort(404)\",\n \"def create_city(states_id):\\n state = storage.get(\\\"State\\\", state_id)\\n if state is None:\\n abort(404)\\n if not request.json:\\n abort(400, \\\"Not a JSON\\\")\\n if 'name' not in request.json:\\n abort(400, \\\"Missing name\\\")\\n city = models.city.City(name=request.json['name'], state_id=states_id)\\n city.save()\\n return jsonify(city.to_dict()), 201\",\n \"def test_CovidCase_creation(self):\\n new_Covid = self.create_CovidCase()\\n\\n self.assertTrue(isinstance(new_Covid, CovidCase))\\n self.assertEqual(new_Covid.country_id, \\\"TE\\\")\",\n \"def test_creates_dict(self):\\n c = City()\\n dic = c.to_dict()\\n self.assertEqual(type(dic), dict)\\n for att in c.__dict__:\\n self.assertTrue(att in dic)\\n self.assertTrue(\\\"__class__\\\" in dic)\",\n \"def check_attributes(self):\\n self.assertEqual(type(self.amenity_1.name), str)\",\n \"def test_city_country(self):\\n santiago_chile = get_city_name('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n dublin_ireland = city_country('dublin', 'ireland')\\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')\",\n \"def test_addr_city_good_values(self):\\n for input_val, output_val in self.known_values:\\n self.line._parse_addr_city(input_val)\\n self.assertEqual(output_val, self.line.addr_city)\",\n \"def test_get_attributes(self):\\n pass\",\n \"def test_init_attributes(self):\\n t = self.Test({'id': 1, 'poop': 'abc'})\\n\\n self.assertEqual(t.id, 1)\\n self.assertEqual(t.name, None)\\n self.assertRaises(AttributeError, t.__getattribute__, 'poop')\",\n \"def post_city(state_id=None):\\n jreq = request.get_json(silent=True)\\n state = storage.get(State, state_id)\\n\\n if state is None:\\n abort(404)\\n if jreq is None:\\n abort(400, 'Not a JSON')\\n if 'name' not in jreq:\\n abort(400, 'Missing name')\\n new_city = City(**jreq)\\n new_city.state_id = state_id\\n storage.save()\\n return jsonify(new_city.to_dict()), 201\",\n \"def test_city_country(self):\\n\\t\\tformatted_address = city_country('santiago', 'chile')\\n\\t\\tself.assertEqual(formatted_address, 'Santiago, Chile')\",\n \"def test_legal_address_is_complete(empty_field):\\n address = LegalAddressFactory.create(country=\\\"US\\\")\\n if empty_field:\\n setattr(address, empty_field, \\\"\\\")\\n assert address.is_complete is False\\n else:\\n assert address.is_complete is True\",\n \"def test_client_tax_information_create(self):\\n pass\",\n \"def city_put(city_id):\\n city_json = request.get_json(silent=True)\\n if city_json is None:\\n abort(400, 'Not a JSON')\\n fetched_obj = storage.get(\\\"City\\\", str(city_id))\\n if fetched_obj is None:\\n abort(404)\\n for key, val in city_json.items():\\n if key not in [\\\"id\\\", \\\"created_at\\\", \\\"updated_at\\\", \\\"state_id\\\"]:\\n setattr(fetched_obj, key, val)\\n fetched_obj.save()\\n return jsonify(fetched_obj.to_json())\",\n \"def test_init(self):\\n self.assertIsInstance(self.obj, Place)\\n self.assertEqual(self.obj.id, self.test_dict[\\\"id\\\"])\\n self.assertEqual(self.obj.created_at,\\n strptime(self.test_dict[\\\"created_at\\\"],\\n '%Y-%m-%dT%H:%M:%S.%f'))\\n self.assertEqual(self.obj.updated_at,\\n strptime(self.test_dict[\\\"updated_at\\\"],\\n '%Y-%m-%dT%H:%M:%S.%f'))\\n self.assertEqual(self.obj.created_at, self.obj.updated_at)\\n self.assertEqual(self.obj.latitude, 0.0)\\n self.assertEqual(self.obj.longitude, 0.0)\\n self.assertEqual(self.obj.city_id, \\\"\\\")\\n self.assertEqual(self.obj.user_id, \\\"\\\")\\n self.assertEqual(self.obj.name, \\\"\\\")\\n self.assertEqual(self.obj.description, \\\"\\\")\\n self.assertEqual(self.obj.number_rooms, 0)\\n self.assertEqual(self.obj.number_bathrooms, 0)\\n self.assertEqual(self.obj.price_by_night, 0)\\n self.assertEqual(self.obj.max_guest, 0)\\n self.assertEqual(self.obj.amenity_ids, [])\\n self.assertIsInstance(self.obj.created_at, datetime.datetime)\\n self.assertIsInstance(self.obj.updated_at, datetime.datetime)\\n self.assertIsInstance(self.obj.id, str)\\n self.assertIsInstance(self.obj.latitude, float)\\n self.assertIsInstance(self.obj.longitude, float)\\n self.assertIsInstance(self.obj.city_id, str)\\n self.assertIsInstance(self.obj.user_id, str)\\n self.assertIsInstance(self.obj.name, str)\\n self.assertIsInstance(self.obj.description, str)\\n self.assertIsInstance(self.obj.number_rooms, int)\\n self.assertIsInstance(self.obj.number_bathrooms, int)\\n self.assertIsInstance(self.obj.price_by_night, int)\\n self.assertIsInstance(self.obj.max_guest, int)\\n self.assertIsInstance(self.obj.amenity_ids, list)\\n\\n 'This is test for else statment'\\n self.obj = Place()\\n\\n self.assertIsInstance(self.obj, Place)\\n self.assertNotEqual(self.obj.id, \\\"\\\")\\n self.assertEqual(self.obj.created_at, self.obj.updated_at)\\n self.assertEqual(self.obj.latitude, 0.0)\\n self.assertEqual(self.obj.longitude, 0.0)\\n self.assertEqual(self.obj.city_id, \\\"\\\")\\n self.assertEqual(self.obj.user_id, \\\"\\\")\\n self.assertEqual(self.obj.name, \\\"\\\")\\n self.assertEqual(self.obj.description, \\\"\\\")\\n self.assertEqual(self.obj.number_rooms, 0)\\n self.assertEqual(self.obj.number_bathrooms, 0)\\n self.assertEqual(self.obj.price_by_night, 0)\\n self.assertEqual(self.obj.max_guest, 0)\\n self.assertEqual(self.obj.amenity_ids, [])\\n self.assertIsInstance(self.obj.created_at, datetime.datetime)\\n self.assertIsInstance(self.obj.updated_at, datetime.datetime)\\n self.assertIsInstance(self.obj.created_at, datetime.datetime)\\n self.assertIsInstance(self.obj.updated_at, datetime.datetime)\\n self.assertIs(self.obj, storage.objects[type(self.obj).__name__ + \\\".\\\" +\\n str(self.obj.id)])\\n self.assertIsInstance(self.obj.id, str)\\n self.assertIsInstance(self.obj.id, str)\\n self.assertIsInstance(self.obj.latitude, float)\\n self.assertIsInstance(self.obj.longitude, float)\\n self.assertIsInstance(self.obj.city_id, str)\\n self.assertIsInstance(self.obj.user_id, str)\\n self.assertIsInstance(self.obj.name, str)\\n self.assertIsInstance(self.obj.description, str)\\n self.assertIsInstance(self.obj.number_rooms, int)\\n self.assertIsInstance(self.obj.number_bathrooms, int)\\n self.assertIsInstance(self.obj.price_by_night, int)\\n self.assertIsInstance(self.obj.max_guest, int)\\n self.assertIsInstance(self.obj.amenity_ids, list)\",\n \"def test_city_country(self):\\n formatted_city = get_full_city(\\\"santiago\\\", \\\"chile\\\")\\n self.assertEqual(formatted_city, \\\"Santiago, Chile\\\")\",\n \"def city(self, city):\\n self._city = city\",\n \"def test_city_country(self):\\n formatted_name = make_formatted_name('santiago', 'chile')\\n self.assertEqual(formatted_name, 'Santiago, Chile')\",\n \"def test_first_name_attr(self):\\n user = User()\\n user_details = {\\\"student_id\\\": user.id, \\\"first_name\\\": \\\"Joe\\\"}\\n student = Student(**user_details)\\n self.assertTrue(hasattr(student, \\\"first_name\\\"))\\n if models.storage_t == 'db':\\n self.assertEqual(student.first_name, \\\"Joe\\\")\\n else:\\n self.assertEqual(student.first_name, \\\"Joe\\\")\",\n \"def test_person_create(self):\\n User.objects.create(\\n first_name='Pepe',\\n last_name='Argento',\\n email='pepe@argento.com.ar',\\n timezone=TIMEZONE_CHOICES[0][0]\\n )\\n self.assertEqual(User.objects.count(), 1)\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_basic_instance_creation(self):\\n first = self.constituencies[0]\\n self.assertEqual(first.slug, 'my-place')\\n self.assertEqual(first.get_absolute_url(),\\n u\\\"/constituency/%s/\\\" % first.slug)\\n count = 0\\n for user in self.users:\\n self.assertEqual(user.postcode, USERS[count]['postcode'])\\n count += 1\",\n \"def test_after_to_dict(self):\\n my_model = City()\\n new_model = City()\\n test_dict = my_model.to_dict()\\n self.assertIsInstance(my_model, City)\\n self.assertEqual(type(my_model).__name__, \\\"City\\\")\\n self.assertEqual(test_dict['__class__'], \\\"City\\\")\\n self.assertTrue(type(test_dict['__class__']), 'str')\\n self.assertTrue(type(test_dict['created_at']), 'str')\\n self.assertTrue(type(test_dict['updated_at']), 'str')\\n self.assertTrue(type(test_dict['id']), 'str')\\n self.assertNotEqual(my_model.id, new_model.id)\",\n \"def test_openmrs_location_uuid_set(self):\\n cape_town = self.locations['Cape Town']\\n case_id = uuid.uuid4().hex\\n form, (case, ) = _create_case(domain=self.domain, case_id=case_id, owner_id=cape_town.location_id)\\n\\n self.assertEqual(\\n get_ancestor_location_openmrs_uuid(case),\\n self.openmrs_capetown_uuid\\n )\",\n \"def test_attributes(self):\\n self.assertTrue(hasattr(User()), \\\"email\\\")\\n self.assertTrue(hasattr(User()), \\\"password\\\")\\n self.assertTrue(hasattr(User()), \\\"first_name\\\")\\n self.assertTrue(hasattr(User()), \\\"last_name\\\")\\n self.assertTrue(hasattr(User()), \\\"__init__\\\")\",\n \"def test_amenity(self):\\n my_amenity = Amenity()\\n my_amenity.name = \\\"Wi-Fi\\\"\\n self.assertEqual(my_amenity.name, 'Wi-Fi')\",\n \"def test_create_restaurant_no_name(self):\\n headers = {'Content-Type': 'application/json'}\\n headers.update(auth_header_cru_restaurants)\\n info = {'creator': 'nobody@gmail.com', 'city': 'Chicago'}\\n resp = self.test_client.post(self.API_BASE + '/create', headers=headers, data=json.dumps(info))\\n self.assertEqual(resp.status_code, 400)\",\n \"def test_create(self):\\n retreat = Retreat.objects.create(\\n name=\\\"random_retreat\\\",\\n details=\\\"This is a description of the retreat.\\\",\\n seats=40,\\n address_line1=\\\"123 random street\\\",\\n postal_code=\\\"123 456\\\",\\n state_province=\\\"Random state\\\",\\n country=\\\"Random country\\\",\\n timezone=\\\"America/Montreal\\\",\\n price=3,\\n start_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 15, 8)),\\n end_time=LOCAL_TIMEZONE.localize(datetime(2130, 1, 17, 12)),\\n min_day_refund=7,\\n min_day_exchange=7,\\n refund_rate=100,\\n is_active=True,\\n accessibility=True,\\n form_url=\\\"example.com\\\",\\n carpool_url='example2.com',\\n review_url='example3.com',\\n has_shared_rooms=True,\\n room_type=Retreat.DOUBLE_OCCUPATION,\\n toilet_gendered=True,\\n )\\n\\n self.assertEqual(retreat.__str__(), \\\"random_retreat\\\")\",\n \"def test_website_companies_create(self):\\n pass\",\n \"def test_attributes(self):\\n user = User()\\n self.assertTrue(hasattr(user, \\\"email\\\"))\\n self.assertTrue(hasattr(user, \\\"password\\\"))\\n self.assertTrue(hasattr(user, \\\"first_name\\\"))\\n self.assertTrue(hasattr(user, \\\"last_name\\\"))\",\n \"def test_State_attr_types(self):\\n U = State()\\n U.name = \\\"State\\\"\\n self.assertEqual(type(U.id), str)\\n self.assertTrue(type(U.created_at) is datetime.datetime)\\n self.assertTrue(type(U.updated_at) is datetime.datetime)\\n self.assertEqual(type(U.name), str)\",\n \"def test_createperson(self):\\n p = model.Person(firstname=\\\"Tobias\\\", lastname=\\\"Thelen\\\",\\n email=\\\"tthelen@uos.de\\\", hobbies=[\\\"singen\\\",\\\"springen\\\",\\\"fröhlichsein\\\"])\\n id = p.store()\\n\\n p2 = model.Person(id=id)\\n self.assertEqual(p.id, p2.id)\\n self.assertEqual(p.firstname, p2.firstname)\\n self.assertEqual(p.lastname, p2.lastname)\\n self.assertEqual(p.email, p2.email)\\n self.assertEqual(p.hobbies, p2.hobbies)\",\n \"def set_City(self, value):\\n super(AddressValidationInputSet, self)._set_input('City', value)\",\n \"def load_random_cities(data):\\n cities = list(set([elem['name'] for elem in data]))\\n city_objects = [City(data=city) for city in cities]\\n City.objects.bulk_create(city_objects)\",\n \"def test_city(self):\\n with patch('sys.stdout', new=StringIO()) as f:\\n HBNBCommand().onecmd(\\\"create City\\\")\\n id = f.getvalue().strip()\\n self.assertTrue(type(f), str)\\n self.assertEqual(len(id), 36)\\n\\n with patch('sys.stdout', new=StringIO()) as f:\\n HBNBCommand().onecmd(\\\"update City \\\" + str(id) + \\\" name Manga\\\")\\n self.assertTrue(type(f), str)\\n self.assertEqual(f.getvalue().strip(), \\\"\\\")\\n\\n with patch('sys.stdout', new=StringIO()) as f:\\n HBNBCommand().onecmd(\\\"show City \\\" + str(id))\\n self.assertTrue(\\\"name\\\" in f.getvalue().strip())\\n self.assertTrue(\\\"Manga\\\" in f.getvalue().strip())\",\n \"def type_city(self, city):\\n\\n\\t\\twith allure.step(\\\"Type payee city\\\"):\\n\\t\\t\\telement = Element(driver=self.driver,\\n\\t\\t\\t explicit_wait_time=self.explicit_wait_time,\\n\\t\\t\\t locator=BillPayPageLocator.CITY_INPUT)\\n\\t\\t\\telement.write(city)\\n\\t\\t\\treturn None\",\n \"def test_place_for_type(self):\\n self.assertEqual(type(self.place.city_id), str)\\n self.assertEqual(type(self.place.user_id), str)\\n self.assertEqual(type(self.place.name), str)\\n self.assertEqual(type(self.place.description), str)\\n self.assertEqual(type(self.place.number_rooms), int)\\n self.assertEqual(type(self.place.number_bathrooms), int)\\n self.assertEqual(type(self.place.max_guest), int)\\n self.assertEqual(type(self.place.price_by_night), int)\\n self.assertEqual(type(self.place.latitude), float)\\n self.assertEqual(type(self.place.longitude), float)\\n self.assertEqual(type(self.place.amenity_ids), list)\",\n \"def test_create_new_address_with_permission(self):\\n data = dict(\\n address_line1='random address 2',\\n postal_code='RAN DOM',\\n city='random city',\\n state_province=self.random_state_province.iso_code,\\n country=self.random_country.iso_code,\\n )\\n\\n self.client.force_authenticate(user=self.admin)\\n\\n response = self.client.post(\\n reverse('location:addresses'),\\n data,\\n format='json',\\n )\\n\\n data['address_line2'] = ''\\n data['state_province'] = dict(\\n name=self.random_state_province.name,\\n iso_code=self.random_state_province.iso_code\\n )\\n data['country'] = dict(\\n name=self.random_country.name,\\n iso_code=self.random_country.iso_code\\n )\\n\\n res = json.loads(response.content)\\n del res['id']\\n\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertEqual(res, data)\",\n \"def test_city_country(self):\\n formatted_name = city_country('santiago', 'chile')\\n self.assertEqual(formatted_name, 'Santiago, Chile')\",\n \"def test_init(self):\\n self.assertEqual(self.new_newssource.id,'id')\\n self.assertEqual(self.new_newssource.name,'name')\\n self.assertEqual(self.new_newssource.description,'description')\\n self.assertEqual(self.new_newssource.url,'www.abcnews.go.com')\\n self.assertEqual(self.new_newssource.category,'category')\\n self.assertEqual(self.new_newssource.language,'language')\\n self.assertEqual(self.new_newssource.country,'country')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79669297","0.7755154","0.76454324","0.75021863","0.7164923","0.7156203","0.70737207","0.70595056","0.70520234","0.6963021","0.69118476","0.69103384","0.67882353","0.67593557","0.67574525","0.6731778","0.6674009","0.66470456","0.6544833","0.6540936","0.6517276","0.65020186","0.6498781","0.6446873","0.6445163","0.6360011","0.63454646","0.63221395","0.62879974","0.6189562","0.61710256","0.6136515","0.60821724","0.6003108","0.5979819","0.592103","0.59120774","0.5911627","0.59031266","0.58985716","0.5880794","0.5870927","0.586905","0.5860724","0.58470106","0.58372074","0.58292896","0.58291996","0.5816593","0.58129674","0.58066636","0.57991445","0.5788364","0.57796395","0.5779095","0.577857","0.5778053","0.577551","0.5774663","0.57709384","0.5761931","0.57609445","0.574938","0.5743531","0.57311714","0.57302487","0.57201076","0.570637","0.5695992","0.5689661","0.5687124","0.56787115","0.5676777","0.56694436","0.56640595","0.5653332","0.56494963","0.5648998","0.56434536","0.56434536","0.56434536","0.56409734","0.56303245","0.5618289","0.5613864","0.56020576","0.56016546","0.55827844","0.5576184","0.5576065","0.5573666","0.55701077","0.55607605","0.5552747","0.55431616","0.55401057","0.55362254","0.553604","0.55332416","0.55325574"],"string":"[\n \"0.79669297\",\n \"0.7755154\",\n \"0.76454324\",\n \"0.75021863\",\n \"0.7164923\",\n \"0.7156203\",\n \"0.70737207\",\n \"0.70595056\",\n \"0.70520234\",\n \"0.6963021\",\n \"0.69118476\",\n \"0.69103384\",\n \"0.67882353\",\n \"0.67593557\",\n \"0.67574525\",\n \"0.6731778\",\n \"0.6674009\",\n \"0.66470456\",\n \"0.6544833\",\n \"0.6540936\",\n \"0.6517276\",\n \"0.65020186\",\n \"0.6498781\",\n \"0.6446873\",\n \"0.6445163\",\n \"0.6360011\",\n \"0.63454646\",\n \"0.63221395\",\n \"0.62879974\",\n \"0.6189562\",\n \"0.61710256\",\n \"0.6136515\",\n \"0.60821724\",\n \"0.6003108\",\n \"0.5979819\",\n \"0.592103\",\n \"0.59120774\",\n \"0.5911627\",\n \"0.59031266\",\n \"0.58985716\",\n \"0.5880794\",\n \"0.5870927\",\n \"0.586905\",\n \"0.5860724\",\n \"0.58470106\",\n \"0.58372074\",\n \"0.58292896\",\n \"0.58291996\",\n \"0.5816593\",\n \"0.58129674\",\n \"0.58066636\",\n \"0.57991445\",\n \"0.5788364\",\n \"0.57796395\",\n \"0.5779095\",\n \"0.577857\",\n \"0.5778053\",\n \"0.577551\",\n \"0.5774663\",\n \"0.57709384\",\n \"0.5761931\",\n \"0.57609445\",\n \"0.574938\",\n \"0.5743531\",\n \"0.57311714\",\n \"0.57302487\",\n \"0.57201076\",\n \"0.570637\",\n \"0.5695992\",\n \"0.5689661\",\n \"0.5687124\",\n \"0.56787115\",\n \"0.5676777\",\n \"0.56694436\",\n \"0.56640595\",\n \"0.5653332\",\n \"0.56494963\",\n \"0.5648998\",\n \"0.56434536\",\n \"0.56434536\",\n \"0.56434536\",\n \"0.56409734\",\n \"0.56303245\",\n \"0.5618289\",\n \"0.5613864\",\n \"0.56020576\",\n \"0.56016546\",\n \"0.55827844\",\n \"0.5576184\",\n \"0.5576065\",\n \"0.5573666\",\n \"0.55701077\",\n \"0.55607605\",\n \"0.5552747\",\n \"0.55431616\",\n \"0.55401057\",\n \"0.55362254\",\n \"0.553604\",\n \"0.55332416\",\n \"0.55325574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76267827"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":17484,"cells":{"query":{"kind":"string","value":"test the attrs of City when set"},"document":{"kind":"string","value":"def test_set_attrs(self):\n city2 = City()\n city2.name = \"Hawaii\"\n self.assertEqual(city2.name, \"Hawaii\")\n city2.state_id = \"<3\"\n self.assertEqual(city2.state_id, \"<3\")\n self.assertEqual(City.name, \"\")\n self.assertEqual(City.state_id, \"\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_attributes(self):\n self.assertTrue(hasattr(self.city, 'name'))\n self.assertTrue(hasattr(self.city, 'state_id'))","def test_city(self):\n c = City()\n self.assertEqual(c.name, \"\")\n self.assertEqual(c.state_id, \"\")\n c.name = \"San Francisco\"\n c.state_id = \"98\"\n self.assertEqual(c.name, \"San Francisco\")\n self.assertEqual(c.state_id, \"98\")\n self.assertEqual(type(c.state_id), str)","def test_city(self):\n my_city = City()\n self.assertTrue(hasattr(mycity, \"name\"))\n self.assertEqual(my_city.name, \"\")\n self.assertTrue(hasattr(mycity, \"state_id\"))\n self.assertEqual(my_city.state_id, \"\")","def test_attr_type(self):\n self.my_city.state_id = \"1c5dd90a-a3df-4516-b1ac-32a8715e5539\"\n self.my_city.name = \"New York\"\n self.assertIsInstance(self.my_city.name, str)\n self.assertIsInstance(self.my_city.state_id, str)","def test_set_attr(self):\n self.my_city.name = \"Denver\"\n self.assertEqual(self.my_city.name, \"Denver\")","def test_attrs(self):\n city = City()\n self.assertEqual(city.name, \"\")\n self.assertEqual(City.name, \"\")\n self.assertEqual(city.state_id, \"\")\n self.assertEqual(City.state_id, \"\")\n self.assertIn(\"id\", city.__dict__)\n self.assertIn(\"created_at\", city.to_dict())\n self.assertIn(\"updated_at\", city.to_dict())","def test_type_of_attributes(self):\n self.assertIsInstance(self.city.name, str)\n self.assertIsInstance(self.city.state_id, str)","def test_city(self):\n self.assertIsInstance(self.address.city, str)\n self.assertEqual(self.address.city, \"Paris\")","def testCityId(self):\n place = Place()\n self.assertTrue(hasattr(place, \"city_id\"))\n self.assertEqual(place.city_id, \"\")","def test_city_id(self):\n place = Place()\n self.assertTrue(hasattr(place, \"city_id\"))\n self.assertEqual(type(place.city_id), str)\n self.assertEqual(place.city_id, \"\")","def test_class_attributes(self):\n self.assertTrue('__tablename__' in City.__dict__)\n self.assertTrue('name' in City.__dict__)\n self.assertTrue('state_id' in City.__dict__)\n self.assertTrue('places' in City.__dict__)","def test_state_id(self):\n c = City()\n self.assertTrue(hasattr(c, \"state_id\"))\n self.assertEqual(c.state_id, \"\")","def set_City(self, value):\n super(AddressValidationInputSet, self)._set_input('City', value)","def test_city(self):\n my_city = City()\n my_city.name = \"Medellin\"\n self.assertEqual(my_city.name, 'Medellin')","def city(self, city):\n self._city = city","def test_addr_city_good_values(self):\n for input_val, output_val in self.known_values:\n self.line._parse_addr_city(input_val)\n self.assertEqual(output_val, self.line.addr_city)","def setUp(self):\n self.my_city = City()","def __init__(self, city):\r\n self.city = city","def test_has_attr(self):\n\n self.assertTrue(hasattr(City, \"save\"))","def test_required_city(self):\r\n self.url_params['city'] = 'New York'\r\n response = self.client.post(self.url, self.url_params)\r\n self.assertEqual(response.status_code, 200)\r\n obj = json.loads(response.content)\r\n self.assertTrue(obj['success'])","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def city(self, city):\n\n self._city = city","def test_name(self):\n c = City()\n self.assertTrue(hasattr(c, \"name\"))\n self.assertEqual(c.name, \"\")","def test_has_attr(self):\n self.assertTrue(hasattr(City, \"save\"))","def test_state_id(self):\n\n self.assertEqual(type(City.state_id), str)","def city_update(self):\n self.city = self.city_finder(self.location.__str__())","def test_attr(self):\n self.assertTrue(hasattr(self.amenity, \"created_at\"))\n self.assertTrue(hasattr(self.amenity, \"id\"))\n self.assertTrue(hasattr(self.amenity, \"updated_at\"))\n self.assertFalse(hasattr(self.amenity, \"random_attr\"))\n self.assertTrue(hasattr(self.amenity, \"name\"))\n self.assertEqual(self.amenity.__class__.__name__, \"Amenity\")\n self.assertEqual(self.amenity.name, \"\")","def city(self, city):\n # type: (string_types) -> None\n\n if city is not None:\n if not isinstance(city, string_types):\n raise TypeError(\"Invalid type for `city`, type has to be `string_types`\")\n\n self._city = city","def test_name(self):\n self.assertEqual(type(City.name), str)","def test_instance(self):\n self.assertIsInstance(self.my_city, City)","def put_city(city_id):\n\n if not request.get_json():\n return make_response(jsonify({\"error\": \"Not a JSON\"}), 400)\n if storage.get(\"City\", city_id) is not None:\n obj = storage.get(\"City\", city_id)\n obj_dict = obj.to_dict()\n dict1 = request.get_json()\n ignored_keys = ['id', 'created_at', 'updated_at', 'state_id']\n for key, value in obj_dict.items():\n if key in ignored_keys:\n pass\n else:\n for k, v in dict1.items():\n if key == k:\n setattr(obj, k, v)\n else:\n pass\n obj.save()\n return make_response(obj.to_dict(), 200)\n else:\n abort(404)","def test_init_without_kwargs(self):\n c = City()\n self.assertTrue('id' in c.__dict__)\n self.assertTrue('created_at' in c.__dict__)\n self.assertTrue('updated_at' in c.__dict__)\n self.assertIsInstance(c, BaseModel)","def test_city_country(self):\n formatted_city = get_full_city(\"santiago\", \"chile\")\n self.assertEqual(formatted_city, \"Santiago, Chile\")","def city(self):\n if \"city\" in self._prop_dict:\n return self._prop_dict[\"city\"]\n else:\n return None","def city(self):\n if \"city\" in self._prop_dict:\n return self._prop_dict[\"city\"]\n else:\n return None","def test_subclass(self):\n self.assertIsInstance(self.city, BaseModel)\n self.assertTrue(hasattr(self.city, \"id\"))\n self.assertTrue(hasattr(self.city, \"created_at\"))\n self.assertTrue(hasattr(self.city, \"updated_at\"))","def test_required_city_missing(self):\r\n self.url_params['city'] = ''\r\n response = self.client.post(self.url, self.url_params)\r\n self.assertEqual(response.status_code, 400)\r\n obj = json.loads(response.content)\r\n self.assertEqual(\r\n obj['value'],\r\n u'A city is required',\r\n )","def test_city_country(self):\n santiago_chile = get_city_name('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def __init__(self):\n self._zipcode = None\n self._city = None","def test_init(self, fixture_environment):\n\n # Generate city object\n city_object = cit.City(environment=fixture_environment)\n\n # Check inheritance from citydistrict object of pycity\n assert city_object._kind == 'citydistrict'","def type_city(self, city):\n\n\t\twith allure.step(\"Type payee city\"):\n\t\t\telement = Element(driver=self.driver,\n\t\t\t explicit_wait_time=self.explicit_wait_time,\n\t\t\t locator=BillPayPageLocator.CITY_INPUT)\n\t\t\telement.write(city)\n\t\t\treturn None","def test_city_country(self):\n\t\tformatted_address = city_country('santiago', 'chile')\n\t\tself.assertEqual(formatted_address, 'Santiago, Chile')","def test_Amenity_attributes(self):\n new_amenity = Amenity()\n self.assertTrue(hasattr(new_amenity, \"name\"))\n self.assertEqual(new_amenity.name, \"\")","def __init__(self, city, state, country):\n self.city = city\n self.state = state\n self.country = country","def setUpClass(cls):\n cls.city = City()","def check_attributes(self):\n self.assertEqual(type(self.amenity_1.name), str)","def test_empty_string(self):\n self.assertEqual(self.place.city_id, \"\")\n self.assertEqual(self.place.user_id, \"\")\n self.assertEqual(self.place.name, \"\")","def test_city_country(self):\n dublin_ireland = city_country('dublin', 'ireland')\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')","def is_default_city_name(self):\n if len(self.text_city.text) == 0 or self.text_city.text == \"Please enter city\":\n return True\n return False","def test_update(self):\n c = city.City(name=\"Paris\")\n p1 = city.Citizen(name=\"Peter\")\n c.add(p1, rel=city.hasInhabitant)\n\n with DataspaceSession(URI) as session:\n wrapper = city.CityWrapper(session=session)\n cw = wrapper.add(c)\n session.commit()\n\n p2 = city.Citizen(name=\"Georg\")\n cw.add(p2, rel=city.hasInhabitant)\n cw.name = \"Freiburg\"\n session.commit()\n\n check_state(self, c, p1, p2, db=DB)","def test_correct_city(self, ip_address, city_correct):\n city, country = get_geo(ip_address=ip_address)\n self.assertEqual(city, city_correct)","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def edit_city(g, city_name, option, value):\n city_code = g.convert[city_name]\n \n if(option == \"country\"):\n g.city_dict[city_code].set_country(value)\n \n if(option == \"continent\"):\n g.city_dict[city_code].set_continent(value)\n \n if(option == \"timezone\"):\n g.city_dict[city_code].set_timezone(int(value)) \n \n if(option == \"coordinates\"):\n g.city_dict[city_code].set_coordinates(value) \n \n if(option == \"population\"):\n g.city_dict[city_code].set_population(int(value))\n \n if(option == \"region\"):\n g.city_dict[city_code].set_region(int(value))\n \n return g","def __config_attributes(self):\n self.__name = self.__data[self.__code][\"airportName\"]\n self.__country = Country(name=self.__data[self.__code][\"countryName\"],\n code=self.__data[self.__code][\"countryCode\"])\n try:\n self.__city = self.__data[self.__code][\"city\"]\n except Exception:\n self.__city = ''","def update_city(city_id):\n obj = models.storage.get(\"City\", city_id)\n json = request.get_json()\n if obj is not None:\n if json is not None:\n for key, value in json.items():\n if key not in [\"id\", \"updated_at\", \"created_at\",\n \"state_id\"]:\n setattr(obj, key, value)\n obj.save()\n return jsonify(obj.to_dict())\n else:\n abort(400, \"Not a JSON\")\n else:\n abort(404)","def expected_city_names_fixture():\n return {'b', 'a', 'c'}","def test_location(self):\n self.assertEqual(self.show.country, None)\n self.assertEqual(self.show.state, None)\n self.assertEqual(self.show.country, None)","def birth_city(self, birth_city):\n\n self._birth_city = birth_city","def payee_city(self, payee_city):\n\n self._payee_city = payee_city","def update_city(city_id):\n obj = storage.get('State', city_id)\n if obj is None:\n abort(400)\n r = request.get_json()\n if r is None:\n abort(400, 'Not a JSON')\n for k, v in r.items():\n if k not in['id', 'created_at', 'updated_at']:\n setattr(obj, k, v)\n storage.save()\n return jsonify(obj.to_dict())","def test_country(self):\n self.assertIsInstance(self.address.country, str)\n self.assertEqual(self.address.country, \"France\")","def city_put(city_id):\n city_json = request.get_json(silent=True)\n if city_json is None:\n abort(400, 'Not a JSON')\n fetched_obj = storage.get(\"City\", str(city_id))\n if fetched_obj is None:\n abort(404)\n for key, val in city_json.items():\n if key not in [\"id\", \"created_at\", \"updated_at\", \"state_id\"]:\n setattr(fetched_obj, key, val)\n fetched_obj.save()\n return jsonify(fetched_obj.to_json())","def audit(dict_city, etelem): \n if etelem.tag == \"node\" or etelem.tag == \"way\":\n for tag in etelem.iter(\"tag\"): \n if tag.attrib['k'] == u\"addr:city\":\n audit_city(dict_city, tag.attrib['v'])\n \n return dict_city","def city_update(city_id):\n found = storage.get(City, city_id)\n if not found:\n abort(404)\n else:\n if not request.get_json():\n return jsonify({\"error\": \"Not a JSON\"}), 400\n invalid_keys = [\"id\", \"created at\", \"updated_at\", \"state_id\"]\n for key, value in request.get_json().items():\n key in invalid_keys or setattr(found, key, value)\n found.save()\n return jsonify(found.to_dict()), 200","def test_verify_attrs_customer(self):\n self.assertEqual(self.customer.name, 'Customer Test')\n self.assertEqual(self.customer.document, '000.000.000-00')","def test_legal_address_is_complete(empty_field):\n address = LegalAddressFactory.create(country=\"US\")\n if empty_field:\n setattr(address, empty_field, \"\")\n assert address.is_complete is False\n else:\n assert address.is_complete is True","def test_attribute(self):\n r1 = Review()\n self.assertEqual(r1.place_id, \"\")","def test_wifi_attribute(self):\n self.amenity.name = \"greatWifi\"\n if hasattr(self.amenity, 'name'):\n actual = self.amenity.name\n else:\n actual = ''\n expected = \"greatWifi\"\n self.assertEqual(expected, actual)","def get_city(self, territory_id: str = \"\"):","def get_city(self, territory_id: str = \"\"):","def test_update_city(self):\n country = CountryFactory()\n city = CityFactory()\n u = UserFactory(role=User.MODERATOR)\n u.set_password('123')\n u.save()\n log_n = LogEntry.objects.count()\n\n auth_url = prepare_url('login')\n data = {\n 'username': u.username,\n 'password': '123'\n }\n response = self.client.post(auth_url, data=data, format='json')\n token = response.data['token']\n\n url = prepare_url('admin-cities-detail', kwargs={'id': str(city.id)})\n data = {\n 'name': 'NewCity name',\n 'country_id': str(country.id),\n 'is_active': False\n }\n n = City.objects.count()\n\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\n response = self.client.patch(url, data=data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(City.objects.count(), n)\n self.assertEqual(response.data['name'], 'NewCity name')\n self.assertEqual(response.data['is_active'], False)\n self.assertEqual(response.data['country_name'], country.name)\n self.assertEqual(LogEntry.objects.count(), log_n+1)","def get_cities(self, city_name: str = \"\"):","def cities(self):\n objs = models.storage.all()\n tmp = []\n for key, value in objs.items():\n name = key.split('.')\n if name[0] == \"City\":\n if value.state_id == str(self.id):\n tmp.append(objs[key])\n return tmp","def test_location(self):\n self.assertEqual(self.show.country, 'USA')\n self.assertEqual(self.show.state, 'VA')\n self.assertEqual(self.show.city, 'Hampton')","def valid_cities(self):\n return random.choice(VALID_CITIES)","def clean(self):\n if self.name is None or self.address is None or self.city_name is None or self.post_code is None:\n raise ValidationError","def get_cities(self, city_name: str = None):","def test_city_country(self):\n formatted_name = city_country('santiago', 'chile')\n self.assertEqual(formatted_name, 'Santiago, Chile')","def test_city_country(self):\n formatted_name = make_formatted_name('santiago', 'chile')\n self.assertEqual(formatted_name, 'Santiago, Chile')","def clean_city(self):\n return self.cleaned_data['city'].strip().title()","def test_inheritence(self):\n self.assertTrue(issubclass(City, BaseModel))","def check_city_response(self, response, city_name):\n body = json_decode(response.body)\n self.assertEqual(type(body), dict)\n self.assertIsNotNone(body.get('city_name'))\n self.assertIsNotNone(body.get('current_temperature'))\n self.assertIsNotNone(body.get('current_weather_description'))\n self.assertIsNotNone(body.get('population'))\n self.assertIsNotNone(body.get('bars'))\n self.assertIsNotNone(body.get('city_score'))\n\n self.assertEqual(body.get('city_name'), city_name)\n\n self.assertIsInstance(body.get('current_temperature'), numbers.Number, \"The current temperature is not numeric\")\n self.assertIsInstance(body.get('current_weather_description'), str, \"The weather description is not a string\")\n self.assertIsInstance(body.get('population'), int, \"The population is not an integer\")\n self.assertIsInstance(body.get('bars'), int, \"The number of bars is not an integer\")\n self.assertIsInstance(body.get('city_score'), numbers.Number, \"The city score is not a number\")","def test_is_subclass(self):\n c = City()\n self.assertIsInstance(c, BaseModel)\n self.assertTrue(hasattr(c, \"id\"))\n self.assertTrue(hasattr(c, \"created_at\"))\n self.assertTrue(hasattr(c, \"updated_at\"))","def test_create_hotel(self):\n amsterdam = City.objects.get(name=\"Amsterdam\")\n ibis = Hotel.objects.get(name=\"Ibis\")\n\n self.assertEqual(ibis.city, amsterdam)\n self.assertEqual(ibis.code, \"AMS01\")\n self.assertEqual(ibis.name, \"Ibis\")","def test_clean_country_flag(self):\n # country_flag = self.cleaned_data.get('country_flag', None)\n # field = self.fields.get(self.country_field_name, None)\n # if not field and hasattr(self, 'computed_fields'):\n # field = self.computed_fields.get(self.country_field_name, None)\n # if field.initial == self.cleaned_data.get(self.country_field_name, None)\n pass","def test_attr_cls(self):\n self.assertEqual(hasattr(self.review, \"text\"), True)\n self.assertEqual(hasattr(self.review, \"place_id\"), True)\n self.assertEqual(hasattr(self.review, \"user_id\"), True)","def __init__(self, model_city):\n\n assert isinstance(\n model_city, city_settings.ModelCity), 'ModelCity expected'\n self.model_city = model_city\n\n self._raw_data = data_io.fetch_service_units(\n self.servicetype, self.model_city)","def test_set_attribute_with_response_returning_place():\n response_ok = {\n 'candidates': [{\n 'formatted_address': '92340 Bourg-la-Reine, France',\n 'name': 'Bourg-la-Reine',\n 'place_id': 'ChIJBY5REypx5kcRgD6LaMOCCwQ'}],\n 'status': 'OK'}\n place = Place()\n place.response = response_ok\n place.set_attribute()\n assert place.status is True\n assert place.place_id == \"ChIJBY5REypx5kcRgD6LaMOCCwQ\"\n assert place.name == \"Bourg-la-Reine\"\n assert place.address == \"92340 Bourg-la-Reine, France\"","def _setAttributes(self, primaryAttr, attrs):\n return False","def cities(self):\n return [value for value in models.storage.all(City).values()\n if value.state_id == self.id]","def put_update_city(city_id):\n city = storage.get(\"City\", city_id)\n if city is None:\n abort(404)\n kwargs = request.get_json()\n if kwargs is None:\n return ('Not a JSON', 400)\n for k, v in kwargs.items():\n setattr(city, k, v)\n city.save()\n return (jsonify(city.to_json()), 200)"],"string":"[\n \"def test_attributes(self):\\n self.assertTrue(hasattr(self.city, 'name'))\\n self.assertTrue(hasattr(self.city, 'state_id'))\",\n \"def test_city(self):\\n c = City()\\n self.assertEqual(c.name, \\\"\\\")\\n self.assertEqual(c.state_id, \\\"\\\")\\n c.name = \\\"San Francisco\\\"\\n c.state_id = \\\"98\\\"\\n self.assertEqual(c.name, \\\"San Francisco\\\")\\n self.assertEqual(c.state_id, \\\"98\\\")\\n self.assertEqual(type(c.state_id), str)\",\n \"def test_city(self):\\n my_city = City()\\n self.assertTrue(hasattr(mycity, \\\"name\\\"))\\n self.assertEqual(my_city.name, \\\"\\\")\\n self.assertTrue(hasattr(mycity, \\\"state_id\\\"))\\n self.assertEqual(my_city.state_id, \\\"\\\")\",\n \"def test_attr_type(self):\\n self.my_city.state_id = \\\"1c5dd90a-a3df-4516-b1ac-32a8715e5539\\\"\\n self.my_city.name = \\\"New York\\\"\\n self.assertIsInstance(self.my_city.name, str)\\n self.assertIsInstance(self.my_city.state_id, str)\",\n \"def test_set_attr(self):\\n self.my_city.name = \\\"Denver\\\"\\n self.assertEqual(self.my_city.name, \\\"Denver\\\")\",\n \"def test_attrs(self):\\n city = City()\\n self.assertEqual(city.name, \\\"\\\")\\n self.assertEqual(City.name, \\\"\\\")\\n self.assertEqual(city.state_id, \\\"\\\")\\n self.assertEqual(City.state_id, \\\"\\\")\\n self.assertIn(\\\"id\\\", city.__dict__)\\n self.assertIn(\\\"created_at\\\", city.to_dict())\\n self.assertIn(\\\"updated_at\\\", city.to_dict())\",\n \"def test_type_of_attributes(self):\\n self.assertIsInstance(self.city.name, str)\\n self.assertIsInstance(self.city.state_id, str)\",\n \"def test_city(self):\\n self.assertIsInstance(self.address.city, str)\\n self.assertEqual(self.address.city, \\\"Paris\\\")\",\n \"def testCityId(self):\\n place = Place()\\n self.assertTrue(hasattr(place, \\\"city_id\\\"))\\n self.assertEqual(place.city_id, \\\"\\\")\",\n \"def test_city_id(self):\\n place = Place()\\n self.assertTrue(hasattr(place, \\\"city_id\\\"))\\n self.assertEqual(type(place.city_id), str)\\n self.assertEqual(place.city_id, \\\"\\\")\",\n \"def test_class_attributes(self):\\n self.assertTrue('__tablename__' in City.__dict__)\\n self.assertTrue('name' in City.__dict__)\\n self.assertTrue('state_id' in City.__dict__)\\n self.assertTrue('places' in City.__dict__)\",\n \"def test_state_id(self):\\n c = City()\\n self.assertTrue(hasattr(c, \\\"state_id\\\"))\\n self.assertEqual(c.state_id, \\\"\\\")\",\n \"def set_City(self, value):\\n super(AddressValidationInputSet, self)._set_input('City', value)\",\n \"def test_city(self):\\n my_city = City()\\n my_city.name = \\\"Medellin\\\"\\n self.assertEqual(my_city.name, 'Medellin')\",\n \"def city(self, city):\\n self._city = city\",\n \"def test_addr_city_good_values(self):\\n for input_val, output_val in self.known_values:\\n self.line._parse_addr_city(input_val)\\n self.assertEqual(output_val, self.line.addr_city)\",\n \"def setUp(self):\\n self.my_city = City()\",\n \"def __init__(self, city):\\r\\n self.city = city\",\n \"def test_has_attr(self):\\n\\n self.assertTrue(hasattr(City, \\\"save\\\"))\",\n \"def test_required_city(self):\\r\\n self.url_params['city'] = 'New York'\\r\\n response = self.client.post(self.url, self.url_params)\\r\\n self.assertEqual(response.status_code, 200)\\r\\n obj = json.loads(response.content)\\r\\n self.assertTrue(obj['success'])\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def city(self, city):\\n\\n self._city = city\",\n \"def test_name(self):\\n c = City()\\n self.assertTrue(hasattr(c, \\\"name\\\"))\\n self.assertEqual(c.name, \\\"\\\")\",\n \"def test_has_attr(self):\\n self.assertTrue(hasattr(City, \\\"save\\\"))\",\n \"def test_state_id(self):\\n\\n self.assertEqual(type(City.state_id), str)\",\n \"def city_update(self):\\n self.city = self.city_finder(self.location.__str__())\",\n \"def test_attr(self):\\n self.assertTrue(hasattr(self.amenity, \\\"created_at\\\"))\\n self.assertTrue(hasattr(self.amenity, \\\"id\\\"))\\n self.assertTrue(hasattr(self.amenity, \\\"updated_at\\\"))\\n self.assertFalse(hasattr(self.amenity, \\\"random_attr\\\"))\\n self.assertTrue(hasattr(self.amenity, \\\"name\\\"))\\n self.assertEqual(self.amenity.__class__.__name__, \\\"Amenity\\\")\\n self.assertEqual(self.amenity.name, \\\"\\\")\",\n \"def city(self, city):\\n # type: (string_types) -> None\\n\\n if city is not None:\\n if not isinstance(city, string_types):\\n raise TypeError(\\\"Invalid type for `city`, type has to be `string_types`\\\")\\n\\n self._city = city\",\n \"def test_name(self):\\n self.assertEqual(type(City.name), str)\",\n \"def test_instance(self):\\n self.assertIsInstance(self.my_city, City)\",\n \"def put_city(city_id):\\n\\n if not request.get_json():\\n return make_response(jsonify({\\\"error\\\": \\\"Not a JSON\\\"}), 400)\\n if storage.get(\\\"City\\\", city_id) is not None:\\n obj = storage.get(\\\"City\\\", city_id)\\n obj_dict = obj.to_dict()\\n dict1 = request.get_json()\\n ignored_keys = ['id', 'created_at', 'updated_at', 'state_id']\\n for key, value in obj_dict.items():\\n if key in ignored_keys:\\n pass\\n else:\\n for k, v in dict1.items():\\n if key == k:\\n setattr(obj, k, v)\\n else:\\n pass\\n obj.save()\\n return make_response(obj.to_dict(), 200)\\n else:\\n abort(404)\",\n \"def test_init_without_kwargs(self):\\n c = City()\\n self.assertTrue('id' in c.__dict__)\\n self.assertTrue('created_at' in c.__dict__)\\n self.assertTrue('updated_at' in c.__dict__)\\n self.assertIsInstance(c, BaseModel)\",\n \"def test_city_country(self):\\n formatted_city = get_full_city(\\\"santiago\\\", \\\"chile\\\")\\n self.assertEqual(formatted_city, \\\"Santiago, Chile\\\")\",\n \"def city(self):\\n if \\\"city\\\" in self._prop_dict:\\n return self._prop_dict[\\\"city\\\"]\\n else:\\n return None\",\n \"def city(self):\\n if \\\"city\\\" in self._prop_dict:\\n return self._prop_dict[\\\"city\\\"]\\n else:\\n return None\",\n \"def test_subclass(self):\\n self.assertIsInstance(self.city, BaseModel)\\n self.assertTrue(hasattr(self.city, \\\"id\\\"))\\n self.assertTrue(hasattr(self.city, \\\"created_at\\\"))\\n self.assertTrue(hasattr(self.city, \\\"updated_at\\\"))\",\n \"def test_required_city_missing(self):\\r\\n self.url_params['city'] = ''\\r\\n response = self.client.post(self.url, self.url_params)\\r\\n self.assertEqual(response.status_code, 400)\\r\\n obj = json.loads(response.content)\\r\\n self.assertEqual(\\r\\n obj['value'],\\r\\n u'A city is required',\\r\\n )\",\n \"def test_city_country(self):\\n santiago_chile = get_city_name('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def __init__(self):\\n self._zipcode = None\\n self._city = None\",\n \"def test_init(self, fixture_environment):\\n\\n # Generate city object\\n city_object = cit.City(environment=fixture_environment)\\n\\n # Check inheritance from citydistrict object of pycity\\n assert city_object._kind == 'citydistrict'\",\n \"def type_city(self, city):\\n\\n\\t\\twith allure.step(\\\"Type payee city\\\"):\\n\\t\\t\\telement = Element(driver=self.driver,\\n\\t\\t\\t explicit_wait_time=self.explicit_wait_time,\\n\\t\\t\\t locator=BillPayPageLocator.CITY_INPUT)\\n\\t\\t\\telement.write(city)\\n\\t\\t\\treturn None\",\n \"def test_city_country(self):\\n\\t\\tformatted_address = city_country('santiago', 'chile')\\n\\t\\tself.assertEqual(formatted_address, 'Santiago, Chile')\",\n \"def test_Amenity_attributes(self):\\n new_amenity = Amenity()\\n self.assertTrue(hasattr(new_amenity, \\\"name\\\"))\\n self.assertEqual(new_amenity.name, \\\"\\\")\",\n \"def __init__(self, city, state, country):\\n self.city = city\\n self.state = state\\n self.country = country\",\n \"def setUpClass(cls):\\n cls.city = City()\",\n \"def check_attributes(self):\\n self.assertEqual(type(self.amenity_1.name), str)\",\n \"def test_empty_string(self):\\n self.assertEqual(self.place.city_id, \\\"\\\")\\n self.assertEqual(self.place.user_id, \\\"\\\")\\n self.assertEqual(self.place.name, \\\"\\\")\",\n \"def test_city_country(self):\\n dublin_ireland = city_country('dublin', 'ireland')\\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')\",\n \"def is_default_city_name(self):\\n if len(self.text_city.text) == 0 or self.text_city.text == \\\"Please enter city\\\":\\n return True\\n return False\",\n \"def test_update(self):\\n c = city.City(name=\\\"Paris\\\")\\n p1 = city.Citizen(name=\\\"Peter\\\")\\n c.add(p1, rel=city.hasInhabitant)\\n\\n with DataspaceSession(URI) as session:\\n wrapper = city.CityWrapper(session=session)\\n cw = wrapper.add(c)\\n session.commit()\\n\\n p2 = city.Citizen(name=\\\"Georg\\\")\\n cw.add(p2, rel=city.hasInhabitant)\\n cw.name = \\\"Freiburg\\\"\\n session.commit()\\n\\n check_state(self, c, p1, p2, db=DB)\",\n \"def test_correct_city(self, ip_address, city_correct):\\n city, country = get_geo(ip_address=ip_address)\\n self.assertEqual(city, city_correct)\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def edit_city(g, city_name, option, value):\\n city_code = g.convert[city_name]\\n \\n if(option == \\\"country\\\"):\\n g.city_dict[city_code].set_country(value)\\n \\n if(option == \\\"continent\\\"):\\n g.city_dict[city_code].set_continent(value)\\n \\n if(option == \\\"timezone\\\"):\\n g.city_dict[city_code].set_timezone(int(value)) \\n \\n if(option == \\\"coordinates\\\"):\\n g.city_dict[city_code].set_coordinates(value) \\n \\n if(option == \\\"population\\\"):\\n g.city_dict[city_code].set_population(int(value))\\n \\n if(option == \\\"region\\\"):\\n g.city_dict[city_code].set_region(int(value))\\n \\n return g\",\n \"def __config_attributes(self):\\n self.__name = self.__data[self.__code][\\\"airportName\\\"]\\n self.__country = Country(name=self.__data[self.__code][\\\"countryName\\\"],\\n code=self.__data[self.__code][\\\"countryCode\\\"])\\n try:\\n self.__city = self.__data[self.__code][\\\"city\\\"]\\n except Exception:\\n self.__city = ''\",\n \"def update_city(city_id):\\n obj = models.storage.get(\\\"City\\\", city_id)\\n json = request.get_json()\\n if obj is not None:\\n if json is not None:\\n for key, value in json.items():\\n if key not in [\\\"id\\\", \\\"updated_at\\\", \\\"created_at\\\",\\n \\\"state_id\\\"]:\\n setattr(obj, key, value)\\n obj.save()\\n return jsonify(obj.to_dict())\\n else:\\n abort(400, \\\"Not a JSON\\\")\\n else:\\n abort(404)\",\n \"def expected_city_names_fixture():\\n return {'b', 'a', 'c'}\",\n \"def test_location(self):\\n self.assertEqual(self.show.country, None)\\n self.assertEqual(self.show.state, None)\\n self.assertEqual(self.show.country, None)\",\n \"def birth_city(self, birth_city):\\n\\n self._birth_city = birth_city\",\n \"def payee_city(self, payee_city):\\n\\n self._payee_city = payee_city\",\n \"def update_city(city_id):\\n obj = storage.get('State', city_id)\\n if obj is None:\\n abort(400)\\n r = request.get_json()\\n if r is None:\\n abort(400, 'Not a JSON')\\n for k, v in r.items():\\n if k not in['id', 'created_at', 'updated_at']:\\n setattr(obj, k, v)\\n storage.save()\\n return jsonify(obj.to_dict())\",\n \"def test_country(self):\\n self.assertIsInstance(self.address.country, str)\\n self.assertEqual(self.address.country, \\\"France\\\")\",\n \"def city_put(city_id):\\n city_json = request.get_json(silent=True)\\n if city_json is None:\\n abort(400, 'Not a JSON')\\n fetched_obj = storage.get(\\\"City\\\", str(city_id))\\n if fetched_obj is None:\\n abort(404)\\n for key, val in city_json.items():\\n if key not in [\\\"id\\\", \\\"created_at\\\", \\\"updated_at\\\", \\\"state_id\\\"]:\\n setattr(fetched_obj, key, val)\\n fetched_obj.save()\\n return jsonify(fetched_obj.to_json())\",\n \"def audit(dict_city, etelem): \\n if etelem.tag == \\\"node\\\" or etelem.tag == \\\"way\\\":\\n for tag in etelem.iter(\\\"tag\\\"): \\n if tag.attrib['k'] == u\\\"addr:city\\\":\\n audit_city(dict_city, tag.attrib['v'])\\n \\n return dict_city\",\n \"def city_update(city_id):\\n found = storage.get(City, city_id)\\n if not found:\\n abort(404)\\n else:\\n if not request.get_json():\\n return jsonify({\\\"error\\\": \\\"Not a JSON\\\"}), 400\\n invalid_keys = [\\\"id\\\", \\\"created at\\\", \\\"updated_at\\\", \\\"state_id\\\"]\\n for key, value in request.get_json().items():\\n key in invalid_keys or setattr(found, key, value)\\n found.save()\\n return jsonify(found.to_dict()), 200\",\n \"def test_verify_attrs_customer(self):\\n self.assertEqual(self.customer.name, 'Customer Test')\\n self.assertEqual(self.customer.document, '000.000.000-00')\",\n \"def test_legal_address_is_complete(empty_field):\\n address = LegalAddressFactory.create(country=\\\"US\\\")\\n if empty_field:\\n setattr(address, empty_field, \\\"\\\")\\n assert address.is_complete is False\\n else:\\n assert address.is_complete is True\",\n \"def test_attribute(self):\\n r1 = Review()\\n self.assertEqual(r1.place_id, \\\"\\\")\",\n \"def test_wifi_attribute(self):\\n self.amenity.name = \\\"greatWifi\\\"\\n if hasattr(self.amenity, 'name'):\\n actual = self.amenity.name\\n else:\\n actual = ''\\n expected = \\\"greatWifi\\\"\\n self.assertEqual(expected, actual)\",\n \"def get_city(self, territory_id: str = \\\"\\\"):\",\n \"def get_city(self, territory_id: str = \\\"\\\"):\",\n \"def test_update_city(self):\\n country = CountryFactory()\\n city = CityFactory()\\n u = UserFactory(role=User.MODERATOR)\\n u.set_password('123')\\n u.save()\\n log_n = LogEntry.objects.count()\\n\\n auth_url = prepare_url('login')\\n data = {\\n 'username': u.username,\\n 'password': '123'\\n }\\n response = self.client.post(auth_url, data=data, format='json')\\n token = response.data['token']\\n\\n url = prepare_url('admin-cities-detail', kwargs={'id': str(city.id)})\\n data = {\\n 'name': 'NewCity name',\\n 'country_id': str(country.id),\\n 'is_active': False\\n }\\n n = City.objects.count()\\n\\n self.client.credentials(HTTP_AUTHORIZATION='{} {}'.format(api_settings.JWT_AUTH_HEADER_PREFIX, token))\\n response = self.client.patch(url, data=data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(City.objects.count(), n)\\n self.assertEqual(response.data['name'], 'NewCity name')\\n self.assertEqual(response.data['is_active'], False)\\n self.assertEqual(response.data['country_name'], country.name)\\n self.assertEqual(LogEntry.objects.count(), log_n+1)\",\n \"def get_cities(self, city_name: str = \\\"\\\"):\",\n \"def cities(self):\\n objs = models.storage.all()\\n tmp = []\\n for key, value in objs.items():\\n name = key.split('.')\\n if name[0] == \\\"City\\\":\\n if value.state_id == str(self.id):\\n tmp.append(objs[key])\\n return tmp\",\n \"def test_location(self):\\n self.assertEqual(self.show.country, 'USA')\\n self.assertEqual(self.show.state, 'VA')\\n self.assertEqual(self.show.city, 'Hampton')\",\n \"def valid_cities(self):\\n return random.choice(VALID_CITIES)\",\n \"def clean(self):\\n if self.name is None or self.address is None or self.city_name is None or self.post_code is None:\\n raise ValidationError\",\n \"def get_cities(self, city_name: str = None):\",\n \"def test_city_country(self):\\n formatted_name = city_country('santiago', 'chile')\\n self.assertEqual(formatted_name, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n formatted_name = make_formatted_name('santiago', 'chile')\\n self.assertEqual(formatted_name, 'Santiago, Chile')\",\n \"def clean_city(self):\\n return self.cleaned_data['city'].strip().title()\",\n \"def test_inheritence(self):\\n self.assertTrue(issubclass(City, BaseModel))\",\n \"def check_city_response(self, response, city_name):\\n body = json_decode(response.body)\\n self.assertEqual(type(body), dict)\\n self.assertIsNotNone(body.get('city_name'))\\n self.assertIsNotNone(body.get('current_temperature'))\\n self.assertIsNotNone(body.get('current_weather_description'))\\n self.assertIsNotNone(body.get('population'))\\n self.assertIsNotNone(body.get('bars'))\\n self.assertIsNotNone(body.get('city_score'))\\n\\n self.assertEqual(body.get('city_name'), city_name)\\n\\n self.assertIsInstance(body.get('current_temperature'), numbers.Number, \\\"The current temperature is not numeric\\\")\\n self.assertIsInstance(body.get('current_weather_description'), str, \\\"The weather description is not a string\\\")\\n self.assertIsInstance(body.get('population'), int, \\\"The population is not an integer\\\")\\n self.assertIsInstance(body.get('bars'), int, \\\"The number of bars is not an integer\\\")\\n self.assertIsInstance(body.get('city_score'), numbers.Number, \\\"The city score is not a number\\\")\",\n \"def test_is_subclass(self):\\n c = City()\\n self.assertIsInstance(c, BaseModel)\\n self.assertTrue(hasattr(c, \\\"id\\\"))\\n self.assertTrue(hasattr(c, \\\"created_at\\\"))\\n self.assertTrue(hasattr(c, \\\"updated_at\\\"))\",\n \"def test_create_hotel(self):\\n amsterdam = City.objects.get(name=\\\"Amsterdam\\\")\\n ibis = Hotel.objects.get(name=\\\"Ibis\\\")\\n\\n self.assertEqual(ibis.city, amsterdam)\\n self.assertEqual(ibis.code, \\\"AMS01\\\")\\n self.assertEqual(ibis.name, \\\"Ibis\\\")\",\n \"def test_clean_country_flag(self):\\n # country_flag = self.cleaned_data.get('country_flag', None)\\n # field = self.fields.get(self.country_field_name, None)\\n # if not field and hasattr(self, 'computed_fields'):\\n # field = self.computed_fields.get(self.country_field_name, None)\\n # if field.initial == self.cleaned_data.get(self.country_field_name, None)\\n pass\",\n \"def test_attr_cls(self):\\n self.assertEqual(hasattr(self.review, \\\"text\\\"), True)\\n self.assertEqual(hasattr(self.review, \\\"place_id\\\"), True)\\n self.assertEqual(hasattr(self.review, \\\"user_id\\\"), True)\",\n \"def __init__(self, model_city):\\n\\n assert isinstance(\\n model_city, city_settings.ModelCity), 'ModelCity expected'\\n self.model_city = model_city\\n\\n self._raw_data = data_io.fetch_service_units(\\n self.servicetype, self.model_city)\",\n \"def test_set_attribute_with_response_returning_place():\\n response_ok = {\\n 'candidates': [{\\n 'formatted_address': '92340 Bourg-la-Reine, France',\\n 'name': 'Bourg-la-Reine',\\n 'place_id': 'ChIJBY5REypx5kcRgD6LaMOCCwQ'}],\\n 'status': 'OK'}\\n place = Place()\\n place.response = response_ok\\n place.set_attribute()\\n assert place.status is True\\n assert place.place_id == \\\"ChIJBY5REypx5kcRgD6LaMOCCwQ\\\"\\n assert place.name == \\\"Bourg-la-Reine\\\"\\n assert place.address == \\\"92340 Bourg-la-Reine, France\\\"\",\n \"def _setAttributes(self, primaryAttr, attrs):\\n return False\",\n \"def cities(self):\\n return [value for value in models.storage.all(City).values()\\n if value.state_id == self.id]\",\n \"def put_update_city(city_id):\\n city = storage.get(\\\"City\\\", city_id)\\n if city is None:\\n abort(404)\\n kwargs = request.get_json()\\n if kwargs is None:\\n return ('Not a JSON', 400)\\n for k, v in kwargs.items():\\n setattr(city, k, v)\\n city.save()\\n return (jsonify(city.to_json()), 200)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77947956","0.74044555","0.7307759","0.7248982","0.71909946","0.71890724","0.69839954","0.68954945","0.6838572","0.6797316","0.6596423","0.6592114","0.6484486","0.64782625","0.6418063","0.6380564","0.6375229","0.62248665","0.62208533","0.61856556","0.6180521","0.6180521","0.6180521","0.6180521","0.6180521","0.6180521","0.6180521","0.6180521","0.6180521","0.6173024","0.61452633","0.6141266","0.60512924","0.60214883","0.5964088","0.5963383","0.5839591","0.5833351","0.5827994","0.58274883","0.5817441","0.5817441","0.58103645","0.5794101","0.57869166","0.57739013","0.57673246","0.5747933","0.5730551","0.5717125","0.5705337","0.5699513","0.5677929","0.5642424","0.5641653","0.5635417","0.5631091","0.5622495","0.55850124","0.55850124","0.55850124","0.55714226","0.55628115","0.5561235","0.55582726","0.55536705","0.55529684","0.5552579","0.5548653","0.5541518","0.5522818","0.54839486","0.5474007","0.54513955","0.544609","0.5441247","0.54179937","0.541734","0.541734","0.54100394","0.5392265","0.5387991","0.5376532","0.53761303","0.5370508","0.53665936","0.53494084","0.5347045","0.5307565","0.5307067","0.5300727","0.5295968","0.5286526","0.5268983","0.5268865","0.52606314","0.52570176","0.5252363","0.5235192","0.5230894"],"string":"[\n \"0.77947956\",\n \"0.74044555\",\n \"0.7307759\",\n \"0.7248982\",\n \"0.71909946\",\n \"0.71890724\",\n \"0.69839954\",\n \"0.68954945\",\n \"0.6838572\",\n \"0.6797316\",\n \"0.6596423\",\n \"0.6592114\",\n \"0.6484486\",\n \"0.64782625\",\n \"0.6418063\",\n \"0.6380564\",\n \"0.6375229\",\n \"0.62248665\",\n \"0.62208533\",\n \"0.61856556\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6180521\",\n \"0.6173024\",\n \"0.61452633\",\n \"0.6141266\",\n \"0.60512924\",\n \"0.60214883\",\n \"0.5964088\",\n \"0.5963383\",\n \"0.5839591\",\n \"0.5833351\",\n \"0.5827994\",\n \"0.58274883\",\n \"0.5817441\",\n \"0.5817441\",\n \"0.58103645\",\n \"0.5794101\",\n \"0.57869166\",\n \"0.57739013\",\n \"0.57673246\",\n \"0.5747933\",\n \"0.5730551\",\n \"0.5717125\",\n \"0.5705337\",\n \"0.5699513\",\n \"0.5677929\",\n \"0.5642424\",\n \"0.5641653\",\n \"0.5635417\",\n \"0.5631091\",\n \"0.5622495\",\n \"0.55850124\",\n \"0.55850124\",\n \"0.55850124\",\n \"0.55714226\",\n \"0.55628115\",\n \"0.5561235\",\n \"0.55582726\",\n \"0.55536705\",\n \"0.55529684\",\n \"0.5552579\",\n \"0.5548653\",\n \"0.5541518\",\n \"0.5522818\",\n \"0.54839486\",\n \"0.5474007\",\n \"0.54513955\",\n \"0.544609\",\n \"0.5441247\",\n \"0.54179937\",\n \"0.541734\",\n \"0.541734\",\n \"0.54100394\",\n \"0.5392265\",\n \"0.5387991\",\n \"0.5376532\",\n \"0.53761303\",\n \"0.5370508\",\n \"0.53665936\",\n \"0.53494084\",\n \"0.5347045\",\n \"0.5307565\",\n \"0.5307067\",\n \"0.5300727\",\n \"0.5295968\",\n \"0.5286526\",\n \"0.5268983\",\n \"0.5268865\",\n \"0.52606314\",\n \"0.52570176\",\n \"0.5252363\",\n \"0.5235192\",\n \"0.5230894\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79602754"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17485,"cells":{"query":{"kind":"string","value":"test the inheritance of City from BaseModel"},"document":{"kind":"string","value":"def test_inheritance(self):\n city3 = City()\n self.assertIsInstance(city3, BaseModel)\n self.assertIsInstance(city3, City)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_inheritence(self):\n self.assertTrue(issubclass(City, BaseModel))","def test_subclass(self):\n self.assertIsInstance(self.city, BaseModel)\n self.assertTrue(hasattr(self.city, \"id\"))\n self.assertTrue(hasattr(self.city, \"created_at\"))\n self.assertTrue(hasattr(self.city, \"updated_at\"))","def test_is_subclass(self):\n c = City()\n self.assertIsInstance(c, BaseModel)\n self.assertTrue(hasattr(c, \"id\"))\n self.assertTrue(hasattr(c, \"created_at\"))\n self.assertTrue(hasattr(c, \"updated_at\"))","def test_subclass(self):\n self.assertTrue(issubclass(self.place.__class__, BaseModel), True)","def test_instance(self):\n self.assertIsInstance(self.my_city, City)","def test_subclass_of_BaseModel(self):\n my_amenity = Amenity()\n self.assertIsInstance(my_amenity, BaseModel)\n self.assertTrue(hasattr(my_amenity, \"id\"))\n self.assertTrue(hasattr(my_amenity, \"created_at\"))\n self.assertTrue(hasattr(my_amenity, \"updated_at\"))","def test_city(self):\n my_city = City()\n self.assertTrue(hasattr(mycity, \"name\"))\n self.assertEqual(my_city.name, \"\")\n self.assertTrue(hasattr(mycity, \"state_id\"))\n self.assertEqual(my_city.state_id, \"\")","def test_is_subclass_place(self):\n place = Place()\n self.assertIsInstance(place, BaseModel)\n self.assertTrue(hasattr(place, \"id\"))\n self.assertTrue(hasattr(place, \"created_at\"))\n self.assertTrue(hasattr(place, \"updated_at\"))","def test_init_without_kwargs(self):\n c = City()\n self.assertTrue('id' in c.__dict__)\n self.assertTrue('created_at' in c.__dict__)\n self.assertTrue('updated_at' in c.__dict__)\n self.assertIsInstance(c, BaseModel)","def test_city(self):\n self.assertIsInstance(self.address.city, str)\n self.assertEqual(self.address.city, \"Paris\")","def test_init(self, fixture_environment):\n\n # Generate city object\n city_object = cit.City(environment=fixture_environment)\n\n # Check inheritance from citydistrict object of pycity\n assert city_object._kind == 'citydistrict'","def test_class(self):\n city1 = City()\n self.assertEqual(city1.__class__.__name__, \"City\")","def test_subclass(self):\n inst = Amenity()\n self.assertIsInstance(inst, BaseModel)\n self.assertTrue(hasattr(inst, \"id\"))\n self.assertTrue(hasattr(inst, \"created_at\"))\n self.assertTrue(hasattr(inst, \"updated_at\"))","def test_city(self):\n c = City()\n self.assertEqual(c.name, \"\")\n self.assertEqual(c.state_id, \"\")\n c.name = \"San Francisco\"\n c.state_id = \"98\"\n self.assertEqual(c.name, \"San Francisco\")\n self.assertEqual(c.state_id, \"98\")\n self.assertEqual(type(c.state_id), str)","def test_type_of_attributes(self):\n self.assertIsInstance(self.city.name, str)\n self.assertIsInstance(self.city.state_id, str)","def test_name(self):\n self.assertEqual(type(City.name), str)","def test_attributes(self):\n self.assertTrue(hasattr(self.city, 'name'))\n self.assertTrue(hasattr(self.city, 'state_id'))","def test_inheritance(self):\n self.assertTrue(issubclass(type(self.user_1), BaseModel))","def test_city(self):\n my_city = City()\n my_city.name = \"Medellin\"\n self.assertEqual(my_city.name, 'Medellin')","def test_class_attributes(self):\n self.assertTrue('__tablename__' in City.__dict__)\n self.assertTrue('name' in City.__dict__)\n self.assertTrue('state_id' in City.__dict__)\n self.assertTrue('places' in City.__dict__)","def test_inherit(self):\n self.assertTrue(issubclass(User, BaseModel))","def testCityId(self):\n place = Place()\n self.assertTrue(hasattr(place, \"city_id\"))\n self.assertEqual(place.city_id, \"\")","def test_set_attrs(self):\n city2 = City()\n city2.name = \"Hawaii\"\n self.assertEqual(city2.name, \"Hawaii\")\n city2.state_id = \"<3\"\n self.assertEqual(city2.state_id, \"<3\")\n self.assertEqual(City.name, \"\")\n self.assertEqual(City.state_id, \"\")","def test_attr_type(self):\n self.my_city.state_id = \"1c5dd90a-a3df-4516-b1ac-32a8715e5539\"\n self.my_city.name = \"New York\"\n self.assertIsInstance(self.my_city.name, str)\n self.assertIsInstance(self.my_city.state_id, str)","def test_city_id(self):\n place = Place()\n self.assertTrue(hasattr(place, \"city_id\"))\n self.assertEqual(type(place.city_id), str)\n self.assertEqual(place.city_id, \"\")","def test_type(self):\n self.assertEqual(type(self.base1), BaseModel)\n self.assertEqual(type(self.base2), BaseModel)","def test_has_attr(self):\n\n self.assertTrue(hasattr(City, \"save\"))","def test_has_attr(self):\n self.assertTrue(hasattr(City, \"save\"))","def test_inherit(self):\n\n new_jawn = Amenity()\n self.assertIsInstance(new_jawn, BaseModel)","def test_save(self):\n\n base_class = BaseModel()","def test_name(self):\n c = City()\n self.assertTrue(hasattr(c, \"name\"))\n self.assertEqual(c.name, \"\")","def setUp(self):\n self.my_city = City()","def test_is_subclass(self):\n user = User()\n user_details = {\"user_id\": user.id, \"first_name\": \"Joe\"}\n student = Student(**user_details)\n self.assertIsInstance(student, BaseModel)\n self.assertTrue(hasattr(student, \"id\"))\n self.assertTrue(hasattr(student, \"created_at\"))\n self.assertTrue(hasattr(student, \"updated_at\"))\n self.assertTrue(hasattr(student, \"user_id\"))","def test_instance_BaseModel(self):\n self.assertTrue(isinstance(self.my_object, BaseModel))","def setUpClass(cls):\n cls.city = City()","def test_father(self):\n review1 = Review()\n self.assertTrue(issubclass(review1.__class__, BaseModel))","def test_attrs(self):\n city = City()\n self.assertEqual(city.name, \"\")\n self.assertEqual(City.name, \"\")\n self.assertEqual(city.state_id, \"\")\n self.assertEqual(City.state_id, \"\")\n self.assertIn(\"id\", city.__dict__)\n self.assertIn(\"created_at\", city.to_dict())\n self.assertIn(\"updated_at\", city.to_dict())","def test_state_id(self):\n c = City()\n self.assertTrue(hasattr(c, \"state_id\"))\n self.assertEqual(c.state_id, \"\")","def test_instance(self):\n self.assertIsInstance(self.test1, BaseModel)","def test_subclass(self):\n am = Review()\n self.assertIsInstance(am, BaseModel)\n self.assertTrue(hasattr(am, \"id\"))\n self.assertTrue(hasattr(am, \"created_at\"))\n self.assertTrue(hasattr(am, \"updated_at\"))\n self.assertTrue(hasattr(am, \"text\"))\n self.assertTrue(hasattr(am, \"user_id\"))\n self.assertTrue(hasattr(am, \"place_id\"))\n self.assertEqual(am.place_id, \"\")\n self.assertEqual(am.user_id, \"\")\n self.assertEqual(am.text, \"\")","def test_BaseModel_methods(self):\n self.assertTrue(hasattr(BaseModel, \"__init__\"))\n self.assertTrue(hasattr(BaseModel, \"__str__\"))\n self.assertTrue(hasattr(BaseModel, \"save\"))\n self.assertTrue(hasattr(BaseModel, \"to_dict\"))","def test_isinstance(self):\n obj = BaseModel()\n self.assertIsInstance(obj, BaseModel)","def test_noarguments(self):\n self.assertEqual(BaseModel, type(BaseModel()))","def test_father(self):\n state1 = State()\n self.assertTrue(issubclass(state1.__class__, BaseModel))","def test_state_id(self):\n\n self.assertEqual(type(City.state_id), str)","def test_after_to_dict(self):\n my_model = City()\n new_model = City()\n test_dict = my_model.to_dict()\n self.assertIsInstance(my_model, City)\n self.assertEqual(type(my_model).__name__, \"City\")\n self.assertEqual(test_dict['__class__'], \"City\")\n self.assertTrue(type(test_dict['__class__']), 'str')\n self.assertTrue(type(test_dict['created_at']), 'str')\n self.assertTrue(type(test_dict['updated_at']), 'str')\n self.assertTrue(type(test_dict['id']), 'str')\n self.assertNotEqual(my_model.id, new_model.id)","def test_issubclass(self):\n self.assertTrue(issubclass(self.rev.__class__, BaseModel), True)","def test_basemodel_basic_instance(self):\n B = BaseModel()\n self.assertIsInstance(B, BaseModel)\n self.assertEqual(BaseModel, type(BaseModel()))\n self.assertTrue(hasattr(B, \"id\"))\n self.assertTrue(hasattr(B, \"created_at\"))\n self.assertTrue(hasattr(B, \"updated_at\"))","def test_set_attr(self):\n self.my_city.name = \"Denver\"\n self.assertEqual(self.my_city.name, \"Denver\")","def test_superclass_fields_are_inherited(self):\r\n class TestModel(Model):\r\n id = columns.UUID(primary_key=True, default=lambda:uuid4())\r\n text = columns.Text()\r\n\r\n class InheritedModel(TestModel):\r\n numbers = columns.Integer()\r\n\r\n assert 'text' in InheritedModel._columns\r\n assert 'numbers' in InheritedModel._columns","def __init__(self, city):\r\n self.city = city","def test_father(self):\n user1 = User()\n self.assertTrue(issubclass(user1.__class__, BaseModel))","def setUp(self):\n self.base1 = BaseModel()","def test_issubclass(self):\n self.assertTrue(issubclass(User()), BaseModel)","def issub_class(self):\n insta = Amenity()\n self.assertIsInstance(insta, BaseModel)\n self.assertTrue(hasattr(insta, \"id\"))\n self.assertTrue(hasattr(insta, \"created_at\"))\n self.assertTrue(hasattr(insta, \"update_at\"))","def setUpClass(self):\n\n base_model = BaseModel()","def test_init_arg(self):\n b1 = BaseModel(12)\n self.assertEqual(type(b1).__name__, \"BaseModel\")\n self.assertFalse(hasattr(b1, \"12\"))","def test_type(self):\n b = BaseModel()\n self.assertIsInstance(self.my_model.id, str)\n self.assertIsInstance(self.my_model.name, str)\n self.assertIsInstance(self.my_model.my_number, int)\n self.assertEqual(type(self.my_new_model.created_at), datetime)","def test_if_exist(self):\r\n self.assertEqual(Place, type(Place()))","def test_init_with_kwargs(self):\n self.base1.name = \"Holberton\"\n model_json = self.base1.to_dict()\n new_model = BaseModel(**model_json)\n self.assertDictEqual(model_json, new_model.to_dict())\n self.assertIn(\"name\", new_model.to_dict())\n self.assertIsNot(self.base1, new_model)","def test_basemodel_attr_types(self):\n B = BaseModel()\n self.assertEqual(type(B.id), str)\n self.assertTrue(type(B.created_at) is datetime.datetime)\n self.assertTrue(type(B.updated_at) is datetime.datetime)","def test_attr(self):\n self.assertTrue(hasattr(BaseModel, \"__str__\"))\n self.assertTrue(hasattr(BaseModel, \"save\"))\n self.assertTrue(hasattr(BaseModel, \"to_dict\"))\n self.assertTrue(\"updated_at\" in self.my_model1.__dict__)\n self.assertTrue(\"created_at\" in self.my_model1.__dict__)\n self.assertTrue(\"id\" in self.my_model1.__dict__)","def test_baseclass_attrs_type(self):\n\n base_class = BaseModel()\n cn = base_class.__class__.__name__\n idn = base_class.id\n attrs = base_class.__dict__\n\n self.assertTrue(type(cn), str)\n self.assertTrue(type(id), str)\n self.assertDictEqual(attrs, base_class.__dict__)","def test_instance_Review(self):\n obj = Review()\n self.assertIsInstance(obj, BaseModel)","def test_baseid(self):\n self.assertEqual(str, type(BaseModel().id))","def test09(self):\n b_0 = Base()\n self.assertEqual(str(type(b_0)), \"<class 'models.base.Base'>\")\n self.assertEqual(b_0.__dict__, {\"id\": 1})","def test_basemodel_query(self):\n\n # GIVEN model that is derived from BaseModel (i.e., no status field present)\n model = TestModelBaseModel\n\n # WHEN retrieving model list\n items = model.objects.all()\n\n # THEN it should succeed\n self.assertEqual(len(items), 0)","def test_valid_model(self):\n model_cls = ModelContainer(APP_LABEL, TestModel2._meta.db_table).model_cls\n self.assertTrue(model_cls.__class__.__name__ is models.Model.__class__.__name__)","def test_get_base_polymorphic_model(self):\n # Finds the base from every level (including lowest)\n self.assertIs(get_base_polymorphic_model(Model2D), Model2A)\n self.assertIs(get_base_polymorphic_model(Model2C), Model2A)\n self.assertIs(get_base_polymorphic_model(Model2B), Model2A)\n self.assertIs(get_base_polymorphic_model(Model2A), Model2A)\n\n # Properly handles multiple inheritance\n self.assertIs(get_base_polymorphic_model(Enhance_Inherit), Enhance_Base)\n\n # Ignores PolymorphicModel itself.\n self.assertIs(get_base_polymorphic_model(PolymorphicModel), None)","def test_place_for_type(self):\n self.assertEqual(type(self.place.city_id), str)\n self.assertEqual(type(self.place.user_id), str)\n self.assertEqual(type(self.place.name), str)\n self.assertEqual(type(self.place.description), str)\n self.assertEqual(type(self.place.number_rooms), int)\n self.assertEqual(type(self.place.number_bathrooms), int)\n self.assertEqual(type(self.place.max_guest), int)\n self.assertEqual(type(self.place.price_by_night), int)\n self.assertEqual(type(self.place.latitude), float)\n self.assertEqual(type(self.place.longitude), float)\n self.assertEqual(type(self.place.amenity_ids), list)","def test_instance(self):\n b = Review()\n self.assertIsInstance(b, Review)\n self.assertTrue(issubclass(type(b), BaseModel))","def test_location_address(self):\n self.assertIsInstance(self.location.address, Address)\n self.assertEqual(self.location.address, self.address)","def test_country(self):\n self.assertIsInstance(self.address.country, str)\n self.assertEqual(self.address.country, \"France\")","def test_model_class(self):\n db = Alchy(self.app)\n\n self.assertEquals(\n db.Model.__dict__['__init__'], alchy.model.ModelBase.__init__)\n self.assertIsInstance(\n db.Model.__dict__['query'], alchy.query.QueryProperty)","def test_core_object_model_inheritance():\n assert issubclass(Question, CoreObject)\n assert issubclass(Fact, CoreObject)\n assert issubclass(Topic, CoreObject)\n assert issubclass(Word, CoreObject)\n assert issubclass(Book, CoreObject)","def test_create_hotel(self):\n amsterdam = City.objects.get(name=\"Amsterdam\")\n ibis = Hotel.objects.get(name=\"Ibis\")\n\n self.assertEqual(ibis.city, amsterdam)\n self.assertEqual(ibis.code, \"AMS01\")\n self.assertEqual(ibis.name, \"Ibis\")","def test_methods_exist(self):\n\n self.assertTrue(hasattr(BaseModel, \"__init__\"))\n self.assertTrue(hasattr(BaseModel, \"__str__\"))\n self.assertTrue(hasattr(BaseModel, \"save\"))\n self.assertTrue(hasattr(BaseModel, \"to_dict\"))","def test_city_country(self):\n dublin_ireland = city_country('dublin', 'ireland')\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')","def test_AmenityClass(self):\n self.assertTrue(issubclass(Amenity, BaseModel))\n self.assertIsInstance(self.Amenity, Amenity)\n self.assertIsNotNone(Amenity.__doc__)","def test_02(self):\n base0 = Base(10)\n self.assertEqual(base0.id, 10)","def test_father_kwargs(self):\n dictonary = {'id': '662a23b3-abc7-4f43-81dc-64c000000c00'}\n state1 = State(**dictonary)\n self.assertTrue(issubclass(state1.__class__, BaseModel))","def test_init_kwarg(self):\n b1 = BaseModel(name=\"Red\")\n self.assertEqual(type(b1).__name__, \"BaseModel\")\n self.assertTrue(hasattr(b1, \"name\"))\n self.assertTrue(hasattr(b1, \"__class__\"))\n self.assertFalse(hasattr(b1, \"id\"))\n self.assertFalse(hasattr(b1, \"created_at\"))\n self.assertFalse(hasattr(b1, \"updated_at\"))","def test_04(self):\n base0 = Base(None)\n self.assertEqual(base0.id, 1)","def test03(self):\n b_0 = Base(\"test\")\n self.assertEqual(b_0.id, \"test\")","def test_override_model_class(self):\n class MyModelBase(object):\n def testing(self):\n return 'testing'\n\n Model = declarative_base(cls=MyModelBase)\n\n class Foo(Model):\n __tablename__ = 'foo'\n _id = Column(types.Integer(), primary_key=True)\n name = Column(types.String())\n\n db = Alchy(self.app, Model=Model)\n\n self.assertTrue(issubclass(db.Model, MyModelBase),\n 'db.Model should be a subclass of MyModelBase')\n\n db.create_all()\n\n self.assertEquals(db.session.query(Foo).all(), Foo.query.all(),\n 'Model classes should have a query property')\n\n record = Foo(name='Name')\n\n self.assertEquals(record.testing(), 'testing')","def test_abstract_attribute_is_not_inherited(self):\r\n assert not ConcreteModel.__abstract__\r\n assert not ConcreteModelWithCol.__abstract__","def test_father_kwargs(self):\n dictonary = {'id': '662a23b3-abc7-4f43-81dc-64c000000c00'}\n review1 = Review(**dictonary)\n self.assertTrue(issubclass(review1.__class__, BaseModel))","def test_basemodel_basic_instance_none(self):\n with self.assertRaises(TypeError):\n BaseModel(None)","def test_updated_type(self):\n\n base_model = BaseModel()\n self.assertTrue(base_model.updated_at, datetime.datetime)","def test_father_kwargs(self):\n dictonary = {'id': '662a23b3-abc7-4f43-81dc-64c000000c00'}\n user1 = User(**dictonary)\n self.assertTrue(issubclass(user1.__class__, BaseModel))","def test_get(self):\n self.assertEqual(self.expected_described_model, self.mapped_model.get(\"described_model_type\"))","def test_hasattribute(self):\n b1 = BaseModel()\n self.assertTrue(hasattr(b1, \"__init__\"))\n self.assertTrue(hasattr(b1, \"created_at\"))\n self.assertTrue(hasattr(b1, \"updated_at\"))\n self.assertTrue(hasattr(b1, \"id\"))","def test_article_subclass_behavior(self):\n print self.base_article.defined_attributes\n print self.sub_article.defined_attributes\n self.assertEqual(self.sub_article.first().attributes,\n self.base_article.first().attributes)","def test_instance_created(self):\n\n base_model = BaseModel()\n self.assertIsInstance(base_model, BaseModel)\n self.assertTrue(hasattr(base_model, \"created_at\"))\n self.assertTrue(hasattr(base_model, \"updated_at\"))","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def set_City(self, value):\n super(AddressValidationInputSet, self)._set_input('City', value)","def model_for_source_url(self, url):\n if 'cities_reduced_fat/city/' in url:\n return City\n elif 'cities_reduced_fat/country/' in url:\n return Country","def test_abstract_model(self):\n self.assertRaises(ImproperlyConfigured, self.site.register, Location)"],"string":"[\n \"def test_inheritence(self):\\n self.assertTrue(issubclass(City, BaseModel))\",\n \"def test_subclass(self):\\n self.assertIsInstance(self.city, BaseModel)\\n self.assertTrue(hasattr(self.city, \\\"id\\\"))\\n self.assertTrue(hasattr(self.city, \\\"created_at\\\"))\\n self.assertTrue(hasattr(self.city, \\\"updated_at\\\"))\",\n \"def test_is_subclass(self):\\n c = City()\\n self.assertIsInstance(c, BaseModel)\\n self.assertTrue(hasattr(c, \\\"id\\\"))\\n self.assertTrue(hasattr(c, \\\"created_at\\\"))\\n self.assertTrue(hasattr(c, \\\"updated_at\\\"))\",\n \"def test_subclass(self):\\n self.assertTrue(issubclass(self.place.__class__, BaseModel), True)\",\n \"def test_instance(self):\\n self.assertIsInstance(self.my_city, City)\",\n \"def test_subclass_of_BaseModel(self):\\n my_amenity = Amenity()\\n self.assertIsInstance(my_amenity, BaseModel)\\n self.assertTrue(hasattr(my_amenity, \\\"id\\\"))\\n self.assertTrue(hasattr(my_amenity, \\\"created_at\\\"))\\n self.assertTrue(hasattr(my_amenity, \\\"updated_at\\\"))\",\n \"def test_city(self):\\n my_city = City()\\n self.assertTrue(hasattr(mycity, \\\"name\\\"))\\n self.assertEqual(my_city.name, \\\"\\\")\\n self.assertTrue(hasattr(mycity, \\\"state_id\\\"))\\n self.assertEqual(my_city.state_id, \\\"\\\")\",\n \"def test_is_subclass_place(self):\\n place = Place()\\n self.assertIsInstance(place, BaseModel)\\n self.assertTrue(hasattr(place, \\\"id\\\"))\\n self.assertTrue(hasattr(place, \\\"created_at\\\"))\\n self.assertTrue(hasattr(place, \\\"updated_at\\\"))\",\n \"def test_init_without_kwargs(self):\\n c = City()\\n self.assertTrue('id' in c.__dict__)\\n self.assertTrue('created_at' in c.__dict__)\\n self.assertTrue('updated_at' in c.__dict__)\\n self.assertIsInstance(c, BaseModel)\",\n \"def test_city(self):\\n self.assertIsInstance(self.address.city, str)\\n self.assertEqual(self.address.city, \\\"Paris\\\")\",\n \"def test_init(self, fixture_environment):\\n\\n # Generate city object\\n city_object = cit.City(environment=fixture_environment)\\n\\n # Check inheritance from citydistrict object of pycity\\n assert city_object._kind == 'citydistrict'\",\n \"def test_class(self):\\n city1 = City()\\n self.assertEqual(city1.__class__.__name__, \\\"City\\\")\",\n \"def test_subclass(self):\\n inst = Amenity()\\n self.assertIsInstance(inst, BaseModel)\\n self.assertTrue(hasattr(inst, \\\"id\\\"))\\n self.assertTrue(hasattr(inst, \\\"created_at\\\"))\\n self.assertTrue(hasattr(inst, \\\"updated_at\\\"))\",\n \"def test_city(self):\\n c = City()\\n self.assertEqual(c.name, \\\"\\\")\\n self.assertEqual(c.state_id, \\\"\\\")\\n c.name = \\\"San Francisco\\\"\\n c.state_id = \\\"98\\\"\\n self.assertEqual(c.name, \\\"San Francisco\\\")\\n self.assertEqual(c.state_id, \\\"98\\\")\\n self.assertEqual(type(c.state_id), str)\",\n \"def test_type_of_attributes(self):\\n self.assertIsInstance(self.city.name, str)\\n self.assertIsInstance(self.city.state_id, str)\",\n \"def test_name(self):\\n self.assertEqual(type(City.name), str)\",\n \"def test_attributes(self):\\n self.assertTrue(hasattr(self.city, 'name'))\\n self.assertTrue(hasattr(self.city, 'state_id'))\",\n \"def test_inheritance(self):\\n self.assertTrue(issubclass(type(self.user_1), BaseModel))\",\n \"def test_city(self):\\n my_city = City()\\n my_city.name = \\\"Medellin\\\"\\n self.assertEqual(my_city.name, 'Medellin')\",\n \"def test_class_attributes(self):\\n self.assertTrue('__tablename__' in City.__dict__)\\n self.assertTrue('name' in City.__dict__)\\n self.assertTrue('state_id' in City.__dict__)\\n self.assertTrue('places' in City.__dict__)\",\n \"def test_inherit(self):\\n self.assertTrue(issubclass(User, BaseModel))\",\n \"def testCityId(self):\\n place = Place()\\n self.assertTrue(hasattr(place, \\\"city_id\\\"))\\n self.assertEqual(place.city_id, \\\"\\\")\",\n \"def test_set_attrs(self):\\n city2 = City()\\n city2.name = \\\"Hawaii\\\"\\n self.assertEqual(city2.name, \\\"Hawaii\\\")\\n city2.state_id = \\\"<3\\\"\\n self.assertEqual(city2.state_id, \\\"<3\\\")\\n self.assertEqual(City.name, \\\"\\\")\\n self.assertEqual(City.state_id, \\\"\\\")\",\n \"def test_attr_type(self):\\n self.my_city.state_id = \\\"1c5dd90a-a3df-4516-b1ac-32a8715e5539\\\"\\n self.my_city.name = \\\"New York\\\"\\n self.assertIsInstance(self.my_city.name, str)\\n self.assertIsInstance(self.my_city.state_id, str)\",\n \"def test_city_id(self):\\n place = Place()\\n self.assertTrue(hasattr(place, \\\"city_id\\\"))\\n self.assertEqual(type(place.city_id), str)\\n self.assertEqual(place.city_id, \\\"\\\")\",\n \"def test_type(self):\\n self.assertEqual(type(self.base1), BaseModel)\\n self.assertEqual(type(self.base2), BaseModel)\",\n \"def test_has_attr(self):\\n\\n self.assertTrue(hasattr(City, \\\"save\\\"))\",\n \"def test_has_attr(self):\\n self.assertTrue(hasattr(City, \\\"save\\\"))\",\n \"def test_inherit(self):\\n\\n new_jawn = Amenity()\\n self.assertIsInstance(new_jawn, BaseModel)\",\n \"def test_save(self):\\n\\n base_class = BaseModel()\",\n \"def test_name(self):\\n c = City()\\n self.assertTrue(hasattr(c, \\\"name\\\"))\\n self.assertEqual(c.name, \\\"\\\")\",\n \"def setUp(self):\\n self.my_city = City()\",\n \"def test_is_subclass(self):\\n user = User()\\n user_details = {\\\"user_id\\\": user.id, \\\"first_name\\\": \\\"Joe\\\"}\\n student = Student(**user_details)\\n self.assertIsInstance(student, BaseModel)\\n self.assertTrue(hasattr(student, \\\"id\\\"))\\n self.assertTrue(hasattr(student, \\\"created_at\\\"))\\n self.assertTrue(hasattr(student, \\\"updated_at\\\"))\\n self.assertTrue(hasattr(student, \\\"user_id\\\"))\",\n \"def test_instance_BaseModel(self):\\n self.assertTrue(isinstance(self.my_object, BaseModel))\",\n \"def setUpClass(cls):\\n cls.city = City()\",\n \"def test_father(self):\\n review1 = Review()\\n self.assertTrue(issubclass(review1.__class__, BaseModel))\",\n \"def test_attrs(self):\\n city = City()\\n self.assertEqual(city.name, \\\"\\\")\\n self.assertEqual(City.name, \\\"\\\")\\n self.assertEqual(city.state_id, \\\"\\\")\\n self.assertEqual(City.state_id, \\\"\\\")\\n self.assertIn(\\\"id\\\", city.__dict__)\\n self.assertIn(\\\"created_at\\\", city.to_dict())\\n self.assertIn(\\\"updated_at\\\", city.to_dict())\",\n \"def test_state_id(self):\\n c = City()\\n self.assertTrue(hasattr(c, \\\"state_id\\\"))\\n self.assertEqual(c.state_id, \\\"\\\")\",\n \"def test_instance(self):\\n self.assertIsInstance(self.test1, BaseModel)\",\n \"def test_subclass(self):\\n am = Review()\\n self.assertIsInstance(am, BaseModel)\\n self.assertTrue(hasattr(am, \\\"id\\\"))\\n self.assertTrue(hasattr(am, \\\"created_at\\\"))\\n self.assertTrue(hasattr(am, \\\"updated_at\\\"))\\n self.assertTrue(hasattr(am, \\\"text\\\"))\\n self.assertTrue(hasattr(am, \\\"user_id\\\"))\\n self.assertTrue(hasattr(am, \\\"place_id\\\"))\\n self.assertEqual(am.place_id, \\\"\\\")\\n self.assertEqual(am.user_id, \\\"\\\")\\n self.assertEqual(am.text, \\\"\\\")\",\n \"def test_BaseModel_methods(self):\\n self.assertTrue(hasattr(BaseModel, \\\"__init__\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"__str__\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"save\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"to_dict\\\"))\",\n \"def test_isinstance(self):\\n obj = BaseModel()\\n self.assertIsInstance(obj, BaseModel)\",\n \"def test_noarguments(self):\\n self.assertEqual(BaseModel, type(BaseModel()))\",\n \"def test_father(self):\\n state1 = State()\\n self.assertTrue(issubclass(state1.__class__, BaseModel))\",\n \"def test_state_id(self):\\n\\n self.assertEqual(type(City.state_id), str)\",\n \"def test_after_to_dict(self):\\n my_model = City()\\n new_model = City()\\n test_dict = my_model.to_dict()\\n self.assertIsInstance(my_model, City)\\n self.assertEqual(type(my_model).__name__, \\\"City\\\")\\n self.assertEqual(test_dict['__class__'], \\\"City\\\")\\n self.assertTrue(type(test_dict['__class__']), 'str')\\n self.assertTrue(type(test_dict['created_at']), 'str')\\n self.assertTrue(type(test_dict['updated_at']), 'str')\\n self.assertTrue(type(test_dict['id']), 'str')\\n self.assertNotEqual(my_model.id, new_model.id)\",\n \"def test_issubclass(self):\\n self.assertTrue(issubclass(self.rev.__class__, BaseModel), True)\",\n \"def test_basemodel_basic_instance(self):\\n B = BaseModel()\\n self.assertIsInstance(B, BaseModel)\\n self.assertEqual(BaseModel, type(BaseModel()))\\n self.assertTrue(hasattr(B, \\\"id\\\"))\\n self.assertTrue(hasattr(B, \\\"created_at\\\"))\\n self.assertTrue(hasattr(B, \\\"updated_at\\\"))\",\n \"def test_set_attr(self):\\n self.my_city.name = \\\"Denver\\\"\\n self.assertEqual(self.my_city.name, \\\"Denver\\\")\",\n \"def test_superclass_fields_are_inherited(self):\\r\\n class TestModel(Model):\\r\\n id = columns.UUID(primary_key=True, default=lambda:uuid4())\\r\\n text = columns.Text()\\r\\n\\r\\n class InheritedModel(TestModel):\\r\\n numbers = columns.Integer()\\r\\n\\r\\n assert 'text' in InheritedModel._columns\\r\\n assert 'numbers' in InheritedModel._columns\",\n \"def __init__(self, city):\\r\\n self.city = city\",\n \"def test_father(self):\\n user1 = User()\\n self.assertTrue(issubclass(user1.__class__, BaseModel))\",\n \"def setUp(self):\\n self.base1 = BaseModel()\",\n \"def test_issubclass(self):\\n self.assertTrue(issubclass(User()), BaseModel)\",\n \"def issub_class(self):\\n insta = Amenity()\\n self.assertIsInstance(insta, BaseModel)\\n self.assertTrue(hasattr(insta, \\\"id\\\"))\\n self.assertTrue(hasattr(insta, \\\"created_at\\\"))\\n self.assertTrue(hasattr(insta, \\\"update_at\\\"))\",\n \"def setUpClass(self):\\n\\n base_model = BaseModel()\",\n \"def test_init_arg(self):\\n b1 = BaseModel(12)\\n self.assertEqual(type(b1).__name__, \\\"BaseModel\\\")\\n self.assertFalse(hasattr(b1, \\\"12\\\"))\",\n \"def test_type(self):\\n b = BaseModel()\\n self.assertIsInstance(self.my_model.id, str)\\n self.assertIsInstance(self.my_model.name, str)\\n self.assertIsInstance(self.my_model.my_number, int)\\n self.assertEqual(type(self.my_new_model.created_at), datetime)\",\n \"def test_if_exist(self):\\r\\n self.assertEqual(Place, type(Place()))\",\n \"def test_init_with_kwargs(self):\\n self.base1.name = \\\"Holberton\\\"\\n model_json = self.base1.to_dict()\\n new_model = BaseModel(**model_json)\\n self.assertDictEqual(model_json, new_model.to_dict())\\n self.assertIn(\\\"name\\\", new_model.to_dict())\\n self.assertIsNot(self.base1, new_model)\",\n \"def test_basemodel_attr_types(self):\\n B = BaseModel()\\n self.assertEqual(type(B.id), str)\\n self.assertTrue(type(B.created_at) is datetime.datetime)\\n self.assertTrue(type(B.updated_at) is datetime.datetime)\",\n \"def test_attr(self):\\n self.assertTrue(hasattr(BaseModel, \\\"__str__\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"save\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"to_dict\\\"))\\n self.assertTrue(\\\"updated_at\\\" in self.my_model1.__dict__)\\n self.assertTrue(\\\"created_at\\\" in self.my_model1.__dict__)\\n self.assertTrue(\\\"id\\\" in self.my_model1.__dict__)\",\n \"def test_baseclass_attrs_type(self):\\n\\n base_class = BaseModel()\\n cn = base_class.__class__.__name__\\n idn = base_class.id\\n attrs = base_class.__dict__\\n\\n self.assertTrue(type(cn), str)\\n self.assertTrue(type(id), str)\\n self.assertDictEqual(attrs, base_class.__dict__)\",\n \"def test_instance_Review(self):\\n obj = Review()\\n self.assertIsInstance(obj, BaseModel)\",\n \"def test_baseid(self):\\n self.assertEqual(str, type(BaseModel().id))\",\n \"def test09(self):\\n b_0 = Base()\\n self.assertEqual(str(type(b_0)), \\\"<class 'models.base.Base'>\\\")\\n self.assertEqual(b_0.__dict__, {\\\"id\\\": 1})\",\n \"def test_basemodel_query(self):\\n\\n # GIVEN model that is derived from BaseModel (i.e., no status field present)\\n model = TestModelBaseModel\\n\\n # WHEN retrieving model list\\n items = model.objects.all()\\n\\n # THEN it should succeed\\n self.assertEqual(len(items), 0)\",\n \"def test_valid_model(self):\\n model_cls = ModelContainer(APP_LABEL, TestModel2._meta.db_table).model_cls\\n self.assertTrue(model_cls.__class__.__name__ is models.Model.__class__.__name__)\",\n \"def test_get_base_polymorphic_model(self):\\n # Finds the base from every level (including lowest)\\n self.assertIs(get_base_polymorphic_model(Model2D), Model2A)\\n self.assertIs(get_base_polymorphic_model(Model2C), Model2A)\\n self.assertIs(get_base_polymorphic_model(Model2B), Model2A)\\n self.assertIs(get_base_polymorphic_model(Model2A), Model2A)\\n\\n # Properly handles multiple inheritance\\n self.assertIs(get_base_polymorphic_model(Enhance_Inherit), Enhance_Base)\\n\\n # Ignores PolymorphicModel itself.\\n self.assertIs(get_base_polymorphic_model(PolymorphicModel), None)\",\n \"def test_place_for_type(self):\\n self.assertEqual(type(self.place.city_id), str)\\n self.assertEqual(type(self.place.user_id), str)\\n self.assertEqual(type(self.place.name), str)\\n self.assertEqual(type(self.place.description), str)\\n self.assertEqual(type(self.place.number_rooms), int)\\n self.assertEqual(type(self.place.number_bathrooms), int)\\n self.assertEqual(type(self.place.max_guest), int)\\n self.assertEqual(type(self.place.price_by_night), int)\\n self.assertEqual(type(self.place.latitude), float)\\n self.assertEqual(type(self.place.longitude), float)\\n self.assertEqual(type(self.place.amenity_ids), list)\",\n \"def test_instance(self):\\n b = Review()\\n self.assertIsInstance(b, Review)\\n self.assertTrue(issubclass(type(b), BaseModel))\",\n \"def test_location_address(self):\\n self.assertIsInstance(self.location.address, Address)\\n self.assertEqual(self.location.address, self.address)\",\n \"def test_country(self):\\n self.assertIsInstance(self.address.country, str)\\n self.assertEqual(self.address.country, \\\"France\\\")\",\n \"def test_model_class(self):\\n db = Alchy(self.app)\\n\\n self.assertEquals(\\n db.Model.__dict__['__init__'], alchy.model.ModelBase.__init__)\\n self.assertIsInstance(\\n db.Model.__dict__['query'], alchy.query.QueryProperty)\",\n \"def test_core_object_model_inheritance():\\n assert issubclass(Question, CoreObject)\\n assert issubclass(Fact, CoreObject)\\n assert issubclass(Topic, CoreObject)\\n assert issubclass(Word, CoreObject)\\n assert issubclass(Book, CoreObject)\",\n \"def test_create_hotel(self):\\n amsterdam = City.objects.get(name=\\\"Amsterdam\\\")\\n ibis = Hotel.objects.get(name=\\\"Ibis\\\")\\n\\n self.assertEqual(ibis.city, amsterdam)\\n self.assertEqual(ibis.code, \\\"AMS01\\\")\\n self.assertEqual(ibis.name, \\\"Ibis\\\")\",\n \"def test_methods_exist(self):\\n\\n self.assertTrue(hasattr(BaseModel, \\\"__init__\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"__str__\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"save\\\"))\\n self.assertTrue(hasattr(BaseModel, \\\"to_dict\\\"))\",\n \"def test_city_country(self):\\n dublin_ireland = city_country('dublin', 'ireland')\\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')\",\n \"def test_AmenityClass(self):\\n self.assertTrue(issubclass(Amenity, BaseModel))\\n self.assertIsInstance(self.Amenity, Amenity)\\n self.assertIsNotNone(Amenity.__doc__)\",\n \"def test_02(self):\\n base0 = Base(10)\\n self.assertEqual(base0.id, 10)\",\n \"def test_father_kwargs(self):\\n dictonary = {'id': '662a23b3-abc7-4f43-81dc-64c000000c00'}\\n state1 = State(**dictonary)\\n self.assertTrue(issubclass(state1.__class__, BaseModel))\",\n \"def test_init_kwarg(self):\\n b1 = BaseModel(name=\\\"Red\\\")\\n self.assertEqual(type(b1).__name__, \\\"BaseModel\\\")\\n self.assertTrue(hasattr(b1, \\\"name\\\"))\\n self.assertTrue(hasattr(b1, \\\"__class__\\\"))\\n self.assertFalse(hasattr(b1, \\\"id\\\"))\\n self.assertFalse(hasattr(b1, \\\"created_at\\\"))\\n self.assertFalse(hasattr(b1, \\\"updated_at\\\"))\",\n \"def test_04(self):\\n base0 = Base(None)\\n self.assertEqual(base0.id, 1)\",\n \"def test03(self):\\n b_0 = Base(\\\"test\\\")\\n self.assertEqual(b_0.id, \\\"test\\\")\",\n \"def test_override_model_class(self):\\n class MyModelBase(object):\\n def testing(self):\\n return 'testing'\\n\\n Model = declarative_base(cls=MyModelBase)\\n\\n class Foo(Model):\\n __tablename__ = 'foo'\\n _id = Column(types.Integer(), primary_key=True)\\n name = Column(types.String())\\n\\n db = Alchy(self.app, Model=Model)\\n\\n self.assertTrue(issubclass(db.Model, MyModelBase),\\n 'db.Model should be a subclass of MyModelBase')\\n\\n db.create_all()\\n\\n self.assertEquals(db.session.query(Foo).all(), Foo.query.all(),\\n 'Model classes should have a query property')\\n\\n record = Foo(name='Name')\\n\\n self.assertEquals(record.testing(), 'testing')\",\n \"def test_abstract_attribute_is_not_inherited(self):\\r\\n assert not ConcreteModel.__abstract__\\r\\n assert not ConcreteModelWithCol.__abstract__\",\n \"def test_father_kwargs(self):\\n dictonary = {'id': '662a23b3-abc7-4f43-81dc-64c000000c00'}\\n review1 = Review(**dictonary)\\n self.assertTrue(issubclass(review1.__class__, BaseModel))\",\n \"def test_basemodel_basic_instance_none(self):\\n with self.assertRaises(TypeError):\\n BaseModel(None)\",\n \"def test_updated_type(self):\\n\\n base_model = BaseModel()\\n self.assertTrue(base_model.updated_at, datetime.datetime)\",\n \"def test_father_kwargs(self):\\n dictonary = {'id': '662a23b3-abc7-4f43-81dc-64c000000c00'}\\n user1 = User(**dictonary)\\n self.assertTrue(issubclass(user1.__class__, BaseModel))\",\n \"def test_get(self):\\n self.assertEqual(self.expected_described_model, self.mapped_model.get(\\\"described_model_type\\\"))\",\n \"def test_hasattribute(self):\\n b1 = BaseModel()\\n self.assertTrue(hasattr(b1, \\\"__init__\\\"))\\n self.assertTrue(hasattr(b1, \\\"created_at\\\"))\\n self.assertTrue(hasattr(b1, \\\"updated_at\\\"))\\n self.assertTrue(hasattr(b1, \\\"id\\\"))\",\n \"def test_article_subclass_behavior(self):\\n print self.base_article.defined_attributes\\n print self.sub_article.defined_attributes\\n self.assertEqual(self.sub_article.first().attributes,\\n self.base_article.first().attributes)\",\n \"def test_instance_created(self):\\n\\n base_model = BaseModel()\\n self.assertIsInstance(base_model, BaseModel)\\n self.assertTrue(hasattr(base_model, \\\"created_at\\\"))\\n self.assertTrue(hasattr(base_model, \\\"updated_at\\\"))\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def set_City(self, value):\\n super(AddressValidationInputSet, self)._set_input('City', value)\",\n \"def model_for_source_url(self, url):\\n if 'cities_reduced_fat/city/' in url:\\n return City\\n elif 'cities_reduced_fat/country/' in url:\\n return Country\",\n \"def test_abstract_model(self):\\n self.assertRaises(ImproperlyConfigured, self.site.register, Location)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8789952","0.8498339","0.8354064","0.7497503","0.7128531","0.70219684","0.700617","0.698174","0.6981573","0.6797616","0.6792511","0.6769275","0.6727983","0.66833794","0.6660511","0.6656261","0.65980506","0.6585381","0.6571345","0.6492039","0.64787066","0.6450331","0.64402336","0.64303166","0.6427531","0.6416702","0.64165545","0.64051247","0.6364319","0.6359309","0.6341275","0.63224703","0.6295962","0.6214925","0.615738","0.61370116","0.61242265","0.6113479","0.6087509","0.60754573","0.6055916","0.6046927","0.6045362","0.60355777","0.59245735","0.5918827","0.59174186","0.5896302","0.58726704","0.58679765","0.58608776","0.58595","0.5813943","0.5779869","0.5774508","0.57732093","0.57606363","0.57372206","0.57296044","0.5721496","0.5688495","0.5649479","0.56436765","0.5629317","0.56289935","0.5623018","0.5622042","0.56216323","0.5600801","0.55888593","0.5579162","0.55688316","0.5564634","0.5558106","0.55524844","0.5544838","0.55260354","0.55157995","0.5511444","0.55111957","0.5511025","0.55001205","0.54875135","0.5487335","0.54715145","0.5467903","0.5467854","0.54619634","0.5456888","0.54226863","0.54012847","0.53921074","0.5389776","0.53812826","0.53719234","0.53719234","0.53719234","0.53689885","0.5359886","0.5355669"],"string":"[\n \"0.8789952\",\n \"0.8498339\",\n \"0.8354064\",\n \"0.7497503\",\n \"0.7128531\",\n \"0.70219684\",\n \"0.700617\",\n \"0.698174\",\n \"0.6981573\",\n \"0.6797616\",\n \"0.6792511\",\n \"0.6769275\",\n \"0.6727983\",\n \"0.66833794\",\n \"0.6660511\",\n \"0.6656261\",\n \"0.65980506\",\n \"0.6585381\",\n \"0.6571345\",\n \"0.6492039\",\n \"0.64787066\",\n \"0.6450331\",\n \"0.64402336\",\n \"0.64303166\",\n \"0.6427531\",\n \"0.6416702\",\n \"0.64165545\",\n \"0.64051247\",\n \"0.6364319\",\n \"0.6359309\",\n \"0.6341275\",\n \"0.63224703\",\n \"0.6295962\",\n \"0.6214925\",\n \"0.615738\",\n \"0.61370116\",\n \"0.61242265\",\n \"0.6113479\",\n \"0.6087509\",\n \"0.60754573\",\n \"0.6055916\",\n \"0.6046927\",\n \"0.6045362\",\n \"0.60355777\",\n \"0.59245735\",\n \"0.5918827\",\n \"0.59174186\",\n \"0.5896302\",\n \"0.58726704\",\n \"0.58679765\",\n \"0.58608776\",\n \"0.58595\",\n \"0.5813943\",\n \"0.5779869\",\n \"0.5774508\",\n \"0.57732093\",\n \"0.57606363\",\n \"0.57372206\",\n \"0.57296044\",\n \"0.5721496\",\n \"0.5688495\",\n \"0.5649479\",\n \"0.56436765\",\n \"0.5629317\",\n \"0.56289935\",\n \"0.5623018\",\n \"0.5622042\",\n \"0.56216323\",\n \"0.5600801\",\n \"0.55888593\",\n \"0.5579162\",\n \"0.55688316\",\n \"0.5564634\",\n \"0.5558106\",\n \"0.55524844\",\n \"0.5544838\",\n \"0.55260354\",\n \"0.55157995\",\n \"0.5511444\",\n \"0.55111957\",\n \"0.5511025\",\n \"0.55001205\",\n \"0.54875135\",\n \"0.5487335\",\n \"0.54715145\",\n \"0.5467903\",\n \"0.5467854\",\n \"0.54619634\",\n \"0.5456888\",\n \"0.54226863\",\n \"0.54012847\",\n \"0.53921074\",\n \"0.5389776\",\n \"0.53812826\",\n \"0.53719234\",\n \"0.53719234\",\n \"0.53719234\",\n \"0.53689885\",\n \"0.5359886\",\n \"0.5355669\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.86559975"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17486,"cells":{"query":{"kind":"string","value":"check that fifo matches expected types and perms, catch security hold were it could be replace with another file"},"document":{"kind":"string","value":"def __checkFifo(path):\n pass # FIXME implement"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def vsys_fifo_exists(path):\n if not os.path.exists(path):\n collectd.error('File does not exist: %s' % path)\n return False\n if not stat.S_ISFIFO(os.stat(path).st_mode):\n collectd.error('File is not a fifo: %s' % path)\n return False\n return True","def _check_fifo(self):\n if not os.path.exists(self.fifo_path):\n raise FifoIsNotAvailable(\"trying to load from session that it's fifo is deleted!\")","def _verify_descriptors(self, msg):\n self.assertTrue(is_writable_file(msg.chlderr))\n self.assertTrue(is_writable_file(msg.chldout))\n self.assertTrue(is_writable_file(msg.chldnul))","def test_noPermission(self):\n log = logfile.LogFile(self.name, self.dir)\n log.write(\"abc\")\n\n # change permissions so rotation would fail\n os.chmod(self.dir, 0555)\n\n # if this succeeds, chmod doesn't restrict us, so we can't\n # do the test\n try:\n f = open(os.path.join(self.dir,\"xxx\"), \"w\")\n except (OSError, IOError):\n pass\n else:\n f.close()\n return\n\n log.rotate() # this should not fail\n\n log.write(\"def\")\n log.flush()\n\n f = log._file\n self.assertEquals(f.tell(), 6)\n f.seek(0, 0)\n self.assertEquals(f.read(), \"abcdef\")\n log.close()","def test_noPermission(self):\n log = logfile.LogFile(self.name, self.dir)\n self.addCleanup(log.close)\n log.write(\"abc\")\n\n # change permissions so rotation would fail\n os.chmod(self.dir, 0o555)\n\n # if this succeeds, chmod doesn't restrict us, so we can't\n # do the test\n try:\n f = open(os.path.join(self.dir, \"xxx\"), \"w\")\n except OSError:\n pass\n else:\n f.close()\n return\n\n log.rotate() # this should not fail\n\n log.write(\"def\")\n log.flush()\n\n f = log._file\n self.assertEqual(f.tell(), 6)\n f.seek(0, 0)\n self.assertEqual(f.read(), b\"abcdef\")","def test_provider_system_hook_file_chmod(change_dir, fix_file_perms):\n tackle(context_file='chmod.yaml', no_input=True)\n assert oct(os.stat('tackle.yaml').st_mode)[-3:] == \"600\"","def _check_stream_writable(self, fe_commit):\n if not self._current_branch.stream_name:\n return\n prefix = self._current_branch.writable_stream_name + '/'\n for fe_file in fe_commit['files']:\n gwt_path = fe_file['path']\n depot_path = self.ctx.gwt_path(gwt_path).to_depot()\n if depot_path.startswith(prefix):\n continue\n\n human_msg = (_(\n \"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\"\n \" Paths not in stream '{stream}' are read-only for branch '{b}'.\")\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\n , gwt_path = gwt_path\n , depot_path = depot_path\n , stream = self._current_branch.writable_stream_name\n , b = self._current_branch.branch_id ))\n raise PreflightException(human_msg)","def test_files(*fns, test_nonzero=False, allow_pipes=False):\n\n for fn in fns:\n is_file = os.path.isfile(fn)\n is_pipe = pathlib.Path(fn).is_fifo()\n if allow_pipes:\n if not is_file or is_pipe:\n error('File \"{}\" does not exist.'.format(fn))\n else:\n if is_pipe:\n if not is_file:\n error('File \"{}\" is a process substitution or a device.'.format(fn))\n else:\n if not is_file:\n error('File \"{}\" does not exist.'.format(fn))\n\n if test_nonzero and not allow_pipes:\n if not file_sizes(fn)[0]:\n error('File \"{}\" has size 0.'.format(fn))","def test_permissions(self):\n self.assertEqual(dir_perm, 0o2750)\n self.assertEqual(file_perm, 0o0440)","def _unblock_open_fifo_operation(self) -> None:\n if os.path.exists(self._fifo_out_path):\n open(self._fifo_out_path, 'wb', buffering=0)\n if os.path.exists(self._fifo_in_path):\n open(self._fifo_in_path, 'rb', buffering=0)","def any(self, fifo: int, /) -> bool:","def test_provider_system_hook_file_remove(change_dir, fix_file_perms):\n o = tackle(context_file='remove.yaml', no_input=True)\n assert o['if_file']\n assert not o['not_file']\n assert o['if_files']\n assert not o['not_files']","def test_is_special_file_socket(mocker: MockerFixture, tmp_path: Path) -> None:\n mocker.patch(\"stat.S_ISSOCK\", return_value=True)\n tmp_file = tmp_path / \"foo\"\n tmp_file.touch()\n assert is_special_file(tmp_file)","def test_rotatePermissionFileNotOk(self):\n log = logfile.DailyLogFile(self.name, self.dir)\n self.addCleanup(log.close)\n\n os.chmod(log.path, 0o444)\n previousFile = log._file\n log.rotate()\n self.assertEqual(previousFile, log._file)","def test_specifiedPermissions(self):\n log1 = logfile.LogFile(self.name, self.dir, defaultMode=0o066)\n self.addCleanup(log1.close)\n mode = stat.S_IMODE(os.stat(self.path)[stat.ST_MODE])\n if runtime.platform.isWindows():\n # The only thing we can get here is global read-only\n self.assertEqual(mode, 0o444)\n else:\n self.assertEqual(mode, 0o066)","def _check_stream_in_classic(self, fe_commit):\n if self._current_branch.stream_name:\n return\n\n depot_re = re.compile(r'^//([^/]+)/([^/]+)/.*$')\n for fe_file in fe_commit['files']:\n gwt_path = fe_file['path']\n depot_path = self.ctx.gwt_path(gwt_path).to_depot()\n m = depot_re.match(depot_path)\n if m:\n depot = m.group(1)\n if depot in self.stream_depots:\n stream = '//{}/{}'.format(m.group(1), m.group(2))\n human_msg = (\n _(\"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\"\n \" Paths in stream '{stream}' are read-only for branch '{b}'.\")\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\n , gwt_path = gwt_path\n , depot_path = depot_path\n , stream = stream\n , b = self._current_branch.branch_id ))\n raise PreflightException(human_msg)","def test_specifiedPermissions(self):\n log1 = logfile.LogFile(self.name, self.dir, defaultMode=0066)\n mode = stat.S_IMODE(os.stat(self.path)[stat.ST_MODE])\n if runtime.platform.isWindows():\n # The only thing we can get here is global read-only\n self.assertEquals(mode, 0444)\n else:\n self.assertEquals(mode, 0066)","def check_perms(resource):\r\n stmode = os.stat(resource).st_mode\r\n return (getattr(stat, 'S_IROTH') & stmode) > 0","def check_perms(resource):\r\n stmode = os.stat(resource).st_mode\r\n return (getattr(stat, 'S_IROTH') & stmode) > 0","def test_modePreservation(self):\n open(self.path, \"w\").close()\n os.chmod(self.path, 0o707)\n mode = os.stat(self.path)[stat.ST_MODE]\n log = logfile.LogFile(self.name, self.dir)\n self.addCleanup(log.close)\n log.write(\"abc\")\n log.rotate()\n self.assertEqual(mode, os.stat(self.path)[stat.ST_MODE])","def io_check(*args, func=None):\n func = func or inspect.stack()[2][3]\n for var in args:\n if not isinstance(var, io.IOBase):\n name = type(var).__name__\n raise IOObjError(\n 'Function {} expected file-like object, {} got instead.'.format(func, name))","def can_handle(file_io):\r\n raise NotImplementedError(\"Please implement this in your importer\")","def test_provider_system_hook_file(change_dir, clean_files):\n tackle(no_input=True)\n assert 'thing.yaml' in os.listdir()\n assert 'stuff' in os.listdir()\n # If the file has been moved properly there should be only one file\n assert len(os.listdir('stuff')) == 3","def test_file_unused(self):\n try:\n with get_temp_file() as (fd, name):\n pass\n except Exception as err:\n self.fail('Failed with exception \"{}\"'.format(err))\n else:\n file_exists = os.access(name, os.F_OK)\n self.assertFalse(file_exists)","def sort_permissions(fl):\n\n if oct(os.stat(fl).st_mode)[4:] != '666':\n os.chmod(fl, 0o666)","def set_file_permissions(host, fqpath, perms):\n command = \"chmod %s %s\" % (perms, fqpath)\n rcode, _, rerr = g.run(host, command)\n\n if rcode == 0:\n return True\n\n g.log.error('chmod failed: %s' % rerr)\n return False","def checkIfAllowedToModify(self):\n\n oldBytes = b''\n testFileName = self.MAPSTUDIO + self.inputFiles[0] + '.msb'\n\n with open(testFileName, 'rb') as oldf:\n oldBytes = oldf.read()\n\n # Try writing something to the file\n\n try:\n with open(testFileName, 'wb') as outf:\n outf.write(b'TESTINGIFICANWRITEINTOTHISFILE')\n except:\n return False\n\n # Because apparently for _some_ reason it doesn't throw an error sometimes(?) so we confirm if the file was actually modified\n\n newBytes = b''\n with open(testFileName, 'rb') as oldf:\n newBytes = oldf.read()\n\n if (oldBytes == newBytes):\n return False\n\n # Restore the file to normal\n\n with open(testFileName, 'wb') as outf:\n outf.write(oldBytes)\n\n oldBytes = b''\n newBytes = b''\n\n return True","def test_command_edit_info_disk_type():\n # this is pathological, don't do this in real life\n def f(inputfile):\n with tempfile.NamedTemporaryFile() as tmp:\n shutil.copy(inputfile, tmp.name)\n\n # disk_type = 1 is ok\n wozardry.parse_args([\"edit\", \"-i\", \"disk_type:1\", tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[\"disk_type\"] == 1\n\n # disk_type = 2 is ok\n wozardry.parse_args([\"edit\", \"-i\", \"disk_type:2\", tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[\"disk_type\"] == 2\n\n # disk_type = 0 is not ok\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskType):\n wozardry.parse_args([\"edit\", \"-i\", \"disk_type:0\", tmp.name])\n\n # disk_type = 3 is not ok\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskType):\n wozardry.parse_args([\"edit\", \"-i\", \"disk_type:3\", tmp.name])\n f(kValid1)\n f(kValid2)","def test_fifo_sync_random():\n pass","def test_overwrite_corrupted_files(overwrite_on_tape_topology, core_config_mock, caches_mock):\n rse1_id, rse2_id, rse3_id, did1, did2 = overwrite_on_tape_topology(did1_corrupted=True, did2_corrupted=True)\n all_rses = [rse1_id, rse2_id, rse3_id]\n\n class _FTSWrapper(FTSWrapper):\n @staticmethod\n def on_receive(job_params):\n for job in (job_params if isinstance(job_params, list) else [job_params]):\n for file in job.get('files', []):\n if (file.get('file_metadata', {}).get('dst_type') == 'TAPE'\n and file.get('file_metadata', {}).get('dst_file', {}).get('file_on_tape') is not None):\n # Fake that dst_file metadata contains file_on_tape == True\n # As we don't really have tape RSEs in our tests, file_on_tape is always false\n file['file_metadata']['dst_file']['file_on_tape'] = True\n return job_params\n\n with patch('rucio.daemons.conveyor.poller.FTS3Transfertool', _FTSWrapper):\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\n # Both transfers must be marked as failed because the file size is incorrect\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.FAILED\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did2)\n assert request['state'] == RequestState.FAILED\n\n # Re-submit the failed requests. They must fail again, because overwrite_corrupted_files is False\n # 2 runs: for multihop, finisher works one hop at a time\n finisher(once=True, partition_wait_time=0)\n finisher(once=True, partition_wait_time=0)\n request = request_core.get_request_by_did(rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.QUEUED\n request = request_core.get_request_by_did(rse_id=rse3_id, **did2)\n assert request['state'] == RequestState.QUEUED\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\n # Set overwrite to True before running the poller or finisher\n core_config.set('transfers', 'overwrite_corrupted_files', True)\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.FAILED\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did2)\n assert request['state'] == RequestState.FAILED\n\n # Re-submit one more time. Now the destination file must be overwritten\n finisher(once=True, partition_wait_time=0)\n finisher(once=True, partition_wait_time=0)\n request = request_core.get_request_by_did(rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.QUEUED\n request = request_core.get_request_by_did(rse_id=rse3_id, **did2)\n assert request['state'] == RequestState.QUEUED\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\n request = request_core.get_request_by_did(rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.SUBMITTED\n assert __wait_for_fts_state(request, expected_state='ARCHIVING') == 'ARCHIVING'\n request = request_core.get_request_by_did(rse_id=rse3_id, **did2)\n assert request['state'] == RequestState.SUBMITTED\n assert __wait_for_fts_state(request, expected_state='ARCHIVING') == 'ARCHIVING'","def test_fileio(tmp_path):\n async def aiotests():\n fio = fileio.FileIO(Path(tmp_path))\n\n with pytest.raises(PermissionError):\n fio.resolve_and_sanitize('../abba123')\n\n with pytest.raises(PermissionError):\n fio.resolve_and_sanitize('/abba123')\n\n with pytest.raises(FileNotFoundError):\n fio.resolve_and_sanitize('abba123', must_exist=True)\n\n with pytest.raises(FileNotFoundError):\n fio.open_and_seek('abba123', 0, for_write=False)\n\n with pytest.raises(PermissionError):\n fio.open_and_seek('../abba123', 0, for_write=False)\n\n with pytest.raises(aiohttp.web.HTTPForbidden):\n await fio.upload_chunk(chunker.FileChunk(path='../abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234'), None)\n\n with pytest.raises(aiohttp.web.HTTPInternalServerError):\n await fio.upload_chunk(chunker.FileChunk(path=False, pos=0, size=123, cmpratio=1, hash='abcdef1234'), None)\n\n assert await fio.copy_chunk_locally(\n chunker.FileChunk(path='abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234'),\n chunker.FileChunk(path='abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234')) is True\n\n with pytest.raises(ValueError):\n assert await fio.copy_chunk_locally(\n chunker.FileChunk(path='abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234'),\n chunker.FileChunk(path='asdfklna', pos=0, size=123, cmpratio=1, hash='ABABABABAB'))\n\n assert fio.try_precreate_large_sparse_file('sparsefiletest.bin', 1234) == fio.resolve_and_sanitize('sparsefiletest.bin').exists()\n await fio.remove_file_and_paths('sparsefiletest.bin')\n\n asyncio.run(aiotests())","def testError(self):\n cmds = \"\"\"chown 0 missingFile\npwd\nexit\n\"\"\"\n\n def _cbCheckResult(res):\n self.assertNotIn(self.testDir.asBytesMode().path, res)\n\n d = self._getBatchOutput(cmds)\n d.addCallback(_cbCheckResult)\n return d","def test_mark_res_file_as_mp_file_type_failure_2(composite_resource, mock_irods):\n\n res, user = composite_resource\n file_path = 'pytest/assets/{}'\n txt_file_path = file_path.format('generic_file.txt')\n vrt_file_path = file_path.format('logan.vrt')\n upload_folder = ''\n file_to_upload = UploadedFile(file=open(txt_file_path, 'rb'),\n name=os.path.basename(txt_file_path))\n\n res_file_txt = add_file_to_resource(\n res, file_to_upload, folder=upload_folder, check_target_folder=True\n )\n\n file_to_upload = UploadedFile(file=open(vrt_file_path, 'rb'),\n name=os.path.basename(vrt_file_path))\n res_file_vrt = add_file_to_resource(\n res, file_to_upload, folder=upload_folder, check_target_folder=True\n )\n assert res.files.count() == 2\n # create model program aggregation\n assert ModelProgramLogicalFile.objects.count() == 0\n # set the txt file to model program aggregation type\n ModelProgramLogicalFile.set_file_type(res, user, res_file_txt.id)\n\n assert ModelProgramResourceFileType.objects.count() == 0\n mp_aggregation = ModelProgramLogicalFile.objects.first()\n # trying set the vrt file as engine for this aggregation should fail as the vrt file is not part of\n # the aggregation\n with pytest.raises(ValidationError):\n ModelProgramResourceFileType.create(file_type='engine', res_file=res_file_vrt,\n mp_metadata=mp_aggregation.metadata)\n\n assert ModelProgramResourceFileType.objects.count() == 0\n assert not res.dangling_aggregations_exist()","def copy_file_check(self):\n pass","def test_mark_res_file_as_mp_file_type_failure_1(composite_resource, mock_irods):\n\n res, user = composite_resource\n file_path = 'pytest/assets/generic_file.txt'\n upload_folder = ''\n file_to_upload = UploadedFile(file=open(file_path, 'rb'),\n name=os.path.basename(file_path))\n\n res_file = add_file_to_resource(\n res, file_to_upload, folder=upload_folder, check_target_folder=True\n )\n assert res.files.count() == 1\n # create model program aggregation\n assert ModelProgramLogicalFile.objects.count() == 0\n # set file to model program aggregation type\n ModelProgramLogicalFile.set_file_type(res, user, res_file.id)\n res_file = res.files.first()\n assert ModelProgramResourceFileType.objects.count() == 0\n mp_aggregation = ModelProgramLogicalFile.objects.first()\n # set the res_file as software for this aggregation\n ModelProgramResourceFileType.create(file_type='software', res_file=res_file,\n mp_metadata=mp_aggregation.metadata)\n\n assert ModelProgramResourceFileType.objects.count() == 1\n mp_res_file_type = ModelProgramResourceFileType.objects.first()\n assert mp_res_file_type.res_file.short_path == res_file.short_path\n assert mp_res_file_type.file_type == ModelProgramResourceFileType.type_from_string('software')\n # trying to set the same file again as mp file type should fail\n with pytest.raises(ValidationError):\n ModelProgramResourceFileType.create(file_type='engine', res_file=res_file,\n mp_metadata=mp_aggregation.metadata)\n\n assert ModelProgramResourceFileType.objects.count() == 1\n assert not res.dangling_aggregations_exist()","def test_add_read_fail(self):\n file_handler = open(self.test_task_filename, 'w')\n file_handler.write('Mock corrupt data')\n file_handler.close()\n os.chmod(self.test_task_filename, 000)\n\n self.assertRaises(IOError, self.task_storage.add, self.my_task)","def check_streaming(self, arg: str):\n if not arg:\n return False\n elif arg.startswith(\"sdo:\"):\n print(\"[check_streaming] File is for streaming\")\n tmp_list = arg.splitlines()\n tmp_list.pop(0)\n for x in tmp_list:\n if self.check_name(x) is False:\n return False\n return True\n else:\n return False","def _checkPHOF(self, op, fifoReadings, counterValue):\n # Relying on indices matching PHOF values.\n PHOFS = np.where(fifoReadings == counterValue)[0]\n # If no PHOF can be found with the target FIFO counter value...\n if not len(PHOFS):\n PHOF = -1 # Set to -1 so LabRAD call can complete. success=False\n\n # For each PHOF for which the FIFO counter equals counterValue, resend\n # the PHOF and check that the FIFO counter indeed equals counterValue.\n # If so, return the PHOF and a flag that the FIFO calibration has been\n # successful.\n success = False\n for PHOF in PHOFS:\n pkt = [0x0700 + PHOF, 0x8700]\n reading = long(((yield self._runSerial(op, pkt))[1] >> 4) & 0xF)\n if reading == counterValue:\n success = True\n break\n ans = int(PHOF), success\n returnValue(ans)","def test_verify_corrupt_archive(self):\n self.backup(u\"full\", u\"testfiles/various_file_types\", options=[])\n output_files = os.listdir(\"testfiles/output\")\n archives = [elem for elem in output_files if \"vol\" in elem]\n for archive in archives:\n # Edit source file\n with open(\"testfiles/output/\" + archive, 'r+') as f:\n f.write('This writes text into each archive file to corrupt it.')\n # Test verify for the file\n try:\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable', options=[])\n except CmdError as e:\n # Should return a 21 error code for \"hash mismatch\"\n self.assertEqual(e.exit_status, 21, str(e))\n else:\n self.fail('Expected Hash Mismatch Error not thrown')","def file_mode_checks(value):\n if not value.endswith(F_SUFFIX):\n raise argparse.ArgumentTypeError(\n \"\\nFile resource must be:\\n\"\n + \" 1) Obfuscated {hex}.cache.js GWT permutation file\"\n )\n return value","def test_compress_fastq_real_with_integrity_fail(\n first_tmp_file, second_tmp_file, spring_tmp_path, real_base_context, mocker\n):\n # GIVEN the path to a existing two existing fastq files and a non existing spring\n runner = CliRunner()\n assert not spring_tmp_path.exists()\n assert first_tmp_file.exists()\n assert second_tmp_file.exists()\n\n dir_path = spring_tmp_path.parent\n assert nr_files(dir_path) == 2\n mocker.patch.object(compare_cmd, \"compare_elements\")\n compare_cmd.compare_elements.return_value = False\n # WHEN running the compress command with an intergrity check\n result = runner.invoke(\n fastq,\n [\n \"--first-read\",\n str(first_tmp_file),\n \"--second-read\",\n str(second_tmp_file),\n \"--spring-path\",\n str(spring_tmp_path),\n \"--check-integrity\",\n ],\n obj=real_base_context,\n )\n # THEN assert the command succedes\n assert result.exit_code == 1\n # THEN assert that the spring file was deleted\n assert not spring_tmp_path.exists()\n # THEN assert that only the original fastq files are left\n assert nr_files(dir_path) == 2","def test_find_read_fail(self):\n file_handler = open(self.test_task_filename, 'w')\n file_handler.write('Mock corrupt data')\n file_handler.close()\n os.chmod(self.test_task_filename, 000)\n\n self.assertRaises(IOError, self.task_storage.find, self.my_task)","def testModePreservation(self):\n f = open(self.path, \"w\").close()\n os.chmod(self.path, 0707)\n mode = os.stat(self.path)[stat.ST_MODE]\n log = logfile.LogFile(self.name, self.dir)\n log.write(\"abc\")\n log.rotate()\n self.assertEquals(mode, os.stat(self.path)[stat.ST_MODE])","def permissions_check(\n basedir='.',\n verbose_level=0,\n):\n # File permissions on Cygwin/Windows filesystems don't work the\n # same way as Linux. Don't try to change them.\n # TODO(dittrich): Is there a Better way to handle perms on Windows?\n fs_type = get_fs_type(basedir)\n if fs_type in ['NTFS', 'FAT', 'FAT32']:\n msg = (\n f\"[-] {basedir} has file system type '{fs_type}': \"\n \"skipping permissions check\"\n )\n logger.info(msg)\n return\n any_other_perms = stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH\n for root, dirs, files in os.walk(basedir, topdown=True):\n for name in files:\n path = os.path.join(root, name)\n try:\n st = os.stat(path)\n perms = st.st_mode & 0o777\n open_perms = (perms & any_other_perms) != 0\n if (open_perms and verbose_level >= 1):\n print(\n f\"[!] file '{path}' is mode {oct(perms)}\",\n file=sys.stderr\n )\n except OSError:\n pass\n for name in dirs:\n path = os.path.join(root, name)\n try:\n st = os.stat(path)\n perms = st.st_mode & 0o777\n open_perms = (perms & any_other_perms) != 0\n if (open_perms and verbose_level >= 1):\n print(\n (\n f\"[!] directory '{path}' is mode \"\n f\"{oct(perms)}\"\n ),\n file=sys.stderr\n )\n except OSError:\n pass","def overwrite_on_tape_topology(rse_factory, did_factory, root_account, vo, file_factory):\n\n rse1 = 'XRD1'\n rse1_id = rse_core.get_rse_id(rse=rse1, vo=vo)\n rse2 = 'XRD3'\n rse2_id = rse_core.get_rse_id(rse=rse2, vo=vo)\n rse3 = 'XRD4'\n rse3_id = rse_core.get_rse_id(rse=rse3, vo=vo)\n\n def __generate_and_upload_file(src_rse, dst_rse, simulate_dst_corrupted=False):\n \"\"\"\n Create and upload real files to source and destination. Don't register it on destination. This way, fts will fail if overwrite = False\n\n If simulate_dst_corrupted is True, will upload a different file to destination, to simulate that it is corrupted\n \"\"\"\n local_file = file_factory.file_generator()\n did = did_factory.random_file_did()\n did_factory.upload_test_file(src_rse, path=local_file, **did)\n did_factory.upload_client.upload(\n [\n {\n 'path': file_factory.file_generator(size=3) if simulate_dst_corrupted else local_file,\n 'rse': dst_rse,\n 'did_scope': did['scope'].external,\n 'did_name': did['name'],\n 'no_register': True,\n }\n ]\n )\n return did\n\n def __create_dids(did1_corrupted=True, did2_corrupted=True):\n \"\"\"\n Uploads two files:\n - one which requires multiple transfer hop to go to destination\n - one which can be transferred in one hop to destination rse\n \"\"\"\n # multihop transfer:\n did1 = __generate_and_upload_file(rse1, rse3, simulate_dst_corrupted=did1_corrupted)\n # direct transfer\n did2 = __generate_and_upload_file(rse2, rse3, simulate_dst_corrupted=did2_corrupted)\n rule_core.add_rule(dids=[did1, did2], account=root_account, copies=1, rse_expression=rse3, grouping='ALL', weight=None, lifetime=None, locked=False, subscription_id=None)\n\n return rse1_id, rse2_id, rse3_id, did1, did2\n\n # Fake that destination RSE is a tape\n rse_core.update_rse(rse_id=rse3_id, parameters={'rse_type': RSEType.TAPE})\n try:\n rse_core.add_rse_attribute(rse3_id, 'archive_timeout', 60)\n yield __create_dids\n finally:\n rse_core.update_rse(rse_id=rse3_id, parameters={'rse_type': RSEType.DISK})\n rse_core.del_rse_attribute(rse3_id, 'archive_timeout')","def fix_file_perms():\n yield\n os.chmod('tackle.yaml', int('0o644', 8))","def test_file_conflict(self):\n dir0, dir1 = self.make_temp_dirs(2)\n self.write_file(dir0, \"foo\")\n self.sync_all()\n\n self.write_file(dir0, \"foo\", \"bar\")\n time.sleep(0.1)\n self.write_file(dir1, \"foo\", \"baz\")\n self.sync_all()\n # File with later mtime wins\n self.assertFile(dir0, \"foo\", \"baz\")\n self.assertFile(dir1, \"foo\", \"baz\")","def test_reject_foo(self):\n self.spawn(\"./binary\").stdin(\"foo\").reject()","def test_provider_system_hook_file_shred(change_dir, clean_files):\n files = ['stuff', 'thing', 'foo']\n for f in files:\n file = open(f, \"w\")\n file.write(f)\n file.close()\n\n tackle('.', no_input=True, context_file='shred.yaml')\n\n for f in files:\n assert not os.path.isfile(f)","def test_inotify(self):\n self.fail(\"write a test\")","def check_filesystem(ssh_connection, disk_fmt, disk):\n if disk_fmt == \"squashfs\":\n return\n cmd = \"fsck.{} -n {}\".format(disk_fmt, disk)\n exit_code, _, stderr = ssh_connection.run(cmd)\n assert exit_code == 0, stderr","def test_create4(self):\n TempfileManager.sequential_files(2)\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertEqual(fname, 'tmp2')\n #\n TempfileManager.unique_files()\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 2)\n fname = os.path.basename(fname)\n self.assertNotEqual(fname, 'tmp3')\n self.assertTrue(fname.startswith('tmp'))","def checks(self, event):\n if ListenerContainer.is_syncing:\n if ListenerContainer.move_to_folder:\n try:\n ListenerContainer.client.delete_folder(ListenerContainer.move_to_folder)\n except error_perm or error_reply: #TODO\n reset()\n # pass # nothing to delete\n # except error_reply:\n # reset()\n ListenerContainer.move_to_folder = None\n if ListenerContainer.move_to_file:\n try:\n ListenerContainer.client.delete_file(ListenerContainer.move_to_file)\n except error_perm or error_reply: # TODO\n reset()\n # pass # nothing to delete\n # except error_reply:\n # reset()\n ListenerContainer.move_to_file = None\n if event.pathname[-1] == '~': # Temp file\n return False\n else:\n return True\n else:\n timer = now()\n if ListenerContainer.move_to_folder:\n x = [timer, 'DELFOLDER', event.pathname]\n ListenerContainer.sync_db.quick_push(x)\n ListenerContainer.move_to_folder = None\n if ListenerContainer.move_to_file:\n x = [timer, 'DELFILE', event.pathname]\n ListenerContainer.sync_db.quick_push(x)\n ListenerContainer.move_to_file = None\n if event.pathname[-1] == '~': # Temp file\n return False\n else:\n return True","def test_permissions(self):\n exist = os.access('models/amenity.py', os.F_OK)\n self.assertTrue(exist)\n read = os.access('models/amenity.py', os.R_OK)\n self.assertTrue(read)\n write = os.access('models/amenity.py', os.W_OK)\n self.assertTrue(write)\n exe = os.access('models/amenity.py', os.X_OK)\n self.assertTrue(exe)","def test_invalid_prompt_files(self):\n invalid_files = [\n \"p01.txt\",\n \"abc.txt\",\n \"p000001s000001.mp3\",\n \"p000001s000001n001.txt\",\n \"u000001.txt\"\n ]\n filtered_files = list(filter(format.is_prompt_file, invalid_files))\n\n assert len(filtered_files) == 0","def test_command_edit_requires_ram():\n def f(inputfile):\n with tempfile.NamedTemporaryFile() as tmp:\n shutil.copy(inputfile, tmp.name)\n\n for ram in wozardry.kRequiresRAM:\n wozardry.parse_args([\"edit\", \"-m\", \"requires_ram:%s\" % ram, tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.meta[\"requires_ram\"] == ram\n\n # invalid required RAM (must be one of enumerated values)\n with pytest.raises(wozardry.WozMETAFormatError_BadRAM):\n wozardry.parse_args([\"edit\", \"-m\", \"requires_ram:65K\", tmp.name])\n\n f(kValid1)\n f(kValid2)","def test_io_flags(pyocf_ctx, cache_mode):\n\n flags = 0x239482\n block_size = 4096\n\n data = bytes(block_size)\n\n pyocf_ctx.register_volume_type(FlagsValVolume)\n\n cache_device = FlagsValVolume(Size.from_MiB(50), flags)\n core_device = FlagsValVolume(Size.from_MiB(50), flags)\n\n cache = Cache.start_on_device(cache_device, cache_mode=cache_mode)\n core = Core.using_device(core_device)\n\n cache.add_core(core)\n vol = CoreVolume(core)\n\n cache_device.set_check(True)\n core_device.set_check(True)\n\n # write miss\n io_to_exp_obj(vol, block_size * 0, block_size, data, 0, IoDir.WRITE, flags)\n assert not cache_device.fail\n assert not core_device.fail\n\n # read miss\n io_to_exp_obj(vol, block_size * 1, block_size, data, 0, IoDir.READ, flags)\n assert not cache_device.fail\n assert not core_device.fail\n\n # \"dirty\" read hit\n io_to_exp_obj(vol, block_size * 0, block_size, data, 0, IoDir.READ, flags)\n assert not cache_device.fail\n assert not core_device.fail\n\n # \"clean\" read hit\n io_to_exp_obj(vol, block_size * 1, block_size, data, 0, IoDir.READ, flags)\n assert not cache_device.fail\n assert not core_device.fail\n\n # \"dirty\" write hit\n io_to_exp_obj(vol, block_size * 0, block_size, data, 0, IoDir.WRITE, flags)\n assert not cache_device.fail\n assert not core_device.fail\n\n # \"clean\" write hit\n io_to_exp_obj(vol, block_size * 1, block_size, data, 0, IoDir.WRITE, flags)\n assert not cache_device.fail\n assert not core_device.fail","def test_get_all_read_fail(self):\n file_handler = open(self.test_task_filename, 'w')\n file_handler.write('Mock corrupt data')\n file_handler.close()\n os.chmod(self.test_task_filename, 000)\n\n self.assertRaises(IOError, self.task_storage.get_all)","def test_create1(self):\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('tmp'))","def test_check_perm(self):\n #Test something that really shouldn't be there\n with pytest.raises(DbException) as err:\n ModulePerm.get_module_perm_by_id(0)\n assert str(err.value) == \"(404, 'Permission not found.')\"\n\n perm = ModulePerm.get_module_perm_by_id(self.permList[0].id)\n assert perm.id == self.permList[0].id\n assert perm.user_id == self.permList[0].user_id\n assert perm.module_id == self.permList[0].module_id\n assert perm.permissions == self.permList[0].permissions\n assert perm.permissions == 15\n\n assert perm.check_perm_read_raw()\n assert perm.check_perm_write_raw()\n assert perm.check_perm_share_raw()\n assert perm.check_perm_own_raw()\n\n perm = ModulePerm.get_module_perm_by_id(self.permList[2].id)\n assert perm.id == self.permList[2].id\n assert perm.user_id == self.permList[2].user_id\n assert perm.module_id == self.permList[2].module_id\n assert perm.permissions == self.permList[2].permissions\n assert perm.permissions == 0\n\n assert not perm.check_perm_read_raw()\n assert not perm.check_perm_write_raw()\n assert not perm.check_perm_share_raw()\n assert not perm.check_perm_own_raw()","def test_file_exists_handled(overwrite_on_tape_topology, caches_mock):\n rse1_id, rse2_id, rse3_id, did1, did2 = overwrite_on_tape_topology(did1_corrupted=False, did2_corrupted=False)\n all_rses = [rse1_id, rse2_id, rse3_id]\n\n class _FTSWrapper(FTSWrapper):\n @staticmethod\n def on_receive(job_params):\n for job in (job_params if isinstance(job_params, list) else [job_params]):\n for file in job.get('files', []):\n if (file.get('file_metadata', {}).get('dst_type') == 'TAPE'\n and file.get('file_metadata', {}).get('dst_file', {}).get('file_on_tape') is not None):\n # Fake that dst_file metadata contains file_on_tape == True\n # As we don't really have tape RSEs in our tests, file_on_tape is always false\n file['file_metadata']['dst_file']['file_on_tape'] = True\n return job_params\n\n with patch('rucio.daemons.conveyor.poller.FTS3Transfertool', _FTSWrapper):\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\n\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.DONE\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did2)\n assert request['state'] == RequestState.DONE","def main():\n\n radio = initialize_radios(0, 25, 0x60)\n\n radio.openWritingPipe(pipes[0])\n radio.openReadingPipe(0, pipes[1])\n\n print(\"Sender Information\")\n radio.printDetails()\n\n payload_list = list()\n\n out = False\n count = 0\n actual_ack = None\n while not out:\n radio.startListening()\n wait_for_data(radio)\n\n # recv_buffer is the array where received data will be placed\n recv_buffer = []\n radio.read(recv_buffer, radio.getDynamicPayloadSize())\n chunk_and_ack = recv_buffer[:-5]\n ack_num = recv_buffer[-7:-5]\n crc = recv_buffer[-5:]\n data = recv_buffer[:-7]\n ack_num_str = bytes(ack_num).decode('utf-8'\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n ''\n )\n data_str = bytes(data)\n\n radio.stopListening()\n if (actual_ack != ack_num_str) and (data_str != b'TH1SPR0GRAMSHOULDBEOVER'):\n if check_crc(chunk_and_ack, crc):\n send_packet(radio, b'1GUTACK')\n payload_list.append(bytes(data))\n actual_ack = ack_num_str\n print(\"Sent ACK number \" + str(count))\n count = count + 1\n else:\n send_packet(radio, b'1BATACK')\n elif (actual_ack == ack_num_str) and (data_str != b'TH1SPR0GRAMSHOULDBEOVER'):\n send_packet(radio, b'1GUTACK')\n elif (actual_ack != ack_num_str) and data_str == b'TH1SPR0GRAMSHOULDBEOVER':\n print(\"Finishing Script\")\n time.sleep(0.5)\n send_packet(radio, b'TH1SISTH3FINALACK')\n out = True\n\n write_file(sys.argv[1], payload_list)","def test_upload_2_generation_shell_failed(self):\n entry = mock.MagicMock()\n device_os = mock.MagicMock()\n families = {\"first_gen\": mock.MagicMock(),\n \"second_gen\": mock.MagicMock()}\n\n device_os.families.get.return_value = families\n vendor = mock.MagicMock(get_device_os=mock.MagicMock(return_value=device_os))\n first_gen = families[\"first_gen\"]\n second_gen = families[\"second_gen\"]\n cs_session = mock.MagicMock()\n resource_name = \"test resource name\"\n self.networking_handler._upload_resource = mock.MagicMock(\n side_effect=[\n None,\n resource_name])\n\n attributes = {\n ResourceModelsAttributes.ENABLE_SNMP: \"False\",\n ResourceModelsAttributes.SNMP_READ_COMMUNITY: entry.snmp_community,\n ResourceModelsAttributes.USER: entry.user,\n ResourceModelsAttributes.PASSWORD: entry.password,\n ResourceModelsAttributes.ENABLE_PASSWORD: entry.enable_password\n }\n\n # act\n self.networking_handler.upload(entry=entry,\n vendor=vendor,\n cs_session=cs_session)\n # verify\n self.networking_handler._upload_resource.assert_any_call(cs_session=cs_session,\n entry=entry,\n resource_family=second_gen[\"family_name\"],\n resource_model=second_gen[\"model_name\"],\n driver_name=second_gen[\"driver_name\"],\n attribute_prefix=\"{}.\".format(\n second_gen[\"model_name\"]))\n\n self.networking_handler._upload_resource.assert_any_call(cs_session=cs_session,\n entry=entry,\n resource_family=first_gen[\"family_name\"],\n resource_model=first_gen[\"model_name\"],\n driver_name=first_gen[\"driver_name\"])","def _make_fifo(self):\n if os.path.exists(self.fifo_path):\n os.remove(self.fifo_path)\n os.mkfifo(self.fifo_path)","def test_move_single_file_aggr_into_model_prog_aggr_failure(composite_resource, aggr_cls, mock_irods):\n\n res, user = composite_resource\n file_path = 'pytest/assets/generic_file.txt'\n mp_folder = 'mp_folder'\n ResourceFile.create_folder(res, mp_folder)\n file_to_upload = UploadedFile(file=open(file_path, 'rb'),\n name=os.path.basename(file_path))\n\n add_file_to_resource(res, file_to_upload, folder=mp_folder, check_target_folder=True)\n assert res.files.count() == 1\n # at this point there should not be any model program/instance aggregation\n assert ModelProgramLogicalFile.objects.count() == 0\n # set folder to model program/instance aggregation type\n ModelProgramLogicalFile.set_file_type(resource=res, user=user, folder_path=mp_folder)\n res_file = res.files.first()\n assert res_file.has_logical_file\n # file has folder\n assert res_file.file_folder == mp_folder\n assert ModelProgramLogicalFile.objects.count() == 1\n # create a single file aggregation\n single_file_name = 'logan.vrt'\n file_path = 'pytest/assets/{}'.format(single_file_name)\n file_to_upload = UploadedFile(file=open(file_path, 'rb'),\n name=os.path.basename(file_path))\n\n res_file = add_file_to_resource(res, file_to_upload, check_target_folder=True)\n # set file to generic/model instance logical file type (aggregation)\n aggr_cls.set_file_type(res, user, res_file.id)\n assert aggr_cls.objects.count() == 1\n # moving the logan.vrt file into the mp_folder should fail\n src_path = 'data/contents/{}'.format(single_file_name)\n tgt_path = 'data/contents/{}/{}'.format(mp_folder, single_file_name)\n with pytest.raises(RF_ValidationError):\n move_or_rename_file_or_folder(user, res.short_id, src_path, tgt_path)\n\n tgt_path = 'data/contents/{}'.format(mp_folder)\n with pytest.raises(RF_ValidationError):\n move_or_rename_file_or_folder(user, res.short_id, src_path, tgt_path)\n\n assert not res.dangling_aggregations_exist()","def test_non_integer_filesystem_limit(self):\n command_line = [\"pool\", \"set-fs-limit\", \"thispool\", \"1.2\"]\n for prefix in [[], [\"-propagate\"]]:\n self.check_system_exit(prefix + command_line, _PARSE_ERROR)","def test_is_not_delicious_file(self):\r\n bad_file = StringIO.StringIO()\r\n bad_file.write('failing tests please')\r\n bad_file.seek(0)\r\n\r\n self.assertTrue(\r\n not DelImporter.can_handle(bad_file),\r\n \"DelImporter cannot handle this file\")\r\n\r\n bad_file.close()","def manage_files(self):\n return 1 << 2","def test_6_1_6_etc_shadow_mode(host):\n assert host.file(ETC_SHADOW).mode == 0o640","def test_Telegramfd (self):\n\t\t# This file is delivered openned while this test.\n\t\ttestfilepath = os.path.join (self.hotfolder, \"fileinuse.txt\")\n\t\tf = open(testfilepath,\"a\") #opens file with name of \"test.txt\"\n\t\tf.write(\"This file is now opened and i'm writting on it \\n\")\n\n\n\t\tknown_values = set ([\n\t\t\t('TESTS/Test3/Telegram Desktop/Printer output.pdf', '.file'),\n\t\t\t('TESTS/Test3/Telegram Desktop/This is a Zero avi file.avi', '.file'),\n\t\t\t('TESTS/Test3/Telegram Desktop/File not in use.CBR', '.file'),\n\t\t\t('TESTS/Test3/Telegram Desktop/This is a dummy rar file.rar', '.rar'),\n\t\t\t])\n\n\t\tEntries = MD.Telegramfd (self.hotfolder)\n\t\tself.assertEqual (known_values, set(Entries))\n\n\t\tf.close ()","def test_10_is_allowed_file_wrong_ext(self):\n\n filename = \"somename.pdf\"\n is_allowed = utils.is_allowed_file(filename)\n self.assertFalse(is_allowed)","def test_verify_corrupt_archive_compare_data(self):\n self.backup(u\"full\", u\"testfiles/various_file_types\", options=[])\n output_files = os.listdir(\"testfiles/output\")\n archives = [elem for elem in output_files if \"vol\" in elem]\n for archive in archives:\n # Edit source file\n with open(\"testfiles/output/\" + archive, 'r+') as f:\n f.write('This writes text into each archive file to corrupt it.')\n # Test verify for the file\n try:\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable',\n options=[u\"--compare-data\"])\n except CmdError as e:\n # Should return a 21 error code for \"hash mismatch\"\n self.assertEqual(e.exit_status, 21, str(e))\n else:\n self.fail('Expected Hash Mismatch Error not thrown')","def get_file (queue):\n\n # get event from queue\n event = queue.get(True)\n\n try:\n # get name of new file\n filename = str(event.src_path)\n filetype = 'new'\n\n except AttributeError as e:\n # instead of event, queue entry is a filename added in\n # [run_blackbox]\n filename = event\n filetype = 'pre-existing'\n\n\n log.info ('detected a {} file: {}'.format(filetype, filename))\n\n\n # only continue if a fits file\n if 'fits' not in filename:\n\n log.info ('{} is not a fits file; skipping it'.format(filename))\n filename = None\n\n else:\n\n # if filename is a temporary rsync copy (default\n # behaviour of rsync is to create a temporary file\n # starting with .[filename].[randomstr]; can be\n # changed with option \"--inplace\"), then let filename\n # refer to the eventual file created by rsync\n fn_head, fn_tail = os.path.split(filename)\n if fn_tail[0] == '.':\n filename = '{}/{}'.format(fn_head, '.'\n .join(fn_tail.split('.')[1:-1]))\n log.info ('changed filename from rsync temporary file {} to {}'\n .format(event.src_path, filename))\n\n # this while loop below replaces the old [copying]\n # function; it times out after wait_max is reached\n wait_max = 180\n t0 = time.time()\n nsleep = 0\n while time.time()-t0 < wait_max:\n\n try:\n # read the file\n data = read_hdulist(filename)\n\n except:\n\n process = False\n if nsleep==0:\n log.warning ('file {} has not completely arrived yet; '\n 'will keep trying to read it in for {}s'\n .format(filename, wait_max))\n\n # give file a bit of time to arrive before next read attempt\n time.sleep(5)\n nsleep += 1\n\n else:\n # if fits file was read fine, set process flag to True\n process = True\n log.info ('successfully read file {} within {:.1f}s'\n .format(filename, time.time()-t0))\n # and break out of while loop\n break\n\n\n if not process:\n log.info ('{}s limit for reading file reached, not processing {}'\n .format(wait_max, filename))\n filename = None\n\n\n return filename","def perform_general():\r\n print(\"Performing the \\\"indirect\\\" check on all blocks\")\r\n \r\n filename_part = \"fusedata.\"\r\n i_start = begining_block_number + 1 #1\r\n i_end = ending_block_number #25\r\n file_name = filename_part\r\n\r\n \r\n for i in range(i_start, (i_end + 1)):\r\n #opening each block available to us\r\n file_name = filename_part + str(i)\r\n #print \"file_name\"+file_name\r\n file_access = open (file_name,\"r+\")\r\n \r\n try:\r\n block_contents = json.load(file_access)\r\n file_access.close()\r\n except ValueError:\r\n print \"\\\"\"+file_name+\"\\\" not in json format (catch 1)\"\r\n file_access.close() \r\n continue\r\n \r\n # first performing the size check\r\n if(type(block_contents) == dict):\r\n # checking if the current parsed block is a file inode\r\n if((\"indirect\" in block_contents) == True):\r\n size_check(block_contents, file_name)\r\n perform_indirect(block_contents, file_name)\r\n \r\n #closing the file to effect changes\r\n file_access_2 = open (file_name,\"w\")\r\n json.dump(block_contents,file_access_2)\r\n file_access_2.close() \r\n \r\n\r\n \r\n print(\"Reached end of test\") \r\n \r\n print(\"-------------------------------\")","def test_compress_fastq_real_with_integrity(\n first_tmp_file, second_tmp_file, spring_tmp_path, real_base_context\n):\n # GIVEN the path to a existing two existing fastq files and a non existing spring\n runner = CliRunner()\n assert not spring_tmp_path.exists()\n assert first_tmp_file.exists()\n assert second_tmp_file.exists()\n\n dir_path = spring_tmp_path.parent\n assert nr_files(dir_path) == 2\n # WHEN running the compress command with an intergrity check\n result = runner.invoke(\n fastq,\n [\n \"--first-read\",\n str(first_tmp_file),\n \"--second-read\",\n str(second_tmp_file),\n \"--spring-path\",\n str(spring_tmp_path),\n \"--check-integrity\",\n ],\n obj=real_base_context,\n )\n # THEN assert the command succedes\n assert result.exit_code == 0\n # THEN assert that the spring file was created\n assert spring_tmp_path.exists()\n # THEN assert that the files created for integrity check was removed\n assert nr_files(dir_path) == 3","def test_ignore_readonly1(tmpdir):\n os.makedirs('%s/foo/bar/baz' % tmpdir)\n os.chmod('%s/foo/bar/baz' % tmpdir, 0o500)\n os.chmod('%s/foo/bar' % tmpdir, 0o500)\n os.chmod('%s/foo' % tmpdir, 0o500)\n\n with pytest.raises(PermissionError):\n sh.remove('%s/foo' % tmpdir, recursive=True)\n assert os.path.exists('%s/foo/bar/baz' % tmpdir)\n\n sh.remove('%s/foo' % tmpdir, force=False, recursive=True,\n ignore_readonly=True)\n assert not os.path.exists('%s/foo' % tmpdir)","def test_read_delete(self):\n\n expected = \"Hello, World! This has been written by Fun Ilrys.\"\n File(\"hi\").write(expected)\n actual = File(\"hi\").read()\n\n self.assertEqual(expected, actual)\n\n expected = False\n File(\"hi\").delete()\n actual = PyFunceble.path.isfile(\"hi\")\n\n self.assertEqual(expected, actual)","def check_file_validity(self, file_):\n if not os.access(file_, os.F_OK):\n raise TailError(\"File '%s' does not exist\" % (file_))\n if not os.access(file_, os.R_OK):\n raise TailError(\"File '%s' not readable\" % (file_))\n if os.path.isdir(file_):\n raise TailError(\"File '%s' is a directory\" % (file_))","def test_verify_pvc_invalid_access_mode(self, interface, pvc_factory):\n expected_err = 'invalid: spec.accessModes: Unsupported value: \"RWO\"'\n\n # Try to create a PVC by providing invalid value of access mode\n try:\n self.pvc_obj = pvc_factory(\n interface=interface,\n project=None,\n storageclass=None,\n size=3,\n access_mode=\"RWO\",\n custom_data=None,\n status=constants.STATUS_BOUND,\n )\n assert not self.pvc_obj, \"Unexpected: PVC creation hasn't failed.\"\n except CommandFailed as err:\n assert expected_err in str(\n err\n ), f\"Couldn't verify PVC creation. Unexpected error {str(err)}\"\n log.info(\"PVC creation failed as expected.\")","def test_06_verify_tar01(self, mock_isfile, mock_call, mock_msg):\n mock_msg.level = 0\n mock_isfile.return_value = False\n mock_call.return_value = 0\n status = udocker.FileUtil(\"tarball.tar\").verify_tar()\n self.assertFalse(status)","def check(src, perm, dest, cmds, comp, verbose=False):\n if comp == Cmp.differ:\n ansiprint(f\"The file '{src}' differs from '{dest}'.\", fg=Color.red, i=True)\n elif comp == Cmp.nodest:\n ansiprint(\n f\"The destination file '{dest}' does not exist\",\n fg=Color.black,\n bg=Color.red,\n )\n elif comp == Cmp.nosrc:\n ansiprint(\n f\"The source file '{src}' does not exist.\", fg=Color.black, bg=Color.red\n )\n elif comp == Cmp.same and verbose:\n ansiprint(f\"The files '{src}' and '{dest}' are the same.\", fg=Color.green)","def processfile(args, fh):\n if args.quick:\n scanner = quickScanZip(args, fh)\n else:\n scanner = findPKHeaders(args, fh)\n\n def checkarg(arg, ent):\n if not arg:\n return False\n return '*' in arg or ent.name in arg\n def checkname(a, b):\n if a and '*' in a: return True\n if b and '*' in b: return True\n l = 0\n if a: l += len(a)\n if b: l += len(b)\n return l > 1\n\n if args.verbose and not (args.cat or args.raw or args.save):\n print(\" 0304 need flgs mth stamp --crc-- compsize fullsize nlen xlen namofs xofs datofs endofs\")\n print(\" 0102 crea need flgs mth stamp --crc-- compsize fullsize nlen xlen clen dsk0 attr osattr datptr namofs xofs cmtofs endofs\")\n for ent in scanner:\n if args.cat or args.raw or args.save:\n if args.quick and isinstance(ent, CentralDirEntry) or \\\n not args.quick and isinstance(ent, LocalFileHeader):\n ent.loaditems(fh)\n do_cat = checkarg(args.cat, ent)\n do_raw = checkarg(args.raw, ent)\n do_save= checkarg(args.save, ent)\n\n do_name= checkname(args.cat, args.raw)\n\n if do_name:\n print(\"\\n===> \" + ent.name + \" <===\\n\")\n\n sys.stdout.flush()\n blks = zipraw(fh, ent)\n\n if args.password and ent.flags&1:\n blks = zip_decrypt(blks, args.password)\n if do_cat or do_save:\n blks = skipbytes(blks, 12, args)\n\n if do_cat:\n sys.stdout.buffer.writelines(zipcat(blks, ent))\n if do_raw:\n sys.stdout.buffer.writelines(blks)\n if do_save:\n savefile(args.outputdir, ent.name, zipcat(blks, ent))\n else:\n ent.loaditems(fh)\n if args.verbose or not args.quick:\n print(\"%08x: %s\" % (ent.pkOffset, ent))\n else:\n print(ent.summary())\n if hasattr(ent, \"comment\") and ent.comment and not args.dumpraw:\n print(ent.comment)\n if args.dumpraw and hasattr(ent, \"extraLength\") and ent.extraLength:\n print(\"%08x: XTRA: %s\" % (ent.extraOffset, binascii.b2a_hex(getbytes(fh, ent.extraOffset, ent.extraLength))))\n if args.dumpraw and hasattr(ent, \"comment\") and ent.comment:\n print(\"%08x: CMT: %s\" % (ent.commentOffset, binascii.b2a_hex(getbytes(fh, ent.commentOffset, ent.commentLength))))\n if args.dumpraw and isinstance(ent, LocalFileHeader):\n blks = zipraw(fh, ent)\n if args.password and ent.flags&1:\n blks = zip_decrypt(blks, args.password)\n\n blockdump(ent.dataOffset, blks)","def test_fifo_sync(args=None):\n if args is None:\n args = Namespace(width=8, size=16, name='test')\n else:\n assert hasattr(args, 'width')\n assert hasattr(args, 'size')\n args = tb_default_args(args)\n\n reset = ResetSignal(0, active=1, isasync=True)\n clock = Clock(0, frequency=50e6)\n glbl = Global(clock, reset)\n fbus = FIFOBus(width=args.width)\n\n @myhdl.block\n def bench_fifo_sync():\n \n tbdut = fifo_sync(glbl, fbus, size=args.size)\n tbclk = clock.gen()\n \n @instance\n def tbstim():\n fbus.write_data.next = 0xFE\n reset.next = reset.active\n yield delay(33)\n reset.next = not reset.active\n for ii in range(5):\n yield clock.posedge\n\n # test the normal cases\n for num_bytes in range(1, args.size+1):\n\n # write some bytes\n for ii in range(num_bytes):\n yield clock.posedge\n fbus.write_data.next = ii + 0xCE\n fbus.write.next = True\n\n yield clock.posedge\n fbus.write.next = False\n fbus.write_data.next = 0xFE\n\n # if 16 bytes written make sure FIFO is full\n yield clock.posedge\n if num_bytes == args.size:\n assert fbus.full, \"FIFO should be full!\"\n assert not fbus.empty, \"FIFO should not be empty\"\n \n # fbus.read.next = True\n # yield clock.posedge\n for ii in range(5):\n yield clock.posedge\n if not fbus.empty:\n break\n\n for ii in range(num_bytes):\n fbus.read.next = True\n yield clock.posedge\n assert fbus.read_valid\n assert fbus.read_data == ii + 0xCE, \\\n \"rdata %x ii %x \" % (fbus.read_data, ii + 0xCE)\n\n fbus.read.next = False\n yield clock.posedge\n assert fbus.empty\n\n raise StopSimulation\n\n w = args.width\n write_data, read_data = Signals(intbv(0)[w:], 2)\n\n @always_comb\n def tbmon():\n write_data.next = fbus.write_data\n read_data.next = fbus.read_data\n\n return tbdut, tbclk, tbstim, tbmon\n\n run_testbench(bench_fifo_sync, args=args)","def cant(self, permissions: Union[str, List]) -> bool:","def test_fileinuse (self):\t\t\n\t\t# open a file:\n\t\ttestfilepath = os.path.join (self.testfolder, \"fileinuse.txt\")\n\t\tf = open(testfilepath,\"a\") #opens file with name of \"test.txt\"\n\t\tf.write(\"This file is now opened and i'm writting on it \\n\")\n\t\tself.assertEqual (MD.fileinuse(testfilepath), True) # Checks a file that it is beign written.\n\t\tself.assertEqual (MD.folderinuse(self.testfolder), True) # Checks if any file inside the folder is beign used.\n\t\tf.close()\n\t\tself.assertEqual (MD.fileinuse(testfilepath), False) # Cheks a file that it is closed.\n\t\tself.assertEqual (MD.folderinuse(self.testfolder), False)","def testChecksumCondition(self):\n file_defs = [\n {'name': 'file_0_byte.txt', 'path': '', 'size': 0},\n {'name': 'file_1_byte.txt', 'path': '', 'size': 1},\n {'name': 'file_320k_minus 1_byte.txt', 'path': 'folder1', 'size': 320 * 1024 - 1, 'mod_inc': -1},\n ]\n\n self._setup_test_store(file_defs)\n\n # Check if this provider supports checksum as change condition\n drive = self.drive_class(self.account_id, self.config_file_dir, self.config_pw)\n if drive.files_differ_on_hash(\n os.path.join(self.test_local_dir, file_defs[0]['path'], file_defs[0]['name']),\n 'dummy_hash') is None:\n self.skipTest('Checksum change condition not supported for this provider.')\n\n self._sync_drives()\n\n # Modify files\n for file_def in file_defs:\n file_path =\\\n os.path.join(self.test_local_dir, file_def['path'], file_def['name'])\n test_utils.make_random_file(\n file_path, file_def['size'], leave_existing=False,\n modify_timestamp_ns=os.stat(file_path).st_mtime_ns)\n\n self._sync_drives()\n self._download_store()\n self.assertDirectoriesAreEqual(self.test_local_dir, self.test_download_dir)","def test_6_1_4_etc_group_mode(host):\n assert host.file(ETC_GROUP).mode == 0o644","def test_08_verify_tar03(self, mock_isfile, mock_call, mock_msg):\n mock_msg.level = 0\n mock_isfile.return_value = True\n mock_call.return_value = 1\n status = udocker.FileUtil(\"tarball.tar\").verify_tar()\n self.assertFalse(status)","def _ensure_read_write_access(tarfileobj):\n dir_perm = tarfile.TUREAD | tarfile.TUWRITE | tarfile.TUEXEC\n file_perm = tarfile.TUREAD | tarfile.TUWRITE\n\n for tarinfo in tarfileobj.getmembers():\n tarinfo.mode |= (dir_perm if tarinfo.isdir() else file_perm)","def test_mark_multiple_res_files_as_mp_file_type(composite_resource, mock_irods):\n\n res, user = composite_resource\n file_path = 'pytest/assets/{}'\n txt_file_path = file_path.format('generic_file.txt')\n vrt_file_path = file_path.format('logan.vrt')\n upload_folder = 'mp_folder'\n ResourceFile.create_folder(res, upload_folder)\n file_to_upload = UploadedFile(file=open(txt_file_path, 'rb'),\n name=os.path.basename(txt_file_path))\n\n res_file_txt = add_file_to_resource(\n res, file_to_upload, folder=upload_folder, check_target_folder=True\n )\n\n file_to_upload = UploadedFile(file=open(vrt_file_path, 'rb'),\n name=os.path.basename(vrt_file_path))\n res_file_vrt = add_file_to_resource(\n res, file_to_upload, folder=upload_folder, check_target_folder=True\n )\n assert res.files.count() == 2\n # create model program aggregation\n assert ModelProgramLogicalFile.objects.count() == 0\n # set the folder to model program aggregation type\n ModelProgramLogicalFile.set_file_type(res, user, folder_path=upload_folder)\n assert ModelProgramLogicalFile.objects.count() == 1\n\n assert ModelProgramResourceFileType.objects.count() == 0\n mp_aggregation = ModelProgramLogicalFile.objects.first()\n mp_type = 'software'\n # set the txt file as software for this aggregation\n ModelProgramResourceFileType.create(file_type=mp_type, res_file=res_file_txt,\n mp_metadata=mp_aggregation.metadata)\n assert ModelProgramResourceFileType.objects.count() == 1\n # set the vrt file as software for this aggregation\n ModelProgramResourceFileType.create(file_type=mp_type, res_file=res_file_vrt,\n mp_metadata=mp_aggregation.metadata)\n assert ModelProgramResourceFileType.objects.count() == 2\n assert not res.dangling_aggregations_exist()","def test_negative_filesystem_limit(self):\n command_line = [\"pool\", \"set-fs-limit\", \"thispool\", \"-1\"]\n for prefix in [[], [\"-propagate\"]]:\n self.check_system_exit(prefix + command_line, _PARSE_ERROR)","def test_multiple_users_cannot_access_each_others_files(self):\n userID1 = 'aaaabbbbccccdddd' # Create three users\n userID2 = 'aaaccccbbbddd'\n userID3 = 'bbbaaadddccccc'\n user1 = ServComs(self.serverIp, userID1)\n user2 = ServComs(self.serverIp, userID2)\n user3 = ServComs(self.serverIp, userID3)\n try:\n nonce1_1 = globals.generate_random_nonce()\n nonce1_2 = globals.generate_random_nonce()\n nonce2_1 = globals.generate_random_nonce()\n nonce2_2 = globals.generate_random_nonce()\n self.send_file(nonce1_1, nonce1_2, user1) # have two send a file\n self.send_file(nonce2_1, nonce2_2, user2)\n files_user_1 = user1.get_file_list() # get their files on server back\n files_user_2 = user2.get_file_list()\n files_user_3 = user3.get_file_list()\n self.assertTrue(len(files_user_3) == 0, # should have right lengths\n \"User 3 has send no files; should have no files.\")\n self.assertTrue(len(files_user_1) == 1 and len(files_user_2) == 1,\n \"User 1 & 2 has send 1 file; should have 1 file.\")\n for encfilename, nonce, ts in files_user_1: #\n self.assertTrue([encfilename, nonce] in [[x[0], x[1]] for x in files_user_1])\n self.assertTrue([encfilename, nonce] not in [[x[0], x[1]] for x in files_user_2])\n for encfilename, nonce, ts in files_user_2:\n self.assertTrue([encfilename, nonce] in [[x[0], x[1]] for x in files_user_2])\n self.assertTrue([encfilename, nonce] not in [[x[0], x[1]] for x in files_user_1])\n user1.get_file(files_user_1[0][0])\n user2.get_file(files_user_2[0][0])\n self.assertRaises(FileNotFoundError, user2.get_file, files_user_1[0][0])\n self.assertRaises(FileNotFoundError, user1.get_file, files_user_2[0][0])\n finally:\n self.unregister_user(userID1)\n self.unregister_user(userID2)\n self.unregister_user(userID3)","def test_delete_file(self):\n # Fake raw_input \n passkeeper.raw_input = lambda x: 'y'\n\n # init\n pk = passkeeper.Passkeeper(directory='.tox/foo')\n # Init directory\n pk.init_dir(passphrase='secret')\n # Decrypt files\n pk.decrypt(passphrase='secret')\n\n # Add input in file\n sample_file = (\"\"\"[bar]\nname = bar access\n\"\"\")\n with open('.tox/foo/bar.ini', 'a') as f:\n f.write(sample_file)\n\n # Add raw dir and file in it\n create_dir('.tox/foo/bar.raw/')\n sample_file = (\"\"\"My private data\"\"\")\n with open('.tox/foo/bar.raw/private', 'w') as f:\n f.write(sample_file)\n\n # Encrypt files\n pk.encrypt(passphrase='secret', commit_message='Add bar entry')\n # Clean old files\n pk.remove_old_encrypted_files(force_remove=True)\n # Call cleanup ini files\n pk.cleanup()\n\n # Decrypt file (last validation)\n pk.decrypt(passphrase='secret')\n os.remove('.tox/foo/bar.ini')\n shutil.rmtree('.tox/foo/bar.raw/')\n\n\n # Encrypt files\n pk.encrypt(passphrase='secret', commit_message='Will remove bar.ini and bar.raw/*')\n\n\n # Control state befor\n self.assertTrue(isfile('.tox/foo/encrypted/bar.ini.passkeeper'))\n self.assertTrue(isfile('.tox/foo/encrypted/bar.raw/private.passkeeper'))\n git_logs = self._get_file_lines(filename='.tox/foo/.git/logs/HEAD')\n self.assertTrue('Will remove bar.ini and bar.raw/*' in git_logs[-1])\n\n # Remove old files\n pk.remove_old_encrypted_files(force_remove=True)\n\n # Call cleanup. file bar.passkeeped should be remove\n pk.cleanup()\n\n # Should have a commit for file deleted\n self.assertFalse(isfile('.tox/foo/encrypted/bar.ini.passkeeper'))\n self.assertTrue(isfile('.tox/foo/encrypted/default.ini.passkeeper'))\n self.assertFalse(isfile('.tox/foo/encrypted/bar.raw/private.passkeeper'))\n self.assertFalse(isdir('.tox/foo/encrypted/bar.raw'))\n git_logs = self._get_file_lines(filename='.tox/foo/.git/logs/HEAD')\n self.assertTrue('Remove file encrypted/bar.raw/private.passkeeper' in git_logs[-2])\n self.assertTrue('Remove file encrypted/bar.ini.passkeeper' in git_logs[-1])","def test_bad_file():\n\n bad = ''.join(random.choices(string.ascii_uppercase + string.digits, k=5))\n rv, out = getstatusoutput(f'{prg} -f {bad}')\n assert rv != 0\n assert re.match('usage:', out, re.I)\n assert re.search(f\"No such file or directory: '{bad}'\", out)","def test_create1a(self):\n fname = TempfileManager.create_tempfile(dir=tempdir)\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('tmp'))","def test_fileAlreadyExistsNoOverwrite(self):\n fp = FilePath(self.mktemp())\n fp.touch()\n\n self.assertRaises(OSError, self.makeConnectedDccFileReceive, fp.path)","def test_6_1_3_etc_group_mode(host):\n assert host.file(ETC_PASSWD_DASH).mode == 0o644","def test_overwrite_hops(overwrite_on_tape_topology, caches_mock, did_factory, file_factory):\n rse1_id, rse2_id, rse3_id, did1, did2 = overwrite_on_tape_topology(did1_corrupted=False, did2_corrupted=True)\n did_factory.upload_client.upload(\n [\n {\n 'path': file_factory.file_generator(size=3),\n 'rse': rse_core.get_rse_name(rse2_id),\n 'did_scope': did1['scope'].external,\n 'did_name': did1['name'],\n 'no_register': True,\n }\n ]\n )\n all_rses = [rse1_id, rse2_id, rse3_id]\n\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\n\n fts_schema_version = FTS3Transfertool(external_host=TEST_FTS_HOST).version()['schema']['major']\n if fts_schema_version >= 8:\n # Newer fts version will honor the overwrite_hop\n request = __wait_for_state_transition(dst_rse_id=rse2_id, **did1)\n assert request['state'] == RequestState.DONE\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.FAILED\n assert 'Destination file exists and overwrite is not enabled' in request['err_msg']\n else:\n # FTS only recently introduced the overwrite_hops parameter. It will be ignored on old\n # fts versions and the first hop will fail with the file exists error\n # TODO: remove this else after FTS 3.12 release and after updating rucio/fts container with the new release\n request = __wait_for_state_transition(dst_rse_id=rse2_id, **did1)\n assert request['state'] == RequestState.FAILED\n assert 'Destination file exists and overwrite is not enabled' in request['err_msg']\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\n assert request['state'] == RequestState.FAILED\n assert 'Unused hop in multi-hop' in request['err_msg']","def test_command_edit_disk_sides():\n with tempfile.NamedTemporaryFile() as tmp:\n shutil.copy(kValid2, tmp.name)\n\n # this file is a 5.25-inch disk image\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[\"disk_type\"] == 1\n assert woz.info[\"disk_sides\"] == 1\n\n # 5.25-inch disk images can only be \"1-sided\"\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskSides):\n wozardry.parse_args([\"edit\", \"-i\", \"disk_sides:2\", tmp.name])\n\n # now change it to a 3.5-inch disk image\n wozardry.parse_args([\"edit\", \"-i\", \"disk_type:2\", tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[\"disk_type\"] == 2\n\n # 3.5-inch disk images can be 1- or 2-sided\n wozardry.parse_args([\"edit\", \"-i\", \"disk_sides:2\", tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[\"disk_sides\"] == 2\n wozardry.parse_args([\"edit\", \"-i\", \"disk_sides:1\", tmp.name])\n with open(tmp.name, \"rb\") as tmpstream:\n woz = wozardry.WozDiskImage(tmpstream)\n assert woz.info[\"disk_sides\"] == 1\n\n # ...but not 3-sided, that's silly\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskSides):\n wozardry.parse_args([\"edit\", \"-i\", \"disk_sides:3\", tmp.name])","def test_make_file_read_only():\n\n filename = os.path.join(tempfile.gettempdir(), \"jade-test-file.txt\")\n if os.path.exists(filename):\n os.chmod(filename, stat.S_IWRITE)\n os.remove(filename)\n\n with open(filename, \"w\") as f:\n f.write(\"Hello World\")\n\n prev_mode = os.stat(filename)\n make_file_read_only(filename)\n # Disabling because it doesn't work on Windows.\n # s = os.stat(filename)\n # assert s.st_mode != prev_mode\n # assert s.st_mode == 33060\n\n if os.path.exists(filename):\n os.chmod(filename, stat.S_IWRITE)\n os.remove(filename)"],"string":"[\n \"def vsys_fifo_exists(path):\\n if not os.path.exists(path):\\n collectd.error('File does not exist: %s' % path)\\n return False\\n if not stat.S_ISFIFO(os.stat(path).st_mode):\\n collectd.error('File is not a fifo: %s' % path)\\n return False\\n return True\",\n \"def _check_fifo(self):\\n if not os.path.exists(self.fifo_path):\\n raise FifoIsNotAvailable(\\\"trying to load from session that it's fifo is deleted!\\\")\",\n \"def _verify_descriptors(self, msg):\\n self.assertTrue(is_writable_file(msg.chlderr))\\n self.assertTrue(is_writable_file(msg.chldout))\\n self.assertTrue(is_writable_file(msg.chldnul))\",\n \"def test_noPermission(self):\\n log = logfile.LogFile(self.name, self.dir)\\n log.write(\\\"abc\\\")\\n\\n # change permissions so rotation would fail\\n os.chmod(self.dir, 0555)\\n\\n # if this succeeds, chmod doesn't restrict us, so we can't\\n # do the test\\n try:\\n f = open(os.path.join(self.dir,\\\"xxx\\\"), \\\"w\\\")\\n except (OSError, IOError):\\n pass\\n else:\\n f.close()\\n return\\n\\n log.rotate() # this should not fail\\n\\n log.write(\\\"def\\\")\\n log.flush()\\n\\n f = log._file\\n self.assertEquals(f.tell(), 6)\\n f.seek(0, 0)\\n self.assertEquals(f.read(), \\\"abcdef\\\")\\n log.close()\",\n \"def test_noPermission(self):\\n log = logfile.LogFile(self.name, self.dir)\\n self.addCleanup(log.close)\\n log.write(\\\"abc\\\")\\n\\n # change permissions so rotation would fail\\n os.chmod(self.dir, 0o555)\\n\\n # if this succeeds, chmod doesn't restrict us, so we can't\\n # do the test\\n try:\\n f = open(os.path.join(self.dir, \\\"xxx\\\"), \\\"w\\\")\\n except OSError:\\n pass\\n else:\\n f.close()\\n return\\n\\n log.rotate() # this should not fail\\n\\n log.write(\\\"def\\\")\\n log.flush()\\n\\n f = log._file\\n self.assertEqual(f.tell(), 6)\\n f.seek(0, 0)\\n self.assertEqual(f.read(), b\\\"abcdef\\\")\",\n \"def test_provider_system_hook_file_chmod(change_dir, fix_file_perms):\\n tackle(context_file='chmod.yaml', no_input=True)\\n assert oct(os.stat('tackle.yaml').st_mode)[-3:] == \\\"600\\\"\",\n \"def _check_stream_writable(self, fe_commit):\\n if not self._current_branch.stream_name:\\n return\\n prefix = self._current_branch.writable_stream_name + '/'\\n for fe_file in fe_commit['files']:\\n gwt_path = fe_file['path']\\n depot_path = self.ctx.gwt_path(gwt_path).to_depot()\\n if depot_path.startswith(prefix):\\n continue\\n\\n human_msg = (_(\\n \\\"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\\"\\n \\\" Paths not in stream '{stream}' are read-only for branch '{b}'.\\\")\\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\\n , gwt_path = gwt_path\\n , depot_path = depot_path\\n , stream = self._current_branch.writable_stream_name\\n , b = self._current_branch.branch_id ))\\n raise PreflightException(human_msg)\",\n \"def test_files(*fns, test_nonzero=False, allow_pipes=False):\\n\\n for fn in fns:\\n is_file = os.path.isfile(fn)\\n is_pipe = pathlib.Path(fn).is_fifo()\\n if allow_pipes:\\n if not is_file or is_pipe:\\n error('File \\\"{}\\\" does not exist.'.format(fn))\\n else:\\n if is_pipe:\\n if not is_file:\\n error('File \\\"{}\\\" is a process substitution or a device.'.format(fn))\\n else:\\n if not is_file:\\n error('File \\\"{}\\\" does not exist.'.format(fn))\\n\\n if test_nonzero and not allow_pipes:\\n if not file_sizes(fn)[0]:\\n error('File \\\"{}\\\" has size 0.'.format(fn))\",\n \"def test_permissions(self):\\n self.assertEqual(dir_perm, 0o2750)\\n self.assertEqual(file_perm, 0o0440)\",\n \"def _unblock_open_fifo_operation(self) -> None:\\n if os.path.exists(self._fifo_out_path):\\n open(self._fifo_out_path, 'wb', buffering=0)\\n if os.path.exists(self._fifo_in_path):\\n open(self._fifo_in_path, 'rb', buffering=0)\",\n \"def any(self, fifo: int, /) -> bool:\",\n \"def test_provider_system_hook_file_remove(change_dir, fix_file_perms):\\n o = tackle(context_file='remove.yaml', no_input=True)\\n assert o['if_file']\\n assert not o['not_file']\\n assert o['if_files']\\n assert not o['not_files']\",\n \"def test_is_special_file_socket(mocker: MockerFixture, tmp_path: Path) -> None:\\n mocker.patch(\\\"stat.S_ISSOCK\\\", return_value=True)\\n tmp_file = tmp_path / \\\"foo\\\"\\n tmp_file.touch()\\n assert is_special_file(tmp_file)\",\n \"def test_rotatePermissionFileNotOk(self):\\n log = logfile.DailyLogFile(self.name, self.dir)\\n self.addCleanup(log.close)\\n\\n os.chmod(log.path, 0o444)\\n previousFile = log._file\\n log.rotate()\\n self.assertEqual(previousFile, log._file)\",\n \"def test_specifiedPermissions(self):\\n log1 = logfile.LogFile(self.name, self.dir, defaultMode=0o066)\\n self.addCleanup(log1.close)\\n mode = stat.S_IMODE(os.stat(self.path)[stat.ST_MODE])\\n if runtime.platform.isWindows():\\n # The only thing we can get here is global read-only\\n self.assertEqual(mode, 0o444)\\n else:\\n self.assertEqual(mode, 0o066)\",\n \"def _check_stream_in_classic(self, fe_commit):\\n if self._current_branch.stream_name:\\n return\\n\\n depot_re = re.compile(r'^//([^/]+)/([^/]+)/.*$')\\n for fe_file in fe_commit['files']:\\n gwt_path = fe_file['path']\\n depot_path = self.ctx.gwt_path(gwt_path).to_depot()\\n m = depot_re.match(depot_path)\\n if m:\\n depot = m.group(1)\\n if depot in self.stream_depots:\\n stream = '//{}/{}'.format(m.group(1), m.group(2))\\n human_msg = (\\n _(\\\"Cannot commit {sha1} '{gwt_path}' to '{depot_path}'.\\\"\\n \\\" Paths in stream '{stream}' are read-only for branch '{b}'.\\\")\\n .format( sha1 = p4gf_util.abbrev(fe_commit['sha1'])\\n , gwt_path = gwt_path\\n , depot_path = depot_path\\n , stream = stream\\n , b = self._current_branch.branch_id ))\\n raise PreflightException(human_msg)\",\n \"def test_specifiedPermissions(self):\\n log1 = logfile.LogFile(self.name, self.dir, defaultMode=0066)\\n mode = stat.S_IMODE(os.stat(self.path)[stat.ST_MODE])\\n if runtime.platform.isWindows():\\n # The only thing we can get here is global read-only\\n self.assertEquals(mode, 0444)\\n else:\\n self.assertEquals(mode, 0066)\",\n \"def check_perms(resource):\\r\\n stmode = os.stat(resource).st_mode\\r\\n return (getattr(stat, 'S_IROTH') & stmode) > 0\",\n \"def check_perms(resource):\\r\\n stmode = os.stat(resource).st_mode\\r\\n return (getattr(stat, 'S_IROTH') & stmode) > 0\",\n \"def test_modePreservation(self):\\n open(self.path, \\\"w\\\").close()\\n os.chmod(self.path, 0o707)\\n mode = os.stat(self.path)[stat.ST_MODE]\\n log = logfile.LogFile(self.name, self.dir)\\n self.addCleanup(log.close)\\n log.write(\\\"abc\\\")\\n log.rotate()\\n self.assertEqual(mode, os.stat(self.path)[stat.ST_MODE])\",\n \"def io_check(*args, func=None):\\n func = func or inspect.stack()[2][3]\\n for var in args:\\n if not isinstance(var, io.IOBase):\\n name = type(var).__name__\\n raise IOObjError(\\n 'Function {} expected file-like object, {} got instead.'.format(func, name))\",\n \"def can_handle(file_io):\\r\\n raise NotImplementedError(\\\"Please implement this in your importer\\\")\",\n \"def test_provider_system_hook_file(change_dir, clean_files):\\n tackle(no_input=True)\\n assert 'thing.yaml' in os.listdir()\\n assert 'stuff' in os.listdir()\\n # If the file has been moved properly there should be only one file\\n assert len(os.listdir('stuff')) == 3\",\n \"def test_file_unused(self):\\n try:\\n with get_temp_file() as (fd, name):\\n pass\\n except Exception as err:\\n self.fail('Failed with exception \\\"{}\\\"'.format(err))\\n else:\\n file_exists = os.access(name, os.F_OK)\\n self.assertFalse(file_exists)\",\n \"def sort_permissions(fl):\\n\\n if oct(os.stat(fl).st_mode)[4:] != '666':\\n os.chmod(fl, 0o666)\",\n \"def set_file_permissions(host, fqpath, perms):\\n command = \\\"chmod %s %s\\\" % (perms, fqpath)\\n rcode, _, rerr = g.run(host, command)\\n\\n if rcode == 0:\\n return True\\n\\n g.log.error('chmod failed: %s' % rerr)\\n return False\",\n \"def checkIfAllowedToModify(self):\\n\\n oldBytes = b''\\n testFileName = self.MAPSTUDIO + self.inputFiles[0] + '.msb'\\n\\n with open(testFileName, 'rb') as oldf:\\n oldBytes = oldf.read()\\n\\n # Try writing something to the file\\n\\n try:\\n with open(testFileName, 'wb') as outf:\\n outf.write(b'TESTINGIFICANWRITEINTOTHISFILE')\\n except:\\n return False\\n\\n # Because apparently for _some_ reason it doesn't throw an error sometimes(?) so we confirm if the file was actually modified\\n\\n newBytes = b''\\n with open(testFileName, 'rb') as oldf:\\n newBytes = oldf.read()\\n\\n if (oldBytes == newBytes):\\n return False\\n\\n # Restore the file to normal\\n\\n with open(testFileName, 'wb') as outf:\\n outf.write(oldBytes)\\n\\n oldBytes = b''\\n newBytes = b''\\n\\n return True\",\n \"def test_command_edit_info_disk_type():\\n # this is pathological, don't do this in real life\\n def f(inputfile):\\n with tempfile.NamedTemporaryFile() as tmp:\\n shutil.copy(inputfile, tmp.name)\\n\\n # disk_type = 1 is ok\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_type:1\\\", tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[\\\"disk_type\\\"] == 1\\n\\n # disk_type = 2 is ok\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_type:2\\\", tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[\\\"disk_type\\\"] == 2\\n\\n # disk_type = 0 is not ok\\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskType):\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_type:0\\\", tmp.name])\\n\\n # disk_type = 3 is not ok\\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskType):\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_type:3\\\", tmp.name])\\n f(kValid1)\\n f(kValid2)\",\n \"def test_fifo_sync_random():\\n pass\",\n \"def test_overwrite_corrupted_files(overwrite_on_tape_topology, core_config_mock, caches_mock):\\n rse1_id, rse2_id, rse3_id, did1, did2 = overwrite_on_tape_topology(did1_corrupted=True, did2_corrupted=True)\\n all_rses = [rse1_id, rse2_id, rse3_id]\\n\\n class _FTSWrapper(FTSWrapper):\\n @staticmethod\\n def on_receive(job_params):\\n for job in (job_params if isinstance(job_params, list) else [job_params]):\\n for file in job.get('files', []):\\n if (file.get('file_metadata', {}).get('dst_type') == 'TAPE'\\n and file.get('file_metadata', {}).get('dst_file', {}).get('file_on_tape') is not None):\\n # Fake that dst_file metadata contains file_on_tape == True\\n # As we don't really have tape RSEs in our tests, file_on_tape is always false\\n file['file_metadata']['dst_file']['file_on_tape'] = True\\n return job_params\\n\\n with patch('rucio.daemons.conveyor.poller.FTS3Transfertool', _FTSWrapper):\\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\\n # Both transfers must be marked as failed because the file size is incorrect\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.FAILED\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did2)\\n assert request['state'] == RequestState.FAILED\\n\\n # Re-submit the failed requests. They must fail again, because overwrite_corrupted_files is False\\n # 2 runs: for multihop, finisher works one hop at a time\\n finisher(once=True, partition_wait_time=0)\\n finisher(once=True, partition_wait_time=0)\\n request = request_core.get_request_by_did(rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.QUEUED\\n request = request_core.get_request_by_did(rse_id=rse3_id, **did2)\\n assert request['state'] == RequestState.QUEUED\\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\\n # Set overwrite to True before running the poller or finisher\\n core_config.set('transfers', 'overwrite_corrupted_files', True)\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.FAILED\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did2)\\n assert request['state'] == RequestState.FAILED\\n\\n # Re-submit one more time. Now the destination file must be overwritten\\n finisher(once=True, partition_wait_time=0)\\n finisher(once=True, partition_wait_time=0)\\n request = request_core.get_request_by_did(rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.QUEUED\\n request = request_core.get_request_by_did(rse_id=rse3_id, **did2)\\n assert request['state'] == RequestState.QUEUED\\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\\n request = request_core.get_request_by_did(rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.SUBMITTED\\n assert __wait_for_fts_state(request, expected_state='ARCHIVING') == 'ARCHIVING'\\n request = request_core.get_request_by_did(rse_id=rse3_id, **did2)\\n assert request['state'] == RequestState.SUBMITTED\\n assert __wait_for_fts_state(request, expected_state='ARCHIVING') == 'ARCHIVING'\",\n \"def test_fileio(tmp_path):\\n async def aiotests():\\n fio = fileio.FileIO(Path(tmp_path))\\n\\n with pytest.raises(PermissionError):\\n fio.resolve_and_sanitize('../abba123')\\n\\n with pytest.raises(PermissionError):\\n fio.resolve_and_sanitize('/abba123')\\n\\n with pytest.raises(FileNotFoundError):\\n fio.resolve_and_sanitize('abba123', must_exist=True)\\n\\n with pytest.raises(FileNotFoundError):\\n fio.open_and_seek('abba123', 0, for_write=False)\\n\\n with pytest.raises(PermissionError):\\n fio.open_and_seek('../abba123', 0, for_write=False)\\n\\n with pytest.raises(aiohttp.web.HTTPForbidden):\\n await fio.upload_chunk(chunker.FileChunk(path='../abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234'), None)\\n\\n with pytest.raises(aiohttp.web.HTTPInternalServerError):\\n await fio.upload_chunk(chunker.FileChunk(path=False, pos=0, size=123, cmpratio=1, hash='abcdef1234'), None)\\n\\n assert await fio.copy_chunk_locally(\\n chunker.FileChunk(path='abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234'),\\n chunker.FileChunk(path='abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234')) is True\\n\\n with pytest.raises(ValueError):\\n assert await fio.copy_chunk_locally(\\n chunker.FileChunk(path='abba1234', pos=0, size=123, cmpratio=1, hash='abcdef1234'),\\n chunker.FileChunk(path='asdfklna', pos=0, size=123, cmpratio=1, hash='ABABABABAB'))\\n\\n assert fio.try_precreate_large_sparse_file('sparsefiletest.bin', 1234) == fio.resolve_and_sanitize('sparsefiletest.bin').exists()\\n await fio.remove_file_and_paths('sparsefiletest.bin')\\n\\n asyncio.run(aiotests())\",\n \"def testError(self):\\n cmds = \\\"\\\"\\\"chown 0 missingFile\\npwd\\nexit\\n\\\"\\\"\\\"\\n\\n def _cbCheckResult(res):\\n self.assertNotIn(self.testDir.asBytesMode().path, res)\\n\\n d = self._getBatchOutput(cmds)\\n d.addCallback(_cbCheckResult)\\n return d\",\n \"def test_mark_res_file_as_mp_file_type_failure_2(composite_resource, mock_irods):\\n\\n res, user = composite_resource\\n file_path = 'pytest/assets/{}'\\n txt_file_path = file_path.format('generic_file.txt')\\n vrt_file_path = file_path.format('logan.vrt')\\n upload_folder = ''\\n file_to_upload = UploadedFile(file=open(txt_file_path, 'rb'),\\n name=os.path.basename(txt_file_path))\\n\\n res_file_txt = add_file_to_resource(\\n res, file_to_upload, folder=upload_folder, check_target_folder=True\\n )\\n\\n file_to_upload = UploadedFile(file=open(vrt_file_path, 'rb'),\\n name=os.path.basename(vrt_file_path))\\n res_file_vrt = add_file_to_resource(\\n res, file_to_upload, folder=upload_folder, check_target_folder=True\\n )\\n assert res.files.count() == 2\\n # create model program aggregation\\n assert ModelProgramLogicalFile.objects.count() == 0\\n # set the txt file to model program aggregation type\\n ModelProgramLogicalFile.set_file_type(res, user, res_file_txt.id)\\n\\n assert ModelProgramResourceFileType.objects.count() == 0\\n mp_aggregation = ModelProgramLogicalFile.objects.first()\\n # trying set the vrt file as engine for this aggregation should fail as the vrt file is not part of\\n # the aggregation\\n with pytest.raises(ValidationError):\\n ModelProgramResourceFileType.create(file_type='engine', res_file=res_file_vrt,\\n mp_metadata=mp_aggregation.metadata)\\n\\n assert ModelProgramResourceFileType.objects.count() == 0\\n assert not res.dangling_aggregations_exist()\",\n \"def copy_file_check(self):\\n pass\",\n \"def test_mark_res_file_as_mp_file_type_failure_1(composite_resource, mock_irods):\\n\\n res, user = composite_resource\\n file_path = 'pytest/assets/generic_file.txt'\\n upload_folder = ''\\n file_to_upload = UploadedFile(file=open(file_path, 'rb'),\\n name=os.path.basename(file_path))\\n\\n res_file = add_file_to_resource(\\n res, file_to_upload, folder=upload_folder, check_target_folder=True\\n )\\n assert res.files.count() == 1\\n # create model program aggregation\\n assert ModelProgramLogicalFile.objects.count() == 0\\n # set file to model program aggregation type\\n ModelProgramLogicalFile.set_file_type(res, user, res_file.id)\\n res_file = res.files.first()\\n assert ModelProgramResourceFileType.objects.count() == 0\\n mp_aggregation = ModelProgramLogicalFile.objects.first()\\n # set the res_file as software for this aggregation\\n ModelProgramResourceFileType.create(file_type='software', res_file=res_file,\\n mp_metadata=mp_aggregation.metadata)\\n\\n assert ModelProgramResourceFileType.objects.count() == 1\\n mp_res_file_type = ModelProgramResourceFileType.objects.first()\\n assert mp_res_file_type.res_file.short_path == res_file.short_path\\n assert mp_res_file_type.file_type == ModelProgramResourceFileType.type_from_string('software')\\n # trying to set the same file again as mp file type should fail\\n with pytest.raises(ValidationError):\\n ModelProgramResourceFileType.create(file_type='engine', res_file=res_file,\\n mp_metadata=mp_aggregation.metadata)\\n\\n assert ModelProgramResourceFileType.objects.count() == 1\\n assert not res.dangling_aggregations_exist()\",\n \"def test_add_read_fail(self):\\n file_handler = open(self.test_task_filename, 'w')\\n file_handler.write('Mock corrupt data')\\n file_handler.close()\\n os.chmod(self.test_task_filename, 000)\\n\\n self.assertRaises(IOError, self.task_storage.add, self.my_task)\",\n \"def check_streaming(self, arg: str):\\n if not arg:\\n return False\\n elif arg.startswith(\\\"sdo:\\\"):\\n print(\\\"[check_streaming] File is for streaming\\\")\\n tmp_list = arg.splitlines()\\n tmp_list.pop(0)\\n for x in tmp_list:\\n if self.check_name(x) is False:\\n return False\\n return True\\n else:\\n return False\",\n \"def _checkPHOF(self, op, fifoReadings, counterValue):\\n # Relying on indices matching PHOF values.\\n PHOFS = np.where(fifoReadings == counterValue)[0]\\n # If no PHOF can be found with the target FIFO counter value...\\n if not len(PHOFS):\\n PHOF = -1 # Set to -1 so LabRAD call can complete. success=False\\n\\n # For each PHOF for which the FIFO counter equals counterValue, resend\\n # the PHOF and check that the FIFO counter indeed equals counterValue.\\n # If so, return the PHOF and a flag that the FIFO calibration has been\\n # successful.\\n success = False\\n for PHOF in PHOFS:\\n pkt = [0x0700 + PHOF, 0x8700]\\n reading = long(((yield self._runSerial(op, pkt))[1] >> 4) & 0xF)\\n if reading == counterValue:\\n success = True\\n break\\n ans = int(PHOF), success\\n returnValue(ans)\",\n \"def test_verify_corrupt_archive(self):\\n self.backup(u\\\"full\\\", u\\\"testfiles/various_file_types\\\", options=[])\\n output_files = os.listdir(\\\"testfiles/output\\\")\\n archives = [elem for elem in output_files if \\\"vol\\\" in elem]\\n for archive in archives:\\n # Edit source file\\n with open(\\\"testfiles/output/\\\" + archive, 'r+') as f:\\n f.write('This writes text into each archive file to corrupt it.')\\n # Test verify for the file\\n try:\\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable', options=[])\\n except CmdError as e:\\n # Should return a 21 error code for \\\"hash mismatch\\\"\\n self.assertEqual(e.exit_status, 21, str(e))\\n else:\\n self.fail('Expected Hash Mismatch Error not thrown')\",\n \"def file_mode_checks(value):\\n if not value.endswith(F_SUFFIX):\\n raise argparse.ArgumentTypeError(\\n \\\"\\\\nFile resource must be:\\\\n\\\"\\n + \\\" 1) Obfuscated {hex}.cache.js GWT permutation file\\\"\\n )\\n return value\",\n \"def test_compress_fastq_real_with_integrity_fail(\\n first_tmp_file, second_tmp_file, spring_tmp_path, real_base_context, mocker\\n):\\n # GIVEN the path to a existing two existing fastq files and a non existing spring\\n runner = CliRunner()\\n assert not spring_tmp_path.exists()\\n assert first_tmp_file.exists()\\n assert second_tmp_file.exists()\\n\\n dir_path = spring_tmp_path.parent\\n assert nr_files(dir_path) == 2\\n mocker.patch.object(compare_cmd, \\\"compare_elements\\\")\\n compare_cmd.compare_elements.return_value = False\\n # WHEN running the compress command with an intergrity check\\n result = runner.invoke(\\n fastq,\\n [\\n \\\"--first-read\\\",\\n str(first_tmp_file),\\n \\\"--second-read\\\",\\n str(second_tmp_file),\\n \\\"--spring-path\\\",\\n str(spring_tmp_path),\\n \\\"--check-integrity\\\",\\n ],\\n obj=real_base_context,\\n )\\n # THEN assert the command succedes\\n assert result.exit_code == 1\\n # THEN assert that the spring file was deleted\\n assert not spring_tmp_path.exists()\\n # THEN assert that only the original fastq files are left\\n assert nr_files(dir_path) == 2\",\n \"def test_find_read_fail(self):\\n file_handler = open(self.test_task_filename, 'w')\\n file_handler.write('Mock corrupt data')\\n file_handler.close()\\n os.chmod(self.test_task_filename, 000)\\n\\n self.assertRaises(IOError, self.task_storage.find, self.my_task)\",\n \"def testModePreservation(self):\\n f = open(self.path, \\\"w\\\").close()\\n os.chmod(self.path, 0707)\\n mode = os.stat(self.path)[stat.ST_MODE]\\n log = logfile.LogFile(self.name, self.dir)\\n log.write(\\\"abc\\\")\\n log.rotate()\\n self.assertEquals(mode, os.stat(self.path)[stat.ST_MODE])\",\n \"def permissions_check(\\n basedir='.',\\n verbose_level=0,\\n):\\n # File permissions on Cygwin/Windows filesystems don't work the\\n # same way as Linux. Don't try to change them.\\n # TODO(dittrich): Is there a Better way to handle perms on Windows?\\n fs_type = get_fs_type(basedir)\\n if fs_type in ['NTFS', 'FAT', 'FAT32']:\\n msg = (\\n f\\\"[-] {basedir} has file system type '{fs_type}': \\\"\\n \\\"skipping permissions check\\\"\\n )\\n logger.info(msg)\\n return\\n any_other_perms = stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH\\n for root, dirs, files in os.walk(basedir, topdown=True):\\n for name in files:\\n path = os.path.join(root, name)\\n try:\\n st = os.stat(path)\\n perms = st.st_mode & 0o777\\n open_perms = (perms & any_other_perms) != 0\\n if (open_perms and verbose_level >= 1):\\n print(\\n f\\\"[!] file '{path}' is mode {oct(perms)}\\\",\\n file=sys.stderr\\n )\\n except OSError:\\n pass\\n for name in dirs:\\n path = os.path.join(root, name)\\n try:\\n st = os.stat(path)\\n perms = st.st_mode & 0o777\\n open_perms = (perms & any_other_perms) != 0\\n if (open_perms and verbose_level >= 1):\\n print(\\n (\\n f\\\"[!] directory '{path}' is mode \\\"\\n f\\\"{oct(perms)}\\\"\\n ),\\n file=sys.stderr\\n )\\n except OSError:\\n pass\",\n \"def overwrite_on_tape_topology(rse_factory, did_factory, root_account, vo, file_factory):\\n\\n rse1 = 'XRD1'\\n rse1_id = rse_core.get_rse_id(rse=rse1, vo=vo)\\n rse2 = 'XRD3'\\n rse2_id = rse_core.get_rse_id(rse=rse2, vo=vo)\\n rse3 = 'XRD4'\\n rse3_id = rse_core.get_rse_id(rse=rse3, vo=vo)\\n\\n def __generate_and_upload_file(src_rse, dst_rse, simulate_dst_corrupted=False):\\n \\\"\\\"\\\"\\n Create and upload real files to source and destination. Don't register it on destination. This way, fts will fail if overwrite = False\\n\\n If simulate_dst_corrupted is True, will upload a different file to destination, to simulate that it is corrupted\\n \\\"\\\"\\\"\\n local_file = file_factory.file_generator()\\n did = did_factory.random_file_did()\\n did_factory.upload_test_file(src_rse, path=local_file, **did)\\n did_factory.upload_client.upload(\\n [\\n {\\n 'path': file_factory.file_generator(size=3) if simulate_dst_corrupted else local_file,\\n 'rse': dst_rse,\\n 'did_scope': did['scope'].external,\\n 'did_name': did['name'],\\n 'no_register': True,\\n }\\n ]\\n )\\n return did\\n\\n def __create_dids(did1_corrupted=True, did2_corrupted=True):\\n \\\"\\\"\\\"\\n Uploads two files:\\n - one which requires multiple transfer hop to go to destination\\n - one which can be transferred in one hop to destination rse\\n \\\"\\\"\\\"\\n # multihop transfer:\\n did1 = __generate_and_upload_file(rse1, rse3, simulate_dst_corrupted=did1_corrupted)\\n # direct transfer\\n did2 = __generate_and_upload_file(rse2, rse3, simulate_dst_corrupted=did2_corrupted)\\n rule_core.add_rule(dids=[did1, did2], account=root_account, copies=1, rse_expression=rse3, grouping='ALL', weight=None, lifetime=None, locked=False, subscription_id=None)\\n\\n return rse1_id, rse2_id, rse3_id, did1, did2\\n\\n # Fake that destination RSE is a tape\\n rse_core.update_rse(rse_id=rse3_id, parameters={'rse_type': RSEType.TAPE})\\n try:\\n rse_core.add_rse_attribute(rse3_id, 'archive_timeout', 60)\\n yield __create_dids\\n finally:\\n rse_core.update_rse(rse_id=rse3_id, parameters={'rse_type': RSEType.DISK})\\n rse_core.del_rse_attribute(rse3_id, 'archive_timeout')\",\n \"def fix_file_perms():\\n yield\\n os.chmod('tackle.yaml', int('0o644', 8))\",\n \"def test_file_conflict(self):\\n dir0, dir1 = self.make_temp_dirs(2)\\n self.write_file(dir0, \\\"foo\\\")\\n self.sync_all()\\n\\n self.write_file(dir0, \\\"foo\\\", \\\"bar\\\")\\n time.sleep(0.1)\\n self.write_file(dir1, \\\"foo\\\", \\\"baz\\\")\\n self.sync_all()\\n # File with later mtime wins\\n self.assertFile(dir0, \\\"foo\\\", \\\"baz\\\")\\n self.assertFile(dir1, \\\"foo\\\", \\\"baz\\\")\",\n \"def test_reject_foo(self):\\n self.spawn(\\\"./binary\\\").stdin(\\\"foo\\\").reject()\",\n \"def test_provider_system_hook_file_shred(change_dir, clean_files):\\n files = ['stuff', 'thing', 'foo']\\n for f in files:\\n file = open(f, \\\"w\\\")\\n file.write(f)\\n file.close()\\n\\n tackle('.', no_input=True, context_file='shred.yaml')\\n\\n for f in files:\\n assert not os.path.isfile(f)\",\n \"def test_inotify(self):\\n self.fail(\\\"write a test\\\")\",\n \"def check_filesystem(ssh_connection, disk_fmt, disk):\\n if disk_fmt == \\\"squashfs\\\":\\n return\\n cmd = \\\"fsck.{} -n {}\\\".format(disk_fmt, disk)\\n exit_code, _, stderr = ssh_connection.run(cmd)\\n assert exit_code == 0, stderr\",\n \"def test_create4(self):\\n TempfileManager.sequential_files(2)\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertEqual(fname, 'tmp2')\\n #\\n TempfileManager.unique_files()\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 2)\\n fname = os.path.basename(fname)\\n self.assertNotEqual(fname, 'tmp3')\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def checks(self, event):\\n if ListenerContainer.is_syncing:\\n if ListenerContainer.move_to_folder:\\n try:\\n ListenerContainer.client.delete_folder(ListenerContainer.move_to_folder)\\n except error_perm or error_reply: #TODO\\n reset()\\n # pass # nothing to delete\\n # except error_reply:\\n # reset()\\n ListenerContainer.move_to_folder = None\\n if ListenerContainer.move_to_file:\\n try:\\n ListenerContainer.client.delete_file(ListenerContainer.move_to_file)\\n except error_perm or error_reply: # TODO\\n reset()\\n # pass # nothing to delete\\n # except error_reply:\\n # reset()\\n ListenerContainer.move_to_file = None\\n if event.pathname[-1] == '~': # Temp file\\n return False\\n else:\\n return True\\n else:\\n timer = now()\\n if ListenerContainer.move_to_folder:\\n x = [timer, 'DELFOLDER', event.pathname]\\n ListenerContainer.sync_db.quick_push(x)\\n ListenerContainer.move_to_folder = None\\n if ListenerContainer.move_to_file:\\n x = [timer, 'DELFILE', event.pathname]\\n ListenerContainer.sync_db.quick_push(x)\\n ListenerContainer.move_to_file = None\\n if event.pathname[-1] == '~': # Temp file\\n return False\\n else:\\n return True\",\n \"def test_permissions(self):\\n exist = os.access('models/amenity.py', os.F_OK)\\n self.assertTrue(exist)\\n read = os.access('models/amenity.py', os.R_OK)\\n self.assertTrue(read)\\n write = os.access('models/amenity.py', os.W_OK)\\n self.assertTrue(write)\\n exe = os.access('models/amenity.py', os.X_OK)\\n self.assertTrue(exe)\",\n \"def test_invalid_prompt_files(self):\\n invalid_files = [\\n \\\"p01.txt\\\",\\n \\\"abc.txt\\\",\\n \\\"p000001s000001.mp3\\\",\\n \\\"p000001s000001n001.txt\\\",\\n \\\"u000001.txt\\\"\\n ]\\n filtered_files = list(filter(format.is_prompt_file, invalid_files))\\n\\n assert len(filtered_files) == 0\",\n \"def test_command_edit_requires_ram():\\n def f(inputfile):\\n with tempfile.NamedTemporaryFile() as tmp:\\n shutil.copy(inputfile, tmp.name)\\n\\n for ram in wozardry.kRequiresRAM:\\n wozardry.parse_args([\\\"edit\\\", \\\"-m\\\", \\\"requires_ram:%s\\\" % ram, tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.meta[\\\"requires_ram\\\"] == ram\\n\\n # invalid required RAM (must be one of enumerated values)\\n with pytest.raises(wozardry.WozMETAFormatError_BadRAM):\\n wozardry.parse_args([\\\"edit\\\", \\\"-m\\\", \\\"requires_ram:65K\\\", tmp.name])\\n\\n f(kValid1)\\n f(kValid2)\",\n \"def test_io_flags(pyocf_ctx, cache_mode):\\n\\n flags = 0x239482\\n block_size = 4096\\n\\n data = bytes(block_size)\\n\\n pyocf_ctx.register_volume_type(FlagsValVolume)\\n\\n cache_device = FlagsValVolume(Size.from_MiB(50), flags)\\n core_device = FlagsValVolume(Size.from_MiB(50), flags)\\n\\n cache = Cache.start_on_device(cache_device, cache_mode=cache_mode)\\n core = Core.using_device(core_device)\\n\\n cache.add_core(core)\\n vol = CoreVolume(core)\\n\\n cache_device.set_check(True)\\n core_device.set_check(True)\\n\\n # write miss\\n io_to_exp_obj(vol, block_size * 0, block_size, data, 0, IoDir.WRITE, flags)\\n assert not cache_device.fail\\n assert not core_device.fail\\n\\n # read miss\\n io_to_exp_obj(vol, block_size * 1, block_size, data, 0, IoDir.READ, flags)\\n assert not cache_device.fail\\n assert not core_device.fail\\n\\n # \\\"dirty\\\" read hit\\n io_to_exp_obj(vol, block_size * 0, block_size, data, 0, IoDir.READ, flags)\\n assert not cache_device.fail\\n assert not core_device.fail\\n\\n # \\\"clean\\\" read hit\\n io_to_exp_obj(vol, block_size * 1, block_size, data, 0, IoDir.READ, flags)\\n assert not cache_device.fail\\n assert not core_device.fail\\n\\n # \\\"dirty\\\" write hit\\n io_to_exp_obj(vol, block_size * 0, block_size, data, 0, IoDir.WRITE, flags)\\n assert not cache_device.fail\\n assert not core_device.fail\\n\\n # \\\"clean\\\" write hit\\n io_to_exp_obj(vol, block_size * 1, block_size, data, 0, IoDir.WRITE, flags)\\n assert not cache_device.fail\\n assert not core_device.fail\",\n \"def test_get_all_read_fail(self):\\n file_handler = open(self.test_task_filename, 'w')\\n file_handler.write('Mock corrupt data')\\n file_handler.close()\\n os.chmod(self.test_task_filename, 000)\\n\\n self.assertRaises(IOError, self.task_storage.get_all)\",\n \"def test_create1(self):\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_check_perm(self):\\n #Test something that really shouldn't be there\\n with pytest.raises(DbException) as err:\\n ModulePerm.get_module_perm_by_id(0)\\n assert str(err.value) == \\\"(404, 'Permission not found.')\\\"\\n\\n perm = ModulePerm.get_module_perm_by_id(self.permList[0].id)\\n assert perm.id == self.permList[0].id\\n assert perm.user_id == self.permList[0].user_id\\n assert perm.module_id == self.permList[0].module_id\\n assert perm.permissions == self.permList[0].permissions\\n assert perm.permissions == 15\\n\\n assert perm.check_perm_read_raw()\\n assert perm.check_perm_write_raw()\\n assert perm.check_perm_share_raw()\\n assert perm.check_perm_own_raw()\\n\\n perm = ModulePerm.get_module_perm_by_id(self.permList[2].id)\\n assert perm.id == self.permList[2].id\\n assert perm.user_id == self.permList[2].user_id\\n assert perm.module_id == self.permList[2].module_id\\n assert perm.permissions == self.permList[2].permissions\\n assert perm.permissions == 0\\n\\n assert not perm.check_perm_read_raw()\\n assert not perm.check_perm_write_raw()\\n assert not perm.check_perm_share_raw()\\n assert not perm.check_perm_own_raw()\",\n \"def test_file_exists_handled(overwrite_on_tape_topology, caches_mock):\\n rse1_id, rse2_id, rse3_id, did1, did2 = overwrite_on_tape_topology(did1_corrupted=False, did2_corrupted=False)\\n all_rses = [rse1_id, rse2_id, rse3_id]\\n\\n class _FTSWrapper(FTSWrapper):\\n @staticmethod\\n def on_receive(job_params):\\n for job in (job_params if isinstance(job_params, list) else [job_params]):\\n for file in job.get('files', []):\\n if (file.get('file_metadata', {}).get('dst_type') == 'TAPE'\\n and file.get('file_metadata', {}).get('dst_file', {}).get('file_on_tape') is not None):\\n # Fake that dst_file metadata contains file_on_tape == True\\n # As we don't really have tape RSEs in our tests, file_on_tape is always false\\n file['file_metadata']['dst_file']['file_on_tape'] = True\\n return job_params\\n\\n with patch('rucio.daemons.conveyor.poller.FTS3Transfertool', _FTSWrapper):\\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\\n\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.DONE\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did2)\\n assert request['state'] == RequestState.DONE\",\n \"def main():\\n\\n radio = initialize_radios(0, 25, 0x60)\\n\\n radio.openWritingPipe(pipes[0])\\n radio.openReadingPipe(0, pipes[1])\\n\\n print(\\\"Sender Information\\\")\\n radio.printDetails()\\n\\n payload_list = list()\\n\\n out = False\\n count = 0\\n actual_ack = None\\n while not out:\\n radio.startListening()\\n wait_for_data(radio)\\n\\n # recv_buffer is the array where received data will be placed\\n recv_buffer = []\\n radio.read(recv_buffer, radio.getDynamicPayloadSize())\\n chunk_and_ack = recv_buffer[:-5]\\n ack_num = recv_buffer[-7:-5]\\n crc = recv_buffer[-5:]\\n data = recv_buffer[:-7]\\n ack_num_str = bytes(ack_num).decode('utf-8'\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n ''\\n )\\n data_str = bytes(data)\\n\\n radio.stopListening()\\n if (actual_ack != ack_num_str) and (data_str != b'TH1SPR0GRAMSHOULDBEOVER'):\\n if check_crc(chunk_and_ack, crc):\\n send_packet(radio, b'1GUTACK')\\n payload_list.append(bytes(data))\\n actual_ack = ack_num_str\\n print(\\\"Sent ACK number \\\" + str(count))\\n count = count + 1\\n else:\\n send_packet(radio, b'1BATACK')\\n elif (actual_ack == ack_num_str) and (data_str != b'TH1SPR0GRAMSHOULDBEOVER'):\\n send_packet(radio, b'1GUTACK')\\n elif (actual_ack != ack_num_str) and data_str == b'TH1SPR0GRAMSHOULDBEOVER':\\n print(\\\"Finishing Script\\\")\\n time.sleep(0.5)\\n send_packet(radio, b'TH1SISTH3FINALACK')\\n out = True\\n\\n write_file(sys.argv[1], payload_list)\",\n \"def test_upload_2_generation_shell_failed(self):\\n entry = mock.MagicMock()\\n device_os = mock.MagicMock()\\n families = {\\\"first_gen\\\": mock.MagicMock(),\\n \\\"second_gen\\\": mock.MagicMock()}\\n\\n device_os.families.get.return_value = families\\n vendor = mock.MagicMock(get_device_os=mock.MagicMock(return_value=device_os))\\n first_gen = families[\\\"first_gen\\\"]\\n second_gen = families[\\\"second_gen\\\"]\\n cs_session = mock.MagicMock()\\n resource_name = \\\"test resource name\\\"\\n self.networking_handler._upload_resource = mock.MagicMock(\\n side_effect=[\\n None,\\n resource_name])\\n\\n attributes = {\\n ResourceModelsAttributes.ENABLE_SNMP: \\\"False\\\",\\n ResourceModelsAttributes.SNMP_READ_COMMUNITY: entry.snmp_community,\\n ResourceModelsAttributes.USER: entry.user,\\n ResourceModelsAttributes.PASSWORD: entry.password,\\n ResourceModelsAttributes.ENABLE_PASSWORD: entry.enable_password\\n }\\n\\n # act\\n self.networking_handler.upload(entry=entry,\\n vendor=vendor,\\n cs_session=cs_session)\\n # verify\\n self.networking_handler._upload_resource.assert_any_call(cs_session=cs_session,\\n entry=entry,\\n resource_family=second_gen[\\\"family_name\\\"],\\n resource_model=second_gen[\\\"model_name\\\"],\\n driver_name=second_gen[\\\"driver_name\\\"],\\n attribute_prefix=\\\"{}.\\\".format(\\n second_gen[\\\"model_name\\\"]))\\n\\n self.networking_handler._upload_resource.assert_any_call(cs_session=cs_session,\\n entry=entry,\\n resource_family=first_gen[\\\"family_name\\\"],\\n resource_model=first_gen[\\\"model_name\\\"],\\n driver_name=first_gen[\\\"driver_name\\\"])\",\n \"def _make_fifo(self):\\n if os.path.exists(self.fifo_path):\\n os.remove(self.fifo_path)\\n os.mkfifo(self.fifo_path)\",\n \"def test_move_single_file_aggr_into_model_prog_aggr_failure(composite_resource, aggr_cls, mock_irods):\\n\\n res, user = composite_resource\\n file_path = 'pytest/assets/generic_file.txt'\\n mp_folder = 'mp_folder'\\n ResourceFile.create_folder(res, mp_folder)\\n file_to_upload = UploadedFile(file=open(file_path, 'rb'),\\n name=os.path.basename(file_path))\\n\\n add_file_to_resource(res, file_to_upload, folder=mp_folder, check_target_folder=True)\\n assert res.files.count() == 1\\n # at this point there should not be any model program/instance aggregation\\n assert ModelProgramLogicalFile.objects.count() == 0\\n # set folder to model program/instance aggregation type\\n ModelProgramLogicalFile.set_file_type(resource=res, user=user, folder_path=mp_folder)\\n res_file = res.files.first()\\n assert res_file.has_logical_file\\n # file has folder\\n assert res_file.file_folder == mp_folder\\n assert ModelProgramLogicalFile.objects.count() == 1\\n # create a single file aggregation\\n single_file_name = 'logan.vrt'\\n file_path = 'pytest/assets/{}'.format(single_file_name)\\n file_to_upload = UploadedFile(file=open(file_path, 'rb'),\\n name=os.path.basename(file_path))\\n\\n res_file = add_file_to_resource(res, file_to_upload, check_target_folder=True)\\n # set file to generic/model instance logical file type (aggregation)\\n aggr_cls.set_file_type(res, user, res_file.id)\\n assert aggr_cls.objects.count() == 1\\n # moving the logan.vrt file into the mp_folder should fail\\n src_path = 'data/contents/{}'.format(single_file_name)\\n tgt_path = 'data/contents/{}/{}'.format(mp_folder, single_file_name)\\n with pytest.raises(RF_ValidationError):\\n move_or_rename_file_or_folder(user, res.short_id, src_path, tgt_path)\\n\\n tgt_path = 'data/contents/{}'.format(mp_folder)\\n with pytest.raises(RF_ValidationError):\\n move_or_rename_file_or_folder(user, res.short_id, src_path, tgt_path)\\n\\n assert not res.dangling_aggregations_exist()\",\n \"def test_non_integer_filesystem_limit(self):\\n command_line = [\\\"pool\\\", \\\"set-fs-limit\\\", \\\"thispool\\\", \\\"1.2\\\"]\\n for prefix in [[], [\\\"-propagate\\\"]]:\\n self.check_system_exit(prefix + command_line, _PARSE_ERROR)\",\n \"def test_is_not_delicious_file(self):\\r\\n bad_file = StringIO.StringIO()\\r\\n bad_file.write('failing tests please')\\r\\n bad_file.seek(0)\\r\\n\\r\\n self.assertTrue(\\r\\n not DelImporter.can_handle(bad_file),\\r\\n \\\"DelImporter cannot handle this file\\\")\\r\\n\\r\\n bad_file.close()\",\n \"def manage_files(self):\\n return 1 << 2\",\n \"def test_6_1_6_etc_shadow_mode(host):\\n assert host.file(ETC_SHADOW).mode == 0o640\",\n \"def test_Telegramfd (self):\\n\\t\\t# This file is delivered openned while this test.\\n\\t\\ttestfilepath = os.path.join (self.hotfolder, \\\"fileinuse.txt\\\")\\n\\t\\tf = open(testfilepath,\\\"a\\\") #opens file with name of \\\"test.txt\\\"\\n\\t\\tf.write(\\\"This file is now opened and i'm writting on it \\\\n\\\")\\n\\n\\n\\t\\tknown_values = set ([\\n\\t\\t\\t('TESTS/Test3/Telegram Desktop/Printer output.pdf', '.file'),\\n\\t\\t\\t('TESTS/Test3/Telegram Desktop/This is a Zero avi file.avi', '.file'),\\n\\t\\t\\t('TESTS/Test3/Telegram Desktop/File not in use.CBR', '.file'),\\n\\t\\t\\t('TESTS/Test3/Telegram Desktop/This is a dummy rar file.rar', '.rar'),\\n\\t\\t\\t])\\n\\n\\t\\tEntries = MD.Telegramfd (self.hotfolder)\\n\\t\\tself.assertEqual (known_values, set(Entries))\\n\\n\\t\\tf.close ()\",\n \"def test_10_is_allowed_file_wrong_ext(self):\\n\\n filename = \\\"somename.pdf\\\"\\n is_allowed = utils.is_allowed_file(filename)\\n self.assertFalse(is_allowed)\",\n \"def test_verify_corrupt_archive_compare_data(self):\\n self.backup(u\\\"full\\\", u\\\"testfiles/various_file_types\\\", options=[])\\n output_files = os.listdir(\\\"testfiles/output\\\")\\n archives = [elem for elem in output_files if \\\"vol\\\" in elem]\\n for archive in archives:\\n # Edit source file\\n with open(\\\"testfiles/output/\\\" + archive, 'r+') as f:\\n f.write('This writes text into each archive file to corrupt it.')\\n # Test verify for the file\\n try:\\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable',\\n options=[u\\\"--compare-data\\\"])\\n except CmdError as e:\\n # Should return a 21 error code for \\\"hash mismatch\\\"\\n self.assertEqual(e.exit_status, 21, str(e))\\n else:\\n self.fail('Expected Hash Mismatch Error not thrown')\",\n \"def get_file (queue):\\n\\n # get event from queue\\n event = queue.get(True)\\n\\n try:\\n # get name of new file\\n filename = str(event.src_path)\\n filetype = 'new'\\n\\n except AttributeError as e:\\n # instead of event, queue entry is a filename added in\\n # [run_blackbox]\\n filename = event\\n filetype = 'pre-existing'\\n\\n\\n log.info ('detected a {} file: {}'.format(filetype, filename))\\n\\n\\n # only continue if a fits file\\n if 'fits' not in filename:\\n\\n log.info ('{} is not a fits file; skipping it'.format(filename))\\n filename = None\\n\\n else:\\n\\n # if filename is a temporary rsync copy (default\\n # behaviour of rsync is to create a temporary file\\n # starting with .[filename].[randomstr]; can be\\n # changed with option \\\"--inplace\\\"), then let filename\\n # refer to the eventual file created by rsync\\n fn_head, fn_tail = os.path.split(filename)\\n if fn_tail[0] == '.':\\n filename = '{}/{}'.format(fn_head, '.'\\n .join(fn_tail.split('.')[1:-1]))\\n log.info ('changed filename from rsync temporary file {} to {}'\\n .format(event.src_path, filename))\\n\\n # this while loop below replaces the old [copying]\\n # function; it times out after wait_max is reached\\n wait_max = 180\\n t0 = time.time()\\n nsleep = 0\\n while time.time()-t0 < wait_max:\\n\\n try:\\n # read the file\\n data = read_hdulist(filename)\\n\\n except:\\n\\n process = False\\n if nsleep==0:\\n log.warning ('file {} has not completely arrived yet; '\\n 'will keep trying to read it in for {}s'\\n .format(filename, wait_max))\\n\\n # give file a bit of time to arrive before next read attempt\\n time.sleep(5)\\n nsleep += 1\\n\\n else:\\n # if fits file was read fine, set process flag to True\\n process = True\\n log.info ('successfully read file {} within {:.1f}s'\\n .format(filename, time.time()-t0))\\n # and break out of while loop\\n break\\n\\n\\n if not process:\\n log.info ('{}s limit for reading file reached, not processing {}'\\n .format(wait_max, filename))\\n filename = None\\n\\n\\n return filename\",\n \"def perform_general():\\r\\n print(\\\"Performing the \\\\\\\"indirect\\\\\\\" check on all blocks\\\")\\r\\n \\r\\n filename_part = \\\"fusedata.\\\"\\r\\n i_start = begining_block_number + 1 #1\\r\\n i_end = ending_block_number #25\\r\\n file_name = filename_part\\r\\n\\r\\n \\r\\n for i in range(i_start, (i_end + 1)):\\r\\n #opening each block available to us\\r\\n file_name = filename_part + str(i)\\r\\n #print \\\"file_name\\\"+file_name\\r\\n file_access = open (file_name,\\\"r+\\\")\\r\\n \\r\\n try:\\r\\n block_contents = json.load(file_access)\\r\\n file_access.close()\\r\\n except ValueError:\\r\\n print \\\"\\\\\\\"\\\"+file_name+\\\"\\\\\\\" not in json format (catch 1)\\\"\\r\\n file_access.close() \\r\\n continue\\r\\n \\r\\n # first performing the size check\\r\\n if(type(block_contents) == dict):\\r\\n # checking if the current parsed block is a file inode\\r\\n if((\\\"indirect\\\" in block_contents) == True):\\r\\n size_check(block_contents, file_name)\\r\\n perform_indirect(block_contents, file_name)\\r\\n \\r\\n #closing the file to effect changes\\r\\n file_access_2 = open (file_name,\\\"w\\\")\\r\\n json.dump(block_contents,file_access_2)\\r\\n file_access_2.close() \\r\\n \\r\\n\\r\\n \\r\\n print(\\\"Reached end of test\\\") \\r\\n \\r\\n print(\\\"-------------------------------\\\")\",\n \"def test_compress_fastq_real_with_integrity(\\n first_tmp_file, second_tmp_file, spring_tmp_path, real_base_context\\n):\\n # GIVEN the path to a existing two existing fastq files and a non existing spring\\n runner = CliRunner()\\n assert not spring_tmp_path.exists()\\n assert first_tmp_file.exists()\\n assert second_tmp_file.exists()\\n\\n dir_path = spring_tmp_path.parent\\n assert nr_files(dir_path) == 2\\n # WHEN running the compress command with an intergrity check\\n result = runner.invoke(\\n fastq,\\n [\\n \\\"--first-read\\\",\\n str(first_tmp_file),\\n \\\"--second-read\\\",\\n str(second_tmp_file),\\n \\\"--spring-path\\\",\\n str(spring_tmp_path),\\n \\\"--check-integrity\\\",\\n ],\\n obj=real_base_context,\\n )\\n # THEN assert the command succedes\\n assert result.exit_code == 0\\n # THEN assert that the spring file was created\\n assert spring_tmp_path.exists()\\n # THEN assert that the files created for integrity check was removed\\n assert nr_files(dir_path) == 3\",\n \"def test_ignore_readonly1(tmpdir):\\n os.makedirs('%s/foo/bar/baz' % tmpdir)\\n os.chmod('%s/foo/bar/baz' % tmpdir, 0o500)\\n os.chmod('%s/foo/bar' % tmpdir, 0o500)\\n os.chmod('%s/foo' % tmpdir, 0o500)\\n\\n with pytest.raises(PermissionError):\\n sh.remove('%s/foo' % tmpdir, recursive=True)\\n assert os.path.exists('%s/foo/bar/baz' % tmpdir)\\n\\n sh.remove('%s/foo' % tmpdir, force=False, recursive=True,\\n ignore_readonly=True)\\n assert not os.path.exists('%s/foo' % tmpdir)\",\n \"def test_read_delete(self):\\n\\n expected = \\\"Hello, World! This has been written by Fun Ilrys.\\\"\\n File(\\\"hi\\\").write(expected)\\n actual = File(\\\"hi\\\").read()\\n\\n self.assertEqual(expected, actual)\\n\\n expected = False\\n File(\\\"hi\\\").delete()\\n actual = PyFunceble.path.isfile(\\\"hi\\\")\\n\\n self.assertEqual(expected, actual)\",\n \"def check_file_validity(self, file_):\\n if not os.access(file_, os.F_OK):\\n raise TailError(\\\"File '%s' does not exist\\\" % (file_))\\n if not os.access(file_, os.R_OK):\\n raise TailError(\\\"File '%s' not readable\\\" % (file_))\\n if os.path.isdir(file_):\\n raise TailError(\\\"File '%s' is a directory\\\" % (file_))\",\n \"def test_verify_pvc_invalid_access_mode(self, interface, pvc_factory):\\n expected_err = 'invalid: spec.accessModes: Unsupported value: \\\"RWO\\\"'\\n\\n # Try to create a PVC by providing invalid value of access mode\\n try:\\n self.pvc_obj = pvc_factory(\\n interface=interface,\\n project=None,\\n storageclass=None,\\n size=3,\\n access_mode=\\\"RWO\\\",\\n custom_data=None,\\n status=constants.STATUS_BOUND,\\n )\\n assert not self.pvc_obj, \\\"Unexpected: PVC creation hasn't failed.\\\"\\n except CommandFailed as err:\\n assert expected_err in str(\\n err\\n ), f\\\"Couldn't verify PVC creation. Unexpected error {str(err)}\\\"\\n log.info(\\\"PVC creation failed as expected.\\\")\",\n \"def test_06_verify_tar01(self, mock_isfile, mock_call, mock_msg):\\n mock_msg.level = 0\\n mock_isfile.return_value = False\\n mock_call.return_value = 0\\n status = udocker.FileUtil(\\\"tarball.tar\\\").verify_tar()\\n self.assertFalse(status)\",\n \"def check(src, perm, dest, cmds, comp, verbose=False):\\n if comp == Cmp.differ:\\n ansiprint(f\\\"The file '{src}' differs from '{dest}'.\\\", fg=Color.red, i=True)\\n elif comp == Cmp.nodest:\\n ansiprint(\\n f\\\"The destination file '{dest}' does not exist\\\",\\n fg=Color.black,\\n bg=Color.red,\\n )\\n elif comp == Cmp.nosrc:\\n ansiprint(\\n f\\\"The source file '{src}' does not exist.\\\", fg=Color.black, bg=Color.red\\n )\\n elif comp == Cmp.same and verbose:\\n ansiprint(f\\\"The files '{src}' and '{dest}' are the same.\\\", fg=Color.green)\",\n \"def processfile(args, fh):\\n if args.quick:\\n scanner = quickScanZip(args, fh)\\n else:\\n scanner = findPKHeaders(args, fh)\\n\\n def checkarg(arg, ent):\\n if not arg:\\n return False\\n return '*' in arg or ent.name in arg\\n def checkname(a, b):\\n if a and '*' in a: return True\\n if b and '*' in b: return True\\n l = 0\\n if a: l += len(a)\\n if b: l += len(b)\\n return l > 1\\n\\n if args.verbose and not (args.cat or args.raw or args.save):\\n print(\\\" 0304 need flgs mth stamp --crc-- compsize fullsize nlen xlen namofs xofs datofs endofs\\\")\\n print(\\\" 0102 crea need flgs mth stamp --crc-- compsize fullsize nlen xlen clen dsk0 attr osattr datptr namofs xofs cmtofs endofs\\\")\\n for ent in scanner:\\n if args.cat or args.raw or args.save:\\n if args.quick and isinstance(ent, CentralDirEntry) or \\\\\\n not args.quick and isinstance(ent, LocalFileHeader):\\n ent.loaditems(fh)\\n do_cat = checkarg(args.cat, ent)\\n do_raw = checkarg(args.raw, ent)\\n do_save= checkarg(args.save, ent)\\n\\n do_name= checkname(args.cat, args.raw)\\n\\n if do_name:\\n print(\\\"\\\\n===> \\\" + ent.name + \\\" <===\\\\n\\\")\\n\\n sys.stdout.flush()\\n blks = zipraw(fh, ent)\\n\\n if args.password and ent.flags&1:\\n blks = zip_decrypt(blks, args.password)\\n if do_cat or do_save:\\n blks = skipbytes(blks, 12, args)\\n\\n if do_cat:\\n sys.stdout.buffer.writelines(zipcat(blks, ent))\\n if do_raw:\\n sys.stdout.buffer.writelines(blks)\\n if do_save:\\n savefile(args.outputdir, ent.name, zipcat(blks, ent))\\n else:\\n ent.loaditems(fh)\\n if args.verbose or not args.quick:\\n print(\\\"%08x: %s\\\" % (ent.pkOffset, ent))\\n else:\\n print(ent.summary())\\n if hasattr(ent, \\\"comment\\\") and ent.comment and not args.dumpraw:\\n print(ent.comment)\\n if args.dumpraw and hasattr(ent, \\\"extraLength\\\") and ent.extraLength:\\n print(\\\"%08x: XTRA: %s\\\" % (ent.extraOffset, binascii.b2a_hex(getbytes(fh, ent.extraOffset, ent.extraLength))))\\n if args.dumpraw and hasattr(ent, \\\"comment\\\") and ent.comment:\\n print(\\\"%08x: CMT: %s\\\" % (ent.commentOffset, binascii.b2a_hex(getbytes(fh, ent.commentOffset, ent.commentLength))))\\n if args.dumpraw and isinstance(ent, LocalFileHeader):\\n blks = zipraw(fh, ent)\\n if args.password and ent.flags&1:\\n blks = zip_decrypt(blks, args.password)\\n\\n blockdump(ent.dataOffset, blks)\",\n \"def test_fifo_sync(args=None):\\n if args is None:\\n args = Namespace(width=8, size=16, name='test')\\n else:\\n assert hasattr(args, 'width')\\n assert hasattr(args, 'size')\\n args = tb_default_args(args)\\n\\n reset = ResetSignal(0, active=1, isasync=True)\\n clock = Clock(0, frequency=50e6)\\n glbl = Global(clock, reset)\\n fbus = FIFOBus(width=args.width)\\n\\n @myhdl.block\\n def bench_fifo_sync():\\n \\n tbdut = fifo_sync(glbl, fbus, size=args.size)\\n tbclk = clock.gen()\\n \\n @instance\\n def tbstim():\\n fbus.write_data.next = 0xFE\\n reset.next = reset.active\\n yield delay(33)\\n reset.next = not reset.active\\n for ii in range(5):\\n yield clock.posedge\\n\\n # test the normal cases\\n for num_bytes in range(1, args.size+1):\\n\\n # write some bytes\\n for ii in range(num_bytes):\\n yield clock.posedge\\n fbus.write_data.next = ii + 0xCE\\n fbus.write.next = True\\n\\n yield clock.posedge\\n fbus.write.next = False\\n fbus.write_data.next = 0xFE\\n\\n # if 16 bytes written make sure FIFO is full\\n yield clock.posedge\\n if num_bytes == args.size:\\n assert fbus.full, \\\"FIFO should be full!\\\"\\n assert not fbus.empty, \\\"FIFO should not be empty\\\"\\n \\n # fbus.read.next = True\\n # yield clock.posedge\\n for ii in range(5):\\n yield clock.posedge\\n if not fbus.empty:\\n break\\n\\n for ii in range(num_bytes):\\n fbus.read.next = True\\n yield clock.posedge\\n assert fbus.read_valid\\n assert fbus.read_data == ii + 0xCE, \\\\\\n \\\"rdata %x ii %x \\\" % (fbus.read_data, ii + 0xCE)\\n\\n fbus.read.next = False\\n yield clock.posedge\\n assert fbus.empty\\n\\n raise StopSimulation\\n\\n w = args.width\\n write_data, read_data = Signals(intbv(0)[w:], 2)\\n\\n @always_comb\\n def tbmon():\\n write_data.next = fbus.write_data\\n read_data.next = fbus.read_data\\n\\n return tbdut, tbclk, tbstim, tbmon\\n\\n run_testbench(bench_fifo_sync, args=args)\",\n \"def cant(self, permissions: Union[str, List]) -> bool:\",\n \"def test_fileinuse (self):\\t\\t\\n\\t\\t# open a file:\\n\\t\\ttestfilepath = os.path.join (self.testfolder, \\\"fileinuse.txt\\\")\\n\\t\\tf = open(testfilepath,\\\"a\\\") #opens file with name of \\\"test.txt\\\"\\n\\t\\tf.write(\\\"This file is now opened and i'm writting on it \\\\n\\\")\\n\\t\\tself.assertEqual (MD.fileinuse(testfilepath), True) # Checks a file that it is beign written.\\n\\t\\tself.assertEqual (MD.folderinuse(self.testfolder), True) # Checks if any file inside the folder is beign used.\\n\\t\\tf.close()\\n\\t\\tself.assertEqual (MD.fileinuse(testfilepath), False) # Cheks a file that it is closed.\\n\\t\\tself.assertEqual (MD.folderinuse(self.testfolder), False)\",\n \"def testChecksumCondition(self):\\n file_defs = [\\n {'name': 'file_0_byte.txt', 'path': '', 'size': 0},\\n {'name': 'file_1_byte.txt', 'path': '', 'size': 1},\\n {'name': 'file_320k_minus 1_byte.txt', 'path': 'folder1', 'size': 320 * 1024 - 1, 'mod_inc': -1},\\n ]\\n\\n self._setup_test_store(file_defs)\\n\\n # Check if this provider supports checksum as change condition\\n drive = self.drive_class(self.account_id, self.config_file_dir, self.config_pw)\\n if drive.files_differ_on_hash(\\n os.path.join(self.test_local_dir, file_defs[0]['path'], file_defs[0]['name']),\\n 'dummy_hash') is None:\\n self.skipTest('Checksum change condition not supported for this provider.')\\n\\n self._sync_drives()\\n\\n # Modify files\\n for file_def in file_defs:\\n file_path =\\\\\\n os.path.join(self.test_local_dir, file_def['path'], file_def['name'])\\n test_utils.make_random_file(\\n file_path, file_def['size'], leave_existing=False,\\n modify_timestamp_ns=os.stat(file_path).st_mtime_ns)\\n\\n self._sync_drives()\\n self._download_store()\\n self.assertDirectoriesAreEqual(self.test_local_dir, self.test_download_dir)\",\n \"def test_6_1_4_etc_group_mode(host):\\n assert host.file(ETC_GROUP).mode == 0o644\",\n \"def test_08_verify_tar03(self, mock_isfile, mock_call, mock_msg):\\n mock_msg.level = 0\\n mock_isfile.return_value = True\\n mock_call.return_value = 1\\n status = udocker.FileUtil(\\\"tarball.tar\\\").verify_tar()\\n self.assertFalse(status)\",\n \"def _ensure_read_write_access(tarfileobj):\\n dir_perm = tarfile.TUREAD | tarfile.TUWRITE | tarfile.TUEXEC\\n file_perm = tarfile.TUREAD | tarfile.TUWRITE\\n\\n for tarinfo in tarfileobj.getmembers():\\n tarinfo.mode |= (dir_perm if tarinfo.isdir() else file_perm)\",\n \"def test_mark_multiple_res_files_as_mp_file_type(composite_resource, mock_irods):\\n\\n res, user = composite_resource\\n file_path = 'pytest/assets/{}'\\n txt_file_path = file_path.format('generic_file.txt')\\n vrt_file_path = file_path.format('logan.vrt')\\n upload_folder = 'mp_folder'\\n ResourceFile.create_folder(res, upload_folder)\\n file_to_upload = UploadedFile(file=open(txt_file_path, 'rb'),\\n name=os.path.basename(txt_file_path))\\n\\n res_file_txt = add_file_to_resource(\\n res, file_to_upload, folder=upload_folder, check_target_folder=True\\n )\\n\\n file_to_upload = UploadedFile(file=open(vrt_file_path, 'rb'),\\n name=os.path.basename(vrt_file_path))\\n res_file_vrt = add_file_to_resource(\\n res, file_to_upload, folder=upload_folder, check_target_folder=True\\n )\\n assert res.files.count() == 2\\n # create model program aggregation\\n assert ModelProgramLogicalFile.objects.count() == 0\\n # set the folder to model program aggregation type\\n ModelProgramLogicalFile.set_file_type(res, user, folder_path=upload_folder)\\n assert ModelProgramLogicalFile.objects.count() == 1\\n\\n assert ModelProgramResourceFileType.objects.count() == 0\\n mp_aggregation = ModelProgramLogicalFile.objects.first()\\n mp_type = 'software'\\n # set the txt file as software for this aggregation\\n ModelProgramResourceFileType.create(file_type=mp_type, res_file=res_file_txt,\\n mp_metadata=mp_aggregation.metadata)\\n assert ModelProgramResourceFileType.objects.count() == 1\\n # set the vrt file as software for this aggregation\\n ModelProgramResourceFileType.create(file_type=mp_type, res_file=res_file_vrt,\\n mp_metadata=mp_aggregation.metadata)\\n assert ModelProgramResourceFileType.objects.count() == 2\\n assert not res.dangling_aggregations_exist()\",\n \"def test_negative_filesystem_limit(self):\\n command_line = [\\\"pool\\\", \\\"set-fs-limit\\\", \\\"thispool\\\", \\\"-1\\\"]\\n for prefix in [[], [\\\"-propagate\\\"]]:\\n self.check_system_exit(prefix + command_line, _PARSE_ERROR)\",\n \"def test_multiple_users_cannot_access_each_others_files(self):\\n userID1 = 'aaaabbbbccccdddd' # Create three users\\n userID2 = 'aaaccccbbbddd'\\n userID3 = 'bbbaaadddccccc'\\n user1 = ServComs(self.serverIp, userID1)\\n user2 = ServComs(self.serverIp, userID2)\\n user3 = ServComs(self.serverIp, userID3)\\n try:\\n nonce1_1 = globals.generate_random_nonce()\\n nonce1_2 = globals.generate_random_nonce()\\n nonce2_1 = globals.generate_random_nonce()\\n nonce2_2 = globals.generate_random_nonce()\\n self.send_file(nonce1_1, nonce1_2, user1) # have two send a file\\n self.send_file(nonce2_1, nonce2_2, user2)\\n files_user_1 = user1.get_file_list() # get their files on server back\\n files_user_2 = user2.get_file_list()\\n files_user_3 = user3.get_file_list()\\n self.assertTrue(len(files_user_3) == 0, # should have right lengths\\n \\\"User 3 has send no files; should have no files.\\\")\\n self.assertTrue(len(files_user_1) == 1 and len(files_user_2) == 1,\\n \\\"User 1 & 2 has send 1 file; should have 1 file.\\\")\\n for encfilename, nonce, ts in files_user_1: #\\n self.assertTrue([encfilename, nonce] in [[x[0], x[1]] for x in files_user_1])\\n self.assertTrue([encfilename, nonce] not in [[x[0], x[1]] for x in files_user_2])\\n for encfilename, nonce, ts in files_user_2:\\n self.assertTrue([encfilename, nonce] in [[x[0], x[1]] for x in files_user_2])\\n self.assertTrue([encfilename, nonce] not in [[x[0], x[1]] for x in files_user_1])\\n user1.get_file(files_user_1[0][0])\\n user2.get_file(files_user_2[0][0])\\n self.assertRaises(FileNotFoundError, user2.get_file, files_user_1[0][0])\\n self.assertRaises(FileNotFoundError, user1.get_file, files_user_2[0][0])\\n finally:\\n self.unregister_user(userID1)\\n self.unregister_user(userID2)\\n self.unregister_user(userID3)\",\n \"def test_delete_file(self):\\n # Fake raw_input \\n passkeeper.raw_input = lambda x: 'y'\\n\\n # init\\n pk = passkeeper.Passkeeper(directory='.tox/foo')\\n # Init directory\\n pk.init_dir(passphrase='secret')\\n # Decrypt files\\n pk.decrypt(passphrase='secret')\\n\\n # Add input in file\\n sample_file = (\\\"\\\"\\\"[bar]\\nname = bar access\\n\\\"\\\"\\\")\\n with open('.tox/foo/bar.ini', 'a') as f:\\n f.write(sample_file)\\n\\n # Add raw dir and file in it\\n create_dir('.tox/foo/bar.raw/')\\n sample_file = (\\\"\\\"\\\"My private data\\\"\\\"\\\")\\n with open('.tox/foo/bar.raw/private', 'w') as f:\\n f.write(sample_file)\\n\\n # Encrypt files\\n pk.encrypt(passphrase='secret', commit_message='Add bar entry')\\n # Clean old files\\n pk.remove_old_encrypted_files(force_remove=True)\\n # Call cleanup ini files\\n pk.cleanup()\\n\\n # Decrypt file (last validation)\\n pk.decrypt(passphrase='secret')\\n os.remove('.tox/foo/bar.ini')\\n shutil.rmtree('.tox/foo/bar.raw/')\\n\\n\\n # Encrypt files\\n pk.encrypt(passphrase='secret', commit_message='Will remove bar.ini and bar.raw/*')\\n\\n\\n # Control state befor\\n self.assertTrue(isfile('.tox/foo/encrypted/bar.ini.passkeeper'))\\n self.assertTrue(isfile('.tox/foo/encrypted/bar.raw/private.passkeeper'))\\n git_logs = self._get_file_lines(filename='.tox/foo/.git/logs/HEAD')\\n self.assertTrue('Will remove bar.ini and bar.raw/*' in git_logs[-1])\\n\\n # Remove old files\\n pk.remove_old_encrypted_files(force_remove=True)\\n\\n # Call cleanup. file bar.passkeeped should be remove\\n pk.cleanup()\\n\\n # Should have a commit for file deleted\\n self.assertFalse(isfile('.tox/foo/encrypted/bar.ini.passkeeper'))\\n self.assertTrue(isfile('.tox/foo/encrypted/default.ini.passkeeper'))\\n self.assertFalse(isfile('.tox/foo/encrypted/bar.raw/private.passkeeper'))\\n self.assertFalse(isdir('.tox/foo/encrypted/bar.raw'))\\n git_logs = self._get_file_lines(filename='.tox/foo/.git/logs/HEAD')\\n self.assertTrue('Remove file encrypted/bar.raw/private.passkeeper' in git_logs[-2])\\n self.assertTrue('Remove file encrypted/bar.ini.passkeeper' in git_logs[-1])\",\n \"def test_bad_file():\\n\\n bad = ''.join(random.choices(string.ascii_uppercase + string.digits, k=5))\\n rv, out = getstatusoutput(f'{prg} -f {bad}')\\n assert rv != 0\\n assert re.match('usage:', out, re.I)\\n assert re.search(f\\\"No such file or directory: '{bad}'\\\", out)\",\n \"def test_create1a(self):\\n fname = TempfileManager.create_tempfile(dir=tempdir)\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_fileAlreadyExistsNoOverwrite(self):\\n fp = FilePath(self.mktemp())\\n fp.touch()\\n\\n self.assertRaises(OSError, self.makeConnectedDccFileReceive, fp.path)\",\n \"def test_6_1_3_etc_group_mode(host):\\n assert host.file(ETC_PASSWD_DASH).mode == 0o644\",\n \"def test_overwrite_hops(overwrite_on_tape_topology, caches_mock, did_factory, file_factory):\\n rse1_id, rse2_id, rse3_id, did1, did2 = overwrite_on_tape_topology(did1_corrupted=False, did2_corrupted=True)\\n did_factory.upload_client.upload(\\n [\\n {\\n 'path': file_factory.file_generator(size=3),\\n 'rse': rse_core.get_rse_name(rse2_id),\\n 'did_scope': did1['scope'].external,\\n 'did_name': did1['name'],\\n 'no_register': True,\\n }\\n ]\\n )\\n all_rses = [rse1_id, rse2_id, rse3_id]\\n\\n submitter(once=True, rses=[{'id': rse_id} for rse_id in all_rses], group_bulk=10, partition_wait_time=0, transfertype='single', filter_transfertool=None)\\n\\n fts_schema_version = FTS3Transfertool(external_host=TEST_FTS_HOST).version()['schema']['major']\\n if fts_schema_version >= 8:\\n # Newer fts version will honor the overwrite_hop\\n request = __wait_for_state_transition(dst_rse_id=rse2_id, **did1)\\n assert request['state'] == RequestState.DONE\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.FAILED\\n assert 'Destination file exists and overwrite is not enabled' in request['err_msg']\\n else:\\n # FTS only recently introduced the overwrite_hops parameter. It will be ignored on old\\n # fts versions and the first hop will fail with the file exists error\\n # TODO: remove this else after FTS 3.12 release and after updating rucio/fts container with the new release\\n request = __wait_for_state_transition(dst_rse_id=rse2_id, **did1)\\n assert request['state'] == RequestState.FAILED\\n assert 'Destination file exists and overwrite is not enabled' in request['err_msg']\\n request = __wait_for_state_transition(dst_rse_id=rse3_id, **did1)\\n assert request['state'] == RequestState.FAILED\\n assert 'Unused hop in multi-hop' in request['err_msg']\",\n \"def test_command_edit_disk_sides():\\n with tempfile.NamedTemporaryFile() as tmp:\\n shutil.copy(kValid2, tmp.name)\\n\\n # this file is a 5.25-inch disk image\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[\\\"disk_type\\\"] == 1\\n assert woz.info[\\\"disk_sides\\\"] == 1\\n\\n # 5.25-inch disk images can only be \\\"1-sided\\\"\\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskSides):\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_sides:2\\\", tmp.name])\\n\\n # now change it to a 3.5-inch disk image\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_type:2\\\", tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[\\\"disk_type\\\"] == 2\\n\\n # 3.5-inch disk images can be 1- or 2-sided\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_sides:2\\\", tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[\\\"disk_sides\\\"] == 2\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_sides:1\\\", tmp.name])\\n with open(tmp.name, \\\"rb\\\") as tmpstream:\\n woz = wozardry.WozDiskImage(tmpstream)\\n assert woz.info[\\\"disk_sides\\\"] == 1\\n\\n # ...but not 3-sided, that's silly\\n with pytest.raises(wozardry.WozINFOFormatError_BadDiskSides):\\n wozardry.parse_args([\\\"edit\\\", \\\"-i\\\", \\\"disk_sides:3\\\", tmp.name])\",\n \"def test_make_file_read_only():\\n\\n filename = os.path.join(tempfile.gettempdir(), \\\"jade-test-file.txt\\\")\\n if os.path.exists(filename):\\n os.chmod(filename, stat.S_IWRITE)\\n os.remove(filename)\\n\\n with open(filename, \\\"w\\\") as f:\\n f.write(\\\"Hello World\\\")\\n\\n prev_mode = os.stat(filename)\\n make_file_read_only(filename)\\n # Disabling because it doesn't work on Windows.\\n # s = os.stat(filename)\\n # assert s.st_mode != prev_mode\\n # assert s.st_mode == 33060\\n\\n if os.path.exists(filename):\\n os.chmod(filename, stat.S_IWRITE)\\n os.remove(filename)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6131964","0.60713536","0.57345945","0.56298536","0.5624965","0.56156325","0.5565259","0.5526661","0.5506482","0.5501304","0.5494585","0.5419358","0.5417479","0.5339467","0.5335473","0.5235633","0.5192825","0.51720446","0.51720446","0.51584595","0.51447344","0.5086432","0.50669557","0.5034095","0.50339377","0.5033747","0.5032271","0.5030001","0.50275093","0.5026844","0.5004081","0.49953365","0.4989964","0.49858424","0.497688","0.49684635","0.4953166","0.4943311","0.4943295","0.49378142","0.49247175","0.4921156","0.4916717","0.49154496","0.4910722","0.4909787","0.49077702","0.49076238","0.489849","0.48978212","0.48971957","0.48913768","0.48751783","0.4873546","0.48693532","0.48590466","0.4858375","0.48555914","0.48525587","0.48464316","0.48462236","0.48460126","0.48454672","0.4843109","0.48415783","0.48398626","0.48371857","0.483709","0.48265344","0.48233822","0.4818234","0.4810645","0.48046878","0.48026624","0.48020014","0.48004085","0.48000446","0.47971687","0.4796556","0.47936523","0.47920412","0.47906092","0.47859058","0.47828168","0.47805354","0.47795385","0.47679964","0.47672296","0.4761318","0.47545218","0.4753726","0.47449118","0.47423747","0.4741863","0.47409636","0.47405186","0.4739573","0.4738623","0.47379655","0.47304553"],"string":"[\n \"0.6131964\",\n \"0.60713536\",\n \"0.57345945\",\n \"0.56298536\",\n \"0.5624965\",\n \"0.56156325\",\n \"0.5565259\",\n \"0.5526661\",\n \"0.5506482\",\n \"0.5501304\",\n \"0.5494585\",\n \"0.5419358\",\n \"0.5417479\",\n \"0.5339467\",\n \"0.5335473\",\n \"0.5235633\",\n \"0.5192825\",\n \"0.51720446\",\n \"0.51720446\",\n \"0.51584595\",\n \"0.51447344\",\n \"0.5086432\",\n \"0.50669557\",\n \"0.5034095\",\n \"0.50339377\",\n \"0.5033747\",\n \"0.5032271\",\n \"0.5030001\",\n \"0.50275093\",\n \"0.5026844\",\n \"0.5004081\",\n \"0.49953365\",\n \"0.4989964\",\n \"0.49858424\",\n \"0.497688\",\n \"0.49684635\",\n \"0.4953166\",\n \"0.4943311\",\n \"0.4943295\",\n \"0.49378142\",\n \"0.49247175\",\n \"0.4921156\",\n \"0.4916717\",\n \"0.49154496\",\n \"0.4910722\",\n \"0.4909787\",\n \"0.49077702\",\n \"0.49076238\",\n \"0.489849\",\n \"0.48978212\",\n \"0.48971957\",\n \"0.48913768\",\n \"0.48751783\",\n \"0.4873546\",\n \"0.48693532\",\n \"0.48590466\",\n \"0.4858375\",\n \"0.48555914\",\n \"0.48525587\",\n \"0.48464316\",\n \"0.48462236\",\n \"0.48460126\",\n \"0.48454672\",\n \"0.4843109\",\n \"0.48415783\",\n \"0.48398626\",\n \"0.48371857\",\n \"0.483709\",\n \"0.48265344\",\n \"0.48233822\",\n \"0.4818234\",\n \"0.4810645\",\n \"0.48046878\",\n \"0.48026624\",\n \"0.48020014\",\n \"0.48004085\",\n \"0.48000446\",\n \"0.47971687\",\n \"0.4796556\",\n \"0.47936523\",\n \"0.47920412\",\n \"0.47906092\",\n \"0.47859058\",\n \"0.47828168\",\n \"0.47805354\",\n \"0.47795385\",\n \"0.47679964\",\n \"0.47672296\",\n \"0.4761318\",\n \"0.47545218\",\n \"0.4753726\",\n \"0.47449118\",\n \"0.47423747\",\n \"0.4741863\",\n \"0.47409636\",\n \"0.47405186\",\n \"0.4739573\",\n \"0.4738623\",\n \"0.47379655\",\n \"0.47304553\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6538033"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17487,"cells":{"query":{"kind":"string","value":"Returns all the grid positions that are currently available for scoring. YB is only available after YZ has been scored other than 0 or NB, NT and GT are never available for scoring If 13 positions have been scored in the grid, no further positions can be filled"},"document":{"kind":"string","value":"def available_positions(self):\n if len([x for x in self.grid.values() if x[0] != None]) < 13:\n return [x for x in assignable_positions if self.grid[x][1] == \"---\"]\n else:\n return []"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def free_positions(self):\n positions = []\n for i in range(self.grid_size):\n for j in range(self.grid_size):\n if self.grid[i][j] == 0:\n positions.append((i, j))\n if positions == []:\n raise GameException('Game Over. No free position left.')\n return positions","def filled_positions(self):\n return [x for x in assignable_positions if self.grid[x][0]]","def available_positions(self):\n available_positions = []\n for i in range(self.positions_count):\n if self.board[i] == 0:\n available_positions.append(i+1)\n return available_positions","def collide_grid(self):\n topleft = self.absolute_collide_topleft\n bottomright = self.absolute_collide_bottomright\n tlx, tly = self.currentLevel.toGridCoord(topleft)\n brx, bry = self.currentLevel.toGridCoord(bottomright)\n collide_grid = []\n for x in range(tlx, brx+1):\n for y in range(tly, bry+1):\n collide_grid.append( (x,y) )\n if not collide_grid:\n collide_grid = [(tlx,tly)]\n return collide_grid","def board_empty_positions(self, x, y):\n board = self.boards[x][y]\n coords = [(x, y, i, j) for (i, j) in board.empty_squares]\n return self.coords_to_positions(coords)","def get_allowed_positions(coordXY, grid):\n\n\tsurrounding_coord = []\n\ttesting_coord = []\n\n\t# Get the coordinates of the external square\n\tfor i in range(coordXY[0] - 1, coordXY[0] + 2, 2):\n\t\tfor j in range(coordXY[1] - 1, coordXY[1] +2, 1):\n\t\t\tif (i,j) == coordXY:\n\t\t\t\tpass\n\t\t\telif i < 0 or j < 0:\n\t\t\t\tsurrounding_coord.append('None')\n\t\t\telse:\n\t\t\t\tsurrounding_coord.append((i,j))\n\n\t# Get the coordinates of the internal square\n\tfor i in range(coordXY[0] - 2, coordXY[0] + 3, 4):\n\t\tfor j in range(coordXY[1] - 2, coordXY[1] + 3, 2):\n\t\t\tif i < 0 or j < 0 or i > 7 or j > 7:\n\t\t\t\ttesting_coord.append('None')\n\t\t\telse:\n\t\t\t\ttesting_coord.append((i,j))\n\n\t# Get the position of Bottom and Top of the 2 squares\n\tTC = [(coordXY[0], coordXY[1] + 2), (coordXY[0], coordXY[1] - 2)]\n\tfor elem in TC:\n\n\t\tif elem[0] not in range(8) or elem[1] not in range(8):\n\t\t\ttesting_coord.append('None')\n\t\telse:\n\t\t\ttesting_coord.append(elem)\n\n\n\tSC = [(coordXY[0], coordXY[1] + 1), (coordXY[0], coordXY[1] - 1)]\n\tfor elem in SC:\n\t\tif elem[0] not in range(8) or elem[1] not in range(8):\n\t\t\tsurrounding_coord.append('None')\n\t\telse:\n\t\t\tsurrounding_coord.append(elem)\n\n\treturn testing_coord, surrounding_coord","def get_positions(self):\r\n null_pos, black_pos, white_pos = set(), set(), set()\r\n for pos in BOARD_POSITIONS:\r\n if self.state[pos[0]][pos[1]] == 0:\r\n null_pos.add(pos)\r\n elif self.state[pos[0]][pos[1]] == 1:\r\n black_pos.add(pos)\r\n else:\r\n white_pos.add(pos)\r\n return null_pos, black_pos, white_pos","def available_moves(self):\n moves = []\n for x, y in self.available_boards:\n moves.extend([self.to_position(x, y, i, j) for (i, j)\n in self.boards[x][y].empty_squares])\n return moves","def _get_neighbours(self, position):\n grid = self._grid\n x, y = position\n neighbours = []\n offsets = [(0,1),(1,0),(0,-1),(-1,0)]\n shuffle(offsets)\n for offset in offsets:\n i, j = offset\n position = (x + i, y + j)\n if grid.valid_position(position) and position not in self.shots:\n neighbours.append(position)\n return neighbours","def get_grid_locations(self, top_left, other_pos):\n cell_x = torch.floor(((other_pos[:, 0] - top_left[:, 0]) / self.neighborhood_size) *self.grid_size)\n\n # Added this part to implementation, otherwise the pooling is going to run into an indexing error\n cell_x[cell_x == self.grid_size] -= 1\n cell_y = torch.floor(((top_left[:, 1] - other_pos[:, 1]) / self.neighborhood_size) *self.grid_size)\n cell_y[cell_y == self.grid_size] -= 1\n grid_pos = cell_x + cell_y * self.grid_size\n\n return grid_pos","def checked_positions():\n for base_position in chain([me.shipyard], me.get_dropoffs()):\n x_shipyard = base_position.position.x\n y_shipyard = base_position.position.y\n for x in range(-search_range, search_range):\n for y in range(-search_range, search_range):\n yield hlt.Position(\n x=x_shipyard + x,\n y=y_shipyard + y)","def empty_spots(self):\n\t\tret = []\n\t\tfor i in range(0, self.size):\n\t\t\tfor j in range(0, self.size):\n\t\t\t\tif(self.grid[i][j] == self.terminal):\n\t\t\t\t\tret.append((i,j))\n\t\treturn ret","def calc_grid(self):\n return int(self._posn.x / cell_size), int(self._posn.y / cell_size)","def available_spots(self):\n occupied_tiles = self.board.keys()\n neighbors = lambda x, y: ((x+1, y), (x-1, y), (x, y+1), (y, y-1))\n tiles_near_occupied = set(neighbor for tile in occupied_tiles\n for neighbor in neighbors(*tile))\n unnoccupied_titles_near_occupied = tiles_near_occupied - set(occupied_tiles)\n return unnoccupied_titles_near_occupied","def grid_points(self):\n for i in range(self.rows):\n for j in range(self.cols):\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon((i,j))\n if i == 0:\n print_gps(max_lat,max_lon,\"grid\")\n if j == 0:\n print_gps(max_lat,min_lon,\"grid\")\n if j == 0:\n print_gps(min_lat,min_lon,\"grid\")\n print_gps(min_lat,max_lon,\"grid\")","def get_grid(self, grid_idx):\n end_idx = self.sample_idx[grid_idx]\n start_idx = self.sample_idx[grid_idx-1] if grid_idx != 0 else 0\n grid = self.im_data[start_idx]\n label = self.label_data[start_idx:end_idx]\n state = self.state_data[start_idx:end_idx]\n goal = self.find_goal(grid[1])\n return grid, state, label, goal","def get_grid_coords(self, count, boundry_x, boundry_y, grid_size):\n\n coords = []\n\n boundry_x = int(boundry_x/10)\n boundry_y = int(boundry_y/10)\n\n while len(coords) < count:\n seed()\n\n\n x = randint(-boundry_x, boundry_x)\n y = randint(-boundry_y, boundry_y)\n\n if len(coords) == 0:\n coords.append((x*grid_size, y*grid_size))\n else:\n for coord in coords:\n if (x not in range(coord[0]-buffer*grid_size, coord[0]+buffer*grid_size)) and (y not in range(coord[1]-buffer, coord[1]+buffer)):\n pass\n else:\n break","def find_excited_locations(self):\n return np.asarray(np.where(self._grid == 8)).T","def _get_gpos ( self ):\n bpos = mgrid[self.x_min:self.x_max:self.nxsteps*1j, \\\n self.y_min:self.y_max:self.nysteps*1j, \\\n self.z_min:self.z_max:self.nzsteps*1j]\n bpos.resize((3, self.size))\n return bpos","def available_boards(self):\n if self.state != State.IN_PROGRESS:\n return []\n if self.last_move is None:\n return self.active_boards\n x, y = self.last_move[-2:]\n if self.boards[x][y].state == State.IN_PROGRESS:\n return [(x, y)]\n return self.active_boards","def get_grid(loc=GRID_LOC):\n\n sref = ncepgrib2.Grib2Decode(loc, gribmsg=False)\n lats, lons = sref[0].grid()\n\n return lats, lons","def get_agent_positions_in_grid(agent, grid):\n grid_position_list = []\n\n for position in agent.positions:\n for (grid_row, grid_row_index) in zip(grid, range(len(grid))):\n grid_col_index = _get_position_grid_column(position, grid_row)\n if grid_col_index or grid_col_index == 0:\n grid_position_list.append(Position(grid_col_index, grid_row_index))\n break\n\n return grid_position_list","def getCoordinates(self):\n return list(self.gridVars.keys())","def get_available_moves(self, board):\n available_moves = []\n for fieldx in range(len(board)):\n column = []\n for fieldy in range(len(board)):\n legit_move = board[self.posy][self.posx].is_valid_move(board, fieldx, fieldy)\n column.append(legit_move)\n available_moves.append(column)\n return available_moves","def get_played_positions(board):\n return np.argwhere(board.state != -1)","def _get_gpos ( self ):\n bpos = mgrid[self.x_min:self.x_max:self.nxsteps*1j, \\\n self.y_min:self.y_max:self.nysteps*1j, \\\n self.z:self.z+0.1]\n bpos.resize((3, self.size))\n return bpos","def get_all_spawnable_cells(self):\n spawnable_positions = []\n\n for i in range(self.grid.width):\n for j in range(self.grid.height):\n n_list = self.grid.get_cell_list_contents([(i, j)])\n\n if len(n_list) <= 0:\n spawnable_positions.append((i, j))\n elif len(n_list) > 0:\n n = n_list[0]\n if not any(map(lambda t: isinstance(n, t), self.not_spawnable_objects)):\n spawnable_positions.append((i, j))\n\n return spawnable_positions","def get_neighbors(self):\n return list(map(self.game.square, [self.position - self.game.rules[\"row_len\"], self.position + 1, self.position + self.game.rules[\"row_len\"], self.position - 1]))","def getGridPoints(x, y, robot):\r\n roundedGrid = (round(x), round(y))\r\n total_radius = (robot.RADIUS + robot.BALL_RADIUS) / robot.grid.scale\r\n scanAmount = math.ceil(total_radius)\r\n scan = range(-scanAmount, scanAmount + 1)\r\n corners = ((0, 0), (0, 1), (1, 1), (1, 0))\r\n points = []\r\n for i in scan:\r\n for j in scan:\r\n for corner in corners:\r\n newX = roundedGrid[0] + i + corner[0]\r\n newY = roundedGrid[1] + j + corner[1]\r\n if grid_distance(newX, newY, x, y) < total_radius:\r\n points.append((newX, newY))\r\n\r\n return points","def boarderPosition(self, gameState):\n if gameState.isOnRedTeam(self.index):\n i = self.midWidth - 1\n else:\n i = self.midWidth + 1\n boudaries = [(i,j) for j in range(self.height)]\n validPositions = []\n for i in boudaries:\n if not gameState.hasWall(i[0],i[1]):\n validPositions.append(i)\n return validPositions","def position_grid(self):\n return self.currentLevel.toGridCoord(self.position)","def get_grid_list (self):\n return [\n 'area', 'ald', 'poi', 'ice', 'lake pond',\n 'drainage', 'degree-day','climate event'\n ]","def get_piles_place(self):\n idx_draw = []\n piles_draw = []\n i = 0\n for p in self.game_state['piles']:\n if not self.game_state['two_player']:\n if not self.game_state['piles_taken'][i] and len(p) < 3:\n idx_draw.append(i)\n piles_draw.append(p)\n else:\n if len(p) <= i and not self.game_state['piles_taken'][i]:\n idx_draw.append(i)\n piles_draw.append(p)\n i += 1\n return (piles_draw,idx_draw)","def allplacement(self, test_pylos):\n placements = []\n player = test_pylos._state['visible']['turn']\n for layer in range(4):\n for row in range(4-layer):\n for column in range(4-layer):\n value = test_pylos.get(layer,row,column)\n if value is None:\n try:\n test_pylos.validPosition(layer, row, column)\n except game.InvalidMoveException:\n pass\n else:\n if test_pylos.createSquare((layer, row, column)) is True:\n if player == 0:\n removableballs = self.removableballs0(test_pylos)\n removableballs.apppend([layer, row, column])\n else:\n removableballs = self.removableballs1(test_pylos)\n removableballs.apppend([layer, row, column])\n for i in removableballs:\n if i[0] == layer - 1 and i[1] == row and i[2] == column:\n removableballs.remove(i)\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column:\n removableballs.remove(i)\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column + 1:\n removableballs.remove(i)\n elif i[0] == layer - 1 and i[1] == row and i[2] == column + 1:\n removableballs.remove(i)\n\n for i in removableballs:\n move = {\n 'move': 'place',\n 'to': [layer, row, column],\n 'remove': i\n }\n\n placements.append(move)\n else:\n move = {\n 'move': 'place',\n 'to': [layer, row, column]\n }\n placements.append(move)\n return placements","def get_visible_cells(self):\r\n ux, uy = self.GetScrollPixelsPerUnit()\r\n sx, sy = self.GetViewStart()\r\n w, h = self.GetGridWindow().GetClientSize().Get()\r\n sx *= ux\r\n sy *= uy\r\n start_col = self.XToCol(sx)\r\n start_row = self.YToRow(sy)\r\n end_col = self.XToCol(sx + w, True)\r\n end_row = self.YToRow(sy + h, True)\r\n return start_row, end_row, start_col, end_col","def get_box_grid(x, y):\n for grid in GRIDS:\n if x >= grid[0][0] and y >= grid[0][1] and \\\n x <= grid[1][0] and y <= grid[1][1]:\n return grid\n return None","def get_empty_board_indecies(self):\n empty_indecies = []\n for row_num in range(len(self.board)):\n for col_num in range(len(self.board)):\n if self.board[row_num][col_num] and self.board[row_num][col_num].state == PegState.EMPTY:\n empty_indecies.append((row_num, col_num))\n return empty_indecies","def grid_coords(self):\n return [(x, y) for y in range(self.height) for x in range(self.width)]","def availableSquares(self):\n List2=[]\n for item in self.all:\n if item.retrieve()==\"\":\n List2.append(item.name())\n return List2","def all_grid_neighbours(grid):\n # create a dictionary of neighbours\n neighbours = {}\n # for each position in the grid\n for position in grid:\n # call the neighbours_of_position function to get the positions \n # neighbours\n position_neighbours = neighbours_of_position(position)\n # build our neighbours dictionary: break up our position_neighbours \n # variable and put it as a neighbour[position] if its in the grid \n neighbours[position] = [p for p in position_neighbours if p in grid]\n return neighbours","def get_positions(self):\n return self.positions","def available_moves(self):\n available_moves = []\n for i in range(self.quadrants_count):\n quadrant_positions = self.play_area[i].available_positions()\n for p in quadrant_positions:\n position = p + i * 9\n for j in range(self.quadrants_count):\n move1 = [str(position), str(j + 1), \"l\"]\n move2 = [str(position), str(j + 1), \"r\"]\n available_moves.append(\" \".join(move1))\n available_moves.append(\" \".join(move2))\n return available_moves","def positions_to_play(self):\r\n\r\n positions = []\r\n\r\n for i in range(0, len(self.matrix)):\r\n for j in range(0, len(self.matrix[i])):\r\n if self.matrix[i][j] == \"0\":\r\n # Add [row, column] to the list\r\n positions.append([i, j])\r\n \r\n return positions","def grid(self):\n return self.__grid","def find_moves(self):\n\n from itertools import product\n free_position = self.find_free()\n return [list(free_position+i) for i in [[0,1],[1,0],[-1,0],[0,-1]] if tuple(i+free_position) in product(range(self.size),repeat=2)]","def position_surroundings(self, neighbour_pos, missed_value):\n pos = []\n for x, y in neighbour_pos:\n position = self._square_matrix[x][y].get_pos_from_number(missed_value)\n if position:\n pos.append(position)\n return pos","def known_points(self):\n return self._cache.space","def get_neighbours(self):\n shape=self.cubeshape[1:]\n neighboursx=np.arange(self.xpos-(self.blocksize-1)/2,(self.xpos+(self.blocksize-1)/2)+1,dtype='int' )\n neighboursx=[x if (x>=0) & (x<=shape[1]-1) else np.nan for x in neighboursx ]\n neighboursy=np.arange(self.ypos-(self.blocksize-1)/2,(self.ypos+(self.blocksize-1)/2)+1,dtype='int' )\n neighboursy=[y if (y>=0) & (y<=shape[0]-1) else np.nan for y in neighboursy ]\n keys=[np.ravel_multi_index([y,x], shape) if np.all(np.isfinite(np.asarray([y,x]))) else np.nan for y in neighboursy for x in neighboursx]\n\n return keys","def populate_positions(self, board, player):\n\t\tprint \"Inside populate_positions!-----\"\n\t\tpositions = []\n\t\tfor i in range(0,3):\n\t\t\tfor j in range(0,3):\n\t\t\t\tif(board[i][j] == player):\n\t\t\t\t\tpositions.append([i,j])\n\t\treturn positions","def occupied_cells(self):\n\n for lm in self.landmarks:\n if self.cell_size < 1:\n # expand the range the landmark exists\n lm_x_range = np.arange(lm[0]-self.R, lm[0]+self.R, self.cell_size)\n lm_y_range = np.arange(lm[1]-self.R, lm[1]+self.R, self.cell_size)\n\n # loop through expanded ranges and compute grid positions\n for lm_x in lm_x_range:\n for lm_y in lm_y_range:\n\n row, col = self.cell_index([lm_x, lm_y])\n\n # apply cost of occupied cell\n try:\n self.world[row][col] = 1000\n except IndexError:\n pass\n\n else:\n # apply cost of occupied cell\n row, col = self.cell_index(lm)\n try:\n self.world[row][col] = 1000\n except IndexError:\n pass","def explore(self):\n \n has_new_pos = False\n while not has_new_pos:\n move = self.biased_choice()\n new_pos = add_lists(move, self.position)\n has_new_pos = check_bounds(new_pos, self.grid_size)\n return new_pos","def getGrid(self):\n\n return self.board","def grid(self):\n return self._grid","def _get_available_spaces(world):\n spaces = []\n for y, row in enumerate(world):\n for x, column in enumerate(row):\n if column == eg.NONE:\n spaces.append((x, y))\n\n return spaces","def generate_nearby_cells(self):\n for y in range(len(self.island_map)):\n for x in range(len(self.island_map[y])):\n list_of_nearby_cells = []\n\n if y != 0:\n self.generate_cell_above(x, y, list_of_nearby_cells)\n\n if x != 0:\n self.generate_cell_left(x, y, list_of_nearby_cells)\n\n if y != len(self.island_map)-1:\n self.generate_cell_below(x, y, list_of_nearby_cells)\n\n if x != len(self.island_map[y])-1:\n self.generate_cell_right(x, y, list_of_nearby_cells)\n\n self.island_map[y][x].nearby_cells = list_of_nearby_cells","def fill_grid_np(self):\n\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\n grid = self.grid_np\n # cell_size = self.cell_size\n for obj in self.levels[self.curient_level].objects:\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\n x, y = obj.position_grid[X], obj.position_grid[Y]\n grid[y*GRID_WIDTH + x] = obj\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\n # cell_size[y*GRID_WIDTH + x] += 1","def get_all_positions(board, white_turn):\n list = []\n for row in range(8):\n for col in range(8):\n # White\n if white_turn and white_piece_on_pos((row, col), board):\n obj = board[row][col]\n if type(obj) is Pawn:\n for valid_pos in valid_positions_pawn_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Tower:\n for valid_pos in valid_positions_tower_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Bishop:\n for valid_pos in valid_positions_bishop_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Horse:\n for valid_pos in valid_positions_horse_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Queen:\n for valid_pos in valid_positions_queen_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is King:\n for valid_pos in valid_positions_king_white((row, col), board):\n list.append(((row, col), valid_pos))\n # Black\n elif (not white_turn) and black_piece_on_pos((row, col), board):\n obj = board[row][col]\n if type(obj) is Pawn:\n for valid_pos in valid_positions_pawn_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Tower:\n for valid_pos in valid_positions_tower_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Bishop:\n for valid_pos in valid_positions_bishop_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Horse:\n for valid_pos in valid_positions_horse_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Queen:\n for valid_pos in valid_positions_queen_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is King:\n for valid_pos in valid_positions_king_black((row, col), board):\n list.append(((row, col), valid_pos))\n return list","def get_all_numbered_cells(self):\r\n num_rows = len(self.mine_field)\r\n num_cols = len(self.mine_field[0])\r\n numbered_cells = [(row, col)\r\n for row in range(num_rows)\r\n for col in range(num_cols)\r\n if self.mine_field[row][col] != '?' and \\\r\n self.mine_field[row][col] != 'x']\r\n return numbered_cells","def grid(self):\n self.best_params_list = clust_grid(self.model, self.param_grid, self.X, self.y, self.model_mask_cols)","def get_empty_square(self) -> list:\n empty_square = []\n for line_index in range(len(self.grid)):\n for col_index in range(len(self.grid[line_index])):\n if self.grid[line_index][col_index].color is None:\n empty_square.append((line_index, col_index))\n\n return empty_square","def available_moves(board_state):\n for x, y in itertools.product(range(len(board_state)), range(len(board_state[0]))):\n if board_state[x][y] == 0:\n yield (x, y)","def get_positions(self):\n\n yield from self._positions","def open_spots(self):\n ret = []\n for i in range(1,25):\n if self.nodes[i].piece == None:\n ret.append(i)\n return ret","def grid_evaluation(self, x, y, size=256):\n\t\t# Create matrix x and y coordinates\n\t\tL = self.L\n\t\t[xx, yy] = meshgrid(linspace(-L, L, size), linspace(-L, L, size))\n\t\tpts = np.array([xx, yy])\n\t\tux = batch_eval(x, pts)\n\t\tuy = batch_eval(y, pts)\n\t\treturn xx, yy, ux, uy","def get_empty_squares(self):\n empty = []\n for row in range(self._dim):\n for col in range(self._dim):\n if self._board[row][col] == EMPTY:\n empty.append((row, col))\n return empty","def find_unsettled_spot(self):\n\t\tfor i in range(9):\n\t\t\tfor j in range(9):\n\t\t\t\tif self.grid[i][j] == 0:\n\t\t\t\t\treturn i, j\n\t\treturn","def get_available_moves(self):\n available = []\n row, col = tuple(self.current_pos)\n if row - 1 >= 0 and self.maze[row - 1][col] != 'x':\n available.append('n')\n if row + 1 < len(self.maze) and self.maze[row + 1][col] != 'x':\n available.append('s')\n if col - 1 >= 0 and self.maze[row][col - 1] != 'x':\n available.append('w')\n if col + 1 < len(self.maze[row]) and self.maze[row][col + 1] != 'x':\n available.append('e')\n return available","def guarded_places(self):\n guarded = []\n for x in range(8):\n for y in range(8):\n if self.squares[x][y].piece and self.squares[x][y].piece.color != self.turn:\n squares = self.squares[x][y].piece.actions(self, (x, y), True)\n if self.squares[x][y].piece.name != 'pawn': # pawns capture in different areas than they move\n guarded.extend(squares[0])\n guarded.extend(squares[1])\n return guarded","def get_gridState(self,real_state=None):\r\n if real_state is None:\r\n # convert unit back to mm\r\n r_state =[self.real_state[0],self.real_state[1],self.real_state[2]]\r\n else:\r\n r_state = real_state\r\n\r\n grid_state = [0,0,0]\r\n # assume orignal point is the center of the starting piece\r\n for i in range(2):\r\n if np.abs(real_state[i]) < (self.grid_size)/2.0:\r\n grid_state[i] =0\r\n else:\r\n # tol = round(self.grid_size/10.0, 2) #tolerance of floating point number\r\n tol =15.0 #1.5cm as tolerance\r\n grid_state[i] = int((real_state[i]-(self.grid_size)/2.0)//self.grid_size)\r\n remain = 1 if (real_state[i]-(self.grid_size)/2.0)%self.grid_size >=tol else 0\r\n grid_state[i] += remain\r\n\r\n # convert rad to degree\r\n real_angle = 180.0*(real_state[2]/math.pi)\r\n a = [abs(real_angle - i) for i in self.angle_set]\r\n i = np.argmin(a)\r\n # calculate approximate degree in grid world\r\n grid_state[2] = self.angle_set[i]\r\n # print(\"grid angle:\",grid_state[2],\"real ag:\",real_angle)\r\n\r\n # correct the degree in rad for moving\r\n # agl_rad = grid_state[2]*(math.pi/180.0)\r\n # correct_rot = -(real_state[2] - agl_rad)\r\n # sp = 100\r\n # t = (correct_rot* (math.pi/180))/sp\r\n # cur_t = time.time()\r\n # past_t = cur_t\r\n # while abs(past_t-cur_t) <=t+0.5:\r\n # self.Roomba.Move(0,sp)\r\n # cur_t = time.time()\r\n # self.Roomba.Move(0,0)\r\n\r\n return grid_state","def get_valid_moves(self):\r\n validMoves = []\r\n\r\n for x in range(BOARD_SIZE):\r\n for y in range(BOARD_SIZE):\r\n pos = np.array([x,y])\r\n if self.board[pos[0],pos[1]] == 0:\r\n if(self.update_board(pos,_testing=True)):\r\n validMoves.append(pos)\r\n\r\n return validMoves","def test_grid():\n with get_test_data('interpolation_test_grid.npz') as fobj:\n data = np.load(fobj)\n return data['xg'], data['yg']","def __FreeTiles(self, grid, log=False):\n\n x_pos, _ = np.where(grid == 0)\n return len(x_pos)","def _buildGridPoints(self):\n self.spacings = []\n for level in xrange(self.depth):\n levelSpacings = []\n refLevel = level + 1\n level = 2**level\n axisData = []\n for axis in self.size:\n spacing = axis / (level+1)\n levelSpacings.append(spacing)\n axisData.append([gridValue*spacing for gridValue in xrange(1, level+1)])\n pointList = [((i, j, k), np.array([axisData[0][i], axisData[1][j], axisData[2][k]]))\n for i in xrange(level)\n for j in xrange(level)\n for k in xrange(level)]\n self.grid[refLevel] = {point[0]: point[1] for point in pointList}\n self.spacings.append(levelSpacings)","def findEmpty(grid):\n for x in range(len(grid.board)):\n for y in range(len(grid.board[0])):\n if grid.board[x][y] == 0:\n return [x,y]","def next_positions(self):\n self.scores = np.array(self.scores)\n improved = self.scores < self._pso_data.best_scores\n\n self._pso_data.best_scores[improved] = self.scores[improved]\n self._pso_data.best_positions[improved] = self.positions[improved]\n\n self._pso_data.velocities = self._new_velocities()\n new_positions = self.positions + self._pso_data.velocities\n return new_positions","def _initiate_board(self):\n grid = []\n for i in range(constant.BOARD_DIMENSION):\n # Starts each row\n current_row = []\n for j in range(constant.BOARD_DIMENSION):\n # Adds the pieces depending on the position\n if i < constant.ROWS_OF_PIECES:\n # Black pieces\n if (j + i) % 2 != 0:\n current_row.append(Piece(i, j, Player.black))\n self.num_black_pieces = self.num_black_pieces + 1\n else:\n current_row.append(None)\n\n elif i >= constant.BOARD_DIMENSION - constant.ROWS_OF_PIECES:\n # White pieces\n if (j + i) % 2 != 0:\n current_row.append(Piece(i, j, Player.white))\n self.num_white_pieces = self.num_white_pieces + 1\n else:\n current_row.append(None)\n\n else:\n current_row.append(None)\n\n grid.append(current_row)\n\n return grid","def get_empty_squares(self):\n return self.empty_squares","def get_relative_grid(self, xgbl, ygbl):\r\n self.rx = xgbl - self.x0\r\n self.ry = ygbl - self.y0\r\n\r\n # flatten the coordinates:\r\n self.rx = self.rx.ravel()\r\n self.ry = self.ry.ravel()","def get_grid(self):\n self.fullws = []\n for row in self.word_search_grid:\n rowdata = []\n for column in row:\n rowdata += [column.entry.get()]\n self.fullws += [rowdata]\n self.logic.set_grid(self.fullws)","def get_occupied_tiles(self):\r\n occupied = np.zeros(self.searchenv.conv.num_tiles)\r\n #Convert current state (positions of agents) to tile indices\r\n tiles = self.searchenv.conv.state_to_tile(self.searchstate.positions)\r\n valid_tiles = tiles[self.searchstate.actives == 1]\r\n occupied[valid_tiles] = 1\r\n return occupied","def get_valid_locations(location_list, grid, shape):","def opponentBoarderPosition(self, gameState):\n if self.red:\n i = self.midWidth\n else:\n i = self.midWidth - 1\n boudaries = [(i,j) for j in range(self.height)]\n validPositions = []\n for i in boudaries:\n if not gameState.hasWall(i[0],i[1]):\n validPositions.append(i)\n return validPositions","def __free_spots(self, pos: Tuple[int, int], tree: int) -> List:\n x, y = pos\n possible_slots = []\n\n if x + 2 == self.__width:\n if self.__exit is None:\n # if we are next to the right edge and the exit is not set, connect and return\n self.__activate(x + 1, y, tree)\n self.__exit = (x + 1, y)\n return []\n\n elif self.__check_row(x + 1, y):\n # iterate through this row and compare maze assignments\n if self.__check_and_join_row(x, y, tree, 1):\n possible_slots.append((x + 1, y))\n\n if x > 1:\n if self.__check_row(x - 1, y):\n if self.__check_and_join_row(x, y, tree, -1):\n possible_slots.append((x - 1, y))\n\n elif self.__entry is None:\n # if we are on the left edge and there's no entry, connect\n self.__activate(0, y, tree)\n self.__entry = (0, y)\n return []\n\n # check left and right.\n if y + 2 < self.__height and self.__check_col(x, y + 1):\n if self.__check_and_join_col(x, y, tree, 1):\n possible_slots.append((x, y + 1))\n\n if y > 1 and self.__check_col(x, y - 1):\n if self.__check_and_join_col(x, y, tree, -1):\n possible_slots.append((x, y - 1))\n\n return possible_slots","def get_grid(self, mol_data):\n\n if self.grid_info is None:\n\n try:\n\n x = mol_data['grid_points/x'][()]\n y = mol_data['grid_points/y'][()]\n z = mol_data['grid_points/z'][()]\n\n except BaseException:\n\n raise ValueError(\n \"Grid points not found in the data file\")\n\n else:\n\n center = mol_data['grid_points/center'][()]\n npts = np.array(self.grid_info['number_of_points'])\n res = np.array(self.grid_info['resolution'])\n\n halfdim = 0.5 * (npts * res)\n\n low_lim = center - halfdim\n hgh_lim = low_lim + res * (npts - 1)\n\n x = np.linspace(low_lim[0], hgh_lim[0], npts[0])\n y = np.linspace(low_lim[1], hgh_lim[1], npts[1])\n z = np.linspace(low_lim[2], hgh_lim[2], npts[2])\n\n # there is stil something strange\n # with the ordering of the grid\n # also noted in GridTools define_grid_points()\n y, x, z = np.meshgrid(y, x, z)\n grid = (x, y, z)\n npts = (len(x), len(y), len(z))\n return grid, npts","def best_frame_indices_in_empty_areas(self, index_y, index_x):\n\n # Go though circles with increasing radius \"distance\" around the current quality area.\n for distance in arange(1, max(self.y_dim, self.x_dim)):\n circle = Miscellaneous.circle_around(index_x, index_y, distance)\n for (compare_x, compare_y) in circle:\n # If the coordinates are within the quality area grid, and if the area at this\n # location has a non-empty list of alignment points, return its list.\n if 0 <= compare_x < self.x_dim and 0 <= compare_y < self.y_dim and \\\n self.quality_areas[compare_y][compare_x]['alignment_point_indices']:\n return self.quality_areas[compare_y][compare_x]['best_frame_indices']\n # This should never happen, because it means that there is not any quality area with an\n # alignment point.\n raise InternalError(\"No quality area contains any alignment point\")","def get_gridline_values(self, x=None, y=None):\n if (x and y) or not (x or y):\n print('[error] use either x or y keyword arguments '\n 'to define the gridline position')\n return\n elif x:\n return self.get_vertical_gridline_values(x)\n elif y:\n return self.get_horizontal_gridline_values(y)","def getChoices(self, grid):\n return [(x, y) for x in range(3) for y in range(3) if grid[x][y] == 0]","def check_for_moves(self) -> list:\r\n avail_moves = []\r\n for x in range(self.size):\r\n for y in range(self.size):\r\n if self.tags[x][y] is None:\r\n avail_moves.append((x, y))\r\n return avail_moves","def get_score(location, grid, shape):","def get_neighbors(grid, x, y):\n out = []\n if x > 0:\n out.append(grid[x-1, y])\n if y > 0:\n out.append(grid[x, y-1])\n if y < grid.shape[1] - 1:\n out.append(grid[x, y+1])\n if x < grid.shape[0] - 1:\n out.append(grid[x+1, y])\n return out","def get_bounds(self):\n occupied_locations = self.board.keys()\n min_x = min(p[0] for p in occupied_locations)\n max_x = max(p[0] for p in occupied_locations)\n min_y = min(p[1] for p in occupied_locations)\n max_y = max(p[1] for p in occupied_locations)\n return ((min_x, max_x), (min_y, max_y))","def getStartSpots(self):\n spots = []\n if self.index == 0:\n startRow = 1\n endRow = 4\n if self.index == 1:\n startRow = 6\n endRow = 9\n for row in range(startRow, endRow):\n for col in range(1,9):\n spots += [(col, row)]\n return spots","def possible_moves(self, pos: Point) -> List[Point]:\n # logger.debug(f\"inside possible_moves {pos}\")\n available_squares = []\n for direction in Direction.cardinal():\n # logger.debug(f\"direction = {direction}\")\n neighbor = pos + direction\n # logger.debug(f\"neighbor = {neighbor}\")\n if neighbor.x < 1 or self.width - 2 < neighbor.x or neighbor.y < 1 or self.height - 2 < neighbor.y:\n # logger.debug(f\"{neighbor} not in bounds\")\n continue\n if self.can_carve(pos, direction):\n # logger.debug(f\"can_carve returned True pos={pos}, direction={direction}\")\n available_squares.append(neighbor)\n # logger.debug(f\"available squares:\")\n # for square in available_squares:\n # logger.debug(f\"square={square}\")\n # logger.add(\"debug.log\")\n return available_squares","def get_unhindered_positions(self, endposition):\n current_position = self.position\n potential_positions = potential_positions = {\n 'left' : [], \n 'right' : [],\n 'up' : [], \n 'down' : []\n }\n space_down = current_position[0]-1\n space_up = self.ncols-current_position[0]\n space_right = self.nrows - (ord('H')-ord(current_position[1]))\n space_left = ord(current_position[1]) - ord('A')\n\n for i in range(1, space_down+1):\n pos = (current_position[0]-i, current_position[1])\n if self.pos_within_bounds(pos):\n potential_positions['down'].append(pos)\n\n for i in range(1, space_up+1):\n pos = (current_position[0]+i, current_position[1])\n if self.pos_within_bounds(pos):\n potential_positions['up'].append(pos)\n \n for i in range(1, space_left+1):\n pos = (current_position[0], chr(ord(current_position[1])-i))\n if self.pos_within_bounds(pos):\n potential_positions['left'].append(pos)\n\n for i in range(1, space_right+1):\n pos = (current_position[0], chr(ord(current_position[1])+i))\n if self.pos_within_bounds(pos):\n potential_positions['right'].append(pos)\n\n for direction, square in potential_positions.items():\n if tuple(endposition) in square:\n return potential_positions[direction]","def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\n pass","def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\n pass","def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\n pass","def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\n pass","def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\n pass","def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\n pass"],"string":"[\n \"def free_positions(self):\\n positions = []\\n for i in range(self.grid_size):\\n for j in range(self.grid_size):\\n if self.grid[i][j] == 0:\\n positions.append((i, j))\\n if positions == []:\\n raise GameException('Game Over. No free position left.')\\n return positions\",\n \"def filled_positions(self):\\n return [x for x in assignable_positions if self.grid[x][0]]\",\n \"def available_positions(self):\\n available_positions = []\\n for i in range(self.positions_count):\\n if self.board[i] == 0:\\n available_positions.append(i+1)\\n return available_positions\",\n \"def collide_grid(self):\\n topleft = self.absolute_collide_topleft\\n bottomright = self.absolute_collide_bottomright\\n tlx, tly = self.currentLevel.toGridCoord(topleft)\\n brx, bry = self.currentLevel.toGridCoord(bottomright)\\n collide_grid = []\\n for x in range(tlx, brx+1):\\n for y in range(tly, bry+1):\\n collide_grid.append( (x,y) )\\n if not collide_grid:\\n collide_grid = [(tlx,tly)]\\n return collide_grid\",\n \"def board_empty_positions(self, x, y):\\n board = self.boards[x][y]\\n coords = [(x, y, i, j) for (i, j) in board.empty_squares]\\n return self.coords_to_positions(coords)\",\n \"def get_allowed_positions(coordXY, grid):\\n\\n\\tsurrounding_coord = []\\n\\ttesting_coord = []\\n\\n\\t# Get the coordinates of the external square\\n\\tfor i in range(coordXY[0] - 1, coordXY[0] + 2, 2):\\n\\t\\tfor j in range(coordXY[1] - 1, coordXY[1] +2, 1):\\n\\t\\t\\tif (i,j) == coordXY:\\n\\t\\t\\t\\tpass\\n\\t\\t\\telif i < 0 or j < 0:\\n\\t\\t\\t\\tsurrounding_coord.append('None')\\n\\t\\t\\telse:\\n\\t\\t\\t\\tsurrounding_coord.append((i,j))\\n\\n\\t# Get the coordinates of the internal square\\n\\tfor i in range(coordXY[0] - 2, coordXY[0] + 3, 4):\\n\\t\\tfor j in range(coordXY[1] - 2, coordXY[1] + 3, 2):\\n\\t\\t\\tif i < 0 or j < 0 or i > 7 or j > 7:\\n\\t\\t\\t\\ttesting_coord.append('None')\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttesting_coord.append((i,j))\\n\\n\\t# Get the position of Bottom and Top of the 2 squares\\n\\tTC = [(coordXY[0], coordXY[1] + 2), (coordXY[0], coordXY[1] - 2)]\\n\\tfor elem in TC:\\n\\n\\t\\tif elem[0] not in range(8) or elem[1] not in range(8):\\n\\t\\t\\ttesting_coord.append('None')\\n\\t\\telse:\\n\\t\\t\\ttesting_coord.append(elem)\\n\\n\\n\\tSC = [(coordXY[0], coordXY[1] + 1), (coordXY[0], coordXY[1] - 1)]\\n\\tfor elem in SC:\\n\\t\\tif elem[0] not in range(8) or elem[1] not in range(8):\\n\\t\\t\\tsurrounding_coord.append('None')\\n\\t\\telse:\\n\\t\\t\\tsurrounding_coord.append(elem)\\n\\n\\treturn testing_coord, surrounding_coord\",\n \"def get_positions(self):\\r\\n null_pos, black_pos, white_pos = set(), set(), set()\\r\\n for pos in BOARD_POSITIONS:\\r\\n if self.state[pos[0]][pos[1]] == 0:\\r\\n null_pos.add(pos)\\r\\n elif self.state[pos[0]][pos[1]] == 1:\\r\\n black_pos.add(pos)\\r\\n else:\\r\\n white_pos.add(pos)\\r\\n return null_pos, black_pos, white_pos\",\n \"def available_moves(self):\\n moves = []\\n for x, y in self.available_boards:\\n moves.extend([self.to_position(x, y, i, j) for (i, j)\\n in self.boards[x][y].empty_squares])\\n return moves\",\n \"def _get_neighbours(self, position):\\n grid = self._grid\\n x, y = position\\n neighbours = []\\n offsets = [(0,1),(1,0),(0,-1),(-1,0)]\\n shuffle(offsets)\\n for offset in offsets:\\n i, j = offset\\n position = (x + i, y + j)\\n if grid.valid_position(position) and position not in self.shots:\\n neighbours.append(position)\\n return neighbours\",\n \"def get_grid_locations(self, top_left, other_pos):\\n cell_x = torch.floor(((other_pos[:, 0] - top_left[:, 0]) / self.neighborhood_size) *self.grid_size)\\n\\n # Added this part to implementation, otherwise the pooling is going to run into an indexing error\\n cell_x[cell_x == self.grid_size] -= 1\\n cell_y = torch.floor(((top_left[:, 1] - other_pos[:, 1]) / self.neighborhood_size) *self.grid_size)\\n cell_y[cell_y == self.grid_size] -= 1\\n grid_pos = cell_x + cell_y * self.grid_size\\n\\n return grid_pos\",\n \"def checked_positions():\\n for base_position in chain([me.shipyard], me.get_dropoffs()):\\n x_shipyard = base_position.position.x\\n y_shipyard = base_position.position.y\\n for x in range(-search_range, search_range):\\n for y in range(-search_range, search_range):\\n yield hlt.Position(\\n x=x_shipyard + x,\\n y=y_shipyard + y)\",\n \"def empty_spots(self):\\n\\t\\tret = []\\n\\t\\tfor i in range(0, self.size):\\n\\t\\t\\tfor j in range(0, self.size):\\n\\t\\t\\t\\tif(self.grid[i][j] == self.terminal):\\n\\t\\t\\t\\t\\tret.append((i,j))\\n\\t\\treturn ret\",\n \"def calc_grid(self):\\n return int(self._posn.x / cell_size), int(self._posn.y / cell_size)\",\n \"def available_spots(self):\\n occupied_tiles = self.board.keys()\\n neighbors = lambda x, y: ((x+1, y), (x-1, y), (x, y+1), (y, y-1))\\n tiles_near_occupied = set(neighbor for tile in occupied_tiles\\n for neighbor in neighbors(*tile))\\n unnoccupied_titles_near_occupied = tiles_near_occupied - set(occupied_tiles)\\n return unnoccupied_titles_near_occupied\",\n \"def grid_points(self):\\n for i in range(self.rows):\\n for j in range(self.cols):\\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon((i,j))\\n if i == 0:\\n print_gps(max_lat,max_lon,\\\"grid\\\")\\n if j == 0:\\n print_gps(max_lat,min_lon,\\\"grid\\\")\\n if j == 0:\\n print_gps(min_lat,min_lon,\\\"grid\\\")\\n print_gps(min_lat,max_lon,\\\"grid\\\")\",\n \"def get_grid(self, grid_idx):\\n end_idx = self.sample_idx[grid_idx]\\n start_idx = self.sample_idx[grid_idx-1] if grid_idx != 0 else 0\\n grid = self.im_data[start_idx]\\n label = self.label_data[start_idx:end_idx]\\n state = self.state_data[start_idx:end_idx]\\n goal = self.find_goal(grid[1])\\n return grid, state, label, goal\",\n \"def get_grid_coords(self, count, boundry_x, boundry_y, grid_size):\\n\\n coords = []\\n\\n boundry_x = int(boundry_x/10)\\n boundry_y = int(boundry_y/10)\\n\\n while len(coords) < count:\\n seed()\\n\\n\\n x = randint(-boundry_x, boundry_x)\\n y = randint(-boundry_y, boundry_y)\\n\\n if len(coords) == 0:\\n coords.append((x*grid_size, y*grid_size))\\n else:\\n for coord in coords:\\n if (x not in range(coord[0]-buffer*grid_size, coord[0]+buffer*grid_size)) and (y not in range(coord[1]-buffer, coord[1]+buffer)):\\n pass\\n else:\\n break\",\n \"def find_excited_locations(self):\\n return np.asarray(np.where(self._grid == 8)).T\",\n \"def _get_gpos ( self ):\\n bpos = mgrid[self.x_min:self.x_max:self.nxsteps*1j, \\\\\\n self.y_min:self.y_max:self.nysteps*1j, \\\\\\n self.z_min:self.z_max:self.nzsteps*1j]\\n bpos.resize((3, self.size))\\n return bpos\",\n \"def available_boards(self):\\n if self.state != State.IN_PROGRESS:\\n return []\\n if self.last_move is None:\\n return self.active_boards\\n x, y = self.last_move[-2:]\\n if self.boards[x][y].state == State.IN_PROGRESS:\\n return [(x, y)]\\n return self.active_boards\",\n \"def get_grid(loc=GRID_LOC):\\n\\n sref = ncepgrib2.Grib2Decode(loc, gribmsg=False)\\n lats, lons = sref[0].grid()\\n\\n return lats, lons\",\n \"def get_agent_positions_in_grid(agent, grid):\\n grid_position_list = []\\n\\n for position in agent.positions:\\n for (grid_row, grid_row_index) in zip(grid, range(len(grid))):\\n grid_col_index = _get_position_grid_column(position, grid_row)\\n if grid_col_index or grid_col_index == 0:\\n grid_position_list.append(Position(grid_col_index, grid_row_index))\\n break\\n\\n return grid_position_list\",\n \"def getCoordinates(self):\\n return list(self.gridVars.keys())\",\n \"def get_available_moves(self, board):\\n available_moves = []\\n for fieldx in range(len(board)):\\n column = []\\n for fieldy in range(len(board)):\\n legit_move = board[self.posy][self.posx].is_valid_move(board, fieldx, fieldy)\\n column.append(legit_move)\\n available_moves.append(column)\\n return available_moves\",\n \"def get_played_positions(board):\\n return np.argwhere(board.state != -1)\",\n \"def _get_gpos ( self ):\\n bpos = mgrid[self.x_min:self.x_max:self.nxsteps*1j, \\\\\\n self.y_min:self.y_max:self.nysteps*1j, \\\\\\n self.z:self.z+0.1]\\n bpos.resize((3, self.size))\\n return bpos\",\n \"def get_all_spawnable_cells(self):\\n spawnable_positions = []\\n\\n for i in range(self.grid.width):\\n for j in range(self.grid.height):\\n n_list = self.grid.get_cell_list_contents([(i, j)])\\n\\n if len(n_list) <= 0:\\n spawnable_positions.append((i, j))\\n elif len(n_list) > 0:\\n n = n_list[0]\\n if not any(map(lambda t: isinstance(n, t), self.not_spawnable_objects)):\\n spawnable_positions.append((i, j))\\n\\n return spawnable_positions\",\n \"def get_neighbors(self):\\n return list(map(self.game.square, [self.position - self.game.rules[\\\"row_len\\\"], self.position + 1, self.position + self.game.rules[\\\"row_len\\\"], self.position - 1]))\",\n \"def getGridPoints(x, y, robot):\\r\\n roundedGrid = (round(x), round(y))\\r\\n total_radius = (robot.RADIUS + robot.BALL_RADIUS) / robot.grid.scale\\r\\n scanAmount = math.ceil(total_radius)\\r\\n scan = range(-scanAmount, scanAmount + 1)\\r\\n corners = ((0, 0), (0, 1), (1, 1), (1, 0))\\r\\n points = []\\r\\n for i in scan:\\r\\n for j in scan:\\r\\n for corner in corners:\\r\\n newX = roundedGrid[0] + i + corner[0]\\r\\n newY = roundedGrid[1] + j + corner[1]\\r\\n if grid_distance(newX, newY, x, y) < total_radius:\\r\\n points.append((newX, newY))\\r\\n\\r\\n return points\",\n \"def boarderPosition(self, gameState):\\n if gameState.isOnRedTeam(self.index):\\n i = self.midWidth - 1\\n else:\\n i = self.midWidth + 1\\n boudaries = [(i,j) for j in range(self.height)]\\n validPositions = []\\n for i in boudaries:\\n if not gameState.hasWall(i[0],i[1]):\\n validPositions.append(i)\\n return validPositions\",\n \"def position_grid(self):\\n return self.currentLevel.toGridCoord(self.position)\",\n \"def get_grid_list (self):\\n return [\\n 'area', 'ald', 'poi', 'ice', 'lake pond',\\n 'drainage', 'degree-day','climate event'\\n ]\",\n \"def get_piles_place(self):\\n idx_draw = []\\n piles_draw = []\\n i = 0\\n for p in self.game_state['piles']:\\n if not self.game_state['two_player']:\\n if not self.game_state['piles_taken'][i] and len(p) < 3:\\n idx_draw.append(i)\\n piles_draw.append(p)\\n else:\\n if len(p) <= i and not self.game_state['piles_taken'][i]:\\n idx_draw.append(i)\\n piles_draw.append(p)\\n i += 1\\n return (piles_draw,idx_draw)\",\n \"def allplacement(self, test_pylos):\\n placements = []\\n player = test_pylos._state['visible']['turn']\\n for layer in range(4):\\n for row in range(4-layer):\\n for column in range(4-layer):\\n value = test_pylos.get(layer,row,column)\\n if value is None:\\n try:\\n test_pylos.validPosition(layer, row, column)\\n except game.InvalidMoveException:\\n pass\\n else:\\n if test_pylos.createSquare((layer, row, column)) is True:\\n if player == 0:\\n removableballs = self.removableballs0(test_pylos)\\n removableballs.apppend([layer, row, column])\\n else:\\n removableballs = self.removableballs1(test_pylos)\\n removableballs.apppend([layer, row, column])\\n for i in removableballs:\\n if i[0] == layer - 1 and i[1] == row and i[2] == column:\\n removableballs.remove(i)\\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column:\\n removableballs.remove(i)\\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column + 1:\\n removableballs.remove(i)\\n elif i[0] == layer - 1 and i[1] == row and i[2] == column + 1:\\n removableballs.remove(i)\\n\\n for i in removableballs:\\n move = {\\n 'move': 'place',\\n 'to': [layer, row, column],\\n 'remove': i\\n }\\n\\n placements.append(move)\\n else:\\n move = {\\n 'move': 'place',\\n 'to': [layer, row, column]\\n }\\n placements.append(move)\\n return placements\",\n \"def get_visible_cells(self):\\r\\n ux, uy = self.GetScrollPixelsPerUnit()\\r\\n sx, sy = self.GetViewStart()\\r\\n w, h = self.GetGridWindow().GetClientSize().Get()\\r\\n sx *= ux\\r\\n sy *= uy\\r\\n start_col = self.XToCol(sx)\\r\\n start_row = self.YToRow(sy)\\r\\n end_col = self.XToCol(sx + w, True)\\r\\n end_row = self.YToRow(sy + h, True)\\r\\n return start_row, end_row, start_col, end_col\",\n \"def get_box_grid(x, y):\\n for grid in GRIDS:\\n if x >= grid[0][0] and y >= grid[0][1] and \\\\\\n x <= grid[1][0] and y <= grid[1][1]:\\n return grid\\n return None\",\n \"def get_empty_board_indecies(self):\\n empty_indecies = []\\n for row_num in range(len(self.board)):\\n for col_num in range(len(self.board)):\\n if self.board[row_num][col_num] and self.board[row_num][col_num].state == PegState.EMPTY:\\n empty_indecies.append((row_num, col_num))\\n return empty_indecies\",\n \"def grid_coords(self):\\n return [(x, y) for y in range(self.height) for x in range(self.width)]\",\n \"def availableSquares(self):\\n List2=[]\\n for item in self.all:\\n if item.retrieve()==\\\"\\\":\\n List2.append(item.name())\\n return List2\",\n \"def all_grid_neighbours(grid):\\n # create a dictionary of neighbours\\n neighbours = {}\\n # for each position in the grid\\n for position in grid:\\n # call the neighbours_of_position function to get the positions \\n # neighbours\\n position_neighbours = neighbours_of_position(position)\\n # build our neighbours dictionary: break up our position_neighbours \\n # variable and put it as a neighbour[position] if its in the grid \\n neighbours[position] = [p for p in position_neighbours if p in grid]\\n return neighbours\",\n \"def get_positions(self):\\n return self.positions\",\n \"def available_moves(self):\\n available_moves = []\\n for i in range(self.quadrants_count):\\n quadrant_positions = self.play_area[i].available_positions()\\n for p in quadrant_positions:\\n position = p + i * 9\\n for j in range(self.quadrants_count):\\n move1 = [str(position), str(j + 1), \\\"l\\\"]\\n move2 = [str(position), str(j + 1), \\\"r\\\"]\\n available_moves.append(\\\" \\\".join(move1))\\n available_moves.append(\\\" \\\".join(move2))\\n return available_moves\",\n \"def positions_to_play(self):\\r\\n\\r\\n positions = []\\r\\n\\r\\n for i in range(0, len(self.matrix)):\\r\\n for j in range(0, len(self.matrix[i])):\\r\\n if self.matrix[i][j] == \\\"0\\\":\\r\\n # Add [row, column] to the list\\r\\n positions.append([i, j])\\r\\n \\r\\n return positions\",\n \"def grid(self):\\n return self.__grid\",\n \"def find_moves(self):\\n\\n from itertools import product\\n free_position = self.find_free()\\n return [list(free_position+i) for i in [[0,1],[1,0],[-1,0],[0,-1]] if tuple(i+free_position) in product(range(self.size),repeat=2)]\",\n \"def position_surroundings(self, neighbour_pos, missed_value):\\n pos = []\\n for x, y in neighbour_pos:\\n position = self._square_matrix[x][y].get_pos_from_number(missed_value)\\n if position:\\n pos.append(position)\\n return pos\",\n \"def known_points(self):\\n return self._cache.space\",\n \"def get_neighbours(self):\\n shape=self.cubeshape[1:]\\n neighboursx=np.arange(self.xpos-(self.blocksize-1)/2,(self.xpos+(self.blocksize-1)/2)+1,dtype='int' )\\n neighboursx=[x if (x>=0) & (x<=shape[1]-1) else np.nan for x in neighboursx ]\\n neighboursy=np.arange(self.ypos-(self.blocksize-1)/2,(self.ypos+(self.blocksize-1)/2)+1,dtype='int' )\\n neighboursy=[y if (y>=0) & (y<=shape[0]-1) else np.nan for y in neighboursy ]\\n keys=[np.ravel_multi_index([y,x], shape) if np.all(np.isfinite(np.asarray([y,x]))) else np.nan for y in neighboursy for x in neighboursx]\\n\\n return keys\",\n \"def populate_positions(self, board, player):\\n\\t\\tprint \\\"Inside populate_positions!-----\\\"\\n\\t\\tpositions = []\\n\\t\\tfor i in range(0,3):\\n\\t\\t\\tfor j in range(0,3):\\n\\t\\t\\t\\tif(board[i][j] == player):\\n\\t\\t\\t\\t\\tpositions.append([i,j])\\n\\t\\treturn positions\",\n \"def occupied_cells(self):\\n\\n for lm in self.landmarks:\\n if self.cell_size < 1:\\n # expand the range the landmark exists\\n lm_x_range = np.arange(lm[0]-self.R, lm[0]+self.R, self.cell_size)\\n lm_y_range = np.arange(lm[1]-self.R, lm[1]+self.R, self.cell_size)\\n\\n # loop through expanded ranges and compute grid positions\\n for lm_x in lm_x_range:\\n for lm_y in lm_y_range:\\n\\n row, col = self.cell_index([lm_x, lm_y])\\n\\n # apply cost of occupied cell\\n try:\\n self.world[row][col] = 1000\\n except IndexError:\\n pass\\n\\n else:\\n # apply cost of occupied cell\\n row, col = self.cell_index(lm)\\n try:\\n self.world[row][col] = 1000\\n except IndexError:\\n pass\",\n \"def explore(self):\\n \\n has_new_pos = False\\n while not has_new_pos:\\n move = self.biased_choice()\\n new_pos = add_lists(move, self.position)\\n has_new_pos = check_bounds(new_pos, self.grid_size)\\n return new_pos\",\n \"def getGrid(self):\\n\\n return self.board\",\n \"def grid(self):\\n return self._grid\",\n \"def _get_available_spaces(world):\\n spaces = []\\n for y, row in enumerate(world):\\n for x, column in enumerate(row):\\n if column == eg.NONE:\\n spaces.append((x, y))\\n\\n return spaces\",\n \"def generate_nearby_cells(self):\\n for y in range(len(self.island_map)):\\n for x in range(len(self.island_map[y])):\\n list_of_nearby_cells = []\\n\\n if y != 0:\\n self.generate_cell_above(x, y, list_of_nearby_cells)\\n\\n if x != 0:\\n self.generate_cell_left(x, y, list_of_nearby_cells)\\n\\n if y != len(self.island_map)-1:\\n self.generate_cell_below(x, y, list_of_nearby_cells)\\n\\n if x != len(self.island_map[y])-1:\\n self.generate_cell_right(x, y, list_of_nearby_cells)\\n\\n self.island_map[y][x].nearby_cells = list_of_nearby_cells\",\n \"def fill_grid_np(self):\\n\\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\\n grid = self.grid_np\\n # cell_size = self.cell_size\\n for obj in self.levels[self.curient_level].objects:\\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\\n x, y = obj.position_grid[X], obj.position_grid[Y]\\n grid[y*GRID_WIDTH + x] = obj\\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\\n # cell_size[y*GRID_WIDTH + x] += 1\",\n \"def get_all_positions(board, white_turn):\\n list = []\\n for row in range(8):\\n for col in range(8):\\n # White\\n if white_turn and white_piece_on_pos((row, col), board):\\n obj = board[row][col]\\n if type(obj) is Pawn:\\n for valid_pos in valid_positions_pawn_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Tower:\\n for valid_pos in valid_positions_tower_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Bishop:\\n for valid_pos in valid_positions_bishop_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Horse:\\n for valid_pos in valid_positions_horse_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Queen:\\n for valid_pos in valid_positions_queen_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is King:\\n for valid_pos in valid_positions_king_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n # Black\\n elif (not white_turn) and black_piece_on_pos((row, col), board):\\n obj = board[row][col]\\n if type(obj) is Pawn:\\n for valid_pos in valid_positions_pawn_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Tower:\\n for valid_pos in valid_positions_tower_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Bishop:\\n for valid_pos in valid_positions_bishop_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Horse:\\n for valid_pos in valid_positions_horse_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Queen:\\n for valid_pos in valid_positions_queen_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is King:\\n for valid_pos in valid_positions_king_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n return list\",\n \"def get_all_numbered_cells(self):\\r\\n num_rows = len(self.mine_field)\\r\\n num_cols = len(self.mine_field[0])\\r\\n numbered_cells = [(row, col)\\r\\n for row in range(num_rows)\\r\\n for col in range(num_cols)\\r\\n if self.mine_field[row][col] != '?' and \\\\\\r\\n self.mine_field[row][col] != 'x']\\r\\n return numbered_cells\",\n \"def grid(self):\\n self.best_params_list = clust_grid(self.model, self.param_grid, self.X, self.y, self.model_mask_cols)\",\n \"def get_empty_square(self) -> list:\\n empty_square = []\\n for line_index in range(len(self.grid)):\\n for col_index in range(len(self.grid[line_index])):\\n if self.grid[line_index][col_index].color is None:\\n empty_square.append((line_index, col_index))\\n\\n return empty_square\",\n \"def available_moves(board_state):\\n for x, y in itertools.product(range(len(board_state)), range(len(board_state[0]))):\\n if board_state[x][y] == 0:\\n yield (x, y)\",\n \"def get_positions(self):\\n\\n yield from self._positions\",\n \"def open_spots(self):\\n ret = []\\n for i in range(1,25):\\n if self.nodes[i].piece == None:\\n ret.append(i)\\n return ret\",\n \"def grid_evaluation(self, x, y, size=256):\\n\\t\\t# Create matrix x and y coordinates\\n\\t\\tL = self.L\\n\\t\\t[xx, yy] = meshgrid(linspace(-L, L, size), linspace(-L, L, size))\\n\\t\\tpts = np.array([xx, yy])\\n\\t\\tux = batch_eval(x, pts)\\n\\t\\tuy = batch_eval(y, pts)\\n\\t\\treturn xx, yy, ux, uy\",\n \"def get_empty_squares(self):\\n empty = []\\n for row in range(self._dim):\\n for col in range(self._dim):\\n if self._board[row][col] == EMPTY:\\n empty.append((row, col))\\n return empty\",\n \"def find_unsettled_spot(self):\\n\\t\\tfor i in range(9):\\n\\t\\t\\tfor j in range(9):\\n\\t\\t\\t\\tif self.grid[i][j] == 0:\\n\\t\\t\\t\\t\\treturn i, j\\n\\t\\treturn\",\n \"def get_available_moves(self):\\n available = []\\n row, col = tuple(self.current_pos)\\n if row - 1 >= 0 and self.maze[row - 1][col] != 'x':\\n available.append('n')\\n if row + 1 < len(self.maze) and self.maze[row + 1][col] != 'x':\\n available.append('s')\\n if col - 1 >= 0 and self.maze[row][col - 1] != 'x':\\n available.append('w')\\n if col + 1 < len(self.maze[row]) and self.maze[row][col + 1] != 'x':\\n available.append('e')\\n return available\",\n \"def guarded_places(self):\\n guarded = []\\n for x in range(8):\\n for y in range(8):\\n if self.squares[x][y].piece and self.squares[x][y].piece.color != self.turn:\\n squares = self.squares[x][y].piece.actions(self, (x, y), True)\\n if self.squares[x][y].piece.name != 'pawn': # pawns capture in different areas than they move\\n guarded.extend(squares[0])\\n guarded.extend(squares[1])\\n return guarded\",\n \"def get_gridState(self,real_state=None):\\r\\n if real_state is None:\\r\\n # convert unit back to mm\\r\\n r_state =[self.real_state[0],self.real_state[1],self.real_state[2]]\\r\\n else:\\r\\n r_state = real_state\\r\\n\\r\\n grid_state = [0,0,0]\\r\\n # assume orignal point is the center of the starting piece\\r\\n for i in range(2):\\r\\n if np.abs(real_state[i]) < (self.grid_size)/2.0:\\r\\n grid_state[i] =0\\r\\n else:\\r\\n # tol = round(self.grid_size/10.0, 2) #tolerance of floating point number\\r\\n tol =15.0 #1.5cm as tolerance\\r\\n grid_state[i] = int((real_state[i]-(self.grid_size)/2.0)//self.grid_size)\\r\\n remain = 1 if (real_state[i]-(self.grid_size)/2.0)%self.grid_size >=tol else 0\\r\\n grid_state[i] += remain\\r\\n\\r\\n # convert rad to degree\\r\\n real_angle = 180.0*(real_state[2]/math.pi)\\r\\n a = [abs(real_angle - i) for i in self.angle_set]\\r\\n i = np.argmin(a)\\r\\n # calculate approximate degree in grid world\\r\\n grid_state[2] = self.angle_set[i]\\r\\n # print(\\\"grid angle:\\\",grid_state[2],\\\"real ag:\\\",real_angle)\\r\\n\\r\\n # correct the degree in rad for moving\\r\\n # agl_rad = grid_state[2]*(math.pi/180.0)\\r\\n # correct_rot = -(real_state[2] - agl_rad)\\r\\n # sp = 100\\r\\n # t = (correct_rot* (math.pi/180))/sp\\r\\n # cur_t = time.time()\\r\\n # past_t = cur_t\\r\\n # while abs(past_t-cur_t) <=t+0.5:\\r\\n # self.Roomba.Move(0,sp)\\r\\n # cur_t = time.time()\\r\\n # self.Roomba.Move(0,0)\\r\\n\\r\\n return grid_state\",\n \"def get_valid_moves(self):\\r\\n validMoves = []\\r\\n\\r\\n for x in range(BOARD_SIZE):\\r\\n for y in range(BOARD_SIZE):\\r\\n pos = np.array([x,y])\\r\\n if self.board[pos[0],pos[1]] == 0:\\r\\n if(self.update_board(pos,_testing=True)):\\r\\n validMoves.append(pos)\\r\\n\\r\\n return validMoves\",\n \"def test_grid():\\n with get_test_data('interpolation_test_grid.npz') as fobj:\\n data = np.load(fobj)\\n return data['xg'], data['yg']\",\n \"def __FreeTiles(self, grid, log=False):\\n\\n x_pos, _ = np.where(grid == 0)\\n return len(x_pos)\",\n \"def _buildGridPoints(self):\\n self.spacings = []\\n for level in xrange(self.depth):\\n levelSpacings = []\\n refLevel = level + 1\\n level = 2**level\\n axisData = []\\n for axis in self.size:\\n spacing = axis / (level+1)\\n levelSpacings.append(spacing)\\n axisData.append([gridValue*spacing for gridValue in xrange(1, level+1)])\\n pointList = [((i, j, k), np.array([axisData[0][i], axisData[1][j], axisData[2][k]]))\\n for i in xrange(level)\\n for j in xrange(level)\\n for k in xrange(level)]\\n self.grid[refLevel] = {point[0]: point[1] for point in pointList}\\n self.spacings.append(levelSpacings)\",\n \"def findEmpty(grid):\\n for x in range(len(grid.board)):\\n for y in range(len(grid.board[0])):\\n if grid.board[x][y] == 0:\\n return [x,y]\",\n \"def next_positions(self):\\n self.scores = np.array(self.scores)\\n improved = self.scores < self._pso_data.best_scores\\n\\n self._pso_data.best_scores[improved] = self.scores[improved]\\n self._pso_data.best_positions[improved] = self.positions[improved]\\n\\n self._pso_data.velocities = self._new_velocities()\\n new_positions = self.positions + self._pso_data.velocities\\n return new_positions\",\n \"def _initiate_board(self):\\n grid = []\\n for i in range(constant.BOARD_DIMENSION):\\n # Starts each row\\n current_row = []\\n for j in range(constant.BOARD_DIMENSION):\\n # Adds the pieces depending on the position\\n if i < constant.ROWS_OF_PIECES:\\n # Black pieces\\n if (j + i) % 2 != 0:\\n current_row.append(Piece(i, j, Player.black))\\n self.num_black_pieces = self.num_black_pieces + 1\\n else:\\n current_row.append(None)\\n\\n elif i >= constant.BOARD_DIMENSION - constant.ROWS_OF_PIECES:\\n # White pieces\\n if (j + i) % 2 != 0:\\n current_row.append(Piece(i, j, Player.white))\\n self.num_white_pieces = self.num_white_pieces + 1\\n else:\\n current_row.append(None)\\n\\n else:\\n current_row.append(None)\\n\\n grid.append(current_row)\\n\\n return grid\",\n \"def get_empty_squares(self):\\n return self.empty_squares\",\n \"def get_relative_grid(self, xgbl, ygbl):\\r\\n self.rx = xgbl - self.x0\\r\\n self.ry = ygbl - self.y0\\r\\n\\r\\n # flatten the coordinates:\\r\\n self.rx = self.rx.ravel()\\r\\n self.ry = self.ry.ravel()\",\n \"def get_grid(self):\\n self.fullws = []\\n for row in self.word_search_grid:\\n rowdata = []\\n for column in row:\\n rowdata += [column.entry.get()]\\n self.fullws += [rowdata]\\n self.logic.set_grid(self.fullws)\",\n \"def get_occupied_tiles(self):\\r\\n occupied = np.zeros(self.searchenv.conv.num_tiles)\\r\\n #Convert current state (positions of agents) to tile indices\\r\\n tiles = self.searchenv.conv.state_to_tile(self.searchstate.positions)\\r\\n valid_tiles = tiles[self.searchstate.actives == 1]\\r\\n occupied[valid_tiles] = 1\\r\\n return occupied\",\n \"def get_valid_locations(location_list, grid, shape):\",\n \"def opponentBoarderPosition(self, gameState):\\n if self.red:\\n i = self.midWidth\\n else:\\n i = self.midWidth - 1\\n boudaries = [(i,j) for j in range(self.height)]\\n validPositions = []\\n for i in boudaries:\\n if not gameState.hasWall(i[0],i[1]):\\n validPositions.append(i)\\n return validPositions\",\n \"def __free_spots(self, pos: Tuple[int, int], tree: int) -> List:\\n x, y = pos\\n possible_slots = []\\n\\n if x + 2 == self.__width:\\n if self.__exit is None:\\n # if we are next to the right edge and the exit is not set, connect and return\\n self.__activate(x + 1, y, tree)\\n self.__exit = (x + 1, y)\\n return []\\n\\n elif self.__check_row(x + 1, y):\\n # iterate through this row and compare maze assignments\\n if self.__check_and_join_row(x, y, tree, 1):\\n possible_slots.append((x + 1, y))\\n\\n if x > 1:\\n if self.__check_row(x - 1, y):\\n if self.__check_and_join_row(x, y, tree, -1):\\n possible_slots.append((x - 1, y))\\n\\n elif self.__entry is None:\\n # if we are on the left edge and there's no entry, connect\\n self.__activate(0, y, tree)\\n self.__entry = (0, y)\\n return []\\n\\n # check left and right.\\n if y + 2 < self.__height and self.__check_col(x, y + 1):\\n if self.__check_and_join_col(x, y, tree, 1):\\n possible_slots.append((x, y + 1))\\n\\n if y > 1 and self.__check_col(x, y - 1):\\n if self.__check_and_join_col(x, y, tree, -1):\\n possible_slots.append((x, y - 1))\\n\\n return possible_slots\",\n \"def get_grid(self, mol_data):\\n\\n if self.grid_info is None:\\n\\n try:\\n\\n x = mol_data['grid_points/x'][()]\\n y = mol_data['grid_points/y'][()]\\n z = mol_data['grid_points/z'][()]\\n\\n except BaseException:\\n\\n raise ValueError(\\n \\\"Grid points not found in the data file\\\")\\n\\n else:\\n\\n center = mol_data['grid_points/center'][()]\\n npts = np.array(self.grid_info['number_of_points'])\\n res = np.array(self.grid_info['resolution'])\\n\\n halfdim = 0.5 * (npts * res)\\n\\n low_lim = center - halfdim\\n hgh_lim = low_lim + res * (npts - 1)\\n\\n x = np.linspace(low_lim[0], hgh_lim[0], npts[0])\\n y = np.linspace(low_lim[1], hgh_lim[1], npts[1])\\n z = np.linspace(low_lim[2], hgh_lim[2], npts[2])\\n\\n # there is stil something strange\\n # with the ordering of the grid\\n # also noted in GridTools define_grid_points()\\n y, x, z = np.meshgrid(y, x, z)\\n grid = (x, y, z)\\n npts = (len(x), len(y), len(z))\\n return grid, npts\",\n \"def best_frame_indices_in_empty_areas(self, index_y, index_x):\\n\\n # Go though circles with increasing radius \\\"distance\\\" around the current quality area.\\n for distance in arange(1, max(self.y_dim, self.x_dim)):\\n circle = Miscellaneous.circle_around(index_x, index_y, distance)\\n for (compare_x, compare_y) in circle:\\n # If the coordinates are within the quality area grid, and if the area at this\\n # location has a non-empty list of alignment points, return its list.\\n if 0 <= compare_x < self.x_dim and 0 <= compare_y < self.y_dim and \\\\\\n self.quality_areas[compare_y][compare_x]['alignment_point_indices']:\\n return self.quality_areas[compare_y][compare_x]['best_frame_indices']\\n # This should never happen, because it means that there is not any quality area with an\\n # alignment point.\\n raise InternalError(\\\"No quality area contains any alignment point\\\")\",\n \"def get_gridline_values(self, x=None, y=None):\\n if (x and y) or not (x or y):\\n print('[error] use either x or y keyword arguments '\\n 'to define the gridline position')\\n return\\n elif x:\\n return self.get_vertical_gridline_values(x)\\n elif y:\\n return self.get_horizontal_gridline_values(y)\",\n \"def getChoices(self, grid):\\n return [(x, y) for x in range(3) for y in range(3) if grid[x][y] == 0]\",\n \"def check_for_moves(self) -> list:\\r\\n avail_moves = []\\r\\n for x in range(self.size):\\r\\n for y in range(self.size):\\r\\n if self.tags[x][y] is None:\\r\\n avail_moves.append((x, y))\\r\\n return avail_moves\",\n \"def get_score(location, grid, shape):\",\n \"def get_neighbors(grid, x, y):\\n out = []\\n if x > 0:\\n out.append(grid[x-1, y])\\n if y > 0:\\n out.append(grid[x, y-1])\\n if y < grid.shape[1] - 1:\\n out.append(grid[x, y+1])\\n if x < grid.shape[0] - 1:\\n out.append(grid[x+1, y])\\n return out\",\n \"def get_bounds(self):\\n occupied_locations = self.board.keys()\\n min_x = min(p[0] for p in occupied_locations)\\n max_x = max(p[0] for p in occupied_locations)\\n min_y = min(p[1] for p in occupied_locations)\\n max_y = max(p[1] for p in occupied_locations)\\n return ((min_x, max_x), (min_y, max_y))\",\n \"def getStartSpots(self):\\n spots = []\\n if self.index == 0:\\n startRow = 1\\n endRow = 4\\n if self.index == 1:\\n startRow = 6\\n endRow = 9\\n for row in range(startRow, endRow):\\n for col in range(1,9):\\n spots += [(col, row)]\\n return spots\",\n \"def possible_moves(self, pos: Point) -> List[Point]:\\n # logger.debug(f\\\"inside possible_moves {pos}\\\")\\n available_squares = []\\n for direction in Direction.cardinal():\\n # logger.debug(f\\\"direction = {direction}\\\")\\n neighbor = pos + direction\\n # logger.debug(f\\\"neighbor = {neighbor}\\\")\\n if neighbor.x < 1 or self.width - 2 < neighbor.x or neighbor.y < 1 or self.height - 2 < neighbor.y:\\n # logger.debug(f\\\"{neighbor} not in bounds\\\")\\n continue\\n if self.can_carve(pos, direction):\\n # logger.debug(f\\\"can_carve returned True pos={pos}, direction={direction}\\\")\\n available_squares.append(neighbor)\\n # logger.debug(f\\\"available squares:\\\")\\n # for square in available_squares:\\n # logger.debug(f\\\"square={square}\\\")\\n # logger.add(\\\"debug.log\\\")\\n return available_squares\",\n \"def get_unhindered_positions(self, endposition):\\n current_position = self.position\\n potential_positions = potential_positions = {\\n 'left' : [], \\n 'right' : [],\\n 'up' : [], \\n 'down' : []\\n }\\n space_down = current_position[0]-1\\n space_up = self.ncols-current_position[0]\\n space_right = self.nrows - (ord('H')-ord(current_position[1]))\\n space_left = ord(current_position[1]) - ord('A')\\n\\n for i in range(1, space_down+1):\\n pos = (current_position[0]-i, current_position[1])\\n if self.pos_within_bounds(pos):\\n potential_positions['down'].append(pos)\\n\\n for i in range(1, space_up+1):\\n pos = (current_position[0]+i, current_position[1])\\n if self.pos_within_bounds(pos):\\n potential_positions['up'].append(pos)\\n \\n for i in range(1, space_left+1):\\n pos = (current_position[0], chr(ord(current_position[1])-i))\\n if self.pos_within_bounds(pos):\\n potential_positions['left'].append(pos)\\n\\n for i in range(1, space_right+1):\\n pos = (current_position[0], chr(ord(current_position[1])+i))\\n if self.pos_within_bounds(pos):\\n potential_positions['right'].append(pos)\\n\\n for direction, square in potential_positions.items():\\n if tuple(endposition) in square:\\n return potential_positions[direction]\",\n \"def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\\n pass\",\n \"def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\\n pass\",\n \"def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\\n pass\",\n \"def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\\n pass\",\n \"def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\\n pass\",\n \"def get_possible_moves(self, current_x: int, current_y: int) -> List[(int, int)]:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6839561","0.6820858","0.6375092","0.6179692","0.6175604","0.61393595","0.6134617","0.6129079","0.61183804","0.6112216","0.6101577","0.6083837","0.5964636","0.59512347","0.58837503","0.5817103","0.5804341","0.5783051","0.5754948","0.57523113","0.5745287","0.5742185","0.5738482","0.57205856","0.5703979","0.56788343","0.56679934","0.56586814","0.5656796","0.56427264","0.5633009","0.56310785","0.5629628","0.5628329","0.56111485","0.5609075","0.5595448","0.5593822","0.5585832","0.5585003","0.5571196","0.5570426","0.5568613","0.55676347","0.55638987","0.5562955","0.5560986","0.5558061","0.55518657","0.5551073","0.5543208","0.5541329","0.55231225","0.5517814","0.55113995","0.5508404","0.5493363","0.54737645","0.54718304","0.5455577","0.5455499","0.545499","0.54502344","0.5444669","0.5442391","0.5437806","0.5436999","0.54313076","0.54301447","0.54265493","0.54246587","0.5421636","0.54215854","0.5420307","0.5420179","0.5418393","0.54145616","0.5409993","0.5407912","0.5407421","0.5403739","0.5400401","0.53892654","0.5387788","0.5384298","0.53795576","0.5377866","0.53718466","0.53707784","0.5359021","0.5354791","0.5352943","0.53523666","0.5351439","0.53499615","0.53499615","0.53499615","0.53499615","0.53499615","0.53499615"],"string":"[\n \"0.6839561\",\n \"0.6820858\",\n \"0.6375092\",\n \"0.6179692\",\n \"0.6175604\",\n \"0.61393595\",\n \"0.6134617\",\n \"0.6129079\",\n \"0.61183804\",\n \"0.6112216\",\n \"0.6101577\",\n \"0.6083837\",\n \"0.5964636\",\n \"0.59512347\",\n \"0.58837503\",\n \"0.5817103\",\n \"0.5804341\",\n \"0.5783051\",\n \"0.5754948\",\n \"0.57523113\",\n \"0.5745287\",\n \"0.5742185\",\n \"0.5738482\",\n \"0.57205856\",\n \"0.5703979\",\n \"0.56788343\",\n \"0.56679934\",\n \"0.56586814\",\n \"0.5656796\",\n \"0.56427264\",\n \"0.5633009\",\n \"0.56310785\",\n \"0.5629628\",\n \"0.5628329\",\n \"0.56111485\",\n \"0.5609075\",\n \"0.5595448\",\n \"0.5593822\",\n \"0.5585832\",\n \"0.5585003\",\n \"0.5571196\",\n \"0.5570426\",\n \"0.5568613\",\n \"0.55676347\",\n \"0.55638987\",\n \"0.5562955\",\n \"0.5560986\",\n \"0.5558061\",\n \"0.55518657\",\n \"0.5551073\",\n \"0.5543208\",\n \"0.5541329\",\n \"0.55231225\",\n \"0.5517814\",\n \"0.55113995\",\n \"0.5508404\",\n \"0.5493363\",\n \"0.54737645\",\n \"0.54718304\",\n \"0.5455577\",\n \"0.5455499\",\n \"0.545499\",\n \"0.54502344\",\n \"0.5444669\",\n \"0.5442391\",\n \"0.5437806\",\n \"0.5436999\",\n \"0.54313076\",\n \"0.54301447\",\n \"0.54265493\",\n \"0.54246587\",\n \"0.5421636\",\n \"0.54215854\",\n \"0.5420307\",\n \"0.5420179\",\n \"0.5418393\",\n \"0.54145616\",\n \"0.5409993\",\n \"0.5407912\",\n \"0.5407421\",\n \"0.5403739\",\n \"0.5400401\",\n \"0.53892654\",\n \"0.5387788\",\n \"0.5384298\",\n \"0.53795576\",\n \"0.5377866\",\n \"0.53718466\",\n \"0.53707784\",\n \"0.5359021\",\n \"0.5354791\",\n \"0.5352943\",\n \"0.53523666\",\n \"0.5351439\",\n \"0.53499615\",\n \"0.53499615\",\n \"0.53499615\",\n \"0.53499615\",\n \"0.53499615\",\n \"0.53499615\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72613794"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17488,"cells":{"query":{"kind":"string","value":"Returns all the grid positions that have been filled in."},"document":{"kind":"string","value":"def filled_positions(self):\n return [x for x in assignable_positions if self.grid[x][0]]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def free_positions(self):\n positions = []\n for i in range(self.grid_size):\n for j in range(self.grid_size):\n if self.grid[i][j] == 0:\n positions.append((i, j))\n if positions == []:\n raise GameException('Game Over. No free position left.')\n return positions","def board_empty_positions(self, x, y):\n board = self.boards[x][y]\n coords = [(x, y, i, j) for (i, j) in board.empty_squares]\n return self.coords_to_positions(coords)","def empty_spots(self):\n\t\tret = []\n\t\tfor i in range(0, self.size):\n\t\t\tfor j in range(0, self.size):\n\t\t\t\tif(self.grid[i][j] == self.terminal):\n\t\t\t\t\tret.append((i,j))\n\t\treturn ret","def get_empty_positions(self):\n\n empty_positions = []\n\n for i in range(self._dimension):\n for j in range(self._dimension):\n if self._board[i][j] == ' ':\n empty_positions.append((i, j))\n\n return empty_positions","def available_positions(self):\n if len([x for x in self.grid.values() if x[0] != None]) < 13:\n return [x for x in assignable_positions if self.grid[x][1] == \"---\"]\n else:\n return []","def available_positions(self):\n available_positions = []\n for i in range(self.positions_count):\n if self.board[i] == 0:\n available_positions.append(i+1)\n return available_positions","def get_empty_board_indecies(self):\n empty_indecies = []\n for row_num in range(len(self.board)):\n for col_num in range(len(self.board)):\n if self.board[row_num][col_num] and self.board[row_num][col_num].state == PegState.EMPTY:\n empty_indecies.append((row_num, col_num))\n return empty_indecies","def get_empty_squares(self):\n empty = []\n for row in range(self._dim):\n for col in range(self._dim):\n if self._board[row][col] == EMPTY:\n empty.append((row, col))\n return empty","def get_empty_square(self) -> list:\n empty_square = []\n for line_index in range(len(self.grid)):\n for col_index in range(len(self.grid[line_index])):\n if self.grid[line_index][col_index].color is None:\n empty_square.append((line_index, col_index))\n\n return empty_square","def get_all_pieces(self):\n occupied = []\n for pieces in self.piece_locs.values():\n occupied += pieces\n return occupied","def findEmpty(grid):\n for x in range(len(grid.board)):\n for y in range(len(grid.board[0])):\n if grid.board[x][y] == 0:\n return [x,y]","def get_positions(self):\r\n null_pos, black_pos, white_pos = set(), set(), set()\r\n for pos in BOARD_POSITIONS:\r\n if self.state[pos[0]][pos[1]] == 0:\r\n null_pos.add(pos)\r\n elif self.state[pos[0]][pos[1]] == 1:\r\n black_pos.add(pos)\r\n else:\r\n white_pos.add(pos)\r\n return null_pos, black_pos, white_pos","def grid_points(self):\n for i in range(self.rows):\n for j in range(self.cols):\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon((i,j))\n if i == 0:\n print_gps(max_lat,max_lon,\"grid\")\n if j == 0:\n print_gps(max_lat,min_lon,\"grid\")\n if j == 0:\n print_gps(min_lat,min_lon,\"grid\")\n print_gps(min_lat,max_lon,\"grid\")","def get_empty_tiles(self) -> List[Point]:\n\t\tempty_tiles = []\n\t\tfor x in range(self.size):\n\t\t\tfor y in range(self.size):\n\t\t\t\tif self.tiles[x][y] == 0:\n\t\t\t\t\tempty_tiles.append(Point(x,y))\n\t\treturn empty_tiles","def fill_grid_np(self):\n\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\n grid = self.grid_np\n # cell_size = self.cell_size\n for obj in self.levels[self.curient_level].objects:\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\n x, y = obj.position_grid[X], obj.position_grid[Y]\n grid[y*GRID_WIDTH + x] = obj\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\n # cell_size[y*GRID_WIDTH + x] += 1","def grid_coords(self):\n return [(x, y) for y in range(self.height) for x in range(self.width)]","def all_cells(self):\n \"\"\"\n Note that we use the convention that the first cell is (1,1)\n \"\"\"\n spart_star = self.circle_star()\n part = Partition(list(spart_star))\n coordinates = part.cells()\n coordinates = [(x+1, y+1) for x, y in coordinates]\n return coordinates","def get_start_grid(cols=4, rows=4):\n grid = [[0]*cols for i in range(rows)]\n for i in range(2):\n empties = get_empty_cells(grid)\n y,x = random.choice(empties)\n grid[y][x] = 2 if random.random() < 0.9 else 4\n return grid","def get_empty_cells(grid):\n empty = []\n for j,row in enumerate(grid):\n for i,val in enumerate(row):\n if not val:\n empty.append((j,i))\n return empty","def get_empty_cells(grid):\n\tempty = []\n\tfor j,row in enumerate(grid):\n\t\tfor i,val in enumerate(row):\n\t\t\tif not val:\n\t\t\t\tempty.append((j,i))\n\treturn empty","def full_board(self):\n board = [[0] * self.rows for _ in range(self.cols)]\n for x in range(0, self.rows):\n for y in range(0, self.cols):\n if Location.objects.filter(board=self, col=x, row=y).exists():\n board[x][y-1] = '1'\n return board","def getStartSpots(self):\n spots = []\n if self.index == 0:\n startRow = 1\n endRow = 4\n if self.index == 1:\n startRow = 6\n endRow = 9\n for row in range(startRow, endRow):\n for col in range(1,9):\n spots += [(col, row)]\n return spots","def fill_grid(self):\n\n for row_margin, row in enumerate(range(self.rows)):\n self.grid.append([])\n\n for col_margin, col in enumerate(range(self.cols)):\n x = col*self.cell_size + col_margin\n y = row*self.cell_size + row_margin\n\n rect = pygame.Rect(x, y, self.cell_size, self.cell_size)\n\n cell = Cell(row, col, rect)\n\n if row == 7 and col == 3:\n cell.root = True\n self.root = cell\n elif row == 7 and col == 16:\n cell.goal = True\n self.goal = cell\n\n self.grid[row].append(cell)","def __FreeTiles(self, grid, log=False):\n\n x_pos, _ = np.where(grid == 0)\n return len(x_pos)","def get_visible_cells(self):\r\n ux, uy = self.GetScrollPixelsPerUnit()\r\n sx, sy = self.GetViewStart()\r\n w, h = self.GetGridWindow().GetClientSize().Get()\r\n sx *= ux\r\n sy *= uy\r\n start_col = self.XToCol(sx)\r\n start_row = self.YToRow(sy)\r\n end_col = self.XToCol(sx + w, True)\r\n end_row = self.YToRow(sy + h, True)\r\n return start_row, end_row, start_col, end_col","def calc_grid(self):\n return int(self._posn.x / cell_size), int(self._posn.y / cell_size)","def get_empty_cells(state):\n cells = []\n for row_index, row in enumerate(state.board):\n for col_index, cell in enumerate(row):\n if cell == 0:\n cells.append([row_index, col_index])\n return cells","def find_empty(puzzle):\r\n empty_squares = []\r\n for y in range(len(puzzle.squares)):\r\n for x in range(len(puzzle.squares[0])):\r\n if puzzle.squares[y][x].is_editable() is True:\r\n empty_squares.append((x, y))\r\n return empty_squares","def get_empty_cells(self):\n empty_cells = []\n for cell_row in self.board:\n for current_cell in cell_row:\n if current_cell is not None:\n if current_cell.get_cell_state() == 0:\n empty_cells.append(current_cell)\n return empty_cells","def find_empty(self):\n num_rows = len(self.board)\n num_cols = len(self.board[0])\n\n for i in range(num_rows):\n for j in range(num_cols):\n if self.board[i][j] == 0:\n return (i, j)","def generate_all_locations(grid, shape):","def get_occupied_tiles(self):\r\n occupied = np.zeros(self.searchenv.conv.num_tiles)\r\n #Convert current state (positions of agents) to tile indices\r\n tiles = self.searchenv.conv.state_to_tile(self.searchstate.positions)\r\n valid_tiles = tiles[self.searchstate.actives == 1]\r\n occupied[valid_tiles] = 1\r\n return occupied","def get_all_neighbors(self):\n m, n = self.board.shape\n return as_strided(self.expanded_board,\n shape = (m,n,3,3), \n strides = self.expanded_board.strides + self.expanded_board.strides)","def occupiedElements(self):\n occupiedMatrix = [[0 for x in range(self.gridSize)] for y in range(self.gridSize)]\n\n for i in range(len(self.changeable)):\n for j in range(self.length[i]):\n if self.direction[i] == \"h\":\n occupiedMatrix[self.fixed[i]][self.changeable[i] + j] = 1\n else:\n occupiedMatrix[self.changeable[i] + j][self.fixed[i]] = 1\n\n return occupiedMatrix","def get_positions(self):\n\n yield from self._positions","def get_all_numbered_cells(self):\r\n num_rows = len(self.mine_field)\r\n num_cols = len(self.mine_field[0])\r\n numbered_cells = [(row, col)\r\n for row in range(num_rows)\r\n for col in range(num_cols)\r\n if self.mine_field[row][col] != '?' and \\\r\n self.mine_field[row][col] != 'x']\r\n return numbered_cells","def get_all_spawnable_cells(self):\n spawnable_positions = []\n\n for i in range(self.grid.width):\n for j in range(self.grid.height):\n n_list = self.grid.get_cell_list_contents([(i, j)])\n\n if len(n_list) <= 0:\n spawnable_positions.append((i, j))\n elif len(n_list) > 0:\n n = n_list[0]\n if not any(map(lambda t: isinstance(n, t), self.not_spawnable_objects)):\n spawnable_positions.append((i, j))\n\n return spawnable_positions","def find_empty_cells(gr):\n l = list()\n for i in range(0,9):\n for j in range(0,9):\n if(gr[i][j] == 0):\n l.append([i, j])\n return l","def getCoordinates(self):\n return list(self.gridVars.keys())","def positions_to_play(self):\r\n\r\n positions = []\r\n\r\n for i in range(0, len(self.matrix)):\r\n for j in range(0, len(self.matrix[i])):\r\n if self.matrix[i][j] == \"0\":\r\n # Add [row, column] to the list\r\n positions.append([i, j])\r\n \r\n return positions","def get_all_open_cells(self):\r\n num_rows = len(self.mine_field)\r\n num_cols = len(self.mine_field[0])\r\n open_cells = [(row, col)\r\n for row in range(num_rows)\r\n for col in range(num_cols)\r\n if not self.mine_field[row][col] == '?']\r\n return open_cells","def get_positions(self):\n return self.positions","def available_moves(self):\n moves = []\n for x, y in self.available_boards:\n moves.extend([self.to_position(x, y, i, j) for (i, j)\n in self.boards[x][y].empty_squares])\n return moves","def generate_nearby_cells(self):\n for y in range(len(self.island_map)):\n for x in range(len(self.island_map[y])):\n list_of_nearby_cells = []\n\n if y != 0:\n self.generate_cell_above(x, y, list_of_nearby_cells)\n\n if x != 0:\n self.generate_cell_left(x, y, list_of_nearby_cells)\n\n if y != len(self.island_map)-1:\n self.generate_cell_below(x, y, list_of_nearby_cells)\n\n if x != len(self.island_map[y])-1:\n self.generate_cell_right(x, y, list_of_nearby_cells)\n\n self.island_map[y][x].nearby_cells = list_of_nearby_cells","def populate_positions(self, board, player):\n\t\tprint \"Inside populate_positions!-----\"\n\t\tpositions = []\n\t\tfor i in range(0,3):\n\t\t\tfor j in range(0,3):\n\t\t\t\tif(board[i][j] == player):\n\t\t\t\t\tpositions.append([i,j])\n\t\treturn positions","def complete_grid(self):\n if self._complete_grid is None:\n self._complete_grid = make_complete_graph(self._num_vertices)\n return self._complete_grid","def get_move_list(self):\n return [\n tuple(x) for x in np.argwhere(self.board == HexBoard.EMPTY).tolist()\n ]","def get_neighbors(self):\n return list(map(self.game.square, [self.position - self.game.rules[\"row_len\"], self.position + 1, self.position + self.game.rules[\"row_len\"], self.position - 1]))","def empty_cells(self) -> List[Cell]:\n return list(ob.pos[0] for ob in self.new_obs())","def remove_filled_positions(self, positions, board):\n\n new_positions = []\n for p in positions:\n if board.check_move(p[0], p[1]):\n new_positions.append(p)\n return new_positions","def makeStartingGrid(self):\n return util.make2DArray(self.xN, self.yN, False)","def get_grid_coords(self, count, boundry_x, boundry_y, grid_size):\n\n coords = []\n\n boundry_x = int(boundry_x/10)\n boundry_y = int(boundry_y/10)\n\n while len(coords) < count:\n seed()\n\n\n x = randint(-boundry_x, boundry_x)\n y = randint(-boundry_y, boundry_y)\n\n if len(coords) == 0:\n coords.append((x*grid_size, y*grid_size))\n else:\n for coord in coords:\n if (x not in range(coord[0]-buffer*grid_size, coord[0]+buffer*grid_size)) and (y not in range(coord[1]-buffer, coord[1]+buffer)):\n pass\n else:\n break","def get_start_grid(cols=4, rows=4):\n\tgrid = [[\"\"]*cols for i in range(rows)]\n\tfor i in range(2):\n\t\tempties = get_empty_cells(grid)\n\t\ty,x = random.choice(empties)\n\t\tgrid[y][x] = 2 if random.random() < prob_2 else 4\n\treturn grid","def blank_board(self):\n return [[False for x in range(self._dim)] for y in range(self._dim)]","def find_excited_locations(self):\n return np.asarray(np.where(self._grid == 8)).T","def empty_cells(state):\r\n cells = []\r\n for x, row in enumerate(state):\r\n for y, cell in enumerate(row):\r\n if cell == 0:\r\n cells.append([x, y])\r\n\r\n return cells","def collide_grid(self):\n topleft = self.absolute_collide_topleft\n bottomright = self.absolute_collide_bottomright\n tlx, tly = self.currentLevel.toGridCoord(topleft)\n brx, bry = self.currentLevel.toGridCoord(bottomright)\n collide_grid = []\n for x in range(tlx, brx+1):\n for y in range(tly, bry+1):\n collide_grid.append( (x,y) )\n if not collide_grid:\n collide_grid = [(tlx,tly)]\n return collide_grid","def reset(self):\n self._cells = [[0 for dummy_col in range(self._grid_width)] for dummy_row in range(self._grid_height)]\n self.new_tile()\n self.new_tile()\n #return self._cells","def find_empty(grid):\n for i in range(LEN_GRID):\n for j in range(LEN_GRID):\n if grid[i][j] == 0:\n return (i, j) # row, col\n return None","def get_grid_locations(self, top_left, other_pos):\n cell_x = torch.floor(((other_pos[:, 0] - top_left[:, 0]) / self.neighborhood_size) *self.grid_size)\n\n # Added this part to implementation, otherwise the pooling is going to run into an indexing error\n cell_x[cell_x == self.grid_size] -= 1\n cell_y = torch.floor(((top_left[:, 1] - other_pos[:, 1]) / self.neighborhood_size) *self.grid_size)\n cell_y[cell_y == self.grid_size] -= 1\n grid_pos = cell_x + cell_y * self.grid_size\n\n return grid_pos","def get_block_positions(self, fig):\n block_positions = []\n\n # Iterates through y + active_piece.y and x + active_piece.x\n for y, row in enumerate(fig, start=self.active_piece.y):\n for x, val in enumerate(row, start=self.active_piece.x):\n if val != 0:\n block_positions.append((x, y))\n\n return block_positions","def get_random_coordinates(self):\n array_shape = np.shape(self.cells) # type: tuple\n points_on_island = []\n for i in range(1, array_shape[0] - 1):\n for j in range(1, array_shape[1] - 1):\n points_on_island.append((i, j))\n random.shuffle(points_on_island)\n return points_on_island","def boarderPosition(self, gameState):\n if gameState.isOnRedTeam(self.index):\n i = self.midWidth - 1\n else:\n i = self.midWidth + 1\n boudaries = [(i,j) for j in range(self.height)]\n validPositions = []\n for i in boudaries:\n if not gameState.hasWall(i[0],i[1]):\n validPositions.append(i)\n return validPositions","def get_grid(self):\n self.fullws = []\n for row in self.word_search_grid:\n rowdata = []\n for column in row:\n rowdata += [column.entry.get()]\n self.fullws += [rowdata]\n self.logic.set_grid(self.fullws)","def get_agent_positions_in_grid(agent, grid):\n grid_position_list = []\n\n for position in agent.positions:\n for (grid_row, grid_row_index) in zip(grid, range(len(grid))):\n grid_col_index = _get_position_grid_column(position, grid_row)\n if grid_col_index or grid_col_index == 0:\n grid_position_list.append(Position(grid_col_index, grid_row_index))\n break\n\n return grid_position_list","def _get_neighbours(self, position):\n grid = self._grid\n x, y = position\n neighbours = []\n offsets = [(0,1),(1,0),(0,-1),(-1,0)]\n shuffle(offsets)\n for offset in offsets:\n i, j = offset\n position = (x + i, y + j)\n if grid.valid_position(position) and position not in self.shots:\n neighbours.append(position)\n return neighbours","def occupied_cells(self):\n\n for lm in self.landmarks:\n if self.cell_size < 1:\n # expand the range the landmark exists\n lm_x_range = np.arange(lm[0]-self.R, lm[0]+self.R, self.cell_size)\n lm_y_range = np.arange(lm[1]-self.R, lm[1]+self.R, self.cell_size)\n\n # loop through expanded ranges and compute grid positions\n for lm_x in lm_x_range:\n for lm_y in lm_y_range:\n\n row, col = self.cell_index([lm_x, lm_y])\n\n # apply cost of occupied cell\n try:\n self.world[row][col] = 1000\n except IndexError:\n pass\n\n else:\n # apply cost of occupied cell\n row, col = self.cell_index(lm)\n try:\n self.world[row][col] = 1000\n except IndexError:\n pass","def find_empty_space(self, state):\r\n for i in range(3):\r\n for j in range(3):\r\n if state[i][j] == 0:\r\n return (i, j)","def filled(self, position):\n i, j = position\n if 0 <= i < self.height and 0 <= j < self.width:\n return self.squares[i][j]\n # all positions outside of the defined area of the maze return as True (filled)\n return 1","def cells(self):\n \"\"\"\n Note that we use the convention that the first cell is (1,1)\n \"\"\"\n spart_star = self.star()\n part = Partition(list(spart_star))\n coordinates = part.cells()\n coordinates = [(x+1, y+1) for x, y in coordinates]\n return coordinates","def cells(self) -> List[Tuple[int, int]]:\n return self._cells","def initialize_grid(self):\n self.grid = np.zeros([self.N, self.N, self.N])\n return self.grid","def get_neighbours(self):\n shape=self.cubeshape[1:]\n neighboursx=np.arange(self.xpos-(self.blocksize-1)/2,(self.xpos+(self.blocksize-1)/2)+1,dtype='int' )\n neighboursx=[x if (x>=0) & (x<=shape[1]-1) else np.nan for x in neighboursx ]\n neighboursy=np.arange(self.ypos-(self.blocksize-1)/2,(self.ypos+(self.blocksize-1)/2)+1,dtype='int' )\n neighboursy=[y if (y>=0) & (y<=shape[0]-1) else np.nan for y in neighboursy ]\n keys=[np.ravel_multi_index([y,x], shape) if np.all(np.isfinite(np.asarray([y,x]))) else np.nan for y in neighboursy for x in neighboursx]\n\n return keys","def get_empty_cells(board):\n empty_cells = [idx for idx, e in enumerate(board) if e == ' ']\n return empty_cells","def open_spots(self):\n ret = []\n for i in range(1,25):\n if self.nodes[i].piece == None:\n ret.append(i)\n return ret","def get_candidate_tiles(self) -> List[Point]:\n\t\tempty_tiles = set()\n\t\tfor x in range(self.size):\n\t\t\tfor y in range(self.size):\n\t\t\t\tif not self.tiles[x][y] == 0:\n\t\t\t\t\tfor d in [[0,1], [1,1], [1,0], [1,-1], [0,-1], [-1,-1], [-1,0], [-1,1]]:\n\t\t\t\t\t\tif x+d[0] >= 0 and y+d[1] >= 0 and x+d[0] < self.size and y+d[1] < self.size and self.tiles[x+d[0]][y+d[1]] == 0:\n\t\t\t\t\t\t\tempty_tiles.add(Point(x+d[0],y+d[1]))\n\t\treturn list(empty_tiles)","def known_mines(self):\n \n if len(self.cells) == self.count:\n return self.cells","def initialize_grid(self):\r\n for i in range(self.height):\r\n for j in range(self.width):\r\n self.grid[i][j] = 0\r\n \r\n # fill up unvisited cells\r\n for r in range(self.height):\r\n for c in range(self.width):\r\n if r % 2 == 0 and c % 2 == 0:\r\n self.unvisited.append((r,c))\r\n\r\n self.visited = []\r\n self.path = dict()\r\n self.generated = False","def get_all_positions(board, white_turn):\n list = []\n for row in range(8):\n for col in range(8):\n # White\n if white_turn and white_piece_on_pos((row, col), board):\n obj = board[row][col]\n if type(obj) is Pawn:\n for valid_pos in valid_positions_pawn_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Tower:\n for valid_pos in valid_positions_tower_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Bishop:\n for valid_pos in valid_positions_bishop_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Horse:\n for valid_pos in valid_positions_horse_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Queen:\n for valid_pos in valid_positions_queen_white((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is King:\n for valid_pos in valid_positions_king_white((row, col), board):\n list.append(((row, col), valid_pos))\n # Black\n elif (not white_turn) and black_piece_on_pos((row, col), board):\n obj = board[row][col]\n if type(obj) is Pawn:\n for valid_pos in valid_positions_pawn_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Tower:\n for valid_pos in valid_positions_tower_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Bishop:\n for valid_pos in valid_positions_bishop_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Horse:\n for valid_pos in valid_positions_horse_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is Queen:\n for valid_pos in valid_positions_queen_black((row, col), board):\n list.append(((row, col), valid_pos))\n elif type(obj) is King:\n for valid_pos in valid_positions_king_black((row, col), board):\n list.append(((row, col), valid_pos))\n return list","def get_pos(self):\n return [self.row, self.col]","def get_empty_squares(self):\n return self.empty_squares","def get_all_closed_cells(self):\r\n num_rows = len(self.mine_field)\r\n num_cols = len(self.mine_field[0])\r\n closed_cells = [(row, col)\r\n for row in range(num_rows)\r\n for col in range(num_cols)\r\n if self.mine_field[row][col] == '?']\r\n return closed_cells","def _initiate_board(self):\n grid = []\n for i in range(constant.BOARD_DIMENSION):\n # Starts each row\n current_row = []\n for j in range(constant.BOARD_DIMENSION):\n # Adds the pieces depending on the position\n if i < constant.ROWS_OF_PIECES:\n # Black pieces\n if (j + i) % 2 != 0:\n current_row.append(Piece(i, j, Player.black))\n self.num_black_pieces = self.num_black_pieces + 1\n else:\n current_row.append(None)\n\n elif i >= constant.BOARD_DIMENSION - constant.ROWS_OF_PIECES:\n # White pieces\n if (j + i) % 2 != 0:\n current_row.append(Piece(i, j, Player.white))\n self.num_white_pieces = self.num_white_pieces + 1\n else:\n current_row.append(None)\n\n else:\n current_row.append(None)\n\n grid.append(current_row)\n\n return grid","def cells(self):\n \"\"\"\n Note that we use the convention that the first cell is (1,1)\n \"\"\"\n part = Partition(list(self))\n coordinates = part.cells()\n coordinates = [(x+1, y+1) for x, y in coordinates]\n return coordinates","def get_all_moves(self):\n # 2d matrix of true/false, true if something can be placed\n legal_move_board = []\n possible_move_list = []\n for row in range(self.size):\n move_row = []\n for col in range(self.size):\n empty = self.board[row][col].state == PegState.EMPTY\n move_row.append(empty)\n if empty:\n possible_move_list.append((row, col))\n legal_move_board.append(move_row)\n \n # every position where something can be placed (list of tuples) (Combined with above)\n \"\"\" possible_move_list = []\n for row in range(self.size):\n for col in range(self.size):\n if legal_move_board[row][col] == True:\n possible_move_list.append((row, col))\n \"\"\"\n return legal_move_board, possible_move_list","def init_grid(self):\n grid = []\n for i in range(self.settings['grid_size']):\n grid.append([])\n for j in range(self.settings['grid_size']):\n if [j, i] in self.settings['walls']:\n grid[i].append(g.WALL)\n else:\n grid[i].append(g.EMPTY)\n return grid","def empty_cells(state):\n cells = []\n\n for i, row in enumerate(state):\n for j, col in enumerate(row):\n if state[i][j] == 0:\n cells.append([i, j])\n\n return cells","def neighbors(self):\n \n # find 0 - blank square\n \n x0 = None\n y0 = None\n \n for i in range(4):\n for j in range(4):\n if self.get_tile(i,j) == 0:\n y0 = i\n x0 = j\n\n if x0 == None or y0 == None:\n return []\n \n neighbor_list = []\n \n # move 0 to the right\n if x0 < 3:\n new_position = Position(self.tiles)\n temp = new_position.get_tile(y0,x0+1)\n new_position.set_tile(y0,x0+1,0)\n new_position.set_tile(y0,x0,temp)\n new_position.directiontomoveto = 'r'\n neighbor_list.append(new_position)\n # move 0 to the left\n if x0 > 0:\n new_position = Position(self.tiles)\n temp = new_position.get_tile(y0,x0-1)\n new_position.set_tile(y0,x0-1,0)\n new_position.set_tile(y0,x0,temp)\n new_position.directiontomoveto = 'l'\n neighbor_list.append(new_position)\n # move 0 up\n if y0 > 0:\n new_position = Position(self.tiles)\n temp = new_position.get_tile(y0-1,x0)\n new_position.set_tile(y0-1,x0,0)\n new_position.set_tile(y0,x0,temp)\n new_position.directiontomoveto = 'u'\n neighbor_list.append(new_position)\n # move 0 down\n if y0 < 3:\n new_position = Position(self.tiles)\n temp = new_position.get_tile(y0+1,x0)\n new_position.set_tile(y0+1,x0,0)\n new_position.set_tile(y0,x0,temp)\n new_position.directiontomoveto = 'd'\n neighbor_list.append(new_position)\n \n return neighbor_list","def cells_list(self):\n xx, yy = np.meshgrid(self.x_spacings, self.y_spacings)\n return np.vstack([yy.ravel(), xx.ravel()]).transpose()","def get_all_positions(self) -> List[\"Position\"]:\n flat_positions = [j for i in self.positions.values() for j in i.values()]\n # filter out non-positions\n flat_positions = [pos for pos in flat_positions if type(pos) is Position]\n return flat_positions","def find_moves(self):\n\n from itertools import product\n free_position = self.find_free()\n return [list(free_position+i) for i in [[0,1],[1,0],[-1,0],[0,-1]] if tuple(i+free_position) in product(range(self.size),repeat=2)]","def check_grid_full(self):\n for row in self.game_state:\n for e in row:\n if e is None:\n return False\n return True","def get_live_cell_coordinates(self):\n\n return np.array(np.where(self.board == 1)).transpose().tolist()","def checked_positions():\n for base_position in chain([me.shipyard], me.get_dropoffs()):\n x_shipyard = base_position.position.x\n y_shipyard = base_position.position.y\n for x in range(-search_range, search_range):\n for y in range(-search_range, search_range):\n yield hlt.Position(\n x=x_shipyard + x,\n y=y_shipyard + y)","def known_mines(self):\n if len(self.cells) <= self.count:\n return self.cells\n return set([])","def get_empty_slots(self):\n slots = np.reshape(range(0, self.size * self.size), (self.size, self.size))\n\n return slots[~self.tiles_taken]","def full_board( self ):\n\n for x in self.__grid:\n if isinstance(x, int):\n return False\n else:\n continue\n\n return True","def reset(self):\n self.grid = np.array([0] * 9) # grid\n self.turn = 1 # whose turn it is\n self.done = False # whether game is done\n return self.grid","def get_open_spaces(board):\n open_spaces = []\n for i in range(3):\n for j in range(3):\n if board[i][j] == 0:\n open_spaces.append(encode_pos(i, j))\n return open_spaces","def opponentBoarderPosition(self, gameState):\n if self.red:\n i = self.midWidth\n else:\n i = self.midWidth - 1\n boudaries = [(i,j) for j in range(self.height)]\n validPositions = []\n for i in boudaries:\n if not gameState.hasWall(i[0],i[1]):\n validPositions.append(i)\n return validPositions"],"string":"[\n \"def free_positions(self):\\n positions = []\\n for i in range(self.grid_size):\\n for j in range(self.grid_size):\\n if self.grid[i][j] == 0:\\n positions.append((i, j))\\n if positions == []:\\n raise GameException('Game Over. No free position left.')\\n return positions\",\n \"def board_empty_positions(self, x, y):\\n board = self.boards[x][y]\\n coords = [(x, y, i, j) for (i, j) in board.empty_squares]\\n return self.coords_to_positions(coords)\",\n \"def empty_spots(self):\\n\\t\\tret = []\\n\\t\\tfor i in range(0, self.size):\\n\\t\\t\\tfor j in range(0, self.size):\\n\\t\\t\\t\\tif(self.grid[i][j] == self.terminal):\\n\\t\\t\\t\\t\\tret.append((i,j))\\n\\t\\treturn ret\",\n \"def get_empty_positions(self):\\n\\n empty_positions = []\\n\\n for i in range(self._dimension):\\n for j in range(self._dimension):\\n if self._board[i][j] == ' ':\\n empty_positions.append((i, j))\\n\\n return empty_positions\",\n \"def available_positions(self):\\n if len([x for x in self.grid.values() if x[0] != None]) < 13:\\n return [x for x in assignable_positions if self.grid[x][1] == \\\"---\\\"]\\n else:\\n return []\",\n \"def available_positions(self):\\n available_positions = []\\n for i in range(self.positions_count):\\n if self.board[i] == 0:\\n available_positions.append(i+1)\\n return available_positions\",\n \"def get_empty_board_indecies(self):\\n empty_indecies = []\\n for row_num in range(len(self.board)):\\n for col_num in range(len(self.board)):\\n if self.board[row_num][col_num] and self.board[row_num][col_num].state == PegState.EMPTY:\\n empty_indecies.append((row_num, col_num))\\n return empty_indecies\",\n \"def get_empty_squares(self):\\n empty = []\\n for row in range(self._dim):\\n for col in range(self._dim):\\n if self._board[row][col] == EMPTY:\\n empty.append((row, col))\\n return empty\",\n \"def get_empty_square(self) -> list:\\n empty_square = []\\n for line_index in range(len(self.grid)):\\n for col_index in range(len(self.grid[line_index])):\\n if self.grid[line_index][col_index].color is None:\\n empty_square.append((line_index, col_index))\\n\\n return empty_square\",\n \"def get_all_pieces(self):\\n occupied = []\\n for pieces in self.piece_locs.values():\\n occupied += pieces\\n return occupied\",\n \"def findEmpty(grid):\\n for x in range(len(grid.board)):\\n for y in range(len(grid.board[0])):\\n if grid.board[x][y] == 0:\\n return [x,y]\",\n \"def get_positions(self):\\r\\n null_pos, black_pos, white_pos = set(), set(), set()\\r\\n for pos in BOARD_POSITIONS:\\r\\n if self.state[pos[0]][pos[1]] == 0:\\r\\n null_pos.add(pos)\\r\\n elif self.state[pos[0]][pos[1]] == 1:\\r\\n black_pos.add(pos)\\r\\n else:\\r\\n white_pos.add(pos)\\r\\n return null_pos, black_pos, white_pos\",\n \"def grid_points(self):\\n for i in range(self.rows):\\n for j in range(self.cols):\\n min_lat,max_lat,min_lon,max_lon = self.coords_to_min_max_lat_lon((i,j))\\n if i == 0:\\n print_gps(max_lat,max_lon,\\\"grid\\\")\\n if j == 0:\\n print_gps(max_lat,min_lon,\\\"grid\\\")\\n if j == 0:\\n print_gps(min_lat,min_lon,\\\"grid\\\")\\n print_gps(min_lat,max_lon,\\\"grid\\\")\",\n \"def get_empty_tiles(self) -> List[Point]:\\n\\t\\tempty_tiles = []\\n\\t\\tfor x in range(self.size):\\n\\t\\t\\tfor y in range(self.size):\\n\\t\\t\\t\\tif self.tiles[x][y] == 0:\\n\\t\\t\\t\\t\\tempty_tiles.append(Point(x,y))\\n\\t\\treturn empty_tiles\",\n \"def fill_grid_np(self):\\n\\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\\n grid = self.grid_np\\n # cell_size = self.cell_size\\n for obj in self.levels[self.curient_level].objects:\\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\\n x, y = obj.position_grid[X], obj.position_grid[Y]\\n grid[y*GRID_WIDTH + x] = obj\\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\\n # cell_size[y*GRID_WIDTH + x] += 1\",\n \"def grid_coords(self):\\n return [(x, y) for y in range(self.height) for x in range(self.width)]\",\n \"def all_cells(self):\\n \\\"\\\"\\\"\\n Note that we use the convention that the first cell is (1,1)\\n \\\"\\\"\\\"\\n spart_star = self.circle_star()\\n part = Partition(list(spart_star))\\n coordinates = part.cells()\\n coordinates = [(x+1, y+1) for x, y in coordinates]\\n return coordinates\",\n \"def get_start_grid(cols=4, rows=4):\\n grid = [[0]*cols for i in range(rows)]\\n for i in range(2):\\n empties = get_empty_cells(grid)\\n y,x = random.choice(empties)\\n grid[y][x] = 2 if random.random() < 0.9 else 4\\n return grid\",\n \"def get_empty_cells(grid):\\n empty = []\\n for j,row in enumerate(grid):\\n for i,val in enumerate(row):\\n if not val:\\n empty.append((j,i))\\n return empty\",\n \"def get_empty_cells(grid):\\n\\tempty = []\\n\\tfor j,row in enumerate(grid):\\n\\t\\tfor i,val in enumerate(row):\\n\\t\\t\\tif not val:\\n\\t\\t\\t\\tempty.append((j,i))\\n\\treturn empty\",\n \"def full_board(self):\\n board = [[0] * self.rows for _ in range(self.cols)]\\n for x in range(0, self.rows):\\n for y in range(0, self.cols):\\n if Location.objects.filter(board=self, col=x, row=y).exists():\\n board[x][y-1] = '1'\\n return board\",\n \"def getStartSpots(self):\\n spots = []\\n if self.index == 0:\\n startRow = 1\\n endRow = 4\\n if self.index == 1:\\n startRow = 6\\n endRow = 9\\n for row in range(startRow, endRow):\\n for col in range(1,9):\\n spots += [(col, row)]\\n return spots\",\n \"def fill_grid(self):\\n\\n for row_margin, row in enumerate(range(self.rows)):\\n self.grid.append([])\\n\\n for col_margin, col in enumerate(range(self.cols)):\\n x = col*self.cell_size + col_margin\\n y = row*self.cell_size + row_margin\\n\\n rect = pygame.Rect(x, y, self.cell_size, self.cell_size)\\n\\n cell = Cell(row, col, rect)\\n\\n if row == 7 and col == 3:\\n cell.root = True\\n self.root = cell\\n elif row == 7 and col == 16:\\n cell.goal = True\\n self.goal = cell\\n\\n self.grid[row].append(cell)\",\n \"def __FreeTiles(self, grid, log=False):\\n\\n x_pos, _ = np.where(grid == 0)\\n return len(x_pos)\",\n \"def get_visible_cells(self):\\r\\n ux, uy = self.GetScrollPixelsPerUnit()\\r\\n sx, sy = self.GetViewStart()\\r\\n w, h = self.GetGridWindow().GetClientSize().Get()\\r\\n sx *= ux\\r\\n sy *= uy\\r\\n start_col = self.XToCol(sx)\\r\\n start_row = self.YToRow(sy)\\r\\n end_col = self.XToCol(sx + w, True)\\r\\n end_row = self.YToRow(sy + h, True)\\r\\n return start_row, end_row, start_col, end_col\",\n \"def calc_grid(self):\\n return int(self._posn.x / cell_size), int(self._posn.y / cell_size)\",\n \"def get_empty_cells(state):\\n cells = []\\n for row_index, row in enumerate(state.board):\\n for col_index, cell in enumerate(row):\\n if cell == 0:\\n cells.append([row_index, col_index])\\n return cells\",\n \"def find_empty(puzzle):\\r\\n empty_squares = []\\r\\n for y in range(len(puzzle.squares)):\\r\\n for x in range(len(puzzle.squares[0])):\\r\\n if puzzle.squares[y][x].is_editable() is True:\\r\\n empty_squares.append((x, y))\\r\\n return empty_squares\",\n \"def get_empty_cells(self):\\n empty_cells = []\\n for cell_row in self.board:\\n for current_cell in cell_row:\\n if current_cell is not None:\\n if current_cell.get_cell_state() == 0:\\n empty_cells.append(current_cell)\\n return empty_cells\",\n \"def find_empty(self):\\n num_rows = len(self.board)\\n num_cols = len(self.board[0])\\n\\n for i in range(num_rows):\\n for j in range(num_cols):\\n if self.board[i][j] == 0:\\n return (i, j)\",\n \"def generate_all_locations(grid, shape):\",\n \"def get_occupied_tiles(self):\\r\\n occupied = np.zeros(self.searchenv.conv.num_tiles)\\r\\n #Convert current state (positions of agents) to tile indices\\r\\n tiles = self.searchenv.conv.state_to_tile(self.searchstate.positions)\\r\\n valid_tiles = tiles[self.searchstate.actives == 1]\\r\\n occupied[valid_tiles] = 1\\r\\n return occupied\",\n \"def get_all_neighbors(self):\\n m, n = self.board.shape\\n return as_strided(self.expanded_board,\\n shape = (m,n,3,3), \\n strides = self.expanded_board.strides + self.expanded_board.strides)\",\n \"def occupiedElements(self):\\n occupiedMatrix = [[0 for x in range(self.gridSize)] for y in range(self.gridSize)]\\n\\n for i in range(len(self.changeable)):\\n for j in range(self.length[i]):\\n if self.direction[i] == \\\"h\\\":\\n occupiedMatrix[self.fixed[i]][self.changeable[i] + j] = 1\\n else:\\n occupiedMatrix[self.changeable[i] + j][self.fixed[i]] = 1\\n\\n return occupiedMatrix\",\n \"def get_positions(self):\\n\\n yield from self._positions\",\n \"def get_all_numbered_cells(self):\\r\\n num_rows = len(self.mine_field)\\r\\n num_cols = len(self.mine_field[0])\\r\\n numbered_cells = [(row, col)\\r\\n for row in range(num_rows)\\r\\n for col in range(num_cols)\\r\\n if self.mine_field[row][col] != '?' and \\\\\\r\\n self.mine_field[row][col] != 'x']\\r\\n return numbered_cells\",\n \"def get_all_spawnable_cells(self):\\n spawnable_positions = []\\n\\n for i in range(self.grid.width):\\n for j in range(self.grid.height):\\n n_list = self.grid.get_cell_list_contents([(i, j)])\\n\\n if len(n_list) <= 0:\\n spawnable_positions.append((i, j))\\n elif len(n_list) > 0:\\n n = n_list[0]\\n if not any(map(lambda t: isinstance(n, t), self.not_spawnable_objects)):\\n spawnable_positions.append((i, j))\\n\\n return spawnable_positions\",\n \"def find_empty_cells(gr):\\n l = list()\\n for i in range(0,9):\\n for j in range(0,9):\\n if(gr[i][j] == 0):\\n l.append([i, j])\\n return l\",\n \"def getCoordinates(self):\\n return list(self.gridVars.keys())\",\n \"def positions_to_play(self):\\r\\n\\r\\n positions = []\\r\\n\\r\\n for i in range(0, len(self.matrix)):\\r\\n for j in range(0, len(self.matrix[i])):\\r\\n if self.matrix[i][j] == \\\"0\\\":\\r\\n # Add [row, column] to the list\\r\\n positions.append([i, j])\\r\\n \\r\\n return positions\",\n \"def get_all_open_cells(self):\\r\\n num_rows = len(self.mine_field)\\r\\n num_cols = len(self.mine_field[0])\\r\\n open_cells = [(row, col)\\r\\n for row in range(num_rows)\\r\\n for col in range(num_cols)\\r\\n if not self.mine_field[row][col] == '?']\\r\\n return open_cells\",\n \"def get_positions(self):\\n return self.positions\",\n \"def available_moves(self):\\n moves = []\\n for x, y in self.available_boards:\\n moves.extend([self.to_position(x, y, i, j) for (i, j)\\n in self.boards[x][y].empty_squares])\\n return moves\",\n \"def generate_nearby_cells(self):\\n for y in range(len(self.island_map)):\\n for x in range(len(self.island_map[y])):\\n list_of_nearby_cells = []\\n\\n if y != 0:\\n self.generate_cell_above(x, y, list_of_nearby_cells)\\n\\n if x != 0:\\n self.generate_cell_left(x, y, list_of_nearby_cells)\\n\\n if y != len(self.island_map)-1:\\n self.generate_cell_below(x, y, list_of_nearby_cells)\\n\\n if x != len(self.island_map[y])-1:\\n self.generate_cell_right(x, y, list_of_nearby_cells)\\n\\n self.island_map[y][x].nearby_cells = list_of_nearby_cells\",\n \"def populate_positions(self, board, player):\\n\\t\\tprint \\\"Inside populate_positions!-----\\\"\\n\\t\\tpositions = []\\n\\t\\tfor i in range(0,3):\\n\\t\\t\\tfor j in range(0,3):\\n\\t\\t\\t\\tif(board[i][j] == player):\\n\\t\\t\\t\\t\\tpositions.append([i,j])\\n\\t\\treturn positions\",\n \"def complete_grid(self):\\n if self._complete_grid is None:\\n self._complete_grid = make_complete_graph(self._num_vertices)\\n return self._complete_grid\",\n \"def get_move_list(self):\\n return [\\n tuple(x) for x in np.argwhere(self.board == HexBoard.EMPTY).tolist()\\n ]\",\n \"def get_neighbors(self):\\n return list(map(self.game.square, [self.position - self.game.rules[\\\"row_len\\\"], self.position + 1, self.position + self.game.rules[\\\"row_len\\\"], self.position - 1]))\",\n \"def empty_cells(self) -> List[Cell]:\\n return list(ob.pos[0] for ob in self.new_obs())\",\n \"def remove_filled_positions(self, positions, board):\\n\\n new_positions = []\\n for p in positions:\\n if board.check_move(p[0], p[1]):\\n new_positions.append(p)\\n return new_positions\",\n \"def makeStartingGrid(self):\\n return util.make2DArray(self.xN, self.yN, False)\",\n \"def get_grid_coords(self, count, boundry_x, boundry_y, grid_size):\\n\\n coords = []\\n\\n boundry_x = int(boundry_x/10)\\n boundry_y = int(boundry_y/10)\\n\\n while len(coords) < count:\\n seed()\\n\\n\\n x = randint(-boundry_x, boundry_x)\\n y = randint(-boundry_y, boundry_y)\\n\\n if len(coords) == 0:\\n coords.append((x*grid_size, y*grid_size))\\n else:\\n for coord in coords:\\n if (x not in range(coord[0]-buffer*grid_size, coord[0]+buffer*grid_size)) and (y not in range(coord[1]-buffer, coord[1]+buffer)):\\n pass\\n else:\\n break\",\n \"def get_start_grid(cols=4, rows=4):\\n\\tgrid = [[\\\"\\\"]*cols for i in range(rows)]\\n\\tfor i in range(2):\\n\\t\\tempties = get_empty_cells(grid)\\n\\t\\ty,x = random.choice(empties)\\n\\t\\tgrid[y][x] = 2 if random.random() < prob_2 else 4\\n\\treturn grid\",\n \"def blank_board(self):\\n return [[False for x in range(self._dim)] for y in range(self._dim)]\",\n \"def find_excited_locations(self):\\n return np.asarray(np.where(self._grid == 8)).T\",\n \"def empty_cells(state):\\r\\n cells = []\\r\\n for x, row in enumerate(state):\\r\\n for y, cell in enumerate(row):\\r\\n if cell == 0:\\r\\n cells.append([x, y])\\r\\n\\r\\n return cells\",\n \"def collide_grid(self):\\n topleft = self.absolute_collide_topleft\\n bottomright = self.absolute_collide_bottomright\\n tlx, tly = self.currentLevel.toGridCoord(topleft)\\n brx, bry = self.currentLevel.toGridCoord(bottomright)\\n collide_grid = []\\n for x in range(tlx, brx+1):\\n for y in range(tly, bry+1):\\n collide_grid.append( (x,y) )\\n if not collide_grid:\\n collide_grid = [(tlx,tly)]\\n return collide_grid\",\n \"def reset(self):\\n self._cells = [[0 for dummy_col in range(self._grid_width)] for dummy_row in range(self._grid_height)]\\n self.new_tile()\\n self.new_tile()\\n #return self._cells\",\n \"def find_empty(grid):\\n for i in range(LEN_GRID):\\n for j in range(LEN_GRID):\\n if grid[i][j] == 0:\\n return (i, j) # row, col\\n return None\",\n \"def get_grid_locations(self, top_left, other_pos):\\n cell_x = torch.floor(((other_pos[:, 0] - top_left[:, 0]) / self.neighborhood_size) *self.grid_size)\\n\\n # Added this part to implementation, otherwise the pooling is going to run into an indexing error\\n cell_x[cell_x == self.grid_size] -= 1\\n cell_y = torch.floor(((top_left[:, 1] - other_pos[:, 1]) / self.neighborhood_size) *self.grid_size)\\n cell_y[cell_y == self.grid_size] -= 1\\n grid_pos = cell_x + cell_y * self.grid_size\\n\\n return grid_pos\",\n \"def get_block_positions(self, fig):\\n block_positions = []\\n\\n # Iterates through y + active_piece.y and x + active_piece.x\\n for y, row in enumerate(fig, start=self.active_piece.y):\\n for x, val in enumerate(row, start=self.active_piece.x):\\n if val != 0:\\n block_positions.append((x, y))\\n\\n return block_positions\",\n \"def get_random_coordinates(self):\\n array_shape = np.shape(self.cells) # type: tuple\\n points_on_island = []\\n for i in range(1, array_shape[0] - 1):\\n for j in range(1, array_shape[1] - 1):\\n points_on_island.append((i, j))\\n random.shuffle(points_on_island)\\n return points_on_island\",\n \"def boarderPosition(self, gameState):\\n if gameState.isOnRedTeam(self.index):\\n i = self.midWidth - 1\\n else:\\n i = self.midWidth + 1\\n boudaries = [(i,j) for j in range(self.height)]\\n validPositions = []\\n for i in boudaries:\\n if not gameState.hasWall(i[0],i[1]):\\n validPositions.append(i)\\n return validPositions\",\n \"def get_grid(self):\\n self.fullws = []\\n for row in self.word_search_grid:\\n rowdata = []\\n for column in row:\\n rowdata += [column.entry.get()]\\n self.fullws += [rowdata]\\n self.logic.set_grid(self.fullws)\",\n \"def get_agent_positions_in_grid(agent, grid):\\n grid_position_list = []\\n\\n for position in agent.positions:\\n for (grid_row, grid_row_index) in zip(grid, range(len(grid))):\\n grid_col_index = _get_position_grid_column(position, grid_row)\\n if grid_col_index or grid_col_index == 0:\\n grid_position_list.append(Position(grid_col_index, grid_row_index))\\n break\\n\\n return grid_position_list\",\n \"def _get_neighbours(self, position):\\n grid = self._grid\\n x, y = position\\n neighbours = []\\n offsets = [(0,1),(1,0),(0,-1),(-1,0)]\\n shuffle(offsets)\\n for offset in offsets:\\n i, j = offset\\n position = (x + i, y + j)\\n if grid.valid_position(position) and position not in self.shots:\\n neighbours.append(position)\\n return neighbours\",\n \"def occupied_cells(self):\\n\\n for lm in self.landmarks:\\n if self.cell_size < 1:\\n # expand the range the landmark exists\\n lm_x_range = np.arange(lm[0]-self.R, lm[0]+self.R, self.cell_size)\\n lm_y_range = np.arange(lm[1]-self.R, lm[1]+self.R, self.cell_size)\\n\\n # loop through expanded ranges and compute grid positions\\n for lm_x in lm_x_range:\\n for lm_y in lm_y_range:\\n\\n row, col = self.cell_index([lm_x, lm_y])\\n\\n # apply cost of occupied cell\\n try:\\n self.world[row][col] = 1000\\n except IndexError:\\n pass\\n\\n else:\\n # apply cost of occupied cell\\n row, col = self.cell_index(lm)\\n try:\\n self.world[row][col] = 1000\\n except IndexError:\\n pass\",\n \"def find_empty_space(self, state):\\r\\n for i in range(3):\\r\\n for j in range(3):\\r\\n if state[i][j] == 0:\\r\\n return (i, j)\",\n \"def filled(self, position):\\n i, j = position\\n if 0 <= i < self.height and 0 <= j < self.width:\\n return self.squares[i][j]\\n # all positions outside of the defined area of the maze return as True (filled)\\n return 1\",\n \"def cells(self):\\n \\\"\\\"\\\"\\n Note that we use the convention that the first cell is (1,1)\\n \\\"\\\"\\\"\\n spart_star = self.star()\\n part = Partition(list(spart_star))\\n coordinates = part.cells()\\n coordinates = [(x+1, y+1) for x, y in coordinates]\\n return coordinates\",\n \"def cells(self) -> List[Tuple[int, int]]:\\n return self._cells\",\n \"def initialize_grid(self):\\n self.grid = np.zeros([self.N, self.N, self.N])\\n return self.grid\",\n \"def get_neighbours(self):\\n shape=self.cubeshape[1:]\\n neighboursx=np.arange(self.xpos-(self.blocksize-1)/2,(self.xpos+(self.blocksize-1)/2)+1,dtype='int' )\\n neighboursx=[x if (x>=0) & (x<=shape[1]-1) else np.nan for x in neighboursx ]\\n neighboursy=np.arange(self.ypos-(self.blocksize-1)/2,(self.ypos+(self.blocksize-1)/2)+1,dtype='int' )\\n neighboursy=[y if (y>=0) & (y<=shape[0]-1) else np.nan for y in neighboursy ]\\n keys=[np.ravel_multi_index([y,x], shape) if np.all(np.isfinite(np.asarray([y,x]))) else np.nan for y in neighboursy for x in neighboursx]\\n\\n return keys\",\n \"def get_empty_cells(board):\\n empty_cells = [idx for idx, e in enumerate(board) if e == ' ']\\n return empty_cells\",\n \"def open_spots(self):\\n ret = []\\n for i in range(1,25):\\n if self.nodes[i].piece == None:\\n ret.append(i)\\n return ret\",\n \"def get_candidate_tiles(self) -> List[Point]:\\n\\t\\tempty_tiles = set()\\n\\t\\tfor x in range(self.size):\\n\\t\\t\\tfor y in range(self.size):\\n\\t\\t\\t\\tif not self.tiles[x][y] == 0:\\n\\t\\t\\t\\t\\tfor d in [[0,1], [1,1], [1,0], [1,-1], [0,-1], [-1,-1], [-1,0], [-1,1]]:\\n\\t\\t\\t\\t\\t\\tif x+d[0] >= 0 and y+d[1] >= 0 and x+d[0] < self.size and y+d[1] < self.size and self.tiles[x+d[0]][y+d[1]] == 0:\\n\\t\\t\\t\\t\\t\\t\\tempty_tiles.add(Point(x+d[0],y+d[1]))\\n\\t\\treturn list(empty_tiles)\",\n \"def known_mines(self):\\n \\n if len(self.cells) == self.count:\\n return self.cells\",\n \"def initialize_grid(self):\\r\\n for i in range(self.height):\\r\\n for j in range(self.width):\\r\\n self.grid[i][j] = 0\\r\\n \\r\\n # fill up unvisited cells\\r\\n for r in range(self.height):\\r\\n for c in range(self.width):\\r\\n if r % 2 == 0 and c % 2 == 0:\\r\\n self.unvisited.append((r,c))\\r\\n\\r\\n self.visited = []\\r\\n self.path = dict()\\r\\n self.generated = False\",\n \"def get_all_positions(board, white_turn):\\n list = []\\n for row in range(8):\\n for col in range(8):\\n # White\\n if white_turn and white_piece_on_pos((row, col), board):\\n obj = board[row][col]\\n if type(obj) is Pawn:\\n for valid_pos in valid_positions_pawn_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Tower:\\n for valid_pos in valid_positions_tower_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Bishop:\\n for valid_pos in valid_positions_bishop_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Horse:\\n for valid_pos in valid_positions_horse_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Queen:\\n for valid_pos in valid_positions_queen_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is King:\\n for valid_pos in valid_positions_king_white((row, col), board):\\n list.append(((row, col), valid_pos))\\n # Black\\n elif (not white_turn) and black_piece_on_pos((row, col), board):\\n obj = board[row][col]\\n if type(obj) is Pawn:\\n for valid_pos in valid_positions_pawn_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Tower:\\n for valid_pos in valid_positions_tower_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Bishop:\\n for valid_pos in valid_positions_bishop_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Horse:\\n for valid_pos in valid_positions_horse_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is Queen:\\n for valid_pos in valid_positions_queen_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n elif type(obj) is King:\\n for valid_pos in valid_positions_king_black((row, col), board):\\n list.append(((row, col), valid_pos))\\n return list\",\n \"def get_pos(self):\\n return [self.row, self.col]\",\n \"def get_empty_squares(self):\\n return self.empty_squares\",\n \"def get_all_closed_cells(self):\\r\\n num_rows = len(self.mine_field)\\r\\n num_cols = len(self.mine_field[0])\\r\\n closed_cells = [(row, col)\\r\\n for row in range(num_rows)\\r\\n for col in range(num_cols)\\r\\n if self.mine_field[row][col] == '?']\\r\\n return closed_cells\",\n \"def _initiate_board(self):\\n grid = []\\n for i in range(constant.BOARD_DIMENSION):\\n # Starts each row\\n current_row = []\\n for j in range(constant.BOARD_DIMENSION):\\n # Adds the pieces depending on the position\\n if i < constant.ROWS_OF_PIECES:\\n # Black pieces\\n if (j + i) % 2 != 0:\\n current_row.append(Piece(i, j, Player.black))\\n self.num_black_pieces = self.num_black_pieces + 1\\n else:\\n current_row.append(None)\\n\\n elif i >= constant.BOARD_DIMENSION - constant.ROWS_OF_PIECES:\\n # White pieces\\n if (j + i) % 2 != 0:\\n current_row.append(Piece(i, j, Player.white))\\n self.num_white_pieces = self.num_white_pieces + 1\\n else:\\n current_row.append(None)\\n\\n else:\\n current_row.append(None)\\n\\n grid.append(current_row)\\n\\n return grid\",\n \"def cells(self):\\n \\\"\\\"\\\"\\n Note that we use the convention that the first cell is (1,1)\\n \\\"\\\"\\\"\\n part = Partition(list(self))\\n coordinates = part.cells()\\n coordinates = [(x+1, y+1) for x, y in coordinates]\\n return coordinates\",\n \"def get_all_moves(self):\\n # 2d matrix of true/false, true if something can be placed\\n legal_move_board = []\\n possible_move_list = []\\n for row in range(self.size):\\n move_row = []\\n for col in range(self.size):\\n empty = self.board[row][col].state == PegState.EMPTY\\n move_row.append(empty)\\n if empty:\\n possible_move_list.append((row, col))\\n legal_move_board.append(move_row)\\n \\n # every position where something can be placed (list of tuples) (Combined with above)\\n \\\"\\\"\\\" possible_move_list = []\\n for row in range(self.size):\\n for col in range(self.size):\\n if legal_move_board[row][col] == True:\\n possible_move_list.append((row, col))\\n \\\"\\\"\\\"\\n return legal_move_board, possible_move_list\",\n \"def init_grid(self):\\n grid = []\\n for i in range(self.settings['grid_size']):\\n grid.append([])\\n for j in range(self.settings['grid_size']):\\n if [j, i] in self.settings['walls']:\\n grid[i].append(g.WALL)\\n else:\\n grid[i].append(g.EMPTY)\\n return grid\",\n \"def empty_cells(state):\\n cells = []\\n\\n for i, row in enumerate(state):\\n for j, col in enumerate(row):\\n if state[i][j] == 0:\\n cells.append([i, j])\\n\\n return cells\",\n \"def neighbors(self):\\n \\n # find 0 - blank square\\n \\n x0 = None\\n y0 = None\\n \\n for i in range(4):\\n for j in range(4):\\n if self.get_tile(i,j) == 0:\\n y0 = i\\n x0 = j\\n\\n if x0 == None or y0 == None:\\n return []\\n \\n neighbor_list = []\\n \\n # move 0 to the right\\n if x0 < 3:\\n new_position = Position(self.tiles)\\n temp = new_position.get_tile(y0,x0+1)\\n new_position.set_tile(y0,x0+1,0)\\n new_position.set_tile(y0,x0,temp)\\n new_position.directiontomoveto = 'r'\\n neighbor_list.append(new_position)\\n # move 0 to the left\\n if x0 > 0:\\n new_position = Position(self.tiles)\\n temp = new_position.get_tile(y0,x0-1)\\n new_position.set_tile(y0,x0-1,0)\\n new_position.set_tile(y0,x0,temp)\\n new_position.directiontomoveto = 'l'\\n neighbor_list.append(new_position)\\n # move 0 up\\n if y0 > 0:\\n new_position = Position(self.tiles)\\n temp = new_position.get_tile(y0-1,x0)\\n new_position.set_tile(y0-1,x0,0)\\n new_position.set_tile(y0,x0,temp)\\n new_position.directiontomoveto = 'u'\\n neighbor_list.append(new_position)\\n # move 0 down\\n if y0 < 3:\\n new_position = Position(self.tiles)\\n temp = new_position.get_tile(y0+1,x0)\\n new_position.set_tile(y0+1,x0,0)\\n new_position.set_tile(y0,x0,temp)\\n new_position.directiontomoveto = 'd'\\n neighbor_list.append(new_position)\\n \\n return neighbor_list\",\n \"def cells_list(self):\\n xx, yy = np.meshgrid(self.x_spacings, self.y_spacings)\\n return np.vstack([yy.ravel(), xx.ravel()]).transpose()\",\n \"def get_all_positions(self) -> List[\\\"Position\\\"]:\\n flat_positions = [j for i in self.positions.values() for j in i.values()]\\n # filter out non-positions\\n flat_positions = [pos for pos in flat_positions if type(pos) is Position]\\n return flat_positions\",\n \"def find_moves(self):\\n\\n from itertools import product\\n free_position = self.find_free()\\n return [list(free_position+i) for i in [[0,1],[1,0],[-1,0],[0,-1]] if tuple(i+free_position) in product(range(self.size),repeat=2)]\",\n \"def check_grid_full(self):\\n for row in self.game_state:\\n for e in row:\\n if e is None:\\n return False\\n return True\",\n \"def get_live_cell_coordinates(self):\\n\\n return np.array(np.where(self.board == 1)).transpose().tolist()\",\n \"def checked_positions():\\n for base_position in chain([me.shipyard], me.get_dropoffs()):\\n x_shipyard = base_position.position.x\\n y_shipyard = base_position.position.y\\n for x in range(-search_range, search_range):\\n for y in range(-search_range, search_range):\\n yield hlt.Position(\\n x=x_shipyard + x,\\n y=y_shipyard + y)\",\n \"def known_mines(self):\\n if len(self.cells) <= self.count:\\n return self.cells\\n return set([])\",\n \"def get_empty_slots(self):\\n slots = np.reshape(range(0, self.size * self.size), (self.size, self.size))\\n\\n return slots[~self.tiles_taken]\",\n \"def full_board( self ):\\n\\n for x in self.__grid:\\n if isinstance(x, int):\\n return False\\n else:\\n continue\\n\\n return True\",\n \"def reset(self):\\n self.grid = np.array([0] * 9) # grid\\n self.turn = 1 # whose turn it is\\n self.done = False # whether game is done\\n return self.grid\",\n \"def get_open_spaces(board):\\n open_spaces = []\\n for i in range(3):\\n for j in range(3):\\n if board[i][j] == 0:\\n open_spaces.append(encode_pos(i, j))\\n return open_spaces\",\n \"def opponentBoarderPosition(self, gameState):\\n if self.red:\\n i = self.midWidth\\n else:\\n i = self.midWidth - 1\\n boudaries = [(i,j) for j in range(self.height)]\\n validPositions = []\\n for i in boudaries:\\n if not gameState.hasWall(i[0],i[1]):\\n validPositions.append(i)\\n return validPositions\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77568364","0.723596","0.7119489","0.70678014","0.7052116","0.69976157","0.68781596","0.68656486","0.68310523","0.6827777","0.67618763","0.66904217","0.6631231","0.65709645","0.65524876","0.65520054","0.65291786","0.65274036","0.6515479","0.6496653","0.6454438","0.6445513","0.6435733","0.64186096","0.64111537","0.64053166","0.6393756","0.63700914","0.63662046","0.63583726","0.63445854","0.6333363","0.6329729","0.63206804","0.63203365","0.6311867","0.6309937","0.62848485","0.6282642","0.626378","0.6252205","0.62501895","0.62379736","0.6220131","0.6218155","0.6202137","0.61923856","0.6182581","0.6177556","0.6142098","0.613799","0.61370754","0.6136937","0.61332095","0.6114049","0.61059284","0.6099764","0.6087413","0.6080937","0.607933","0.60738677","0.60671276","0.60635906","0.60597956","0.6055019","0.6050606","0.60413027","0.60398525","0.6038735","0.60310286","0.60287464","0.6026677","0.6019085","0.6013974","0.6009997","0.60094357","0.60018116","0.59963906","0.59942526","0.5985177","0.5981083","0.5978216","0.5965847","0.5962802","0.59584314","0.5949296","0.59482574","0.5938955","0.59327424","0.59254014","0.5920402","0.5914715","0.5912537","0.59065086","0.590559","0.59053737","0.589972","0.5897942","0.5897556","0.5897367"],"string":"[\n \"0.77568364\",\n \"0.723596\",\n \"0.7119489\",\n \"0.70678014\",\n \"0.7052116\",\n \"0.69976157\",\n \"0.68781596\",\n \"0.68656486\",\n \"0.68310523\",\n \"0.6827777\",\n \"0.67618763\",\n \"0.66904217\",\n \"0.6631231\",\n \"0.65709645\",\n \"0.65524876\",\n \"0.65520054\",\n \"0.65291786\",\n \"0.65274036\",\n \"0.6515479\",\n \"0.6496653\",\n \"0.6454438\",\n \"0.6445513\",\n \"0.6435733\",\n \"0.64186096\",\n \"0.64111537\",\n \"0.64053166\",\n \"0.6393756\",\n \"0.63700914\",\n \"0.63662046\",\n \"0.63583726\",\n \"0.63445854\",\n \"0.6333363\",\n \"0.6329729\",\n \"0.63206804\",\n \"0.63203365\",\n \"0.6311867\",\n \"0.6309937\",\n \"0.62848485\",\n \"0.6282642\",\n \"0.626378\",\n \"0.6252205\",\n \"0.62501895\",\n \"0.62379736\",\n \"0.6220131\",\n \"0.6218155\",\n \"0.6202137\",\n \"0.61923856\",\n \"0.6182581\",\n \"0.6177556\",\n \"0.6142098\",\n \"0.613799\",\n \"0.61370754\",\n \"0.6136937\",\n \"0.61332095\",\n \"0.6114049\",\n \"0.61059284\",\n \"0.6099764\",\n \"0.6087413\",\n \"0.6080937\",\n \"0.607933\",\n \"0.60738677\",\n \"0.60671276\",\n \"0.60635906\",\n \"0.60597956\",\n \"0.6055019\",\n \"0.6050606\",\n \"0.60413027\",\n \"0.60398525\",\n \"0.6038735\",\n \"0.60310286\",\n \"0.60287464\",\n \"0.6026677\",\n \"0.6019085\",\n \"0.6013974\",\n \"0.6009997\",\n \"0.60094357\",\n \"0.60018116\",\n \"0.59963906\",\n \"0.59942526\",\n \"0.5985177\",\n \"0.5981083\",\n \"0.5978216\",\n \"0.5965847\",\n \"0.5962802\",\n \"0.59584314\",\n \"0.5949296\",\n \"0.59482574\",\n \"0.5938955\",\n \"0.59327424\",\n \"0.59254014\",\n \"0.5920402\",\n \"0.5914715\",\n \"0.5912537\",\n \"0.59065086\",\n \"0.590559\",\n \"0.59053737\",\n \"0.589972\",\n \"0.5897942\",\n \"0.5897556\",\n \"0.5897367\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80966496"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17489,"cells":{"query":{"kind":"string","value":"Assigns a tuple to a position in the grid, of the form (hand, score of this hand for this position)"},"document":{"kind":"string","value":"def assign(self, hand, position):\n assert isinstance(hand, h.Hand)\n # print \"POSITION:\", position\n # print self\n try:\n assert self.grid[position][1] == \"---\"\n except AssertionError:\n raise FilledInError\n self.grid[position] = (hand, self.score(hand, position))\n self.update_totals()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def score_tuple( hand ):\n m = matches(hand)\n #print( m )\n #royal_flush -- a special case of straight flush.\n if flush(hand) and straight(hand) and hand[4].rank == 14:\n return (8, hand[4].rank, 0)\n #straight_flush\n elif flush(hand) and straight(hand):\n return (8, hand[4].rank, 0)\n #four_of_a_kind\n elif len(m) == 2 and m[0].count == 4:\n return (7, m[0].card.rank, 0)\n #full_house\n elif len(m) == 2 and m[0].count == 3 and m[1].count == 2:\n return (6, m[0].card.rank, m[1].card.rank)\n #flush\n elif flush(hand):\n return (5, hand[4].rank, 0)\n #straight\n elif straight(hand):\n return (4, hand[4].rank, 0)\n #three_of_a_kind\n elif len(m) == 3 and m[0].count == 3:\n return (3, m[0].card.rank, 0)\n #two_pair\n elif len(m) == 3 and m[0].count == 2 and m[1].count == 2:\n return (2, m[0].card.rank, m[1].card.rank)\n #one_pair\n elif len(m) == 4 and m[0].count == 2 and m[1].count == 1:\n return (1, m[0].card.rank, m[1].card.rank)\n # Simple high card. Is this adequate? We'll know if we get ties.\n else:\n return (0, hand[4].rank, 0) # or (0, m[0].card.rank, 0)","def set(self,row,col,value):\r\n self.puzzle[row][col] = value\r\n print(\"Entered value \",value)\r\n if self.puzzle[row][col] == self.rows[row][col]:\r\n self.score = self.score+5\r\n else:\r\n self.score = self.score-5","def mark(board, player, row, col):\r\n pass","def put(self, choice, token):\n x, y = choice\n self.grid[x][y] = token","def make_move(board, picked_column, player):\n row = find_first_free_cell(board, picked_column)\n board[row][picked_column] = player\n return board, row","def mark(board, player, row, col):\n pass","def get_score(location, grid, shape):","def place_marker(board, score, marker, position):\n board[position] = marker\n inc = 0\n if marker == \"X\":\n inc = 1\n else:\n inc = -1\n if position == 7:\n score[0] += inc\n score[3] += inc\n score[6] += inc\n elif position == 8:\n score[0] += inc\n score[4] += inc\n elif position == 9:\n score[0] += inc\n score[5] += inc\n score[7] += inc\n elif position == 4:\n score[1] += inc\n score[3] += inc\n elif position == 5:\n score[1] += inc\n score[4] += inc\n score[6] += inc\n score[7] += inc\n elif position == 6:\n score[1] += inc\n score[5] += inc\n elif position == 1:\n score[2] += inc\n score[3] += inc\n score[7] += inc\n elif position == 2:\n score[2] += inc\n score[4] += inc\n elif position == 3:\n score[2] += inc\n score[5] += inc\n score[6] += inc","def mm_move(board, player):\r\n if board.check_win() == provided.PLAYERX:\r\n return SCORES[provided.PLAYERX],(-1,-1)\r\n elif board.check_win() == provided.PLAYERO:\r\n return SCORES[provided.PLAYERO],(-1,-1)\r\n elif board.check_win() == provided.DRAW:\r\n return SCORES[provided.DRAW],(-1,-1)\r\n else:\r\n empty_tuple_list = board.get_empty_squares()\r\n score_pos_tuple_list = []\r\n best_score = None\r\n best_pos = None\r\n for idx1 in range(len(empty_tuple_list)):\r\n empty_tuple = empty_tuple_list[idx1]\r\n board_clone = board.clone()\r\n board_clone.move(empty_tuple[0],empty_tuple[1],player)\r\n score_pos_tuple = mm_move(board_clone,provided.switch_player(player))\r\n score_pos_tuple_list.append(score_pos_tuple)\r\n\r\n #decide best score and pos fast!!!\r\n if score_pos_tuple[0]*SCORES[player] == 1:\r\n return (score_pos_tuple[0],empty_tuple)\r\n\r\n #decide best score and pos\r\n for idx2 in range(len(score_pos_tuple_list)):\r\n if idx2 == 0:\r\n best_score = score_pos_tuple_list[idx2][0]\r\n best_pos = empty_tuple_list[idx2]\r\n else:\r\n if score_pos_tuple_list[idx2][0]*SCORES[player] > best_score*SCORES[player]:\r\n best_score = score_pos_tuple_list[idx2][0]\r\n best_pos = empty_tuple_list[idx2]\r\n\r\n return (best_score,best_pos)","def guess(self, row, col) -> Tuple[int, Optional[ship.Ship]]:\n my_ship: ship.Ship = self._board_matrix[row][col]\n\n # if my_ship is None the guess is a miss, otherwise its a hit\n\n # --------- BEGIN YOUR CODE ----------\n\n # This is exactly the same as Human.guess, just copy the code over\n\n # --------- END YOUR CODE ----------","def setPiece(self, column, rank, piece):\n try:\n self.values[int(int(rank)-1)*8+self.getColIdx(column)] = piece\n except:\n print column, rank\n rospy.loginfo(\"setPiece: invalid row/column\")","def setPiece(self, column, rank, piece):\n try:\n self.values[int(int(rank)-1)*8+self.getColIdx(column)] = piece\n except:\n print column, rank\n rospy.loginfo(\"setPiece: invalid row/column\")","def loc_from_tuple(self, coords):\n self.x, self.y = coords","def set_tile(self, row, col, value):\n # replace with your code\n self.grid[row][col] = value","def change_position(board: Board, position: Position, character: str) -> Board:\n board = list(board)\n \n row = board[position[0]]\n new_row = row[:position[-1]] + character + row[position[-1] + 1:]\n board[position[0]] = new_row\n\n board = tuple(board) \n\n return board","def set_position(self, position):\n self.position = tuple(position)","def __init__(self, pos, score=0):\n self.__pos = pos\n self.__score = score","def set_game_params(self, board):\n self.board = np.copy(board)\n self.board_min_len = np.min(len(board))\n\n tmp_player_pos = np.where(board == 1)\n self.player_pos = (tmp_player_pos[0][0], tmp_player_pos[1][0])\n tmp_rival_pos = np.where(board == 2)\n self.rival_pos = (tmp_rival_pos[0][0], tmp_rival_pos[1][0])","def position(self, value):\n if type(value) is not tuple or len(value) != 2 or \\\n type(value[0]) is not int or value[0] < 0 or \\\n type(value[1]) is not int or value[1] < 0:\n raise TypeError(\"position must be a tuple of 2 positive integers\")\n else:\n self.__position = value","def swap_tile(grid: tuple[int, ...], move: int) -> tuple[int, ...]:\n tile_to_swap: int = grid.index(0) + move\n value_to_swap: int = grid[tile_to_swap]\n\n mutable_grid: list[int] = list(grid)\n mutable_grid[grid.index(0)] = value_to_swap\n mutable_grid[tile_to_swap] = 0\n swapped_grid = tuple(mutable_grid)\n\n return swapped_grid","def __init__(self):\n self.played_pos = []\n self.grid = [['-', '-', '-'],\n ['-', '-', '-'],\n ['-', '-', '-']]\n self.player_played_pos = {'p1': set(), 'p2': set()}","def set_tile(self, row, col, value):\n # replace with your code\n self._grid[row][col] = value","def set_tile(self, row, col, value):\n # replace with your code\n self._grid[row][col] = value","def getGameState(self):\n ### Student code goes here\n row1 = [-1, -1, -1]\n row2 = [-1, -1, -1]\n row3 = [-1, -1, -1]\n for i in self.kb.kb_ask(parse_input(\"fact: (pos ?t ?px ?py\")):\n if str(i.bindings_dict['?t'])=='empty':\n t = -1\n else:\n t = int(i.bindings_dict['?t'][4])\n xpx = int(i.bindings_dict['?px'][3])\n xpy = int(i.bindings_dict['?py'][3])\n if xpy == 1:\n row1[xpx-1] = t\n elif xpy == 2:\n row2[xpx-1] = t\n elif xpy == 3:\n row3[xpx-1] = t\n return tuple((tuple(row1),tuple(row2),tuple(row3)))","def set_tile(self, row, col, value):\n # replace with your code\n self._grid[row][col] = value;","def position(self, value):\n if type(value) is not tuple or len(value) != 2 \\\n or type(value[0]) is not int or type(value[1]) is not int \\\n or value[0] < 0 or value[1] < 0:\n raise TypeError(\"position must be a tuple of 2 positive integers\")\n else:\n self.__position = value","def position(square):\n first = square[0]\n second = square[1]\n col = parseCol(first)\n row = parseRow(second)\n return (row, col)","def score(self, hand, position):\n\n try:\n assert self.grid[position][1] == \"---\"\n except AssertionError:\n print self\n print position\n raise FilledInError\n except KeyError:\n print \"\\nCheck your code. This is not a valid position:\", position, \"\\n\"\n raise\n\n if position.startswith(\"n\"): # Return sum of relevant number\n n = int(position[1])\n return sum(d for d in hand.dice if d == n)\n\n elif position in [\"k3\", \"k4\", \"ch\"]: # Return total sum\n if position == \"k3\" and hand.max_tally()[0] < 3:\n return 0 # The is not a three of a kind\n elif position == \"k4\" and hand.max_tally()[0] < 4:\n return 0 # The is not a four of a kind\n return sum(hand.dice)\n\n elif position in [\"fh\", \"ss\", \"ls\", \"yz\", \"yb\"]: # Return fixed score\n if position == \"fh\":\n tallies = hand.get_dicedict().values()\n if 1 in tallies:\n return 0 # This is not a full house\n\n elif position in [\"ss\", \"ls\"]:\n ds = \"\".join(str(x) for x in hand.sort_by_value())\n if position == [\"ss\"]:\n if \"1234\" not in ds and \"2345\" not in ds and \"3456\" not in ds:\n return 0\n else:\n if \"12345\" not in ds and \"23456\" not in ds:\n return 0\n\n else:\n if hand.max_tally()[0] < 5:\n return 0 # This is not a yahtzee\n if position == \"yb\" and self.grid[\"yz\"] == \"---\":\n return 0 # YB only scores points if there already is a YZ\n\n return fixed_scores[position]\n\n else:\n raise InvalidPositionError","def __getitem__(self, item):\n if type(item) != int:\n raise TypeError('Grid index must be int, not {}'.format(type(item)))\n if not 0 <= item < len(self):\n raise IndexError('Grid index out of range')\n point = []\n for i in range(len(self.sizes)):\n point.append(item % self.sizes[i])\n item //= self.sizes[i]\n return tuple(point)","def _insert_piece(cls, board, coord, piece):\n rank = board[coord[0]][:coord[1]] + (piece,) + board[coord[0]][coord[1]+1:]\n return board[:coord[0]] + (rank,) + board[coord[0]+1:]","def set_tile(self, row, col, value):\n # replace with your code\n if col < self.grid_height and row < self.grid_width:\n self.board[row][col] = value","def place(self, board):\r\n self.board = board","def compute_new_position_from_dice(self, player_index, thr):\r\n new_position = self.player_positions[player_index] + thr.get_amount()\r\n\r\n if new_position >= len(self.squares):\r\n new_position -= len(self.squares)\r\n\r\n return new_position","def set_tile(self, row, col, value):\r\n # replace with your code\r\n self._grid_tile[row][col] = value","def place_obj(self):\r\n for pos in BOARD_POSITIONS:\r\n self.board[pos[0]][pos[1]] = Stone(color=self.state[pos[0]][pos[1]], pos=(pos[0], pos[1]))\r\n self.board[pos[0]][pos[1]].liberty = self.board[pos[0]][pos[1]].compute_liberty(self.state)","def __init__(self, layout, player):\n self.layout = [x[:] for x in layout] #this state's layout is a copy\n self.height = len(layout[0])\n self.width = len(layout)\n self.who_played = player\n self.score = self._scoring() #score for this board","def get_score(self):\n return tuple(self.score)","def set_item(self, row, col, value):\n self.board[row][col] = value","def update_grid_pos(self):\n self.grid_pos = self.get_tile_of_position(self.tank.body.position)","def move(self, row, col, player):\n self.board[row][col] = player","def update_scores(self):\n self.score[0] = (-1)*sum(self.board[self.board == -1])\n self.score[1] = sum(self.board[self.board == 1])\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)","def set_tile(self, row, col, value):\r\n self.grid[row][col] = value","def move(self, row, col, player):","def get_ai_move(board, player):\r\n row, col = 0, 0\r\n return row, col","def position(self) -> Tuple[int, int]:\n return self.row, self.col","def turrel(pos, board):\n row = pos[0]\n col = pos[1]\n for i in range(len(board)):\n board[i][col] = 1\n for j in range(len(board)):\n board[row][j] = 1","def make_position(data) -> Position:\n return (data[\"x\"], data[\"y\"])","def set_tile(self, row, col, value):\n # replace with your code\n self._grid_2048[row][col] = value","def getScore(self,board):\n return board.getScore()[self.tile]","def score_pos(pos = UPPER_LEFT):\n return {\n UPPER_LEFT : (0.5, 0.5),\n UPPER_RIGHT: ((Globals.instance.WIDTH/Globals.instance.GRID_SIZE) - 0.5, 0.5),\n LOWER_LEFT: (0.5, (Globals.instance.HEIGHT/Globals.instance.GRID_SIZE) - 1),\n LOWER_RIGHT: ((Globals.instance.WIDTH/Globals.instance.GRID_SIZE) - 0.5, (Globals.instance.HEIGHT/Globals.instance.GRID_SIZE) - 1)\n }.get(pos, UPPER_LEFT)","def position(self, position):\n if type(position) is not tuple or len(position) is not 2\\\n or type(position[0]) is not int or position[0] < 0\\\n or type(position[1]) is not int or position[1] < 0:\n\n raise TypeError(\"position must be a tuple of 2 positive integers\")\n\n self.__position = position","def position(self, value):\n if (not isinstance(value, tuple) or\n len(value) != 2 or\n not all(isinstance(num, int) for num in value) or\n not all(num >= 0 for num in value)):\n raise TypeError(\"position must be a tuple of 2 positive integers\")\n else:\n self.__position = value","def set_piece(self, square, piece):\n self.board[square.row][square.col] = piece","def set_piece(self, square, piece):\n self.board[square.row][square.col] = piece","def set_tile(self, row, col, value):\r\n self._grid[row][col]=value","def set_square(self, col, row, value):\n row_index = row - 1\n col_index = ord(col.lower()) - 97 # ord('a') is 97\n self.state[row_index][col_index] = value","def set_tile(self, row, col, value):\n self.grid[row][col] = value","def set_tile(self, row, col, value):\n self.grid[row][col] = value","def place_piece(piece, px, py, pc):\n \n\n for i, j in piece:\n x = px + i\n y = py + j\n if not (0 <= x < BOARD_WIDTH):\n continue\n if not (0 <= y < BOARD_HEIGHT):\n continue\n board[y][x] = pc","def update_positions(self, grid):\r\n self.grid = grid","def set_pos(self, p: tuple) -> None:\n self.pos = p","def __getitem__(self, pair):\n return self._scores[pair]","def put(self, column):\n column -= 1\n if self.occupied[column] >= 4 or self.won:\n return 'ERROR'\n self.history.append(column + 1)\n row = self.occupied[column]\n # assign 1 to player 1, and -1 to player 2\n if len(self.history) % 2 == 1:\n player = 1\n self.board[3 - row][column] = 1\n else:\n player = -1\n self.board[3 - row][column] = 2\n # add player score to column, row and diagonal scores\n self.columnScore[column] += player\n self.rowScore[row] += player\n self.occupied[column] += 1;\n if column == row:\n self.diagonal += player\n if column + row == 3:\n self.antidiagonal += player\n # check column, row and diagonal scores\n # if absolute value of one of them is 4\n # which means the original value is either 4 or -4\n # and one of the player has occupied all 4 of them\n # which means the player has won in that row/column/diagonal\n # and thus return \"WIN\"\n if (abs(self.rowScore[row]) == 4 or abs(self.columnScore[column]) == 4\n or abs(self.diagonal) == 4 or abs(self.antidiagonal) == 4):\n self.won = True\n return 'WIN'\n # check if there is still non-full columns\n # in other words check if the board is full\n for i in range(0, self.size):\n # if board is not full return \"OK\"\n if self.occupied[i] < 4:\n return 'OK'\n # if the board is full, return \"DRAW\"\n return 'DRAW'","def put(brickheight,bricklength,row,column,walllength,wallheight,occupied,answer):\n temp =[]\n for x in range(column,column+bricklength):\n for y in range(row,row+brickheight):\n occupied.append((x,y))\n temp.append(((y)*walllength+x))\n else:\n tempp = tuple(temp)\n answer.append(tempp)\n return occupied","def __init__(self, board, index):\n self.board = board\n self.index = index\n self._x, self._y = None, None","def l_point(self):\n self.l_score += 1\n self.update_scoreboard()","def set_game_params(self, board):\n self.board = board.copy()\n self.n_rows = len(self.board[0]) # cols number\n self.n_cols = len(self.board) # rows number\n self.fruits_ttl = min(self.n_rows,self.n_cols)+1\n player_pos = np.where(board == 1)\n rival_pos = np.where(board == 2)\n self.locations[PLAYER] = tuple(ax[0] for ax in player_pos)\n self.locations[RIVAL] = tuple(ax[0] for ax in rival_pos)\n self.turns = 0\n self.max_turns = reachables(self.board,self.locations[PLAYER])\n self.player_turns = self.max_turns // 2","def set_tile(self, row, col, value):\r\n self._board[row][col] = value","def apply_move(board_state, move, side):\n move_x, move_y = move\n\n def get_tuples():\n for x in range(len(board_state)):\n if move_x == x:\n temp = list(board_state[x])\n temp[move_y] = side\n yield tuple(temp)\n else:\n yield board_state[x]\n\n return tuple(get_tuples())","def get_ai_move(board, player):\n row, col = 0, 0\n return row, col","def assign_points(players):\n pass","def compute_coordinates(self):\n self._x, self._y = self.board.index_to_coordinates(self.index)","def check_score(self) -> None:\n self.player_1, self.player_2 = 0, 0\n for cell in self.cells:\n if cell.player == 1:\n self.player_1 += 1\n elif cell.player == 2:\n self.player_2 += 1","def new_move(self, grid_x, grid_y, player):\n #duplication /!\\\n if player == self.X:\n self.draw_X(grid_x, grid_y)\n self.board[grid_y][grid_x] = self.X\n\n elif player == self.O:\n self.draw_O(grid_x, grid_y)\n self.board[grid_y][grid_x] = self.O","def set_tile(self, row, col, value):\r\n # replace with your code\r\n self._cells[row][col] = value","def __key__(self) -> Tuple[int, int]:\n return self.col, self.row","def place_player(self, player, position):\n self.board[position] = player","def set_tile(self, row, col, value):\n # replace with your code\n self._cells[row][col] = value","def position_to_index(self, position, grid_size):\n x, y = position\n return x * grid_size + y","def __getitem__(self, args):\n x, y = args\n xpos, ypos = self.move(x, y)\n return self.board[xpos][ypos]","def make_move(board, player_num, row, col):\n board[row][col] = 'X' if player_num == 1 else 'O'","def player_location(self):\n x = 0\n y = 0\n for line in self.grid:\n for i in line:\n if i == \"P\":\n return x, y\n \n y+=1\n x += 1\n y = 0","def score_update(scoreboard, compare):\r\n if compare == 'Victory':\r\n scoreboard['W'] += 1\r\n elif compare == 'Defeat':\r\n scoreboard['L'] += 1\r\n elif compare == 'Tie':\r\n scoreboard['T'] += 1","def move(self, row, col, player):\n score = 1 if player == 1 else -1\n self.rows[row] += score\n self.cols[col] += score\n if row == col:\n self.diagonal1 += score\n if row + col == self.n - 1:\n self.diagonal2 += score\n win = {self.rows[row], self.cols[col], self.diagonal1, self.diagonal2}\n if self.n in win or -self.n in win:\n return player\n return 0","def update(self, move):\n\n if not 0 <= move < 7:\n raise InvalidMove\n\n placed = False\n x = None\n y = None\n\n for row in reversed(xrange(self._rows)):\n if not self._board[row][move]:\n self._board[row][move] = self.current_player\n placed = True\n x = move\n y = row\n break\n\n if not placed:\n raise InvalidMove\n\n return (x, y)","def populate_board(self):\n for row in range(10):\n for col in range(10):\n coord = Coordinate(row, col)\n coord_attack = Coordinate(row, col)\n self.player_table.setItem(row, col, coord)\n self.attack_table.setItem(row, col, coord_attack)","def getGameState(self):\n row1 = [0, 0, 0]\n row2 = [0, 0, 0]\n row3 = [0, 0, 0]\n tilePosStatement = Statement()\n posTerm1 = Term('?x')\n posTerm2 = Term('?y')\n posTerm3 = Term('?tile')\n tilePosStatement.terms = (posTerm1, posTerm2, posTerm3)\n tilePosStatement.predicate = 'tilePos'\n for fact in self.kb.facts:\n if match(fact.statement, tilePosStatement):\n if fact.statement.terms[2] == Term(Constant('tile1')):\n term = 1\n if fact.statement.terms[2] == Term(Constant('tile2')):\n term = 2\n if fact.statement.terms[2] == Term(Constant('tile3')):\n term = 3\n if fact.statement.terms[2] == Term(Constant('tile4')):\n term = 4\n if fact.statement.terms[2] == Term(Constant('tile5')):\n term = 5\n if fact.statement.terms[2] == Term(Constant('tile6')):\n term = 6\n if fact.statement.terms[2] == Term(Constant('tile7')):\n term = 7\n if fact.statement.terms[2] == Term(Constant('tile8')):\n term = 8\n if fact.statement.terms[2] == Term(Constant('empty')):\n term = -1\n if fact.statement.terms[0] == Term(Constant('pos1')):\n col = 0\n elif fact.statement.terms[0] == Term(Constant('pos2')):\n col = 1\n elif fact.statement.terms[0] == Term(Constant('pos3')):\n col = 2\n if fact.statement.terms[1] == Term(Constant('pos1')):\n row1[col] = term\n\n elif fact.statement.terms[1] == Term(Constant('pos2')):\n row2[col] = term\n\n elif fact.statement.terms[1] == Term(Constant('pos3')):\n row3[col] = term\n\n row1 = tuple(row1)\n row2 = tuple(row2)\n row3 = tuple(row3)\n result = (row1, row2, row3)\n return result\n\n ### Student code goes here","def place_at(self, row, col, piece):\n self.board[row + PADDING][col + PADDING] = piece","def mc_update_scores(scores, board, player):\n for row in range(board.get_dim()):\n for col in range(board.get_dim()):\n if board.check_win()==player:\n if board.square(row,col)==player:\n scores[row][col]+=SCORE_CURRENT\n elif board.square(row,col)==switch_player(player):\n scores[row][col]-=SCORE_OTHER\n elif board.check_win()==switch_player(player):\n if board.square(row,col)==player:\n scores[row][col]-=SCORE_CURRENT\n elif board.square(row,col)==switch_player(player):\n scores[row][col]+=SCORE_OTHER\n return","def update_board_with_move(self, cell, mark):\n row, col = cell\n self.storage[row][col] = mark","def get_score_pos(pos, board):\n if piece_on_pos(pos, board):\n return board[pos[0]][pos[1]].points\n else:\n return 0","def makePiece(self, val, column, rank, name):\n p = ChessPiece()\n p.header.frame_id = name # used to be \"chess_board\" but we already know that\n p.pose.position.x = SQUARE_SIZE * (0.5 + self.getColIdx(column))\n p.pose.position.y = SQUARE_SIZE * (0.5 + rank - 1)\n p.pose.position.z = 0.03\n p.type = val\n return p","def _set_tuple_structure(self, key):\n if len(key) == 2:\n self.ks = list(np.array(key[1]))\n self.set_neighs(key[0])","def placeship(point1: tuple, point2: tuple, board: list):\n # Is horizontal\n if point1[0] == point2[0]:\n for i in range(min(point1[1], point2[1]), max(point1[1], point2[1]) + 1):\n board[point1[0]][i] = \"O\"\n # Is vertical\n elif point1[1] == point2[1]:\n for i in range(min(point1[0], point2[0]), max(point1[0], point2[0]) + 1):\n board[i][point1[1]] = \"O\"\n return board","def get_position(self) -> typing.Tuple[int, int]:\n raise NotImplementedError","def set_tile(self, row, col, value):\n self._grid[row][col] = value","def get(self):\n return (self.x,self.y);","def coord (i, j):\r\n return j, i","def changeTile (self, posY, posX, tile=\"t\"):\r\n self.grid[posY][posX] = tile","def scoreGame(self):\n # create valueLs[card1,card2,...], pass it to sumHandReturnPoints(valueLs) or twoCardReturnPoints(valueLs)\n scoreLs = []\n ### Score of row\n for rowKey in self.table:\n valueLs = self.table[rowKey]\n points = self.sumHandReturnPoints(valueLs)\n scoreLs.append(points)\n\n ### Score of 4-card column\n for offset in range(0,3): # 0,1,2\n tmpLs = []\n for rowKey in self.table:\n valueLs = self.table[rowKey]\n if len(valueLs) == 5:\n iterStart = 1\n else:\n iterStart = 0\n card = valueLs[iterStart+offset]\n tmpLs.append(card)\n points = self.sumHandReturnPoints(tmpLs)\n scoreLs.append(points) \n\n ### Score of 2-card column\n #(1) 1st column\n valueLs1 = self.table['row1']\n valueLs2 = self.table['row2']\n tmpLs = []\n tmpLs.append(valueLs1[0].get_rank())\n tmpLs.append(valueLs2[0].get_rank())\n points = self.twoCardReturnPoints(tmpLs)\n scoreLs.append(points)\n #(2) 5th column\n valueLs3 = self.table['row1']\n valueLs4 = self.table['row2']\n tmpLs = []\n tmpLs.append(valueLs3[-1].get_rank())\n tmpLs.append(valueLs4[-1].get_rank())\n points = self.twoCardReturnPoints(tmpLs)\n scoreLs.append(points) \n\n ### Add up scoreLs\n sumPoints = 0\n for points in scoreLs:\n sumPoints += points\n return sumPoints"],"string":"[\n \"def score_tuple( hand ):\\n m = matches(hand)\\n #print( m )\\n #royal_flush -- a special case of straight flush.\\n if flush(hand) and straight(hand) and hand[4].rank == 14:\\n return (8, hand[4].rank, 0)\\n #straight_flush\\n elif flush(hand) and straight(hand):\\n return (8, hand[4].rank, 0)\\n #four_of_a_kind\\n elif len(m) == 2 and m[0].count == 4:\\n return (7, m[0].card.rank, 0)\\n #full_house\\n elif len(m) == 2 and m[0].count == 3 and m[1].count == 2:\\n return (6, m[0].card.rank, m[1].card.rank)\\n #flush\\n elif flush(hand):\\n return (5, hand[4].rank, 0)\\n #straight\\n elif straight(hand):\\n return (4, hand[4].rank, 0)\\n #three_of_a_kind\\n elif len(m) == 3 and m[0].count == 3:\\n return (3, m[0].card.rank, 0)\\n #two_pair\\n elif len(m) == 3 and m[0].count == 2 and m[1].count == 2:\\n return (2, m[0].card.rank, m[1].card.rank)\\n #one_pair\\n elif len(m) == 4 and m[0].count == 2 and m[1].count == 1:\\n return (1, m[0].card.rank, m[1].card.rank)\\n # Simple high card. Is this adequate? We'll know if we get ties.\\n else:\\n return (0, hand[4].rank, 0) # or (0, m[0].card.rank, 0)\",\n \"def set(self,row,col,value):\\r\\n self.puzzle[row][col] = value\\r\\n print(\\\"Entered value \\\",value)\\r\\n if self.puzzle[row][col] == self.rows[row][col]:\\r\\n self.score = self.score+5\\r\\n else:\\r\\n self.score = self.score-5\",\n \"def mark(board, player, row, col):\\r\\n pass\",\n \"def put(self, choice, token):\\n x, y = choice\\n self.grid[x][y] = token\",\n \"def make_move(board, picked_column, player):\\n row = find_first_free_cell(board, picked_column)\\n board[row][picked_column] = player\\n return board, row\",\n \"def mark(board, player, row, col):\\n pass\",\n \"def get_score(location, grid, shape):\",\n \"def place_marker(board, score, marker, position):\\n board[position] = marker\\n inc = 0\\n if marker == \\\"X\\\":\\n inc = 1\\n else:\\n inc = -1\\n if position == 7:\\n score[0] += inc\\n score[3] += inc\\n score[6] += inc\\n elif position == 8:\\n score[0] += inc\\n score[4] += inc\\n elif position == 9:\\n score[0] += inc\\n score[5] += inc\\n score[7] += inc\\n elif position == 4:\\n score[1] += inc\\n score[3] += inc\\n elif position == 5:\\n score[1] += inc\\n score[4] += inc\\n score[6] += inc\\n score[7] += inc\\n elif position == 6:\\n score[1] += inc\\n score[5] += inc\\n elif position == 1:\\n score[2] += inc\\n score[3] += inc\\n score[7] += inc\\n elif position == 2:\\n score[2] += inc\\n score[4] += inc\\n elif position == 3:\\n score[2] += inc\\n score[5] += inc\\n score[6] += inc\",\n \"def mm_move(board, player):\\r\\n if board.check_win() == provided.PLAYERX:\\r\\n return SCORES[provided.PLAYERX],(-1,-1)\\r\\n elif board.check_win() == provided.PLAYERO:\\r\\n return SCORES[provided.PLAYERO],(-1,-1)\\r\\n elif board.check_win() == provided.DRAW:\\r\\n return SCORES[provided.DRAW],(-1,-1)\\r\\n else:\\r\\n empty_tuple_list = board.get_empty_squares()\\r\\n score_pos_tuple_list = []\\r\\n best_score = None\\r\\n best_pos = None\\r\\n for idx1 in range(len(empty_tuple_list)):\\r\\n empty_tuple = empty_tuple_list[idx1]\\r\\n board_clone = board.clone()\\r\\n board_clone.move(empty_tuple[0],empty_tuple[1],player)\\r\\n score_pos_tuple = mm_move(board_clone,provided.switch_player(player))\\r\\n score_pos_tuple_list.append(score_pos_tuple)\\r\\n\\r\\n #decide best score and pos fast!!!\\r\\n if score_pos_tuple[0]*SCORES[player] == 1:\\r\\n return (score_pos_tuple[0],empty_tuple)\\r\\n\\r\\n #decide best score and pos\\r\\n for idx2 in range(len(score_pos_tuple_list)):\\r\\n if idx2 == 0:\\r\\n best_score = score_pos_tuple_list[idx2][0]\\r\\n best_pos = empty_tuple_list[idx2]\\r\\n else:\\r\\n if score_pos_tuple_list[idx2][0]*SCORES[player] > best_score*SCORES[player]:\\r\\n best_score = score_pos_tuple_list[idx2][0]\\r\\n best_pos = empty_tuple_list[idx2]\\r\\n\\r\\n return (best_score,best_pos)\",\n \"def guess(self, row, col) -> Tuple[int, Optional[ship.Ship]]:\\n my_ship: ship.Ship = self._board_matrix[row][col]\\n\\n # if my_ship is None the guess is a miss, otherwise its a hit\\n\\n # --------- BEGIN YOUR CODE ----------\\n\\n # This is exactly the same as Human.guess, just copy the code over\\n\\n # --------- END YOUR CODE ----------\",\n \"def setPiece(self, column, rank, piece):\\n try:\\n self.values[int(int(rank)-1)*8+self.getColIdx(column)] = piece\\n except:\\n print column, rank\\n rospy.loginfo(\\\"setPiece: invalid row/column\\\")\",\n \"def setPiece(self, column, rank, piece):\\n try:\\n self.values[int(int(rank)-1)*8+self.getColIdx(column)] = piece\\n except:\\n print column, rank\\n rospy.loginfo(\\\"setPiece: invalid row/column\\\")\",\n \"def loc_from_tuple(self, coords):\\n self.x, self.y = coords\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n self.grid[row][col] = value\",\n \"def change_position(board: Board, position: Position, character: str) -> Board:\\n board = list(board)\\n \\n row = board[position[0]]\\n new_row = row[:position[-1]] + character + row[position[-1] + 1:]\\n board[position[0]] = new_row\\n\\n board = tuple(board) \\n\\n return board\",\n \"def set_position(self, position):\\n self.position = tuple(position)\",\n \"def __init__(self, pos, score=0):\\n self.__pos = pos\\n self.__score = score\",\n \"def set_game_params(self, board):\\n self.board = np.copy(board)\\n self.board_min_len = np.min(len(board))\\n\\n tmp_player_pos = np.where(board == 1)\\n self.player_pos = (tmp_player_pos[0][0], tmp_player_pos[1][0])\\n tmp_rival_pos = np.where(board == 2)\\n self.rival_pos = (tmp_rival_pos[0][0], tmp_rival_pos[1][0])\",\n \"def position(self, value):\\n if type(value) is not tuple or len(value) != 2 or \\\\\\n type(value[0]) is not int or value[0] < 0 or \\\\\\n type(value[1]) is not int or value[1] < 0:\\n raise TypeError(\\\"position must be a tuple of 2 positive integers\\\")\\n else:\\n self.__position = value\",\n \"def swap_tile(grid: tuple[int, ...], move: int) -> tuple[int, ...]:\\n tile_to_swap: int = grid.index(0) + move\\n value_to_swap: int = grid[tile_to_swap]\\n\\n mutable_grid: list[int] = list(grid)\\n mutable_grid[grid.index(0)] = value_to_swap\\n mutable_grid[tile_to_swap] = 0\\n swapped_grid = tuple(mutable_grid)\\n\\n return swapped_grid\",\n \"def __init__(self):\\n self.played_pos = []\\n self.grid = [['-', '-', '-'],\\n ['-', '-', '-'],\\n ['-', '-', '-']]\\n self.player_played_pos = {'p1': set(), 'p2': set()}\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n self._grid[row][col] = value\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n self._grid[row][col] = value\",\n \"def getGameState(self):\\n ### Student code goes here\\n row1 = [-1, -1, -1]\\n row2 = [-1, -1, -1]\\n row3 = [-1, -1, -1]\\n for i in self.kb.kb_ask(parse_input(\\\"fact: (pos ?t ?px ?py\\\")):\\n if str(i.bindings_dict['?t'])=='empty':\\n t = -1\\n else:\\n t = int(i.bindings_dict['?t'][4])\\n xpx = int(i.bindings_dict['?px'][3])\\n xpy = int(i.bindings_dict['?py'][3])\\n if xpy == 1:\\n row1[xpx-1] = t\\n elif xpy == 2:\\n row2[xpx-1] = t\\n elif xpy == 3:\\n row3[xpx-1] = t\\n return tuple((tuple(row1),tuple(row2),tuple(row3)))\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n self._grid[row][col] = value;\",\n \"def position(self, value):\\n if type(value) is not tuple or len(value) != 2 \\\\\\n or type(value[0]) is not int or type(value[1]) is not int \\\\\\n or value[0] < 0 or value[1] < 0:\\n raise TypeError(\\\"position must be a tuple of 2 positive integers\\\")\\n else:\\n self.__position = value\",\n \"def position(square):\\n first = square[0]\\n second = square[1]\\n col = parseCol(first)\\n row = parseRow(second)\\n return (row, col)\",\n \"def score(self, hand, position):\\n\\n try:\\n assert self.grid[position][1] == \\\"---\\\"\\n except AssertionError:\\n print self\\n print position\\n raise FilledInError\\n except KeyError:\\n print \\\"\\\\nCheck your code. This is not a valid position:\\\", position, \\\"\\\\n\\\"\\n raise\\n\\n if position.startswith(\\\"n\\\"): # Return sum of relevant number\\n n = int(position[1])\\n return sum(d for d in hand.dice if d == n)\\n\\n elif position in [\\\"k3\\\", \\\"k4\\\", \\\"ch\\\"]: # Return total sum\\n if position == \\\"k3\\\" and hand.max_tally()[0] < 3:\\n return 0 # The is not a three of a kind\\n elif position == \\\"k4\\\" and hand.max_tally()[0] < 4:\\n return 0 # The is not a four of a kind\\n return sum(hand.dice)\\n\\n elif position in [\\\"fh\\\", \\\"ss\\\", \\\"ls\\\", \\\"yz\\\", \\\"yb\\\"]: # Return fixed score\\n if position == \\\"fh\\\":\\n tallies = hand.get_dicedict().values()\\n if 1 in tallies:\\n return 0 # This is not a full house\\n\\n elif position in [\\\"ss\\\", \\\"ls\\\"]:\\n ds = \\\"\\\".join(str(x) for x in hand.sort_by_value())\\n if position == [\\\"ss\\\"]:\\n if \\\"1234\\\" not in ds and \\\"2345\\\" not in ds and \\\"3456\\\" not in ds:\\n return 0\\n else:\\n if \\\"12345\\\" not in ds and \\\"23456\\\" not in ds:\\n return 0\\n\\n else:\\n if hand.max_tally()[0] < 5:\\n return 0 # This is not a yahtzee\\n if position == \\\"yb\\\" and self.grid[\\\"yz\\\"] == \\\"---\\\":\\n return 0 # YB only scores points if there already is a YZ\\n\\n return fixed_scores[position]\\n\\n else:\\n raise InvalidPositionError\",\n \"def __getitem__(self, item):\\n if type(item) != int:\\n raise TypeError('Grid index must be int, not {}'.format(type(item)))\\n if not 0 <= item < len(self):\\n raise IndexError('Grid index out of range')\\n point = []\\n for i in range(len(self.sizes)):\\n point.append(item % self.sizes[i])\\n item //= self.sizes[i]\\n return tuple(point)\",\n \"def _insert_piece(cls, board, coord, piece):\\n rank = board[coord[0]][:coord[1]] + (piece,) + board[coord[0]][coord[1]+1:]\\n return board[:coord[0]] + (rank,) + board[coord[0]+1:]\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n if col < self.grid_height and row < self.grid_width:\\n self.board[row][col] = value\",\n \"def place(self, board):\\r\\n self.board = board\",\n \"def compute_new_position_from_dice(self, player_index, thr):\\r\\n new_position = self.player_positions[player_index] + thr.get_amount()\\r\\n\\r\\n if new_position >= len(self.squares):\\r\\n new_position -= len(self.squares)\\r\\n\\r\\n return new_position\",\n \"def set_tile(self, row, col, value):\\r\\n # replace with your code\\r\\n self._grid_tile[row][col] = value\",\n \"def place_obj(self):\\r\\n for pos in BOARD_POSITIONS:\\r\\n self.board[pos[0]][pos[1]] = Stone(color=self.state[pos[0]][pos[1]], pos=(pos[0], pos[1]))\\r\\n self.board[pos[0]][pos[1]].liberty = self.board[pos[0]][pos[1]].compute_liberty(self.state)\",\n \"def __init__(self, layout, player):\\n self.layout = [x[:] for x in layout] #this state's layout is a copy\\n self.height = len(layout[0])\\n self.width = len(layout)\\n self.who_played = player\\n self.score = self._scoring() #score for this board\",\n \"def get_score(self):\\n return tuple(self.score)\",\n \"def set_item(self, row, col, value):\\n self.board[row][col] = value\",\n \"def update_grid_pos(self):\\n self.grid_pos = self.get_tile_of_position(self.tank.body.position)\",\n \"def move(self, row, col, player):\\n self.board[row][col] = player\",\n \"def update_scores(self):\\n self.score[0] = (-1)*sum(self.board[self.board == -1])\\n self.score[1] = sum(self.board[self.board == 1])\\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)\",\n \"def set_tile(self, row, col, value):\\r\\n self.grid[row][col] = value\",\n \"def move(self, row, col, player):\",\n \"def get_ai_move(board, player):\\r\\n row, col = 0, 0\\r\\n return row, col\",\n \"def position(self) -> Tuple[int, int]:\\n return self.row, self.col\",\n \"def turrel(pos, board):\\n row = pos[0]\\n col = pos[1]\\n for i in range(len(board)):\\n board[i][col] = 1\\n for j in range(len(board)):\\n board[row][j] = 1\",\n \"def make_position(data) -> Position:\\n return (data[\\\"x\\\"], data[\\\"y\\\"])\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n self._grid_2048[row][col] = value\",\n \"def getScore(self,board):\\n return board.getScore()[self.tile]\",\n \"def score_pos(pos = UPPER_LEFT):\\n return {\\n UPPER_LEFT : (0.5, 0.5),\\n UPPER_RIGHT: ((Globals.instance.WIDTH/Globals.instance.GRID_SIZE) - 0.5, 0.5),\\n LOWER_LEFT: (0.5, (Globals.instance.HEIGHT/Globals.instance.GRID_SIZE) - 1),\\n LOWER_RIGHT: ((Globals.instance.WIDTH/Globals.instance.GRID_SIZE) - 0.5, (Globals.instance.HEIGHT/Globals.instance.GRID_SIZE) - 1)\\n }.get(pos, UPPER_LEFT)\",\n \"def position(self, position):\\n if type(position) is not tuple or len(position) is not 2\\\\\\n or type(position[0]) is not int or position[0] < 0\\\\\\n or type(position[1]) is not int or position[1] < 0:\\n\\n raise TypeError(\\\"position must be a tuple of 2 positive integers\\\")\\n\\n self.__position = position\",\n \"def position(self, value):\\n if (not isinstance(value, tuple) or\\n len(value) != 2 or\\n not all(isinstance(num, int) for num in value) or\\n not all(num >= 0 for num in value)):\\n raise TypeError(\\\"position must be a tuple of 2 positive integers\\\")\\n else:\\n self.__position = value\",\n \"def set_piece(self, square, piece):\\n self.board[square.row][square.col] = piece\",\n \"def set_piece(self, square, piece):\\n self.board[square.row][square.col] = piece\",\n \"def set_tile(self, row, col, value):\\r\\n self._grid[row][col]=value\",\n \"def set_square(self, col, row, value):\\n row_index = row - 1\\n col_index = ord(col.lower()) - 97 # ord('a') is 97\\n self.state[row_index][col_index] = value\",\n \"def set_tile(self, row, col, value):\\n self.grid[row][col] = value\",\n \"def set_tile(self, row, col, value):\\n self.grid[row][col] = value\",\n \"def place_piece(piece, px, py, pc):\\n \\n\\n for i, j in piece:\\n x = px + i\\n y = py + j\\n if not (0 <= x < BOARD_WIDTH):\\n continue\\n if not (0 <= y < BOARD_HEIGHT):\\n continue\\n board[y][x] = pc\",\n \"def update_positions(self, grid):\\r\\n self.grid = grid\",\n \"def set_pos(self, p: tuple) -> None:\\n self.pos = p\",\n \"def __getitem__(self, pair):\\n return self._scores[pair]\",\n \"def put(self, column):\\n column -= 1\\n if self.occupied[column] >= 4 or self.won:\\n return 'ERROR'\\n self.history.append(column + 1)\\n row = self.occupied[column]\\n # assign 1 to player 1, and -1 to player 2\\n if len(self.history) % 2 == 1:\\n player = 1\\n self.board[3 - row][column] = 1\\n else:\\n player = -1\\n self.board[3 - row][column] = 2\\n # add player score to column, row and diagonal scores\\n self.columnScore[column] += player\\n self.rowScore[row] += player\\n self.occupied[column] += 1;\\n if column == row:\\n self.diagonal += player\\n if column + row == 3:\\n self.antidiagonal += player\\n # check column, row and diagonal scores\\n # if absolute value of one of them is 4\\n # which means the original value is either 4 or -4\\n # and one of the player has occupied all 4 of them\\n # which means the player has won in that row/column/diagonal\\n # and thus return \\\"WIN\\\"\\n if (abs(self.rowScore[row]) == 4 or abs(self.columnScore[column]) == 4\\n or abs(self.diagonal) == 4 or abs(self.antidiagonal) == 4):\\n self.won = True\\n return 'WIN'\\n # check if there is still non-full columns\\n # in other words check if the board is full\\n for i in range(0, self.size):\\n # if board is not full return \\\"OK\\\"\\n if self.occupied[i] < 4:\\n return 'OK'\\n # if the board is full, return \\\"DRAW\\\"\\n return 'DRAW'\",\n \"def put(brickheight,bricklength,row,column,walllength,wallheight,occupied,answer):\\n temp =[]\\n for x in range(column,column+bricklength):\\n for y in range(row,row+brickheight):\\n occupied.append((x,y))\\n temp.append(((y)*walllength+x))\\n else:\\n tempp = tuple(temp)\\n answer.append(tempp)\\n return occupied\",\n \"def __init__(self, board, index):\\n self.board = board\\n self.index = index\\n self._x, self._y = None, None\",\n \"def l_point(self):\\n self.l_score += 1\\n self.update_scoreboard()\",\n \"def set_game_params(self, board):\\n self.board = board.copy()\\n self.n_rows = len(self.board[0]) # cols number\\n self.n_cols = len(self.board) # rows number\\n self.fruits_ttl = min(self.n_rows,self.n_cols)+1\\n player_pos = np.where(board == 1)\\n rival_pos = np.where(board == 2)\\n self.locations[PLAYER] = tuple(ax[0] for ax in player_pos)\\n self.locations[RIVAL] = tuple(ax[0] for ax in rival_pos)\\n self.turns = 0\\n self.max_turns = reachables(self.board,self.locations[PLAYER])\\n self.player_turns = self.max_turns // 2\",\n \"def set_tile(self, row, col, value):\\r\\n self._board[row][col] = value\",\n \"def apply_move(board_state, move, side):\\n move_x, move_y = move\\n\\n def get_tuples():\\n for x in range(len(board_state)):\\n if move_x == x:\\n temp = list(board_state[x])\\n temp[move_y] = side\\n yield tuple(temp)\\n else:\\n yield board_state[x]\\n\\n return tuple(get_tuples())\",\n \"def get_ai_move(board, player):\\n row, col = 0, 0\\n return row, col\",\n \"def assign_points(players):\\n pass\",\n \"def compute_coordinates(self):\\n self._x, self._y = self.board.index_to_coordinates(self.index)\",\n \"def check_score(self) -> None:\\n self.player_1, self.player_2 = 0, 0\\n for cell in self.cells:\\n if cell.player == 1:\\n self.player_1 += 1\\n elif cell.player == 2:\\n self.player_2 += 1\",\n \"def new_move(self, grid_x, grid_y, player):\\n #duplication /!\\\\\\n if player == self.X:\\n self.draw_X(grid_x, grid_y)\\n self.board[grid_y][grid_x] = self.X\\n\\n elif player == self.O:\\n self.draw_O(grid_x, grid_y)\\n self.board[grid_y][grid_x] = self.O\",\n \"def set_tile(self, row, col, value):\\r\\n # replace with your code\\r\\n self._cells[row][col] = value\",\n \"def __key__(self) -> Tuple[int, int]:\\n return self.col, self.row\",\n \"def place_player(self, player, position):\\n self.board[position] = player\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n self._cells[row][col] = value\",\n \"def position_to_index(self, position, grid_size):\\n x, y = position\\n return x * grid_size + y\",\n \"def __getitem__(self, args):\\n x, y = args\\n xpos, ypos = self.move(x, y)\\n return self.board[xpos][ypos]\",\n \"def make_move(board, player_num, row, col):\\n board[row][col] = 'X' if player_num == 1 else 'O'\",\n \"def player_location(self):\\n x = 0\\n y = 0\\n for line in self.grid:\\n for i in line:\\n if i == \\\"P\\\":\\n return x, y\\n \\n y+=1\\n x += 1\\n y = 0\",\n \"def score_update(scoreboard, compare):\\r\\n if compare == 'Victory':\\r\\n scoreboard['W'] += 1\\r\\n elif compare == 'Defeat':\\r\\n scoreboard['L'] += 1\\r\\n elif compare == 'Tie':\\r\\n scoreboard['T'] += 1\",\n \"def move(self, row, col, player):\\n score = 1 if player == 1 else -1\\n self.rows[row] += score\\n self.cols[col] += score\\n if row == col:\\n self.diagonal1 += score\\n if row + col == self.n - 1:\\n self.diagonal2 += score\\n win = {self.rows[row], self.cols[col], self.diagonal1, self.diagonal2}\\n if self.n in win or -self.n in win:\\n return player\\n return 0\",\n \"def update(self, move):\\n\\n if not 0 <= move < 7:\\n raise InvalidMove\\n\\n placed = False\\n x = None\\n y = None\\n\\n for row in reversed(xrange(self._rows)):\\n if not self._board[row][move]:\\n self._board[row][move] = self.current_player\\n placed = True\\n x = move\\n y = row\\n break\\n\\n if not placed:\\n raise InvalidMove\\n\\n return (x, y)\",\n \"def populate_board(self):\\n for row in range(10):\\n for col in range(10):\\n coord = Coordinate(row, col)\\n coord_attack = Coordinate(row, col)\\n self.player_table.setItem(row, col, coord)\\n self.attack_table.setItem(row, col, coord_attack)\",\n \"def getGameState(self):\\n row1 = [0, 0, 0]\\n row2 = [0, 0, 0]\\n row3 = [0, 0, 0]\\n tilePosStatement = Statement()\\n posTerm1 = Term('?x')\\n posTerm2 = Term('?y')\\n posTerm3 = Term('?tile')\\n tilePosStatement.terms = (posTerm1, posTerm2, posTerm3)\\n tilePosStatement.predicate = 'tilePos'\\n for fact in self.kb.facts:\\n if match(fact.statement, tilePosStatement):\\n if fact.statement.terms[2] == Term(Constant('tile1')):\\n term = 1\\n if fact.statement.terms[2] == Term(Constant('tile2')):\\n term = 2\\n if fact.statement.terms[2] == Term(Constant('tile3')):\\n term = 3\\n if fact.statement.terms[2] == Term(Constant('tile4')):\\n term = 4\\n if fact.statement.terms[2] == Term(Constant('tile5')):\\n term = 5\\n if fact.statement.terms[2] == Term(Constant('tile6')):\\n term = 6\\n if fact.statement.terms[2] == Term(Constant('tile7')):\\n term = 7\\n if fact.statement.terms[2] == Term(Constant('tile8')):\\n term = 8\\n if fact.statement.terms[2] == Term(Constant('empty')):\\n term = -1\\n if fact.statement.terms[0] == Term(Constant('pos1')):\\n col = 0\\n elif fact.statement.terms[0] == Term(Constant('pos2')):\\n col = 1\\n elif fact.statement.terms[0] == Term(Constant('pos3')):\\n col = 2\\n if fact.statement.terms[1] == Term(Constant('pos1')):\\n row1[col] = term\\n\\n elif fact.statement.terms[1] == Term(Constant('pos2')):\\n row2[col] = term\\n\\n elif fact.statement.terms[1] == Term(Constant('pos3')):\\n row3[col] = term\\n\\n row1 = tuple(row1)\\n row2 = tuple(row2)\\n row3 = tuple(row3)\\n result = (row1, row2, row3)\\n return result\\n\\n ### Student code goes here\",\n \"def place_at(self, row, col, piece):\\n self.board[row + PADDING][col + PADDING] = piece\",\n \"def mc_update_scores(scores, board, player):\\n for row in range(board.get_dim()):\\n for col in range(board.get_dim()):\\n if board.check_win()==player:\\n if board.square(row,col)==player:\\n scores[row][col]+=SCORE_CURRENT\\n elif board.square(row,col)==switch_player(player):\\n scores[row][col]-=SCORE_OTHER\\n elif board.check_win()==switch_player(player):\\n if board.square(row,col)==player:\\n scores[row][col]-=SCORE_CURRENT\\n elif board.square(row,col)==switch_player(player):\\n scores[row][col]+=SCORE_OTHER\\n return\",\n \"def update_board_with_move(self, cell, mark):\\n row, col = cell\\n self.storage[row][col] = mark\",\n \"def get_score_pos(pos, board):\\n if piece_on_pos(pos, board):\\n return board[pos[0]][pos[1]].points\\n else:\\n return 0\",\n \"def makePiece(self, val, column, rank, name):\\n p = ChessPiece()\\n p.header.frame_id = name # used to be \\\"chess_board\\\" but we already know that\\n p.pose.position.x = SQUARE_SIZE * (0.5 + self.getColIdx(column))\\n p.pose.position.y = SQUARE_SIZE * (0.5 + rank - 1)\\n p.pose.position.z = 0.03\\n p.type = val\\n return p\",\n \"def _set_tuple_structure(self, key):\\n if len(key) == 2:\\n self.ks = list(np.array(key[1]))\\n self.set_neighs(key[0])\",\n \"def placeship(point1: tuple, point2: tuple, board: list):\\n # Is horizontal\\n if point1[0] == point2[0]:\\n for i in range(min(point1[1], point2[1]), max(point1[1], point2[1]) + 1):\\n board[point1[0]][i] = \\\"O\\\"\\n # Is vertical\\n elif point1[1] == point2[1]:\\n for i in range(min(point1[0], point2[0]), max(point1[0], point2[0]) + 1):\\n board[i][point1[1]] = \\\"O\\\"\\n return board\",\n \"def get_position(self) -> typing.Tuple[int, int]:\\n raise NotImplementedError\",\n \"def set_tile(self, row, col, value):\\n self._grid[row][col] = value\",\n \"def get(self):\\n return (self.x,self.y);\",\n \"def coord (i, j):\\r\\n return j, i\",\n \"def changeTile (self, posY, posX, tile=\\\"t\\\"):\\r\\n self.grid[posY][posX] = tile\",\n \"def scoreGame(self):\\n # create valueLs[card1,card2,...], pass it to sumHandReturnPoints(valueLs) or twoCardReturnPoints(valueLs)\\n scoreLs = []\\n ### Score of row\\n for rowKey in self.table:\\n valueLs = self.table[rowKey]\\n points = self.sumHandReturnPoints(valueLs)\\n scoreLs.append(points)\\n\\n ### Score of 4-card column\\n for offset in range(0,3): # 0,1,2\\n tmpLs = []\\n for rowKey in self.table:\\n valueLs = self.table[rowKey]\\n if len(valueLs) == 5:\\n iterStart = 1\\n else:\\n iterStart = 0\\n card = valueLs[iterStart+offset]\\n tmpLs.append(card)\\n points = self.sumHandReturnPoints(tmpLs)\\n scoreLs.append(points) \\n\\n ### Score of 2-card column\\n #(1) 1st column\\n valueLs1 = self.table['row1']\\n valueLs2 = self.table['row2']\\n tmpLs = []\\n tmpLs.append(valueLs1[0].get_rank())\\n tmpLs.append(valueLs2[0].get_rank())\\n points = self.twoCardReturnPoints(tmpLs)\\n scoreLs.append(points)\\n #(2) 5th column\\n valueLs3 = self.table['row1']\\n valueLs4 = self.table['row2']\\n tmpLs = []\\n tmpLs.append(valueLs3[-1].get_rank())\\n tmpLs.append(valueLs4[-1].get_rank())\\n points = self.twoCardReturnPoints(tmpLs)\\n scoreLs.append(points) \\n\\n ### Add up scoreLs\\n sumPoints = 0\\n for points in scoreLs:\\n sumPoints += points\\n return sumPoints\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6446688","0.60046214","0.5860571","0.5825233","0.57931954","0.5780548","0.57786965","0.5750296","0.57402545","0.57364994","0.5714486","0.5714486","0.57086986","0.5672831","0.5660482","0.5629688","0.5591937","0.5587683","0.55868065","0.55848455","0.5583773","0.5582667","0.5582667","0.55697054","0.5556777","0.55557","0.5551011","0.55397934","0.5536432","0.5532568","0.55306387","0.5522122","0.55142385","0.55085415","0.5506215","0.549045","0.548767","0.5481562","0.5479544","0.5475866","0.5469146","0.54669744","0.54605967","0.54566956","0.54548806","0.5436429","0.5433337","0.543161","0.5430682","0.54270554","0.5426301","0.54226345","0.54086626","0.54086626","0.5407771","0.5406419","0.53963774","0.53963774","0.5389649","0.5386631","0.53796786","0.5379231","0.53782046","0.5375505","0.5372994","0.53705484","0.5368257","0.5365924","0.5364758","0.53574646","0.53534794","0.53531533","0.53517944","0.5351659","0.5350299","0.5349879","0.5335359","0.5329615","0.53293043","0.53231627","0.5313319","0.5309624","0.5305263","0.5304799","0.53032875","0.53008485","0.5286482","0.52831495","0.52823716","0.52740973","0.5266391","0.5262887","0.52622265","0.526101","0.5258827","0.5256599","0.5255831","0.5255409","0.5253588","0.5245909"],"string":"[\n \"0.6446688\",\n \"0.60046214\",\n \"0.5860571\",\n \"0.5825233\",\n \"0.57931954\",\n \"0.5780548\",\n \"0.57786965\",\n \"0.5750296\",\n \"0.57402545\",\n \"0.57364994\",\n \"0.5714486\",\n \"0.5714486\",\n \"0.57086986\",\n \"0.5672831\",\n \"0.5660482\",\n \"0.5629688\",\n \"0.5591937\",\n \"0.5587683\",\n \"0.55868065\",\n \"0.55848455\",\n \"0.5583773\",\n \"0.5582667\",\n \"0.5582667\",\n \"0.55697054\",\n \"0.5556777\",\n \"0.55557\",\n \"0.5551011\",\n \"0.55397934\",\n \"0.5536432\",\n \"0.5532568\",\n \"0.55306387\",\n \"0.5522122\",\n \"0.55142385\",\n \"0.55085415\",\n \"0.5506215\",\n \"0.549045\",\n \"0.548767\",\n \"0.5481562\",\n \"0.5479544\",\n \"0.5475866\",\n \"0.5469146\",\n \"0.54669744\",\n \"0.54605967\",\n \"0.54566956\",\n \"0.54548806\",\n \"0.5436429\",\n \"0.5433337\",\n \"0.543161\",\n \"0.5430682\",\n \"0.54270554\",\n \"0.5426301\",\n \"0.54226345\",\n \"0.54086626\",\n \"0.54086626\",\n \"0.5407771\",\n \"0.5406419\",\n \"0.53963774\",\n \"0.53963774\",\n \"0.5389649\",\n \"0.5386631\",\n \"0.53796786\",\n \"0.5379231\",\n \"0.53782046\",\n \"0.5375505\",\n \"0.5372994\",\n \"0.53705484\",\n \"0.5368257\",\n \"0.5365924\",\n \"0.5364758\",\n \"0.53574646\",\n \"0.53534794\",\n \"0.53531533\",\n \"0.53517944\",\n \"0.5351659\",\n \"0.5350299\",\n \"0.5349879\",\n \"0.5335359\",\n \"0.5329615\",\n \"0.53293043\",\n \"0.53231627\",\n \"0.5313319\",\n \"0.5309624\",\n \"0.5305263\",\n \"0.5304799\",\n \"0.53032875\",\n \"0.53008485\",\n \"0.5286482\",\n \"0.52831495\",\n \"0.52823716\",\n \"0.52740973\",\n \"0.5266391\",\n \"0.5262887\",\n \"0.52622265\",\n \"0.526101\",\n \"0.5258827\",\n \"0.5256599\",\n \"0.5255831\",\n \"0.5255409\",\n \"0.5253588\",\n \"0.5245909\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7689742"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17490,"cells":{"query":{"kind":"string","value":"This function checks how many points you would get if the hand would be filled in at the given position."},"document":{"kind":"string","value":"def score(self, hand, position):\n\n try:\n assert self.grid[position][1] == \"---\"\n except AssertionError:\n print self\n print position\n raise FilledInError\n except KeyError:\n print \"\\nCheck your code. This is not a valid position:\", position, \"\\n\"\n raise\n\n if position.startswith(\"n\"): # Return sum of relevant number\n n = int(position[1])\n return sum(d for d in hand.dice if d == n)\n\n elif position in [\"k3\", \"k4\", \"ch\"]: # Return total sum\n if position == \"k3\" and hand.max_tally()[0] < 3:\n return 0 # The is not a three of a kind\n elif position == \"k4\" and hand.max_tally()[0] < 4:\n return 0 # The is not a four of a kind\n return sum(hand.dice)\n\n elif position in [\"fh\", \"ss\", \"ls\", \"yz\", \"yb\"]: # Return fixed score\n if position == \"fh\":\n tallies = hand.get_dicedict().values()\n if 1 in tallies:\n return 0 # This is not a full house\n\n elif position in [\"ss\", \"ls\"]:\n ds = \"\".join(str(x) for x in hand.sort_by_value())\n if position == [\"ss\"]:\n if \"1234\" not in ds and \"2345\" not in ds and \"3456\" not in ds:\n return 0\n else:\n if \"12345\" not in ds and \"23456\" not in ds:\n return 0\n\n else:\n if hand.max_tally()[0] < 5:\n return 0 # This is not a yahtzee\n if position == \"yb\" and self.grid[\"yz\"] == \"---\":\n return 0 # YB only scores points if there already is a YZ\n\n return fixed_scores[position]\n\n else:\n raise InvalidPositionError"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def check_masked (self, pos : list,) :\n count = 0\n total = 0\n for x in range(pos[0],min(pos[0] + AUTO_width1, self.m_x)) :\n for y in range(pos[1], min(pos[1] + AUTO_width1, self.m_y)) :\n total += 1\n if self.current_grid[x][y] :\n count += 1\n if count/total > 0.5 :\n return True\n else :\n return False","def filled(self, position):\n i, j = position\n if 0 <= i < self.height and 0 <= j < self.width:\n return self.squares[i][j]\n # all positions outside of the defined area of the maze return as True (filled)\n return 1","def full():\r\n\r\n count = 0\r\n for slot in board:\r\n if slot not in '012345678':\r\n count += 1\r\n return count == 9","def is_miss_deal(hand: list, mighty: Card) -> bool:\n point_card_count = 0\n for card in hand:\n if card.is_pointcard() and card != mighty:\n point_card_count += 1\n\n if point_card_count <= 1:\n return True\n else:\n return False","def checkNumNeighbors():","def game_tie(self):\n\n shape = self.board.shape\n if np.count_nonzero(self.board) == (shape[0] * shape[1]):\n # The board is full\n player = 0\n return True\n else:\n return False","def check_rows(self):\n\t\tfor i in range(len(self.board)):\n\t\t\tpts = 0\n\t\t\tfor j in range(len(self.board[i])):\n\t\t\t\tif self.board[i][j] == self.marker:\n\t\t\t\t\tpts+=1\n\t\t\tif pts == 3:\n\t\t\t\tprint('YOU WON')\n\t\t\t\treturn True","def valid(point):\n index = offset(point)\n if tiles[index] == 0:\n return False\n\n index = offset(point + 19)\n\n if tiles[index] == 0:\n return False\n\n return point.x % 20 == 0 or point.y % 20 == 0","def _board_is_full(self):\n return (self.get_counts()[0] + self.get_counts()[1] == self._num_rows * self._num_cols)","def test_check_point():\n board = Board(640, 640, 8)\n board.start_game()\n assert board.check_point(board.SPACE_SIZE/2, board.SPACE_SIZE/2) is not None\n assert board.check_point(0, 0) is None","def check_ship_fits(self, ship_length, row, column, orientation):\n if orientation == 'H':\n if column + ship_length > 10:\n if self.user == 'player':\n print('SHIP DOES NOT FIT, TRY AGAIN!\\n')\n return False\n else:\n return False\n else:\n return True\n else:\n if row + ship_length > 10:\n if self.user == 'player':\n print('SHIP DOES NOT FIT, TRY AGAIN!\\n')\n return False\n else:\n return False\n else:\n return True","def checkMove(guess, xPos, yPos):\n\n\t# Return 0 if x position or y position are not valid\n\tif(xPos not in range(0, 5) or yPos not in range(0, 5)):\n\t\treturn 0\n\n\t# Return 0 f the guessed position is not water\n\tif(guess[yPos][xPos] != \"~\"):\n\t\treturn 0\n\n\treturn 1","def __len__(self):\n return len(self.points)","def __len__(self):\n return len(self.points)","def available(self, position):\n if position is not None:\n x, y = position\n return self.grid[x][y] == 0","def is_occupied(self, p):\r\n return 0 <= p[0] < self.width and 0 <= p[1] < self.height and self.grid[p[1]][p[0]] == '#'","def _check_occupied(self, col, row):\n if self.board[row - 1][col - 1] == EMPTY:\n return False\n else:\n return True","def isOver(self):\n\t\tisFull = Piece.BLANK\n\t\tfor a,b,c in [[0, 1, 2], [3, 4, 5], [6, 7, 8], [0, 3, 6], [1, 4, 7], [2, 5, 8], [0, 4, 8], [2, 4, 6]]:\n\t\t\tif (self.board[a] is self.board[b] is self.board[c] and self.board[a] is not Piece.BLANK):\n\t\t\t\treturn self.board[a]\n\t\t\tif (self.board[a] is Piece.BLANK or self.board[b] is Piece.BLANK or self.board[c] is Piece.BLANK):\n\t\t\t\tisFull = False\n\t\treturn isFull","def hand_points(hand):\n points = [[]]\n branch = 1\n for card in hand:\n if not card[\"is_hidden\"]:\n if card[\"value\"].isnumeric():\n for possibility in range(branch):\n points[possibility].append(int(card[\"value\"]))\n elif card[\"value\"] == \"A\":\n for possibility in range(branch):\n # Ace is 1 or 11. Creating the two possibility\n points.append(points[possibility] + [11]) \n points[possibility].append(1)\n branch += 1\n else:\n # Left are the face value of 10\n for possibility in range(branch):\n points[possibility].append(10)\n\n score = list(zip([sum(branch) for branch in points], points))\n score.sort(key=lambda x: x[0], reverse=True)\n\n for total, points in score:\n if total == 21 and len(hand) == 2:\n return total, \"BlackJack!\"\n if total <= 21:\n if 1 in points and 11 in points:\n return total, None\n if 1 in points: \n return total, \"Soft\"\n if 11 in points:\n return total, \"Hard\"\n else:\n return total, None\n\n # If you get there, you have lost or you had empty hand \n # or all card in hand was hiddien\n if score:\n return score[-1][0], None\n else:\n return 0, None","def check_position_free(self, pos=None):\n if pos is None:\n pos = self.draw.position\n return self.board.board[pos] == 0","def _is_valid_count(self, count: int, gp: GriddedPerm) -> bool:\n return self._point_in_fuse_region(gp) + 1 == count","def __len__(self):\n return len(self._points)","def check_place(self, positions):\n return self.size == len(set(positions[i] + i for i in range(self.size))) == len(\n set(positions[i] - i for i in range(self.size)))","def obstacle_count(self):\n found_something = False\n count = 0\n starting_postion = self.get_heading()\n self.right(primary=60, counter=60)\n time.sleep(0.5)\n while self.get_heading() != starting_postion:\n if self.read_distance() < 250 and not found_something:\n found_something = True\n count += 1\n print (\"I found something\")\n elif self.read_distance() > 250 and found_something:\n found_something = False\n print(\"I have a clear view\")\n self.stop()\n\n print(\"I have found this many things: %d\" % count)\n return count","def is_full(self):\r\n return self.num_checkers == self.width * self.height","def test(brickheight,bricklength,row,column,walllength,wallheight,occupied,answer):\n if brickheight>wallheight or bricklength>walllength:\n return False\n elif over(brickheight,bricklength,row,column,walllength,wallheight):\n return False\n else:\n for x in range(column,column+bricklength):\n for y in range(row,row+brickheight):\n if (x,y) in occupied:\n return False \n break\n else:\n return True","def num_quadrature_points(self) -> int:","def isFree(point):\n global grid\n for i in point:\n if i < 0:\n return False\n try:\n value = grid[point[0]][point[1]][point[2]]\n # print value\n except:\n print \"point \", point, \"lies outside of grid\"\n value = False\n\n return value","def check_if_board_full(self, board):\n for i in range(self.height // 80):\n for j in range(self.width // 80):\n if board[(j, i)] == 0:\n return False\n elif j == self.width // 80:\n break\n else:\n pass\n print(\"Board full! :(\")\n return True","def verify_coords(self, piece_coords):\n if piece_coords[0] >= self.size or piece_coords[0] < 0:\n return False\n if piece_coords[1] >= self.size or piece_coords[1] < 0:\n return False\n return True","def nr_of_bees_at(self, point):\n \"\"\" Responsible for transformations \"\"\"\n pos, com, success = self.perception\n \n point = point.copy()\n \n if self.phase == 2:\n point -= self.position\n elif self.phase > 2:\n point = self.transform(point - self.position)\n\n return sum(map(lambda x: array_equal(point,x),pos))","def check_if_full(self):\n pass","def is_full(board):\r\n return False","def test_failed_draw_case():\n hands = draw_hands(4)[0]\n start_tile = pick_start_tile(hands)\n\n\n # testing that I'm handling weird start players right\n fail_draws = 0\n good_draws = 0\n for i in range(10000):\n hands = draw_hands(4)[0]\n start_tile = pick_start_tile(hands)[0]\n if start_tile is None:\n fail_draws += 1\n else:\n good_draws += 1\n\n return fail_draws, good_draws","def check_neighbors(self, position: typing.Tuple[int, int, int]):\n raise NotImplementedError","def check_won (grid):\r\n p=0\r\n for k in range(len(grid)):\r\n for g in range(len(grid[k])): \r\n if grid[k][g]>=32:\r\n p+=1\r\n else:\r\n ()\r\n if p>0:\r\n return True\r\n else:\r\n return False","def is_full(self):\n return len(self.walls) == 4","def _check_for_win(self, piece, piece_x, piece_y):\n for slope_x, slope_y in ((1,0), (0,1), (1,1), (1,-1)):\n for start_x, start_y in ((piece_x - slope_x*i, piece_y - slope_y*i) for i in range(self.consecutive_pieces_to_win)):\n winning_piece_positions = []\n for x, y in ((start_x + slope_x*i, start_y + slope_y*i)\n for i in range(self.consecutive_pieces_to_win)):\n winning_piece_positions.append((x, y))\n # Don't need to check the piece that is being placed\n if (x, y) == (piece_x, piece_y):\n continue\n current_piece = self._get_piece_at_opening_or_none(x, y)\n if current_piece != piece:\n winning_piece_positions = None\n break\n if winning_piece_positions:\n # Check if there are additional winning pieces that exceed the number required to win.\n # e.g. a 5-in-a-row when only 4 are required\n if slope_x != 0:\n num_previous_pieces_to_check = self.consecutive_pieces_to_win + (start_x - piece_x)\n for x, y in ((start_x - slope_x*i, start_y - slope_y*i)\n for i in range(1, 1 + num_previous_pieces_to_check)):\n current_piece = self._get_piece_at_opening_or_none(x, y)\n if current_piece == piece:\n winning_piece_positions.insert(0, (x, y))\n else:\n break\n return winning_piece_positions\n\n return False","def goalReached(self, rewards):\n return len(rewards) >= 100 and np.mean(rewards[-100:]) >= 18","def occupied(self, (xIndex, yIndex)):\n return xIndex < 0 or yIndex < 0 or \\\n xIndex >= self.xN or yIndex >= self.yN or \\\n self.grid[xIndex][yIndex]","def ok(self, point):\n [x1, x2, x3, x4, x5, x6] = point.decisions\n if x1 + x2 -2 < 0:\n return False\n if 6 - x1 - x2 < 0:\n return False\n if 2 - x2 + x1 < 0:\n return False\n if 2 - x1 + 3*x2 < 0:\n return False\n if 4 - (x3 - 3)**2 - x4 < 0:\n return False\n if (x5 - 3)**3 + x6 - 4 < 0:\n return False\n for i, d in enumerate(point.decisions):\n if d < self.decisions[i].low or d > self.decisions[i].high:\n print i, d, self.decisions[i].low, self.decisions[i].high\n return False\n return True","def is_full(self):\n return len(self.__occupied_slots__) >= self.__size__","def is_full(board):\n return False","def is_full_house(hand):\n count = {c:0 for c in cards.keys()}\n for card in hand:\n count[card[0]] += 1\n for c in count:\n if count[c] != 0 and count[c] != 2 and count[c] != 3:\n return None\n triple = 0\n for k in count:\n if count[k] == 3:\n triple = cards[k]\n return (True, triple)","def is_valid(field):\n one = 0\n two = 0\n three = 0\n four = 0\n for i in range(1, 11):\n for j in range(1, 11):\n if ship_size(field, (i, j)) == 1:\n one += 1\n if ship_size(field, (i, j)) == 2:\n two += 1\n if ship_size(field, (i, j)) == 3:\n three += 1\n if ship_size(field, (i, j)) == 4:\n four += 1\n if ship_size(field, (i, j)) > 4:\n return False\n if one == 4 and two == 6 and three == 6 and four == 4:\n return True\n print(one, two, three, four)\n\n return False","def check_points_and_level_up(self):\n if self.points > 20 * self.level:\n self.level += 1\n self.refresh_rate = self.refresh_rate * 0.75","def is_fullp(self):\n front = (self.front + 1) % self.length\n return front == self.rear","def can_reach(self, position: tuple) -> bool:\n x = position[0]\n y = position[1]\n dist_to_shoulder = math.sqrt(x**2 + y**2)\n max_dist = self.bicep_length + self.forearm_length\n if dist_to_shoulder > max_dist:\n return False\n return True","def __len__(self):\n return self.num_points","def is_solved(self):\n\n marker = self._marker\n amount_of_pegs = 0\n for row in marker:\n for i in row:\n if i == \"*\":\n amount_of_pegs += 1\n return amount_of_pegs == 1","def calculate_points(hand): \r\n hand_value = 0\r\n ace_count = 0 \r\n \r\n #Finds value of non-Ace cards, and counts number of Aces.\r\n for card in hand:\r\n if card[0] == 'Ace':\r\n ace_count += 1\r\n else:\r\n # Calls card_value function to evaluate the card.\r\n hand_value += card_value(card) \r\n \r\n #Ace card present\r\n if ace_count > 0:\r\n return ace_hand_value(ace_count, hand_value)\r\n \r\n #Implied \"if ace_count == 0:\"\r\n return hand_value","def is_full(self):\n\n current_board = self.current_board\n remaining_rows = 0\n\n for row in current_board:\n if \"-\" in set(row):\n remaining_rows += 1\n\n if remaining_rows == 0:\n return True\n else:\n return False","def checkPointInLampsReach(self, p):\n v1 = XYPoint(self.Lime.x - self.Red.x, self.Lime.y - self.Red.y)\n v2 = XYPoint(self.Blue.x - self.Red.x, self.Blue.y - self.Red.y)\n\n q = XYPoint(p.x - self.Red.x, p.y - self.Red.y)\n s = self.crossProduct(q, v2) / self.crossProduct(v1, v2)\n t = self.crossProduct(v1, q) / self.crossProduct(v1, v2)\n\n return (s >= 0.0) and (t >= 0.0) and (s + t <= 1.0)","def can_draw(self,point):\n if point <= 0:\n return False\n else:\n return True","def check_miss_count(self):\n first = self.attk_arry[-1]\n second = self.attk_arry[-2]\n third = self.attk_arry[-3]\n fourth = self.attk_arry[-4]\n sum_of_attk = first + second + third + fourth\n if sum_of_attk == 8:\n self.column_arry.append(10)\n self.row_arry.append(10)\n else:\n pass","def check_offset(self):\n\n for d in range(self.n_dmps):\n if abs(self.y0[d] - self.goal[d]) < 1e-4:\n self.goal[d] += 1e-4","def check_reached(self):\n m_x, m_y = self.destination.get_pos()\n m_radius = self.destination.radius\n distance_centre = math.sqrt((m_x - self.x)**2 + (m_y - self.y)**2)\n sum_radii = m_radius + self.radius\n if distance_centre < sum_radii:\n self.color = pygame.colordict.THECOLORS['green']\n self.has_reached = True","def is_occupied(n):\n global _tracks\n if n >= len(_tracks):\n return False\n else:\n return _tracks[n]","def check_lengths(self, length: Expr) -> bool:\n for point1 in self.points:\n for point2 in self.points - {point1}:\n if abs(point2 - point1) == length:\n print(f'Length {length} found between points: {point1} and {point2}')\n return True\n return False","def on_board(self, pos):\n i, j = pos\n return 0 <= i < COLS and 0 <= j < ROWS","def isFull(self) -> bool:\n return (self.rear + 1) % self.capacity == self.front","def is_full(self):\n return self.remaining_space_in_hold() == 0","def answer_ok(a):\n (rightly_positioned, permutated) = a\n if (rightly_positioned + permutated > number_of_positions) \\\n or (rightly_positioned + permutated < len(colours) - number_of_positions):\n return False\n if rightly_positioned == 3 and permutated == 1:\n return False\n return True","def check(self):\n winner = None\n count = 0\n\n for y in range(self.gridSize):\n if winner != None:\n return winner\n P1, P2 = 0, 0\n for item in self.grid[y]:\n # Check row of the grid\n if item == \"P1\":\n P1 += 1\n elif item == \"P2\":\n P2 += 1\n winner = self.checkval(P1, P2, self.gridSize)\n if winner != None:\n return winner\n P1, P2 = 0, 0\n for x in range(self.gridSize):\n # Check column of the grid\n if self.grid[x][y] == \"P1\":\n P1 += 1\n elif self.grid[x][y] == \"P2\":\n P2 += 1\n winner = self.checkval(P1, P2, self.gridSize)\n if winner != None:\n return winner\n P1, P2 = 0, 0\n for y in range(self.gridSize):\n # Check right top to left bottom across the grid\n for x in range(self.gridSize):\n if x == y:\n if self.grid[x][y] == \"P1\":\n P1 += 1\n elif self.grid[x][y] == \"P2\":\n P2 += 1\n winner = self.checkval(P1, P2, self.gridSize)\n if winner != None:\n return winner\n P1, P2 = 0, 0\n for y in range(self.gridSize):\n # Check the left top to the right bottom across the grid\n for x in range(self.gridSize - 1, -1, -1):\n # Check how many filled spaces there are\n if \".\" not in self.grid[y][x]:\n count += 1\n if x + y == self.gridSize - 1:\n if self.grid[y][x] == \"P1\":\n P1 += 1\n elif self.grid[y][x] == \"P2\":\n P2 += 1\n winner = self.checkval(P1, P2, self.gridSize)\n # Check if there is a winner if so return the winner\n if winner != None:\n return winner\n # Check if the fields that are filled are equal to the possible spaces to be filled in the grid\n if count == self.gridSize**2:\n return \"Tie\"","def validatePosition(boardsize, pos):\n return pos.x in range(0, boardsize) and pos.y in range(0,boardsize)","def valid(self, pos):\n\t\tpos = Point(pos)\n\t\treturn 0 <= pos.x < self.dims.width and 0 <= pos.y < self.dims.height","def checkRow(self, x):\n used = []\n for y in range(len(self.board[0])):\n cur = self.board[x][y]\n if cur not in used:\n if cur !=0:\n used += [cur]\n else:\n return False\n return True","def inSmallBlindPosition(self):\n return len(self.in_game) > 0 and ((self.dealer + 1) % len(self.in_game)) == self.position","def check_bounds (position, size):\n \n for item in position:\n # checks whether item is out of bounds\n if item < 0 or item >= size:\n return False\n return True","def is_board_full(self):\n for position in self.positions:\n if self.is_position_availible(position):\n return False\n return True","def is_straight(hand):\n\ti = 0\n\twhile i < 8:\n\t\tif hand[i] == 1 and hand[i+1] == 1 and hand[i+2] == 1 and hand[i+3] == 1 and hand[i+4] == 1:\n\t\t\treturn True, i + 4\n\t\ti += 1\n\treturn False","def xy_occupied(xy, board):\n return True if board[xy[0]][xy[1]] else False","def won(self):\n for y in range(self.size):\n winning = []\n for x in range(self.size):\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n for x in range(self.size):\n winning = []\n for y in range(self.size):\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n winning = []\n for y in range(self.size):\n x = y\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n winning = []\n for y in range(self.size):\n x = self.size-1-y\n if self.fields[x, y] == self.opponent:\n winning.append((x, y))\n if len(winning) == self.size:\n return winning\n return None","def check_position(self, position):\n x_axis, y_axis = position\n try:\n return bool(self.full_map[x_axis][y_axis] not in \"#\"\n and 0 <= x_axis <= self.x_axis\n and 0 <= y_axis <= self.y_axis)\n\n except IndexError:\n return False","def check_won(grid):\r\n for i in range(len(grid)):\r\n for j in range(len(grid[i])):\r\n if grid[i][j] >= 32:\r\n return True \r\n return False","def hasBlackjack(self):\n return len(self.cards) == 2 and self.getPoints() == 21","def _position_is_empty_in_board(position, board):\n return board[position[0]][position[1]] == \"-\"","def check_hand(self, player):\n\n total = player.score()\n if total > 21:\n status = 'bust'\n elif total == 21:\n status = 'win'\n else:\n status = 'okay'\n\n if self.verbose:\n print(total, 'points')\n \n return status","def is_spot_possible(left, right, bottom, top):\n return True\n if right < 6 or bottom < 6:\n # print(\"IMPOSSIBLE\", left, right, top, bottom)\n return False\n if left > 18 or top > 18:\n # print(\"IMPOSSIBLE\", left, right, top, bottom)\n return False\n if abs(top - bottom) > 16 or abs(right - left) > 16:\n # print(\"IMPOSSIBLE\", left, right, top, bottom)\n return False\n return True","def check_if_user_won(self, board, pos, player_no):\n\n has_player_got_4 = set()\n has_player_got_4.add(pos)\n\n self.check_horizontal(has_player_got_4, board, pos, player_no)\n\n if len(has_player_got_4) >= 4:\n return True\n\n has_player_got_4 = set()\n has_player_got_4.add(pos)\n\n self.check_vertical(has_player_got_4, board, pos, player_no)\n\n if len(has_player_got_4) >= 4:\n return True\n\n has_player_got_4 = set()\n has_player_got_4.add(pos)\n\n self.check_diagonal(has_player_got_4, board, pos, player_no)\n\n if len(has_player_got_4) >= 4:\n return True\n\n has_player_got_4 = set()\n has_player_got_4.add(pos)\n\n self.check_inverted_diagonal(has_player_got_4, board, pos, player_no)\n\n if len(has_player_got_4) >= 4:\n return True\n\n if self.check_if_board_full(board):\n self.draw = True\n return True","def is_full(self):\n return self.top == self.size - 1","def check_pile(self, iteration):\n\n for r in range(0, self.number_radial, 1):\n for theta in range(0, self.number_angles, 1):\n\n if self.array[r][theta] < self.max_peak:\n self.array[r][theta] = self.array[r][theta]\n\n else:\n if self.force:\n self.topple_sumulate_centrifugal_force(r, theta, iteration)\n else:\n self.topple(r, theta, iteration)","def is_full(self):\n for i in range(self.width):\n if self.can_add_to(i) == True:\n return False\n return True","def valid_position(self, new_coords: tuple) -> bool:\n x, y = new_coords\n min_allowed_value = self.offset\n max_allowed_value = 10 - self.offset\n\n # If the value is outside of the board on the left or up, return false\n if x < min_allowed_value or y < min_allowed_value:\n return False\n # If the value is outside of the board on the right or down sides, return false\n if x > max_allowed_value or y > max_allowed_value:\n return False\n\n # If the position is taken by any piece, return false\n if self.board[y][x] != 0:\n print(\"Error: Position taken by %d\" % self.board[y][x])\n return False\n return True","def is_valid(field):\r\n taken_coordinates = []\r\n count_ships = [0]*4\r\n # counting ships\r\n try:\r\n for row in range(10):\r\n for cell in range(10):\r\n if (row, cell) not in taken_coordinates and\\\r\n has_ship((row, cell), field):\r\n taken_coordinates.extend(ship_coordinates((row, cell), field))\r\n count_ships[ship_size((row, cell), field) - 1] += 1\r\n except IndexError:\r\n return False\r\n # check if the amount of ship is correct and if they are not crossing\r\n if count_ships == [i for i in range(4, 0, -1)] and\\\r\n len(taken_coordinates) == len(set(taken_coordinates)):\r\n return True\r\n return False","def checkPoint(self):\n self.__stack.append((self.__current_pos, self.line, self.linePos))","def check_win(self):\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)","def on_board(pos, size):\n\n row, col = pos\n height, width = size\n\n return 0 <= row <= width and 0 <= col <= height","def check_none_zero_values(self):\n hits = -1\n if (len(self.data.shape) == 3):\n hits = 0\n frames = self.data.shape[0]\n pixels = self.data.shape[1]\n bins = self.data.shape[2]\n for i in range(frames):\n for j in range(pixels):\n for k in range(bins):\n value = self.data[i][j][k]\n if value > 0:\n hits += 1\n print(\"self.data[{}][{}][{}]; Sum so far = {}\".format(i, j, k, hits))\n return hits","def check_if_enough_skill_points(self):\r\n for skill_string in self.__skills:\r\n if (self.__skills[skill_string].points_to_up >\r\n self.__skill_points):\r\n self.skill_up_disable(skill_string)","def get_color_count(self, color, filled):\n count = 0\n for cell in filled:\n coord_x = cell[1]\n coord_y = cell[0]\n\n # up\n if coord_y - 1 >= 0:\n new_cell = (coord_y-1, coord_x)\n cell_up_color = self.get_color(new_cell)\n if cell_up_color == color and new_cell not in filled:\n count += 1\n filled.append(new_cell)\n\n # down\n if coord_y + 1 < self.height:\n new_cell = (coord_y+1, coord_x)\n cell_down_color = self.get_color(new_cell)\n if cell_down_color == color and new_cell not in filled:\n count += 1\n filled.append(new_cell)\n\n # left\n if coord_x - 1 >= 0:\n new_cell = (coord_y, coord_x-1)\n cell_left_color = self.get_color(new_cell)\n if cell_left_color == color and new_cell not in filled:\n count += 1\n filled.append(new_cell)\n\n # right\n if coord_x + 1 < self.width:\n new_cell = (coord_y, coord_x+1)\n cell_right_color = self.get_color(new_cell)\n if cell_right_color == color and new_cell not in filled:\n count += 1\n filled.append(new_cell)\n\n return count","def nr_points(self):\n return len(self.x)","def is_full(self) -> bool:","def check_won (grid):\r\n for i in range(4): \r\n for j in range(4):\r\n if grid[i][j] >= 32:\r\n return True\r\n return False","def test_is_filled(self):\n n = 10\n matrix = g.np.random.uniform(size=(n + 1,) * 3) > 0.5\n not_matrix = g.np.logical_not(matrix)\n pitch = 1. / n\n origin = g.np.random.uniform(size=(3,))\n vox = g.trimesh.voxel.VoxelGrid(matrix)\n vox = vox.apply_scale(pitch).apply_translation(origin)\n not_vox = g.trimesh.voxel.VoxelGrid(not_matrix)\n not_vox = not_vox.apply_scale(pitch).apply_translation(origin)\n for a, b in ((vox, not_vox), (not_vox, vox)):\n points = a.points\n # slight jitter - shouldn't change indices\n points += (\n g.np.random.uniform(size=points.shape) - 1) * 0.4 * pitch\n g.np.random.shuffle(points)\n\n # all points are filled, and no empty points are filled\n assert g.np.all(a.is_filled(points))\n assert not g.np.any(b.is_filled(points))\n\n # test different number of dimensions\n points = g.np.stack([points, points[-1::-1]], axis=1)\n assert g.np.all(a.is_filled(points))\n assert not g.np.any(b.is_filled(points))","def check_won (grid):\r\n w=False\r\n for row in range(4):\r\n for col in range(4):\r\n if grid[row][col]>=32:\r\n w=True\r\n break\r\n return w","def __len__(self):\n\t\treturn len(self._coords)","def check_won (grid):\r\n for i in range (4):\r\n for j in range (4):\r\n if grid[i][j] >= 32:\r\n return True\r\n return False","def draw_by_insufficient(self):\n \n if self.cap_counter > 100:\n self.draw_loop(\"draw due to none in 50\")\n \n if self.board.count(self.empty) == 62:\n self.draw_loop(\"draw due to insufficient\")\n\n \n if self.board.count(self.empty) == 61:\n for i in self.board:\n if i != self.empty:\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Bishop] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Bishop]:\n self.draw_loop(\"draw due to insufficient\")\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Knight] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Knight]:\n self.draw_loop(\"draw due to insufficient\")","def is_board_full(board):\n for i in range(1, 10):\n if check_empty_space(board, i):\n return False\n return True"],"string":"[\n \"def check_masked (self, pos : list,) :\\n count = 0\\n total = 0\\n for x in range(pos[0],min(pos[0] + AUTO_width1, self.m_x)) :\\n for y in range(pos[1], min(pos[1] + AUTO_width1, self.m_y)) :\\n total += 1\\n if self.current_grid[x][y] :\\n count += 1\\n if count/total > 0.5 :\\n return True\\n else :\\n return False\",\n \"def filled(self, position):\\n i, j = position\\n if 0 <= i < self.height and 0 <= j < self.width:\\n return self.squares[i][j]\\n # all positions outside of the defined area of the maze return as True (filled)\\n return 1\",\n \"def full():\\r\\n\\r\\n count = 0\\r\\n for slot in board:\\r\\n if slot not in '012345678':\\r\\n count += 1\\r\\n return count == 9\",\n \"def is_miss_deal(hand: list, mighty: Card) -> bool:\\n point_card_count = 0\\n for card in hand:\\n if card.is_pointcard() and card != mighty:\\n point_card_count += 1\\n\\n if point_card_count <= 1:\\n return True\\n else:\\n return False\",\n \"def checkNumNeighbors():\",\n \"def game_tie(self):\\n\\n shape = self.board.shape\\n if np.count_nonzero(self.board) == (shape[0] * shape[1]):\\n # The board is full\\n player = 0\\n return True\\n else:\\n return False\",\n \"def check_rows(self):\\n\\t\\tfor i in range(len(self.board)):\\n\\t\\t\\tpts = 0\\n\\t\\t\\tfor j in range(len(self.board[i])):\\n\\t\\t\\t\\tif self.board[i][j] == self.marker:\\n\\t\\t\\t\\t\\tpts+=1\\n\\t\\t\\tif pts == 3:\\n\\t\\t\\t\\tprint('YOU WON')\\n\\t\\t\\t\\treturn True\",\n \"def valid(point):\\n index = offset(point)\\n if tiles[index] == 0:\\n return False\\n\\n index = offset(point + 19)\\n\\n if tiles[index] == 0:\\n return False\\n\\n return point.x % 20 == 0 or point.y % 20 == 0\",\n \"def _board_is_full(self):\\n return (self.get_counts()[0] + self.get_counts()[1] == self._num_rows * self._num_cols)\",\n \"def test_check_point():\\n board = Board(640, 640, 8)\\n board.start_game()\\n assert board.check_point(board.SPACE_SIZE/2, board.SPACE_SIZE/2) is not None\\n assert board.check_point(0, 0) is None\",\n \"def check_ship_fits(self, ship_length, row, column, orientation):\\n if orientation == 'H':\\n if column + ship_length > 10:\\n if self.user == 'player':\\n print('SHIP DOES NOT FIT, TRY AGAIN!\\\\n')\\n return False\\n else:\\n return False\\n else:\\n return True\\n else:\\n if row + ship_length > 10:\\n if self.user == 'player':\\n print('SHIP DOES NOT FIT, TRY AGAIN!\\\\n')\\n return False\\n else:\\n return False\\n else:\\n return True\",\n \"def checkMove(guess, xPos, yPos):\\n\\n\\t# Return 0 if x position or y position are not valid\\n\\tif(xPos not in range(0, 5) or yPos not in range(0, 5)):\\n\\t\\treturn 0\\n\\n\\t# Return 0 f the guessed position is not water\\n\\tif(guess[yPos][xPos] != \\\"~\\\"):\\n\\t\\treturn 0\\n\\n\\treturn 1\",\n \"def __len__(self):\\n return len(self.points)\",\n \"def __len__(self):\\n return len(self.points)\",\n \"def available(self, position):\\n if position is not None:\\n x, y = position\\n return self.grid[x][y] == 0\",\n \"def is_occupied(self, p):\\r\\n return 0 <= p[0] < self.width and 0 <= p[1] < self.height and self.grid[p[1]][p[0]] == '#'\",\n \"def _check_occupied(self, col, row):\\n if self.board[row - 1][col - 1] == EMPTY:\\n return False\\n else:\\n return True\",\n \"def isOver(self):\\n\\t\\tisFull = Piece.BLANK\\n\\t\\tfor a,b,c in [[0, 1, 2], [3, 4, 5], [6, 7, 8], [0, 3, 6], [1, 4, 7], [2, 5, 8], [0, 4, 8], [2, 4, 6]]:\\n\\t\\t\\tif (self.board[a] is self.board[b] is self.board[c] and self.board[a] is not Piece.BLANK):\\n\\t\\t\\t\\treturn self.board[a]\\n\\t\\t\\tif (self.board[a] is Piece.BLANK or self.board[b] is Piece.BLANK or self.board[c] is Piece.BLANK):\\n\\t\\t\\t\\tisFull = False\\n\\t\\treturn isFull\",\n \"def hand_points(hand):\\n points = [[]]\\n branch = 1\\n for card in hand:\\n if not card[\\\"is_hidden\\\"]:\\n if card[\\\"value\\\"].isnumeric():\\n for possibility in range(branch):\\n points[possibility].append(int(card[\\\"value\\\"]))\\n elif card[\\\"value\\\"] == \\\"A\\\":\\n for possibility in range(branch):\\n # Ace is 1 or 11. Creating the two possibility\\n points.append(points[possibility] + [11]) \\n points[possibility].append(1)\\n branch += 1\\n else:\\n # Left are the face value of 10\\n for possibility in range(branch):\\n points[possibility].append(10)\\n\\n score = list(zip([sum(branch) for branch in points], points))\\n score.sort(key=lambda x: x[0], reverse=True)\\n\\n for total, points in score:\\n if total == 21 and len(hand) == 2:\\n return total, \\\"BlackJack!\\\"\\n if total <= 21:\\n if 1 in points and 11 in points:\\n return total, None\\n if 1 in points: \\n return total, \\\"Soft\\\"\\n if 11 in points:\\n return total, \\\"Hard\\\"\\n else:\\n return total, None\\n\\n # If you get there, you have lost or you had empty hand \\n # or all card in hand was hiddien\\n if score:\\n return score[-1][0], None\\n else:\\n return 0, None\",\n \"def check_position_free(self, pos=None):\\n if pos is None:\\n pos = self.draw.position\\n return self.board.board[pos] == 0\",\n \"def _is_valid_count(self, count: int, gp: GriddedPerm) -> bool:\\n return self._point_in_fuse_region(gp) + 1 == count\",\n \"def __len__(self):\\n return len(self._points)\",\n \"def check_place(self, positions):\\n return self.size == len(set(positions[i] + i for i in range(self.size))) == len(\\n set(positions[i] - i for i in range(self.size)))\",\n \"def obstacle_count(self):\\n found_something = False\\n count = 0\\n starting_postion = self.get_heading()\\n self.right(primary=60, counter=60)\\n time.sleep(0.5)\\n while self.get_heading() != starting_postion:\\n if self.read_distance() < 250 and not found_something:\\n found_something = True\\n count += 1\\n print (\\\"I found something\\\")\\n elif self.read_distance() > 250 and found_something:\\n found_something = False\\n print(\\\"I have a clear view\\\")\\n self.stop()\\n\\n print(\\\"I have found this many things: %d\\\" % count)\\n return count\",\n \"def is_full(self):\\r\\n return self.num_checkers == self.width * self.height\",\n \"def test(brickheight,bricklength,row,column,walllength,wallheight,occupied,answer):\\n if brickheight>wallheight or bricklength>walllength:\\n return False\\n elif over(brickheight,bricklength,row,column,walllength,wallheight):\\n return False\\n else:\\n for x in range(column,column+bricklength):\\n for y in range(row,row+brickheight):\\n if (x,y) in occupied:\\n return False \\n break\\n else:\\n return True\",\n \"def num_quadrature_points(self) -> int:\",\n \"def isFree(point):\\n global grid\\n for i in point:\\n if i < 0:\\n return False\\n try:\\n value = grid[point[0]][point[1]][point[2]]\\n # print value\\n except:\\n print \\\"point \\\", point, \\\"lies outside of grid\\\"\\n value = False\\n\\n return value\",\n \"def check_if_board_full(self, board):\\n for i in range(self.height // 80):\\n for j in range(self.width // 80):\\n if board[(j, i)] == 0:\\n return False\\n elif j == self.width // 80:\\n break\\n else:\\n pass\\n print(\\\"Board full! :(\\\")\\n return True\",\n \"def verify_coords(self, piece_coords):\\n if piece_coords[0] >= self.size or piece_coords[0] < 0:\\n return False\\n if piece_coords[1] >= self.size or piece_coords[1] < 0:\\n return False\\n return True\",\n \"def nr_of_bees_at(self, point):\\n \\\"\\\"\\\" Responsible for transformations \\\"\\\"\\\"\\n pos, com, success = self.perception\\n \\n point = point.copy()\\n \\n if self.phase == 2:\\n point -= self.position\\n elif self.phase > 2:\\n point = self.transform(point - self.position)\\n\\n return sum(map(lambda x: array_equal(point,x),pos))\",\n \"def check_if_full(self):\\n pass\",\n \"def is_full(board):\\r\\n return False\",\n \"def test_failed_draw_case():\\n hands = draw_hands(4)[0]\\n start_tile = pick_start_tile(hands)\\n\\n\\n # testing that I'm handling weird start players right\\n fail_draws = 0\\n good_draws = 0\\n for i in range(10000):\\n hands = draw_hands(4)[0]\\n start_tile = pick_start_tile(hands)[0]\\n if start_tile is None:\\n fail_draws += 1\\n else:\\n good_draws += 1\\n\\n return fail_draws, good_draws\",\n \"def check_neighbors(self, position: typing.Tuple[int, int, int]):\\n raise NotImplementedError\",\n \"def check_won (grid):\\r\\n p=0\\r\\n for k in range(len(grid)):\\r\\n for g in range(len(grid[k])): \\r\\n if grid[k][g]>=32:\\r\\n p+=1\\r\\n else:\\r\\n ()\\r\\n if p>0:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def is_full(self):\\n return len(self.walls) == 4\",\n \"def _check_for_win(self, piece, piece_x, piece_y):\\n for slope_x, slope_y in ((1,0), (0,1), (1,1), (1,-1)):\\n for start_x, start_y in ((piece_x - slope_x*i, piece_y - slope_y*i) for i in range(self.consecutive_pieces_to_win)):\\n winning_piece_positions = []\\n for x, y in ((start_x + slope_x*i, start_y + slope_y*i)\\n for i in range(self.consecutive_pieces_to_win)):\\n winning_piece_positions.append((x, y))\\n # Don't need to check the piece that is being placed\\n if (x, y) == (piece_x, piece_y):\\n continue\\n current_piece = self._get_piece_at_opening_or_none(x, y)\\n if current_piece != piece:\\n winning_piece_positions = None\\n break\\n if winning_piece_positions:\\n # Check if there are additional winning pieces that exceed the number required to win.\\n # e.g. a 5-in-a-row when only 4 are required\\n if slope_x != 0:\\n num_previous_pieces_to_check = self.consecutive_pieces_to_win + (start_x - piece_x)\\n for x, y in ((start_x - slope_x*i, start_y - slope_y*i)\\n for i in range(1, 1 + num_previous_pieces_to_check)):\\n current_piece = self._get_piece_at_opening_or_none(x, y)\\n if current_piece == piece:\\n winning_piece_positions.insert(0, (x, y))\\n else:\\n break\\n return winning_piece_positions\\n\\n return False\",\n \"def goalReached(self, rewards):\\n return len(rewards) >= 100 and np.mean(rewards[-100:]) >= 18\",\n \"def occupied(self, (xIndex, yIndex)):\\n return xIndex < 0 or yIndex < 0 or \\\\\\n xIndex >= self.xN or yIndex >= self.yN or \\\\\\n self.grid[xIndex][yIndex]\",\n \"def ok(self, point):\\n [x1, x2, x3, x4, x5, x6] = point.decisions\\n if x1 + x2 -2 < 0:\\n return False\\n if 6 - x1 - x2 < 0:\\n return False\\n if 2 - x2 + x1 < 0:\\n return False\\n if 2 - x1 + 3*x2 < 0:\\n return False\\n if 4 - (x3 - 3)**2 - x4 < 0:\\n return False\\n if (x5 - 3)**3 + x6 - 4 < 0:\\n return False\\n for i, d in enumerate(point.decisions):\\n if d < self.decisions[i].low or d > self.decisions[i].high:\\n print i, d, self.decisions[i].low, self.decisions[i].high\\n return False\\n return True\",\n \"def is_full(self):\\n return len(self.__occupied_slots__) >= self.__size__\",\n \"def is_full(board):\\n return False\",\n \"def is_full_house(hand):\\n count = {c:0 for c in cards.keys()}\\n for card in hand:\\n count[card[0]] += 1\\n for c in count:\\n if count[c] != 0 and count[c] != 2 and count[c] != 3:\\n return None\\n triple = 0\\n for k in count:\\n if count[k] == 3:\\n triple = cards[k]\\n return (True, triple)\",\n \"def is_valid(field):\\n one = 0\\n two = 0\\n three = 0\\n four = 0\\n for i in range(1, 11):\\n for j in range(1, 11):\\n if ship_size(field, (i, j)) == 1:\\n one += 1\\n if ship_size(field, (i, j)) == 2:\\n two += 1\\n if ship_size(field, (i, j)) == 3:\\n three += 1\\n if ship_size(field, (i, j)) == 4:\\n four += 1\\n if ship_size(field, (i, j)) > 4:\\n return False\\n if one == 4 and two == 6 and three == 6 and four == 4:\\n return True\\n print(one, two, three, four)\\n\\n return False\",\n \"def check_points_and_level_up(self):\\n if self.points > 20 * self.level:\\n self.level += 1\\n self.refresh_rate = self.refresh_rate * 0.75\",\n \"def is_fullp(self):\\n front = (self.front + 1) % self.length\\n return front == self.rear\",\n \"def can_reach(self, position: tuple) -> bool:\\n x = position[0]\\n y = position[1]\\n dist_to_shoulder = math.sqrt(x**2 + y**2)\\n max_dist = self.bicep_length + self.forearm_length\\n if dist_to_shoulder > max_dist:\\n return False\\n return True\",\n \"def __len__(self):\\n return self.num_points\",\n \"def is_solved(self):\\n\\n marker = self._marker\\n amount_of_pegs = 0\\n for row in marker:\\n for i in row:\\n if i == \\\"*\\\":\\n amount_of_pegs += 1\\n return amount_of_pegs == 1\",\n \"def calculate_points(hand): \\r\\n hand_value = 0\\r\\n ace_count = 0 \\r\\n \\r\\n #Finds value of non-Ace cards, and counts number of Aces.\\r\\n for card in hand:\\r\\n if card[0] == 'Ace':\\r\\n ace_count += 1\\r\\n else:\\r\\n # Calls card_value function to evaluate the card.\\r\\n hand_value += card_value(card) \\r\\n \\r\\n #Ace card present\\r\\n if ace_count > 0:\\r\\n return ace_hand_value(ace_count, hand_value)\\r\\n \\r\\n #Implied \\\"if ace_count == 0:\\\"\\r\\n return hand_value\",\n \"def is_full(self):\\n\\n current_board = self.current_board\\n remaining_rows = 0\\n\\n for row in current_board:\\n if \\\"-\\\" in set(row):\\n remaining_rows += 1\\n\\n if remaining_rows == 0:\\n return True\\n else:\\n return False\",\n \"def checkPointInLampsReach(self, p):\\n v1 = XYPoint(self.Lime.x - self.Red.x, self.Lime.y - self.Red.y)\\n v2 = XYPoint(self.Blue.x - self.Red.x, self.Blue.y - self.Red.y)\\n\\n q = XYPoint(p.x - self.Red.x, p.y - self.Red.y)\\n s = self.crossProduct(q, v2) / self.crossProduct(v1, v2)\\n t = self.crossProduct(v1, q) / self.crossProduct(v1, v2)\\n\\n return (s >= 0.0) and (t >= 0.0) and (s + t <= 1.0)\",\n \"def can_draw(self,point):\\n if point <= 0:\\n return False\\n else:\\n return True\",\n \"def check_miss_count(self):\\n first = self.attk_arry[-1]\\n second = self.attk_arry[-2]\\n third = self.attk_arry[-3]\\n fourth = self.attk_arry[-4]\\n sum_of_attk = first + second + third + fourth\\n if sum_of_attk == 8:\\n self.column_arry.append(10)\\n self.row_arry.append(10)\\n else:\\n pass\",\n \"def check_offset(self):\\n\\n for d in range(self.n_dmps):\\n if abs(self.y0[d] - self.goal[d]) < 1e-4:\\n self.goal[d] += 1e-4\",\n \"def check_reached(self):\\n m_x, m_y = self.destination.get_pos()\\n m_radius = self.destination.radius\\n distance_centre = math.sqrt((m_x - self.x)**2 + (m_y - self.y)**2)\\n sum_radii = m_radius + self.radius\\n if distance_centre < sum_radii:\\n self.color = pygame.colordict.THECOLORS['green']\\n self.has_reached = True\",\n \"def is_occupied(n):\\n global _tracks\\n if n >= len(_tracks):\\n return False\\n else:\\n return _tracks[n]\",\n \"def check_lengths(self, length: Expr) -> bool:\\n for point1 in self.points:\\n for point2 in self.points - {point1}:\\n if abs(point2 - point1) == length:\\n print(f'Length {length} found between points: {point1} and {point2}')\\n return True\\n return False\",\n \"def on_board(self, pos):\\n i, j = pos\\n return 0 <= i < COLS and 0 <= j < ROWS\",\n \"def isFull(self) -> bool:\\n return (self.rear + 1) % self.capacity == self.front\",\n \"def is_full(self):\\n return self.remaining_space_in_hold() == 0\",\n \"def answer_ok(a):\\n (rightly_positioned, permutated) = a\\n if (rightly_positioned + permutated > number_of_positions) \\\\\\n or (rightly_positioned + permutated < len(colours) - number_of_positions):\\n return False\\n if rightly_positioned == 3 and permutated == 1:\\n return False\\n return True\",\n \"def check(self):\\n winner = None\\n count = 0\\n\\n for y in range(self.gridSize):\\n if winner != None:\\n return winner\\n P1, P2 = 0, 0\\n for item in self.grid[y]:\\n # Check row of the grid\\n if item == \\\"P1\\\":\\n P1 += 1\\n elif item == \\\"P2\\\":\\n P2 += 1\\n winner = self.checkval(P1, P2, self.gridSize)\\n if winner != None:\\n return winner\\n P1, P2 = 0, 0\\n for x in range(self.gridSize):\\n # Check column of the grid\\n if self.grid[x][y] == \\\"P1\\\":\\n P1 += 1\\n elif self.grid[x][y] == \\\"P2\\\":\\n P2 += 1\\n winner = self.checkval(P1, P2, self.gridSize)\\n if winner != None:\\n return winner\\n P1, P2 = 0, 0\\n for y in range(self.gridSize):\\n # Check right top to left bottom across the grid\\n for x in range(self.gridSize):\\n if x == y:\\n if self.grid[x][y] == \\\"P1\\\":\\n P1 += 1\\n elif self.grid[x][y] == \\\"P2\\\":\\n P2 += 1\\n winner = self.checkval(P1, P2, self.gridSize)\\n if winner != None:\\n return winner\\n P1, P2 = 0, 0\\n for y in range(self.gridSize):\\n # Check the left top to the right bottom across the grid\\n for x in range(self.gridSize - 1, -1, -1):\\n # Check how many filled spaces there are\\n if \\\".\\\" not in self.grid[y][x]:\\n count += 1\\n if x + y == self.gridSize - 1:\\n if self.grid[y][x] == \\\"P1\\\":\\n P1 += 1\\n elif self.grid[y][x] == \\\"P2\\\":\\n P2 += 1\\n winner = self.checkval(P1, P2, self.gridSize)\\n # Check if there is a winner if so return the winner\\n if winner != None:\\n return winner\\n # Check if the fields that are filled are equal to the possible spaces to be filled in the grid\\n if count == self.gridSize**2:\\n return \\\"Tie\\\"\",\n \"def validatePosition(boardsize, pos):\\n return pos.x in range(0, boardsize) and pos.y in range(0,boardsize)\",\n \"def valid(self, pos):\\n\\t\\tpos = Point(pos)\\n\\t\\treturn 0 <= pos.x < self.dims.width and 0 <= pos.y < self.dims.height\",\n \"def checkRow(self, x):\\n used = []\\n for y in range(len(self.board[0])):\\n cur = self.board[x][y]\\n if cur not in used:\\n if cur !=0:\\n used += [cur]\\n else:\\n return False\\n return True\",\n \"def inSmallBlindPosition(self):\\n return len(self.in_game) > 0 and ((self.dealer + 1) % len(self.in_game)) == self.position\",\n \"def check_bounds (position, size):\\n \\n for item in position:\\n # checks whether item is out of bounds\\n if item < 0 or item >= size:\\n return False\\n return True\",\n \"def is_board_full(self):\\n for position in self.positions:\\n if self.is_position_availible(position):\\n return False\\n return True\",\n \"def is_straight(hand):\\n\\ti = 0\\n\\twhile i < 8:\\n\\t\\tif hand[i] == 1 and hand[i+1] == 1 and hand[i+2] == 1 and hand[i+3] == 1 and hand[i+4] == 1:\\n\\t\\t\\treturn True, i + 4\\n\\t\\ti += 1\\n\\treturn False\",\n \"def xy_occupied(xy, board):\\n return True if board[xy[0]][xy[1]] else False\",\n \"def won(self):\\n for y in range(self.size):\\n winning = []\\n for x in range(self.size):\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n for x in range(self.size):\\n winning = []\\n for y in range(self.size):\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n winning = []\\n for y in range(self.size):\\n x = y\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n winning = []\\n for y in range(self.size):\\n x = self.size-1-y\\n if self.fields[x, y] == self.opponent:\\n winning.append((x, y))\\n if len(winning) == self.size:\\n return winning\\n return None\",\n \"def check_position(self, position):\\n x_axis, y_axis = position\\n try:\\n return bool(self.full_map[x_axis][y_axis] not in \\\"#\\\"\\n and 0 <= x_axis <= self.x_axis\\n and 0 <= y_axis <= self.y_axis)\\n\\n except IndexError:\\n return False\",\n \"def check_won(grid):\\r\\n for i in range(len(grid)):\\r\\n for j in range(len(grid[i])):\\r\\n if grid[i][j] >= 32:\\r\\n return True \\r\\n return False\",\n \"def hasBlackjack(self):\\n return len(self.cards) == 2 and self.getPoints() == 21\",\n \"def _position_is_empty_in_board(position, board):\\n return board[position[0]][position[1]] == \\\"-\\\"\",\n \"def check_hand(self, player):\\n\\n total = player.score()\\n if total > 21:\\n status = 'bust'\\n elif total == 21:\\n status = 'win'\\n else:\\n status = 'okay'\\n\\n if self.verbose:\\n print(total, 'points')\\n \\n return status\",\n \"def is_spot_possible(left, right, bottom, top):\\n return True\\n if right < 6 or bottom < 6:\\n # print(\\\"IMPOSSIBLE\\\", left, right, top, bottom)\\n return False\\n if left > 18 or top > 18:\\n # print(\\\"IMPOSSIBLE\\\", left, right, top, bottom)\\n return False\\n if abs(top - bottom) > 16 or abs(right - left) > 16:\\n # print(\\\"IMPOSSIBLE\\\", left, right, top, bottom)\\n return False\\n return True\",\n \"def check_if_user_won(self, board, pos, player_no):\\n\\n has_player_got_4 = set()\\n has_player_got_4.add(pos)\\n\\n self.check_horizontal(has_player_got_4, board, pos, player_no)\\n\\n if len(has_player_got_4) >= 4:\\n return True\\n\\n has_player_got_4 = set()\\n has_player_got_4.add(pos)\\n\\n self.check_vertical(has_player_got_4, board, pos, player_no)\\n\\n if len(has_player_got_4) >= 4:\\n return True\\n\\n has_player_got_4 = set()\\n has_player_got_4.add(pos)\\n\\n self.check_diagonal(has_player_got_4, board, pos, player_no)\\n\\n if len(has_player_got_4) >= 4:\\n return True\\n\\n has_player_got_4 = set()\\n has_player_got_4.add(pos)\\n\\n self.check_inverted_diagonal(has_player_got_4, board, pos, player_no)\\n\\n if len(has_player_got_4) >= 4:\\n return True\\n\\n if self.check_if_board_full(board):\\n self.draw = True\\n return True\",\n \"def is_full(self):\\n return self.top == self.size - 1\",\n \"def check_pile(self, iteration):\\n\\n for r in range(0, self.number_radial, 1):\\n for theta in range(0, self.number_angles, 1):\\n\\n if self.array[r][theta] < self.max_peak:\\n self.array[r][theta] = self.array[r][theta]\\n\\n else:\\n if self.force:\\n self.topple_sumulate_centrifugal_force(r, theta, iteration)\\n else:\\n self.topple(r, theta, iteration)\",\n \"def is_full(self):\\n for i in range(self.width):\\n if self.can_add_to(i) == True:\\n return False\\n return True\",\n \"def valid_position(self, new_coords: tuple) -> bool:\\n x, y = new_coords\\n min_allowed_value = self.offset\\n max_allowed_value = 10 - self.offset\\n\\n # If the value is outside of the board on the left or up, return false\\n if x < min_allowed_value or y < min_allowed_value:\\n return False\\n # If the value is outside of the board on the right or down sides, return false\\n if x > max_allowed_value or y > max_allowed_value:\\n return False\\n\\n # If the position is taken by any piece, return false\\n if self.board[y][x] != 0:\\n print(\\\"Error: Position taken by %d\\\" % self.board[y][x])\\n return False\\n return True\",\n \"def is_valid(field):\\r\\n taken_coordinates = []\\r\\n count_ships = [0]*4\\r\\n # counting ships\\r\\n try:\\r\\n for row in range(10):\\r\\n for cell in range(10):\\r\\n if (row, cell) not in taken_coordinates and\\\\\\r\\n has_ship((row, cell), field):\\r\\n taken_coordinates.extend(ship_coordinates((row, cell), field))\\r\\n count_ships[ship_size((row, cell), field) - 1] += 1\\r\\n except IndexError:\\r\\n return False\\r\\n # check if the amount of ship is correct and if they are not crossing\\r\\n if count_ships == [i for i in range(4, 0, -1)] and\\\\\\r\\n len(taken_coordinates) == len(set(taken_coordinates)):\\r\\n return True\\r\\n return False\",\n \"def checkPoint(self):\\n self.__stack.append((self.__current_pos, self.line, self.linePos))\",\n \"def check_win(self):\\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)\",\n \"def on_board(pos, size):\\n\\n row, col = pos\\n height, width = size\\n\\n return 0 <= row <= width and 0 <= col <= height\",\n \"def check_none_zero_values(self):\\n hits = -1\\n if (len(self.data.shape) == 3):\\n hits = 0\\n frames = self.data.shape[0]\\n pixels = self.data.shape[1]\\n bins = self.data.shape[2]\\n for i in range(frames):\\n for j in range(pixels):\\n for k in range(bins):\\n value = self.data[i][j][k]\\n if value > 0:\\n hits += 1\\n print(\\\"self.data[{}][{}][{}]; Sum so far = {}\\\".format(i, j, k, hits))\\n return hits\",\n \"def check_if_enough_skill_points(self):\\r\\n for skill_string in self.__skills:\\r\\n if (self.__skills[skill_string].points_to_up >\\r\\n self.__skill_points):\\r\\n self.skill_up_disable(skill_string)\",\n \"def get_color_count(self, color, filled):\\n count = 0\\n for cell in filled:\\n coord_x = cell[1]\\n coord_y = cell[0]\\n\\n # up\\n if coord_y - 1 >= 0:\\n new_cell = (coord_y-1, coord_x)\\n cell_up_color = self.get_color(new_cell)\\n if cell_up_color == color and new_cell not in filled:\\n count += 1\\n filled.append(new_cell)\\n\\n # down\\n if coord_y + 1 < self.height:\\n new_cell = (coord_y+1, coord_x)\\n cell_down_color = self.get_color(new_cell)\\n if cell_down_color == color and new_cell not in filled:\\n count += 1\\n filled.append(new_cell)\\n\\n # left\\n if coord_x - 1 >= 0:\\n new_cell = (coord_y, coord_x-1)\\n cell_left_color = self.get_color(new_cell)\\n if cell_left_color == color and new_cell not in filled:\\n count += 1\\n filled.append(new_cell)\\n\\n # right\\n if coord_x + 1 < self.width:\\n new_cell = (coord_y, coord_x+1)\\n cell_right_color = self.get_color(new_cell)\\n if cell_right_color == color and new_cell not in filled:\\n count += 1\\n filled.append(new_cell)\\n\\n return count\",\n \"def nr_points(self):\\n return len(self.x)\",\n \"def is_full(self) -> bool:\",\n \"def check_won (grid):\\r\\n for i in range(4): \\r\\n for j in range(4):\\r\\n if grid[i][j] >= 32:\\r\\n return True\\r\\n return False\",\n \"def test_is_filled(self):\\n n = 10\\n matrix = g.np.random.uniform(size=(n + 1,) * 3) > 0.5\\n not_matrix = g.np.logical_not(matrix)\\n pitch = 1. / n\\n origin = g.np.random.uniform(size=(3,))\\n vox = g.trimesh.voxel.VoxelGrid(matrix)\\n vox = vox.apply_scale(pitch).apply_translation(origin)\\n not_vox = g.trimesh.voxel.VoxelGrid(not_matrix)\\n not_vox = not_vox.apply_scale(pitch).apply_translation(origin)\\n for a, b in ((vox, not_vox), (not_vox, vox)):\\n points = a.points\\n # slight jitter - shouldn't change indices\\n points += (\\n g.np.random.uniform(size=points.shape) - 1) * 0.4 * pitch\\n g.np.random.shuffle(points)\\n\\n # all points are filled, and no empty points are filled\\n assert g.np.all(a.is_filled(points))\\n assert not g.np.any(b.is_filled(points))\\n\\n # test different number of dimensions\\n points = g.np.stack([points, points[-1::-1]], axis=1)\\n assert g.np.all(a.is_filled(points))\\n assert not g.np.any(b.is_filled(points))\",\n \"def check_won (grid):\\r\\n w=False\\r\\n for row in range(4):\\r\\n for col in range(4):\\r\\n if grid[row][col]>=32:\\r\\n w=True\\r\\n break\\r\\n return w\",\n \"def __len__(self):\\n\\t\\treturn len(self._coords)\",\n \"def check_won (grid):\\r\\n for i in range (4):\\r\\n for j in range (4):\\r\\n if grid[i][j] >= 32:\\r\\n return True\\r\\n return False\",\n \"def draw_by_insufficient(self):\\n \\n if self.cap_counter > 100:\\n self.draw_loop(\\\"draw due to none in 50\\\")\\n \\n if self.board.count(self.empty) == 62:\\n self.draw_loop(\\\"draw due to insufficient\\\")\\n\\n \\n if self.board.count(self.empty) == 61:\\n for i in self.board:\\n if i != self.empty:\\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Bishop] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Bishop]:\\n self.draw_loop(\\\"draw due to insufficient\\\")\\n if i.graphic == piece_class.PIECEDICT[WHITE][piece_class.Knight] or i.graphic == piece_class.PIECEDICT[BLACK][piece_class.Knight]:\\n self.draw_loop(\\\"draw due to insufficient\\\")\",\n \"def is_board_full(board):\\n for i in range(1, 10):\\n if check_empty_space(board, i):\\n return False\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6565153","0.63837904","0.63379383","0.6248904","0.62295264","0.6200083","0.6106756","0.6105779","0.60929984","0.6092066","0.60837704","0.60635835","0.60598457","0.60598457","0.6055476","0.60407025","0.60384566","0.60374635","0.6029937","0.60184675","0.5996415","0.5992634","0.59691954","0.59656775","0.59553266","0.5954451","0.5936567","0.59223574","0.59066504","0.59002703","0.5893284","0.58752966","0.58736855","0.5872578","0.5870295","0.5855914","0.5855574","0.58490324","0.58485216","0.5836238","0.5832257","0.5828152","0.5820035","0.5815387","0.5815132","0.58106464","0.5807958","0.5802414","0.57900375","0.5787989","0.5773406","0.57712024","0.57691574","0.5760047","0.57587856","0.5757616","0.57518303","0.57474005","0.5741846","0.57370096","0.5733321","0.57314026","0.57260305","0.57225686","0.56966305","0.56923604","0.56915575","0.5689491","0.5688712","0.56850666","0.56820685","0.56768864","0.56722987","0.56633","0.56628114","0.56623197","0.5656841","0.5655074","0.56544906","0.5653806","0.56518364","0.5646529","0.56454396","0.56443244","0.5643086","0.56342703","0.5633522","0.5633288","0.5630973","0.56308484","0.56279546","0.562204","0.5619941","0.56170833","0.5616192","0.56154835","0.56149656","0.56120604","0.5611796","0.5611349"],"string":"[\n \"0.6565153\",\n \"0.63837904\",\n \"0.63379383\",\n \"0.6248904\",\n \"0.62295264\",\n \"0.6200083\",\n \"0.6106756\",\n \"0.6105779\",\n \"0.60929984\",\n \"0.6092066\",\n \"0.60837704\",\n \"0.60635835\",\n \"0.60598457\",\n \"0.60598457\",\n \"0.6055476\",\n \"0.60407025\",\n \"0.60384566\",\n \"0.60374635\",\n \"0.6029937\",\n \"0.60184675\",\n \"0.5996415\",\n \"0.5992634\",\n \"0.59691954\",\n \"0.59656775\",\n \"0.59553266\",\n \"0.5954451\",\n \"0.5936567\",\n \"0.59223574\",\n \"0.59066504\",\n \"0.59002703\",\n \"0.5893284\",\n \"0.58752966\",\n \"0.58736855\",\n \"0.5872578\",\n \"0.5870295\",\n \"0.5855914\",\n \"0.5855574\",\n \"0.58490324\",\n \"0.58485216\",\n \"0.5836238\",\n \"0.5832257\",\n \"0.5828152\",\n \"0.5820035\",\n \"0.5815387\",\n \"0.5815132\",\n \"0.58106464\",\n \"0.5807958\",\n \"0.5802414\",\n \"0.57900375\",\n \"0.5787989\",\n \"0.5773406\",\n \"0.57712024\",\n \"0.57691574\",\n \"0.5760047\",\n \"0.57587856\",\n \"0.5757616\",\n \"0.57518303\",\n \"0.57474005\",\n \"0.5741846\",\n \"0.57370096\",\n \"0.5733321\",\n \"0.57314026\",\n \"0.57260305\",\n \"0.57225686\",\n \"0.56966305\",\n \"0.56923604\",\n \"0.56915575\",\n \"0.5689491\",\n \"0.5688712\",\n \"0.56850666\",\n \"0.56820685\",\n \"0.56768864\",\n \"0.56722987\",\n \"0.56633\",\n \"0.56628114\",\n \"0.56623197\",\n \"0.5656841\",\n \"0.5655074\",\n \"0.56544906\",\n \"0.5653806\",\n \"0.56518364\",\n \"0.5646529\",\n \"0.56454396\",\n \"0.56443244\",\n \"0.5643086\",\n \"0.56342703\",\n \"0.5633522\",\n \"0.5633288\",\n \"0.5630973\",\n \"0.56308484\",\n \"0.56279546\",\n \"0.562204\",\n \"0.5619941\",\n \"0.56170833\",\n \"0.5616192\",\n \"0.56154835\",\n \"0.56149656\",\n \"0.56120604\",\n \"0.5611796\",\n \"0.5611349\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5970365"},"document_rank":{"kind":"string","value":"22"}}},{"rowIdx":17491,"cells":{"query":{"kind":"string","value":"Sums all scores of the currently filled number positions"},"document":{"kind":"string","value":"def number_total(self):\n return sum(self.grid[pos][1] for pos in [\"n1\", \"n2\", \"n3\", \"n4\", \"n5\", \"n6\"] if self.grid[pos][0])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_scores(self):\n self.score[0] = (-1)*sum(self.board[self.board == -1])\n self.score[1] = sum(self.board[self.board == 1])\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)","def grades_sum(grades_input):\n total = 0\n for score in scores: \n total += score\n\n return total","def update_scores(self):\r\n totals = [0, 0, 0, 0]\r\n for player in range(0, 4):\r\n for round_num in range(0, 17):\r\n try:\r\n bid = int(self.spin_bids[player][round_num].get())\r\n tricks = int(self.spin_tricks[player][round_num].get())\r\n except ValueError:\r\n bid = -1\r\n tricks = -1\r\n score = calc_score(min(round_num+1, 13), bid, tricks)\r\n self.lbl_scores[player][round_num].configure(text=str(score))\r\n totals[player] += score\r\n for player in range(0, 4):\r\n self.lbl_totals[player].configure(text=str(totals[player]))\r\n return totals[0] + totals[1] + totals[2] + totals[3]","def __get_score(self):\n for pair in zip(self.nu[self.nu_idx:], self.sw[self.sw_idx:]):\n if pair[0] == pair[1]:\n self.score += 1\n else:\n break","def calculate_total(score_sheet):\n total_score = EMPTY_BOX()\n if EMPTY_BOX() not in score_sheet:\n if sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) >= UPPER_BONUS_REQUIREMENT():\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)]) + UPPER_SECTION_BONUS()\n elif sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) < UPPER_BONUS_REQUIREMENT():\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)])\n else:\n total_score = EMPTY_BOX()\n\n return total_score","def _calculate_score(self):\n mul = self._check_board()\n if mul > 0:\n inc = 100 * mul + ((mul - 1) * 25)\n self.score += inc","def sum_points(self) -> int:\n return sum([card.rank_value for card in self.deck.cards])","def get_value(self):\n #Finds all of the values in the cards\n score_list=[Card.get_value(card) for card in self.cards]\n #Sums the scores\n if self.num_cards() > 0:\n total_score=reduce((lambda x,y: x+y),score_list)\n return total_score\n else:\n return 0","def calculScore(self):\n for cell in self.notComputeRouter:\n if(cell.isCovered==True):\n self.score += 1000\n self.score += self.budget","def scoreGame(self):\n # create valueLs[card1,card2,...], pass it to sumHandReturnPoints(valueLs) or twoCardReturnPoints(valueLs)\n scoreLs = []\n ### Score of row\n for rowKey in self.table:\n valueLs = self.table[rowKey]\n points = self.sumHandReturnPoints(valueLs)\n scoreLs.append(points)\n\n ### Score of 4-card column\n for offset in range(0,3): # 0,1,2\n tmpLs = []\n for rowKey in self.table:\n valueLs = self.table[rowKey]\n if len(valueLs) == 5:\n iterStart = 1\n else:\n iterStart = 0\n card = valueLs[iterStart+offset]\n tmpLs.append(card)\n points = self.sumHandReturnPoints(tmpLs)\n scoreLs.append(points) \n\n ### Score of 2-card column\n #(1) 1st column\n valueLs1 = self.table['row1']\n valueLs2 = self.table['row2']\n tmpLs = []\n tmpLs.append(valueLs1[0].get_rank())\n tmpLs.append(valueLs2[0].get_rank())\n points = self.twoCardReturnPoints(tmpLs)\n scoreLs.append(points)\n #(2) 5th column\n valueLs3 = self.table['row1']\n valueLs4 = self.table['row2']\n tmpLs = []\n tmpLs.append(valueLs3[-1].get_rank())\n tmpLs.append(valueLs4[-1].get_rank())\n points = self.twoCardReturnPoints(tmpLs)\n scoreLs.append(points) \n\n ### Add up scoreLs\n sumPoints = 0\n for points in scoreLs:\n sumPoints += points\n return sumPoints","def update_score(self, blanks=0, letters=0, words=0):\n points = 0\n points += letters * LETTER_POINT\n points += words * WORD_POINT\n points += blanks * BLANK_POINT\n self.score += points","def calculate_score(player_cards):\n score = sum(player_cards)\n return score","def to_total_scores(ht_score, ft_score, et_score, pen_score):\n total_score = ft_score\n try:\n for score in [et_score, pen_score]:\n if None in score:\n continue\n score[0] = total_score[0] + score[0]\n score[1] = total_score[1] + score[1]\n total_score = score\n except Exception:\n return ht_score, ft_score, et_score, pen_score\n return ht_score, ft_score, et_score, pen_score","def merge(self):\n for i in range(len(self.main_grid_values)):\n for j in range(len(self.main_grid_values) - 1):\n if self.main_grid_values[i][j] == self.main_grid_values[i][j+1]:\n self.score_value.set(str(int(self.score_value.get()) + self.main_grid_values[i][j]*2))\n self.main_grid_values[i][j] *= 2\n self.main_grid_values[i][j+1] = 0","def add_total_score(self, sheet_number):\n cell_format = self.workbook.add_format(self.base_format)\n # cell_format.set_bg_color('yellow')\n # total score of rows\n self.worksheets[sheet_number].write(0, len(self.arrays[sheet_number][0]), 'total score', cell_format)\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), 0, 'total score', cell_format)\n for i in range(2, len(self.arrays[sheet_number])):\n count = 0\n for j in range(1, len(self.arrays[sheet_number][0])):\n count += self.arrays[sheet_number][i][j]\n self.worksheets[sheet_number].write(i, len(self.arrays[sheet_number][0]), count, cell_format)\n # total score of columns\n for i in range(1, len(self.arrays[sheet_number][0])):\n count = 0\n for j in range(2, len(self.arrays[sheet_number])):\n count += self.arrays[sheet_number][j][i]\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), i, count, cell_format)","def _scoring(self):\n val = 0 #score will be totaled here\n\n for c in range(0, self.width): #for every column in the board\n for r in range(0, self.height): #for every row of a column\n #see if we can move...\n possible_up = (r + 3 < self.height) #up?\n possible_left = (c - 3 > 0) #left?\n possible_right = (c + 3 < self.width) #right?\n\n #diagonally up, left\n if possible_up and possible_left:\n val+= self._up_left(c, r)\n\n #up\n if possible_up:\n val+= self._up(c,r)\n\n #diagonally up, right\n if possible_up and possible_right:\n val+= self._up_right(c,r)\n\n #right\n if possible_right:\n val+= self._right(c,r)\n\n\n return val","def _compute_scores(self, triples):\n # compute scores as sum(s * p * o)\n scores = tf.reduce_sum(triples[0] * triples[1] * triples[2], 1)\n return scores","def get_local_score(self):\n for candidate in self.candidate_list:\n self.score += candidate.get_score()","def sumHandReturnPoints(self, valueLs): # card is the element in valueLs\n #Ace is dealt with here, assume Ace to be 11 initially, decreasing by 10 per Ace if sum > 21\n rowSum = 0\n AceCount = 0\n for ele in valueLs:\n rank = ele.get_rank()\n if rank == 1:\n rank = 11\n AceCount += 1 # serve as flag\n rowSum += rank\n while(AceCount!=0):\n if rowSum > 21:\n rowSum -= 10\n AceCount -= 1\n points = self.countPoints(rowSum)\n return points","def calculate_score(cards):\n if sum(cards) == 21 and len(cards) == 2:\n return 0\n \n if 11 in cards and sum(cards) > 21:\n cards.remove 11\n cards.append 1\n return sum(cards)","def getScore(self):\n return sum(self.field)","def score(self):\n return sum(len(i) for i in self.firework)","def calculate_scores(self):\n words = self.walk_board()\n player_scores = {}\n for word in words:\n player = word.get_owning_player()\n if player not in player_scores:\n player_scores[player] = 0\n player_scores[player] += word.get_score()\n return player_scores","def sum_(hand: list):\n vals = [card.rank for card in hand]\n intvals = []\n while len(vals) > 0:\n value = vals.pop()\n try:\n intvals.append(int(value))\n except ValueError:\n if value in ['K', 'Q', 'J']:\n intvals.append(10)\n elif value == 'A':\n intvals.append(1) # Keep it simple for the sake of example\n if intvals == [1, 10] or intvals == [10, 1]:\n print(\" Blackjack!\")\n return(21)\n else:\n points = sum(intvals)\n print(\" Current score: {}\".format(str(points)))\n return(points)","def add_score(self, points: int) -> None:\n self.__score += points\n\n for rank in self.__ranks.keys():\n if self.__score >= rank:\n self.__level = self.__ranks[rank]\n else:\n break","def scores_for(self, board):\r\n scores = [50] * board.width\r\n\r\n for col in range(board.width):\r\n if not board.can_add_to(col):\r\n scores[col] = -1\r\n elif board.is_win_for(self.checker):\r\n scores[col] = 100\r\n elif board.is_win_for(self.opponent_checker()):\r\n scores[col] = 0\r\n elif self.lookahead == 0:\r\n scores[col] = 50\r\n else: \r\n board.add_checker(self.checker, col)\r\n opponent = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead - 1)\r\n opp_scores = opponent.scores_for(board)\r\n if max(opp_scores) == 100:\r\n scores[col] = 0\r\n elif max(opp_scores) == 0:\r\n scores[col] = 100\r\n else:\r\n scores[col] = 50\r\n board.remove_checker(col)\r\n\r\n return scores","def set_score(self, points):\n self.score += points","def scores_for(self, board):\r\n scores = [1]*board.width\r\n for i in range(board.width):\r\n if not board.can_add_to(i):\r\n scores[i] = -1\r\n elif board.is_win_for(self.checker):\r\n scores[i] = 100\r\n elif board.is_win_for(self.opponent_checker()):\r\n scores[i] = 0\r\n elif self.lookahead == 0:\r\n scores[i] = 50\r\n else:\r\n board.add_checker(self.checker, i)\r\n other = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead-1)\r\n other_scores = other.scores_for(board)\r\n if max(other_scores) == 100:\r\n scores[i] = 0\r\n elif max(other_scores) == 50:\r\n scores[i] = 50\r\n elif max(other_scores) == 0:\r\n scores[i] = 100\r\n board.remove_checker(i)\r\n return scores","def grand_total(self):\n return sum(self.grid[pos][1] for pos in assignable_positions if self.grid[pos][0]) + self.grid[\"nb\"][1]","def score(self):","def score(self, robot):\n scores = [grupo.score(robot) for grupo in self.get_grupos()]\n return reduce(lambda acumulador, score: tuple([ a + b for a, b in zip(acumulador, score)]), scores, (0, 0, 0, 0, 0, 0, 0, 0))","def aver_score(datalist):\n scores_per_position = []\n \n for tupl in datalist:\n count = 0\n sum_of_position = 0\n for element in tupl[3]:\n sum_of_position += element\n count +=1\n aver_pos = sum_of_position/ count\n scores_per_position += [aver_pos]\n \n return scores_per_position","def get_score(self):\n return sum([Letters.get_value(tile.letter) for tile in self.tiles])","def score(self, board: Block) -> int:\n grid = _flatten(board)\n left = grid[0]\n right = grid[-1]\n top = [i[0] for i in grid]\n bottom = [i[-1] for i in grid]\n score0 = left.count(self.colour)\n score1 = right.count(self.colour)\n score2 = top.count(self.colour)\n score3 = bottom.count(self.colour)\n return score0 + score1 + score2 + score3","def get_score_matrix(self) -> int:","def global_score_count(self):\n score_count_mat = np.ndarray((5,10))\n\n for n in range(0,5):\n score_count = [\n np.count_nonzero(self.__data[...,n*5:n*5+5] == score)\n for score\n in np.arange(10)+1\n ]\n\n score_count_mat[n,...] = score_count\n\n return score_count_mat","def update_points(self):\n #Calculate Upper Section\n total = 0\n for box in self._upper_section:\n total += box.points\n self._upperSum = total\n\n if total >= 63:\n self._bonus = 35\n total += 35\n self._upperTotal = total\n\n # Calculate Lower Section\n total = 0\n for box in self._lower_section:\n total += box.points\n\n if self.get_box(\"Yahtzee\").points > 0:\n total = total + (self._yahtzee_count - 1) * 100 # Yahtzee Bonus\n\n self._lowerTotal = total\n\n #Total Points\n self._grandTotal = self._upperTotal + self._lowerTotal","def get_score(self, pos):\n score = 0\n gap_count = 0\n align_query, align_target = '', ''\n for xval, yval in pos:\n xunit = self.get_seq(self.target, xval)\n yunit = self.get_seq(self.query, yval)\n align_query += yunit\n align_target += xunit\n if xval != 0 and yval != 0:\n score += self.subs_matrix[xunit][yunit]\n gap_count = 0\n else:\n gap_count += 1\n if gap_count > 1 and len(self.gap_penatly) > 1:\n score += self.gap_penatly[1]\n else:\n score += self.gap_penatly[0]\n return score, align_query, align_target","def score(self):\n result = 0\n\n idx = self.cups.index(1)\n idx += 1\n if idx >= len(self.cups):\n idx = 0\n # ok, keep adding things until we get back to 1\n while 1 != self.cups[idx]:\n # add this value..\n result *= 10\n result += self.cups[idx]\n # and on to the next one..\n idx += 1\n if idx >= len(self.cups):\n idx = 0\n\n return result","def update_score(self, score: int) -> int:\n self.score += score\n return self.score","def sum_scores(scores):\n segs = itertools.chain(*[x[\"segments\"][:] for x in scores]) #all the segs in flat list\n d_counts = sum_dicts([x[\"events\"][\"d_counts\"] for x in scores])\n t_counts = sum_dicts([x[\"events\"][\"t_counts\"] for x in scores])\n d_rates = pct_dict(d_counts)\n t_rates = pct_dict(t_counts)\n return dict(frame_scores=score_frames(segs), event_scores=dict(d_counts=d_counts, t_counts=t_counts, d_rates=d_rates, t_rates=t_rates))","def get_score_list(self) -> List[int]:\n\n result, values = [], []\n for i in range(len(self._cards)):\n for value in self._cards[i]:\n values.append(value[0])\n if sum(values) > 21:\n for num in range(len(values)):\n if values[num] == 11:\n values[num] = 1\n result.append(sum(values))\n values = []\n return result","def merge_scores(self): # pragma: no cover\n\n merged_scores = []\n score_end_indices = []\n\n for i, score in enumerate(self.anom_scores):\n if not score['start_idx']-1 in score_end_indices:\n merged_scores.append(score['score'])\n score_end_indices.append(score['end_idx'])","def column_sums(square):\n total = 0","def calculate_player_position_score(marbles: list):\n prime = Evaluator.prime_positions\n good = Evaluator.good_positions\n position_score = 0\n for marble in marbles:\n if marble in prime:\n position_score += 10\n elif marble in good:\n position_score += 5\n else:\n position_score -= 1\n return position_score","def score_of_nodes(self, score):\n for hypervisor_id in self.model.get_all_hypervisors():\n hypervisor = self.model. \\\n get_hypervisor_from_id(hypervisor_id)\n count = self.model.get_mapping(). \\\n get_node_vms_from_id(hypervisor_id)\n if len(count) > 0:\n result = self.calculate_score_node(hypervisor)\n else:\n # The hypervisor has not VMs\n result = 0\n if len(count) > 0:\n score.append((hypervisor_id, result))\n return score","def _update_score(self) -> None:\n\n # setting new score by iterating over players\n self.score_play[self.n_play_turns, ] = [\n self._score_table[(\n self.contract.level,\n self.contract.suit,\n self.tricks[i],\n self.contract.player_vulnerability[i],\n int(self.contract.double + self.contract.redouble)\n )]\n for i in range(NUM_PLAYERS)\n ]","def calc_score_for_lang(self, lang_tree):\n scores = []\n lang_tree.tree.find_all(self.text, lambda index, score: scores.append(score))\n self.scores[lang_tree.lang] = sum(scores)","def score(self):\r\n totN = 0\r\n totB = 0\r\n for l in range(SIZE):\r\n for c in range(len(COLONNES)):\r\n if self.jeu[l][c] == NOIR:\r\n totN += 1\r\n elif self.jeu[l][c] == BLANC:\r\n totB += 1\r\n return (totN, totB)","def update_score():\n pass","def scoring(self):\n pass","def total_score(self, logits):\n previous = torch.full((1, self.tag_size), -10000., device=device)\n previous[0][self.tag_map[self.start_tag]] = 0.\n\n for index in range(len(logits)):\n previous = previous.expand(self.tag_size, self.tag_size).t()\n emit = logits[index].view(1, -1).expand(self.tag_size, self.tag_size)\n scores = previous + emit + self.transitions\n previous = log_sum_exp(scores)\n\n # previous = previous + self.transitions[:, self.tag_map[self.stop_tag]]\n # previous += self.transitions[self.tag_map[self.stop_tag]]\n previous += self.transitions[self.tag_map[:, self.stop_tag]]\n total_scores = log_sum_exp(previous.t())[0]\n return total_scores","def calc_sum_fitness(self):\n fitness: float = 0\n for c in self.characters:\n fitness += c.fitness\n self.sum_fitness = round(fitness, 3)","def update_score(self):\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)","def calculate_score(list_of_cards):\n if sum(list_of_cards) == 21 and len(list_of_cards) == 2:\n return 0\n if 11 in list_of_cards and sum(list_of_cards) > 21:\n list_of_cards.remove(11)\n list_of_cards.append(1)\n return sum(list_of_cards)","def _tally(self, score):\n self._score[self._turn] += score","def _calc_multiple_alignment_score(wrapped_data : tuple) -> int: \n (start, finish) = wrapped_data \n score_sum = 0.\n for dna_record in tqdm(dna_sequences[start : finish + 1], total=(finish + 1 - start), desc=\"Training process\"):\n score_sum += self.aligner.score(seq, dna_record.seq)\n return score_sum","def compute_score(self):\n for i in xrange(FRAMES):\n # STRIKE\n if self.frames[i][0] == 10:\n # CONSECUTIVE STRIKE\n if self.frames[i + 1][0] == 10:\n self.scores.append(self.frames[i][0] +\n self.frames[i + 1][0] +\n self.frames[i + 2][0])\n else:\n self.scores.append(self.frames[i][0] +\n self.frames[i + 1][0] +\n self.frames[i + 1][1])\n # SPARE\n elif (self.frames[i][0] + self.frames[i][1] == 10):\n self.scores.append(self.frames[i][0] + self.frames[i][1] +\n self.frames[i + 1][0])\n # NEITHER\n else:\n self.scores.append(self.frames[i][0] + self.frames[i][1])\n # Total Score\n for score in self.scores:\n self.score += score","def run_compute_price_score(self):\n for home_data in self.homes:\n score_result = self.compute_price_score(home_data.home.price)\n if score_result == -1:\n home_data.eliminate_home()\n home_data.accumulated_points = score_result * self.price_user_scale_factor * self.price_question_weight\n home_data.total_possible_points = self.price_user_scale_factor * self.price_question_weight","def update_scores(self, score):\n self.result_list.append(score)\n\n if self.best_score == 0 and self.worst_score == 0:\n self.best_score = score\n self.worst_score = score\n\n if score < self.best_score:\n self.best_score = score\n\n if score > self.worst_score:\n self.worst_score = score","def score(self):\n xg, yg = self.goal\n xe, ye = self.empty_node()\n score = len(self.history) + 4*(xg + yg)\n if xg == 1:\n score -= 3\n if ye > 1:\n score += ye - 1\n dx = abs(xe - xg + 1)\n if xg and dx:\n score += dx\n return score","def adjust_score(self):\n self.score += game.temporary_score","def schwefel222fcn(x: np.ndarray) -> np.ndarray:\n absx = np.abs(x)\n scores = np.sum(absx, axis=1) + np.prod(absx, axis=1)\n return scores","def mc_update_scores(scores, board, player):\r\n dim = board.get_dim()\r\n winner = board.check_win()\r\n if winner == provided.DRAW:\r\n return\r\n flag = (player == winner)\r\n if flag:\r\n offset = 1\r\n else:\r\n offset = -1\r\n for idx in range(dim):\r\n for jdx in range(dim):\r\n if board.square(idx,jdx) == player:\r\n scores[idx][jdx] += (offset * SCORE_CURRENT)\r\n elif board.square(idx,jdx) != provided.EMPTY:\r\n scores[idx][jdx] -= (offset * SCORE_OTHER)","def sum_squares(X):\n pref_sum = np.cumsum(X)\n pref_sum_squares = np.cumsum(X**2)\n n = len(X)\n sum_squares_left = np.zeros(n+1)\n sum_squares_right = np.zeros(n+1)\n for i in range(1, n+1):\n mean_left = pref_sum[i-1]/i \n mean_right = (pref_sum[-1] - pref_sum[i-1])/(n-i) # bad for i = n overwritten later\n sum_squares_left[i] = pref_sum_squares[i-1] + i * mean_left**2 - 2 * mean_left * pref_sum[i-1] \n sum_squares_right[i] = (pref_sum_squares[-1] - pref_sum_squares [i-1]) + mean_right**2 * (n-i) - 2 * mean_right *(pref_sum[-1] - pref_sum[i-1])\n sum_squares_left[0] = 0\n sum_squares_right[0] = sum_squares_left[-1]\n sum_squares_right[-1] = 0\n return sum_squares_left, sum_squares_right","def disp_score():","def _aggregate_scores(self,\n game_scores: List[Dict[str, int]]) -> task.ScoreData:\n\n relative_score = []\n recall = []\n num_incorrect = []\n precision = []\n\n for score_dict in game_scores:\n\n relative_score.append(score_dict['score']/score_dict['max_score'])\n recall.append(score_dict['num_correct']/score_dict['word_count'])\n num_incorrect.append(score_dict['num_incorrect'])\n\n # Precision -- defined to be 1 if no words are guessed.\n num_guesses = score_dict['num_correct'] + score_dict['num_incorrect']\n if num_guesses > 0:\n precision.append(score_dict['num_correct'] / num_guesses)\n else:\n precision.append(1.0)\n\n relative_score = np.mean(relative_score)\n recall = np.mean(recall)\n num_incorrect = np.mean(num_incorrect)\n precision = np.mean(precision)\n\n score_data = task.ScoreData(\n {'relative_score': relative_score,\n 'recall': recall,\n 'num_incorrect': num_incorrect,\n 'precision': precision},\n preferred_score='relative_score',\n low_score=0.0,\n high_score=1.0,\n number_of_shots=-1,\n subtask_description=('A simplified implementation of the NYT '\n 'Spelling Bee game. Individual game scoring is '\n 'defined in the class deffinition. Reported '\n 'scores are normalized to game maximums.'\n 'All scores are averaged over each game played '\n 'in an evaluation run.'))\n\n return score_data","def get_total_score(list, board):\n obj_list = []\n\n # Get board to end-state\n for pos_tuple in list:\n obj_list.append(board[pos_tuple[1][0]][pos_tuple[1][1]])\n move_on_own_board(pos_tuple[0], pos_tuple[1], board)\n\n # Calculate the score\n tot_score = 0\n for row in range(8):\n for col in range(8):\n if piece_on_pos((row, col), board):\n obj = board[row][col]\n tot_score += obj.points\n\n # Improve score calculation\n if obj.string == \"PW\":\n if row == 7:\n tot_score += 5 # Pawn becomes queen\n else:\n tot_score += 0.01 * (row * row)\n elif obj.string == \"PB\":\n if row == 0:\n tot_score -= 5 # Pawn becomes queen\n else:\n tot_score -= 0.01 * ((7 - row) * (7 - row))\n elif (obj.string == \"HW\") | (obj.string == \"BW\") | (obj.string == \"QW\"):\n if row > 0:\n tot_score += 0.1\n\n # Get board back to init-state\n for i in reversed(range(len(list))):\n move_on_own_board(list[i][1], list[i][0], board)\n board[list[i][1][0]][list[i][1][1]] = obj_list[i] # Listception\n\n return tot_score","def score( self ):\r\n result = 0.0\r\n for rr in self.ee.getRsrcs( ):\r\n value = self.scoreRsrc( rr )\r\n result += value\r\n print( \"INFO: Value for the schedule is %s \" % ( rr, result ) )\r\n return( result )","def add_to_score(self, to_add):\n self.score += to_add","def get_score(self):\n for response in self.response_list:\n self.score += response.get_score","def summation(self):\n return sum(self.read_ints())","def increase(self, points):\n self.score += points","def score(self):\n return self.aggregate(Sum('score')).values()[0] or 0","def sum1d_pos(summand):\n total = 0\n for i in range(summand.size):\n total += summand[i]\n return total","def score(self, hand, position):\n\n try:\n assert self.grid[position][1] == \"---\"\n except AssertionError:\n print self\n print position\n raise FilledInError\n except KeyError:\n print \"\\nCheck your code. This is not a valid position:\", position, \"\\n\"\n raise\n\n if position.startswith(\"n\"): # Return sum of relevant number\n n = int(position[1])\n return sum(d for d in hand.dice if d == n)\n\n elif position in [\"k3\", \"k4\", \"ch\"]: # Return total sum\n if position == \"k3\" and hand.max_tally()[0] < 3:\n return 0 # The is not a three of a kind\n elif position == \"k4\" and hand.max_tally()[0] < 4:\n return 0 # The is not a four of a kind\n return sum(hand.dice)\n\n elif position in [\"fh\", \"ss\", \"ls\", \"yz\", \"yb\"]: # Return fixed score\n if position == \"fh\":\n tallies = hand.get_dicedict().values()\n if 1 in tallies:\n return 0 # This is not a full house\n\n elif position in [\"ss\", \"ls\"]:\n ds = \"\".join(str(x) for x in hand.sort_by_value())\n if position == [\"ss\"]:\n if \"1234\" not in ds and \"2345\" not in ds and \"3456\" not in ds:\n return 0\n else:\n if \"12345\" not in ds and \"23456\" not in ds:\n return 0\n\n else:\n if hand.max_tally()[0] < 5:\n return 0 # This is not a yahtzee\n if position == \"yb\" and self.grid[\"yz\"] == \"---\":\n return 0 # YB only scores points if there already is a YZ\n\n return fixed_scores[position]\n\n else:\n raise InvalidPositionError","def fit_score(self, solution):\r\n illegal_neighbours = 0\r\n legal_neighbours = 0\r\n for polygon in solution.genetic_units.values():\r\n for neighbour_id in polygon.neighbours_ids:\r\n if polygon.color is solution.genetic_units[neighbour_id].color:\r\n illegal_neighbours += 1\r\n else:\r\n legal_neighbours += 1\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return illegal_neighbours\r\n else:\r\n return legal_neighbours","def calc_score(pins_stats):\n count = 0\n new = pins_stats[:, :2] - ORIG_PINS_LOC\n for p in new:\n if np.linalg.norm(p) > R_PIN / 2:\n count += 1\n return count","def score(self, hand, top_card):\n powerset = GameState.powerset(list(hand).append(top_card))\n score = 0\n for meld in powerset:\n score += self.score_meld(meld)\n return score","def sum_cards(self):\n has_ace = False\n sum = 0\n\n # Add up players cards\n for card in self.cards:\n if card.card_value == \"ace\":\n has_ace = True\n sum += card.game_value\n\n # Handle case where ace plays low\n if sum > 21 and has_ace:\n sum -= 10\n\n return sum","def game_score(matrix):\n total_score = 0\n\n for i in range(len(matrix)):\n for j in range(len(matrix[0])):\n\n value_to_multiply_tile = 0\n tile_copy = copy.deepcopy(matrix[i][j])\n\n # a tile of worth 0 = an empty tile in game\n if tile_copy != 0 or tile_copy != 2:\n while tile_copy > 2:\n value_to_multiply_tile += 1\n tile_copy /= 2\n total_score += matrix[i][j] * value_to_multiply_tile\n\n return total_score","def get_score(self):\n rewards, resets = self.runner.get_rewards_resets()\n self.runner.clear_rewards_resets()\n assert rewards.ndim == 1 and resets.ndim == 1, (rewards.ndim, resets.ndim)\n assert rewards.shape[0] == resets.shape[0], (rewards.shape, resets.shape)\n scores = [0]\n for t in reversed(range(rewards.shape[0])):\n if resets[t]:\n scores.append(0)\n scores[-1] += rewards[t]\n return np.mean(scores)","def _draw_score(self) -> None:\n score_digits = [int(x) for x in list(str(self.game.score))]\n total_width = 0 # total width of all numbers to be printed\n\n for digit in score_digits:\n total_width += self.images['numbers'][digit].get_width()\n\n x_offset = (self._screen_width - total_width) / 2\n\n for digit in score_digits:\n self.surface.blit(self.images['numbers'][digit],\n (x_offset, self._screen_height * 0.1))\n x_offset += self.images['numbers'][digit].get_width()","def updateScore(self, score):\n self.__score += score","def game_nim_sum():\n global piles\n global num_piles \n nim_sum = 0\n for x in piles:\n nim_sum = nim_sum^x\n \n return nim_sum","def calculate_score(card_list):\n if sum(card_list) == 21 and len(card_list) == 2:\n return 0\n if sum(card_list) > 21 and 11 in card_list:\n card_list.remove(11)\n card_list.append(1)\n return sum(card_list)","def get_score(self):\r\n correct = 0\r\n for key in self.correct_map:\r\n try:\r\n correct += self.correct_map.get_npoints(key)\r\n except Exception:\r\n log.error('key=%s, correct_map = %s', key, self.correct_map)\r\n raise\r\n\r\n if (not self.student_answers) or len(self.student_answers) == 0:\r\n return {'score': 0,\r\n 'total': self.get_max_score()}\r\n else:\r\n return {'score': correct,\r\n 'total': self.get_max_score()}","def update_score(self, board):\n self._score += 1","def getScore(self, seq, start):\n sum = 0.0\n seqdata = seq.getSequence()[start : start+self.cols]\n for pos in range(len(seqdata)):\n q = self.counts[pos].getFreq(seqdata[pos])\n if q == 0:\n q = 0.0001 # to avoid log(0) == -Infinity\n logodds = math.log(q / self.background.getFreq(seqdata[pos]))\n sum += logodds\n return sum","def add_score(self, score):\n self._score += score","def mc_update_scores(scores, board, player):\n winner = board.check_win()\n\n for col in range(board.get_dim()):\n for row in range(board.get_dim()):\n\n if winner == provided.DRAW:\n scores[row][col] += 0\n\n elif winner == player and board.square(row, col) == player:\n scores[row][col] += SCORE_CURRENT\n elif winner == player and board.square(row, col) != player and board.square(row, col) != provided.EMPTY:\n scores[row][col] -= SCORE_OTHER\n elif winner != player and board.square(row, col) == player:\n scores[row][col] -= SCORE_CURRENT\n elif winner != player and board.square(row, col) != player and board.square(row, col) != provided.EMPTY:\n scores[row][col] += SCORE_CURRENT\n elif board.square(row, col) == provided.EMPTY:\n scores[row][col] += 0","def mc_update_scores(scores, board, player):\n winner = board.check_win()\n mc_match = 0\n mc_other = 0\n \n if winner == player:\n mc_match = SCORE_CURRENT\n mc_other = -SCORE_OTHER\n elif winner == provided.switch_player(player):\n mc_match = -SCORE_CURRENT\n mc_other = SCORE_OTHER\n \n for row in range(board.get_dim()):\n for col in range(board.get_dim()):\n if board.square(row, col) == player:\n scores[row][col] += mc_match\n elif board.square(row, col) == provided.switch_player(player):\n scores[row][col] += mc_other","def score(self) -> int:\n card_values = {\n '0': 0,\n '1': 1,\n '2': 2,\n '3': 3,\n '4': 4,\n '5': 5,\n '6': 6,\n '7': 7,\n '8': 8,\n '9': 9,\n '10': 10,\n 'JACK': 10,\n 'QUEEN': 10,\n 'KING': 10,\n 'ACE': 11}\n hand_value = []\n for i in self.cards:\n hand_value.append(card_values[i.value])\n while sum(hand_value) > 21 and 11 in hand_value:\n for i, j in enumerate(hand_value):\n if j == 11:\n hand_value[i] = 1\n break\n else:\n pass\n return sum(hand_value)","def check_score(self, move):\n\n i = int(move) // 7\n j = int(move) % 7 #find the corresponding index of the input move\n\n if i == 0: #top\n if self.board[i+1][j-1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i+2][j] != \"\":\n return 1\n return 0\n if i == 6: #bottom\n if self.board[i-1][j-1] != \"\" and self.board[i-1][j+1] != \"\" and self.board[i-2][j] != \"\":\n return 1\n return 0\n if j == 0: #left\n if self.board[i-1][j+1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i][j+2] != \"\":\n return 1\n return 0\n if j == 6: #right\n if self.board[i-1][j-1] != \"\" and self.board[i+1][j-1] != \"\" and self.board[i][j-2] != \"\":\n return 1\n return 0\n if i == 2 or i == 4: # horizontal\n score = 0\n if self.board[i-1][j-1] != \"\" and self.board[i-1][j+1] != \"\" and self.board[i-2][j] != \"\":\n score += 1\n if self.board[i+1][j-1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i+2][j] != \"\":\n score += 1\n return score\n\n if j == 2 or j == 4: # vertical\n score = 0\n if self.board[i-1][j-1] != \"\" and self.board[i+1][j-1] != \"\" and self.board[i][j-2] != \"\":\n score += 1\n if self.board[i-1][j+1] != \"\" and self.board[i+1][j+1] != \"\" and self.board[i][j+2] != \"\":\n score += 1\n return score","def finalize(self):\n\t\tif self._sum_factor != 0.0:\n\t\t\tself._last_score = self._current_score / self._sum_factor\n\t\telse:\n\t\t\tself._last_score = 0.0\n\n\t\tself._scores.append(self._last_score)\n\t\tself._scores = self._scores[-self._range[1]:]\n\t\n\t\tself._sum_factor = 0.0\n\t\tself._current_score = 0.0","def sumAllValues(self,*toSkip):\n sum=0\n for counterKey in self.counters.keys():\n if not counterKey in toSkip: sum += self.counters[counterKey]\n # 026 #self.debug.mainLogger.debug(\"Sumation of all counters finished with result %i.\"%(sum))\n return sum","def schwefel220fcn(x: np.ndarray) -> np.ndarray:\n scores = np.sum(np.abs(x), axis=1)\n return scores","def alignScore():\n matrix = mapMatrix(\"BLOSUM62\")\n \n path = \"./data/\"\n for file in os.listdir(path):\n if file.endswith(\".fa\") or file.endswith(\".fasta\"):\n sequences = []\n input_sequences = SeqIO.parse(path + file, \"fasta\", \\\n IUPAC.protein)\n\n for record in input_sequences:\n seq = str(record.seq)\n sequences.append(seq) \n \n SumOfPairs = 0\n for pair in combinations(sequences, 2): \n SumOfPairs += pairwiseScore(pair[0], pair[1], matrix)\n \n print SumOfPairs","def sumsquaresfcn(x: np.ndarray) -> np.ndarray:\n m, n = x.shape\n x2 = x**2\n I = np.tile(np.arange(1, n + 1), (m, 1))\n scores = np.sum(I * x2, axis=1, keepdims=True)\n\n return scores","def evaluate(self):\n self.matrix = pair_matrix(self)\n score = 0\n for x in range(len(self.seq)):\n for y in range(x, len(self.seq)):\n if self.matrix[x, y] == 1:\n if abs(x - y) < 5:\n score -= 7\n if self.seq[x] == complementary(self.seq[y]):\n score += 2\n elif self.seq[x] == 'U' and self.seq[y] == 'G' or self.seq[x] == 'G' and self.seq[y] == 'U':\n score += 1\n else:\n score -= 5\n return score"],"string":"[\n \"def update_scores(self):\\n self.score[0] = (-1)*sum(self.board[self.board == -1])\\n self.score[1] = sum(self.board[self.board == 1])\\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)\",\n \"def grades_sum(grades_input):\\n total = 0\\n for score in scores: \\n total += score\\n\\n return total\",\n \"def update_scores(self):\\r\\n totals = [0, 0, 0, 0]\\r\\n for player in range(0, 4):\\r\\n for round_num in range(0, 17):\\r\\n try:\\r\\n bid = int(self.spin_bids[player][round_num].get())\\r\\n tricks = int(self.spin_tricks[player][round_num].get())\\r\\n except ValueError:\\r\\n bid = -1\\r\\n tricks = -1\\r\\n score = calc_score(min(round_num+1, 13), bid, tricks)\\r\\n self.lbl_scores[player][round_num].configure(text=str(score))\\r\\n totals[player] += score\\r\\n for player in range(0, 4):\\r\\n self.lbl_totals[player].configure(text=str(totals[player]))\\r\\n return totals[0] + totals[1] + totals[2] + totals[3]\",\n \"def __get_score(self):\\n for pair in zip(self.nu[self.nu_idx:], self.sw[self.sw_idx:]):\\n if pair[0] == pair[1]:\\n self.score += 1\\n else:\\n break\",\n \"def calculate_total(score_sheet):\\n total_score = EMPTY_BOX()\\n if EMPTY_BOX() not in score_sheet:\\n if sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) >= UPPER_BONUS_REQUIREMENT():\\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)]) + UPPER_SECTION_BONUS()\\n elif sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) < UPPER_BONUS_REQUIREMENT():\\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)])\\n else:\\n total_score = EMPTY_BOX()\\n\\n return total_score\",\n \"def _calculate_score(self):\\n mul = self._check_board()\\n if mul > 0:\\n inc = 100 * mul + ((mul - 1) * 25)\\n self.score += inc\",\n \"def sum_points(self) -> int:\\n return sum([card.rank_value for card in self.deck.cards])\",\n \"def get_value(self):\\n #Finds all of the values in the cards\\n score_list=[Card.get_value(card) for card in self.cards]\\n #Sums the scores\\n if self.num_cards() > 0:\\n total_score=reduce((lambda x,y: x+y),score_list)\\n return total_score\\n else:\\n return 0\",\n \"def calculScore(self):\\n for cell in self.notComputeRouter:\\n if(cell.isCovered==True):\\n self.score += 1000\\n self.score += self.budget\",\n \"def scoreGame(self):\\n # create valueLs[card1,card2,...], pass it to sumHandReturnPoints(valueLs) or twoCardReturnPoints(valueLs)\\n scoreLs = []\\n ### Score of row\\n for rowKey in self.table:\\n valueLs = self.table[rowKey]\\n points = self.sumHandReturnPoints(valueLs)\\n scoreLs.append(points)\\n\\n ### Score of 4-card column\\n for offset in range(0,3): # 0,1,2\\n tmpLs = []\\n for rowKey in self.table:\\n valueLs = self.table[rowKey]\\n if len(valueLs) == 5:\\n iterStart = 1\\n else:\\n iterStart = 0\\n card = valueLs[iterStart+offset]\\n tmpLs.append(card)\\n points = self.sumHandReturnPoints(tmpLs)\\n scoreLs.append(points) \\n\\n ### Score of 2-card column\\n #(1) 1st column\\n valueLs1 = self.table['row1']\\n valueLs2 = self.table['row2']\\n tmpLs = []\\n tmpLs.append(valueLs1[0].get_rank())\\n tmpLs.append(valueLs2[0].get_rank())\\n points = self.twoCardReturnPoints(tmpLs)\\n scoreLs.append(points)\\n #(2) 5th column\\n valueLs3 = self.table['row1']\\n valueLs4 = self.table['row2']\\n tmpLs = []\\n tmpLs.append(valueLs3[-1].get_rank())\\n tmpLs.append(valueLs4[-1].get_rank())\\n points = self.twoCardReturnPoints(tmpLs)\\n scoreLs.append(points) \\n\\n ### Add up scoreLs\\n sumPoints = 0\\n for points in scoreLs:\\n sumPoints += points\\n return sumPoints\",\n \"def update_score(self, blanks=0, letters=0, words=0):\\n points = 0\\n points += letters * LETTER_POINT\\n points += words * WORD_POINT\\n points += blanks * BLANK_POINT\\n self.score += points\",\n \"def calculate_score(player_cards):\\n score = sum(player_cards)\\n return score\",\n \"def to_total_scores(ht_score, ft_score, et_score, pen_score):\\n total_score = ft_score\\n try:\\n for score in [et_score, pen_score]:\\n if None in score:\\n continue\\n score[0] = total_score[0] + score[0]\\n score[1] = total_score[1] + score[1]\\n total_score = score\\n except Exception:\\n return ht_score, ft_score, et_score, pen_score\\n return ht_score, ft_score, et_score, pen_score\",\n \"def merge(self):\\n for i in range(len(self.main_grid_values)):\\n for j in range(len(self.main_grid_values) - 1):\\n if self.main_grid_values[i][j] == self.main_grid_values[i][j+1]:\\n self.score_value.set(str(int(self.score_value.get()) + self.main_grid_values[i][j]*2))\\n self.main_grid_values[i][j] *= 2\\n self.main_grid_values[i][j+1] = 0\",\n \"def add_total_score(self, sheet_number):\\n cell_format = self.workbook.add_format(self.base_format)\\n # cell_format.set_bg_color('yellow')\\n # total score of rows\\n self.worksheets[sheet_number].write(0, len(self.arrays[sheet_number][0]), 'total score', cell_format)\\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), 0, 'total score', cell_format)\\n for i in range(2, len(self.arrays[sheet_number])):\\n count = 0\\n for j in range(1, len(self.arrays[sheet_number][0])):\\n count += self.arrays[sheet_number][i][j]\\n self.worksheets[sheet_number].write(i, len(self.arrays[sheet_number][0]), count, cell_format)\\n # total score of columns\\n for i in range(1, len(self.arrays[sheet_number][0])):\\n count = 0\\n for j in range(2, len(self.arrays[sheet_number])):\\n count += self.arrays[sheet_number][j][i]\\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), i, count, cell_format)\",\n \"def _scoring(self):\\n val = 0 #score will be totaled here\\n\\n for c in range(0, self.width): #for every column in the board\\n for r in range(0, self.height): #for every row of a column\\n #see if we can move...\\n possible_up = (r + 3 < self.height) #up?\\n possible_left = (c - 3 > 0) #left?\\n possible_right = (c + 3 < self.width) #right?\\n\\n #diagonally up, left\\n if possible_up and possible_left:\\n val+= self._up_left(c, r)\\n\\n #up\\n if possible_up:\\n val+= self._up(c,r)\\n\\n #diagonally up, right\\n if possible_up and possible_right:\\n val+= self._up_right(c,r)\\n\\n #right\\n if possible_right:\\n val+= self._right(c,r)\\n\\n\\n return val\",\n \"def _compute_scores(self, triples):\\n # compute scores as sum(s * p * o)\\n scores = tf.reduce_sum(triples[0] * triples[1] * triples[2], 1)\\n return scores\",\n \"def get_local_score(self):\\n for candidate in self.candidate_list:\\n self.score += candidate.get_score()\",\n \"def sumHandReturnPoints(self, valueLs): # card is the element in valueLs\\n #Ace is dealt with here, assume Ace to be 11 initially, decreasing by 10 per Ace if sum > 21\\n rowSum = 0\\n AceCount = 0\\n for ele in valueLs:\\n rank = ele.get_rank()\\n if rank == 1:\\n rank = 11\\n AceCount += 1 # serve as flag\\n rowSum += rank\\n while(AceCount!=0):\\n if rowSum > 21:\\n rowSum -= 10\\n AceCount -= 1\\n points = self.countPoints(rowSum)\\n return points\",\n \"def calculate_score(cards):\\n if sum(cards) == 21 and len(cards) == 2:\\n return 0\\n \\n if 11 in cards and sum(cards) > 21:\\n cards.remove 11\\n cards.append 1\\n return sum(cards)\",\n \"def getScore(self):\\n return sum(self.field)\",\n \"def score(self):\\n return sum(len(i) for i in self.firework)\",\n \"def calculate_scores(self):\\n words = self.walk_board()\\n player_scores = {}\\n for word in words:\\n player = word.get_owning_player()\\n if player not in player_scores:\\n player_scores[player] = 0\\n player_scores[player] += word.get_score()\\n return player_scores\",\n \"def sum_(hand: list):\\n vals = [card.rank for card in hand]\\n intvals = []\\n while len(vals) > 0:\\n value = vals.pop()\\n try:\\n intvals.append(int(value))\\n except ValueError:\\n if value in ['K', 'Q', 'J']:\\n intvals.append(10)\\n elif value == 'A':\\n intvals.append(1) # Keep it simple for the sake of example\\n if intvals == [1, 10] or intvals == [10, 1]:\\n print(\\\" Blackjack!\\\")\\n return(21)\\n else:\\n points = sum(intvals)\\n print(\\\" Current score: {}\\\".format(str(points)))\\n return(points)\",\n \"def add_score(self, points: int) -> None:\\n self.__score += points\\n\\n for rank in self.__ranks.keys():\\n if self.__score >= rank:\\n self.__level = self.__ranks[rank]\\n else:\\n break\",\n \"def scores_for(self, board):\\r\\n scores = [50] * board.width\\r\\n\\r\\n for col in range(board.width):\\r\\n if not board.can_add_to(col):\\r\\n scores[col] = -1\\r\\n elif board.is_win_for(self.checker):\\r\\n scores[col] = 100\\r\\n elif board.is_win_for(self.opponent_checker()):\\r\\n scores[col] = 0\\r\\n elif self.lookahead == 0:\\r\\n scores[col] = 50\\r\\n else: \\r\\n board.add_checker(self.checker, col)\\r\\n opponent = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead - 1)\\r\\n opp_scores = opponent.scores_for(board)\\r\\n if max(opp_scores) == 100:\\r\\n scores[col] = 0\\r\\n elif max(opp_scores) == 0:\\r\\n scores[col] = 100\\r\\n else:\\r\\n scores[col] = 50\\r\\n board.remove_checker(col)\\r\\n\\r\\n return scores\",\n \"def set_score(self, points):\\n self.score += points\",\n \"def scores_for(self, board):\\r\\n scores = [1]*board.width\\r\\n for i in range(board.width):\\r\\n if not board.can_add_to(i):\\r\\n scores[i] = -1\\r\\n elif board.is_win_for(self.checker):\\r\\n scores[i] = 100\\r\\n elif board.is_win_for(self.opponent_checker()):\\r\\n scores[i] = 0\\r\\n elif self.lookahead == 0:\\r\\n scores[i] = 50\\r\\n else:\\r\\n board.add_checker(self.checker, i)\\r\\n other = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead-1)\\r\\n other_scores = other.scores_for(board)\\r\\n if max(other_scores) == 100:\\r\\n scores[i] = 0\\r\\n elif max(other_scores) == 50:\\r\\n scores[i] = 50\\r\\n elif max(other_scores) == 0:\\r\\n scores[i] = 100\\r\\n board.remove_checker(i)\\r\\n return scores\",\n \"def grand_total(self):\\n return sum(self.grid[pos][1] for pos in assignable_positions if self.grid[pos][0]) + self.grid[\\\"nb\\\"][1]\",\n \"def score(self):\",\n \"def score(self, robot):\\n scores = [grupo.score(robot) for grupo in self.get_grupos()]\\n return reduce(lambda acumulador, score: tuple([ a + b for a, b in zip(acumulador, score)]), scores, (0, 0, 0, 0, 0, 0, 0, 0))\",\n \"def aver_score(datalist):\\n scores_per_position = []\\n \\n for tupl in datalist:\\n count = 0\\n sum_of_position = 0\\n for element in tupl[3]:\\n sum_of_position += element\\n count +=1\\n aver_pos = sum_of_position/ count\\n scores_per_position += [aver_pos]\\n \\n return scores_per_position\",\n \"def get_score(self):\\n return sum([Letters.get_value(tile.letter) for tile in self.tiles])\",\n \"def score(self, board: Block) -> int:\\n grid = _flatten(board)\\n left = grid[0]\\n right = grid[-1]\\n top = [i[0] for i in grid]\\n bottom = [i[-1] for i in grid]\\n score0 = left.count(self.colour)\\n score1 = right.count(self.colour)\\n score2 = top.count(self.colour)\\n score3 = bottom.count(self.colour)\\n return score0 + score1 + score2 + score3\",\n \"def get_score_matrix(self) -> int:\",\n \"def global_score_count(self):\\n score_count_mat = np.ndarray((5,10))\\n\\n for n in range(0,5):\\n score_count = [\\n np.count_nonzero(self.__data[...,n*5:n*5+5] == score)\\n for score\\n in np.arange(10)+1\\n ]\\n\\n score_count_mat[n,...] = score_count\\n\\n return score_count_mat\",\n \"def update_points(self):\\n #Calculate Upper Section\\n total = 0\\n for box in self._upper_section:\\n total += box.points\\n self._upperSum = total\\n\\n if total >= 63:\\n self._bonus = 35\\n total += 35\\n self._upperTotal = total\\n\\n # Calculate Lower Section\\n total = 0\\n for box in self._lower_section:\\n total += box.points\\n\\n if self.get_box(\\\"Yahtzee\\\").points > 0:\\n total = total + (self._yahtzee_count - 1) * 100 # Yahtzee Bonus\\n\\n self._lowerTotal = total\\n\\n #Total Points\\n self._grandTotal = self._upperTotal + self._lowerTotal\",\n \"def get_score(self, pos):\\n score = 0\\n gap_count = 0\\n align_query, align_target = '', ''\\n for xval, yval in pos:\\n xunit = self.get_seq(self.target, xval)\\n yunit = self.get_seq(self.query, yval)\\n align_query += yunit\\n align_target += xunit\\n if xval != 0 and yval != 0:\\n score += self.subs_matrix[xunit][yunit]\\n gap_count = 0\\n else:\\n gap_count += 1\\n if gap_count > 1 and len(self.gap_penatly) > 1:\\n score += self.gap_penatly[1]\\n else:\\n score += self.gap_penatly[0]\\n return score, align_query, align_target\",\n \"def score(self):\\n result = 0\\n\\n idx = self.cups.index(1)\\n idx += 1\\n if idx >= len(self.cups):\\n idx = 0\\n # ok, keep adding things until we get back to 1\\n while 1 != self.cups[idx]:\\n # add this value..\\n result *= 10\\n result += self.cups[idx]\\n # and on to the next one..\\n idx += 1\\n if idx >= len(self.cups):\\n idx = 0\\n\\n return result\",\n \"def update_score(self, score: int) -> int:\\n self.score += score\\n return self.score\",\n \"def sum_scores(scores):\\n segs = itertools.chain(*[x[\\\"segments\\\"][:] for x in scores]) #all the segs in flat list\\n d_counts = sum_dicts([x[\\\"events\\\"][\\\"d_counts\\\"] for x in scores])\\n t_counts = sum_dicts([x[\\\"events\\\"][\\\"t_counts\\\"] for x in scores])\\n d_rates = pct_dict(d_counts)\\n t_rates = pct_dict(t_counts)\\n return dict(frame_scores=score_frames(segs), event_scores=dict(d_counts=d_counts, t_counts=t_counts, d_rates=d_rates, t_rates=t_rates))\",\n \"def get_score_list(self) -> List[int]:\\n\\n result, values = [], []\\n for i in range(len(self._cards)):\\n for value in self._cards[i]:\\n values.append(value[0])\\n if sum(values) > 21:\\n for num in range(len(values)):\\n if values[num] == 11:\\n values[num] = 1\\n result.append(sum(values))\\n values = []\\n return result\",\n \"def merge_scores(self): # pragma: no cover\\n\\n merged_scores = []\\n score_end_indices = []\\n\\n for i, score in enumerate(self.anom_scores):\\n if not score['start_idx']-1 in score_end_indices:\\n merged_scores.append(score['score'])\\n score_end_indices.append(score['end_idx'])\",\n \"def column_sums(square):\\n total = 0\",\n \"def calculate_player_position_score(marbles: list):\\n prime = Evaluator.prime_positions\\n good = Evaluator.good_positions\\n position_score = 0\\n for marble in marbles:\\n if marble in prime:\\n position_score += 10\\n elif marble in good:\\n position_score += 5\\n else:\\n position_score -= 1\\n return position_score\",\n \"def score_of_nodes(self, score):\\n for hypervisor_id in self.model.get_all_hypervisors():\\n hypervisor = self.model. \\\\\\n get_hypervisor_from_id(hypervisor_id)\\n count = self.model.get_mapping(). \\\\\\n get_node_vms_from_id(hypervisor_id)\\n if len(count) > 0:\\n result = self.calculate_score_node(hypervisor)\\n else:\\n # The hypervisor has not VMs\\n result = 0\\n if len(count) > 0:\\n score.append((hypervisor_id, result))\\n return score\",\n \"def _update_score(self) -> None:\\n\\n # setting new score by iterating over players\\n self.score_play[self.n_play_turns, ] = [\\n self._score_table[(\\n self.contract.level,\\n self.contract.suit,\\n self.tricks[i],\\n self.contract.player_vulnerability[i],\\n int(self.contract.double + self.contract.redouble)\\n )]\\n for i in range(NUM_PLAYERS)\\n ]\",\n \"def calc_score_for_lang(self, lang_tree):\\n scores = []\\n lang_tree.tree.find_all(self.text, lambda index, score: scores.append(score))\\n self.scores[lang_tree.lang] = sum(scores)\",\n \"def score(self):\\r\\n totN = 0\\r\\n totB = 0\\r\\n for l in range(SIZE):\\r\\n for c in range(len(COLONNES)):\\r\\n if self.jeu[l][c] == NOIR:\\r\\n totN += 1\\r\\n elif self.jeu[l][c] == BLANC:\\r\\n totB += 1\\r\\n return (totN, totB)\",\n \"def update_score():\\n pass\",\n \"def scoring(self):\\n pass\",\n \"def total_score(self, logits):\\n previous = torch.full((1, self.tag_size), -10000., device=device)\\n previous[0][self.tag_map[self.start_tag]] = 0.\\n\\n for index in range(len(logits)):\\n previous = previous.expand(self.tag_size, self.tag_size).t()\\n emit = logits[index].view(1, -1).expand(self.tag_size, self.tag_size)\\n scores = previous + emit + self.transitions\\n previous = log_sum_exp(scores)\\n\\n # previous = previous + self.transitions[:, self.tag_map[self.stop_tag]]\\n # previous += self.transitions[self.tag_map[self.stop_tag]]\\n previous += self.transitions[self.tag_map[:, self.stop_tag]]\\n total_scores = log_sum_exp(previous.t())[0]\\n return total_scores\",\n \"def calc_sum_fitness(self):\\n fitness: float = 0\\n for c in self.characters:\\n fitness += c.fitness\\n self.sum_fitness = round(fitness, 3)\",\n \"def update_score(self):\\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)\",\n \"def calculate_score(list_of_cards):\\n if sum(list_of_cards) == 21 and len(list_of_cards) == 2:\\n return 0\\n if 11 in list_of_cards and sum(list_of_cards) > 21:\\n list_of_cards.remove(11)\\n list_of_cards.append(1)\\n return sum(list_of_cards)\",\n \"def _tally(self, score):\\n self._score[self._turn] += score\",\n \"def _calc_multiple_alignment_score(wrapped_data : tuple) -> int: \\n (start, finish) = wrapped_data \\n score_sum = 0.\\n for dna_record in tqdm(dna_sequences[start : finish + 1], total=(finish + 1 - start), desc=\\\"Training process\\\"):\\n score_sum += self.aligner.score(seq, dna_record.seq)\\n return score_sum\",\n \"def compute_score(self):\\n for i in xrange(FRAMES):\\n # STRIKE\\n if self.frames[i][0] == 10:\\n # CONSECUTIVE STRIKE\\n if self.frames[i + 1][0] == 10:\\n self.scores.append(self.frames[i][0] +\\n self.frames[i + 1][0] +\\n self.frames[i + 2][0])\\n else:\\n self.scores.append(self.frames[i][0] +\\n self.frames[i + 1][0] +\\n self.frames[i + 1][1])\\n # SPARE\\n elif (self.frames[i][0] + self.frames[i][1] == 10):\\n self.scores.append(self.frames[i][0] + self.frames[i][1] +\\n self.frames[i + 1][0])\\n # NEITHER\\n else:\\n self.scores.append(self.frames[i][0] + self.frames[i][1])\\n # Total Score\\n for score in self.scores:\\n self.score += score\",\n \"def run_compute_price_score(self):\\n for home_data in self.homes:\\n score_result = self.compute_price_score(home_data.home.price)\\n if score_result == -1:\\n home_data.eliminate_home()\\n home_data.accumulated_points = score_result * self.price_user_scale_factor * self.price_question_weight\\n home_data.total_possible_points = self.price_user_scale_factor * self.price_question_weight\",\n \"def update_scores(self, score):\\n self.result_list.append(score)\\n\\n if self.best_score == 0 and self.worst_score == 0:\\n self.best_score = score\\n self.worst_score = score\\n\\n if score < self.best_score:\\n self.best_score = score\\n\\n if score > self.worst_score:\\n self.worst_score = score\",\n \"def score(self):\\n xg, yg = self.goal\\n xe, ye = self.empty_node()\\n score = len(self.history) + 4*(xg + yg)\\n if xg == 1:\\n score -= 3\\n if ye > 1:\\n score += ye - 1\\n dx = abs(xe - xg + 1)\\n if xg and dx:\\n score += dx\\n return score\",\n \"def adjust_score(self):\\n self.score += game.temporary_score\",\n \"def schwefel222fcn(x: np.ndarray) -> np.ndarray:\\n absx = np.abs(x)\\n scores = np.sum(absx, axis=1) + np.prod(absx, axis=1)\\n return scores\",\n \"def mc_update_scores(scores, board, player):\\r\\n dim = board.get_dim()\\r\\n winner = board.check_win()\\r\\n if winner == provided.DRAW:\\r\\n return\\r\\n flag = (player == winner)\\r\\n if flag:\\r\\n offset = 1\\r\\n else:\\r\\n offset = -1\\r\\n for idx in range(dim):\\r\\n for jdx in range(dim):\\r\\n if board.square(idx,jdx) == player:\\r\\n scores[idx][jdx] += (offset * SCORE_CURRENT)\\r\\n elif board.square(idx,jdx) != provided.EMPTY:\\r\\n scores[idx][jdx] -= (offset * SCORE_OTHER)\",\n \"def sum_squares(X):\\n pref_sum = np.cumsum(X)\\n pref_sum_squares = np.cumsum(X**2)\\n n = len(X)\\n sum_squares_left = np.zeros(n+1)\\n sum_squares_right = np.zeros(n+1)\\n for i in range(1, n+1):\\n mean_left = pref_sum[i-1]/i \\n mean_right = (pref_sum[-1] - pref_sum[i-1])/(n-i) # bad for i = n overwritten later\\n sum_squares_left[i] = pref_sum_squares[i-1] + i * mean_left**2 - 2 * mean_left * pref_sum[i-1] \\n sum_squares_right[i] = (pref_sum_squares[-1] - pref_sum_squares [i-1]) + mean_right**2 * (n-i) - 2 * mean_right *(pref_sum[-1] - pref_sum[i-1])\\n sum_squares_left[0] = 0\\n sum_squares_right[0] = sum_squares_left[-1]\\n sum_squares_right[-1] = 0\\n return sum_squares_left, sum_squares_right\",\n \"def disp_score():\",\n \"def _aggregate_scores(self,\\n game_scores: List[Dict[str, int]]) -> task.ScoreData:\\n\\n relative_score = []\\n recall = []\\n num_incorrect = []\\n precision = []\\n\\n for score_dict in game_scores:\\n\\n relative_score.append(score_dict['score']/score_dict['max_score'])\\n recall.append(score_dict['num_correct']/score_dict['word_count'])\\n num_incorrect.append(score_dict['num_incorrect'])\\n\\n # Precision -- defined to be 1 if no words are guessed.\\n num_guesses = score_dict['num_correct'] + score_dict['num_incorrect']\\n if num_guesses > 0:\\n precision.append(score_dict['num_correct'] / num_guesses)\\n else:\\n precision.append(1.0)\\n\\n relative_score = np.mean(relative_score)\\n recall = np.mean(recall)\\n num_incorrect = np.mean(num_incorrect)\\n precision = np.mean(precision)\\n\\n score_data = task.ScoreData(\\n {'relative_score': relative_score,\\n 'recall': recall,\\n 'num_incorrect': num_incorrect,\\n 'precision': precision},\\n preferred_score='relative_score',\\n low_score=0.0,\\n high_score=1.0,\\n number_of_shots=-1,\\n subtask_description=('A simplified implementation of the NYT '\\n 'Spelling Bee game. Individual game scoring is '\\n 'defined in the class deffinition. Reported '\\n 'scores are normalized to game maximums.'\\n 'All scores are averaged over each game played '\\n 'in an evaluation run.'))\\n\\n return score_data\",\n \"def get_total_score(list, board):\\n obj_list = []\\n\\n # Get board to end-state\\n for pos_tuple in list:\\n obj_list.append(board[pos_tuple[1][0]][pos_tuple[1][1]])\\n move_on_own_board(pos_tuple[0], pos_tuple[1], board)\\n\\n # Calculate the score\\n tot_score = 0\\n for row in range(8):\\n for col in range(8):\\n if piece_on_pos((row, col), board):\\n obj = board[row][col]\\n tot_score += obj.points\\n\\n # Improve score calculation\\n if obj.string == \\\"PW\\\":\\n if row == 7:\\n tot_score += 5 # Pawn becomes queen\\n else:\\n tot_score += 0.01 * (row * row)\\n elif obj.string == \\\"PB\\\":\\n if row == 0:\\n tot_score -= 5 # Pawn becomes queen\\n else:\\n tot_score -= 0.01 * ((7 - row) * (7 - row))\\n elif (obj.string == \\\"HW\\\") | (obj.string == \\\"BW\\\") | (obj.string == \\\"QW\\\"):\\n if row > 0:\\n tot_score += 0.1\\n\\n # Get board back to init-state\\n for i in reversed(range(len(list))):\\n move_on_own_board(list[i][1], list[i][0], board)\\n board[list[i][1][0]][list[i][1][1]] = obj_list[i] # Listception\\n\\n return tot_score\",\n \"def score( self ):\\r\\n result = 0.0\\r\\n for rr in self.ee.getRsrcs( ):\\r\\n value = self.scoreRsrc( rr )\\r\\n result += value\\r\\n print( \\\"INFO: Value for the schedule is %s \\\" % ( rr, result ) )\\r\\n return( result )\",\n \"def add_to_score(self, to_add):\\n self.score += to_add\",\n \"def get_score(self):\\n for response in self.response_list:\\n self.score += response.get_score\",\n \"def summation(self):\\n return sum(self.read_ints())\",\n \"def increase(self, points):\\n self.score += points\",\n \"def score(self):\\n return self.aggregate(Sum('score')).values()[0] or 0\",\n \"def sum1d_pos(summand):\\n total = 0\\n for i in range(summand.size):\\n total += summand[i]\\n return total\",\n \"def score(self, hand, position):\\n\\n try:\\n assert self.grid[position][1] == \\\"---\\\"\\n except AssertionError:\\n print self\\n print position\\n raise FilledInError\\n except KeyError:\\n print \\\"\\\\nCheck your code. This is not a valid position:\\\", position, \\\"\\\\n\\\"\\n raise\\n\\n if position.startswith(\\\"n\\\"): # Return sum of relevant number\\n n = int(position[1])\\n return sum(d for d in hand.dice if d == n)\\n\\n elif position in [\\\"k3\\\", \\\"k4\\\", \\\"ch\\\"]: # Return total sum\\n if position == \\\"k3\\\" and hand.max_tally()[0] < 3:\\n return 0 # The is not a three of a kind\\n elif position == \\\"k4\\\" and hand.max_tally()[0] < 4:\\n return 0 # The is not a four of a kind\\n return sum(hand.dice)\\n\\n elif position in [\\\"fh\\\", \\\"ss\\\", \\\"ls\\\", \\\"yz\\\", \\\"yb\\\"]: # Return fixed score\\n if position == \\\"fh\\\":\\n tallies = hand.get_dicedict().values()\\n if 1 in tallies:\\n return 0 # This is not a full house\\n\\n elif position in [\\\"ss\\\", \\\"ls\\\"]:\\n ds = \\\"\\\".join(str(x) for x in hand.sort_by_value())\\n if position == [\\\"ss\\\"]:\\n if \\\"1234\\\" not in ds and \\\"2345\\\" not in ds and \\\"3456\\\" not in ds:\\n return 0\\n else:\\n if \\\"12345\\\" not in ds and \\\"23456\\\" not in ds:\\n return 0\\n\\n else:\\n if hand.max_tally()[0] < 5:\\n return 0 # This is not a yahtzee\\n if position == \\\"yb\\\" and self.grid[\\\"yz\\\"] == \\\"---\\\":\\n return 0 # YB only scores points if there already is a YZ\\n\\n return fixed_scores[position]\\n\\n else:\\n raise InvalidPositionError\",\n \"def fit_score(self, solution):\\r\\n illegal_neighbours = 0\\r\\n legal_neighbours = 0\\r\\n for polygon in solution.genetic_units.values():\\r\\n for neighbour_id in polygon.neighbours_ids:\\r\\n if polygon.color is solution.genetic_units[neighbour_id].color:\\r\\n illegal_neighbours += 1\\r\\n else:\\r\\n legal_neighbours += 1\\r\\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n return illegal_neighbours\\r\\n else:\\r\\n return legal_neighbours\",\n \"def calc_score(pins_stats):\\n count = 0\\n new = pins_stats[:, :2] - ORIG_PINS_LOC\\n for p in new:\\n if np.linalg.norm(p) > R_PIN / 2:\\n count += 1\\n return count\",\n \"def score(self, hand, top_card):\\n powerset = GameState.powerset(list(hand).append(top_card))\\n score = 0\\n for meld in powerset:\\n score += self.score_meld(meld)\\n return score\",\n \"def sum_cards(self):\\n has_ace = False\\n sum = 0\\n\\n # Add up players cards\\n for card in self.cards:\\n if card.card_value == \\\"ace\\\":\\n has_ace = True\\n sum += card.game_value\\n\\n # Handle case where ace plays low\\n if sum > 21 and has_ace:\\n sum -= 10\\n\\n return sum\",\n \"def game_score(matrix):\\n total_score = 0\\n\\n for i in range(len(matrix)):\\n for j in range(len(matrix[0])):\\n\\n value_to_multiply_tile = 0\\n tile_copy = copy.deepcopy(matrix[i][j])\\n\\n # a tile of worth 0 = an empty tile in game\\n if tile_copy != 0 or tile_copy != 2:\\n while tile_copy > 2:\\n value_to_multiply_tile += 1\\n tile_copy /= 2\\n total_score += matrix[i][j] * value_to_multiply_tile\\n\\n return total_score\",\n \"def get_score(self):\\n rewards, resets = self.runner.get_rewards_resets()\\n self.runner.clear_rewards_resets()\\n assert rewards.ndim == 1 and resets.ndim == 1, (rewards.ndim, resets.ndim)\\n assert rewards.shape[0] == resets.shape[0], (rewards.shape, resets.shape)\\n scores = [0]\\n for t in reversed(range(rewards.shape[0])):\\n if resets[t]:\\n scores.append(0)\\n scores[-1] += rewards[t]\\n return np.mean(scores)\",\n \"def _draw_score(self) -> None:\\n score_digits = [int(x) for x in list(str(self.game.score))]\\n total_width = 0 # total width of all numbers to be printed\\n\\n for digit in score_digits:\\n total_width += self.images['numbers'][digit].get_width()\\n\\n x_offset = (self._screen_width - total_width) / 2\\n\\n for digit in score_digits:\\n self.surface.blit(self.images['numbers'][digit],\\n (x_offset, self._screen_height * 0.1))\\n x_offset += self.images['numbers'][digit].get_width()\",\n \"def updateScore(self, score):\\n self.__score += score\",\n \"def game_nim_sum():\\n global piles\\n global num_piles \\n nim_sum = 0\\n for x in piles:\\n nim_sum = nim_sum^x\\n \\n return nim_sum\",\n \"def calculate_score(card_list):\\n if sum(card_list) == 21 and len(card_list) == 2:\\n return 0\\n if sum(card_list) > 21 and 11 in card_list:\\n card_list.remove(11)\\n card_list.append(1)\\n return sum(card_list)\",\n \"def get_score(self):\\r\\n correct = 0\\r\\n for key in self.correct_map:\\r\\n try:\\r\\n correct += self.correct_map.get_npoints(key)\\r\\n except Exception:\\r\\n log.error('key=%s, correct_map = %s', key, self.correct_map)\\r\\n raise\\r\\n\\r\\n if (not self.student_answers) or len(self.student_answers) == 0:\\r\\n return {'score': 0,\\r\\n 'total': self.get_max_score()}\\r\\n else:\\r\\n return {'score': correct,\\r\\n 'total': self.get_max_score()}\",\n \"def update_score(self, board):\\n self._score += 1\",\n \"def getScore(self, seq, start):\\n sum = 0.0\\n seqdata = seq.getSequence()[start : start+self.cols]\\n for pos in range(len(seqdata)):\\n q = self.counts[pos].getFreq(seqdata[pos])\\n if q == 0:\\n q = 0.0001 # to avoid log(0) == -Infinity\\n logodds = math.log(q / self.background.getFreq(seqdata[pos]))\\n sum += logodds\\n return sum\",\n \"def add_score(self, score):\\n self._score += score\",\n \"def mc_update_scores(scores, board, player):\\n winner = board.check_win()\\n\\n for col in range(board.get_dim()):\\n for row in range(board.get_dim()):\\n\\n if winner == provided.DRAW:\\n scores[row][col] += 0\\n\\n elif winner == player and board.square(row, col) == player:\\n scores[row][col] += SCORE_CURRENT\\n elif winner == player and board.square(row, col) != player and board.square(row, col) != provided.EMPTY:\\n scores[row][col] -= SCORE_OTHER\\n elif winner != player and board.square(row, col) == player:\\n scores[row][col] -= SCORE_CURRENT\\n elif winner != player and board.square(row, col) != player and board.square(row, col) != provided.EMPTY:\\n scores[row][col] += SCORE_CURRENT\\n elif board.square(row, col) == provided.EMPTY:\\n scores[row][col] += 0\",\n \"def mc_update_scores(scores, board, player):\\n winner = board.check_win()\\n mc_match = 0\\n mc_other = 0\\n \\n if winner == player:\\n mc_match = SCORE_CURRENT\\n mc_other = -SCORE_OTHER\\n elif winner == provided.switch_player(player):\\n mc_match = -SCORE_CURRENT\\n mc_other = SCORE_OTHER\\n \\n for row in range(board.get_dim()):\\n for col in range(board.get_dim()):\\n if board.square(row, col) == player:\\n scores[row][col] += mc_match\\n elif board.square(row, col) == provided.switch_player(player):\\n scores[row][col] += mc_other\",\n \"def score(self) -> int:\\n card_values = {\\n '0': 0,\\n '1': 1,\\n '2': 2,\\n '3': 3,\\n '4': 4,\\n '5': 5,\\n '6': 6,\\n '7': 7,\\n '8': 8,\\n '9': 9,\\n '10': 10,\\n 'JACK': 10,\\n 'QUEEN': 10,\\n 'KING': 10,\\n 'ACE': 11}\\n hand_value = []\\n for i in self.cards:\\n hand_value.append(card_values[i.value])\\n while sum(hand_value) > 21 and 11 in hand_value:\\n for i, j in enumerate(hand_value):\\n if j == 11:\\n hand_value[i] = 1\\n break\\n else:\\n pass\\n return sum(hand_value)\",\n \"def check_score(self, move):\\n\\n i = int(move) // 7\\n j = int(move) % 7 #find the corresponding index of the input move\\n\\n if i == 0: #top\\n if self.board[i+1][j-1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i+2][j] != \\\"\\\":\\n return 1\\n return 0\\n if i == 6: #bottom\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i-1][j+1] != \\\"\\\" and self.board[i-2][j] != \\\"\\\":\\n return 1\\n return 0\\n if j == 0: #left\\n if self.board[i-1][j+1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i][j+2] != \\\"\\\":\\n return 1\\n return 0\\n if j == 6: #right\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i+1][j-1] != \\\"\\\" and self.board[i][j-2] != \\\"\\\":\\n return 1\\n return 0\\n if i == 2 or i == 4: # horizontal\\n score = 0\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i-1][j+1] != \\\"\\\" and self.board[i-2][j] != \\\"\\\":\\n score += 1\\n if self.board[i+1][j-1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i+2][j] != \\\"\\\":\\n score += 1\\n return score\\n\\n if j == 2 or j == 4: # vertical\\n score = 0\\n if self.board[i-1][j-1] != \\\"\\\" and self.board[i+1][j-1] != \\\"\\\" and self.board[i][j-2] != \\\"\\\":\\n score += 1\\n if self.board[i-1][j+1] != \\\"\\\" and self.board[i+1][j+1] != \\\"\\\" and self.board[i][j+2] != \\\"\\\":\\n score += 1\\n return score\",\n \"def finalize(self):\\n\\t\\tif self._sum_factor != 0.0:\\n\\t\\t\\tself._last_score = self._current_score / self._sum_factor\\n\\t\\telse:\\n\\t\\t\\tself._last_score = 0.0\\n\\n\\t\\tself._scores.append(self._last_score)\\n\\t\\tself._scores = self._scores[-self._range[1]:]\\n\\t\\n\\t\\tself._sum_factor = 0.0\\n\\t\\tself._current_score = 0.0\",\n \"def sumAllValues(self,*toSkip):\\n sum=0\\n for counterKey in self.counters.keys():\\n if not counterKey in toSkip: sum += self.counters[counterKey]\\n # 026 #self.debug.mainLogger.debug(\\\"Sumation of all counters finished with result %i.\\\"%(sum))\\n return sum\",\n \"def schwefel220fcn(x: np.ndarray) -> np.ndarray:\\n scores = np.sum(np.abs(x), axis=1)\\n return scores\",\n \"def alignScore():\\n matrix = mapMatrix(\\\"BLOSUM62\\\")\\n \\n path = \\\"./data/\\\"\\n for file in os.listdir(path):\\n if file.endswith(\\\".fa\\\") or file.endswith(\\\".fasta\\\"):\\n sequences = []\\n input_sequences = SeqIO.parse(path + file, \\\"fasta\\\", \\\\\\n IUPAC.protein)\\n\\n for record in input_sequences:\\n seq = str(record.seq)\\n sequences.append(seq) \\n \\n SumOfPairs = 0\\n for pair in combinations(sequences, 2): \\n SumOfPairs += pairwiseScore(pair[0], pair[1], matrix)\\n \\n print SumOfPairs\",\n \"def sumsquaresfcn(x: np.ndarray) -> np.ndarray:\\n m, n = x.shape\\n x2 = x**2\\n I = np.tile(np.arange(1, n + 1), (m, 1))\\n scores = np.sum(I * x2, axis=1, keepdims=True)\\n\\n return scores\",\n \"def evaluate(self):\\n self.matrix = pair_matrix(self)\\n score = 0\\n for x in range(len(self.seq)):\\n for y in range(x, len(self.seq)):\\n if self.matrix[x, y] == 1:\\n if abs(x - y) < 5:\\n score -= 7\\n if self.seq[x] == complementary(self.seq[y]):\\n score += 2\\n elif self.seq[x] == 'U' and self.seq[y] == 'G' or self.seq[x] == 'G' and self.seq[y] == 'U':\\n score += 1\\n else:\\n score -= 5\\n return score\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7418236","0.6753837","0.6666424","0.64958215","0.6413631","0.6385383","0.63700944","0.6284259","0.62794924","0.62260115","0.62230796","0.620351","0.62015486","0.6172633","0.6159928","0.61399513","0.6136497","0.6111552","0.6099969","0.6067096","0.60524994","0.6019855","0.6017741","0.6017433","0.6006695","0.59794974","0.59664863","0.59581894","0.59487104","0.59476703","0.5946251","0.5938703","0.59257334","0.5913759","0.59044415","0.5892139","0.58813053","0.5877422","0.5875836","0.583622","0.5826298","0.5822777","0.5811936","0.5804668","0.5799133","0.579887","0.57910377","0.5788769","0.5785301","0.5778407","0.57659394","0.5765666","0.57608306","0.5744664","0.5742389","0.5740637","0.5734399","0.5733357","0.5721067","0.5716066","0.5702005","0.56846714","0.5665171","0.5664594","0.5661402","0.5656996","0.5653792","0.5638988","0.5633888","0.5625836","0.5623112","0.56187785","0.5614857","0.5612693","0.5603815","0.5592419","0.5586103","0.55848455","0.5581266","0.5579144","0.5578639","0.55767405","0.5575976","0.55716485","0.556644","0.5565841","0.55603296","0.5553369","0.5552507","0.554719","0.5542908","0.55405587","0.5536273","0.55337757","0.5499198","0.5490823","0.5490164","0.54854894","0.5480157","0.5475498"],"string":"[\n \"0.7418236\",\n \"0.6753837\",\n \"0.6666424\",\n \"0.64958215\",\n \"0.6413631\",\n \"0.6385383\",\n \"0.63700944\",\n \"0.6284259\",\n \"0.62794924\",\n \"0.62260115\",\n \"0.62230796\",\n \"0.620351\",\n \"0.62015486\",\n \"0.6172633\",\n \"0.6159928\",\n \"0.61399513\",\n \"0.6136497\",\n \"0.6111552\",\n \"0.6099969\",\n \"0.6067096\",\n \"0.60524994\",\n \"0.6019855\",\n \"0.6017741\",\n \"0.6017433\",\n \"0.6006695\",\n \"0.59794974\",\n \"0.59664863\",\n \"0.59581894\",\n \"0.59487104\",\n \"0.59476703\",\n \"0.5946251\",\n \"0.5938703\",\n \"0.59257334\",\n \"0.5913759\",\n \"0.59044415\",\n \"0.5892139\",\n \"0.58813053\",\n \"0.5877422\",\n \"0.5875836\",\n \"0.583622\",\n \"0.5826298\",\n \"0.5822777\",\n \"0.5811936\",\n \"0.5804668\",\n \"0.5799133\",\n \"0.579887\",\n \"0.57910377\",\n \"0.5788769\",\n \"0.5785301\",\n \"0.5778407\",\n \"0.57659394\",\n \"0.5765666\",\n \"0.57608306\",\n \"0.5744664\",\n \"0.5742389\",\n \"0.5740637\",\n \"0.5734399\",\n \"0.5733357\",\n \"0.5721067\",\n \"0.5716066\",\n \"0.5702005\",\n \"0.56846714\",\n \"0.5665171\",\n \"0.5664594\",\n \"0.5661402\",\n \"0.5656996\",\n \"0.5653792\",\n \"0.5638988\",\n \"0.5633888\",\n \"0.5625836\",\n \"0.5623112\",\n \"0.56187785\",\n \"0.5614857\",\n \"0.5612693\",\n \"0.5603815\",\n \"0.5592419\",\n \"0.5586103\",\n \"0.55848455\",\n \"0.5581266\",\n \"0.5579144\",\n \"0.5578639\",\n \"0.55767405\",\n \"0.5575976\",\n \"0.55716485\",\n \"0.556644\",\n \"0.5565841\",\n \"0.55603296\",\n \"0.5553369\",\n \"0.5552507\",\n \"0.554719\",\n \"0.5542908\",\n \"0.55405587\",\n \"0.5536273\",\n \"0.55337757\",\n \"0.5499198\",\n \"0.5490823\",\n \"0.5490164\",\n \"0.54854894\",\n \"0.5480157\",\n \"0.5475498\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5861139"},"document_rank":{"kind":"string","value":"39"}}},{"rowIdx":17492,"cells":{"query":{"kind":"string","value":"Sums all scores of the currently filled assignable positions plus the number bonus"},"document":{"kind":"string","value":"def grand_total(self):\n return sum(self.grid[pos][1] for pos in assignable_positions if self.grid[pos][0]) + self.grid[\"nb\"][1]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_scores(self):\n self.score[0] = (-1)*sum(self.board[self.board == -1])\n self.score[1] = sum(self.board[self.board == 1])\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)","def update_scores(self):\r\n totals = [0, 0, 0, 0]\r\n for player in range(0, 4):\r\n for round_num in range(0, 17):\r\n try:\r\n bid = int(self.spin_bids[player][round_num].get())\r\n tricks = int(self.spin_tricks[player][round_num].get())\r\n except ValueError:\r\n bid = -1\r\n tricks = -1\r\n score = calc_score(min(round_num+1, 13), bid, tricks)\r\n self.lbl_scores[player][round_num].configure(text=str(score))\r\n totals[player] += score\r\n for player in range(0, 4):\r\n self.lbl_totals[player].configure(text=str(totals[player]))\r\n return totals[0] + totals[1] + totals[2] + totals[3]","def calculScore(self):\n for cell in self.notComputeRouter:\n if(cell.isCovered==True):\n self.score += 1000\n self.score += self.budget","def grades_sum(grades_input):\n total = 0\n for score in scores: \n total += score\n\n return total","def calculate_total(score_sheet):\n total_score = EMPTY_BOX()\n if EMPTY_BOX() not in score_sheet:\n if sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) >= UPPER_BONUS_REQUIREMENT():\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)]) + UPPER_SECTION_BONUS()\n elif sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) < UPPER_BONUS_REQUIREMENT():\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)])\n else:\n total_score = EMPTY_BOX()\n\n return total_score","def __get_score(self):\n for pair in zip(self.nu[self.nu_idx:], self.sw[self.sw_idx:]):\n if pair[0] == pair[1]:\n self.score += 1\n else:\n break","def calculate_score(player_cards):\n score = sum(player_cards)\n return score","def _calculate_score(self):\n mul = self._check_board()\n if mul > 0:\n inc = 100 * mul + ((mul - 1) * 25)\n self.score += inc","def add_score(self, points: int) -> None:\n self.__score += points\n\n for rank in self.__ranks.keys():\n if self.__score >= rank:\n self.__level = self.__ranks[rank]\n else:\n break","def getScore(self):\n return sum(self.field)","def get_value(self):\n #Finds all of the values in the cards\n score_list=[Card.get_value(card) for card in self.cards]\n #Sums the scores\n if self.num_cards() > 0:\n total_score=reduce((lambda x,y: x+y),score_list)\n return total_score\n else:\n return 0","def get_local_score(self):\n for candidate in self.candidate_list:\n self.score += candidate.get_score()","def score(self):\n return sum(len(i) for i in self.firework)","def score(self):","def calculate_scores(self):\n words = self.walk_board()\n player_scores = {}\n for word in words:\n player = word.get_owning_player()\n if player not in player_scores:\n player_scores[player] = 0\n player_scores[player] += word.get_score()\n return player_scores","def set_score(self, points):\n self.score += points","def _tally(self, score):\n self._score[self._turn] += score","def update_score():\n pass","def scoreGame(self):\n # create valueLs[card1,card2,...], pass it to sumHandReturnPoints(valueLs) or twoCardReturnPoints(valueLs)\n scoreLs = []\n ### Score of row\n for rowKey in self.table:\n valueLs = self.table[rowKey]\n points = self.sumHandReturnPoints(valueLs)\n scoreLs.append(points)\n\n ### Score of 4-card column\n for offset in range(0,3): # 0,1,2\n tmpLs = []\n for rowKey in self.table:\n valueLs = self.table[rowKey]\n if len(valueLs) == 5:\n iterStart = 1\n else:\n iterStart = 0\n card = valueLs[iterStart+offset]\n tmpLs.append(card)\n points = self.sumHandReturnPoints(tmpLs)\n scoreLs.append(points) \n\n ### Score of 2-card column\n #(1) 1st column\n valueLs1 = self.table['row1']\n valueLs2 = self.table['row2']\n tmpLs = []\n tmpLs.append(valueLs1[0].get_rank())\n tmpLs.append(valueLs2[0].get_rank())\n points = self.twoCardReturnPoints(tmpLs)\n scoreLs.append(points)\n #(2) 5th column\n valueLs3 = self.table['row1']\n valueLs4 = self.table['row2']\n tmpLs = []\n tmpLs.append(valueLs3[-1].get_rank())\n tmpLs.append(valueLs4[-1].get_rank())\n points = self.twoCardReturnPoints(tmpLs)\n scoreLs.append(points) \n\n ### Add up scoreLs\n sumPoints = 0\n for points in scoreLs:\n sumPoints += points\n return sumPoints","def update_points(self):\n #Calculate Upper Section\n total = 0\n for box in self._upper_section:\n total += box.points\n self._upperSum = total\n\n if total >= 63:\n self._bonus = 35\n total += 35\n self._upperTotal = total\n\n # Calculate Lower Section\n total = 0\n for box in self._lower_section:\n total += box.points\n\n if self.get_box(\"Yahtzee\").points > 0:\n total = total + (self._yahtzee_count - 1) * 100 # Yahtzee Bonus\n\n self._lowerTotal = total\n\n #Total Points\n self._grandTotal = self._upperTotal + self._lowerTotal","def adjust_score(self):\n self.score += game.temporary_score","def scoring(self):\n pass","def update_score(self, blanks=0, letters=0, words=0):\n points = 0\n points += letters * LETTER_POINT\n points += words * WORD_POINT\n points += blanks * BLANK_POINT\n self.score += points","def sum_points(self) -> int:\n return sum([card.rank_value for card in self.deck.cards])","def hit(self):\r\n self.life -= 1\r\n total_score = 1\r\n if self.life == 0:\r\n self.alive = False\r\n total_score += self.bonus\r\n return total_score","def get_score(self):\n for response in self.response_list:\n self.score += response.get_score","def calculate_score(cards):\n if sum(cards) == 21 and len(cards) == 2:\n return 0\n \n if 11 in cards and sum(cards) > 21:\n cards.remove 11\n cards.append 1\n return sum(cards)","def _update_score(self) -> None:\n\n # setting new score by iterating over players\n self.score_play[self.n_play_turns, ] = [\n self._score_table[(\n self.contract.level,\n self.contract.suit,\n self.tricks[i],\n self.contract.player_vulnerability[i],\n int(self.contract.double + self.contract.redouble)\n )]\n for i in range(NUM_PLAYERS)\n ]","def add_score(home, away, outcome, curr_score_dict, points=3):\n if outcome == \"Draw\":\n \"\"\"Roept hier zichzelf aan en geeft beide teams 1 point\"\"\"\n curr_score_dict = add_score(home, away, home, curr_score_dict, 1)\n curr_score_dict = add_score(home, away, away, curr_score_dict, 1)\n else:\n curr_score_dict[outcome] += points\n return curr_score_dict","def calculate_player_position_score(marbles: list):\n prime = Evaluator.prime_positions\n good = Evaluator.good_positions\n position_score = 0\n for marble in marbles:\n if marble in prime:\n position_score += 10\n elif marble in good:\n position_score += 5\n else:\n position_score -= 1\n return position_score","def disp_score():","def update_score(self, score: int) -> int:\n self.score += score\n return self.score","def get_score(self):\n return sum([Letters.get_value(tile.letter) for tile in self.tiles])","def check_score(self) -> None:\n self.player_1, self.player_2 = 0, 0\n for cell in self.cells:\n if cell.player == 1:\n self.player_1 += 1\n elif cell.player == 2:\n self.player_2 += 1","def add_to_score(self, to_add):\n self.score += to_add","def score(self):\n result = 0\n\n idx = self.cups.index(1)\n idx += 1\n if idx >= len(self.cups):\n idx = 0\n # ok, keep adding things until we get back to 1\n while 1 != self.cups[idx]:\n # add this value..\n result *= 10\n result += self.cups[idx]\n # and on to the next one..\n idx += 1\n if idx >= len(self.cups):\n idx = 0\n\n return result","def to_total_scores(ht_score, ft_score, et_score, pen_score):\n total_score = ft_score\n try:\n for score in [et_score, pen_score]:\n if None in score:\n continue\n score[0] = total_score[0] + score[0]\n score[1] = total_score[1] + score[1]\n total_score = score\n except Exception:\n return ht_score, ft_score, et_score, pen_score\n return ht_score, ft_score, et_score, pen_score","def increase(self, points):\n self.score += points","def total_reward(self):\n return np.sum(self.rewards)","def judge(name):\n score = 0\n for scoreID, scorer, weight in weights:\n subscore = scorer(name)\n score += subscore * weight\n name.scores[scoreID] = subscore\n name.score = score\n return score","def _compute_scores(self, triples):\n # compute scores as sum(s * p * o)\n scores = tf.reduce_sum(triples[0] * triples[1] * triples[2], 1)\n return scores","def get_score(self):\r\n correct = 0\r\n for key in self.correct_map:\r\n try:\r\n correct += self.correct_map.get_npoints(key)\r\n except Exception:\r\n log.error('key=%s, correct_map = %s', key, self.correct_map)\r\n raise\r\n\r\n if (not self.student_answers) or len(self.student_answers) == 0:\r\n return {'score': 0,\r\n 'total': self.get_max_score()}\r\n else:\r\n return {'score': correct,\r\n 'total': self.get_max_score()}","def calculate_score(list_of_cards):\n if sum(list_of_cards) == 21 and len(list_of_cards) == 2:\n return 0\n if 11 in list_of_cards and sum(list_of_cards) > 21:\n list_of_cards.remove(11)\n list_of_cards.append(1)\n return sum(list_of_cards)","def get_score(self, pos):\n score = 0\n gap_count = 0\n align_query, align_target = '', ''\n for xval, yval in pos:\n xunit = self.get_seq(self.target, xval)\n yunit = self.get_seq(self.query, yval)\n align_query += yunit\n align_target += xunit\n if xval != 0 and yval != 0:\n score += self.subs_matrix[xunit][yunit]\n gap_count = 0\n else:\n gap_count += 1\n if gap_count > 1 and len(self.gap_penatly) > 1:\n score += self.gap_penatly[1]\n else:\n score += self.gap_penatly[0]\n return score, align_query, align_target","def get_score(self):\n rewards, resets = self.runner.get_rewards_resets()\n self.runner.clear_rewards_resets()\n assert rewards.ndim == 1 and resets.ndim == 1, (rewards.ndim, resets.ndim)\n assert rewards.shape[0] == resets.shape[0], (rewards.shape, resets.shape)\n scores = [0]\n for t in reversed(range(rewards.shape[0])):\n if resets[t]:\n scores.append(0)\n scores[-1] += rewards[t]\n return np.mean(scores)","def calc_sum_fitness(self):\n fitness: float = 0\n for c in self.characters:\n fitness += c.fitness\n self.sum_fitness = round(fitness, 3)","def score(self):\r\n totN = 0\r\n totB = 0\r\n for l in range(SIZE):\r\n for c in range(len(COLONNES)):\r\n if self.jeu[l][c] == NOIR:\r\n totN += 1\r\n elif self.jeu[l][c] == BLANC:\r\n totB += 1\r\n return (totN, totB)","def totalScore(x, air = 5, safety = 5, job = 5, green = 5, price = 5):\n #\"price\", \"contamination\", \"distanceToDangerZone\", \"distanceToMedianJobLocation\",\"GreenConc\"\n return -x[0]*air + x[1]*safety - x[2]*job + x[3]*green - x[4]*price","def calculate_score(friendly_tiles, enemy_tiles):\n friendly_permanent_tiles = [tile for tile in friendly_tiles if tile.is_permanently_owned()]\n enemy_permanent_tiles = [tile for tile in enemy_tiles if tile.is_permanently_owned()]\n num_friendly_permanent_tiles = len(friendly_permanent_tiles)\n num_enemy_permanent_tiles = len(enemy_permanent_tiles)\n num_friendly_non_permanent_tiles = len(friendly_tiles) - num_friendly_permanent_tiles\n num_enemy_non_permanent_tiles = len(enemy_tiles) - num_enemy_permanent_tiles\n return (num_friendly_non_permanent_tiles + 2 * num_friendly_permanent_tiles, num_enemy_non_permanent_tiles + 2 * num_enemy_permanent_tiles)","def scores_for(self, board):\r\n scores = [50] * board.width\r\n\r\n for col in range(board.width):\r\n if not board.can_add_to(col):\r\n scores[col] = -1\r\n elif board.is_win_for(self.checker):\r\n scores[col] = 100\r\n elif board.is_win_for(self.opponent_checker()):\r\n scores[col] = 0\r\n elif self.lookahead == 0:\r\n scores[col] = 50\r\n else: \r\n board.add_checker(self.checker, col)\r\n opponent = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead - 1)\r\n opp_scores = opponent.scores_for(board)\r\n if max(opp_scores) == 100:\r\n scores[col] = 0\r\n elif max(opp_scores) == 0:\r\n scores[col] = 100\r\n else:\r\n scores[col] = 50\r\n board.remove_checker(col)\r\n\r\n return scores","def score(self):\n xg, yg = self.goal\n xe, ye = self.empty_node()\n score = len(self.history) + 4*(xg + yg)\n if xg == 1:\n score -= 3\n if ye > 1:\n score += ye - 1\n dx = abs(xe - xg + 1)\n if xg and dx:\n score += dx\n return score","def sumHandReturnPoints(self, valueLs): # card is the element in valueLs\n #Ace is dealt with here, assume Ace to be 11 initially, decreasing by 10 per Ace if sum > 21\n rowSum = 0\n AceCount = 0\n for ele in valueLs:\n rank = ele.get_rank()\n if rank == 1:\n rank = 11\n AceCount += 1 # serve as flag\n rowSum += rank\n while(AceCount!=0):\n if rowSum > 21:\n rowSum -= 10\n AceCount -= 1\n points = self.countPoints(rowSum)\n return points","def _scoring(self):\n val = 0 #score will be totaled here\n\n for c in range(0, self.width): #for every column in the board\n for r in range(0, self.height): #for every row of a column\n #see if we can move...\n possible_up = (r + 3 < self.height) #up?\n possible_left = (c - 3 > 0) #left?\n possible_right = (c + 3 < self.width) #right?\n\n #diagonally up, left\n if possible_up and possible_left:\n val+= self._up_left(c, r)\n\n #up\n if possible_up:\n val+= self._up(c,r)\n\n #diagonally up, right\n if possible_up and possible_right:\n val+= self._up_right(c,r)\n\n #right\n if possible_right:\n val+= self._right(c,r)\n\n\n return val","def score(self, robot):\n scores = [grupo.score(robot) for grupo in self.get_grupos()]\n return reduce(lambda acumulador, score: tuple([ a + b for a, b in zip(acumulador, score)]), scores, (0, 0, 0, 0, 0, 0, 0, 0))","def calculate_scores(players):\n scores = {}\n for player in players.tuple_:\n scores[player.id_] = player.score()\n return scores","def sum_(hand: list):\n vals = [card.rank for card in hand]\n intvals = []\n while len(vals) > 0:\n value = vals.pop()\n try:\n intvals.append(int(value))\n except ValueError:\n if value in ['K', 'Q', 'J']:\n intvals.append(10)\n elif value == 'A':\n intvals.append(1) # Keep it simple for the sake of example\n if intvals == [1, 10] or intvals == [10, 1]:\n print(\" Blackjack!\")\n return(21)\n else:\n points = sum(intvals)\n print(\" Current score: {}\".format(str(points)))\n return(points)","def commit_score(self):\n\n # Update the player's total score and total roll count\n self._total_score += self._current_score","def add_total_score(self, sheet_number):\n cell_format = self.workbook.add_format(self.base_format)\n # cell_format.set_bg_color('yellow')\n # total score of rows\n self.worksheets[sheet_number].write(0, len(self.arrays[sheet_number][0]), 'total score', cell_format)\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), 0, 'total score', cell_format)\n for i in range(2, len(self.arrays[sheet_number])):\n count = 0\n for j in range(1, len(self.arrays[sheet_number][0])):\n count += self.arrays[sheet_number][i][j]\n self.worksheets[sheet_number].write(i, len(self.arrays[sheet_number][0]), count, cell_format)\n # total score of columns\n for i in range(1, len(self.arrays[sheet_number][0])):\n count = 0\n for j in range(2, len(self.arrays[sheet_number])):\n count += self.arrays[sheet_number][j][i]\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), i, count, cell_format)","def score(self) -> float:\n assert hasattr(self, \"characterized_inventory\"), \"Must do LCIA first\"\n if hasattr(self, \"weighted_inventory\"):\n return float(self.weighted_inventory.sum())\n elif hasattr(self, \"normalized_inventory\"):\n return float(self.normalized_inventory.sum())\n else:\n return float(self.characterized_inventory.sum())","def scores_for(self, board):\r\n scores = [1]*board.width\r\n for i in range(board.width):\r\n if not board.can_add_to(i):\r\n scores[i] = -1\r\n elif board.is_win_for(self.checker):\r\n scores[i] = 100\r\n elif board.is_win_for(self.opponent_checker()):\r\n scores[i] = 0\r\n elif self.lookahead == 0:\r\n scores[i] = 50\r\n else:\r\n board.add_checker(self.checker, i)\r\n other = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead-1)\r\n other_scores = other.scores_for(board)\r\n if max(other_scores) == 100:\r\n scores[i] = 0\r\n elif max(other_scores) == 50:\r\n scores[i] = 50\r\n elif max(other_scores) == 0:\r\n scores[i] = 100\r\n board.remove_checker(i)\r\n return scores","def calculate_production_bonus(self, number_of_improvements, max_slots):\n pass","def score(self):\n return self.aggregate(Sum('score')).values()[0] or 0","def add_score(self, score):\n self._score += score","def addScore(self, index, lstGross):\n # update gross score for hole\n for player, gross in zip(self.scores, lstGross):\n player.gross[index] = gross\n # update team score\n for team in self.team_list:\n team.calculate_score(index)\n # update team points using other team score\n self.team_list[0].update_points(index, self.team_list[1])\n self.team_list[1].update_points(index, self.team_list[0])","def increase_score(self):\n self.score += 1","def update_score(self):\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)","def extras_total(self):\n total = self.wides + self.no_balls + self.byes + self.leg_byes\n return total","def calculate_score(card_list):\n if sum(card_list) == 21 and len(card_list) == 2:\n return 0\n if sum(card_list) > 21 and 11 in card_list:\n card_list.remove(11)\n card_list.append(1)\n return sum(card_list)","def adjusted_score(self) -> Tuple[List[str], np.ndarray]:\n nb_agents = self.game.configuration.nb_agents\n current_scores = np.zeros((1, nb_agents), dtype=np.float32)\n\n eq_agent_states = dict(\n (\n agent_pbk,\n AgentState(\n self.game.initialization.eq_money_holdings[i],\n [int(h) for h in self.game.initialization.eq_good_holdings[i]],\n self.game.initialization.utility_params[i],\n ),\n )\n for agent_pbk, i in zip(\n self.game.configuration.agent_pbks,\n range(self.game.configuration.nb_agents),\n )\n ) # type: Dict[str, AgentState]\n\n result = np.zeros((1, nb_agents), dtype=np.float32)\n\n eq_scores = np.zeros((1, nb_agents), dtype=np.float32)\n eq_scores[0, :] = [\n eq_agent_state.get_score() for eq_agent_state in eq_agent_states.values()\n ]\n\n temp_game = Game(self.game.configuration, self.game.initialization)\n\n # initial scores\n initial_scores = np.zeros((1, nb_agents), dtype=np.float32)\n scores_dict = temp_game.get_scores()\n initial_scores[0, :] = list(scores_dict.values())\n keys = list(scores_dict.keys())\n current_scores = np.zeros((1, nb_agents), dtype=np.float32)\n current_scores[0, :] = initial_scores[0, :]\n\n # compute the partial scores for every agent after every transaction\n # (remember that indexes of the transaction start from one, because index 0 is reserved for the initial scores)\n for idx, tx in enumerate(self.game.transactions):\n temp_game.settle_transaction(tx)\n scores_dict = temp_game.get_scores()\n current_scores[0, :] = list(scores_dict.values())\n\n result[0, :] = np.divide(\n np.subtract(current_scores, initial_scores),\n np.subtract(eq_scores, initial_scores),\n )\n result = np.transpose(result)\n\n return keys, result","def updateScore(score):\n return score + 1","def totalPoisonRatingBonus(self):\n return int(self._basePoisonRatingBonus + self.equipmentPoisonRatingBonus +\n self._statusPoisonRatingBonus)","def get_score_matrix(self) -> int:","def _calc_multiple_alignment_score(wrapped_data : tuple) -> int: \n (start, finish) = wrapped_data \n score_sum = 0.\n for dna_record in tqdm(dna_sequences[start : finish + 1], total=(finish + 1 - start), desc=\"Training process\"):\n score_sum += self.aligner.score(seq, dna_record.seq)\n return score_sum","def score(self, hand, position):\n\n try:\n assert self.grid[position][1] == \"---\"\n except AssertionError:\n print self\n print position\n raise FilledInError\n except KeyError:\n print \"\\nCheck your code. This is not a valid position:\", position, \"\\n\"\n raise\n\n if position.startswith(\"n\"): # Return sum of relevant number\n n = int(position[1])\n return sum(d for d in hand.dice if d == n)\n\n elif position in [\"k3\", \"k4\", \"ch\"]: # Return total sum\n if position == \"k3\" and hand.max_tally()[0] < 3:\n return 0 # The is not a three of a kind\n elif position == \"k4\" and hand.max_tally()[0] < 4:\n return 0 # The is not a four of a kind\n return sum(hand.dice)\n\n elif position in [\"fh\", \"ss\", \"ls\", \"yz\", \"yb\"]: # Return fixed score\n if position == \"fh\":\n tallies = hand.get_dicedict().values()\n if 1 in tallies:\n return 0 # This is not a full house\n\n elif position in [\"ss\", \"ls\"]:\n ds = \"\".join(str(x) for x in hand.sort_by_value())\n if position == [\"ss\"]:\n if \"1234\" not in ds and \"2345\" not in ds and \"3456\" not in ds:\n return 0\n else:\n if \"12345\" not in ds and \"23456\" not in ds:\n return 0\n\n else:\n if hand.max_tally()[0] < 5:\n return 0 # This is not a yahtzee\n if position == \"yb\" and self.grid[\"yz\"] == \"---\":\n return 0 # YB only scores points if there already is a YZ\n\n return fixed_scores[position]\n\n else:\n raise InvalidPositionError","def add_indicator_values(self, flood_safety_score, biodiversity_score,\n cost_score, total_cost, turn=0): \n if turn < len(self.turn):\n self.flood_safety[turn] = flood_safety_score\n self.biodiversity[turn] = biodiversity_score * 100\n self.cost_score[turn] = cost_score * 100\n self.total_costs[turn] = total_cost\n else:\n self.flood_safety.append(flood_safety_score)\n self.biodiversity.append(biodiversity_score * 100)\n self.total_costs.append(total_cost)\n self.cost_score.append(cost_score * 100)\n self.turn.append(turn)\n return","def add_point(self):\n self.total_score = self.total_score + 1\n if self.total_score // 10 == 0:\n self.level = self.total_score / 10","def score(self) -> int:\n card_values = {\n '0': 0,\n '1': 1,\n '2': 2,\n '3': 3,\n '4': 4,\n '5': 5,\n '6': 6,\n '7': 7,\n '8': 8,\n '9': 9,\n '10': 10,\n 'JACK': 10,\n 'QUEEN': 10,\n 'KING': 10,\n 'ACE': 11}\n hand_value = []\n for i in self.cards:\n hand_value.append(card_values[i.value])\n while sum(hand_value) > 21 and 11 in hand_value:\n for i, j in enumerate(hand_value):\n if j == 11:\n hand_value[i] = 1\n break\n else:\n pass\n return sum(hand_value)","def get_total_assigned(self):\n return sum(self.n_assigned_list)","def score(self):\n raise NotImplementedError()","def set_scores(apps, schema_editor):\n\n Game = apps.get_model(\"stats\", \"Game\")\n for game in Game.objects.all():\n score_allies = 0\n score_opponents = 0\n player_stats = game.playerstat_set.all()\n for stat in player_stats:\n if stat.is_opponent:\n score_opponents += stat.scored\n else:\n score_allies += stat.scored\n\n game.score_allies = score_allies\n game.score_opponents = score_opponents\n game.save()","def sum_cards(self):\n has_ace = False\n sum = 0\n\n # Add up players cards\n for card in self.cards:\n if card.card_value == \"ace\":\n has_ace = True\n sum += card.game_value\n\n # Handle case where ace plays low\n if sum > 21 and has_ace:\n sum -= 10\n\n return sum","def custom_score(game, player):\n \n # get avaliable moves for each player\n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n \n # return different between # of my agent's move and oppenent's\n return float(own_moves - opp_moves)","def score_update(scoreboard, compare):\r\n if compare == 'Victory':\r\n scoreboard['W'] += 1\r\n elif compare == 'Defeat':\r\n scoreboard['L'] += 1\r\n elif compare == 'Tie':\r\n scoreboard['T'] += 1","def print_sheet(score_sheet, total_score):\n print('-------------------------\\nYahtzee Score Sheet\\n-------------------------')\n name_list = ['Player', '1. Aces', '2. 2s', '3. 3s', '4. 4s', '5. 5s', '6. 6s', '7. 3 of a kind', '8. 4 of '\n 'a kind', '9. Full House', '10. Small Straight', '11. Large Straight', '12. Yahtzee', '13. Chance']\n temp_list = [\"\" if score == EMPTY_BOX() else score for score in score_sheet]\n for i in range(len(score_sheet)):\n print(f'{name_list[i]}:{temp_list[i]}')\n if EMPTY_BOX() not in score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]:\n if sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) >= UPPER_BONUS_REQUIREMENT():\n print(f'Sum of Upper Section:{sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)])}\\nBonus:35')\n elif sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) < UPPER_BONUS_REQUIREMENT():\n print(f'Sum of Upper Section:{sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)])}\\nBonus:0')\n else:\n print('Sum of Upper Section:\\nBonus:')\n if EMPTY_BOX() not in score_sheet[slice(LOWER_START(), LOWER_STOP()+1)]:\n print(f'Sum of Lower Section:{sum(score_sheet[slice(LOWER_START(), LOWER_STOP()+1+1)])}')\n else:\n print('Sum of Lower Section:')\n if EMPTY_BOX() not in score_sheet:\n print(f'Total Score:{total_score}\\n-------------------------')\n else:\n print(f'Total Score:\\n-------------------------')","def updateScore(self, score):\n self.__score += score","def update(self):\n for pl, result in zip(self._players, self.golf_round.doc.results):\n for score in result.scores:\n n = score.num-1\n # update net \n pl.dct_net['holes'][n] = score.gross - pl._bumps[n]\n pl.update_totals(pl.dct_net)","def centre_priority_evaluate(self):\r\n evaluation = 0\r\n for player in range(2):\r\n player_sign = player * 2 - 1\r\n for i in range(4):\r\n score = i + 1\r\n evaluation += player_sign * score * count_bits(self.bitboard_king[player] &\r\n self.CENTRE_PRIORITY_BITMASKS[i])\r\n evaluation += player_sign * score * count_bits(self.bitboard_pawns[player] &\r\n self.CENTRE_PRIORITY_BITMASKS[i])\r\n return evaluation","def compute_scores(self, *scorers):\n if self.nodes[0]:\n list_ = self.nodes\n else:\n list_ = self.reaction_trees\n\n for idx, item in enumerate(list_):\n scores = {repr(scorer): scorer(item) for scorer in scorers}\n self.all_scores[idx].update(scores)\n self._update_route_dict(self.all_scores, \"all_score\")","def _aggregate_scores(self,\n game_scores: List[Dict[str, int]]) -> task.ScoreData:\n\n relative_score = []\n recall = []\n num_incorrect = []\n precision = []\n\n for score_dict in game_scores:\n\n relative_score.append(score_dict['score']/score_dict['max_score'])\n recall.append(score_dict['num_correct']/score_dict['word_count'])\n num_incorrect.append(score_dict['num_incorrect'])\n\n # Precision -- defined to be 1 if no words are guessed.\n num_guesses = score_dict['num_correct'] + score_dict['num_incorrect']\n if num_guesses > 0:\n precision.append(score_dict['num_correct'] / num_guesses)\n else:\n precision.append(1.0)\n\n relative_score = np.mean(relative_score)\n recall = np.mean(recall)\n num_incorrect = np.mean(num_incorrect)\n precision = np.mean(precision)\n\n score_data = task.ScoreData(\n {'relative_score': relative_score,\n 'recall': recall,\n 'num_incorrect': num_incorrect,\n 'precision': precision},\n preferred_score='relative_score',\n low_score=0.0,\n high_score=1.0,\n number_of_shots=-1,\n subtask_description=('A simplified implementation of the NYT '\n 'Spelling Bee game. Individual game scoring is '\n 'defined in the class deffinition. Reported '\n 'scores are normalized to game maximums.'\n 'All scores are averaged over each game played '\n 'in an evaluation run.'))\n\n return score_data","def set_ability_scores(self, scores: List[int]):\n for s in range(6):\n self.dna[s] = scores[s]","def account_for_new_score(self):\n self.rolls += 1\n if self.continued is True:\n self.total_score += self.current_roll.score\n self.dice_remaining = self.current_roll.dice_remaining\n\n if self.dice_remaining == 0:\n self.resets += 1\n self.dice_remaining = 5","def compute_score(self):\n for i in xrange(FRAMES):\n # STRIKE\n if self.frames[i][0] == 10:\n # CONSECUTIVE STRIKE\n if self.frames[i + 1][0] == 10:\n self.scores.append(self.frames[i][0] +\n self.frames[i + 1][0] +\n self.frames[i + 2][0])\n else:\n self.scores.append(self.frames[i][0] +\n self.frames[i + 1][0] +\n self.frames[i + 1][1])\n # SPARE\n elif (self.frames[i][0] + self.frames[i][1] == 10):\n self.scores.append(self.frames[i][0] + self.frames[i][1] +\n self.frames[i + 1][0])\n # NEITHER\n else:\n self.scores.append(self.frames[i][0] + self.frames[i][1])\n # Total Score\n for score in self.scores:\n self.score += score","def calculate_profit(self):","def score( self ):\r\n result = 0.0\r\n for rr in self.ee.getRsrcs( ):\r\n value = self.scoreRsrc( rr )\r\n result += value\r\n print( \"INFO: Value for the schedule is %s \" % ( rr, result ) )\r\n return( result )","def score(self):\n self.set_idx()\n if self.idx:\n diffs = self.diffs()\n weights = self.weights\n return np.sum(weights * diffs) / np.sum(weights)\n else:\n return 0.0","def get_score(self, student_answers):\r\n pass","def score(self, hand, top_card):\n powerset = GameState.powerset(list(hand).append(top_card))\n score = 0\n for meld in powerset:\n score += self.score_meld(meld)\n return score","def compute_stability_scores(self):\n self.mutations, self.scores, self.matrix = stability(\n self.seq,\n alphabet='ACGU',\n fold_vectorize=self.fold_vectorize)","def total_cost(self):\n if self.goal:\n return self.goal + (self.community_contribution or 0)\n else:\n return 0","def score(self):\n # loop over aminoacids in protein and calculate how often H and C are surrounded by H and C\n for aminoacid in self.aminoacids:\n if aminoacid.aminoacid_type == \"H\":\n self.stability = self.stability + (-1 * self.surrounded_by(aminoacid.location, \"H\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \"C\", aminoacid.aminoacid_number))\n elif aminoacid.aminoacid_type == \"C\":\n self.stability = self.stability + (-5 * self.surrounded_by(aminoacid.location, \"C\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \"H\", aminoacid.aminoacid_number))\n self.stability = self.stability/2\n return int(self.stability)"],"string":"[\n \"def update_scores(self):\\n self.score[0] = (-1)*sum(self.board[self.board == -1])\\n self.score[1] = sum(self.board[self.board == 1])\\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)\",\n \"def update_scores(self):\\r\\n totals = [0, 0, 0, 0]\\r\\n for player in range(0, 4):\\r\\n for round_num in range(0, 17):\\r\\n try:\\r\\n bid = int(self.spin_bids[player][round_num].get())\\r\\n tricks = int(self.spin_tricks[player][round_num].get())\\r\\n except ValueError:\\r\\n bid = -1\\r\\n tricks = -1\\r\\n score = calc_score(min(round_num+1, 13), bid, tricks)\\r\\n self.lbl_scores[player][round_num].configure(text=str(score))\\r\\n totals[player] += score\\r\\n for player in range(0, 4):\\r\\n self.lbl_totals[player].configure(text=str(totals[player]))\\r\\n return totals[0] + totals[1] + totals[2] + totals[3]\",\n \"def calculScore(self):\\n for cell in self.notComputeRouter:\\n if(cell.isCovered==True):\\n self.score += 1000\\n self.score += self.budget\",\n \"def grades_sum(grades_input):\\n total = 0\\n for score in scores: \\n total += score\\n\\n return total\",\n \"def calculate_total(score_sheet):\\n total_score = EMPTY_BOX()\\n if EMPTY_BOX() not in score_sheet:\\n if sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) >= UPPER_BONUS_REQUIREMENT():\\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)]) + UPPER_SECTION_BONUS()\\n elif sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) < UPPER_BONUS_REQUIREMENT():\\n total_score = sum(score_sheet[slice(UPPER_START(), LOWER_STOP()+1)])\\n else:\\n total_score = EMPTY_BOX()\\n\\n return total_score\",\n \"def __get_score(self):\\n for pair in zip(self.nu[self.nu_idx:], self.sw[self.sw_idx:]):\\n if pair[0] == pair[1]:\\n self.score += 1\\n else:\\n break\",\n \"def calculate_score(player_cards):\\n score = sum(player_cards)\\n return score\",\n \"def _calculate_score(self):\\n mul = self._check_board()\\n if mul > 0:\\n inc = 100 * mul + ((mul - 1) * 25)\\n self.score += inc\",\n \"def add_score(self, points: int) -> None:\\n self.__score += points\\n\\n for rank in self.__ranks.keys():\\n if self.__score >= rank:\\n self.__level = self.__ranks[rank]\\n else:\\n break\",\n \"def getScore(self):\\n return sum(self.field)\",\n \"def get_value(self):\\n #Finds all of the values in the cards\\n score_list=[Card.get_value(card) for card in self.cards]\\n #Sums the scores\\n if self.num_cards() > 0:\\n total_score=reduce((lambda x,y: x+y),score_list)\\n return total_score\\n else:\\n return 0\",\n \"def get_local_score(self):\\n for candidate in self.candidate_list:\\n self.score += candidate.get_score()\",\n \"def score(self):\\n return sum(len(i) for i in self.firework)\",\n \"def score(self):\",\n \"def calculate_scores(self):\\n words = self.walk_board()\\n player_scores = {}\\n for word in words:\\n player = word.get_owning_player()\\n if player not in player_scores:\\n player_scores[player] = 0\\n player_scores[player] += word.get_score()\\n return player_scores\",\n \"def set_score(self, points):\\n self.score += points\",\n \"def _tally(self, score):\\n self._score[self._turn] += score\",\n \"def update_score():\\n pass\",\n \"def scoreGame(self):\\n # create valueLs[card1,card2,...], pass it to sumHandReturnPoints(valueLs) or twoCardReturnPoints(valueLs)\\n scoreLs = []\\n ### Score of row\\n for rowKey in self.table:\\n valueLs = self.table[rowKey]\\n points = self.sumHandReturnPoints(valueLs)\\n scoreLs.append(points)\\n\\n ### Score of 4-card column\\n for offset in range(0,3): # 0,1,2\\n tmpLs = []\\n for rowKey in self.table:\\n valueLs = self.table[rowKey]\\n if len(valueLs) == 5:\\n iterStart = 1\\n else:\\n iterStart = 0\\n card = valueLs[iterStart+offset]\\n tmpLs.append(card)\\n points = self.sumHandReturnPoints(tmpLs)\\n scoreLs.append(points) \\n\\n ### Score of 2-card column\\n #(1) 1st column\\n valueLs1 = self.table['row1']\\n valueLs2 = self.table['row2']\\n tmpLs = []\\n tmpLs.append(valueLs1[0].get_rank())\\n tmpLs.append(valueLs2[0].get_rank())\\n points = self.twoCardReturnPoints(tmpLs)\\n scoreLs.append(points)\\n #(2) 5th column\\n valueLs3 = self.table['row1']\\n valueLs4 = self.table['row2']\\n tmpLs = []\\n tmpLs.append(valueLs3[-1].get_rank())\\n tmpLs.append(valueLs4[-1].get_rank())\\n points = self.twoCardReturnPoints(tmpLs)\\n scoreLs.append(points) \\n\\n ### Add up scoreLs\\n sumPoints = 0\\n for points in scoreLs:\\n sumPoints += points\\n return sumPoints\",\n \"def update_points(self):\\n #Calculate Upper Section\\n total = 0\\n for box in self._upper_section:\\n total += box.points\\n self._upperSum = total\\n\\n if total >= 63:\\n self._bonus = 35\\n total += 35\\n self._upperTotal = total\\n\\n # Calculate Lower Section\\n total = 0\\n for box in self._lower_section:\\n total += box.points\\n\\n if self.get_box(\\\"Yahtzee\\\").points > 0:\\n total = total + (self._yahtzee_count - 1) * 100 # Yahtzee Bonus\\n\\n self._lowerTotal = total\\n\\n #Total Points\\n self._grandTotal = self._upperTotal + self._lowerTotal\",\n \"def adjust_score(self):\\n self.score += game.temporary_score\",\n \"def scoring(self):\\n pass\",\n \"def update_score(self, blanks=0, letters=0, words=0):\\n points = 0\\n points += letters * LETTER_POINT\\n points += words * WORD_POINT\\n points += blanks * BLANK_POINT\\n self.score += points\",\n \"def sum_points(self) -> int:\\n return sum([card.rank_value for card in self.deck.cards])\",\n \"def hit(self):\\r\\n self.life -= 1\\r\\n total_score = 1\\r\\n if self.life == 0:\\r\\n self.alive = False\\r\\n total_score += self.bonus\\r\\n return total_score\",\n \"def get_score(self):\\n for response in self.response_list:\\n self.score += response.get_score\",\n \"def calculate_score(cards):\\n if sum(cards) == 21 and len(cards) == 2:\\n return 0\\n \\n if 11 in cards and sum(cards) > 21:\\n cards.remove 11\\n cards.append 1\\n return sum(cards)\",\n \"def _update_score(self) -> None:\\n\\n # setting new score by iterating over players\\n self.score_play[self.n_play_turns, ] = [\\n self._score_table[(\\n self.contract.level,\\n self.contract.suit,\\n self.tricks[i],\\n self.contract.player_vulnerability[i],\\n int(self.contract.double + self.contract.redouble)\\n )]\\n for i in range(NUM_PLAYERS)\\n ]\",\n \"def add_score(home, away, outcome, curr_score_dict, points=3):\\n if outcome == \\\"Draw\\\":\\n \\\"\\\"\\\"Roept hier zichzelf aan en geeft beide teams 1 point\\\"\\\"\\\"\\n curr_score_dict = add_score(home, away, home, curr_score_dict, 1)\\n curr_score_dict = add_score(home, away, away, curr_score_dict, 1)\\n else:\\n curr_score_dict[outcome] += points\\n return curr_score_dict\",\n \"def calculate_player_position_score(marbles: list):\\n prime = Evaluator.prime_positions\\n good = Evaluator.good_positions\\n position_score = 0\\n for marble in marbles:\\n if marble in prime:\\n position_score += 10\\n elif marble in good:\\n position_score += 5\\n else:\\n position_score -= 1\\n return position_score\",\n \"def disp_score():\",\n \"def update_score(self, score: int) -> int:\\n self.score += score\\n return self.score\",\n \"def get_score(self):\\n return sum([Letters.get_value(tile.letter) for tile in self.tiles])\",\n \"def check_score(self) -> None:\\n self.player_1, self.player_2 = 0, 0\\n for cell in self.cells:\\n if cell.player == 1:\\n self.player_1 += 1\\n elif cell.player == 2:\\n self.player_2 += 1\",\n \"def add_to_score(self, to_add):\\n self.score += to_add\",\n \"def score(self):\\n result = 0\\n\\n idx = self.cups.index(1)\\n idx += 1\\n if idx >= len(self.cups):\\n idx = 0\\n # ok, keep adding things until we get back to 1\\n while 1 != self.cups[idx]:\\n # add this value..\\n result *= 10\\n result += self.cups[idx]\\n # and on to the next one..\\n idx += 1\\n if idx >= len(self.cups):\\n idx = 0\\n\\n return result\",\n \"def to_total_scores(ht_score, ft_score, et_score, pen_score):\\n total_score = ft_score\\n try:\\n for score in [et_score, pen_score]:\\n if None in score:\\n continue\\n score[0] = total_score[0] + score[0]\\n score[1] = total_score[1] + score[1]\\n total_score = score\\n except Exception:\\n return ht_score, ft_score, et_score, pen_score\\n return ht_score, ft_score, et_score, pen_score\",\n \"def increase(self, points):\\n self.score += points\",\n \"def total_reward(self):\\n return np.sum(self.rewards)\",\n \"def judge(name):\\n score = 0\\n for scoreID, scorer, weight in weights:\\n subscore = scorer(name)\\n score += subscore * weight\\n name.scores[scoreID] = subscore\\n name.score = score\\n return score\",\n \"def _compute_scores(self, triples):\\n # compute scores as sum(s * p * o)\\n scores = tf.reduce_sum(triples[0] * triples[1] * triples[2], 1)\\n return scores\",\n \"def get_score(self):\\r\\n correct = 0\\r\\n for key in self.correct_map:\\r\\n try:\\r\\n correct += self.correct_map.get_npoints(key)\\r\\n except Exception:\\r\\n log.error('key=%s, correct_map = %s', key, self.correct_map)\\r\\n raise\\r\\n\\r\\n if (not self.student_answers) or len(self.student_answers) == 0:\\r\\n return {'score': 0,\\r\\n 'total': self.get_max_score()}\\r\\n else:\\r\\n return {'score': correct,\\r\\n 'total': self.get_max_score()}\",\n \"def calculate_score(list_of_cards):\\n if sum(list_of_cards) == 21 and len(list_of_cards) == 2:\\n return 0\\n if 11 in list_of_cards and sum(list_of_cards) > 21:\\n list_of_cards.remove(11)\\n list_of_cards.append(1)\\n return sum(list_of_cards)\",\n \"def get_score(self, pos):\\n score = 0\\n gap_count = 0\\n align_query, align_target = '', ''\\n for xval, yval in pos:\\n xunit = self.get_seq(self.target, xval)\\n yunit = self.get_seq(self.query, yval)\\n align_query += yunit\\n align_target += xunit\\n if xval != 0 and yval != 0:\\n score += self.subs_matrix[xunit][yunit]\\n gap_count = 0\\n else:\\n gap_count += 1\\n if gap_count > 1 and len(self.gap_penatly) > 1:\\n score += self.gap_penatly[1]\\n else:\\n score += self.gap_penatly[0]\\n return score, align_query, align_target\",\n \"def get_score(self):\\n rewards, resets = self.runner.get_rewards_resets()\\n self.runner.clear_rewards_resets()\\n assert rewards.ndim == 1 and resets.ndim == 1, (rewards.ndim, resets.ndim)\\n assert rewards.shape[0] == resets.shape[0], (rewards.shape, resets.shape)\\n scores = [0]\\n for t in reversed(range(rewards.shape[0])):\\n if resets[t]:\\n scores.append(0)\\n scores[-1] += rewards[t]\\n return np.mean(scores)\",\n \"def calc_sum_fitness(self):\\n fitness: float = 0\\n for c in self.characters:\\n fitness += c.fitness\\n self.sum_fitness = round(fitness, 3)\",\n \"def score(self):\\r\\n totN = 0\\r\\n totB = 0\\r\\n for l in range(SIZE):\\r\\n for c in range(len(COLONNES)):\\r\\n if self.jeu[l][c] == NOIR:\\r\\n totN += 1\\r\\n elif self.jeu[l][c] == BLANC:\\r\\n totB += 1\\r\\n return (totN, totB)\",\n \"def totalScore(x, air = 5, safety = 5, job = 5, green = 5, price = 5):\\n #\\\"price\\\", \\\"contamination\\\", \\\"distanceToDangerZone\\\", \\\"distanceToMedianJobLocation\\\",\\\"GreenConc\\\"\\n return -x[0]*air + x[1]*safety - x[2]*job + x[3]*green - x[4]*price\",\n \"def calculate_score(friendly_tiles, enemy_tiles):\\n friendly_permanent_tiles = [tile for tile in friendly_tiles if tile.is_permanently_owned()]\\n enemy_permanent_tiles = [tile for tile in enemy_tiles if tile.is_permanently_owned()]\\n num_friendly_permanent_tiles = len(friendly_permanent_tiles)\\n num_enemy_permanent_tiles = len(enemy_permanent_tiles)\\n num_friendly_non_permanent_tiles = len(friendly_tiles) - num_friendly_permanent_tiles\\n num_enemy_non_permanent_tiles = len(enemy_tiles) - num_enemy_permanent_tiles\\n return (num_friendly_non_permanent_tiles + 2 * num_friendly_permanent_tiles, num_enemy_non_permanent_tiles + 2 * num_enemy_permanent_tiles)\",\n \"def scores_for(self, board):\\r\\n scores = [50] * board.width\\r\\n\\r\\n for col in range(board.width):\\r\\n if not board.can_add_to(col):\\r\\n scores[col] = -1\\r\\n elif board.is_win_for(self.checker):\\r\\n scores[col] = 100\\r\\n elif board.is_win_for(self.opponent_checker()):\\r\\n scores[col] = 0\\r\\n elif self.lookahead == 0:\\r\\n scores[col] = 50\\r\\n else: \\r\\n board.add_checker(self.checker, col)\\r\\n opponent = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead - 1)\\r\\n opp_scores = opponent.scores_for(board)\\r\\n if max(opp_scores) == 100:\\r\\n scores[col] = 0\\r\\n elif max(opp_scores) == 0:\\r\\n scores[col] = 100\\r\\n else:\\r\\n scores[col] = 50\\r\\n board.remove_checker(col)\\r\\n\\r\\n return scores\",\n \"def score(self):\\n xg, yg = self.goal\\n xe, ye = self.empty_node()\\n score = len(self.history) + 4*(xg + yg)\\n if xg == 1:\\n score -= 3\\n if ye > 1:\\n score += ye - 1\\n dx = abs(xe - xg + 1)\\n if xg and dx:\\n score += dx\\n return score\",\n \"def sumHandReturnPoints(self, valueLs): # card is the element in valueLs\\n #Ace is dealt with here, assume Ace to be 11 initially, decreasing by 10 per Ace if sum > 21\\n rowSum = 0\\n AceCount = 0\\n for ele in valueLs:\\n rank = ele.get_rank()\\n if rank == 1:\\n rank = 11\\n AceCount += 1 # serve as flag\\n rowSum += rank\\n while(AceCount!=0):\\n if rowSum > 21:\\n rowSum -= 10\\n AceCount -= 1\\n points = self.countPoints(rowSum)\\n return points\",\n \"def _scoring(self):\\n val = 0 #score will be totaled here\\n\\n for c in range(0, self.width): #for every column in the board\\n for r in range(0, self.height): #for every row of a column\\n #see if we can move...\\n possible_up = (r + 3 < self.height) #up?\\n possible_left = (c - 3 > 0) #left?\\n possible_right = (c + 3 < self.width) #right?\\n\\n #diagonally up, left\\n if possible_up and possible_left:\\n val+= self._up_left(c, r)\\n\\n #up\\n if possible_up:\\n val+= self._up(c,r)\\n\\n #diagonally up, right\\n if possible_up and possible_right:\\n val+= self._up_right(c,r)\\n\\n #right\\n if possible_right:\\n val+= self._right(c,r)\\n\\n\\n return val\",\n \"def score(self, robot):\\n scores = [grupo.score(robot) for grupo in self.get_grupos()]\\n return reduce(lambda acumulador, score: tuple([ a + b for a, b in zip(acumulador, score)]), scores, (0, 0, 0, 0, 0, 0, 0, 0))\",\n \"def calculate_scores(players):\\n scores = {}\\n for player in players.tuple_:\\n scores[player.id_] = player.score()\\n return scores\",\n \"def sum_(hand: list):\\n vals = [card.rank for card in hand]\\n intvals = []\\n while len(vals) > 0:\\n value = vals.pop()\\n try:\\n intvals.append(int(value))\\n except ValueError:\\n if value in ['K', 'Q', 'J']:\\n intvals.append(10)\\n elif value == 'A':\\n intvals.append(1) # Keep it simple for the sake of example\\n if intvals == [1, 10] or intvals == [10, 1]:\\n print(\\\" Blackjack!\\\")\\n return(21)\\n else:\\n points = sum(intvals)\\n print(\\\" Current score: {}\\\".format(str(points)))\\n return(points)\",\n \"def commit_score(self):\\n\\n # Update the player's total score and total roll count\\n self._total_score += self._current_score\",\n \"def add_total_score(self, sheet_number):\\n cell_format = self.workbook.add_format(self.base_format)\\n # cell_format.set_bg_color('yellow')\\n # total score of rows\\n self.worksheets[sheet_number].write(0, len(self.arrays[sheet_number][0]), 'total score', cell_format)\\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), 0, 'total score', cell_format)\\n for i in range(2, len(self.arrays[sheet_number])):\\n count = 0\\n for j in range(1, len(self.arrays[sheet_number][0])):\\n count += self.arrays[sheet_number][i][j]\\n self.worksheets[sheet_number].write(i, len(self.arrays[sheet_number][0]), count, cell_format)\\n # total score of columns\\n for i in range(1, len(self.arrays[sheet_number][0])):\\n count = 0\\n for j in range(2, len(self.arrays[sheet_number])):\\n count += self.arrays[sheet_number][j][i]\\n self.worksheets[sheet_number].write(len(self.arrays[sheet_number]), i, count, cell_format)\",\n \"def score(self) -> float:\\n assert hasattr(self, \\\"characterized_inventory\\\"), \\\"Must do LCIA first\\\"\\n if hasattr(self, \\\"weighted_inventory\\\"):\\n return float(self.weighted_inventory.sum())\\n elif hasattr(self, \\\"normalized_inventory\\\"):\\n return float(self.normalized_inventory.sum())\\n else:\\n return float(self.characterized_inventory.sum())\",\n \"def scores_for(self, board):\\r\\n scores = [1]*board.width\\r\\n for i in range(board.width):\\r\\n if not board.can_add_to(i):\\r\\n scores[i] = -1\\r\\n elif board.is_win_for(self.checker):\\r\\n scores[i] = 100\\r\\n elif board.is_win_for(self.opponent_checker()):\\r\\n scores[i] = 0\\r\\n elif self.lookahead == 0:\\r\\n scores[i] = 50\\r\\n else:\\r\\n board.add_checker(self.checker, i)\\r\\n other = AIPlayer(self.opponent_checker(), self.tiebreak, self.lookahead-1)\\r\\n other_scores = other.scores_for(board)\\r\\n if max(other_scores) == 100:\\r\\n scores[i] = 0\\r\\n elif max(other_scores) == 50:\\r\\n scores[i] = 50\\r\\n elif max(other_scores) == 0:\\r\\n scores[i] = 100\\r\\n board.remove_checker(i)\\r\\n return scores\",\n \"def calculate_production_bonus(self, number_of_improvements, max_slots):\\n pass\",\n \"def score(self):\\n return self.aggregate(Sum('score')).values()[0] or 0\",\n \"def add_score(self, score):\\n self._score += score\",\n \"def addScore(self, index, lstGross):\\n # update gross score for hole\\n for player, gross in zip(self.scores, lstGross):\\n player.gross[index] = gross\\n # update team score\\n for team in self.team_list:\\n team.calculate_score(index)\\n # update team points using other team score\\n self.team_list[0].update_points(index, self.team_list[1])\\n self.team_list[1].update_points(index, self.team_list[0])\",\n \"def increase_score(self):\\n self.score += 1\",\n \"def update_score(self):\\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)\",\n \"def extras_total(self):\\n total = self.wides + self.no_balls + self.byes + self.leg_byes\\n return total\",\n \"def calculate_score(card_list):\\n if sum(card_list) == 21 and len(card_list) == 2:\\n return 0\\n if sum(card_list) > 21 and 11 in card_list:\\n card_list.remove(11)\\n card_list.append(1)\\n return sum(card_list)\",\n \"def adjusted_score(self) -> Tuple[List[str], np.ndarray]:\\n nb_agents = self.game.configuration.nb_agents\\n current_scores = np.zeros((1, nb_agents), dtype=np.float32)\\n\\n eq_agent_states = dict(\\n (\\n agent_pbk,\\n AgentState(\\n self.game.initialization.eq_money_holdings[i],\\n [int(h) for h in self.game.initialization.eq_good_holdings[i]],\\n self.game.initialization.utility_params[i],\\n ),\\n )\\n for agent_pbk, i in zip(\\n self.game.configuration.agent_pbks,\\n range(self.game.configuration.nb_agents),\\n )\\n ) # type: Dict[str, AgentState]\\n\\n result = np.zeros((1, nb_agents), dtype=np.float32)\\n\\n eq_scores = np.zeros((1, nb_agents), dtype=np.float32)\\n eq_scores[0, :] = [\\n eq_agent_state.get_score() for eq_agent_state in eq_agent_states.values()\\n ]\\n\\n temp_game = Game(self.game.configuration, self.game.initialization)\\n\\n # initial scores\\n initial_scores = np.zeros((1, nb_agents), dtype=np.float32)\\n scores_dict = temp_game.get_scores()\\n initial_scores[0, :] = list(scores_dict.values())\\n keys = list(scores_dict.keys())\\n current_scores = np.zeros((1, nb_agents), dtype=np.float32)\\n current_scores[0, :] = initial_scores[0, :]\\n\\n # compute the partial scores for every agent after every transaction\\n # (remember that indexes of the transaction start from one, because index 0 is reserved for the initial scores)\\n for idx, tx in enumerate(self.game.transactions):\\n temp_game.settle_transaction(tx)\\n scores_dict = temp_game.get_scores()\\n current_scores[0, :] = list(scores_dict.values())\\n\\n result[0, :] = np.divide(\\n np.subtract(current_scores, initial_scores),\\n np.subtract(eq_scores, initial_scores),\\n )\\n result = np.transpose(result)\\n\\n return keys, result\",\n \"def updateScore(score):\\n return score + 1\",\n \"def totalPoisonRatingBonus(self):\\n return int(self._basePoisonRatingBonus + self.equipmentPoisonRatingBonus +\\n self._statusPoisonRatingBonus)\",\n \"def get_score_matrix(self) -> int:\",\n \"def _calc_multiple_alignment_score(wrapped_data : tuple) -> int: \\n (start, finish) = wrapped_data \\n score_sum = 0.\\n for dna_record in tqdm(dna_sequences[start : finish + 1], total=(finish + 1 - start), desc=\\\"Training process\\\"):\\n score_sum += self.aligner.score(seq, dna_record.seq)\\n return score_sum\",\n \"def score(self, hand, position):\\n\\n try:\\n assert self.grid[position][1] == \\\"---\\\"\\n except AssertionError:\\n print self\\n print position\\n raise FilledInError\\n except KeyError:\\n print \\\"\\\\nCheck your code. This is not a valid position:\\\", position, \\\"\\\\n\\\"\\n raise\\n\\n if position.startswith(\\\"n\\\"): # Return sum of relevant number\\n n = int(position[1])\\n return sum(d for d in hand.dice if d == n)\\n\\n elif position in [\\\"k3\\\", \\\"k4\\\", \\\"ch\\\"]: # Return total sum\\n if position == \\\"k3\\\" and hand.max_tally()[0] < 3:\\n return 0 # The is not a three of a kind\\n elif position == \\\"k4\\\" and hand.max_tally()[0] < 4:\\n return 0 # The is not a four of a kind\\n return sum(hand.dice)\\n\\n elif position in [\\\"fh\\\", \\\"ss\\\", \\\"ls\\\", \\\"yz\\\", \\\"yb\\\"]: # Return fixed score\\n if position == \\\"fh\\\":\\n tallies = hand.get_dicedict().values()\\n if 1 in tallies:\\n return 0 # This is not a full house\\n\\n elif position in [\\\"ss\\\", \\\"ls\\\"]:\\n ds = \\\"\\\".join(str(x) for x in hand.sort_by_value())\\n if position == [\\\"ss\\\"]:\\n if \\\"1234\\\" not in ds and \\\"2345\\\" not in ds and \\\"3456\\\" not in ds:\\n return 0\\n else:\\n if \\\"12345\\\" not in ds and \\\"23456\\\" not in ds:\\n return 0\\n\\n else:\\n if hand.max_tally()[0] < 5:\\n return 0 # This is not a yahtzee\\n if position == \\\"yb\\\" and self.grid[\\\"yz\\\"] == \\\"---\\\":\\n return 0 # YB only scores points if there already is a YZ\\n\\n return fixed_scores[position]\\n\\n else:\\n raise InvalidPositionError\",\n \"def add_indicator_values(self, flood_safety_score, biodiversity_score,\\n cost_score, total_cost, turn=0): \\n if turn < len(self.turn):\\n self.flood_safety[turn] = flood_safety_score\\n self.biodiversity[turn] = biodiversity_score * 100\\n self.cost_score[turn] = cost_score * 100\\n self.total_costs[turn] = total_cost\\n else:\\n self.flood_safety.append(flood_safety_score)\\n self.biodiversity.append(biodiversity_score * 100)\\n self.total_costs.append(total_cost)\\n self.cost_score.append(cost_score * 100)\\n self.turn.append(turn)\\n return\",\n \"def add_point(self):\\n self.total_score = self.total_score + 1\\n if self.total_score // 10 == 0:\\n self.level = self.total_score / 10\",\n \"def score(self) -> int:\\n card_values = {\\n '0': 0,\\n '1': 1,\\n '2': 2,\\n '3': 3,\\n '4': 4,\\n '5': 5,\\n '6': 6,\\n '7': 7,\\n '8': 8,\\n '9': 9,\\n '10': 10,\\n 'JACK': 10,\\n 'QUEEN': 10,\\n 'KING': 10,\\n 'ACE': 11}\\n hand_value = []\\n for i in self.cards:\\n hand_value.append(card_values[i.value])\\n while sum(hand_value) > 21 and 11 in hand_value:\\n for i, j in enumerate(hand_value):\\n if j == 11:\\n hand_value[i] = 1\\n break\\n else:\\n pass\\n return sum(hand_value)\",\n \"def get_total_assigned(self):\\n return sum(self.n_assigned_list)\",\n \"def score(self):\\n raise NotImplementedError()\",\n \"def set_scores(apps, schema_editor):\\n\\n Game = apps.get_model(\\\"stats\\\", \\\"Game\\\")\\n for game in Game.objects.all():\\n score_allies = 0\\n score_opponents = 0\\n player_stats = game.playerstat_set.all()\\n for stat in player_stats:\\n if stat.is_opponent:\\n score_opponents += stat.scored\\n else:\\n score_allies += stat.scored\\n\\n game.score_allies = score_allies\\n game.score_opponents = score_opponents\\n game.save()\",\n \"def sum_cards(self):\\n has_ace = False\\n sum = 0\\n\\n # Add up players cards\\n for card in self.cards:\\n if card.card_value == \\\"ace\\\":\\n has_ace = True\\n sum += card.game_value\\n\\n # Handle case where ace plays low\\n if sum > 21 and has_ace:\\n sum -= 10\\n\\n return sum\",\n \"def custom_score(game, player):\\n \\n # get avaliable moves for each player\\n own_moves = len(game.get_legal_moves(player))\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n \\n # return different between # of my agent's move and oppenent's\\n return float(own_moves - opp_moves)\",\n \"def score_update(scoreboard, compare):\\r\\n if compare == 'Victory':\\r\\n scoreboard['W'] += 1\\r\\n elif compare == 'Defeat':\\r\\n scoreboard['L'] += 1\\r\\n elif compare == 'Tie':\\r\\n scoreboard['T'] += 1\",\n \"def print_sheet(score_sheet, total_score):\\n print('-------------------------\\\\nYahtzee Score Sheet\\\\n-------------------------')\\n name_list = ['Player', '1. Aces', '2. 2s', '3. 3s', '4. 4s', '5. 5s', '6. 6s', '7. 3 of a kind', '8. 4 of '\\n 'a kind', '9. Full House', '10. Small Straight', '11. Large Straight', '12. Yahtzee', '13. Chance']\\n temp_list = [\\\"\\\" if score == EMPTY_BOX() else score for score in score_sheet]\\n for i in range(len(score_sheet)):\\n print(f'{name_list[i]}:{temp_list[i]}')\\n if EMPTY_BOX() not in score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]:\\n if sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) >= UPPER_BONUS_REQUIREMENT():\\n print(f'Sum of Upper Section:{sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)])}\\\\nBonus:35')\\n elif sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)]) < UPPER_BONUS_REQUIREMENT():\\n print(f'Sum of Upper Section:{sum(score_sheet[slice(UPPER_START(), UPPER_STOP()+1)])}\\\\nBonus:0')\\n else:\\n print('Sum of Upper Section:\\\\nBonus:')\\n if EMPTY_BOX() not in score_sheet[slice(LOWER_START(), LOWER_STOP()+1)]:\\n print(f'Sum of Lower Section:{sum(score_sheet[slice(LOWER_START(), LOWER_STOP()+1+1)])}')\\n else:\\n print('Sum of Lower Section:')\\n if EMPTY_BOX() not in score_sheet:\\n print(f'Total Score:{total_score}\\\\n-------------------------')\\n else:\\n print(f'Total Score:\\\\n-------------------------')\",\n \"def updateScore(self, score):\\n self.__score += score\",\n \"def update(self):\\n for pl, result in zip(self._players, self.golf_round.doc.results):\\n for score in result.scores:\\n n = score.num-1\\n # update net \\n pl.dct_net['holes'][n] = score.gross - pl._bumps[n]\\n pl.update_totals(pl.dct_net)\",\n \"def centre_priority_evaluate(self):\\r\\n evaluation = 0\\r\\n for player in range(2):\\r\\n player_sign = player * 2 - 1\\r\\n for i in range(4):\\r\\n score = i + 1\\r\\n evaluation += player_sign * score * count_bits(self.bitboard_king[player] &\\r\\n self.CENTRE_PRIORITY_BITMASKS[i])\\r\\n evaluation += player_sign * score * count_bits(self.bitboard_pawns[player] &\\r\\n self.CENTRE_PRIORITY_BITMASKS[i])\\r\\n return evaluation\",\n \"def compute_scores(self, *scorers):\\n if self.nodes[0]:\\n list_ = self.nodes\\n else:\\n list_ = self.reaction_trees\\n\\n for idx, item in enumerate(list_):\\n scores = {repr(scorer): scorer(item) for scorer in scorers}\\n self.all_scores[idx].update(scores)\\n self._update_route_dict(self.all_scores, \\\"all_score\\\")\",\n \"def _aggregate_scores(self,\\n game_scores: List[Dict[str, int]]) -> task.ScoreData:\\n\\n relative_score = []\\n recall = []\\n num_incorrect = []\\n precision = []\\n\\n for score_dict in game_scores:\\n\\n relative_score.append(score_dict['score']/score_dict['max_score'])\\n recall.append(score_dict['num_correct']/score_dict['word_count'])\\n num_incorrect.append(score_dict['num_incorrect'])\\n\\n # Precision -- defined to be 1 if no words are guessed.\\n num_guesses = score_dict['num_correct'] + score_dict['num_incorrect']\\n if num_guesses > 0:\\n precision.append(score_dict['num_correct'] / num_guesses)\\n else:\\n precision.append(1.0)\\n\\n relative_score = np.mean(relative_score)\\n recall = np.mean(recall)\\n num_incorrect = np.mean(num_incorrect)\\n precision = np.mean(precision)\\n\\n score_data = task.ScoreData(\\n {'relative_score': relative_score,\\n 'recall': recall,\\n 'num_incorrect': num_incorrect,\\n 'precision': precision},\\n preferred_score='relative_score',\\n low_score=0.0,\\n high_score=1.0,\\n number_of_shots=-1,\\n subtask_description=('A simplified implementation of the NYT '\\n 'Spelling Bee game. Individual game scoring is '\\n 'defined in the class deffinition. Reported '\\n 'scores are normalized to game maximums.'\\n 'All scores are averaged over each game played '\\n 'in an evaluation run.'))\\n\\n return score_data\",\n \"def set_ability_scores(self, scores: List[int]):\\n for s in range(6):\\n self.dna[s] = scores[s]\",\n \"def account_for_new_score(self):\\n self.rolls += 1\\n if self.continued is True:\\n self.total_score += self.current_roll.score\\n self.dice_remaining = self.current_roll.dice_remaining\\n\\n if self.dice_remaining == 0:\\n self.resets += 1\\n self.dice_remaining = 5\",\n \"def compute_score(self):\\n for i in xrange(FRAMES):\\n # STRIKE\\n if self.frames[i][0] == 10:\\n # CONSECUTIVE STRIKE\\n if self.frames[i + 1][0] == 10:\\n self.scores.append(self.frames[i][0] +\\n self.frames[i + 1][0] +\\n self.frames[i + 2][0])\\n else:\\n self.scores.append(self.frames[i][0] +\\n self.frames[i + 1][0] +\\n self.frames[i + 1][1])\\n # SPARE\\n elif (self.frames[i][0] + self.frames[i][1] == 10):\\n self.scores.append(self.frames[i][0] + self.frames[i][1] +\\n self.frames[i + 1][0])\\n # NEITHER\\n else:\\n self.scores.append(self.frames[i][0] + self.frames[i][1])\\n # Total Score\\n for score in self.scores:\\n self.score += score\",\n \"def calculate_profit(self):\",\n \"def score( self ):\\r\\n result = 0.0\\r\\n for rr in self.ee.getRsrcs( ):\\r\\n value = self.scoreRsrc( rr )\\r\\n result += value\\r\\n print( \\\"INFO: Value for the schedule is %s \\\" % ( rr, result ) )\\r\\n return( result )\",\n \"def score(self):\\n self.set_idx()\\n if self.idx:\\n diffs = self.diffs()\\n weights = self.weights\\n return np.sum(weights * diffs) / np.sum(weights)\\n else:\\n return 0.0\",\n \"def get_score(self, student_answers):\\r\\n pass\",\n \"def score(self, hand, top_card):\\n powerset = GameState.powerset(list(hand).append(top_card))\\n score = 0\\n for meld in powerset:\\n score += self.score_meld(meld)\\n return score\",\n \"def compute_stability_scores(self):\\n self.mutations, self.scores, self.matrix = stability(\\n self.seq,\\n alphabet='ACGU',\\n fold_vectorize=self.fold_vectorize)\",\n \"def total_cost(self):\\n if self.goal:\\n return self.goal + (self.community_contribution or 0)\\n else:\\n return 0\",\n \"def score(self):\\n # loop over aminoacids in protein and calculate how often H and C are surrounded by H and C\\n for aminoacid in self.aminoacids:\\n if aminoacid.aminoacid_type == \\\"H\\\":\\n self.stability = self.stability + (-1 * self.surrounded_by(aminoacid.location, \\\"H\\\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \\\"C\\\", aminoacid.aminoacid_number))\\n elif aminoacid.aminoacid_type == \\\"C\\\":\\n self.stability = self.stability + (-5 * self.surrounded_by(aminoacid.location, \\\"C\\\", aminoacid.aminoacid_number)) + (-1 * self.surrounded_by(aminoacid.location, \\\"H\\\", aminoacid.aminoacid_number))\\n self.stability = self.stability/2\\n return int(self.stability)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69169897","0.6778899","0.66893876","0.6492624","0.6446686","0.64220786","0.6389673","0.63117045","0.62862825","0.627944","0.62590975","0.62550724","0.6167822","0.6160577","0.6156003","0.6153973","0.6143272","0.6137463","0.6123372","0.6122803","0.61142373","0.608828","0.6081575","0.60813975","0.6075664","0.6067807","0.60674155","0.6066803","0.6059975","0.6035494","0.6024585","0.6015036","0.5986326","0.59804314","0.597255","0.5950737","0.5944914","0.5944688","0.5935898","0.5904515","0.58931255","0.5889207","0.5880248","0.58753","0.5854381","0.584618","0.5836179","0.58124244","0.58051616","0.57947505","0.5793925","0.57924646","0.5792433","0.57904917","0.57894474","0.57875955","0.57846254","0.5779751","0.5777058","0.57707876","0.57688737","0.5767375","0.576262","0.5758588","0.5723662","0.57191575","0.57134706","0.5698731","0.56983596","0.5691474","0.5691017","0.56891865","0.5687572","0.56766117","0.5674365","0.5666595","0.56597996","0.56582344","0.5654614","0.5654149","0.5651182","0.5646639","0.5642143","0.56405276","0.5634994","0.56223065","0.5618118","0.56051236","0.56036794","0.55833286","0.55809736","0.5580156","0.55790234","0.5578898","0.5578016","0.55613434","0.5557734","0.5554167","0.55496943","0.5549228"],"string":"[\n \"0.69169897\",\n \"0.6778899\",\n \"0.66893876\",\n \"0.6492624\",\n \"0.6446686\",\n \"0.64220786\",\n \"0.6389673\",\n \"0.63117045\",\n \"0.62862825\",\n \"0.627944\",\n \"0.62590975\",\n \"0.62550724\",\n \"0.6167822\",\n \"0.6160577\",\n \"0.6156003\",\n \"0.6153973\",\n \"0.6143272\",\n \"0.6137463\",\n \"0.6123372\",\n \"0.6122803\",\n \"0.61142373\",\n \"0.608828\",\n \"0.6081575\",\n \"0.60813975\",\n \"0.6075664\",\n \"0.6067807\",\n \"0.60674155\",\n \"0.6066803\",\n \"0.6059975\",\n \"0.6035494\",\n \"0.6024585\",\n \"0.6015036\",\n \"0.5986326\",\n \"0.59804314\",\n \"0.597255\",\n \"0.5950737\",\n \"0.5944914\",\n \"0.5944688\",\n \"0.5935898\",\n \"0.5904515\",\n \"0.58931255\",\n \"0.5889207\",\n \"0.5880248\",\n \"0.58753\",\n \"0.5854381\",\n \"0.584618\",\n \"0.5836179\",\n \"0.58124244\",\n \"0.58051616\",\n \"0.57947505\",\n \"0.5793925\",\n \"0.57924646\",\n \"0.5792433\",\n \"0.57904917\",\n \"0.57894474\",\n \"0.57875955\",\n \"0.57846254\",\n \"0.5779751\",\n \"0.5777058\",\n \"0.57707876\",\n \"0.57688737\",\n \"0.5767375\",\n \"0.576262\",\n \"0.5758588\",\n \"0.5723662\",\n \"0.57191575\",\n \"0.57134706\",\n \"0.5698731\",\n \"0.56983596\",\n \"0.5691474\",\n \"0.5691017\",\n \"0.56891865\",\n \"0.5687572\",\n \"0.56766117\",\n \"0.5674365\",\n \"0.5666595\",\n \"0.56597996\",\n \"0.56582344\",\n \"0.5654614\",\n \"0.5654149\",\n \"0.5651182\",\n \"0.5646639\",\n \"0.5642143\",\n \"0.56405276\",\n \"0.5634994\",\n \"0.56223065\",\n \"0.5618118\",\n \"0.56051236\",\n \"0.56036794\",\n \"0.55833286\",\n \"0.55809736\",\n \"0.5580156\",\n \"0.55790234\",\n \"0.5578898\",\n \"0.5578016\",\n \"0.55613434\",\n \"0.5557734\",\n \"0.5554167\",\n \"0.55496943\",\n \"0.5549228\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6580822"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":17493,"cells":{"query":{"kind":"string","value":"Aggregate the statistics of a log dict"},"document":{"kind":"string","value":"def aggregate_log_dict(agg_dict, new_dict) -> dict:\n for k in new_dict:\n # init new if not present\n if k not in agg_dict:\n agg_dict[k] = {\n 'n': 0,\n 'sum': 0.0,\n 'max': new_dict[k],\n 'min': new_dict[k],\n }\n # aggregate\n agg_dict[k]['n'] += 1\n agg_dict[k]['sum'] += new_dict[k]\n agg_dict[k]['max'] = max(new_dict[k], agg_dict[k]['max'])\n agg_dict[k]['min'] = min(new_dict[k], agg_dict[k]['min'])\n # TODO: add more stats (e.g. stdev, max, minin the future)\n\n return agg_dict"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def aggregate_logging_outputs(logging_outputs):\n cider_sum = sum(log.get('cider', 0) for log in logging_outputs)\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n sc_loss_sum = sum(log.get('sc_loss', 0) for log in logging_outputs)\n n_pos_sum = sum(log.get('n_positive', 0) for log in logging_outputs)\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n\n # print('| n_pos_sum: {} | nsentences: {}'.format(n_pos_sum, nsentences))\n agg_output = {\n 'cider': cider_sum / nsentences,\n 'loss': loss_sum / sample_size / math.log(2),\n 'sc_loss': sc_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.,\n 'n_positive_rate': n_pos_sum / nsentences,\n 'ntokens': ntokens,\n 'nsentences': nsentences,\n 'sample_size': sample_size,\n }\n return agg_output","def aggregate_logging_outputs(logging_outputs):\n loss = sum(log.get('loss', 0) for log in logging_outputs)\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n acc = sum(log.get('acc', 0) for log in logging_outputs)\n\n agg_output = {\n 'loss': loss / sample_size / math.log(2),\n 'nll_loss': sum(log.get('nll_loss', 0) for log in logging_outputs) / sample_size / math.log(\n 2) if ntokens > 0 else 0.,\n 'ntokens': ntokens,\n 'nsentences': nsentences,\n 'sample_size': sample_size,\n 'acc': acc / sample_size,\n }\n\n if 'mlm_loss' in logging_outputs[0]:\n mlm_loss = sum(log.get('mlm_loss', 0) for log in logging_outputs)\n mlm_acc = sum(log.get('mlm_acc', 0) for log in logging_outputs)\n agg_output['mlm_loss'] = mlm_loss / sample_size / math.log(2)\n agg_output['mlm_acc'] = mlm_acc / sample_size\n return agg_output","def aggregate_logging_outputs(logging_outputs):\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n downstream_loss = sum(log.get('downstream_loss', 0) for log in logging_outputs)\n bleu_stats = reduce(utils.reduce_bleu_stats,\n [log.get('bleu', utils.get_zero_bleu_stats()) for log in logging_outputs])\n intermediate_loss = defaultdict(lambda: AverageMeter())\n all_loss_dicts = [log.get('intermediate_loss', defaultdict(lambda: AverageMeter())) for log in logging_outputs]\n # Aggregate\n for single_dict in all_loss_dicts:\n for key, value in single_dict.items():\n intermediate_loss[key] = utils.reduce_average_meter(intermediate_loss[key], value)\n # same for bleu\n intermediate_bleu = defaultdict(lambda: AverageMeter())\n all_bleu_dicts = [log.get('intermediate_bleu', defaultdict(lambda: AverageMeter()))\n for log in logging_outputs]\n # Aggregate\n for single_dict in all_bleu_dicts:\n for key, value in single_dict.items():\n intermediate_bleu[key] = utils.reduce_average_meter(intermediate_bleu[key], value)\n agg_output = {'loss': loss_sum / float(sample_size), 'sample_size': sample_size, 'ntokens': ntokens,\n 'nsentences': nsentences, 'downstream_loss': downstream_loss / float(sample_size),\n 'bleu': bleu_stats, **intermediate_loss, **intermediate_bleu}\n return agg_output","def aggregate_logging_outputs(logging_outputs):\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n agg_output = {\n 'loss': loss_sum,\n 'ntokens': ntokens,\n 'nsentences': nsentences,\n 'sample_size': sample_size,\n }\n if sample_size != ntokens:\n agg_output['nll_loss'] = loss_sum / math.log(2)\n return agg_output","def _aggregate_log_values(self, source, dest):\n remove = []\n for key, item in source.items():\n if \"data\" not in item:\n # Assume it's a sub-group\n dest[key] = {}\n self._aggregate_log_values(item, dest[key])\n else:\n aggregator = self._get_aggregator_for_key(key, item['agg'])\n value = aggregator(item['data'])\n if item['precision'] is not None:\n value = round(value, item['precision'])\n dest[key] = value\n if item['scope'] == 'get':\n remove.append(key)\n for key in remove:\n del source[key]","def aggregate_logging_outputs(logging_outputs):\n loss_sum = sum(log.get(\"loss\", 0) for log in logging_outputs)\n nll_loss_sum = sum(log.get(\"nll_loss\", 0) for log in logging_outputs)\n ntokens = sum(log.get(\"ntokens\", 0) for log in logging_outputs)\n nsentences = sum(log.get(\"nsentences\", 0) for log in logging_outputs)\n sample_size = sum(log.get(\"sample_size\", 0) for log in logging_outputs)\n return {\n \"loss\": loss_sum / sample_size if sample_size > 0 else 0.0,\n \"nll_loss\": nll_loss_sum / ntokens / math.log(2),\n \"ntokens\": ntokens,\n \"nsentences\": nsentences,\n \"sample_size\": sample_size,\n }","def aggregate_logging_outputs(logging_outputs):\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n return {\n 'loss': sum(log.get('loss', 0) for log in logging_outputs) / sample_size / math.log(2) if sample_size > 0 else 0.,\n 'nll_loss': sum(log.get('nll_loss', 0) for log in logging_outputs) / ntokens / math.log(2) if ntokens > 0 else 0.,\n 'ntokens': ntokens,\n 'nsentences': nsentences,\n 'sample_size': sample_size,\n }","def aggregate_logging_outputs(logging_outputs):\n # assert len(logging_outputs) == 1\n log = logging_outputs[0]\n loss = log.get('loss', 0)\n ntokens = log.get('ntokens', 0)\n batch_sizes = log.get('nsentences', 0)\n sample_size = log.get('sample_size', 0)\n agg_output = {\n 'loss': loss,\n 'ntokens': ntokens,\n 'nsentences': batch_sizes,\n 'sample_size': sample_size,\n }\n return agg_output","def log_dict(self, source, agg=\"auto\", group=None):\n for key, val in source.items():\n if isinstance(val, dict):\n sub_group = key if group is None else group+\"->\"+key\n self.log_dict(val, agg=agg, group=sub_group)\n else:\n self.log(key, val, group=group, agg=agg)","def means(self) -> dict:\n return {k: 0 if not vs else sum(vs) / len(vs) for k, vs in self.logs.items()}","def _aggregate_traj_stats(traj_stats_a, traj_stats_b):\n merged_stats = {}\n for k in traj_stats_a:\n n_a, avg_a, M2_a = traj_stats_a[k][\"n\"], traj_stats_a[k][\"mean\"], traj_stats_a[k][\"sqdiff\"]\n n_b, avg_b, M2_b = traj_stats_b[k][\"n\"], traj_stats_b[k][\"mean\"], traj_stats_b[k][\"sqdiff\"]\n n = n_a + n_b\n mean = (n_a * avg_a + n_b * avg_b) / n\n delta = (avg_b - avg_a)\n M2 = M2_a + M2_b + (delta ** 2) * (n_a * n_b) / n\n merged_stats[k] = dict(n=n, mean=mean, sqdiff=M2)\n return merged_stats","def aggregate_metrics(metrics):\n if len(metrics) == 1:\n return metrics[0]\n else:\n agg_metrics = metrics[0]\n for metric in agg_metrics.keys():\n vals = [x[metric] for x in metrics]\n agg_metrics[metric] = [np.mean(vals), np.std(vals)]\n return agg_metrics","def aggregate_results(self, results):\n result = dict()\n result['MAE'] = self.average_dict_items(results, 'MAE')\n result['MdAE'] = self.average_dict_items(results, 'MdAE')\n result['RMSE'] = self.average_dict_items(results, 'RMSE')\n result['SMAPE'] = self.average_dict_items(results, 'SMAPE')\n result['num_values'] = self.average_dict_items(results, 'num_values')\n return result","def get_total_counts(self):\n ret = {}\n all_loggers_count = 0\n for logger, name_map in self.acc_map.items():\n cur_logger_count = 0\n ret[logger.name] = {}\n for name, status_map in name_map.items():\n cur_name_count = 0\n ret[logger.name][name] = {}\n for status, acc in status_map.items():\n cur_count = acc.total_count\n ret[logger.name][name][status] = cur_count\n cur_name_count += cur_count\n cur_logger_count += cur_count\n all_loggers_count += cur_count\n ret[logger.name][name]['__all__'] = cur_name_count\n ret[logger.name]['__all__'] = cur_logger_count\n ret['__all__'] = all_loggers_count\n return ret","def extract_test_log(self, log: dict) -> dict:\n test_log = self.extract_generic_log(log, 'test')\n test_log['adv_acc'] = (log['correct_adv'] / log['total']) * 100.0\n\n return test_log","def sum_dstats(self, stats, smetrics):\n avg = {}\n\n for disk, metrics in stats.iteritems():\n for mname, metric in metrics.iteritems():\n if mname not in smetrics:\n continue\n if mname in avg:\n avg[mname] += metric\n else:\n avg[mname] = metric\n\n return avg","def summaries(e_dict, m_dict):\n for key, value in m_dict.items():\n e_dict[key].append(np.mean(value))\n return e_dict","def collect_log_output(activity_log, result):\n\n test_name = activity_log.get('identifier')\n if test_name:\n result.append(test_name['_value'])\n\n duration = activity_log.get('duration')\n if duration:\n output = str(\"{:.2f}\".format(float(duration['_value'])))\n result.append(output)\n\n performance_metrics = activity_log.get('performanceMetrics')\n if not performance_metrics is None:\n metrics = performance_metrics.get('_values')\n for metric in metrics:\n measurement = metric.get('measurements')\n values = measurement.get('_values')\n value_sum = 0\n for value in values:\n value_sum += float(value.get('_value'))\n output = str(value_sum / len(values))\n result.append(output)","def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)","def aggregate_statistics(self, stat_col, stat_agg):\n self.module.aggregate_statistics(stat_col, stat_agg)","def apply(self):\n counter = {}\n for act in self.activities:\n freq = []\n for trace in self.log:\n freq.append(len(self.project_trace(trace, [act])))\n if not len(freq) == 0:\n counter[act] = {'sum': sum(freq), 'min': min(freq),\n 'max': max(freq)}\n return counter","def score_aggregate(results):\n scores = []\n truth_count = detected_count = segment_count = 0\n\n for res in results:\n scores.append(res[\"scores\"])\n truth_count += len(res[\"labels\"])\n detected_count += len(res[\"detected\"])\n segment_count += len(res[\"scores\"][\"segments\"])\n\n ret = dict()\n ret[\"scores\"] = sum_scores(scores)\n ret[\"stats\"] = dict(truth_count=truth_count, detected_count=detected_count, segment_count=segment_count)\n return ret","def summarize_rec_data(data):\n\n # Warning: not all collectible data has a summary stats implemented below!\n # See get_rec_stats() above!\n\n stats = {}\n\n if 'hc_ro' in data:\n # Entropy across HC units average over samples.\n hc_ro_arr = np.array(list(data['hc_ro'].values()))\n stats['H HC ro'] = utils.entropy(hc_ro_arr.T).mean()\n\n if 'vs_state' in data:\n # Sum of vS reward estimates change (from first to last sample).\n vs_state = data['vs_state']\n stats['d vS'] = sum(vs_state[max(vs_state.keys())] - vs_state[0])\n\n if 'co_occs' in data:\n # Mean entropy of real location and HC state co-occurance frequencies.\n co_occs = data['co_occs'][max(data['co_occs'].keys())]\n stats['H HC co'] = np.nanmean(get_hc_co_occ_entropy(co_occs))\n stats['H loc co'] = np.nanmean(get_loc_co_occ_entropy(co_occs))\n\n return stats","def get_all_stat(self):\n all_stat=dict()\n for stat_type in self.log_book.keys():\n stat = self.get_stat(stat_type)\n all_stat[stat_type] = stat\n return all_stat","def aggregate_statistics(self, new_stats):\n \n if isinstance(new_stats,RunStatistics):\n new_stats = [new_stats, ]\n elif isinstance(new_stats,list):\n if any(not isinstance(_,RunStatistics) for _ in new_stats):\n raise MadGraph5Error, \"The 'new_stats' argument of the function \"+\\\n \"'updtate_statistics' must be a (possibly list of) \"+\\\n \"RunStatistics instance.\"\n \n keys = set([])\n for stat in [self,]+new_stats:\n keys |= set(stat.keys())\n\n new_stats = new_stats+[self,]\n for key in keys:\n # Define special rules\n if key=='max_precision':\n # The minimal precision corresponds to the maximal value for PREC\n self[key] = min( _[key] for _ in new_stats if key in _)\n elif key=='min_precision':\n # The maximal precision corresponds to the minimal value for PREC\n self[key] = max( _[key] for _ in new_stats if key in _)\n elif key=='averaged_timing':\n n_madloop_calls = sum(_['n_madloop_calls'] for _ in new_stats if\n 'n_madloop_calls' in _)\n if n_madloop_calls > 0 :\n self[key] = sum(_[key]*_['n_madloop_calls'] for _ in \n new_stats if (key in _ and 'n_madloop_calls' in _) )/n_madloop_calls\n else:\n # Now assume all other quantities are cumulative\n self[key] = sum(_[key] for _ in new_stats if key in _)","def get_errors_summary(statistics):\n all_errors = (report['errors'] for report in statistics) \n \n errors_summary = defaultdict(int)\n for doc in all_errors:\n for key, value in doc.items():\n errors_summary[key] += value\n\n return errors_summary","def _aggregate_perf_data(perf_all_ordinals: List[str]):\n aggregate = {}\n\n pd = PerfData()\n for data in perf_all_ordinals:\n worker_pd = PerfData(**json.loads(data))\n if len(perf_all_ordinals) > 1:\n aggregate.setdefault(\"ordinals\", [])\n aggregate[\"ordinals\"].append(worker_pd.throughput_dict())\n\n pd.merge(worker_pd)\n\n aggregate.update(dataclasses.asdict(pd))\n return aggregate","def collect_filterstats_from_log(fp):\n D = {}\n while (line := fp.readline()): # noqa: E203, E231\n if \"Filter results for\" in line:\n filt = line.split()[-1]\n fp.readline() # empty\n fp.readline() # \"Filtering\"\n fp.readline() # Array size\n fp.readline() # cumulative masked\n fp.readline() # previously masked\n filtered_line = fp.readline() # new filtered\n if \"New filtered\" not in filtered_line:\n raise ValueError(\"Expected 'filtered' six lines after \"\n f\"'filter results', got {filtered_line:s}\")\n count = int(filtered_line.split()[-1])\n if filt in D:\n raise ValueError(f\"Found results for {filt:s} multiple times!\")\n D[filt] = count\n return D","def parse_stats(stats: dict, res: dict):\n for k, v in stats.items():\n if k not in res.keys():\n res.update({k: {}})\n if isinstance(v, list):\n for element in v:\n for metric, value in element.items():\n res[k].update({metric: [value]})","def get_logs_with_timestamp(res: List[Dict[str, Any]]) -> Dict[str, List[str]]:\n\n list_of_logs = (\n {log[\"result\"][\"timestamp\"]: log[\"result\"][\"logs\"]}\n for log in res\n if log[\"result\"][\"logs\"]\n )\n\n logs_with_timestamp = defaultdict(list)\n\n for result in list_of_logs:\n k, v = list(result.items())[0]\n logs_with_timestamp[parser.parse(k)] += v\n\n return logs_with_timestamp","def summary(self) -> Dict[str, Dict[str, float]]:\n vals: Dict[str, List[float]] = defaultdict(list)\n if not self.steps: # pragma: no cover\n return {}\n\n for timing_dict in self._timings:\n for step in self.steps:\n if step in timing_dict:\n vals[step].append(timing_dict[step])\n summary = {}\n for step in self.steps:\n if vals[step]:\n summary[step] = {\n \"cnt\": len(vals[step]),\n \"sum\": sum(vals[step]),\n \"min\": min(vals[step]),\n \"max\": max(vals[step]),\n \"avg\": sum(vals[step]) / len(vals[step]),\n }\n return summary","def aggregate_statistics(self, stat_col, stat_agg):\n precision_sums = stat_col[self.key_precision]\n recall_sums = stat_col[self.key_recall]\n f1score_sums = stat_col[self.key_f1score]\n supports = stat_col[self.key_f1score+'_support']\n\n # Special case - no samples!\n if sum(supports) == 0:\n stat_agg[self.key_precision] = 0\n stat_agg[self.key_precision+'_std'] = 0\n stat_agg[self.key_recall] = 0\n stat_agg[self.key_recall+'_std'] = 0\n stat_agg[self.key_f1score] = 0\n stat_agg[self.key_f1score+'_std'] = 0\n\n else: \n # Else: calculate weighted precision.\n precisions_avg = np.average(precision_sums, weights=supports)\n precisions_var = np.average((precision_sums-precisions_avg)**2, weights=supports)\n \n stat_agg[self.key_precision] = precisions_avg\n stat_agg[self.key_precision+'_std'] = math.sqrt(precisions_var)\n\n # Calculate weighted recall.\n recalls_avg = np.average(recall_sums, weights=supports)\n recalls_var = np.average((recall_sums-recalls_avg)**2, weights=supports)\n\n stat_agg[self.key_recall] = recalls_avg\n stat_agg[self.key_recall+'_std'] = math.sqrt(recalls_var)\n\n # Calculate weighted f1 score.\n f1scores_avg = np.average(f1score_sums, weights=supports)\n f1scores_var = np.average((f1score_sums-f1scores_avg)**2, weights=supports)\n\n stat_agg[self.key_f1score] = f1scores_avg\n stat_agg[self.key_f1score+'_std'] = math.sqrt(f1scores_var)","def output_stats(self):\n elapsed = self.timer.elapsed.total_seconds()\n count = self.copied + self.errored\n total = self.total\n # Time per key in milliseconds\n avg = round(elapsed / count * 1000, 3)\n # Time remaining in seconds\n remaining = 1.0 * elapsed / count * (total - count)\n # Time remaining in minutes\n remaining = round(remaining / 60.0, 1)\n # Time taken in minutes\n elapsed = round(elapsed / 60.0, 1)\n\n self.log.info(f\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \"\n f\"{remaining}min remaining. ({count:,}/{total:,})\")","def reduce_metrics(logging_outputs) -> None:\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n loss_sum_vad = sum(log.get('loss_vad', 0) for log in logging_outputs)\n\n ntokens_vad = sum(log.get('ntokens_vad', 0) for log in logging_outputs)\n total_n_ts = sum(log.get('n_ts', 0) for log in logging_outputs)\n total_n_pred_ts = sum(log.get('n_pred_ts', 0) for log in logging_outputs)\n\n nsentences_vad = sum(log.get('nsentences_vad', 0) for log in logging_outputs)\n\n sample_size_vad = sum(log.get('sample_size_vad', 0) for log in logging_outputs)\n sample_size_asr = sum(log.get('sample_size', 0) for log in logging_outputs)\n\n sample_size = sample_size_vad + sample_size_asr\n\n metrics.log_scalar('loss', loss_sum / sample_size, sample_size, round=3)\n\n if sample_size_vad:\n metrics.log_scalar('loss_vad', loss_sum_vad / sample_size_vad, sample_size_vad, round=3)\n else:\n metrics.log_scalar('loss_vad', loss_sum_vad / 999999999999, sample_size_vad, round=3)\n\n ncorrect_vad = sum(log.get('ncorrect_vad', 0) for log in logging_outputs)\n if nsentences_vad:\n metrics.log_scalar('accuracy_vad', 100.0 * ncorrect_vad / ntokens_vad, ntokens_vad, round=1)\n else:\n metrics.log_scalar('accuracy_vad', 100.0 * ncorrect_vad / 999999999999, ntokens_vad, round=1)\n\n if total_n_ts:\n ncorrect_ts = sum(log.get('ncorrect_ts', 0) for log in logging_outputs)\n metrics.log_scalar('recall_TS', 100.0 * ncorrect_ts / total_n_ts, total_n_ts, round=1)\n else:\n metrics.log_scalar('recall_TS', 100.0 * 0 / ntokens_vad, ntokens_vad, round=1)\n\n if total_n_pred_ts:\n ncorrect_ts = sum(log.get('ncorrect_ts', 0) for log in logging_outputs)\n metrics.log_scalar('precision_TS', 100.0 * ncorrect_ts / total_n_pred_ts, total_n_pred_ts, round=1)\n else:\n metrics.log_scalar('precision_TS', 100.0 * 0 / ntokens_vad, ntokens_vad, round=1)","def get_log_summary():\n # reads the session\n session = request.args.get('session', type=str)\n # reads the requested process name\n process = request.args.get('process', default='receipt', type=str)\n\n dictio = {}\n\n if check_session_validity(session):\n user = get_user_from_session(session)\n if lh.check_user_log_visibility(user, process):\n this_variants_number = lh.get_handler_for_process_and_session(process, session).variants_number\n this_cases_number = lh.get_handler_for_process_and_session(process, session).cases_number\n this_events_number = lh.get_handler_for_process_and_session(process, session).events_number\n\n ancestor_variants_number = lh.get_handler_for_process_and_session(process,\n session).first_ancestor.variants_number\n ancestor_cases_number = lh.get_handler_for_process_and_session(process, session).first_ancestor.cases_number\n ancestor_events_number = lh.get_handler_for_process_and_session(process,\n session).first_ancestor.events_number\n\n dictio = {\"this_variants_number\": this_variants_number, \"this_cases_number\": this_cases_number,\n \"this_events_number\": this_events_number, \"ancestor_variants_number\": ancestor_variants_number,\n \"ancestor_cases_number\": ancestor_cases_number, \"ancestor_events_number\": ancestor_events_number}\n print(dictio)\n\n ret = jsonify(dictio)\n return ret","def _print_aggregate_results(\n task: Task, task_results: Dict[Task, List[List[Dict[str, Any]]]]\n) -> None:\n aggregate_task_result = aggregate_nvs_results(task_results[task])\n print(\"\")\n print(f\"Aggregate results for task={task}:\")\n pretty_print_nvs_metrics(aggregate_task_result)\n print(\"\")","def _compute_traj_stats(traj_obs_dict):\n traj_stats = { k : {} for k in traj_obs_dict }\n for k in traj_obs_dict:\n traj_stats[k][\"n\"] = traj_obs_dict[k].shape[0]\n traj_stats[k][\"mean\"] = traj_obs_dict[k].mean(axis=0, keepdims=True) # [1, ...]\n traj_stats[k][\"sqdiff\"] = ((traj_obs_dict[k] - traj_stats[k][\"mean\"]) ** 2).sum(axis=0, keepdims=True) # [1, ...]\n return traj_stats","def data_statistics(title, data, stats=None, normalize=False, log_fun=logging.INFO):\n log_fun(\"Statistics %s\" % title)\n\n all_data = []\n for key in data:\n if not isinstance(data[key], list):\n all_data.append(data[key])\n else:\n all_data.extend(data[key])\n\n if normalize:\n minimum = min(all_data)\n maximum = max(all_data)\n all_data = [(d - minimum) / (maximum - minimum) for d in all_data]\n\n result = Statistics.calculate(all_data, stats, log_fun)\n FileWriter.write_dict_to_file(FileWriter.STATISTICS, title + \".txt\", result)","def reduce_metrics(logging_outputs) -> None:\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n admloss_sum = sum(log.get('admloss', 0) for log in logging_outputs)\n margin_n = sum(log.get('margin_n', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)\n metrics.log_scalar('admloss', admloss_sum / sample_size / math.log(2), sample_size, round=3)\n metrics.log_scalar('margin_norm', margin_n / nsentences, 32, round=3)\n metrics.log_derived('ppl', lambda meters: round(2**meters['loss'].avg, 3))","def average_data(counts, observable):\n if not isinstance(observable, dict):\n observable = make_dict_observable(observable)\n temp = 0\n tot = sum(counts.values())\n for key in counts:\n if key in observable:\n temp += counts[key] * observable[key] / tot\n return temp","def counts_to_log_counts(forms_by_count):\n forms_by_log_count={}\n for count, forms in forms_by_count.items():\n try:\n log_count=math.log(count)\n forms_by_log_count[log_count] = forms\n except:\n print(count)\n return forms_by_log_count","def sum_stats(stats_data):\n t_bounces = 0\n t_complaints = 0\n t_delivery_attempts = 0\n t_rejects = 0\n for dp in stats_data:\n t_bounces += int(dp['Bounces'])\n t_complaints += int(dp['Complaints'])\n t_delivery_attempts += int(dp['DeliveryAttempts'])\n t_rejects += int(dp['Rejects'])\n\n return {\n 'Bounces': t_bounces,\n 'Complaints': t_complaints,\n 'DeliveryAttempts': t_delivery_attempts,\n 'Rejects': t_rejects,\n }","def reduce_metrics(logging_outputs) -> None:\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n neg_elbo_sum = sum(log.get('neg_elbo', 0) for log in logging_outputs)\n recon_loss_sum = sum(log.get('recon_loss', 0) for log in logging_outputs)\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\n KLz_sum = sum(log.get('KLz', 0) for log in logging_outputs)\n KLt_sum = sum(log.get('KLt', 0) for log in logging_outputs)\n KLtheta_sum = sum(log.get('KLtheta', 0) for log in logging_outputs)\n\n if 'nll_iw' in logging_outputs[0]:\n nll_iw_sum = sum(log.get('nll_iw', 0) for log in logging_outputs)\n metrics.log_scalar('nll_iw_s', nll_iw_sum / nsentences, \n nsentences, round=3, priority=4)\n metrics.log_scalar('nll_iw_t', nll_iw_sum / ntokens / math.log(2), \n ntokens, round=3, priority=5) \n metrics.log_derived('ppl_iw', lambda meters: utils.get_perplexity(meters['nll_iw_t'].avg), priority=6)\n\n else:\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), \n sample_size, round=3, priority=3)\n\n metrics.log_scalar('neg_elbo_s', neg_elbo_sum / nsentences, \n nsentences, round=3, priority=4)\n metrics.log_scalar('recon_loss_s', recon_loss_sum / nsentences, \n nsentences, round=3, priority=4)\n\n metrics.log_scalar('neg_elbo_t', neg_elbo_sum / ntokens / math.log(2), \n ntokens, round=3, priority=5)\n metrics.log_scalar('recon_loss_t', recon_loss_sum / ntokens / math.log(2), \n ntokens, round=3, priority=5)\n\n metrics.log_scalar('KLz', KLz_sum / nsentences, nsentences, round=1, priority=8)\n metrics.log_scalar('KLt', KLt_sum / nsentences, nsentences, round=1, priority=8)\n metrics.log_scalar('KLtheta', KLtheta_sum / nsentences, nsentences, round=1, priority=8)\n\n metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['neg_elbo_t'].avg), priority=6)\n metrics.log_derived('recon_ppl', lambda meters: utils.get_perplexity(meters['recon_loss_t'].avg), priority=7)\n\n if 'active' in logging_outputs[0]:\n metrics.log_scalar('active', logging_outputs[0]['active'], weight=0, round=1, priority=10)\n metrics.log_scalar('percent', logging_outputs[0]['percent'], weight=0, round=2, priority=10)\n # metrics.log_scalar('nlow', logging_outputs[0]['nlow'], weight=0, priority=10)\n # metrics.log_scalar('nhigh', logging_outputs[0]['nhigh'], weight=0, priority=10)","def compute_metrics(self, results: list) -> dict:","def average_metrics_results(results):\n res = {}\n\n if len(results) == 0:\n return {}\n\n for key in results[0]:\n try:\n res[key] = sum([r[key] for r in results]) / len(results)\n except TypeError:\n res[key] = [sum(b)/len(results) for b in zip(*[s[key] for s in results])]\n return res","def collect_stat(self):\n\n cnstat_dict, ratestat_dict = self.get_cnstat()\n self.cnstat_dict.update(cnstat_dict)\n self.ratestat_dict.update(ratestat_dict)","def aggregate_results(results):\n\n for (config,con,dec),folds in results.iteritems():\n m = MODEL_PATTERN.match(config)\n if m:\n mode = m.groupdict()['mode'] # mle, rl, mrt, ...\n model = m.groupdict()['model'] # haem, hacm, hard, ...\n align = m.groupdict()['align'] # crp, cls ...\n else:\n mode, model, align = '', '', ''\n # mean accuracies across seeds for each fold\n foldaccuracies = []\n # we count number of models over folds and seeds\n num_individual_models = 0\n\n for foldname,fold in folds.items():\n if 'Q' in options.mode:\n seedaccurracies = fold.values()[:1] if fold.values() else [] # pick one\n# SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\n else:\n seedaccurracies = []\n for seed_acc in fold.values():\n seedaccurracies.append(seed_acc)\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\n # aggregate on fold level\n fold['__MEAN__'] = float(np.mean(seedaccurracies))\n fold['__SD__'] = float(np.std(seedaccurracies))\n l = len(seedaccurracies)\n num_individual_models += l\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__MEAN__')] += l\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__SD__')] += l\n\n # statistics over seeds for this fold\n fold['__STATS__'] = fold['__MEAN__'], fold['__SD__'], l\n foldaccuracies.append(fold['__MEAN__'])\n # aggregate on (config, condition, decoding) level\n folds['__MEAN__'] = float(np.mean(foldaccuracies))\n folds['__SD__'] = float(np.std(foldaccuracies))\n # statistics over folds for this (config, condition, decoding)\n folds['__STATS__'] = folds['__MEAN__'], folds['__SD__'], num_individual_models","def aggregate_results(self):\n\n raise NotImplementedError","def merge_logs(self):\n ourlog = LogData()\n for l in self.data_set:\n ourlog.entries = ourlog.entries + l.entries\n ourlog.sort_time()\n self.finalized_data = ourlog","def get_summarized_results(self):\n stats = [v.stats() for (k, v) in self.examples.items() if v.is_ready()]\n res = self.ExampleClass.average_stats(stats)\n\n res['loss'] = self.loss/self.loss_cnt\n res['recent_loss'] = sum(self.recent_loss_array) / sum(self.recent_loss_bs_array)\n\n return res","def count_results(self):\n for record in self._traced_records:\n if self._statistical_results.get(record.name) == None:\n self._statistical_results[record.name] = StatisticalResult(\n record.name)\n self._statistical_results[record.name].insert_record(\n record.kernel_time)\n self._total_time_ms += record.kernel_time\n self._statistical_results = {\n k: v\n for k, v in sorted(self._statistical_results.items(),\n key=lambda item: item[1],\n reverse=True)\n }","def get_best_result(log_dict, mode=\"regress\"):\n logs = OrderedDict()\n sum_score = 0\n for log_item in log_dict:\n csv_log = utils.parse_csv_log(log_dict[log_item])\n if mode == \"regress\":\n logs[log_item] = np.min(csv_log[\"val_mean_squared_error\"])\n sum_score += logs[log_item]\n elif mode == \"class\":\n logs[log_item] = np.max(csv_log[\"val_accuracy\"])\n sum_score += logs[log_item]\n elif mode == \"binary\":\n logs[log_item] = np.max(csv_log[\"val_binary_accuracy\"])\n sum_score += logs[log_item]\n\n return logs, sum_score","def _aggregate_metrics(metrics, aggfunc, base):\n return base.Struct(**_UNCOMPRESSED_METRICS)(\n left_side_bearing=aggfunc(_m.left_side_bearing for _m in metrics),\n right_side_bearing=aggfunc(_m.right_side_bearing for _m in metrics),\n character_width=aggfunc(_m.character_width for _m in metrics),\n character_ascent=aggfunc(_m.character_ascent for _m in metrics),\n character_descent=aggfunc(_m.character_descent for _m in metrics),\n character_attributes=0,\n )","def reduce_metrics(logging_outputs) -> None:\n\n loss_sum = utils.item(sum(log.get(\"loss\", 0) for log in logging_outputs))\n ntokens = utils.item(sum(log.get(\"ntokens\", 0) for log in logging_outputs))\n nsentences = utils.item(\n sum(log.get(\"nsentences\", 0) for log in logging_outputs)\n )\n sample_size = utils.item(\n sum(log.get(\"sample_size\", 0) for log in logging_outputs)\n )\n\n metrics.log_scalar(\n \"loss\", loss_sum / sample_size / math.log(2), sample_size, round=3\n )\n metrics.log_scalar(\"ntokens\", ntokens)\n metrics.log_scalar(\"nsentences\", nsentences)\n if sample_size != ntokens:\n metrics.log_scalar(\n \"nll_loss\", loss_sum / ntokens / math.log(2), ntokens, round=3\n )","def aggregate_by_event_type(record):\n return record.map(parse_entry)\\\n .map(lambda record: (record['event'], 1))\\\n .reduceByKey(lambda a, b: a+b)","def summary(self):\n summaries = self.summaries()\n # Reduce to a single summary with weighted averages for each\n # field except \"total_instances\", which is summed.\n total_instances = sum(d['total_instances'].value() for d in summaries)\n summary = {'total_instances': total_instances}\n for field in ('average_qps', 'average_latency', 'average_memory'):\n instance_weighted_sum = sum(\n d['total_instances'].value() * d[field].value()\n for d in summaries)\n summary[field] = float(instance_weighted_sum) / total_instances\n # Beautify rounding precision to match the App Engine UI.\n summary['average_qps'] = round(summary['average_qps'], 3)\n summary['average_latency_ms'] = round(summary['average_latency'], 1)\n summary['average_memory_mb'] = round(summary['average_memory'], 1)\n del summary['average_latency']\n del summary['average_memory']\n return summary","def summarize_metrics(metrics):\n summarized = {}\n for k in metrics:\n if k.endswith('mse'):\n summarized[k[:-3] + 'rmse'] = np.sqrt(np.mean(metrics[k]))\n elif k.startswith('err'):\n summarized[k + '_mean'] = np.mean(metrics[k])\n summarized[k + '_rmse'] = np.sqrt(np.mean(metrics[k]**2))\n elif k.endswith('nomean'):\n summarized[k] = metrics[k]\n else:\n summarized[k] = np.mean(metrics[k])\n\n return summarized","def calculate_averages(data):\n def mean(item_key):\n all_items = [i[item_key] for i in data]\n return sum(all_items)/float(len(all_items))\n\n return {\n \"mean_error_count\": mean('error_count'),\n \"mean_success_count\": mean('success_count'),\n \"mean_mean_response_time\": mean('mean_respone_time'),\n \"mean_data_sent_mb\": mean('data_sent_mb'),\n }","def log_weights_statistics(self):\n for weight_name, weight_parameter in self._weights.items():\n for statistic_function in self._statistics_functions:\n self._weights_statistics[statistic_function.__name__][\n weight_name\n ].append(float(statistic_function(weight_parameter)))","def aggregate_dict(x):\n agg_x = {}\n\n for ele in x:\n assert isinstance(ele, dict)\n\n for k, v in ele.items():\n if k not in agg_x:\n agg_x[k] = []\n\n if isinstance(v, (tuple, list)):\n agg_x[k].extend(list(v))\n else:\n agg_x[k].append(v)\n\n # Stack if possible\n new_agg_x = {}\n for k, v in agg_x.items():\n try:\n v = torch.cat(v, dim=0)\n except Exception:\n pass\n new_agg_x[k] = v\n\n return new_agg_x","def aggregate(self):\n data_to_track = {}\n for possession in self.possessions_to_track_aggregate:\n data_to_track[possession] = self._haves[possession]\n\n for variable in self.variables_to_track_aggregate:\n try:\n data_to_track[variable] = self.__dict__[variable]\n except KeyError:\n pass\n self.database_connection.put([\"aggregate\",\n data_to_track,\n self.group,\n self.round])","def log_scalars(self, scalars_dict, accum_reduction=None):\n for k, v in scalars_dict.items():\n self.log_scalar(k, v, accum_reduction)","def aggregate_stats(self):\n if self.split_bn.track_running_stats:\n (\n self.bn.running_mean.data,\n self.bn.running_var.data,\n ) = self._get_aggregated_mean_std(\n self.split_bn.running_mean,\n self.split_bn.running_var,\n self.num_splits,\n )","def calculate_statistics(self, trace, **input_test):\n inputs = self._clean_inputs(input_test)\n mc_logp = self._logp(trace, **inputs)\n mean_mse = self._mse(trace, **inputs)\n mse2 = self._mse2(trace, **inputs)\n mu, sd, zscore = self._alpha_stats(trace)\n return {'logp': mc_logp,\n 'mse': mean_mse,\n 'mse2': mse2,\n 'mu': mu,\n 'sd': sd,\n 'zscore': zscore}","def compute_query_summary_statistics(data):\n return {\n nameserver: _compute_summary_stats(entries)\n for nameserver, entries in data.items()\n }","def totals_map():\n totals_map = [*map(sum,poke_stats)]\n\n return(totals_map)","def statistic_aggregation(self, resource=None, resource_type=None,\n meter_name=None, period=300, aggregate='mean',\n granularity=300):\n\n pass","def calculate_times(log):\n log['processing_time'] = 0\n log['multitasking'] = 0\n log = log.to_dict('records')\n log = sorted(log, key=lambda x: (x['source'], x['caseid']))\n for _, group in itertools.groupby(log, key=lambda x: (x['source'], x['caseid'])):\n events = list(group)\n events = sorted(events, key=itemgetter('start_timestamp'))\n for i in range(0, len(events)):\n # In one-timestamp approach the first activity of the trace\n # is taken as instantsince there is no previous timestamp\n # to find a range\n dur = (events[i]['end_timestamp'] -\n events[i]['start_timestamp']).total_seconds()\n if i == 0:\n wit = 0\n else:\n wit = (events[i]['start_timestamp'] -\n events[i-1]['end_timestamp']).total_seconds()\n events[i]['waiting_time'] = wit if wit >= 0 else 0\n events[i]['processing_time'] = dur\n return pd.DataFrame.from_dict(log)","def aggregate(self, **aggregations):\n # Before we iterate, reset the aggregations\n for _, agg in aggregations.items():\n agg.reset()\n # Do the accumulation\n for attrs in self:\n for _, agg in aggregations.items():\n agg.accumulate(attrs)\n # Return the results\n return {name: agg.result for name, agg in aggregations.items()}","def compute_propagation_summary_statistics(data):\n return {\n type: _compute_summary_stats(entries)\n for type, entries in data.items()\n }","def add_aggregators(self, stat_agg):\n stat_agg.add_aggregator(self.key_precision, '{:05.4f}') \n stat_agg.add_aggregator(self.key_precision+'_std', '{:05.4f}')\n stat_agg.add_aggregator(self.key_recall, '{:05.4f}') \n stat_agg.add_aggregator(self.key_recall+'_std', '{:05.4f}')\n stat_agg.add_aggregator(self.key_f1score, '{:05.4f}') \n stat_agg.add_aggregator(self.key_f1score+'_std', '{:05.4f}')","def _calculate_aggregated_metrics(self):\n\n # using the historical values, calculate the aggregate\n # there are two kinds of metrics:\n # a) cumulative metrics - only the delta of the last 2 recorded values is used (eg cpu cycles)\n # b) absolute metrics - the last absolute value is used\n\n running_pids_set = set(self.__pids)\n\n for pid, process_metrics in self.__metrics_history.items():\n for _metric, _metric_values in process_metrics.items():\n if not self.__aggregated_metrics.get(_metric):\n self.__aggregated_metrics[_metric] = 0\n if _metric.is_cumulative:\n if pid in running_pids_set:\n if len(_metric_values) > 1:\n # only report the cumulative metrics for more than one sample\n self.__aggregated_metrics[_metric] += (\n _metric_values[-1] - _metric_values[-2]\n )\n else:\n if pid in running_pids_set:\n # absolute metric - accumulate the last reported value\n self.__aggregated_metrics[_metric] += _metric_values[-1]","def Analyze(self):\n \n self._analyzeLogs()\n for user in self._start_times:\n self._result[user] = self._zipTimes(user)","def _compute_summary_stats(entries):\n result = {\n \"average\": None,\n \"median\": None,\n \"min\": None,\n \"max\": None,\n \"per66\": None,\n \"per75\": None,\n \"per90\": None,\n \"per95\": None,\n \"per99\": None,\n \"success_count\": len(entries['success']),\n \"error_count\": len(entries['error']),\n }\n # sort by the duration\n success = sorted(entries['success'], key=lambda x: x[1])\n if len(success) == 0:\n return result\n\n result['average'] = _compute_average(success)\n result['min'] = success[0][1]\n result['max'] = success[-1][1]\n result['median'] = _compute_percentile(success, 50)\n result['per66'] = _compute_percentile(success, 66)\n result['per75'] = _compute_percentile(success, 75)\n result['per90'] = _compute_percentile(success, 90)\n result['per95'] = _compute_percentile(success, 95)\n result['per99'] = _compute_percentile(success, 99)\n assert result['min'] <= result['median']\n assert result['median'] <= result['per66']\n assert result['per66'] <= result['per75']\n assert result['per75'] <= result['per90']\n assert result['per90'] <= result['per95']\n assert result['per95'] <= result['per99']\n assert result['per99'] <= result['max']\n return result","def reduce_metrics(logging_outputs) -> None:\n loss_sum = utils.item(sum(log.get('loss', 0) for log in logging_outputs))\n sample_size = utils.item(sum(log.get('sample_size', 0) for log in logging_outputs))\n\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)\n metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg))","def summarize_dat(totalData):\n import pandas as pd\n from numpy import log\n pkRed = totalData.loc[:, ['PEAK_NUM', 'pk_LON', 'pk_LAT', 'pk_maxCH4_AB', 'numtimes',\n 'min_read']].drop_duplicates().reset_index().loc[:,\n ['PEAK_NUM', 'pk_LON', 'pk_LAT', 'pk_maxCH4_AB', 'numtimes', 'min_read']]\n verLoc = weighted_loc(pkRed, 'pk_LAT', 'pk_LON', 'min_read', 'pk_maxCH4_AB').rename(\n columns={'pk_LAT': 'overallLAT', 'pk_LON': 'overallLON'})\n pkRed['logCH4'] = pkRed.apply(lambda y: log(y.pk_maxCH4_AB), axis=1)\n mnVals = pkRed.groupby('min_read', as_index=False).logCH4.mean().rename(columns={'logCH4': 'mnlogCH4'}).loc[:,\n ['min_read', 'mnlogCH4']]\n together = pd.merge(verLoc, mnVals, on=['min_read'])\n final = pd.merge(together, totalData, on=['min_read'])\n return (final)","def avg_added(self):\n avg = {}\n for path, lines in self.lines_added.items():\n avg[path] = round(statistics.mean(lines))\n\n return avg","def extract_streams(log):\n # key = (weapon, enemy_name), value = [(timestamp, damage_amount),...]\n your_damage_streams = {}\n enemy_damage_streams = {}\n # key = name, value = (ticker, set([ship1, ship2,...]))\n enemy_info_map = {}\n for e in combat_entries(log):\n if e.attacker.strip().lower() == 'you':\n enemy = e.target\n enemy_name, ship, ticker = enemy_info(enemy)\n stream = your_damage_streams.setdefault((e.weapon, enemy_name), [])\n else:\n enemy = e.attacker\n enemy_name, ship, ticker = enemy_info(enemy)\n stream = enemy_damage_streams.setdefault((e.weapon, enemy_name), [])\n\n timestamp = e.timestamp\n amount = e.damage\n if stream and stream[-1][0] == timestamp:\n stream[-1] = (timestamp, stream[-1][1] + amount)\n else:\n stream.append((timestamp, amount))\n\n if ship is not None:\n t, ship_types = enemy_info_map.setdefault(enemy_name,\n (ticker, set()))\n ship_types.add(ship)\n\n damage_streams = []\n def add_streams(stream_map, enemy_attacks):\n for k, stream in stream_map.iteritems():\n weapon, enemy = k\n ticker, ships = enemy_info_map.setdefault(enemy, ('Unknown', []))\n if enemy_attacks:\n attacker = enemy\n target = 'You'\n else:\n attacker = 'You'\n target = enemy\n damage_streams.append(\n DamageStream(attacker, target, stream, ticker, weapon, ships))\n\n add_streams(your_damage_streams, False)\n add_streams(enemy_damage_streams, True)\n\n return damage_streams","def errdump_statistics(errdump_aggregated_df, raslog_message_details_df, raslog_message_id_details_df):\n \n # filter log messages based on date, message tag and merge devices behind same port for single event\n errdump_filtered_df = errdump_filter(errdump_aggregated_df)\n\n # set message date and time as index\n errdump_filtered_df.set_index(errdump_filtered_df['Message_date'], inplace=True)\n errdump_filtered_df.drop(columns=['Message_date'], inplace=True)\n \n # grouping and message counting\n errdump_grp_columns = ['configname', 'chassis_name', 'chassis_wwn',\n 'switchName', 'switchWwn',\n 'Fabric_name', 'Fabric_label', 'config_collection_date',\n 'Message_ID', 'Severity', 'Message_portIndex', 'Message_portType','slot', 'port',\n 'Condition', 'Dashboard_category', 'obj', 'Message_status',\n 'portIndex', 'Index_slot_port', 'portType', 'portState', 'speed',\n 'tx_port', 'rx_port', 'sid', 'did', 'wwn', 'IP_Address',\n 'Connected_portId', 'Connected_portWwn', 'Device_Host_Name_Port', 'alias', 'deviceType']\n \n # group log messages by month, device and log message\n errdump_grouper = errdump_filtered_df.groupby([pd.Grouper(freq='M', kind='period'), *errdump_grp_columns])\n # count events in each group\n raslog_counter_sr = errdump_grouper['Condition'].count()\n raslog_counter_df = pd.DataFrame(raslog_counter_sr)\n # rename column Condition to avoid duplication with one of Indexies\n raslog_counter_df.rename(columns={'Condition': 'Quantity'}, inplace=True)\n raslog_counter_df.reset_index(inplace=True)\n # replace na_cell\n raslog_counter_df.replace({'na_cell': np.nan}, inplace=True)\n if raslog_counter_df['alias'].notna().any():\n raslog_counter_df['alias'].replace('na_cell(?:, )?', value='', regex=True, inplace=True)\n raslog_counter_df['alias'] = raslog_counter_df['alias'].str.rstrip(', ')\n\n # apply date format to remove day and time\n raslog_counter_df['config_collection_date'] = pd.to_datetime(raslog_counter_df['config_collection_date']).dt.date\n raslog_counter_df['Message_date'] = raslog_counter_df['Message_date'].dt.strftime('%Y-%m')\n # sort values\n errdump_sort_columns = ['chassis_name', 'Fabric_label', 'Fabric_name', 'switchName', 'Message_date', 'Quantity']\n raslog_counter_df.sort_values(by=errdump_sort_columns, ascending=[*[True]*5, False], inplace=True)\n raslog_counter_df.reset_index(drop=True, inplace=True) \n # drop columns with all empty empty cells\n raslog_counter_df.dropna(axis=1, how='all', inplace=True)\n\n raslog_counter_df = dfop.dataframe_fillna(raslog_counter_df, raslog_message_details_df, join_lst=['Condition'], \n filled_lst=['Details', 'Recommended_action']) \n\n raslog_counter_df = dfop.dataframe_fillna(raslog_counter_df, raslog_message_id_details_df, join_lst=['Message_ID'], \n filled_lst=['Details', 'Recommended_action']) \n \n # find log messages which appear more then three times a month\n mask_frequent = raslog_counter_df['Quantity'] > 3\n raslog_frequent_df = raslog_counter_df.loc[mask_frequent].copy()\n \n # remove INFO Messages for report DataFrame except securitu violations messages\n mask_not_info = raslog_frequent_df['Severity'] != 'INFO'\n mask_sec_violation_condition = raslog_frequent_df['Condition'].str.contains('security violation', case=False, na=False)\n mask_sec_violation_dashboard = raslog_frequent_df['Dashboard_category'].str.contains('security violation', case=False, na=False)\n raslog_frequent_df = raslog_frequent_df.loc[mask_not_info | mask_sec_violation_condition | mask_sec_violation_dashboard].copy()\n\n raslog_frequent_df.reset_index(drop=True, inplace=True) \n return raslog_counter_df, raslog_frequent_df","def process_log(self, log: Dict, placeholder_switcher: int, placeholder_geo: List) -> Dict:\n\n line_cnt = 0\n distance_counter = {'human': 0, 'autopilot': 0} # human is 0, autopilot is 1 (checked )\n\n start = time.monotonic()\n\n for ts, event in log.items():\n line_cnt += 1\n\n current_switcher_str = 'autopilot' if placeholder_switcher else 'human'\n converted_ts = self.convert_time(ts)\n\n if isinstance(event, Dict):\n\n if self.validate_coordinates(event):\n # if line contains geo coordinates - calculate distance and update values in dict\n driven_distance = self.calculate_distance(placeholder_geo, list(event.values()))\n distance_counter[current_switcher_str] += driven_distance\n\n # update current geo position\n placeholder_geo = event.values()\n\n logging.info(f\"{line_cnt}|{converted_ts}|{current_switcher_str}|\"\n f\"{driven_distance}|{distance_counter}|{list(placeholder_geo)}|{list(event.values())}\")\n\n elif isinstance(event, int):\n\n placeholder_switcher = self.check_switcher_state(event, placeholder_switcher)\n\n logging.info(f\"{line_cnt}|{converted_ts}|{current_switcher_str}|\"\n f\"{driven_distance}|{distance_counter}|{list(placeholder_geo)}|{event}\")\n else:\n pass\n self.monitor.warning(f'Something went wrong on line {line_cnt}')\n\n end = time.monotonic()\n self.monitor.info(\" Total time spent on calculating report: {}\".format(\"%6.2f\" % (end - start)))\n\n return distance_counter","def exportcsvsumdata(self, log):\r\n csvdata= None\r\n\r\n if (log):\r\n csvdata = ('%s\\t'%(log['CALLSIGN']))\r\n csvdata += ('%s\\t'%(log['OPERATORS']))\r\n csvdata += ('%s\\t'%(log['LOCATION']))\r\n csvdata += ('%d\\t'%(log['COUNT']))\r\n csvdata += ('%s\\t'%(log['NAMES']))\r\n if(log['LASTWORKED']): \r\n csvdata += ('%s/%s UTC'%(log['LASTWORKED'],\r\n log['LWTIME'])) \r\n\r\n return csvdata","async def log_stats(self, stats_dict):\n\n cmd = \"PRAGMA table_info(trainer_stats)\"\n cur = self.sql.cur\n data = cur.execute(cmd).fetchall()\n valid_keys = []\n for entry in data:\n valid_keys.append(entry['name'])\n self.log.info(valid_keys)\n\n for key in stats_dict:\n if key not in valid_keys:\n raise ValueError()\n trainer_id = self.trainer_id\n for key in stats_dict:\n value = stats_dict[key]\n cmd = f\"\"\"UPDATE trainer_stats\n SET {key} = {key} + :value\n WHERE trainer_id = :trainer_id\"\"\"\n cur.execute(cmd, locals())\n await self.sql.commit(now=True)\n self.log.info(\"log completed\")","def collect_log_output(activity_log, result):\n output = activity_log.get('emittedOutput')\n if output:\n result.append(output['_value'])\n else:\n subsections = activity_log.get('subsections')\n if subsections:\n for subsection in subsections['_values']:\n collect_log_output(subsection, result)","def parse_stats(output):\n lines = [line for line in output if \"[Stats]\" in line]\n stats = {\n 'totals': {'time': 0, 'tasks': 0, 'avg': 0}\n }\n for line in lines:\n m = re.search(r'\\((\\d+) ms\\).+\\((\\d+)\\).+\\((\\d+) us.+\\)', line)\n if not m:\n continue\n dt, tasks, avg = map(int, m.groups())\n if 'totals' in line:\n stats['totals'] = {'time': dt, 'tasks': tasks, 'avg': avg}\n return stats","def get_output_stats(self, output):\n\t\ttype_dict = {}\n\t\ttype_full_dict = {}\n\t\ttotal = 0\n\t\tfor key in output.keys():\n\t\t\tif output[key]['deleted'] == \"0\":\n\t\t\t\ttry:\n\t\t\t\t\ttype_dict[output[key]['type']] += 1\n\t\t\t\texcept:\n\t\t\t\t\ttype_dict[output[key]['type']] = 1\n\t\t\t\ttry:\n\t\t\t\t\ttype_full_dict[output[key]['type_full']] += 1\n\t\t\t\texcept:\n\t\t\t\t\ttype_full_dict[output[key]['type_full']] = 1\n\t\t\t\ttotal += 1\n\t\tfinal_stats = {}\n\t\tfor key in type_dict.keys():\n\t\t\tfinal_stats[key + \"(\" + str(type_dict[key]) + \"/\" + str(int(np.rint((type_dict[key]/total)*100))) + \"%)\"] = {'count': type_dict[key], 'percent_of_total': (type_dict[key]/total)*100}\n\t\t\tfor k in type_full_dict.keys():\n\t\t\t\tif k == None:\n\t\t\t\t\tpass\n\t\t\t\telif k.startswith(key):\n\t\t\t\t\tfinal_stats[key + \"(\" + str(type_dict[key]) + \"/\" + str(int(np.rint((type_dict[key]/total)*100))) + \"%)\"][k + \"(\" + str(type_full_dict[k]) + \"/\" + str(int(np.rint((type_full_dict[k]/total)*100))) + \"%)\"] = {'count': type_full_dict[k], 'percent_of_total': (type_full_dict[k]/total)*100, 'percent_of_' + key + '(s)': (type_full_dict[k]/type_dict[key])*100}\n\t\treturn (final_stats, total)","def __call__(self, **kwargs: float):\n for k, v in kwargs.items():\n if k not in self.logs:\n self.logs[k] = []\n self.logs[k].append(v)","def get_results_stats(self, results, key_by_date=True):\n stats = {}\n for r in results:\n if key_by_date:\n key = r.get(1)\n value = r.get(2)\n else:\n key = r.get(2)\n value = r.get(1)\n\n if key not in stats:\n stats[key] = {}\n\n if value not in stats[key]:\n stats[key][value] = 1\n else:\n stats[key][value] += 1\n\n # sort each keyset\n if not key_by_date:\n stats[key] = self._stats_sort(stats[key])\n\n if stats:\n # only if sorted per key\n if key_by_date:\n stats = self._stats_sort(stats)\n\n return stats","def clients_histogram_aggregates(**kwargs):\n attributes_list = [\n \"sample_id\",\n \"client_id\",\n \"ping_type\",\n \"os\",\n \"app_version\",\n \"app_build_id\",\n \"channel\",\n ]\n fixed_attributes = [\"app_version\", \"channel\"]\n cubed_attributes = [x for x in attributes_list if x not in fixed_attributes]\n return dict(\n attributes_list=attributes_list,\n attributes=\",\".join(attributes_list),\n cubed_attributes=cubed_attributes,\n attribute_combinations=compute_datacube_groupings(cubed_attributes),\n metric_attributes=\"\"\"\n metric,\n metric_type,\n key,\n agg_type\n \"\"\",\n **kwargs,\n )","def add_metrics(_dict):\n for key, itr in _dict.items():\n if key not in self.metric_cols:\n self.metric_cols.append(key)","def stats():\r\n times_lst = []\r\n time_dict = {}\r\n for album, details in dbase().items():\r\n time_m = 0\r\n time_s = 0\r\n for songs, details_s in details[0].items():\r\n time = details_s[1].split(\":\")\r\n min = int(time[0])\r\n sec = int(time[1])\r\n time_m += min\r\n time_s += sec\r\n time_s = datetime.timedelta(seconds=time_s)\r\n time_m = datetime.timedelta(seconds=time_m)\r\n time = time_m + time_s\r\n time = str(time)\r\n times_lst.append(time)\r\n time_dict[album] = time\r\n\r\n time_dict = sorted(time_dict.items(), key=lambda x: x[1], reverse=True)\r\n return time_dict","def get_metrics(self, objs_metrics):\n d = {}\n _S = DiffStatus\n\n for status in _S.iter():\n d[status] = [dict(obj_m) for obj_m in objs_metrics if obj_m['status'] == status]\n\n count_a_only = len(d.get(_S.deleted, []))\n count_b_only = len(d.get(_S.added, []))\n count_modified = len(d.get(_S.modified, []))\n count_unchanged = len(d.get(_S.unchanged, []))\n count_common = count_modified + count_unchanged\n\n count_a = count_common + count_a_only\n count_b = count_common + count_b_only\n\n d['count'] = {\n 'a': count_a,\n 'b': count_b,\n 'a_only': count_a_only,\n 'b_only': count_b_only,\n 'modified': count_modified,\n 'unchanged': count_unchanged,\n }\n\n d['summary'] = {\n _S.added: {\n 'count': count_b_only,\n 'relative_to': {\n 'a': count_b_only / count_a,\n 'b': count_b_only / count_b,\n }\n },\n _S.deleted: {\n 'count': count_a_only,\n 'relative_to': {\n 'a': count_a_only / count_a,\n 'b': count_a_only / count_b,\n },\n },\n _S.modified: {\n 'count': count_modified,\n 'relative_to': {\n 'a': count_modified / count_a,\n 'b': count_modified / count_b,\n }\n },\n _S.unchanged: {\n 'count': count_unchanged,\n 'relative_to': {\n 'a': count_unchanged / count_a,\n 'b': count_unchanged / count_b,\n }\n },\n }\n\n return d","def compute_metrics(self, results: list) -> dict:\n dump(results, self.out_file_path)\n print_log(\n f'Results has been saved to {self.out_file_path}.',\n logger='current')\n return {}","def collect_statistics(self, stat_col, data_streams):\n # Calculate all four statistics.\n _, precisions, recalls, f1scores, supports = self.calculate_statistics(data_streams)\n\n # Calculate weighted averages.\n precision_sum = sum([pi*si for (pi,si) in zip(precisions,supports)])\n recall_sum = sum([ri*si for (ri,si) in zip(recalls,supports)])\n f1score_sum = sum([fi*si for (fi,si) in zip(f1scores,supports)])\n support_sum = sum(supports)\n\n if support_sum > 0:\n precision_avg = precision_sum / support_sum \n recall_avg = recall_sum / support_sum\n f1score_avg = f1score_sum / support_sum\n else:\n precision_avg = 0\n recall_avg = 0\n f1score_avg = 0\n\n # Export averages to statistics.\n stat_col[self.key_precision] = precision_avg\n stat_col[self.key_recall] = recall_avg\n stat_col[self.key_f1score] = f1score_avg\n\n # Export support to statistics.\n stat_col[self.key_f1score+'_support'] = support_sum","def print_metrics(result):\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '------------------------------------------------')\n logging.log(LOG_LEVEL_OUTPUT_INFO, ' KEY METRICS: ')\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '------------------------------------------------')\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* pages_count: %d',\n get_counter_metric(result, 'pages_count'))\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_count: %d',\n get_counter_metric(result, 'revisions_count'))\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* very_long_page_histories_count: %d',\n get_counter_metric(result, 'very_long_page_histories_count'))\n revisions_per_page_distr = get_distributions_metric(\n result, 'revisions_per_page_distr')\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.mean: %d',\n revisions_per_page_distr.mean)\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.sum: %d',\n revisions_per_page_distr.sum)\n cumulative_page_rev_size_distr = get_distributions_metric(\n result, 'cumulative_page_rev_size_distr')\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '* cumulative_page_rev_size_distr.mean: %d',\n cumulative_page_rev_size_distr.mean)\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* cumulative_page_rev_size_distr.sum: %d',\n cumulative_page_rev_size_distr.sum)","def stats(self):\n stats = {\n 'lines' : '', # This will count the lines under each split\n 'status_code': self.status_code,\n 'content_type': self.mime,\n 'hop': self.hop_path[-1:],\n 'sum:content_length': self.content_length,\n 'host': self.host(),\n 'source': self.source\n }\n # Add in annotations:\n for annot in self.annotations:\n # Set a prefix based on what it is:\n prefix = ''\n if self.re_tries.match(annot):\n prefix = 'tries:'\n elif self.re_ip.match(annot):\n prefix = \"ip:\"\n # Only emit lines with annotations:\n if annot != \"-\":\n stats[\"%s%s\" % (prefix, annot)] = \"\"\n return stats","def flatten_log(self, log):\n pass","def aggregate(all_metrics, reducer, suffix):\n # Collect metric separately\n separated_metrics = {} # type: dict[frozenset, list[dict]]\n for el in all_metrics:\n key = frozenset(el[\"metric\"][\"dimensions\"].items())\n if key not in separated_metrics:\n separated_metrics[key] = [el]\n else:\n separated_metrics[key].append(el)\n\n # Collect all dimensions\n dims = {}\n for metric_dims in separated_metrics.keys():\n for prop, val in dict(metric_dims).iteritems():\n if prop in dims:\n dims[prop].add(val)\n else:\n dims[prop] = set(val)\n\n # Sort each metric\n for _, metric in separated_metrics.iteritems():\n metric.sort(key=lambda v: v[\"metric\"][\"timestamp\"])\n\n separated_metrics = sorted(separated_metrics.values(), key=len)\n separated_metrics.reverse()\n\n # Compute the new values\n new_values = []\n all_timestamps = map(\n lambda l: map(\n lambda x: x[\"metric\"][\"timestamp\"], l),\n separated_metrics)\n metric_count = len(separated_metrics)\n for index in range(0, len(separated_metrics[0])):\n new_value = reducer[0](\n separated_metrics[0][index][\"metric\"][\"value\"],\n metric_count)\n new_timestamp = separated_metrics[0][index][\"metric\"][\"timestamp\"]\n for metric_index in range(1, metric_count):\n new_value = reducer[1](new_value, helpers.interpolate(\n new_timestamp,\n separated_metrics[metric_index],\n all_timestamps[metric_index]\n ), metric_count)\n new_values.append((new_timestamp, new_value))\n\n # Aggregate the other details:\n metric_name = separated_metrics[0][0][\"metric\"][\"name\"] + suffix\n meta = separated_metrics[0][0][\"meta\"]\n new_metrics = [\n helpers.create_agg_metric(\n metric_name,\n meta,\n dims,\n val[0],\n val[1]\n ) for val in new_values\n ]\n return new_metrics","def _fill_mean_dict(self, running_metrics: Dict, mean_dict: Dict) -> None:\n for key, value in running_metrics.items():\n mean = np.mean(value)\n mean_dict[key].append(mean)","def aggregator(index_keynames, value_keynames, ts_keyname, func, interval = 60 * 5):\n data = {}\n ts = None\n #print ts_keyname\n for parsts, parsdata in func():\n #print parsdata\n #print parsdata[\"log_timestamp\"]\n if ts is None:\n ts = parsts\n key = tuple((parsdata[key] for key in index_keynames))\n values = tuple((int(parsdata[key]) for key in value_keynames))\n if key not in data:\n data[key] = values\n else:\n data[key] = tuple((data[key][index] + int(values[index]) for index in range(len(values))))\n if parsts > (ts + interval):\n for keys, values in data.items():\n yield \"%s\\t%s\\t%s\" % (ts, \"\\t\".join((str(index_key) for index_key in keys)), \"\\t\".join((str(value_key) for value_key in values)))\n ts = None\n data = {}","def aggregate_data(mts, feature, target):\r\n set_dict = dict()\r\n set_dict['mt'] = mts\r\n set_dict['feature'] = feature\r\n set_dict['target'] = target\r\n \r\n return set_dict"],"string":"[\n \"def aggregate_logging_outputs(logging_outputs):\\n cider_sum = sum(log.get('cider', 0) for log in logging_outputs)\\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\\n sc_loss_sum = sum(log.get('sc_loss', 0) for log in logging_outputs)\\n n_pos_sum = sum(log.get('n_positive', 0) for log in logging_outputs)\\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n\\n # print('| n_pos_sum: {} | nsentences: {}'.format(n_pos_sum, nsentences))\\n agg_output = {\\n 'cider': cider_sum / nsentences,\\n 'loss': loss_sum / sample_size / math.log(2),\\n 'sc_loss': sc_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.,\\n 'n_positive_rate': n_pos_sum / nsentences,\\n 'ntokens': ntokens,\\n 'nsentences': nsentences,\\n 'sample_size': sample_size,\\n }\\n return agg_output\",\n \"def aggregate_logging_outputs(logging_outputs):\\n loss = sum(log.get('loss', 0) for log in logging_outputs)\\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n acc = sum(log.get('acc', 0) for log in logging_outputs)\\n\\n agg_output = {\\n 'loss': loss / sample_size / math.log(2),\\n 'nll_loss': sum(log.get('nll_loss', 0) for log in logging_outputs) / sample_size / math.log(\\n 2) if ntokens > 0 else 0.,\\n 'ntokens': ntokens,\\n 'nsentences': nsentences,\\n 'sample_size': sample_size,\\n 'acc': acc / sample_size,\\n }\\n\\n if 'mlm_loss' in logging_outputs[0]:\\n mlm_loss = sum(log.get('mlm_loss', 0) for log in logging_outputs)\\n mlm_acc = sum(log.get('mlm_acc', 0) for log in logging_outputs)\\n agg_output['mlm_loss'] = mlm_loss / sample_size / math.log(2)\\n agg_output['mlm_acc'] = mlm_acc / sample_size\\n return agg_output\",\n \"def aggregate_logging_outputs(logging_outputs):\\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n downstream_loss = sum(log.get('downstream_loss', 0) for log in logging_outputs)\\n bleu_stats = reduce(utils.reduce_bleu_stats,\\n [log.get('bleu', utils.get_zero_bleu_stats()) for log in logging_outputs])\\n intermediate_loss = defaultdict(lambda: AverageMeter())\\n all_loss_dicts = [log.get('intermediate_loss', defaultdict(lambda: AverageMeter())) for log in logging_outputs]\\n # Aggregate\\n for single_dict in all_loss_dicts:\\n for key, value in single_dict.items():\\n intermediate_loss[key] = utils.reduce_average_meter(intermediate_loss[key], value)\\n # same for bleu\\n intermediate_bleu = defaultdict(lambda: AverageMeter())\\n all_bleu_dicts = [log.get('intermediate_bleu', defaultdict(lambda: AverageMeter()))\\n for log in logging_outputs]\\n # Aggregate\\n for single_dict in all_bleu_dicts:\\n for key, value in single_dict.items():\\n intermediate_bleu[key] = utils.reduce_average_meter(intermediate_bleu[key], value)\\n agg_output = {'loss': loss_sum / float(sample_size), 'sample_size': sample_size, 'ntokens': ntokens,\\n 'nsentences': nsentences, 'downstream_loss': downstream_loss / float(sample_size),\\n 'bleu': bleu_stats, **intermediate_loss, **intermediate_bleu}\\n return agg_output\",\n \"def aggregate_logging_outputs(logging_outputs):\\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n agg_output = {\\n 'loss': loss_sum,\\n 'ntokens': ntokens,\\n 'nsentences': nsentences,\\n 'sample_size': sample_size,\\n }\\n if sample_size != ntokens:\\n agg_output['nll_loss'] = loss_sum / math.log(2)\\n return agg_output\",\n \"def _aggregate_log_values(self, source, dest):\\n remove = []\\n for key, item in source.items():\\n if \\\"data\\\" not in item:\\n # Assume it's a sub-group\\n dest[key] = {}\\n self._aggregate_log_values(item, dest[key])\\n else:\\n aggregator = self._get_aggregator_for_key(key, item['agg'])\\n value = aggregator(item['data'])\\n if item['precision'] is not None:\\n value = round(value, item['precision'])\\n dest[key] = value\\n if item['scope'] == 'get':\\n remove.append(key)\\n for key in remove:\\n del source[key]\",\n \"def aggregate_logging_outputs(logging_outputs):\\n loss_sum = sum(log.get(\\\"loss\\\", 0) for log in logging_outputs)\\n nll_loss_sum = sum(log.get(\\\"nll_loss\\\", 0) for log in logging_outputs)\\n ntokens = sum(log.get(\\\"ntokens\\\", 0) for log in logging_outputs)\\n nsentences = sum(log.get(\\\"nsentences\\\", 0) for log in logging_outputs)\\n sample_size = sum(log.get(\\\"sample_size\\\", 0) for log in logging_outputs)\\n return {\\n \\\"loss\\\": loss_sum / sample_size if sample_size > 0 else 0.0,\\n \\\"nll_loss\\\": nll_loss_sum / ntokens / math.log(2),\\n \\\"ntokens\\\": ntokens,\\n \\\"nsentences\\\": nsentences,\\n \\\"sample_size\\\": sample_size,\\n }\",\n \"def aggregate_logging_outputs(logging_outputs):\\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n return {\\n 'loss': sum(log.get('loss', 0) for log in logging_outputs) / sample_size / math.log(2) if sample_size > 0 else 0.,\\n 'nll_loss': sum(log.get('nll_loss', 0) for log in logging_outputs) / ntokens / math.log(2) if ntokens > 0 else 0.,\\n 'ntokens': ntokens,\\n 'nsentences': nsentences,\\n 'sample_size': sample_size,\\n }\",\n \"def aggregate_logging_outputs(logging_outputs):\\n # assert len(logging_outputs) == 1\\n log = logging_outputs[0]\\n loss = log.get('loss', 0)\\n ntokens = log.get('ntokens', 0)\\n batch_sizes = log.get('nsentences', 0)\\n sample_size = log.get('sample_size', 0)\\n agg_output = {\\n 'loss': loss,\\n 'ntokens': ntokens,\\n 'nsentences': batch_sizes,\\n 'sample_size': sample_size,\\n }\\n return agg_output\",\n \"def log_dict(self, source, agg=\\\"auto\\\", group=None):\\n for key, val in source.items():\\n if isinstance(val, dict):\\n sub_group = key if group is None else group+\\\"->\\\"+key\\n self.log_dict(val, agg=agg, group=sub_group)\\n else:\\n self.log(key, val, group=group, agg=agg)\",\n \"def means(self) -> dict:\\n return {k: 0 if not vs else sum(vs) / len(vs) for k, vs in self.logs.items()}\",\n \"def _aggregate_traj_stats(traj_stats_a, traj_stats_b):\\n merged_stats = {}\\n for k in traj_stats_a:\\n n_a, avg_a, M2_a = traj_stats_a[k][\\\"n\\\"], traj_stats_a[k][\\\"mean\\\"], traj_stats_a[k][\\\"sqdiff\\\"]\\n n_b, avg_b, M2_b = traj_stats_b[k][\\\"n\\\"], traj_stats_b[k][\\\"mean\\\"], traj_stats_b[k][\\\"sqdiff\\\"]\\n n = n_a + n_b\\n mean = (n_a * avg_a + n_b * avg_b) / n\\n delta = (avg_b - avg_a)\\n M2 = M2_a + M2_b + (delta ** 2) * (n_a * n_b) / n\\n merged_stats[k] = dict(n=n, mean=mean, sqdiff=M2)\\n return merged_stats\",\n \"def aggregate_metrics(metrics):\\n if len(metrics) == 1:\\n return metrics[0]\\n else:\\n agg_metrics = metrics[0]\\n for metric in agg_metrics.keys():\\n vals = [x[metric] for x in metrics]\\n agg_metrics[metric] = [np.mean(vals), np.std(vals)]\\n return agg_metrics\",\n \"def aggregate_results(self, results):\\n result = dict()\\n result['MAE'] = self.average_dict_items(results, 'MAE')\\n result['MdAE'] = self.average_dict_items(results, 'MdAE')\\n result['RMSE'] = self.average_dict_items(results, 'RMSE')\\n result['SMAPE'] = self.average_dict_items(results, 'SMAPE')\\n result['num_values'] = self.average_dict_items(results, 'num_values')\\n return result\",\n \"def get_total_counts(self):\\n ret = {}\\n all_loggers_count = 0\\n for logger, name_map in self.acc_map.items():\\n cur_logger_count = 0\\n ret[logger.name] = {}\\n for name, status_map in name_map.items():\\n cur_name_count = 0\\n ret[logger.name][name] = {}\\n for status, acc in status_map.items():\\n cur_count = acc.total_count\\n ret[logger.name][name][status] = cur_count\\n cur_name_count += cur_count\\n cur_logger_count += cur_count\\n all_loggers_count += cur_count\\n ret[logger.name][name]['__all__'] = cur_name_count\\n ret[logger.name]['__all__'] = cur_logger_count\\n ret['__all__'] = all_loggers_count\\n return ret\",\n \"def extract_test_log(self, log: dict) -> dict:\\n test_log = self.extract_generic_log(log, 'test')\\n test_log['adv_acc'] = (log['correct_adv'] / log['total']) * 100.0\\n\\n return test_log\",\n \"def sum_dstats(self, stats, smetrics):\\n avg = {}\\n\\n for disk, metrics in stats.iteritems():\\n for mname, metric in metrics.iteritems():\\n if mname not in smetrics:\\n continue\\n if mname in avg:\\n avg[mname] += metric\\n else:\\n avg[mname] = metric\\n\\n return avg\",\n \"def summaries(e_dict, m_dict):\\n for key, value in m_dict.items():\\n e_dict[key].append(np.mean(value))\\n return e_dict\",\n \"def collect_log_output(activity_log, result):\\n\\n test_name = activity_log.get('identifier')\\n if test_name:\\n result.append(test_name['_value'])\\n\\n duration = activity_log.get('duration')\\n if duration:\\n output = str(\\\"{:.2f}\\\".format(float(duration['_value'])))\\n result.append(output)\\n\\n performance_metrics = activity_log.get('performanceMetrics')\\n if not performance_metrics is None:\\n metrics = performance_metrics.get('_values')\\n for metric in metrics:\\n measurement = metric.get('measurements')\\n values = measurement.get('_values')\\n value_sum = 0\\n for value in values:\\n value_sum += float(value.get('_value'))\\n output = str(value_sum / len(values))\\n result.append(output)\",\n \"def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:\\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n\\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)\",\n \"def aggregate_statistics(self, stat_col, stat_agg):\\n self.module.aggregate_statistics(stat_col, stat_agg)\",\n \"def apply(self):\\n counter = {}\\n for act in self.activities:\\n freq = []\\n for trace in self.log:\\n freq.append(len(self.project_trace(trace, [act])))\\n if not len(freq) == 0:\\n counter[act] = {'sum': sum(freq), 'min': min(freq),\\n 'max': max(freq)}\\n return counter\",\n \"def score_aggregate(results):\\n scores = []\\n truth_count = detected_count = segment_count = 0\\n\\n for res in results:\\n scores.append(res[\\\"scores\\\"])\\n truth_count += len(res[\\\"labels\\\"])\\n detected_count += len(res[\\\"detected\\\"])\\n segment_count += len(res[\\\"scores\\\"][\\\"segments\\\"])\\n\\n ret = dict()\\n ret[\\\"scores\\\"] = sum_scores(scores)\\n ret[\\\"stats\\\"] = dict(truth_count=truth_count, detected_count=detected_count, segment_count=segment_count)\\n return ret\",\n \"def summarize_rec_data(data):\\n\\n # Warning: not all collectible data has a summary stats implemented below!\\n # See get_rec_stats() above!\\n\\n stats = {}\\n\\n if 'hc_ro' in data:\\n # Entropy across HC units average over samples.\\n hc_ro_arr = np.array(list(data['hc_ro'].values()))\\n stats['H HC ro'] = utils.entropy(hc_ro_arr.T).mean()\\n\\n if 'vs_state' in data:\\n # Sum of vS reward estimates change (from first to last sample).\\n vs_state = data['vs_state']\\n stats['d vS'] = sum(vs_state[max(vs_state.keys())] - vs_state[0])\\n\\n if 'co_occs' in data:\\n # Mean entropy of real location and HC state co-occurance frequencies.\\n co_occs = data['co_occs'][max(data['co_occs'].keys())]\\n stats['H HC co'] = np.nanmean(get_hc_co_occ_entropy(co_occs))\\n stats['H loc co'] = np.nanmean(get_loc_co_occ_entropy(co_occs))\\n\\n return stats\",\n \"def get_all_stat(self):\\n all_stat=dict()\\n for stat_type in self.log_book.keys():\\n stat = self.get_stat(stat_type)\\n all_stat[stat_type] = stat\\n return all_stat\",\n \"def aggregate_statistics(self, new_stats):\\n \\n if isinstance(new_stats,RunStatistics):\\n new_stats = [new_stats, ]\\n elif isinstance(new_stats,list):\\n if any(not isinstance(_,RunStatistics) for _ in new_stats):\\n raise MadGraph5Error, \\\"The 'new_stats' argument of the function \\\"+\\\\\\n \\\"'updtate_statistics' must be a (possibly list of) \\\"+\\\\\\n \\\"RunStatistics instance.\\\"\\n \\n keys = set([])\\n for stat in [self,]+new_stats:\\n keys |= set(stat.keys())\\n\\n new_stats = new_stats+[self,]\\n for key in keys:\\n # Define special rules\\n if key=='max_precision':\\n # The minimal precision corresponds to the maximal value for PREC\\n self[key] = min( _[key] for _ in new_stats if key in _)\\n elif key=='min_precision':\\n # The maximal precision corresponds to the minimal value for PREC\\n self[key] = max( _[key] for _ in new_stats if key in _)\\n elif key=='averaged_timing':\\n n_madloop_calls = sum(_['n_madloop_calls'] for _ in new_stats if\\n 'n_madloop_calls' in _)\\n if n_madloop_calls > 0 :\\n self[key] = sum(_[key]*_['n_madloop_calls'] for _ in \\n new_stats if (key in _ and 'n_madloop_calls' in _) )/n_madloop_calls\\n else:\\n # Now assume all other quantities are cumulative\\n self[key] = sum(_[key] for _ in new_stats if key in _)\",\n \"def get_errors_summary(statistics):\\n all_errors = (report['errors'] for report in statistics) \\n \\n errors_summary = defaultdict(int)\\n for doc in all_errors:\\n for key, value in doc.items():\\n errors_summary[key] += value\\n\\n return errors_summary\",\n \"def _aggregate_perf_data(perf_all_ordinals: List[str]):\\n aggregate = {}\\n\\n pd = PerfData()\\n for data in perf_all_ordinals:\\n worker_pd = PerfData(**json.loads(data))\\n if len(perf_all_ordinals) > 1:\\n aggregate.setdefault(\\\"ordinals\\\", [])\\n aggregate[\\\"ordinals\\\"].append(worker_pd.throughput_dict())\\n\\n pd.merge(worker_pd)\\n\\n aggregate.update(dataclasses.asdict(pd))\\n return aggregate\",\n \"def collect_filterstats_from_log(fp):\\n D = {}\\n while (line := fp.readline()): # noqa: E203, E231\\n if \\\"Filter results for\\\" in line:\\n filt = line.split()[-1]\\n fp.readline() # empty\\n fp.readline() # \\\"Filtering\\\"\\n fp.readline() # Array size\\n fp.readline() # cumulative masked\\n fp.readline() # previously masked\\n filtered_line = fp.readline() # new filtered\\n if \\\"New filtered\\\" not in filtered_line:\\n raise ValueError(\\\"Expected 'filtered' six lines after \\\"\\n f\\\"'filter results', got {filtered_line:s}\\\")\\n count = int(filtered_line.split()[-1])\\n if filt in D:\\n raise ValueError(f\\\"Found results for {filt:s} multiple times!\\\")\\n D[filt] = count\\n return D\",\n \"def parse_stats(stats: dict, res: dict):\\n for k, v in stats.items():\\n if k not in res.keys():\\n res.update({k: {}})\\n if isinstance(v, list):\\n for element in v:\\n for metric, value in element.items():\\n res[k].update({metric: [value]})\",\n \"def get_logs_with_timestamp(res: List[Dict[str, Any]]) -> Dict[str, List[str]]:\\n\\n list_of_logs = (\\n {log[\\\"result\\\"][\\\"timestamp\\\"]: log[\\\"result\\\"][\\\"logs\\\"]}\\n for log in res\\n if log[\\\"result\\\"][\\\"logs\\\"]\\n )\\n\\n logs_with_timestamp = defaultdict(list)\\n\\n for result in list_of_logs:\\n k, v = list(result.items())[0]\\n logs_with_timestamp[parser.parse(k)] += v\\n\\n return logs_with_timestamp\",\n \"def summary(self) -> Dict[str, Dict[str, float]]:\\n vals: Dict[str, List[float]] = defaultdict(list)\\n if not self.steps: # pragma: no cover\\n return {}\\n\\n for timing_dict in self._timings:\\n for step in self.steps:\\n if step in timing_dict:\\n vals[step].append(timing_dict[step])\\n summary = {}\\n for step in self.steps:\\n if vals[step]:\\n summary[step] = {\\n \\\"cnt\\\": len(vals[step]),\\n \\\"sum\\\": sum(vals[step]),\\n \\\"min\\\": min(vals[step]),\\n \\\"max\\\": max(vals[step]),\\n \\\"avg\\\": sum(vals[step]) / len(vals[step]),\\n }\\n return summary\",\n \"def aggregate_statistics(self, stat_col, stat_agg):\\n precision_sums = stat_col[self.key_precision]\\n recall_sums = stat_col[self.key_recall]\\n f1score_sums = stat_col[self.key_f1score]\\n supports = stat_col[self.key_f1score+'_support']\\n\\n # Special case - no samples!\\n if sum(supports) == 0:\\n stat_agg[self.key_precision] = 0\\n stat_agg[self.key_precision+'_std'] = 0\\n stat_agg[self.key_recall] = 0\\n stat_agg[self.key_recall+'_std'] = 0\\n stat_agg[self.key_f1score] = 0\\n stat_agg[self.key_f1score+'_std'] = 0\\n\\n else: \\n # Else: calculate weighted precision.\\n precisions_avg = np.average(precision_sums, weights=supports)\\n precisions_var = np.average((precision_sums-precisions_avg)**2, weights=supports)\\n \\n stat_agg[self.key_precision] = precisions_avg\\n stat_agg[self.key_precision+'_std'] = math.sqrt(precisions_var)\\n\\n # Calculate weighted recall.\\n recalls_avg = np.average(recall_sums, weights=supports)\\n recalls_var = np.average((recall_sums-recalls_avg)**2, weights=supports)\\n\\n stat_agg[self.key_recall] = recalls_avg\\n stat_agg[self.key_recall+'_std'] = math.sqrt(recalls_var)\\n\\n # Calculate weighted f1 score.\\n f1scores_avg = np.average(f1score_sums, weights=supports)\\n f1scores_var = np.average((f1score_sums-f1scores_avg)**2, weights=supports)\\n\\n stat_agg[self.key_f1score] = f1scores_avg\\n stat_agg[self.key_f1score+'_std'] = math.sqrt(f1scores_var)\",\n \"def output_stats(self):\\n elapsed = self.timer.elapsed.total_seconds()\\n count = self.copied + self.errored\\n total = self.total\\n # Time per key in milliseconds\\n avg = round(elapsed / count * 1000, 3)\\n # Time remaining in seconds\\n remaining = 1.0 * elapsed / count * (total - count)\\n # Time remaining in minutes\\n remaining = round(remaining / 60.0, 1)\\n # Time taken in minutes\\n elapsed = round(elapsed / 60.0, 1)\\n\\n self.log.info(f\\\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \\\"\\n f\\\"{remaining}min remaining. ({count:,}/{total:,})\\\")\",\n \"def reduce_metrics(logging_outputs) -> None:\\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\\n loss_sum_vad = sum(log.get('loss_vad', 0) for log in logging_outputs)\\n\\n ntokens_vad = sum(log.get('ntokens_vad', 0) for log in logging_outputs)\\n total_n_ts = sum(log.get('n_ts', 0) for log in logging_outputs)\\n total_n_pred_ts = sum(log.get('n_pred_ts', 0) for log in logging_outputs)\\n\\n nsentences_vad = sum(log.get('nsentences_vad', 0) for log in logging_outputs)\\n\\n sample_size_vad = sum(log.get('sample_size_vad', 0) for log in logging_outputs)\\n sample_size_asr = sum(log.get('sample_size', 0) for log in logging_outputs)\\n\\n sample_size = sample_size_vad + sample_size_asr\\n\\n metrics.log_scalar('loss', loss_sum / sample_size, sample_size, round=3)\\n\\n if sample_size_vad:\\n metrics.log_scalar('loss_vad', loss_sum_vad / sample_size_vad, sample_size_vad, round=3)\\n else:\\n metrics.log_scalar('loss_vad', loss_sum_vad / 999999999999, sample_size_vad, round=3)\\n\\n ncorrect_vad = sum(log.get('ncorrect_vad', 0) for log in logging_outputs)\\n if nsentences_vad:\\n metrics.log_scalar('accuracy_vad', 100.0 * ncorrect_vad / ntokens_vad, ntokens_vad, round=1)\\n else:\\n metrics.log_scalar('accuracy_vad', 100.0 * ncorrect_vad / 999999999999, ntokens_vad, round=1)\\n\\n if total_n_ts:\\n ncorrect_ts = sum(log.get('ncorrect_ts', 0) for log in logging_outputs)\\n metrics.log_scalar('recall_TS', 100.0 * ncorrect_ts / total_n_ts, total_n_ts, round=1)\\n else:\\n metrics.log_scalar('recall_TS', 100.0 * 0 / ntokens_vad, ntokens_vad, round=1)\\n\\n if total_n_pred_ts:\\n ncorrect_ts = sum(log.get('ncorrect_ts', 0) for log in logging_outputs)\\n metrics.log_scalar('precision_TS', 100.0 * ncorrect_ts / total_n_pred_ts, total_n_pred_ts, round=1)\\n else:\\n metrics.log_scalar('precision_TS', 100.0 * 0 / ntokens_vad, ntokens_vad, round=1)\",\n \"def get_log_summary():\\n # reads the session\\n session = request.args.get('session', type=str)\\n # reads the requested process name\\n process = request.args.get('process', default='receipt', type=str)\\n\\n dictio = {}\\n\\n if check_session_validity(session):\\n user = get_user_from_session(session)\\n if lh.check_user_log_visibility(user, process):\\n this_variants_number = lh.get_handler_for_process_and_session(process, session).variants_number\\n this_cases_number = lh.get_handler_for_process_and_session(process, session).cases_number\\n this_events_number = lh.get_handler_for_process_and_session(process, session).events_number\\n\\n ancestor_variants_number = lh.get_handler_for_process_and_session(process,\\n session).first_ancestor.variants_number\\n ancestor_cases_number = lh.get_handler_for_process_and_session(process, session).first_ancestor.cases_number\\n ancestor_events_number = lh.get_handler_for_process_and_session(process,\\n session).first_ancestor.events_number\\n\\n dictio = {\\\"this_variants_number\\\": this_variants_number, \\\"this_cases_number\\\": this_cases_number,\\n \\\"this_events_number\\\": this_events_number, \\\"ancestor_variants_number\\\": ancestor_variants_number,\\n \\\"ancestor_cases_number\\\": ancestor_cases_number, \\\"ancestor_events_number\\\": ancestor_events_number}\\n print(dictio)\\n\\n ret = jsonify(dictio)\\n return ret\",\n \"def _print_aggregate_results(\\n task: Task, task_results: Dict[Task, List[List[Dict[str, Any]]]]\\n) -> None:\\n aggregate_task_result = aggregate_nvs_results(task_results[task])\\n print(\\\"\\\")\\n print(f\\\"Aggregate results for task={task}:\\\")\\n pretty_print_nvs_metrics(aggregate_task_result)\\n print(\\\"\\\")\",\n \"def _compute_traj_stats(traj_obs_dict):\\n traj_stats = { k : {} for k in traj_obs_dict }\\n for k in traj_obs_dict:\\n traj_stats[k][\\\"n\\\"] = traj_obs_dict[k].shape[0]\\n traj_stats[k][\\\"mean\\\"] = traj_obs_dict[k].mean(axis=0, keepdims=True) # [1, ...]\\n traj_stats[k][\\\"sqdiff\\\"] = ((traj_obs_dict[k] - traj_stats[k][\\\"mean\\\"]) ** 2).sum(axis=0, keepdims=True) # [1, ...]\\n return traj_stats\",\n \"def data_statistics(title, data, stats=None, normalize=False, log_fun=logging.INFO):\\n log_fun(\\\"Statistics %s\\\" % title)\\n\\n all_data = []\\n for key in data:\\n if not isinstance(data[key], list):\\n all_data.append(data[key])\\n else:\\n all_data.extend(data[key])\\n\\n if normalize:\\n minimum = min(all_data)\\n maximum = max(all_data)\\n all_data = [(d - minimum) / (maximum - minimum) for d in all_data]\\n\\n result = Statistics.calculate(all_data, stats, log_fun)\\n FileWriter.write_dict_to_file(FileWriter.STATISTICS, title + \\\".txt\\\", result)\",\n \"def reduce_metrics(logging_outputs) -> None:\\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\\n admloss_sum = sum(log.get('admloss', 0) for log in logging_outputs)\\n margin_n = sum(log.get('margin_n', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n\\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)\\n metrics.log_scalar('admloss', admloss_sum / sample_size / math.log(2), sample_size, round=3)\\n metrics.log_scalar('margin_norm', margin_n / nsentences, 32, round=3)\\n metrics.log_derived('ppl', lambda meters: round(2**meters['loss'].avg, 3))\",\n \"def average_data(counts, observable):\\n if not isinstance(observable, dict):\\n observable = make_dict_observable(observable)\\n temp = 0\\n tot = sum(counts.values())\\n for key in counts:\\n if key in observable:\\n temp += counts[key] * observable[key] / tot\\n return temp\",\n \"def counts_to_log_counts(forms_by_count):\\n forms_by_log_count={}\\n for count, forms in forms_by_count.items():\\n try:\\n log_count=math.log(count)\\n forms_by_log_count[log_count] = forms\\n except:\\n print(count)\\n return forms_by_log_count\",\n \"def sum_stats(stats_data):\\n t_bounces = 0\\n t_complaints = 0\\n t_delivery_attempts = 0\\n t_rejects = 0\\n for dp in stats_data:\\n t_bounces += int(dp['Bounces'])\\n t_complaints += int(dp['Complaints'])\\n t_delivery_attempts += int(dp['DeliveryAttempts'])\\n t_rejects += int(dp['Rejects'])\\n\\n return {\\n 'Bounces': t_bounces,\\n 'Complaints': t_complaints,\\n 'DeliveryAttempts': t_delivery_attempts,\\n 'Rejects': t_rejects,\\n }\",\n \"def reduce_metrics(logging_outputs) -> None:\\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\\n neg_elbo_sum = sum(log.get('neg_elbo', 0) for log in logging_outputs)\\n recon_loss_sum = sum(log.get('recon_loss', 0) for log in logging_outputs)\\n ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)\\n sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)\\n nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)\\n KLz_sum = sum(log.get('KLz', 0) for log in logging_outputs)\\n KLt_sum = sum(log.get('KLt', 0) for log in logging_outputs)\\n KLtheta_sum = sum(log.get('KLtheta', 0) for log in logging_outputs)\\n\\n if 'nll_iw' in logging_outputs[0]:\\n nll_iw_sum = sum(log.get('nll_iw', 0) for log in logging_outputs)\\n metrics.log_scalar('nll_iw_s', nll_iw_sum / nsentences, \\n nsentences, round=3, priority=4)\\n metrics.log_scalar('nll_iw_t', nll_iw_sum / ntokens / math.log(2), \\n ntokens, round=3, priority=5) \\n metrics.log_derived('ppl_iw', lambda meters: utils.get_perplexity(meters['nll_iw_t'].avg), priority=6)\\n\\n else:\\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), \\n sample_size, round=3, priority=3)\\n\\n metrics.log_scalar('neg_elbo_s', neg_elbo_sum / nsentences, \\n nsentences, round=3, priority=4)\\n metrics.log_scalar('recon_loss_s', recon_loss_sum / nsentences, \\n nsentences, round=3, priority=4)\\n\\n metrics.log_scalar('neg_elbo_t', neg_elbo_sum / ntokens / math.log(2), \\n ntokens, round=3, priority=5)\\n metrics.log_scalar('recon_loss_t', recon_loss_sum / ntokens / math.log(2), \\n ntokens, round=3, priority=5)\\n\\n metrics.log_scalar('KLz', KLz_sum / nsentences, nsentences, round=1, priority=8)\\n metrics.log_scalar('KLt', KLt_sum / nsentences, nsentences, round=1, priority=8)\\n metrics.log_scalar('KLtheta', KLtheta_sum / nsentences, nsentences, round=1, priority=8)\\n\\n metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['neg_elbo_t'].avg), priority=6)\\n metrics.log_derived('recon_ppl', lambda meters: utils.get_perplexity(meters['recon_loss_t'].avg), priority=7)\\n\\n if 'active' in logging_outputs[0]:\\n metrics.log_scalar('active', logging_outputs[0]['active'], weight=0, round=1, priority=10)\\n metrics.log_scalar('percent', logging_outputs[0]['percent'], weight=0, round=2, priority=10)\\n # metrics.log_scalar('nlow', logging_outputs[0]['nlow'], weight=0, priority=10)\\n # metrics.log_scalar('nhigh', logging_outputs[0]['nhigh'], weight=0, priority=10)\",\n \"def compute_metrics(self, results: list) -> dict:\",\n \"def average_metrics_results(results):\\n res = {}\\n\\n if len(results) == 0:\\n return {}\\n\\n for key in results[0]:\\n try:\\n res[key] = sum([r[key] for r in results]) / len(results)\\n except TypeError:\\n res[key] = [sum(b)/len(results) for b in zip(*[s[key] for s in results])]\\n return res\",\n \"def collect_stat(self):\\n\\n cnstat_dict, ratestat_dict = self.get_cnstat()\\n self.cnstat_dict.update(cnstat_dict)\\n self.ratestat_dict.update(ratestat_dict)\",\n \"def aggregate_results(results):\\n\\n for (config,con,dec),folds in results.iteritems():\\n m = MODEL_PATTERN.match(config)\\n if m:\\n mode = m.groupdict()['mode'] # mle, rl, mrt, ...\\n model = m.groupdict()['model'] # haem, hacm, hard, ...\\n align = m.groupdict()['align'] # crp, cls ...\\n else:\\n mode, model, align = '', '', ''\\n # mean accuracies across seeds for each fold\\n foldaccuracies = []\\n # we count number of models over folds and seeds\\n num_individual_models = 0\\n\\n for foldname,fold in folds.items():\\n if 'Q' in options.mode:\\n seedaccurracies = fold.values()[:1] if fold.values() else [] # pick one\\n# SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\\n else:\\n seedaccurracies = []\\n for seed_acc in fold.values():\\n seedaccurracies.append(seed_acc)\\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,foldname)] += 1\\n # aggregate on fold level\\n fold['__MEAN__'] = float(np.mean(seedaccurracies))\\n fold['__SD__'] = float(np.std(seedaccurracies))\\n l = len(seedaccurracies)\\n num_individual_models += l\\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__MEAN__')] += l\\n SUPPORT_STATISTICS[(config,con,dec,model,align,mode,'__SD__')] += l\\n\\n # statistics over seeds for this fold\\n fold['__STATS__'] = fold['__MEAN__'], fold['__SD__'], l\\n foldaccuracies.append(fold['__MEAN__'])\\n # aggregate on (config, condition, decoding) level\\n folds['__MEAN__'] = float(np.mean(foldaccuracies))\\n folds['__SD__'] = float(np.std(foldaccuracies))\\n # statistics over folds for this (config, condition, decoding)\\n folds['__STATS__'] = folds['__MEAN__'], folds['__SD__'], num_individual_models\",\n \"def aggregate_results(self):\\n\\n raise NotImplementedError\",\n \"def merge_logs(self):\\n ourlog = LogData()\\n for l in self.data_set:\\n ourlog.entries = ourlog.entries + l.entries\\n ourlog.sort_time()\\n self.finalized_data = ourlog\",\n \"def get_summarized_results(self):\\n stats = [v.stats() for (k, v) in self.examples.items() if v.is_ready()]\\n res = self.ExampleClass.average_stats(stats)\\n\\n res['loss'] = self.loss/self.loss_cnt\\n res['recent_loss'] = sum(self.recent_loss_array) / sum(self.recent_loss_bs_array)\\n\\n return res\",\n \"def count_results(self):\\n for record in self._traced_records:\\n if self._statistical_results.get(record.name) == None:\\n self._statistical_results[record.name] = StatisticalResult(\\n record.name)\\n self._statistical_results[record.name].insert_record(\\n record.kernel_time)\\n self._total_time_ms += record.kernel_time\\n self._statistical_results = {\\n k: v\\n for k, v in sorted(self._statistical_results.items(),\\n key=lambda item: item[1],\\n reverse=True)\\n }\",\n \"def get_best_result(log_dict, mode=\\\"regress\\\"):\\n logs = OrderedDict()\\n sum_score = 0\\n for log_item in log_dict:\\n csv_log = utils.parse_csv_log(log_dict[log_item])\\n if mode == \\\"regress\\\":\\n logs[log_item] = np.min(csv_log[\\\"val_mean_squared_error\\\"])\\n sum_score += logs[log_item]\\n elif mode == \\\"class\\\":\\n logs[log_item] = np.max(csv_log[\\\"val_accuracy\\\"])\\n sum_score += logs[log_item]\\n elif mode == \\\"binary\\\":\\n logs[log_item] = np.max(csv_log[\\\"val_binary_accuracy\\\"])\\n sum_score += logs[log_item]\\n\\n return logs, sum_score\",\n \"def _aggregate_metrics(metrics, aggfunc, base):\\n return base.Struct(**_UNCOMPRESSED_METRICS)(\\n left_side_bearing=aggfunc(_m.left_side_bearing for _m in metrics),\\n right_side_bearing=aggfunc(_m.right_side_bearing for _m in metrics),\\n character_width=aggfunc(_m.character_width for _m in metrics),\\n character_ascent=aggfunc(_m.character_ascent for _m in metrics),\\n character_descent=aggfunc(_m.character_descent for _m in metrics),\\n character_attributes=0,\\n )\",\n \"def reduce_metrics(logging_outputs) -> None:\\n\\n loss_sum = utils.item(sum(log.get(\\\"loss\\\", 0) for log in logging_outputs))\\n ntokens = utils.item(sum(log.get(\\\"ntokens\\\", 0) for log in logging_outputs))\\n nsentences = utils.item(\\n sum(log.get(\\\"nsentences\\\", 0) for log in logging_outputs)\\n )\\n sample_size = utils.item(\\n sum(log.get(\\\"sample_size\\\", 0) for log in logging_outputs)\\n )\\n\\n metrics.log_scalar(\\n \\\"loss\\\", loss_sum / sample_size / math.log(2), sample_size, round=3\\n )\\n metrics.log_scalar(\\\"ntokens\\\", ntokens)\\n metrics.log_scalar(\\\"nsentences\\\", nsentences)\\n if sample_size != ntokens:\\n metrics.log_scalar(\\n \\\"nll_loss\\\", loss_sum / ntokens / math.log(2), ntokens, round=3\\n )\",\n \"def aggregate_by_event_type(record):\\n return record.map(parse_entry)\\\\\\n .map(lambda record: (record['event'], 1))\\\\\\n .reduceByKey(lambda a, b: a+b)\",\n \"def summary(self):\\n summaries = self.summaries()\\n # Reduce to a single summary with weighted averages for each\\n # field except \\\"total_instances\\\", which is summed.\\n total_instances = sum(d['total_instances'].value() for d in summaries)\\n summary = {'total_instances': total_instances}\\n for field in ('average_qps', 'average_latency', 'average_memory'):\\n instance_weighted_sum = sum(\\n d['total_instances'].value() * d[field].value()\\n for d in summaries)\\n summary[field] = float(instance_weighted_sum) / total_instances\\n # Beautify rounding precision to match the App Engine UI.\\n summary['average_qps'] = round(summary['average_qps'], 3)\\n summary['average_latency_ms'] = round(summary['average_latency'], 1)\\n summary['average_memory_mb'] = round(summary['average_memory'], 1)\\n del summary['average_latency']\\n del summary['average_memory']\\n return summary\",\n \"def summarize_metrics(metrics):\\n summarized = {}\\n for k in metrics:\\n if k.endswith('mse'):\\n summarized[k[:-3] + 'rmse'] = np.sqrt(np.mean(metrics[k]))\\n elif k.startswith('err'):\\n summarized[k + '_mean'] = np.mean(metrics[k])\\n summarized[k + '_rmse'] = np.sqrt(np.mean(metrics[k]**2))\\n elif k.endswith('nomean'):\\n summarized[k] = metrics[k]\\n else:\\n summarized[k] = np.mean(metrics[k])\\n\\n return summarized\",\n \"def calculate_averages(data):\\n def mean(item_key):\\n all_items = [i[item_key] for i in data]\\n return sum(all_items)/float(len(all_items))\\n\\n return {\\n \\\"mean_error_count\\\": mean('error_count'),\\n \\\"mean_success_count\\\": mean('success_count'),\\n \\\"mean_mean_response_time\\\": mean('mean_respone_time'),\\n \\\"mean_data_sent_mb\\\": mean('data_sent_mb'),\\n }\",\n \"def log_weights_statistics(self):\\n for weight_name, weight_parameter in self._weights.items():\\n for statistic_function in self._statistics_functions:\\n self._weights_statistics[statistic_function.__name__][\\n weight_name\\n ].append(float(statistic_function(weight_parameter)))\",\n \"def aggregate_dict(x):\\n agg_x = {}\\n\\n for ele in x:\\n assert isinstance(ele, dict)\\n\\n for k, v in ele.items():\\n if k not in agg_x:\\n agg_x[k] = []\\n\\n if isinstance(v, (tuple, list)):\\n agg_x[k].extend(list(v))\\n else:\\n agg_x[k].append(v)\\n\\n # Stack if possible\\n new_agg_x = {}\\n for k, v in agg_x.items():\\n try:\\n v = torch.cat(v, dim=0)\\n except Exception:\\n pass\\n new_agg_x[k] = v\\n\\n return new_agg_x\",\n \"def aggregate(self):\\n data_to_track = {}\\n for possession in self.possessions_to_track_aggregate:\\n data_to_track[possession] = self._haves[possession]\\n\\n for variable in self.variables_to_track_aggregate:\\n try:\\n data_to_track[variable] = self.__dict__[variable]\\n except KeyError:\\n pass\\n self.database_connection.put([\\\"aggregate\\\",\\n data_to_track,\\n self.group,\\n self.round])\",\n \"def log_scalars(self, scalars_dict, accum_reduction=None):\\n for k, v in scalars_dict.items():\\n self.log_scalar(k, v, accum_reduction)\",\n \"def aggregate_stats(self):\\n if self.split_bn.track_running_stats:\\n (\\n self.bn.running_mean.data,\\n self.bn.running_var.data,\\n ) = self._get_aggregated_mean_std(\\n self.split_bn.running_mean,\\n self.split_bn.running_var,\\n self.num_splits,\\n )\",\n \"def calculate_statistics(self, trace, **input_test):\\n inputs = self._clean_inputs(input_test)\\n mc_logp = self._logp(trace, **inputs)\\n mean_mse = self._mse(trace, **inputs)\\n mse2 = self._mse2(trace, **inputs)\\n mu, sd, zscore = self._alpha_stats(trace)\\n return {'logp': mc_logp,\\n 'mse': mean_mse,\\n 'mse2': mse2,\\n 'mu': mu,\\n 'sd': sd,\\n 'zscore': zscore}\",\n \"def compute_query_summary_statistics(data):\\n return {\\n nameserver: _compute_summary_stats(entries)\\n for nameserver, entries in data.items()\\n }\",\n \"def totals_map():\\n totals_map = [*map(sum,poke_stats)]\\n\\n return(totals_map)\",\n \"def statistic_aggregation(self, resource=None, resource_type=None,\\n meter_name=None, period=300, aggregate='mean',\\n granularity=300):\\n\\n pass\",\n \"def calculate_times(log):\\n log['processing_time'] = 0\\n log['multitasking'] = 0\\n log = log.to_dict('records')\\n log = sorted(log, key=lambda x: (x['source'], x['caseid']))\\n for _, group in itertools.groupby(log, key=lambda x: (x['source'], x['caseid'])):\\n events = list(group)\\n events = sorted(events, key=itemgetter('start_timestamp'))\\n for i in range(0, len(events)):\\n # In one-timestamp approach the first activity of the trace\\n # is taken as instantsince there is no previous timestamp\\n # to find a range\\n dur = (events[i]['end_timestamp'] -\\n events[i]['start_timestamp']).total_seconds()\\n if i == 0:\\n wit = 0\\n else:\\n wit = (events[i]['start_timestamp'] -\\n events[i-1]['end_timestamp']).total_seconds()\\n events[i]['waiting_time'] = wit if wit >= 0 else 0\\n events[i]['processing_time'] = dur\\n return pd.DataFrame.from_dict(log)\",\n \"def aggregate(self, **aggregations):\\n # Before we iterate, reset the aggregations\\n for _, agg in aggregations.items():\\n agg.reset()\\n # Do the accumulation\\n for attrs in self:\\n for _, agg in aggregations.items():\\n agg.accumulate(attrs)\\n # Return the results\\n return {name: agg.result for name, agg in aggregations.items()}\",\n \"def compute_propagation_summary_statistics(data):\\n return {\\n type: _compute_summary_stats(entries)\\n for type, entries in data.items()\\n }\",\n \"def add_aggregators(self, stat_agg):\\n stat_agg.add_aggregator(self.key_precision, '{:05.4f}') \\n stat_agg.add_aggregator(self.key_precision+'_std', '{:05.4f}')\\n stat_agg.add_aggregator(self.key_recall, '{:05.4f}') \\n stat_agg.add_aggregator(self.key_recall+'_std', '{:05.4f}')\\n stat_agg.add_aggregator(self.key_f1score, '{:05.4f}') \\n stat_agg.add_aggregator(self.key_f1score+'_std', '{:05.4f}')\",\n \"def _calculate_aggregated_metrics(self):\\n\\n # using the historical values, calculate the aggregate\\n # there are two kinds of metrics:\\n # a) cumulative metrics - only the delta of the last 2 recorded values is used (eg cpu cycles)\\n # b) absolute metrics - the last absolute value is used\\n\\n running_pids_set = set(self.__pids)\\n\\n for pid, process_metrics in self.__metrics_history.items():\\n for _metric, _metric_values in process_metrics.items():\\n if not self.__aggregated_metrics.get(_metric):\\n self.__aggregated_metrics[_metric] = 0\\n if _metric.is_cumulative:\\n if pid in running_pids_set:\\n if len(_metric_values) > 1:\\n # only report the cumulative metrics for more than one sample\\n self.__aggregated_metrics[_metric] += (\\n _metric_values[-1] - _metric_values[-2]\\n )\\n else:\\n if pid in running_pids_set:\\n # absolute metric - accumulate the last reported value\\n self.__aggregated_metrics[_metric] += _metric_values[-1]\",\n \"def Analyze(self):\\n \\n self._analyzeLogs()\\n for user in self._start_times:\\n self._result[user] = self._zipTimes(user)\",\n \"def _compute_summary_stats(entries):\\n result = {\\n \\\"average\\\": None,\\n \\\"median\\\": None,\\n \\\"min\\\": None,\\n \\\"max\\\": None,\\n \\\"per66\\\": None,\\n \\\"per75\\\": None,\\n \\\"per90\\\": None,\\n \\\"per95\\\": None,\\n \\\"per99\\\": None,\\n \\\"success_count\\\": len(entries['success']),\\n \\\"error_count\\\": len(entries['error']),\\n }\\n # sort by the duration\\n success = sorted(entries['success'], key=lambda x: x[1])\\n if len(success) == 0:\\n return result\\n\\n result['average'] = _compute_average(success)\\n result['min'] = success[0][1]\\n result['max'] = success[-1][1]\\n result['median'] = _compute_percentile(success, 50)\\n result['per66'] = _compute_percentile(success, 66)\\n result['per75'] = _compute_percentile(success, 75)\\n result['per90'] = _compute_percentile(success, 90)\\n result['per95'] = _compute_percentile(success, 95)\\n result['per99'] = _compute_percentile(success, 99)\\n assert result['min'] <= result['median']\\n assert result['median'] <= result['per66']\\n assert result['per66'] <= result['per75']\\n assert result['per75'] <= result['per90']\\n assert result['per90'] <= result['per95']\\n assert result['per95'] <= result['per99']\\n assert result['per99'] <= result['max']\\n return result\",\n \"def reduce_metrics(logging_outputs) -> None:\\n loss_sum = utils.item(sum(log.get('loss', 0) for log in logging_outputs))\\n sample_size = utils.item(sum(log.get('sample_size', 0) for log in logging_outputs))\\n\\n metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)\\n metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg))\",\n \"def summarize_dat(totalData):\\n import pandas as pd\\n from numpy import log\\n pkRed = totalData.loc[:, ['PEAK_NUM', 'pk_LON', 'pk_LAT', 'pk_maxCH4_AB', 'numtimes',\\n 'min_read']].drop_duplicates().reset_index().loc[:,\\n ['PEAK_NUM', 'pk_LON', 'pk_LAT', 'pk_maxCH4_AB', 'numtimes', 'min_read']]\\n verLoc = weighted_loc(pkRed, 'pk_LAT', 'pk_LON', 'min_read', 'pk_maxCH4_AB').rename(\\n columns={'pk_LAT': 'overallLAT', 'pk_LON': 'overallLON'})\\n pkRed['logCH4'] = pkRed.apply(lambda y: log(y.pk_maxCH4_AB), axis=1)\\n mnVals = pkRed.groupby('min_read', as_index=False).logCH4.mean().rename(columns={'logCH4': 'mnlogCH4'}).loc[:,\\n ['min_read', 'mnlogCH4']]\\n together = pd.merge(verLoc, mnVals, on=['min_read'])\\n final = pd.merge(together, totalData, on=['min_read'])\\n return (final)\",\n \"def avg_added(self):\\n avg = {}\\n for path, lines in self.lines_added.items():\\n avg[path] = round(statistics.mean(lines))\\n\\n return avg\",\n \"def extract_streams(log):\\n # key = (weapon, enemy_name), value = [(timestamp, damage_amount),...]\\n your_damage_streams = {}\\n enemy_damage_streams = {}\\n # key = name, value = (ticker, set([ship1, ship2,...]))\\n enemy_info_map = {}\\n for e in combat_entries(log):\\n if e.attacker.strip().lower() == 'you':\\n enemy = e.target\\n enemy_name, ship, ticker = enemy_info(enemy)\\n stream = your_damage_streams.setdefault((e.weapon, enemy_name), [])\\n else:\\n enemy = e.attacker\\n enemy_name, ship, ticker = enemy_info(enemy)\\n stream = enemy_damage_streams.setdefault((e.weapon, enemy_name), [])\\n\\n timestamp = e.timestamp\\n amount = e.damage\\n if stream and stream[-1][0] == timestamp:\\n stream[-1] = (timestamp, stream[-1][1] + amount)\\n else:\\n stream.append((timestamp, amount))\\n\\n if ship is not None:\\n t, ship_types = enemy_info_map.setdefault(enemy_name,\\n (ticker, set()))\\n ship_types.add(ship)\\n\\n damage_streams = []\\n def add_streams(stream_map, enemy_attacks):\\n for k, stream in stream_map.iteritems():\\n weapon, enemy = k\\n ticker, ships = enemy_info_map.setdefault(enemy, ('Unknown', []))\\n if enemy_attacks:\\n attacker = enemy\\n target = 'You'\\n else:\\n attacker = 'You'\\n target = enemy\\n damage_streams.append(\\n DamageStream(attacker, target, stream, ticker, weapon, ships))\\n\\n add_streams(your_damage_streams, False)\\n add_streams(enemy_damage_streams, True)\\n\\n return damage_streams\",\n \"def errdump_statistics(errdump_aggregated_df, raslog_message_details_df, raslog_message_id_details_df):\\n \\n # filter log messages based on date, message tag and merge devices behind same port for single event\\n errdump_filtered_df = errdump_filter(errdump_aggregated_df)\\n\\n # set message date and time as index\\n errdump_filtered_df.set_index(errdump_filtered_df['Message_date'], inplace=True)\\n errdump_filtered_df.drop(columns=['Message_date'], inplace=True)\\n \\n # grouping and message counting\\n errdump_grp_columns = ['configname', 'chassis_name', 'chassis_wwn',\\n 'switchName', 'switchWwn',\\n 'Fabric_name', 'Fabric_label', 'config_collection_date',\\n 'Message_ID', 'Severity', 'Message_portIndex', 'Message_portType','slot', 'port',\\n 'Condition', 'Dashboard_category', 'obj', 'Message_status',\\n 'portIndex', 'Index_slot_port', 'portType', 'portState', 'speed',\\n 'tx_port', 'rx_port', 'sid', 'did', 'wwn', 'IP_Address',\\n 'Connected_portId', 'Connected_portWwn', 'Device_Host_Name_Port', 'alias', 'deviceType']\\n \\n # group log messages by month, device and log message\\n errdump_grouper = errdump_filtered_df.groupby([pd.Grouper(freq='M', kind='period'), *errdump_grp_columns])\\n # count events in each group\\n raslog_counter_sr = errdump_grouper['Condition'].count()\\n raslog_counter_df = pd.DataFrame(raslog_counter_sr)\\n # rename column Condition to avoid duplication with one of Indexies\\n raslog_counter_df.rename(columns={'Condition': 'Quantity'}, inplace=True)\\n raslog_counter_df.reset_index(inplace=True)\\n # replace na_cell\\n raslog_counter_df.replace({'na_cell': np.nan}, inplace=True)\\n if raslog_counter_df['alias'].notna().any():\\n raslog_counter_df['alias'].replace('na_cell(?:, )?', value='', regex=True, inplace=True)\\n raslog_counter_df['alias'] = raslog_counter_df['alias'].str.rstrip(', ')\\n\\n # apply date format to remove day and time\\n raslog_counter_df['config_collection_date'] = pd.to_datetime(raslog_counter_df['config_collection_date']).dt.date\\n raslog_counter_df['Message_date'] = raslog_counter_df['Message_date'].dt.strftime('%Y-%m')\\n # sort values\\n errdump_sort_columns = ['chassis_name', 'Fabric_label', 'Fabric_name', 'switchName', 'Message_date', 'Quantity']\\n raslog_counter_df.sort_values(by=errdump_sort_columns, ascending=[*[True]*5, False], inplace=True)\\n raslog_counter_df.reset_index(drop=True, inplace=True) \\n # drop columns with all empty empty cells\\n raslog_counter_df.dropna(axis=1, how='all', inplace=True)\\n\\n raslog_counter_df = dfop.dataframe_fillna(raslog_counter_df, raslog_message_details_df, join_lst=['Condition'], \\n filled_lst=['Details', 'Recommended_action']) \\n\\n raslog_counter_df = dfop.dataframe_fillna(raslog_counter_df, raslog_message_id_details_df, join_lst=['Message_ID'], \\n filled_lst=['Details', 'Recommended_action']) \\n \\n # find log messages which appear more then three times a month\\n mask_frequent = raslog_counter_df['Quantity'] > 3\\n raslog_frequent_df = raslog_counter_df.loc[mask_frequent].copy()\\n \\n # remove INFO Messages for report DataFrame except securitu violations messages\\n mask_not_info = raslog_frequent_df['Severity'] != 'INFO'\\n mask_sec_violation_condition = raslog_frequent_df['Condition'].str.contains('security violation', case=False, na=False)\\n mask_sec_violation_dashboard = raslog_frequent_df['Dashboard_category'].str.contains('security violation', case=False, na=False)\\n raslog_frequent_df = raslog_frequent_df.loc[mask_not_info | mask_sec_violation_condition | mask_sec_violation_dashboard].copy()\\n\\n raslog_frequent_df.reset_index(drop=True, inplace=True) \\n return raslog_counter_df, raslog_frequent_df\",\n \"def process_log(self, log: Dict, placeholder_switcher: int, placeholder_geo: List) -> Dict:\\n\\n line_cnt = 0\\n distance_counter = {'human': 0, 'autopilot': 0} # human is 0, autopilot is 1 (checked )\\n\\n start = time.monotonic()\\n\\n for ts, event in log.items():\\n line_cnt += 1\\n\\n current_switcher_str = 'autopilot' if placeholder_switcher else 'human'\\n converted_ts = self.convert_time(ts)\\n\\n if isinstance(event, Dict):\\n\\n if self.validate_coordinates(event):\\n # if line contains geo coordinates - calculate distance and update values in dict\\n driven_distance = self.calculate_distance(placeholder_geo, list(event.values()))\\n distance_counter[current_switcher_str] += driven_distance\\n\\n # update current geo position\\n placeholder_geo = event.values()\\n\\n logging.info(f\\\"{line_cnt}|{converted_ts}|{current_switcher_str}|\\\"\\n f\\\"{driven_distance}|{distance_counter}|{list(placeholder_geo)}|{list(event.values())}\\\")\\n\\n elif isinstance(event, int):\\n\\n placeholder_switcher = self.check_switcher_state(event, placeholder_switcher)\\n\\n logging.info(f\\\"{line_cnt}|{converted_ts}|{current_switcher_str}|\\\"\\n f\\\"{driven_distance}|{distance_counter}|{list(placeholder_geo)}|{event}\\\")\\n else:\\n pass\\n self.monitor.warning(f'Something went wrong on line {line_cnt}')\\n\\n end = time.monotonic()\\n self.monitor.info(\\\" Total time spent on calculating report: {}\\\".format(\\\"%6.2f\\\" % (end - start)))\\n\\n return distance_counter\",\n \"def exportcsvsumdata(self, log):\\r\\n csvdata= None\\r\\n\\r\\n if (log):\\r\\n csvdata = ('%s\\\\t'%(log['CALLSIGN']))\\r\\n csvdata += ('%s\\\\t'%(log['OPERATORS']))\\r\\n csvdata += ('%s\\\\t'%(log['LOCATION']))\\r\\n csvdata += ('%d\\\\t'%(log['COUNT']))\\r\\n csvdata += ('%s\\\\t'%(log['NAMES']))\\r\\n if(log['LASTWORKED']): \\r\\n csvdata += ('%s/%s UTC'%(log['LASTWORKED'],\\r\\n log['LWTIME'])) \\r\\n\\r\\n return csvdata\",\n \"async def log_stats(self, stats_dict):\\n\\n cmd = \\\"PRAGMA table_info(trainer_stats)\\\"\\n cur = self.sql.cur\\n data = cur.execute(cmd).fetchall()\\n valid_keys = []\\n for entry in data:\\n valid_keys.append(entry['name'])\\n self.log.info(valid_keys)\\n\\n for key in stats_dict:\\n if key not in valid_keys:\\n raise ValueError()\\n trainer_id = self.trainer_id\\n for key in stats_dict:\\n value = stats_dict[key]\\n cmd = f\\\"\\\"\\\"UPDATE trainer_stats\\n SET {key} = {key} + :value\\n WHERE trainer_id = :trainer_id\\\"\\\"\\\"\\n cur.execute(cmd, locals())\\n await self.sql.commit(now=True)\\n self.log.info(\\\"log completed\\\")\",\n \"def collect_log_output(activity_log, result):\\n output = activity_log.get('emittedOutput')\\n if output:\\n result.append(output['_value'])\\n else:\\n subsections = activity_log.get('subsections')\\n if subsections:\\n for subsection in subsections['_values']:\\n collect_log_output(subsection, result)\",\n \"def parse_stats(output):\\n lines = [line for line in output if \\\"[Stats]\\\" in line]\\n stats = {\\n 'totals': {'time': 0, 'tasks': 0, 'avg': 0}\\n }\\n for line in lines:\\n m = re.search(r'\\\\((\\\\d+) ms\\\\).+\\\\((\\\\d+)\\\\).+\\\\((\\\\d+) us.+\\\\)', line)\\n if not m:\\n continue\\n dt, tasks, avg = map(int, m.groups())\\n if 'totals' in line:\\n stats['totals'] = {'time': dt, 'tasks': tasks, 'avg': avg}\\n return stats\",\n \"def get_output_stats(self, output):\\n\\t\\ttype_dict = {}\\n\\t\\ttype_full_dict = {}\\n\\t\\ttotal = 0\\n\\t\\tfor key in output.keys():\\n\\t\\t\\tif output[key]['deleted'] == \\\"0\\\":\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\ttype_dict[output[key]['type']] += 1\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\ttype_dict[output[key]['type']] = 1\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\ttype_full_dict[output[key]['type_full']] += 1\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\ttype_full_dict[output[key]['type_full']] = 1\\n\\t\\t\\t\\ttotal += 1\\n\\t\\tfinal_stats = {}\\n\\t\\tfor key in type_dict.keys():\\n\\t\\t\\tfinal_stats[key + \\\"(\\\" + str(type_dict[key]) + \\\"/\\\" + str(int(np.rint((type_dict[key]/total)*100))) + \\\"%)\\\"] = {'count': type_dict[key], 'percent_of_total': (type_dict[key]/total)*100}\\n\\t\\t\\tfor k in type_full_dict.keys():\\n\\t\\t\\t\\tif k == None:\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif k.startswith(key):\\n\\t\\t\\t\\t\\tfinal_stats[key + \\\"(\\\" + str(type_dict[key]) + \\\"/\\\" + str(int(np.rint((type_dict[key]/total)*100))) + \\\"%)\\\"][k + \\\"(\\\" + str(type_full_dict[k]) + \\\"/\\\" + str(int(np.rint((type_full_dict[k]/total)*100))) + \\\"%)\\\"] = {'count': type_full_dict[k], 'percent_of_total': (type_full_dict[k]/total)*100, 'percent_of_' + key + '(s)': (type_full_dict[k]/type_dict[key])*100}\\n\\t\\treturn (final_stats, total)\",\n \"def __call__(self, **kwargs: float):\\n for k, v in kwargs.items():\\n if k not in self.logs:\\n self.logs[k] = []\\n self.logs[k].append(v)\",\n \"def get_results_stats(self, results, key_by_date=True):\\n stats = {}\\n for r in results:\\n if key_by_date:\\n key = r.get(1)\\n value = r.get(2)\\n else:\\n key = r.get(2)\\n value = r.get(1)\\n\\n if key not in stats:\\n stats[key] = {}\\n\\n if value not in stats[key]:\\n stats[key][value] = 1\\n else:\\n stats[key][value] += 1\\n\\n # sort each keyset\\n if not key_by_date:\\n stats[key] = self._stats_sort(stats[key])\\n\\n if stats:\\n # only if sorted per key\\n if key_by_date:\\n stats = self._stats_sort(stats)\\n\\n return stats\",\n \"def clients_histogram_aggregates(**kwargs):\\n attributes_list = [\\n \\\"sample_id\\\",\\n \\\"client_id\\\",\\n \\\"ping_type\\\",\\n \\\"os\\\",\\n \\\"app_version\\\",\\n \\\"app_build_id\\\",\\n \\\"channel\\\",\\n ]\\n fixed_attributes = [\\\"app_version\\\", \\\"channel\\\"]\\n cubed_attributes = [x for x in attributes_list if x not in fixed_attributes]\\n return dict(\\n attributes_list=attributes_list,\\n attributes=\\\",\\\".join(attributes_list),\\n cubed_attributes=cubed_attributes,\\n attribute_combinations=compute_datacube_groupings(cubed_attributes),\\n metric_attributes=\\\"\\\"\\\"\\n metric,\\n metric_type,\\n key,\\n agg_type\\n \\\"\\\"\\\",\\n **kwargs,\\n )\",\n \"def add_metrics(_dict):\\n for key, itr in _dict.items():\\n if key not in self.metric_cols:\\n self.metric_cols.append(key)\",\n \"def stats():\\r\\n times_lst = []\\r\\n time_dict = {}\\r\\n for album, details in dbase().items():\\r\\n time_m = 0\\r\\n time_s = 0\\r\\n for songs, details_s in details[0].items():\\r\\n time = details_s[1].split(\\\":\\\")\\r\\n min = int(time[0])\\r\\n sec = int(time[1])\\r\\n time_m += min\\r\\n time_s += sec\\r\\n time_s = datetime.timedelta(seconds=time_s)\\r\\n time_m = datetime.timedelta(seconds=time_m)\\r\\n time = time_m + time_s\\r\\n time = str(time)\\r\\n times_lst.append(time)\\r\\n time_dict[album] = time\\r\\n\\r\\n time_dict = sorted(time_dict.items(), key=lambda x: x[1], reverse=True)\\r\\n return time_dict\",\n \"def get_metrics(self, objs_metrics):\\n d = {}\\n _S = DiffStatus\\n\\n for status in _S.iter():\\n d[status] = [dict(obj_m) for obj_m in objs_metrics if obj_m['status'] == status]\\n\\n count_a_only = len(d.get(_S.deleted, []))\\n count_b_only = len(d.get(_S.added, []))\\n count_modified = len(d.get(_S.modified, []))\\n count_unchanged = len(d.get(_S.unchanged, []))\\n count_common = count_modified + count_unchanged\\n\\n count_a = count_common + count_a_only\\n count_b = count_common + count_b_only\\n\\n d['count'] = {\\n 'a': count_a,\\n 'b': count_b,\\n 'a_only': count_a_only,\\n 'b_only': count_b_only,\\n 'modified': count_modified,\\n 'unchanged': count_unchanged,\\n }\\n\\n d['summary'] = {\\n _S.added: {\\n 'count': count_b_only,\\n 'relative_to': {\\n 'a': count_b_only / count_a,\\n 'b': count_b_only / count_b,\\n }\\n },\\n _S.deleted: {\\n 'count': count_a_only,\\n 'relative_to': {\\n 'a': count_a_only / count_a,\\n 'b': count_a_only / count_b,\\n },\\n },\\n _S.modified: {\\n 'count': count_modified,\\n 'relative_to': {\\n 'a': count_modified / count_a,\\n 'b': count_modified / count_b,\\n }\\n },\\n _S.unchanged: {\\n 'count': count_unchanged,\\n 'relative_to': {\\n 'a': count_unchanged / count_a,\\n 'b': count_unchanged / count_b,\\n }\\n },\\n }\\n\\n return d\",\n \"def compute_metrics(self, results: list) -> dict:\\n dump(results, self.out_file_path)\\n print_log(\\n f'Results has been saved to {self.out_file_path}.',\\n logger='current')\\n return {}\",\n \"def collect_statistics(self, stat_col, data_streams):\\n # Calculate all four statistics.\\n _, precisions, recalls, f1scores, supports = self.calculate_statistics(data_streams)\\n\\n # Calculate weighted averages.\\n precision_sum = sum([pi*si for (pi,si) in zip(precisions,supports)])\\n recall_sum = sum([ri*si for (ri,si) in zip(recalls,supports)])\\n f1score_sum = sum([fi*si for (fi,si) in zip(f1scores,supports)])\\n support_sum = sum(supports)\\n\\n if support_sum > 0:\\n precision_avg = precision_sum / support_sum \\n recall_avg = recall_sum / support_sum\\n f1score_avg = f1score_sum / support_sum\\n else:\\n precision_avg = 0\\n recall_avg = 0\\n f1score_avg = 0\\n\\n # Export averages to statistics.\\n stat_col[self.key_precision] = precision_avg\\n stat_col[self.key_recall] = recall_avg\\n stat_col[self.key_f1score] = f1score_avg\\n\\n # Export support to statistics.\\n stat_col[self.key_f1score+'_support'] = support_sum\",\n \"def print_metrics(result):\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, ' KEY METRICS: ')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* pages_count: %d',\\n get_counter_metric(result, 'pages_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_count: %d',\\n get_counter_metric(result, 'revisions_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* very_long_page_histories_count: %d',\\n get_counter_metric(result, 'very_long_page_histories_count'))\\n revisions_per_page_distr = get_distributions_metric(\\n result, 'revisions_per_page_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.mean: %d',\\n revisions_per_page_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.sum: %d',\\n revisions_per_page_distr.sum)\\n cumulative_page_rev_size_distr = get_distributions_metric(\\n result, 'cumulative_page_rev_size_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '* cumulative_page_rev_size_distr.mean: %d',\\n cumulative_page_rev_size_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* cumulative_page_rev_size_distr.sum: %d',\\n cumulative_page_rev_size_distr.sum)\",\n \"def stats(self):\\n stats = {\\n 'lines' : '', # This will count the lines under each split\\n 'status_code': self.status_code,\\n 'content_type': self.mime,\\n 'hop': self.hop_path[-1:],\\n 'sum:content_length': self.content_length,\\n 'host': self.host(),\\n 'source': self.source\\n }\\n # Add in annotations:\\n for annot in self.annotations:\\n # Set a prefix based on what it is:\\n prefix = ''\\n if self.re_tries.match(annot):\\n prefix = 'tries:'\\n elif self.re_ip.match(annot):\\n prefix = \\\"ip:\\\"\\n # Only emit lines with annotations:\\n if annot != \\\"-\\\":\\n stats[\\\"%s%s\\\" % (prefix, annot)] = \\\"\\\"\\n return stats\",\n \"def flatten_log(self, log):\\n pass\",\n \"def aggregate(all_metrics, reducer, suffix):\\n # Collect metric separately\\n separated_metrics = {} # type: dict[frozenset, list[dict]]\\n for el in all_metrics:\\n key = frozenset(el[\\\"metric\\\"][\\\"dimensions\\\"].items())\\n if key not in separated_metrics:\\n separated_metrics[key] = [el]\\n else:\\n separated_metrics[key].append(el)\\n\\n # Collect all dimensions\\n dims = {}\\n for metric_dims in separated_metrics.keys():\\n for prop, val in dict(metric_dims).iteritems():\\n if prop in dims:\\n dims[prop].add(val)\\n else:\\n dims[prop] = set(val)\\n\\n # Sort each metric\\n for _, metric in separated_metrics.iteritems():\\n metric.sort(key=lambda v: v[\\\"metric\\\"][\\\"timestamp\\\"])\\n\\n separated_metrics = sorted(separated_metrics.values(), key=len)\\n separated_metrics.reverse()\\n\\n # Compute the new values\\n new_values = []\\n all_timestamps = map(\\n lambda l: map(\\n lambda x: x[\\\"metric\\\"][\\\"timestamp\\\"], l),\\n separated_metrics)\\n metric_count = len(separated_metrics)\\n for index in range(0, len(separated_metrics[0])):\\n new_value = reducer[0](\\n separated_metrics[0][index][\\\"metric\\\"][\\\"value\\\"],\\n metric_count)\\n new_timestamp = separated_metrics[0][index][\\\"metric\\\"][\\\"timestamp\\\"]\\n for metric_index in range(1, metric_count):\\n new_value = reducer[1](new_value, helpers.interpolate(\\n new_timestamp,\\n separated_metrics[metric_index],\\n all_timestamps[metric_index]\\n ), metric_count)\\n new_values.append((new_timestamp, new_value))\\n\\n # Aggregate the other details:\\n metric_name = separated_metrics[0][0][\\\"metric\\\"][\\\"name\\\"] + suffix\\n meta = separated_metrics[0][0][\\\"meta\\\"]\\n new_metrics = [\\n helpers.create_agg_metric(\\n metric_name,\\n meta,\\n dims,\\n val[0],\\n val[1]\\n ) for val in new_values\\n ]\\n return new_metrics\",\n \"def _fill_mean_dict(self, running_metrics: Dict, mean_dict: Dict) -> None:\\n for key, value in running_metrics.items():\\n mean = np.mean(value)\\n mean_dict[key].append(mean)\",\n \"def aggregator(index_keynames, value_keynames, ts_keyname, func, interval = 60 * 5):\\n data = {}\\n ts = None\\n #print ts_keyname\\n for parsts, parsdata in func():\\n #print parsdata\\n #print parsdata[\\\"log_timestamp\\\"]\\n if ts is None:\\n ts = parsts\\n key = tuple((parsdata[key] for key in index_keynames))\\n values = tuple((int(parsdata[key]) for key in value_keynames))\\n if key not in data:\\n data[key] = values\\n else:\\n data[key] = tuple((data[key][index] + int(values[index]) for index in range(len(values))))\\n if parsts > (ts + interval):\\n for keys, values in data.items():\\n yield \\\"%s\\\\t%s\\\\t%s\\\" % (ts, \\\"\\\\t\\\".join((str(index_key) for index_key in keys)), \\\"\\\\t\\\".join((str(value_key) for value_key in values)))\\n ts = None\\n data = {}\",\n \"def aggregate_data(mts, feature, target):\\r\\n set_dict = dict()\\r\\n set_dict['mt'] = mts\\r\\n set_dict['feature'] = feature\\r\\n set_dict['target'] = target\\r\\n \\r\\n return set_dict\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72006446","0.7089349","0.7069627","0.6966819","0.6952808","0.6841634","0.683194","0.6784331","0.65914315","0.64268196","0.606027","0.60507745","0.6043965","0.6035893","0.60186166","0.5961135","0.59582734","0.59497863","0.5914297","0.5878846","0.5870959","0.5861917","0.57707447","0.5761949","0.57606405","0.57302153","0.57230663","0.57104474","0.5689965","0.56839955","0.5666973","0.5665829","0.5659399","0.5658338","0.5649764","0.56433487","0.56381977","0.5609969","0.5602755","0.56002474","0.5596269","0.55949837","0.5594494","0.55907315","0.5589697","0.55829924","0.5579076","0.5573548","0.55674344","0.5563498","0.5546906","0.553464","0.55322516","0.55201495","0.5514164","0.5512517","0.55099994","0.5506025","0.5505022","0.54931694","0.54849684","0.5469714","0.5461159","0.54573596","0.54438275","0.54378146","0.54055876","0.5398452","0.53963494","0.5386544","0.5376442","0.53757185","0.53687984","0.53449154","0.5338717","0.53261876","0.53258353","0.5320672","0.5317941","0.5309693","0.5306987","0.5297628","0.52959687","0.52947336","0.52900076","0.5275797","0.5268787","0.5259276","0.52589864","0.52573377","0.5247524","0.52474874","0.52319425","0.5231003","0.52302426","0.5229575","0.5228397","0.5226896","0.522649","0.5225937"],"string":"[\n \"0.72006446\",\n \"0.7089349\",\n \"0.7069627\",\n \"0.6966819\",\n \"0.6952808\",\n \"0.6841634\",\n \"0.683194\",\n \"0.6784331\",\n \"0.65914315\",\n \"0.64268196\",\n \"0.606027\",\n \"0.60507745\",\n \"0.6043965\",\n \"0.6035893\",\n \"0.60186166\",\n \"0.5961135\",\n \"0.59582734\",\n \"0.59497863\",\n \"0.5914297\",\n \"0.5878846\",\n \"0.5870959\",\n \"0.5861917\",\n \"0.57707447\",\n \"0.5761949\",\n \"0.57606405\",\n \"0.57302153\",\n \"0.57230663\",\n \"0.57104474\",\n \"0.5689965\",\n \"0.56839955\",\n \"0.5666973\",\n \"0.5665829\",\n \"0.5659399\",\n \"0.5658338\",\n \"0.5649764\",\n \"0.56433487\",\n \"0.56381977\",\n \"0.5609969\",\n \"0.5602755\",\n \"0.56002474\",\n \"0.5596269\",\n \"0.55949837\",\n \"0.5594494\",\n \"0.55907315\",\n \"0.5589697\",\n \"0.55829924\",\n \"0.5579076\",\n \"0.5573548\",\n \"0.55674344\",\n \"0.5563498\",\n \"0.5546906\",\n \"0.553464\",\n \"0.55322516\",\n \"0.55201495\",\n \"0.5514164\",\n \"0.5512517\",\n \"0.55099994\",\n \"0.5506025\",\n \"0.5505022\",\n \"0.54931694\",\n \"0.54849684\",\n \"0.5469714\",\n \"0.5461159\",\n \"0.54573596\",\n \"0.54438275\",\n \"0.54378146\",\n \"0.54055876\",\n \"0.5398452\",\n \"0.53963494\",\n \"0.5386544\",\n \"0.5376442\",\n \"0.53757185\",\n \"0.53687984\",\n \"0.53449154\",\n \"0.5338717\",\n \"0.53261876\",\n \"0.53258353\",\n \"0.5320672\",\n \"0.5317941\",\n \"0.5309693\",\n \"0.5306987\",\n \"0.5297628\",\n \"0.52959687\",\n \"0.52947336\",\n \"0.52900076\",\n \"0.5275797\",\n \"0.5268787\",\n \"0.5259276\",\n \"0.52589864\",\n \"0.52573377\",\n \"0.5247524\",\n \"0.52474874\",\n \"0.52319425\",\n \"0.5231003\",\n \"0.52302426\",\n \"0.5229575\",\n \"0.5228397\",\n \"0.5226896\",\n \"0.522649\",\n \"0.5225937\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7331248"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17494,"cells":{"query":{"kind":"string","value":"Initializes the path to write the log to"},"document":{"kind":"string","value":"def init_logger(self, logger_path,\n logger_name='Experiment') -> logging.Logger:\n self.logger = logging.getLogger(logger_name)\n\n self.logger.setLevel(logging.INFO)\n\n file_handler = logging.FileHandler(logger_path) # TOD bug here\n formatter = logging.Formatter('%(asctime)s||%(message)s')\n file_handler.setFormatter(formatter)\n self.logger.addHandler(file_handler)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def init_log(path):\n file = open(path, 'w+')\n file.close()","def _init_log(self):\n if not os_path_exists(self.log_file):\n self._write('', 'w')","def __init__(self, log_path, on=True):\n self.log_path = log_path\n self.on = on\n\n if self.on:\n while os.path.isfile(self.log_path):\n self.log_path += '+'","def init_logger():\n logpath = Path(f\"logs/{time.strftime('%Y.%m.%d %H:%M')}.txt\")\n logpath.parent.mkdir(exist_ok=True)\n logging.basicConfig(filename=logpath, level=logging.DEBUG)","def init_logs_directory(self):\n \n return self.join_and_init_path(self.get_data_general_directory, PATH_FOR_LOGS)","def init_log_file(self):\r\n try:\r\n os.makedirs(config[\"server_log_path\"])\r\n except OSError:\r\n if not os.path.isdir(config[\"server_log_path\"]):\r\n raise\r\n server_log_file = logging.FileHandler(\r\n config[\"server_log_path\"] + 'server_log_' + time.strftime('%Y-%m-%d_%H.%M.%S') + '.txt')\r\n server_log_file.setLevel(logging.DEBUG)\r\n server_log_file.setFormatter(file_formatter)\r\n server_log.addHandler(server_log_file)","def setPath(logPath):\n GlobalLogger.logger.close()\n GlobalLogger.logger = FileLogger(logPath)","def __init_log_folder():\n try:\n os.makedirs(Logger.__log_dir)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise e","def __init__(self):\r\n self.file_object = './ExecutionLogs/PredictFromModel.log'\r\n\r\n \"\"\" Initialize logger class for log writing \"\"\"\r\n self.log_writer = logger.logger(self.file_object)","def logs_directory(self):","def init_log_files(self): \n \n dir_path = self.init_logs_directory()\n log_files = self.join_path(dir_path, PATH_FOR_LOG_FILES)\n \n return log_files","def Init(log_file_path):\n global _LOG_FILE\n _LOG_FILE = log_file_path\n print \"Using log file: %s\" % _LOG_FILE","def new_custom_log_dir(self) -> str:","def get_log_path():\n return LOG_PATH","def initialize_logger():\n if not os.path.exists(LOGGING_DIRECTORY):\n os.makedirs(LOGGING_DIRECTORY)\n os.chmod(LOGGING_DIRECTORY, 0o777)","def __init__(self, path, cwd, aux=[]):\n logging.Logger.__init__(self, \"\") # run parent __init__ class\\\n self.setLevel(logging.DEBUG)\n self.path = os.path.dirname(path)\n self.file_name = path.split('/')[-1]\n self.cwd = cwd\n\n log_name = self.file_name.split('.')[0]\n self.log_file = \"%s.log\" % log_name\n self._set_console_handler()\n self._set_file_handler()\n\n if aux:\n self.aux = aux\n self._set_aux_file_handler()\n\n self.print_basic_info()","def logpath(self):\n return self.outpath","def __init__(self, logging_level, root_dir, logs_path):\n logging.basicConfig(level=logging_level,\n filename=os.path.join(root_dir, logs_path))","def log_path(self):\n return os.path.join(self._sandbox, 'log')","def initialize():\n if not os.path.isfile(WORK_LOG_FILENAME):\n with open(WORK_LOG_FILENAME, 'a', newline='') as work_log:\n work_log_writer = csv.DictWriter(work_log, fieldnames=FIELDNAMES)\n work_log_writer.writeheader()","def _default_log_dir():\n config_dir = os.path.abspath(os.path.dirname(self.config_filepath))\n log_dir = os.path.join(config_dir, \"logs\")\n if not os.path.isdir(log_dir):\n os.mkdir(log_dir)\n return log_dir","def init_logs():\n\n #Ensure that the directories are made\n make_dirs()\n\n #Create FileHandler logging handler, set it's log level, configure the log storage format,\n # and add the formatter to the root logger\n fh = logging.FileHandler(log_file)\n fh.setLevel(logging.INFO)\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n fh.setFormatter(formatter)\n logging.root.addHandler(fh)\n logging.root.setLevel(logging.INFO)\n\n #Report it to the world!\n logging.info(\"Saving logs to \" + log_file)","def setupLogging(self):\n\t\ttry:\n\t\t\tself.logger = logging.getLogger(__name__)\n\t\t\thandler = RotatingFileHandler(self.logFile, maxBytes=500000, backupCount=5)\n\t\t\tformat = \"%(asctime)s %(levelname)-8s %(message)s\"\n\t\t\thandler.setFormatter(logging.Formatter(format))\n\t\t\thandler.setLevel(logging.INFO)\n\t\t\tself.logger.addHandler(handler)\n\t\t\tself.logger.setLevel(logging.INFO)\n\t\texcept Exception as err:\n\t\t\terrorStr = 'Error initializing log file, ',err\n\t\t\tprint(errorStr)\n\t\t\texit(1)","def _logFile_default(self):\n print \"choosing default log file\"\n return os.path.join(self.rpiADCLogFolder,time.strftime(\"rpiADC-%Y-%m-%d.csv\", self.currentLocalTime))","def initialize_logger(self, exp_dir):\n env = EnvSing.get_instance()\n # configure logger\n self.log_file = exp_dir + \"/pruner.log\"\n\n if not env.exists(self.log_file):\n env.dump(\"\", self.log_file)\n self.fd = env.open_file(self.log_file, flags=\"w\")\n self._log(\"Initialized Pruner Logger\")","def _initialize_log_file(config):\n for settings in config[\"handlers\"].values():\n if _is_file_handler(settings):\n log_path = Path(settings[\"filename\"])\n log_path.parent.mkdir(parents=True, exist_ok=True)\n log_path.touch(exist_ok=True)","def log_path(self):\n return LOGS_RESOURCES_PATH / (self.daemon_id + '.log')","def __init__(self, file_path, print_too=True, override=False):\n self.file_path = file_path\n self.print_too = print_too\n if override:\n if os.path.exists(file_path):\n print('Overriding - deleting previous log...')\n os.remove(file_path)\n os.makedirs(os.path.dirname(file_path), exist_ok=True)","def log_file_path(self, log_file_path):\n self._log_file_path = log_file_path\n return self","def init_log():\n os.system('rm -rf /target/testdriver.log || true')\n os.system('touch /target/testdriver.log')\n os.system(f\"chown {uid_gid_output} /target/testdriver.log\")\n os.system('chmod 664 /target/testdriver.log')","def init_log_file(folder_path, suffix=None, log_level=logging.INFO):\n\n timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n log_format = '[%(levelname)s]: %(asctime)s %(message)s'\n\n if suffix is not None:\n file_name = timestamp + '_' + suffix\n else:\n file_name = timestamp\n\n file_path = os.path.join(folder_path, file_name)\n logging.basicConfig(filename=file_path, level=log_level, format=log_format)\n logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))\n\n return file_path","def getLogPath():\n pwd = os.path.dirname(os.path.abspath(__file__))\n log_file = os.path.join(pwd, 'log.txt')\n\n return log_file","def init(ctx, project_root, mkdir, level, formatter, log_path):\n conf_content = get_tpl('logme', level=level, formatter=formatter, filename=log_path)\n\n config = get_config(conf_content)\n\n abs_path = Path(project_root).resolve()\n conf_location = abs_path.joinpath('logme.ini')\n\n if not abs_path.exists():\n if not mkdir:\n raise NotADirectoryError(f\"{abs_path.parent.resolve() / project_root} does not exist. If you'd \"\n f\"like to make the directory, please use '-mk' flag.\")\n else:\n abs_path.mkdir(parents=True, exist_ok=True)\n\n with conf_location.open('w') as conf:\n config.write(conf)","def _setup_file_logger(self):\n if self._file_log_handler is not None:\n raise RuntimeError(\"{}: File logger already exists\".format(self))\n\n # Note that in unit test driver's runpath might not be set\n if self.cfg.file_logger and self.runpath is not None:\n formatter = logging.Formatter(\n \"%(asctime)s %(levelname)s %(message)s\"\n )\n self._file_log_handler = logging.FileHandler(\n os.path.join(self.runpath, self.cfg.file_logger)\n )\n self._file_log_handler.setFormatter(formatter)\n self.logger.addHandler(self._file_log_handler)\n self.logger.propagate = False # No console logs","def initialize_logging(self):\n logging_config_path = self.pyleus_config.get('logging_config_path')\n if logging_config_path:\n logging.config.fileConfig(logging_config_path)\n elif os.path.isfile(DEFAULT_LOGGING_CONFIG_PATH):\n logging.config.fileConfig(DEFAULT_LOGGING_CONFIG_PATH)","def __init__(self,\n logdir,\n mode='a',\n delete=False,\n clearmem=True):\n Logger.__init__(self)\n self.logdir = logdir\n self.mode = mode\n self.delete = delete\n self.clearmem = clearmem\n if not os.path.exists(self.logdir):\n os.mkdir(self.logdir)","def open_log(fn):\n\n global log_file\n if fn is not None:\n d = os.path.dirname(fn)\n if d != \"\":\n makedirs(d)\n log_file = open(fn, \"a+\")","def get_log_file_path(self):\n dir_path = self._get_log_file_dir()\n self._check_make_dirs(dir_path)\n return join(dir_path, self.LOG_FILE_NAME)","def setup_logdir(self, default_logdir: Union[str, Path]) -> Path:\n self._default_logdir = Path(default_logdir)\n\n if self._create_logdir:\n self.logdir_path.mkdir(parents=True, exist_ok=True)\n\n if not self.logdir_path.is_dir():\n raise ValueError(f\"logdir '{self.logdir_path}' must be a directory.\")\n\n return self.logdir_path","def init (path = None, filename = None, create = True, path_add = None) :\n if path_add is None:\n path_add=[]\n if path is None :\n path = sys.hal_log_values [\"__log_path\"]\n \n if path == \"###\" :\n if sys.platform.startswith(\"win\") :\n path = \"d:\\\\temp\" if os.path.exists (\"d:\\\\temp\") else \"c:\\\\temp\"\n path = os.path.join (path, \"log_pyquickhelper\")\n else :\n path = \"/tmp\"\n path = os.path.join (path, \"log_pyquickhelper\")\n \n if len (path_add) > 0 : \n if not isinstance (path_add, list) : path_add = [ path_add ]\n temp = []\n for p in path_add :\n spl = os.path.splitext (p)\n temp.append (spl [0])\n path = os.path.join (path, *temp)\n \n if filename is None :\n filename = sys.hal_log_values [\"__log_file_name\"]\n \n if (sys.hal_log_values [\"__log_path\"] != path or sys.hal_log_values [\"__log_file_name\"] != filename) \\\n and sys.hal_log_values [\"__log_file\"] != None :\n sys.hal_log_values [\"__log_file\"].close ()\n sys.hal_log_values [\"__log_file\"] = None\n sys.hal_log_values [\"__log_path\"] = path\n sys.hal_log_values [\"__log_file_name\"] = filename\n \n if create :\n if not os.path.exists (sys.hal_log_values [\"__log_path\"]) :\n os.makedirs (sys.hal_log_values [\"__log_path\"])\n else :\n if not os.path.exists (sys.hal_log_values [\"__log_path\"]) :\n raise PQHException (\"unable to find path \" + sys.hal_log_values [\"__log_path\"])","def init_logging(filepath=os.path.dirname(os.path.abspath(__file__))):\r\n logfile = filepath\r\n logfile += '\\\\Logs\\\\'\r\n if not os.path.isdir(logfile):\r\n os.makedirs(logfile)\r\n logfile += datetime.now().strftime('%m-%d-%Y') + '_File_Moving.log'\r\n with open(logfile, 'w'):\r\n pass\r\n logging.basicConfig(filename=logfile, level=logging.DEBUG,\r\n format='%(levelname)s: -- %(asctime)s -- %(message)s',\r\n datefmt='%m/%d/%Y %H:%M:%S %p')","def get_log_file(self):\n self.log_file = os.path.join(\n self.directory,\n \"ts\",\n self.ts.reaction_label,\n \"conformers\",\n \"{}_{}_{}.log\".format(self.ts.reaction_label, self.ts.direction, self.ts.index))\n return self.log_file","def set_log_file(filename):\n pass","def __init__ (self,\n path_to_json = None):\n self.default_path = path_to_json\n self.__setup_logging(default_path = self.default_path)","def _init_logger(self):\n # Create log directory, if it doesn't already exist.\n self._create_directory(directory=self._log_directory)\n log_filename = \"{0}/{1}.log\".format(self._log_directory, self._program)\n\n # Add the date to the log file names.\n logging.basicConfig(\n filename=log_filename,\n filemode='w',\n level=logging.DEBUG,\n format='%(asctime)s|%(name)s|%(levelname)-5s| %(message)s',\n datefmt='%Y-%m-%d %I:%M:%S %p')\n\n # define a Handler which writes LOG messages or higher to the sys.stderr\n console = logging.StreamHandler()\n #\n # Note: Anything above the logging level is displayed to stdout.\n #\n # Level Numeric value\n # CRITICAL\t50\n # ERROR \t40\n # WARNING\t30\n # LOG 25 (our log level)\n # INFO\t 20\n # DEBUG \t10\n # NOTSET\t0\n #\n # Add a logging level to always display to stderr.\n logging.addLevelName(self._LOG_LEVEL, self._LOG_NAME)\n if self._debug:\n console.setLevel(logging.DEBUG)\n else:\n console.setLevel(self._LOG_LEVEL)\n # Set a format which is simpler for console use.\n formatter = logging.Formatter('%(name)s|%(levelname)-5s| %(message)s')\n console.setFormatter(formatter)\n # Add the handler to the root logger.\n logging.getLogger('').addHandler(console)\n self._logger = logging.getLogger()","def _output_log_path(name):\n output = Path(\"../Raw Data/\").joinpath(str(date.today()))\n output.mkdir(parents=True, exist_ok=True)\n return output.joinpath(\"000_logging.hdf5\")","def logdir(self) -> Path:\n assert (\n self._logdir\n ), \"Log provider has not been tied to a SummaryWriter yet\"\n return self._logdir","def _setup_dir(self):\n if not os.path.exists(self._save_dir):\n logger.info(\"save_dir {} does not exist, \"\n \"creating it\".format(self._save_dir))\n os.makedirs(self._save_dir)\n\n # Log the run parameters.\n logger.info(\"Writing logs to {}\".format(self._log_dir))\n\n if not os.path.exists(self._log_dir):\n logger.info(\"log path {} does not exist, \"\n \"creating it\".format(self._log_dir))\n os.makedirs(self._log_dir)","def __init__(self, log_path):\n # create a map for storing LogImg objects\n self.log_img_map = OrderedDict()\n\n # set the path to the log directory\n self.log_path = log_path\n\n # check if log directory already exists or create it\n if not os.path.exists(self.log_path):\n os.makedirs(self.log_path)\n\n # set current training step\n self.train_step = 0","def _set_output_file(self):\n dictConfig(self.DEFAULT_LOGGING)","def init() -> None:\n log_format = logging.Formatter(\"%(levelname)s || %(name)s || %(asctime)s || %(message)s\")\n\n log_file = Path(\"logs\", \"rl_snake.log\")\n log_file.parent.mkdir(exist_ok=True)\n\n file_handler = handlers.RotatingFileHandler(\n log_file,\n maxBytes=3000000,\n backupCount=5\n )\n file_handler.setFormatter(log_format)\n\n root_logger = logging.getLogger()\n root_logger.addHandler(file_handler)\n root_logger.setLevel(logging.DEBUG if constants.Misc.debug else logging.INFO)\n\n root_logger.info(\"Root logger initilised\")","def set_log_dir(self, model_path=None):\n # Set date and epoch counter as if starting a new model\n self.epoch = 0\n now = datetime.datetime.now()\n\n # If we have a model path with date and epochs use them\n if model_path:\n # Continue from we left of. Get epoch and date from the file name\n # A sample model path might look like:\n # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5\n regex = r\".*/\\w+(\\d{4})(\\d{2})(\\d{2})T(\\d{2})(\\d{2})/mask\\_rcnn\\_\\w+(\\d{4})\\.h5\"\n m = re.match(regex, model_path)\n if m:\n now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\n int(m.group(4)), int(m.group(5)))\n self.epoch = int(m.group(6)) + 1\n\n # Directory for training logs\n self.log_dir = os.path.join(self.model_dir, \"{}{:%Y%m%dT%H%M}\".format(\n self.config.NAME.lower(), now))\n\n # Path to save after each epoch. Include placeholders that get filled by Keras.\n self.checkpoint_path = os.path.join(self.log_dir, \"yolo_V3_{}_*epoch*.ckpt\".format(\n self.config.NAME.lower()))\n self.checkpoint_path = self.checkpoint_path.replace(\n \"*epoch*\", \"{epoch:04d}\")","def set_log_dir(dir):\r\n LogOptions._LOG_DIR = dir","def setUp(self):\n self.path = tempfile.mkdtemp()\n self.log = log.Log(self.path)","def set_log_dir(self, model_path=None):\n # Set date and epoch counter as if starting a new model\n self.epoch = 0\n now = datetime.datetime.now()\n\n # If we have a model path with date and epochs use them\n if model_path:\n # Continue from we left of. Get epoch and date from the file name\n # A sample model path might look like:\n\n regex = r\".*[/\\\\][\\w-]+(\\d{4})(\\d{2})(\\d{2})T(\\d{2})(\\d{2})[/\\\\]OOD\\_[\\w-]+(\\d{4})\\.h5\"\n m = re.match(regex, model_path)\n if m:\n now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\n int(m.group(4)), int(m.group(5)))\n # Epoch number in file is 1-based, and in Keras code it's 0-based.\n # So, adjust for that then increment by one to start from the next epoch\n self.epoch = int(m.group(6)) - 1 + 1\n\n # Directory for training logs\n self.log_dir = os.path.join(self.model_dir, f\"{self.config.NAME.lower()}{now:%Y%m%dT%H%M}\")\n\n # Path to save after each epoch. Include placeholders that get filled by Keras.\n self.checkpoint_path = os.path.join(self.log_dir, f\"OOD_{self.config.NAME.lower()}_*epoch*.h5\")\n self.checkpoint_path = self.checkpoint_path.replace(\"*epoch*\", \"{epoch:04d}\")","def test_path(self):\n self.assertEqual(\n self.log.current_log_path,\n f'{self.path}/.{datetime.now(ET).date().isoformat()}.log'\n )","def __init__(self, abs_path_logfile):\n\n self.logger = logging.getLogger()\n self.logger.setLevel(logging.DEBUG)\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n handler = logging.handlers.TimedRotatingFileHandler(abs_path_logfile, when='D', interval=1)\n handler.setLevel(logging.DEBUG)\n handler.setFormatter(formatter)\n self.logger.addHandler(handler)","def writeLog(self, log_path):\r\n f = open(log_path, 'w')\r\n f.write(str(self))\r\n f.close()","def log_dir():\r\n if LogOptions._LOG_DIR is None:\r\n LogOptions._LOG_DIR = app.get_options().twitter_common_log_log_dir\r\n return LogOptions._LOG_DIR","def set_log_dir(self, model_path=None):\n # Set date and epoch counter as if starting a new model\n self.epoch = 0\n now = datetime.datetime.now()\n\n # If we have a model path with date and epochs use them\n if model_path:\n # Continue from we left of. Get epoch and date from the file name\n # A sample model path might look like:\n # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5\n regex = r\".*/\\w+(\\d{4})(\\d{2})(\\d{2})T(\\d{2})(\\d{2})/FCN\\_DenseNet\\_\\w+(\\d{4})\\.h5\"\n m = re.match(regex, model_path)\n if m:\n now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\n int(m.group(4)), int(m.group(5)))\n self.epoch = int(m.group(6)) + 1\n\n # Directory for training logs\n self.log_dir = os.path.join(self.model_dir, \"{}{:%Y%m%dT%H%M}\".format(\n self.config.NAME.lower(), now))\n\n # Path to save after each epoch. Include placeholders that get filled by Keras.\n self.checkpoint_path = os.path.join(self.log_dir, \"FCN_DenseNet_{}_*epoch*.h5\".format(\n self.config.NAME.lower()))\n self.checkpoint_path = self.checkpoint_path.replace(\n \"*epoch*\", \"{epoch:04d}\")","def _begin_logging(self):\n logconf.set_up_root_logger(self.opts.logfile)","def get_log_path():\n forch_log_dir = os.getenv('FORCH_LOG_DIR')\n if not forch_log_dir:\n return None\n return os.path.join(forch_log_dir, 'forch.log')","def __init__(self, level, general_log_path, outputs_folder):\n self.log_level = level\n\n # self.general_log_file = general_log_path.open('w')\n self.general_log_file = GCOpen(general_log_path, 'w')\n self.general_log_file.open()\n\n self.file_outputs_dir = outputs_folder / 'output_files'\n # self.file_outputs_dir.mkdir(exist_ok=True)\n\n exp_name = str(outputs_folder).split('/')[-1]\n\n self.summary_writer = SummaryWriter(log_dir=str(TEMP_FOLDER),\n filename_suffix='.' + exp_name)\n tf_filename = find_tf_event(exp_name)\n self.sw_local_path = Path(TEMP_FOLDER) / tf_filename\n self.sw_gc_path = outputs_folder / tf_filename\n\n self.log(\"Starting new experiment at \" +\n datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n self.log(\"User: \" + getpass.getuser())\n self.log(\"Host: \" + socket.gethostname())\n\n Logger.unique_logger = self","def setLogFile(filename):\n\tglobal logfile\n\tlogfile = filename","def initLogging ( logFile ):\n logging.basicConfig(\n filename=logFile,\n level=logging.INFO,\n format='%(asctime)s %(levelname)-8s %(message)s',\n filemode='w'\n )","def setup_log_dir():\n log_dir = get_log_dir()\n if log_dir.endswith('latest'):\n shutil.rmtree(log_dir, ignore_errors=True)\n mkdirs(log_dir)\n return log_dir","def __init__(self,\r\n default_path = None,\r\n default_level = None,\r\n logging_dir = None,\r\n log_file = None,\r\n log_file_dir = None,\r\n log_conf_full = None\r\n ):\r\n self.logger_is_set = False\r\n\r\n '''\r\n Get ready to setup everything.\r\n TO DO: read from structure is badly needed. \r\n '''\r\n self.default_path = default_path\r\n self.default_level = default_level\r\n self.logging_dir = logging_dir\r\n self.log_file = log_file\r\n self.log_file_dir = log_file_dir\r\n self.log_conf_full = log_conf_full\r\n\r\n\r\n\r\n self.setup_logging(self.default_path,\r\n self.default_level,\r\n self.logging_dir,\r\n self.log_file,\r\n self.log_file_dir,\r\n self.log_conf_full\r\n )","def _logger(self):\r\n\r\n # Create filename for log\r\n filenameF = self._vna.getDateFormatted() + \".txt\"\r\n filenameF = \"Logs/\" + filenameF \r\n f = open(filenameF, \"a+\") # Log saved in directory named logs located in same directory as this file\r\n \r\n # if self._vna.isTwoComponents():\r\n # for i in range(len(self._voltages)):\r\n # f.write('%s\\t\\t\\t' % self._voltages[i][0])\r\n # else:\r\n for i in range(len(self._voltages)):\r\n f.write('%s\\t\\t' % self._voltages[i][0])\r\n f.write('\\n')\r\n\r\n # if self._vna.isTwoComponents():\r\n # for i in range(len(self._voltages[0])):\r\n # line = \"\"\r\n # for j in range(len(self._voltages)):\r\n # line = line + str(self._frequency[j][i]) + '\\t' + str(self._intensity[j][2*i]) + \\\r\n # str(self._intensity[j][2*i + 1]) + '\\t'\r\n # f.write(line)\r\n # f.write('\\n')\r\n # else: \r\n for i in range(len(self._voltages[0])):\r\n line = \"\"\r\n for j in range(len(self._voltages)):\r\n line = line + str(self._frequency[j][i]) + '\\t' + str(self._intensity[j][i]) + '\\t' \r\n f.write(line)\r\n f.write('\\n')","def __init__(self, file_path: str = \"..\\\\Resource\\\\log.txt\", name: str = 'TangoServerLogger'):\r\n self.logger = logging.getLogger(name)\r\n self.logger.setLevel(logging.DEBUG)\r\n\r\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\r\n\r\n logfile = logging.FileHandler(file_path)\r\n logfile.setLevel(logging.DEBUG)\r\n logfile.setFormatter(formatter)\r\n\r\n self.logger.addHandler(logfile)","def initialize_file_handler(\n dir_path=os.path.dirname(os.path.realpath(__file__)),\n date_time=datetime.datetime.now(),\n):\n file_name = \"{}.log\".format(\n \"-\".join([str(date_time.year), str(date_time.month), str(date_time.day)])\n )\n file_handler_path = \"{}/logFiles/{}\".format(dir_path, file_name)\n hdlr = logging.FileHandler(file_handler_path)\n formatter = logging.Formatter(\n \"%(levelname)s:%(asctime)s:%(name)s:%(message)s\"\n )\n hdlr.setFormatter(formatter)\n hdlr.setLevel(logging.ERROR)\n logging.getLogger(\"\").addHandler(hdlr)","def __init__(self, api_path=None, log_path=None, log_level=\"DEBUG\"):\n\n # Construct the log path. \n if log_path:\n self.log_path = log_path\n else:\n defaultlog_path = \"~/Spirent/CTA/Logs/\"\n\n now = datetime.datetime.now()\n defaultlog_path += now.strftime(\"%Y-%m-%d-%H-%M-%S\")\n defaultlog_path += \"_PID\"\n defaultlog_path += str(os.getpid())\n defaultlog_path = os.path.expanduser(defaultlog_path)\n \n # The environment variable overwrites the default path. \n self.log_path = os.getenv(\"CTA_LOG_OUTPUT_DIRECTORY\", defaultlog_path) \n\n self.log_path = os.path.abspath(self.log_path)\n self.logfile = os.path.join(self.log_path, \"cta_python.log\") \n\n if not os.path.exists(self.log_path):\n os.makedirs(self.log_path)\n\n # NOTE: Consider limiting the number of log directories that are created.\n # It would mean deleting older directories.\n\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - stc::get automationoptions -suppressTclErrors\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - return false\n #2016-05-19 14:05:56,382 UserID =mjefferson\n #2016-05-19 14:05:56,382 Log Level=INFO\n\n if log_level == \"CRITICAL\":\n log_level = logging.CRITICAL\n elif log_level == \"ERROR\":\n log_level = logging.ERROR\n elif log_level == \"WARNING\":\n log_level = logging.WARNING\n elif log_level == \"INFO\": \n log_level = logging.INFO\n else:\n # DEBUG is the default log level.\n log_level = logging.DEBUG \n \n logging.basicConfig(filename=self.logfile, filemode=\"w\", level=log_level, format=\"%(asctime)s %(levelname)s %(message)s\")\n #logging.Formatter(fmt='%(asctime)s.%(msecs)03d',datefmt='%Y/%m/%d %H:%M:%S')\n # Add timestamps to each log message.\n #logging.basicConfig()\n # The logger is now ready. \n\n logging.info(\"Spirent TestCenter Conformance Application Python API is starting up...\")\n logging.info(\"OS Type = \" + os.name)\n logging.info(\"API Path = \" + api_path)\n logging.info(\"UserID = \" + getpass.getuser())\n logging.info(\"Log Level = \" + logging.getLevelName(log_level)) \n logging.info(\"Current Path = \" + os.path.abspath(os.getcwd())) \n logging.info(\"Log Path = \" + self.log_path)\n\n # Instantiate the Tcl interpreter.\n self.tcl = Tcl()\n\n self.tcl.eval(\"lappend ::auto_path {\" + api_path + \"}\")\n\n logging.info(\"Tcl Version = \" + self.tcl.eval(\"info patchlevel\"))\n logging.info(\"Tcl ::auto_path = \" + self.tcl.eval('set ::auto_path'))\n logging.info(\"Loading the Spirent TestCenter Conformance Application in the Tcl interpreter...\")\n self.Exec(\"package require SpirentTestCenterConformance\")\n\n return","def init_logging(input_file_parameters, dir_stacks):\r\n fl_name = '{0}_log_{1}_{2}.txt'.format(NAME,\r\n START_TIME,\r\n input_file_parameters.job_name)\r\n #NOTICE! Current_log_path.path is changed here!\r\n CurrentLogPath.path = os.path.join(input_file_parameters.output_dir,\r\n fl_name)\r\n logging.basicConfig(filename=CurrentLogPath.path, filemode='w',\r\n format='%(asctime)s %(levelname)s:%(message)s',\r\n level=logging.INFO)\r\n logging.info('{0} v. {1} started'.format(NAME, VERSION))\r\n logging.info('Job name: {0}'.format(input_file_parameters.job_name))\r\n logging.info('Starting point directory:\\n{0}'.format(dir_stacks[0]\r\n .path))\r\n logging.info('Output directory:\\n{0}'.format(input_file_parameters.output_dir))\r\n logging.info('-'*80)\r\n logging.info('staplefile contents:\\n{0}'.format('\\n'.join(input_file_parameters.staplefile)))\r\n logging.info('-'*80)\r\n logging.info('config.txt contents:\\n{0}'\r\n .format(utils.get_config_file()))\r\n logging.info('-'*80)","def init(directory,name):\n \n import os,time\n fname=os.path.join(directory,'Log_'+name+'_'+time.strftime('%Y-%b-%d')+'.txt')\n if not os.path.isfile(os.path.join(directory,fname)):\n try:\n fhandle = open(fname, 'a')\n fhandle.write('-----------------------------------------------------\\n')\n fhandle.write('Program initialized! on '+time.strftime('%Y-%b-%d')+'\\n')\n fhandle.write('-----------------------------------------------------\\n')\n print('======begin log!======')\n return(fhandle)\n except:\n print('Could not create Log file with name : '+\n ' Log_'+name+'_'+date.today().isoformat()+'.txt')\n else:\n fhandle=open(fname,'a')\n fhandle.write('-----------------------------------------------------\\n')\n fhandle.write('Program initialized! on '+time.strftime('%Y-%b-%d')+'\\n')\n fhandle.write('-----------------------------------------------------\\n')\n print('======begin log!======')\n return(fhandle)","def __init__(self, log_dir, prefix, when_interval=None):\n\n log_dir = os.path.abspath(log_dir)\n\n if not os.path.exists(log_dir):\n os.mkdir(log_dir)\n\n if not isinstance(prefix, str):\n raise ValueError(\"Prefix string required for output files\")\n\n if when_interval is None:\n when, interval = 'H', 1\n else:\n when, interval = when_interval\n if not (isinstance(when, str) and isinstance(interval, int)):\n raise ValueError(\"Rollover interval should be like ('h', 1)\")\n\n log_path = os.path.join(log_dir, prefix)\n\n _LOGGER.info(\"Saving data to path {}\".format(log_path))\n _LOGGER.info(\"Rollover interval {}\"\n .format(\"{}{}\".format(when, interval)))\n\n # Create the logger.\n self._logger = TimedRotatingFileHandler(filename=log_path,\n encoding='utf-8',\n when=when,\n interval=interval)","def log_data(self):\n\n self.check_dir()\n with open(self.log_file, \"a\") as logger_file:\n logger_file.write(\"{}, {}\\n\".format(self.time, self.msg))","def logdir(self) -> str:\n return self._logdir","def logsave(self):\n log_file = open(self.conf[\"output_prefix\"] + \"_log.txt\", \"w\")\n try:\n log_file.write(self.log)\n finally:\n log_file.close()","def setLog(path):\n logger = logging.getLogger('current function')\n FORMAT = '%(asctime)s - %(levelname)s - %(message)s'\n logging.basicConfig( level = logging.DEBUG,\n format = FORMAT,\n filename = path,\n filemode = 'wa' )\n console = logging.StreamHandler()\n console.setLevel(logging.INFO)\n formatter = logging.Formatter(FORMAT)\n console.setFormatter(formatter)\n logger.addHandler(console)\n return logger","def pytest_logger_logsdir(self, config):","def GetLogFilePath():\n global _LOG_FILE\n return _LOG_FILE","def set_log_dir(self, model_path=None):\n # Set date and epoch counter as if starting a new model\n self.epoch = 0\n# now = datetime.datetime.now()\n# \n# # If we have a model path with date and epochs use them\n# if model_path:\n# # Continue from we left of. Get epoch and date from the file name\n# # A sample model path might look like:\n# # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5\n# regex = r\".*/[\\w-]+(\\d{4})(\\d{2})(\\d{2})T(\\d{2})(\\d{2})/mask\\_rcnn\\_[\\w-]+(\\d{4})\\.h5\"\n# m = re.match(regex, model_path)\n# if m:\n# now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\n# int(m.group(4)), int(m.group(5)))\n# # Epoch number in file is 1-based, and in Keras code it's 0-based.\n# # So, adjust for that then increment by one to start from the next epoch\n# self.epoch = int(m.group(6)) - 1 + 1\n# print('Re-starting from epoch %d' % self.epoch)\n\n # Directory for training logs\n self.log_dir = os.path.join(self.model_dir, \n \"siamese_{}_{}_{}\".format(self.config.MODEL.lower(), \n self.config.NAME.lower(), \n self.config.EXPERIMENT.lower()))\n\n # Create log_dir if not exists\n if not os.path.exists(self.log_dir):\n os.makedirs(self.log_dir)\n\n # Path to save after each epoch. Include placeholders that get filled by Keras.\n self.checkpoint_path = os.path.join(self.log_dir, \"siamese_mrcnn_*epoch*.h5\")\n self.checkpoint_path = self.checkpoint_path.replace(\"*epoch*\", \"{epoch:04d}\")","def init(self) -> None:\n logger.debug(f\"[nbtutorial]: Outdir is: {self.outdir}\")","def set_config(self, file_path_name):\n level = logging.DEBUG\n format = '%(asctime)s %(levelname)-8s %(message)s' \n datefmt = '%a, %d %b %Y %H:%M:%S'\n filemode = 'a'\n \n\n logging.basicConfig(level = level,\n format = format,\n datefmt = datefmt,\n filename = file_path_name,\n filemode = filemode)","def instantiate_logs(self):\n\n # Log file\n timestamp = datetime.now().strftime(\"%Y-%m-%dT%H%M%S\")\n self.log_dir = os.path.join(\"experiment_logs\", timestamp)\n\n # Create Log directory if it does not exist\n try:\n os.makedirs(self.log_dir)\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise\n\n self.info_file = os.path.join(self.log_dir, \"run_info.txt\")\n self.log_file = os.path.join(self.log_dir, \"data.csv\")\n\n with open(self.info_file, \"w+\") as f:\n f.write(\"Period = {}\\nMaxVel = {}\".format(self.period, self.max_vel))\n\n self.log_file_desc = open(self.log_file, \"w+\")\n self.log_file_desc.write(\"t, current_vel, current_h_angle, current_v_angle, x, y, z, roll, pitch, yaw\")","def GetPath () :\n return sys.hal_log_values [\"__log_path\"]","def __init__(self, logfile):\n\n self.logfile = logfile\n if self. logfile:\n self.file = open(logfile, \"w\")\n self.starttime = time.time()\n self.file.write(\"%.2f %s Starting log\\n\" % (time.time() - self.starttime, time.asctime()))","def __init__(self, log_dir):\n self.writer = SummaryWriter(log_dir)","def initLogger(self):\n loglevel = self.loglevels[self.loglevel]\n log_format = '%(asctime)s name=%(name)s loglevel=%(levelname)s message=%(message)s'\n logging.basicConfig(format=log_format,\n level=loglevel)\n \tmultiprocessing.log_to_stderr(loglevel)","def _createlog(self):\n\t\tif self.toemail and self.fromemail and self.smtphost:\n\t\t\t# Use the email logger as the first logger, so that when sending the email (in :meth:`EmailLogger.close`) fails, it will still be logged to the log file/stdout/stderr\n\t\t\tself._loggers.append(EmailLogger(self))\n\t\tif self.log2stderr:\n\t\t\tself._loggers.append(StreamLogger(self, sys.stderr, self._formatlogline))\n\t\tif self.log2stdout:\n\t\t\tself._loggers.append(StreamLogger(self, sys.stdout, self._formatlogline))\n\t\tif self.log2file:\n\t\t\t# Create the log file\n\t\t\tlogfilename = ul4c.Template(self.logfilename, \"logfilename\").renders(job=self)\n\t\t\tlogfilename = url.File(logfilename).abs()\n\t\t\tself.logfileurl = str(url.Ssh(misc.sysinfo.user_name, misc.sysinfo.host_fqdn or misc.sysinfo.host_name, logfilename.local()))\n\t\t\tskipurls = [logfilename]\n\t\t\tlogfile = logfilename.open(mode=\"w\", encoding=self.encoding, errors=self.errors)\n\t\t\tif self.loglinkname is not None:\n\t\t\t\t# Create the log link\n\t\t\t\tloglinkname = ul4c.Template(self.loglinkname, \"loglinkname\").renders(job=self)\n\t\t\t\tloglinkname = url.File(loglinkname).abs()\n\t\t\t\tskipurls.append(loglinkname)\n\t\t\t\tlogfilename = logfilename.relative(loglinkname)\n\t\t\t\ttry:\n\t\t\t\t\tlogfilename.symlink(loglinkname)\n\t\t\t\texcept OSError as exc:\n\t\t\t\t\tif exc.errno == errno.EEXIST:\n\t\t\t\t\t\tloglinkname.remove()\n\t\t\t\t\t\tlogfilename.symlink(loglinkname)\n\t\t\t\t\telse:\n\t\t\t\t\t\traise\n\t\t\tself._loggers.append(URLResourceLogger(self, logfile, skipurls, self._formatlogline))","def initLogger(filename, dir_name='data/logs/'):\n \n filename = os.path.join(dir_name, filename)\n \n if not os.path.exists(dir_name):\n os.makedirs(dir_name)\n \n if os.path.exists(filename):\n os.remove(filename)\n \n formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(message)s', \n '%m-%d-%Y %H:%M:%S')\n fh = logging.FileHandler(filename)\n fh.setFormatter(formatter)\n \n logger.addHandler(fh)","def __init__(self):\n\n self.__logger = logging.getLogger()\n\n formatter = logging.Formatter('%(asctime)s :: %(levelname)s :: ' +\n '%(message)s')\n\n file_handler = RotatingFileHandler('.log', 'a', 1000000, 1)\n\n file_handler.setLevel(logging.DEBUG)\n file_handler.setFormatter(formatter)\n self.__logger.addHandler(file_handler)\n\n stream_handler = logging.StreamHandler()\n stream_handler.setLevel(logging.INFO)\n self.__logger.addHandler(stream_handler)","def setup_log(self, log_file):\n directory = os.path.dirname(log_file)\n if directory:\n os.makedirs(directory, exist_ok=True)\n\n logger = logging.getLogger(log_file)\n formatter = logging.Formatter(config.LOG_FORMAT)\n\n file_handler = logging.FileHandler(log_file, mode='a')\n file_handler.setFormatter(formatter)\n\n logger.setLevel(logging.INFO)\n logger.addHandler(file_handler)\n\n return logger","def init_logging(file_name, log_path=None, level=logging.INFO):\n global logger\n\n try:\n if not log_path:\n log_path = '../logs'\n except KeyError as e:\n raise LoggerException(message=str(e))\n\n log_path = os.path.join(log_path, file_name + \"-\" + datetime.utcnow().strftime(\"%Y%m%d-%H%M%S\") + '.log')\n\n logging.basicConfig(\n filename=log_path,\n format='%(asctime)s - %(levelname)s: %(message)s',\n level=level,\n )","def __init_logging(self):\n\n logger = logging.getLogger('__name__')\n if os.path.exists(constants.LOG_FILE):\n logger.setLevel(logging.DEBUG)\n logger_file_handler = logging.FileHandler(constants.LOG_FILE)\n logger_formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')\n logger_file_handler.setFormatter(logger_formatter)\n logger.addHandler(logger_file_handler)\n else:\n logger.disabled = True","def __init__(self):\r\n try:\r\n self.file = open(REPORT_FILE, 'w')\r\n except OSError:\r\n print('Problem opening log file')\r\n exit(1)","def log(length, file):\n\n if user_init.check_pre_init() and user_utility.check_drive_init() == 'True':\n \n data = user_utility.read_log(length, file)\n\n for log in data:\n print(log)\n\n\n else:\n user_utility.print_error(\"Sink folder not Found! Initialise folder first or reset your configuration!\")","def get_logging_dir(self):\n return self.logging_dir","def get_logdir(self):\n return self.event_writer.get_logdir()","def __init__(self):\n #path = \"/\".join( ( os.path.abspath( __file__ ).replace( \"\\\\\", \"/\" ) ).split( \"/\" )[:-1])\n #logging.config.fileConfig( os.path.join ( path, \"..\\\\resources\\\\config-log.ini\" ), disable_existing_loggers=False ) # working in local\n #logging.config.fileConfig ( \"config-log.ini\", disable_existing_loggers=False) # working in local\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(logging.INFO)\n ch = logging.StreamHandler()\n ch.setLevel(logging.INFO)\n # create formatter and add it to the handlers\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n #fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n # add the handlers to the logger\n self.logger.addHandler(ch)\n # TODO decorator it doesnt create file handler which logs even debug messages\n # fh = logging.FileHandler('spam.log')\n # fh.setLevel(logging.DEBUG)\n # create console handler with a higher log level\n # fh.setFormatter(formatter)\n # logger.addHandler(fh)","def init_logging(input_dir, file_name):\n create_dir(input_dir)\n config(file_name, log_level=logging.DEBUG)"],"string":"[\n \"def init_log(path):\\n file = open(path, 'w+')\\n file.close()\",\n \"def _init_log(self):\\n if not os_path_exists(self.log_file):\\n self._write('', 'w')\",\n \"def __init__(self, log_path, on=True):\\n self.log_path = log_path\\n self.on = on\\n\\n if self.on:\\n while os.path.isfile(self.log_path):\\n self.log_path += '+'\",\n \"def init_logger():\\n logpath = Path(f\\\"logs/{time.strftime('%Y.%m.%d %H:%M')}.txt\\\")\\n logpath.parent.mkdir(exist_ok=True)\\n logging.basicConfig(filename=logpath, level=logging.DEBUG)\",\n \"def init_logs_directory(self):\\n \\n return self.join_and_init_path(self.get_data_general_directory, PATH_FOR_LOGS)\",\n \"def init_log_file(self):\\r\\n try:\\r\\n os.makedirs(config[\\\"server_log_path\\\"])\\r\\n except OSError:\\r\\n if not os.path.isdir(config[\\\"server_log_path\\\"]):\\r\\n raise\\r\\n server_log_file = logging.FileHandler(\\r\\n config[\\\"server_log_path\\\"] + 'server_log_' + time.strftime('%Y-%m-%d_%H.%M.%S') + '.txt')\\r\\n server_log_file.setLevel(logging.DEBUG)\\r\\n server_log_file.setFormatter(file_formatter)\\r\\n server_log.addHandler(server_log_file)\",\n \"def setPath(logPath):\\n GlobalLogger.logger.close()\\n GlobalLogger.logger = FileLogger(logPath)\",\n \"def __init_log_folder():\\n try:\\n os.makedirs(Logger.__log_dir)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise e\",\n \"def __init__(self):\\r\\n self.file_object = './ExecutionLogs/PredictFromModel.log'\\r\\n\\r\\n \\\"\\\"\\\" Initialize logger class for log writing \\\"\\\"\\\"\\r\\n self.log_writer = logger.logger(self.file_object)\",\n \"def logs_directory(self):\",\n \"def init_log_files(self): \\n \\n dir_path = self.init_logs_directory()\\n log_files = self.join_path(dir_path, PATH_FOR_LOG_FILES)\\n \\n return log_files\",\n \"def Init(log_file_path):\\n global _LOG_FILE\\n _LOG_FILE = log_file_path\\n print \\\"Using log file: %s\\\" % _LOG_FILE\",\n \"def new_custom_log_dir(self) -> str:\",\n \"def get_log_path():\\n return LOG_PATH\",\n \"def initialize_logger():\\n if not os.path.exists(LOGGING_DIRECTORY):\\n os.makedirs(LOGGING_DIRECTORY)\\n os.chmod(LOGGING_DIRECTORY, 0o777)\",\n \"def __init__(self, path, cwd, aux=[]):\\n logging.Logger.__init__(self, \\\"\\\") # run parent __init__ class\\\\\\n self.setLevel(logging.DEBUG)\\n self.path = os.path.dirname(path)\\n self.file_name = path.split('/')[-1]\\n self.cwd = cwd\\n\\n log_name = self.file_name.split('.')[0]\\n self.log_file = \\\"%s.log\\\" % log_name\\n self._set_console_handler()\\n self._set_file_handler()\\n\\n if aux:\\n self.aux = aux\\n self._set_aux_file_handler()\\n\\n self.print_basic_info()\",\n \"def logpath(self):\\n return self.outpath\",\n \"def __init__(self, logging_level, root_dir, logs_path):\\n logging.basicConfig(level=logging_level,\\n filename=os.path.join(root_dir, logs_path))\",\n \"def log_path(self):\\n return os.path.join(self._sandbox, 'log')\",\n \"def initialize():\\n if not os.path.isfile(WORK_LOG_FILENAME):\\n with open(WORK_LOG_FILENAME, 'a', newline='') as work_log:\\n work_log_writer = csv.DictWriter(work_log, fieldnames=FIELDNAMES)\\n work_log_writer.writeheader()\",\n \"def _default_log_dir():\\n config_dir = os.path.abspath(os.path.dirname(self.config_filepath))\\n log_dir = os.path.join(config_dir, \\\"logs\\\")\\n if not os.path.isdir(log_dir):\\n os.mkdir(log_dir)\\n return log_dir\",\n \"def init_logs():\\n\\n #Ensure that the directories are made\\n make_dirs()\\n\\n #Create FileHandler logging handler, set it's log level, configure the log storage format,\\n # and add the formatter to the root logger\\n fh = logging.FileHandler(log_file)\\n fh.setLevel(logging.INFO)\\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\\n fh.setFormatter(formatter)\\n logging.root.addHandler(fh)\\n logging.root.setLevel(logging.INFO)\\n\\n #Report it to the world!\\n logging.info(\\\"Saving logs to \\\" + log_file)\",\n \"def setupLogging(self):\\n\\t\\ttry:\\n\\t\\t\\tself.logger = logging.getLogger(__name__)\\n\\t\\t\\thandler = RotatingFileHandler(self.logFile, maxBytes=500000, backupCount=5)\\n\\t\\t\\tformat = \\\"%(asctime)s %(levelname)-8s %(message)s\\\"\\n\\t\\t\\thandler.setFormatter(logging.Formatter(format))\\n\\t\\t\\thandler.setLevel(logging.INFO)\\n\\t\\t\\tself.logger.addHandler(handler)\\n\\t\\t\\tself.logger.setLevel(logging.INFO)\\n\\t\\texcept Exception as err:\\n\\t\\t\\terrorStr = 'Error initializing log file, ',err\\n\\t\\t\\tprint(errorStr)\\n\\t\\t\\texit(1)\",\n \"def _logFile_default(self):\\n print \\\"choosing default log file\\\"\\n return os.path.join(self.rpiADCLogFolder,time.strftime(\\\"rpiADC-%Y-%m-%d.csv\\\", self.currentLocalTime))\",\n \"def initialize_logger(self, exp_dir):\\n env = EnvSing.get_instance()\\n # configure logger\\n self.log_file = exp_dir + \\\"/pruner.log\\\"\\n\\n if not env.exists(self.log_file):\\n env.dump(\\\"\\\", self.log_file)\\n self.fd = env.open_file(self.log_file, flags=\\\"w\\\")\\n self._log(\\\"Initialized Pruner Logger\\\")\",\n \"def _initialize_log_file(config):\\n for settings in config[\\\"handlers\\\"].values():\\n if _is_file_handler(settings):\\n log_path = Path(settings[\\\"filename\\\"])\\n log_path.parent.mkdir(parents=True, exist_ok=True)\\n log_path.touch(exist_ok=True)\",\n \"def log_path(self):\\n return LOGS_RESOURCES_PATH / (self.daemon_id + '.log')\",\n \"def __init__(self, file_path, print_too=True, override=False):\\n self.file_path = file_path\\n self.print_too = print_too\\n if override:\\n if os.path.exists(file_path):\\n print('Overriding - deleting previous log...')\\n os.remove(file_path)\\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\",\n \"def log_file_path(self, log_file_path):\\n self._log_file_path = log_file_path\\n return self\",\n \"def init_log():\\n os.system('rm -rf /target/testdriver.log || true')\\n os.system('touch /target/testdriver.log')\\n os.system(f\\\"chown {uid_gid_output} /target/testdriver.log\\\")\\n os.system('chmod 664 /target/testdriver.log')\",\n \"def init_log_file(folder_path, suffix=None, log_level=logging.INFO):\\n\\n timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n log_format = '[%(levelname)s]: %(asctime)s %(message)s'\\n\\n if suffix is not None:\\n file_name = timestamp + '_' + suffix\\n else:\\n file_name = timestamp\\n\\n file_path = os.path.join(folder_path, file_name)\\n logging.basicConfig(filename=file_path, level=log_level, format=log_format)\\n logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))\\n\\n return file_path\",\n \"def getLogPath():\\n pwd = os.path.dirname(os.path.abspath(__file__))\\n log_file = os.path.join(pwd, 'log.txt')\\n\\n return log_file\",\n \"def init(ctx, project_root, mkdir, level, formatter, log_path):\\n conf_content = get_tpl('logme', level=level, formatter=formatter, filename=log_path)\\n\\n config = get_config(conf_content)\\n\\n abs_path = Path(project_root).resolve()\\n conf_location = abs_path.joinpath('logme.ini')\\n\\n if not abs_path.exists():\\n if not mkdir:\\n raise NotADirectoryError(f\\\"{abs_path.parent.resolve() / project_root} does not exist. If you'd \\\"\\n f\\\"like to make the directory, please use '-mk' flag.\\\")\\n else:\\n abs_path.mkdir(parents=True, exist_ok=True)\\n\\n with conf_location.open('w') as conf:\\n config.write(conf)\",\n \"def _setup_file_logger(self):\\n if self._file_log_handler is not None:\\n raise RuntimeError(\\\"{}: File logger already exists\\\".format(self))\\n\\n # Note that in unit test driver's runpath might not be set\\n if self.cfg.file_logger and self.runpath is not None:\\n formatter = logging.Formatter(\\n \\\"%(asctime)s %(levelname)s %(message)s\\\"\\n )\\n self._file_log_handler = logging.FileHandler(\\n os.path.join(self.runpath, self.cfg.file_logger)\\n )\\n self._file_log_handler.setFormatter(formatter)\\n self.logger.addHandler(self._file_log_handler)\\n self.logger.propagate = False # No console logs\",\n \"def initialize_logging(self):\\n logging_config_path = self.pyleus_config.get('logging_config_path')\\n if logging_config_path:\\n logging.config.fileConfig(logging_config_path)\\n elif os.path.isfile(DEFAULT_LOGGING_CONFIG_PATH):\\n logging.config.fileConfig(DEFAULT_LOGGING_CONFIG_PATH)\",\n \"def __init__(self,\\n logdir,\\n mode='a',\\n delete=False,\\n clearmem=True):\\n Logger.__init__(self)\\n self.logdir = logdir\\n self.mode = mode\\n self.delete = delete\\n self.clearmem = clearmem\\n if not os.path.exists(self.logdir):\\n os.mkdir(self.logdir)\",\n \"def open_log(fn):\\n\\n global log_file\\n if fn is not None:\\n d = os.path.dirname(fn)\\n if d != \\\"\\\":\\n makedirs(d)\\n log_file = open(fn, \\\"a+\\\")\",\n \"def get_log_file_path(self):\\n dir_path = self._get_log_file_dir()\\n self._check_make_dirs(dir_path)\\n return join(dir_path, self.LOG_FILE_NAME)\",\n \"def setup_logdir(self, default_logdir: Union[str, Path]) -> Path:\\n self._default_logdir = Path(default_logdir)\\n\\n if self._create_logdir:\\n self.logdir_path.mkdir(parents=True, exist_ok=True)\\n\\n if not self.logdir_path.is_dir():\\n raise ValueError(f\\\"logdir '{self.logdir_path}' must be a directory.\\\")\\n\\n return self.logdir_path\",\n \"def init (path = None, filename = None, create = True, path_add = None) :\\n if path_add is None:\\n path_add=[]\\n if path is None :\\n path = sys.hal_log_values [\\\"__log_path\\\"]\\n \\n if path == \\\"###\\\" :\\n if sys.platform.startswith(\\\"win\\\") :\\n path = \\\"d:\\\\\\\\temp\\\" if os.path.exists (\\\"d:\\\\\\\\temp\\\") else \\\"c:\\\\\\\\temp\\\"\\n path = os.path.join (path, \\\"log_pyquickhelper\\\")\\n else :\\n path = \\\"/tmp\\\"\\n path = os.path.join (path, \\\"log_pyquickhelper\\\")\\n \\n if len (path_add) > 0 : \\n if not isinstance (path_add, list) : path_add = [ path_add ]\\n temp = []\\n for p in path_add :\\n spl = os.path.splitext (p)\\n temp.append (spl [0])\\n path = os.path.join (path, *temp)\\n \\n if filename is None :\\n filename = sys.hal_log_values [\\\"__log_file_name\\\"]\\n \\n if (sys.hal_log_values [\\\"__log_path\\\"] != path or sys.hal_log_values [\\\"__log_file_name\\\"] != filename) \\\\\\n and sys.hal_log_values [\\\"__log_file\\\"] != None :\\n sys.hal_log_values [\\\"__log_file\\\"].close ()\\n sys.hal_log_values [\\\"__log_file\\\"] = None\\n sys.hal_log_values [\\\"__log_path\\\"] = path\\n sys.hal_log_values [\\\"__log_file_name\\\"] = filename\\n \\n if create :\\n if not os.path.exists (sys.hal_log_values [\\\"__log_path\\\"]) :\\n os.makedirs (sys.hal_log_values [\\\"__log_path\\\"])\\n else :\\n if not os.path.exists (sys.hal_log_values [\\\"__log_path\\\"]) :\\n raise PQHException (\\\"unable to find path \\\" + sys.hal_log_values [\\\"__log_path\\\"])\",\n \"def init_logging(filepath=os.path.dirname(os.path.abspath(__file__))):\\r\\n logfile = filepath\\r\\n logfile += '\\\\\\\\Logs\\\\\\\\'\\r\\n if not os.path.isdir(logfile):\\r\\n os.makedirs(logfile)\\r\\n logfile += datetime.now().strftime('%m-%d-%Y') + '_File_Moving.log'\\r\\n with open(logfile, 'w'):\\r\\n pass\\r\\n logging.basicConfig(filename=logfile, level=logging.DEBUG,\\r\\n format='%(levelname)s: -- %(asctime)s -- %(message)s',\\r\\n datefmt='%m/%d/%Y %H:%M:%S %p')\",\n \"def get_log_file(self):\\n self.log_file = os.path.join(\\n self.directory,\\n \\\"ts\\\",\\n self.ts.reaction_label,\\n \\\"conformers\\\",\\n \\\"{}_{}_{}.log\\\".format(self.ts.reaction_label, self.ts.direction, self.ts.index))\\n return self.log_file\",\n \"def set_log_file(filename):\\n pass\",\n \"def __init__ (self,\\n path_to_json = None):\\n self.default_path = path_to_json\\n self.__setup_logging(default_path = self.default_path)\",\n \"def _init_logger(self):\\n # Create log directory, if it doesn't already exist.\\n self._create_directory(directory=self._log_directory)\\n log_filename = \\\"{0}/{1}.log\\\".format(self._log_directory, self._program)\\n\\n # Add the date to the log file names.\\n logging.basicConfig(\\n filename=log_filename,\\n filemode='w',\\n level=logging.DEBUG,\\n format='%(asctime)s|%(name)s|%(levelname)-5s| %(message)s',\\n datefmt='%Y-%m-%d %I:%M:%S %p')\\n\\n # define a Handler which writes LOG messages or higher to the sys.stderr\\n console = logging.StreamHandler()\\n #\\n # Note: Anything above the logging level is displayed to stdout.\\n #\\n # Level Numeric value\\n # CRITICAL\\t50\\n # ERROR \\t40\\n # WARNING\\t30\\n # LOG 25 (our log level)\\n # INFO\\t 20\\n # DEBUG \\t10\\n # NOTSET\\t0\\n #\\n # Add a logging level to always display to stderr.\\n logging.addLevelName(self._LOG_LEVEL, self._LOG_NAME)\\n if self._debug:\\n console.setLevel(logging.DEBUG)\\n else:\\n console.setLevel(self._LOG_LEVEL)\\n # Set a format which is simpler for console use.\\n formatter = logging.Formatter('%(name)s|%(levelname)-5s| %(message)s')\\n console.setFormatter(formatter)\\n # Add the handler to the root logger.\\n logging.getLogger('').addHandler(console)\\n self._logger = logging.getLogger()\",\n \"def _output_log_path(name):\\n output = Path(\\\"../Raw Data/\\\").joinpath(str(date.today()))\\n output.mkdir(parents=True, exist_ok=True)\\n return output.joinpath(\\\"000_logging.hdf5\\\")\",\n \"def logdir(self) -> Path:\\n assert (\\n self._logdir\\n ), \\\"Log provider has not been tied to a SummaryWriter yet\\\"\\n return self._logdir\",\n \"def _setup_dir(self):\\n if not os.path.exists(self._save_dir):\\n logger.info(\\\"save_dir {} does not exist, \\\"\\n \\\"creating it\\\".format(self._save_dir))\\n os.makedirs(self._save_dir)\\n\\n # Log the run parameters.\\n logger.info(\\\"Writing logs to {}\\\".format(self._log_dir))\\n\\n if not os.path.exists(self._log_dir):\\n logger.info(\\\"log path {} does not exist, \\\"\\n \\\"creating it\\\".format(self._log_dir))\\n os.makedirs(self._log_dir)\",\n \"def __init__(self, log_path):\\n # create a map for storing LogImg objects\\n self.log_img_map = OrderedDict()\\n\\n # set the path to the log directory\\n self.log_path = log_path\\n\\n # check if log directory already exists or create it\\n if not os.path.exists(self.log_path):\\n os.makedirs(self.log_path)\\n\\n # set current training step\\n self.train_step = 0\",\n \"def _set_output_file(self):\\n dictConfig(self.DEFAULT_LOGGING)\",\n \"def init() -> None:\\n log_format = logging.Formatter(\\\"%(levelname)s || %(name)s || %(asctime)s || %(message)s\\\")\\n\\n log_file = Path(\\\"logs\\\", \\\"rl_snake.log\\\")\\n log_file.parent.mkdir(exist_ok=True)\\n\\n file_handler = handlers.RotatingFileHandler(\\n log_file,\\n maxBytes=3000000,\\n backupCount=5\\n )\\n file_handler.setFormatter(log_format)\\n\\n root_logger = logging.getLogger()\\n root_logger.addHandler(file_handler)\\n root_logger.setLevel(logging.DEBUG if constants.Misc.debug else logging.INFO)\\n\\n root_logger.info(\\\"Root logger initilised\\\")\",\n \"def set_log_dir(self, model_path=None):\\n # Set date and epoch counter as if starting a new model\\n self.epoch = 0\\n now = datetime.datetime.now()\\n\\n # If we have a model path with date and epochs use them\\n if model_path:\\n # Continue from we left of. Get epoch and date from the file name\\n # A sample model path might look like:\\n # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5\\n regex = r\\\".*/\\\\w+(\\\\d{4})(\\\\d{2})(\\\\d{2})T(\\\\d{2})(\\\\d{2})/mask\\\\_rcnn\\\\_\\\\w+(\\\\d{4})\\\\.h5\\\"\\n m = re.match(regex, model_path)\\n if m:\\n now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\\n int(m.group(4)), int(m.group(5)))\\n self.epoch = int(m.group(6)) + 1\\n\\n # Directory for training logs\\n self.log_dir = os.path.join(self.model_dir, \\\"{}{:%Y%m%dT%H%M}\\\".format(\\n self.config.NAME.lower(), now))\\n\\n # Path to save after each epoch. Include placeholders that get filled by Keras.\\n self.checkpoint_path = os.path.join(self.log_dir, \\\"yolo_V3_{}_*epoch*.ckpt\\\".format(\\n self.config.NAME.lower()))\\n self.checkpoint_path = self.checkpoint_path.replace(\\n \\\"*epoch*\\\", \\\"{epoch:04d}\\\")\",\n \"def set_log_dir(dir):\\r\\n LogOptions._LOG_DIR = dir\",\n \"def setUp(self):\\n self.path = tempfile.mkdtemp()\\n self.log = log.Log(self.path)\",\n \"def set_log_dir(self, model_path=None):\\n # Set date and epoch counter as if starting a new model\\n self.epoch = 0\\n now = datetime.datetime.now()\\n\\n # If we have a model path with date and epochs use them\\n if model_path:\\n # Continue from we left of. Get epoch and date from the file name\\n # A sample model path might look like:\\n\\n regex = r\\\".*[/\\\\\\\\][\\\\w-]+(\\\\d{4})(\\\\d{2})(\\\\d{2})T(\\\\d{2})(\\\\d{2})[/\\\\\\\\]OOD\\\\_[\\\\w-]+(\\\\d{4})\\\\.h5\\\"\\n m = re.match(regex, model_path)\\n if m:\\n now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\\n int(m.group(4)), int(m.group(5)))\\n # Epoch number in file is 1-based, and in Keras code it's 0-based.\\n # So, adjust for that then increment by one to start from the next epoch\\n self.epoch = int(m.group(6)) - 1 + 1\\n\\n # Directory for training logs\\n self.log_dir = os.path.join(self.model_dir, f\\\"{self.config.NAME.lower()}{now:%Y%m%dT%H%M}\\\")\\n\\n # Path to save after each epoch. Include placeholders that get filled by Keras.\\n self.checkpoint_path = os.path.join(self.log_dir, f\\\"OOD_{self.config.NAME.lower()}_*epoch*.h5\\\")\\n self.checkpoint_path = self.checkpoint_path.replace(\\\"*epoch*\\\", \\\"{epoch:04d}\\\")\",\n \"def test_path(self):\\n self.assertEqual(\\n self.log.current_log_path,\\n f'{self.path}/.{datetime.now(ET).date().isoformat()}.log'\\n )\",\n \"def __init__(self, abs_path_logfile):\\n\\n self.logger = logging.getLogger()\\n self.logger.setLevel(logging.DEBUG)\\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\\n handler = logging.handlers.TimedRotatingFileHandler(abs_path_logfile, when='D', interval=1)\\n handler.setLevel(logging.DEBUG)\\n handler.setFormatter(formatter)\\n self.logger.addHandler(handler)\",\n \"def writeLog(self, log_path):\\r\\n f = open(log_path, 'w')\\r\\n f.write(str(self))\\r\\n f.close()\",\n \"def log_dir():\\r\\n if LogOptions._LOG_DIR is None:\\r\\n LogOptions._LOG_DIR = app.get_options().twitter_common_log_log_dir\\r\\n return LogOptions._LOG_DIR\",\n \"def set_log_dir(self, model_path=None):\\n # Set date and epoch counter as if starting a new model\\n self.epoch = 0\\n now = datetime.datetime.now()\\n\\n # If we have a model path with date and epochs use them\\n if model_path:\\n # Continue from we left of. Get epoch and date from the file name\\n # A sample model path might look like:\\n # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5\\n regex = r\\\".*/\\\\w+(\\\\d{4})(\\\\d{2})(\\\\d{2})T(\\\\d{2})(\\\\d{2})/FCN\\\\_DenseNet\\\\_\\\\w+(\\\\d{4})\\\\.h5\\\"\\n m = re.match(regex, model_path)\\n if m:\\n now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\\n int(m.group(4)), int(m.group(5)))\\n self.epoch = int(m.group(6)) + 1\\n\\n # Directory for training logs\\n self.log_dir = os.path.join(self.model_dir, \\\"{}{:%Y%m%dT%H%M}\\\".format(\\n self.config.NAME.lower(), now))\\n\\n # Path to save after each epoch. Include placeholders that get filled by Keras.\\n self.checkpoint_path = os.path.join(self.log_dir, \\\"FCN_DenseNet_{}_*epoch*.h5\\\".format(\\n self.config.NAME.lower()))\\n self.checkpoint_path = self.checkpoint_path.replace(\\n \\\"*epoch*\\\", \\\"{epoch:04d}\\\")\",\n \"def _begin_logging(self):\\n logconf.set_up_root_logger(self.opts.logfile)\",\n \"def get_log_path():\\n forch_log_dir = os.getenv('FORCH_LOG_DIR')\\n if not forch_log_dir:\\n return None\\n return os.path.join(forch_log_dir, 'forch.log')\",\n \"def __init__(self, level, general_log_path, outputs_folder):\\n self.log_level = level\\n\\n # self.general_log_file = general_log_path.open('w')\\n self.general_log_file = GCOpen(general_log_path, 'w')\\n self.general_log_file.open()\\n\\n self.file_outputs_dir = outputs_folder / 'output_files'\\n # self.file_outputs_dir.mkdir(exist_ok=True)\\n\\n exp_name = str(outputs_folder).split('/')[-1]\\n\\n self.summary_writer = SummaryWriter(log_dir=str(TEMP_FOLDER),\\n filename_suffix='.' + exp_name)\\n tf_filename = find_tf_event(exp_name)\\n self.sw_local_path = Path(TEMP_FOLDER) / tf_filename\\n self.sw_gc_path = outputs_folder / tf_filename\\n\\n self.log(\\\"Starting new experiment at \\\" +\\n datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\"))\\n self.log(\\\"User: \\\" + getpass.getuser())\\n self.log(\\\"Host: \\\" + socket.gethostname())\\n\\n Logger.unique_logger = self\",\n \"def setLogFile(filename):\\n\\tglobal logfile\\n\\tlogfile = filename\",\n \"def initLogging ( logFile ):\\n logging.basicConfig(\\n filename=logFile,\\n level=logging.INFO,\\n format='%(asctime)s %(levelname)-8s %(message)s',\\n filemode='w'\\n )\",\n \"def setup_log_dir():\\n log_dir = get_log_dir()\\n if log_dir.endswith('latest'):\\n shutil.rmtree(log_dir, ignore_errors=True)\\n mkdirs(log_dir)\\n return log_dir\",\n \"def __init__(self,\\r\\n default_path = None,\\r\\n default_level = None,\\r\\n logging_dir = None,\\r\\n log_file = None,\\r\\n log_file_dir = None,\\r\\n log_conf_full = None\\r\\n ):\\r\\n self.logger_is_set = False\\r\\n\\r\\n '''\\r\\n Get ready to setup everything.\\r\\n TO DO: read from structure is badly needed. \\r\\n '''\\r\\n self.default_path = default_path\\r\\n self.default_level = default_level\\r\\n self.logging_dir = logging_dir\\r\\n self.log_file = log_file\\r\\n self.log_file_dir = log_file_dir\\r\\n self.log_conf_full = log_conf_full\\r\\n\\r\\n\\r\\n\\r\\n self.setup_logging(self.default_path,\\r\\n self.default_level,\\r\\n self.logging_dir,\\r\\n self.log_file,\\r\\n self.log_file_dir,\\r\\n self.log_conf_full\\r\\n )\",\n \"def _logger(self):\\r\\n\\r\\n # Create filename for log\\r\\n filenameF = self._vna.getDateFormatted() + \\\".txt\\\"\\r\\n filenameF = \\\"Logs/\\\" + filenameF \\r\\n f = open(filenameF, \\\"a+\\\") # Log saved in directory named logs located in same directory as this file\\r\\n \\r\\n # if self._vna.isTwoComponents():\\r\\n # for i in range(len(self._voltages)):\\r\\n # f.write('%s\\\\t\\\\t\\\\t' % self._voltages[i][0])\\r\\n # else:\\r\\n for i in range(len(self._voltages)):\\r\\n f.write('%s\\\\t\\\\t' % self._voltages[i][0])\\r\\n f.write('\\\\n')\\r\\n\\r\\n # if self._vna.isTwoComponents():\\r\\n # for i in range(len(self._voltages[0])):\\r\\n # line = \\\"\\\"\\r\\n # for j in range(len(self._voltages)):\\r\\n # line = line + str(self._frequency[j][i]) + '\\\\t' + str(self._intensity[j][2*i]) + \\\\\\r\\n # str(self._intensity[j][2*i + 1]) + '\\\\t'\\r\\n # f.write(line)\\r\\n # f.write('\\\\n')\\r\\n # else: \\r\\n for i in range(len(self._voltages[0])):\\r\\n line = \\\"\\\"\\r\\n for j in range(len(self._voltages)):\\r\\n line = line + str(self._frequency[j][i]) + '\\\\t' + str(self._intensity[j][i]) + '\\\\t' \\r\\n f.write(line)\\r\\n f.write('\\\\n')\",\n \"def __init__(self, file_path: str = \\\"..\\\\\\\\Resource\\\\\\\\log.txt\\\", name: str = 'TangoServerLogger'):\\r\\n self.logger = logging.getLogger(name)\\r\\n self.logger.setLevel(logging.DEBUG)\\r\\n\\r\\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\\r\\n\\r\\n logfile = logging.FileHandler(file_path)\\r\\n logfile.setLevel(logging.DEBUG)\\r\\n logfile.setFormatter(formatter)\\r\\n\\r\\n self.logger.addHandler(logfile)\",\n \"def initialize_file_handler(\\n dir_path=os.path.dirname(os.path.realpath(__file__)),\\n date_time=datetime.datetime.now(),\\n):\\n file_name = \\\"{}.log\\\".format(\\n \\\"-\\\".join([str(date_time.year), str(date_time.month), str(date_time.day)])\\n )\\n file_handler_path = \\\"{}/logFiles/{}\\\".format(dir_path, file_name)\\n hdlr = logging.FileHandler(file_handler_path)\\n formatter = logging.Formatter(\\n \\\"%(levelname)s:%(asctime)s:%(name)s:%(message)s\\\"\\n )\\n hdlr.setFormatter(formatter)\\n hdlr.setLevel(logging.ERROR)\\n logging.getLogger(\\\"\\\").addHandler(hdlr)\",\n \"def __init__(self, api_path=None, log_path=None, log_level=\\\"DEBUG\\\"):\\n\\n # Construct the log path. \\n if log_path:\\n self.log_path = log_path\\n else:\\n defaultlog_path = \\\"~/Spirent/CTA/Logs/\\\"\\n\\n now = datetime.datetime.now()\\n defaultlog_path += now.strftime(\\\"%Y-%m-%d-%H-%M-%S\\\")\\n defaultlog_path += \\\"_PID\\\"\\n defaultlog_path += str(os.getpid())\\n defaultlog_path = os.path.expanduser(defaultlog_path)\\n \\n # The environment variable overwrites the default path. \\n self.log_path = os.getenv(\\\"CTA_LOG_OUTPUT_DIRECTORY\\\", defaultlog_path) \\n\\n self.log_path = os.path.abspath(self.log_path)\\n self.logfile = os.path.join(self.log_path, \\\"cta_python.log\\\") \\n\\n if not os.path.exists(self.log_path):\\n os.makedirs(self.log_path)\\n\\n # NOTE: Consider limiting the number of log directories that are created.\\n # It would mean deleting older directories.\\n\\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - stc::get automationoptions -suppressTclErrors\\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - return false\\n #2016-05-19 14:05:56,382 UserID =mjefferson\\n #2016-05-19 14:05:56,382 Log Level=INFO\\n\\n if log_level == \\\"CRITICAL\\\":\\n log_level = logging.CRITICAL\\n elif log_level == \\\"ERROR\\\":\\n log_level = logging.ERROR\\n elif log_level == \\\"WARNING\\\":\\n log_level = logging.WARNING\\n elif log_level == \\\"INFO\\\": \\n log_level = logging.INFO\\n else:\\n # DEBUG is the default log level.\\n log_level = logging.DEBUG \\n \\n logging.basicConfig(filename=self.logfile, filemode=\\\"w\\\", level=log_level, format=\\\"%(asctime)s %(levelname)s %(message)s\\\")\\n #logging.Formatter(fmt='%(asctime)s.%(msecs)03d',datefmt='%Y/%m/%d %H:%M:%S')\\n # Add timestamps to each log message.\\n #logging.basicConfig()\\n # The logger is now ready. \\n\\n logging.info(\\\"Spirent TestCenter Conformance Application Python API is starting up...\\\")\\n logging.info(\\\"OS Type = \\\" + os.name)\\n logging.info(\\\"API Path = \\\" + api_path)\\n logging.info(\\\"UserID = \\\" + getpass.getuser())\\n logging.info(\\\"Log Level = \\\" + logging.getLevelName(log_level)) \\n logging.info(\\\"Current Path = \\\" + os.path.abspath(os.getcwd())) \\n logging.info(\\\"Log Path = \\\" + self.log_path)\\n\\n # Instantiate the Tcl interpreter.\\n self.tcl = Tcl()\\n\\n self.tcl.eval(\\\"lappend ::auto_path {\\\" + api_path + \\\"}\\\")\\n\\n logging.info(\\\"Tcl Version = \\\" + self.tcl.eval(\\\"info patchlevel\\\"))\\n logging.info(\\\"Tcl ::auto_path = \\\" + self.tcl.eval('set ::auto_path'))\\n logging.info(\\\"Loading the Spirent TestCenter Conformance Application in the Tcl interpreter...\\\")\\n self.Exec(\\\"package require SpirentTestCenterConformance\\\")\\n\\n return\",\n \"def init_logging(input_file_parameters, dir_stacks):\\r\\n fl_name = '{0}_log_{1}_{2}.txt'.format(NAME,\\r\\n START_TIME,\\r\\n input_file_parameters.job_name)\\r\\n #NOTICE! Current_log_path.path is changed here!\\r\\n CurrentLogPath.path = os.path.join(input_file_parameters.output_dir,\\r\\n fl_name)\\r\\n logging.basicConfig(filename=CurrentLogPath.path, filemode='w',\\r\\n format='%(asctime)s %(levelname)s:%(message)s',\\r\\n level=logging.INFO)\\r\\n logging.info('{0} v. {1} started'.format(NAME, VERSION))\\r\\n logging.info('Job name: {0}'.format(input_file_parameters.job_name))\\r\\n logging.info('Starting point directory:\\\\n{0}'.format(dir_stacks[0]\\r\\n .path))\\r\\n logging.info('Output directory:\\\\n{0}'.format(input_file_parameters.output_dir))\\r\\n logging.info('-'*80)\\r\\n logging.info('staplefile contents:\\\\n{0}'.format('\\\\n'.join(input_file_parameters.staplefile)))\\r\\n logging.info('-'*80)\\r\\n logging.info('config.txt contents:\\\\n{0}'\\r\\n .format(utils.get_config_file()))\\r\\n logging.info('-'*80)\",\n \"def init(directory,name):\\n \\n import os,time\\n fname=os.path.join(directory,'Log_'+name+'_'+time.strftime('%Y-%b-%d')+'.txt')\\n if not os.path.isfile(os.path.join(directory,fname)):\\n try:\\n fhandle = open(fname, 'a')\\n fhandle.write('-----------------------------------------------------\\\\n')\\n fhandle.write('Program initialized! on '+time.strftime('%Y-%b-%d')+'\\\\n')\\n fhandle.write('-----------------------------------------------------\\\\n')\\n print('======begin log!======')\\n return(fhandle)\\n except:\\n print('Could not create Log file with name : '+\\n ' Log_'+name+'_'+date.today().isoformat()+'.txt')\\n else:\\n fhandle=open(fname,'a')\\n fhandle.write('-----------------------------------------------------\\\\n')\\n fhandle.write('Program initialized! on '+time.strftime('%Y-%b-%d')+'\\\\n')\\n fhandle.write('-----------------------------------------------------\\\\n')\\n print('======begin log!======')\\n return(fhandle)\",\n \"def __init__(self, log_dir, prefix, when_interval=None):\\n\\n log_dir = os.path.abspath(log_dir)\\n\\n if not os.path.exists(log_dir):\\n os.mkdir(log_dir)\\n\\n if not isinstance(prefix, str):\\n raise ValueError(\\\"Prefix string required for output files\\\")\\n\\n if when_interval is None:\\n when, interval = 'H', 1\\n else:\\n when, interval = when_interval\\n if not (isinstance(when, str) and isinstance(interval, int)):\\n raise ValueError(\\\"Rollover interval should be like ('h', 1)\\\")\\n\\n log_path = os.path.join(log_dir, prefix)\\n\\n _LOGGER.info(\\\"Saving data to path {}\\\".format(log_path))\\n _LOGGER.info(\\\"Rollover interval {}\\\"\\n .format(\\\"{}{}\\\".format(when, interval)))\\n\\n # Create the logger.\\n self._logger = TimedRotatingFileHandler(filename=log_path,\\n encoding='utf-8',\\n when=when,\\n interval=interval)\",\n \"def log_data(self):\\n\\n self.check_dir()\\n with open(self.log_file, \\\"a\\\") as logger_file:\\n logger_file.write(\\\"{}, {}\\\\n\\\".format(self.time, self.msg))\",\n \"def logdir(self) -> str:\\n return self._logdir\",\n \"def logsave(self):\\n log_file = open(self.conf[\\\"output_prefix\\\"] + \\\"_log.txt\\\", \\\"w\\\")\\n try:\\n log_file.write(self.log)\\n finally:\\n log_file.close()\",\n \"def setLog(path):\\n logger = logging.getLogger('current function')\\n FORMAT = '%(asctime)s - %(levelname)s - %(message)s'\\n logging.basicConfig( level = logging.DEBUG,\\n format = FORMAT,\\n filename = path,\\n filemode = 'wa' )\\n console = logging.StreamHandler()\\n console.setLevel(logging.INFO)\\n formatter = logging.Formatter(FORMAT)\\n console.setFormatter(formatter)\\n logger.addHandler(console)\\n return logger\",\n \"def pytest_logger_logsdir(self, config):\",\n \"def GetLogFilePath():\\n global _LOG_FILE\\n return _LOG_FILE\",\n \"def set_log_dir(self, model_path=None):\\n # Set date and epoch counter as if starting a new model\\n self.epoch = 0\\n# now = datetime.datetime.now()\\n# \\n# # If we have a model path with date and epochs use them\\n# if model_path:\\n# # Continue from we left of. Get epoch and date from the file name\\n# # A sample model path might look like:\\n# # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5\\n# regex = r\\\".*/[\\\\w-]+(\\\\d{4})(\\\\d{2})(\\\\d{2})T(\\\\d{2})(\\\\d{2})/mask\\\\_rcnn\\\\_[\\\\w-]+(\\\\d{4})\\\\.h5\\\"\\n# m = re.match(regex, model_path)\\n# if m:\\n# now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)),\\n# int(m.group(4)), int(m.group(5)))\\n# # Epoch number in file is 1-based, and in Keras code it's 0-based.\\n# # So, adjust for that then increment by one to start from the next epoch\\n# self.epoch = int(m.group(6)) - 1 + 1\\n# print('Re-starting from epoch %d' % self.epoch)\\n\\n # Directory for training logs\\n self.log_dir = os.path.join(self.model_dir, \\n \\\"siamese_{}_{}_{}\\\".format(self.config.MODEL.lower(), \\n self.config.NAME.lower(), \\n self.config.EXPERIMENT.lower()))\\n\\n # Create log_dir if not exists\\n if not os.path.exists(self.log_dir):\\n os.makedirs(self.log_dir)\\n\\n # Path to save after each epoch. Include placeholders that get filled by Keras.\\n self.checkpoint_path = os.path.join(self.log_dir, \\\"siamese_mrcnn_*epoch*.h5\\\")\\n self.checkpoint_path = self.checkpoint_path.replace(\\\"*epoch*\\\", \\\"{epoch:04d}\\\")\",\n \"def init(self) -> None:\\n logger.debug(f\\\"[nbtutorial]: Outdir is: {self.outdir}\\\")\",\n \"def set_config(self, file_path_name):\\n level = logging.DEBUG\\n format = '%(asctime)s %(levelname)-8s %(message)s' \\n datefmt = '%a, %d %b %Y %H:%M:%S'\\n filemode = 'a'\\n \\n\\n logging.basicConfig(level = level,\\n format = format,\\n datefmt = datefmt,\\n filename = file_path_name,\\n filemode = filemode)\",\n \"def instantiate_logs(self):\\n\\n # Log file\\n timestamp = datetime.now().strftime(\\\"%Y-%m-%dT%H%M%S\\\")\\n self.log_dir = os.path.join(\\\"experiment_logs\\\", timestamp)\\n\\n # Create Log directory if it does not exist\\n try:\\n os.makedirs(self.log_dir)\\n except OSError as exception:\\n if exception.errno != errno.EEXIST:\\n raise\\n\\n self.info_file = os.path.join(self.log_dir, \\\"run_info.txt\\\")\\n self.log_file = os.path.join(self.log_dir, \\\"data.csv\\\")\\n\\n with open(self.info_file, \\\"w+\\\") as f:\\n f.write(\\\"Period = {}\\\\nMaxVel = {}\\\".format(self.period, self.max_vel))\\n\\n self.log_file_desc = open(self.log_file, \\\"w+\\\")\\n self.log_file_desc.write(\\\"t, current_vel, current_h_angle, current_v_angle, x, y, z, roll, pitch, yaw\\\")\",\n \"def GetPath () :\\n return sys.hal_log_values [\\\"__log_path\\\"]\",\n \"def __init__(self, logfile):\\n\\n self.logfile = logfile\\n if self. logfile:\\n self.file = open(logfile, \\\"w\\\")\\n self.starttime = time.time()\\n self.file.write(\\\"%.2f %s Starting log\\\\n\\\" % (time.time() - self.starttime, time.asctime()))\",\n \"def __init__(self, log_dir):\\n self.writer = SummaryWriter(log_dir)\",\n \"def initLogger(self):\\n loglevel = self.loglevels[self.loglevel]\\n log_format = '%(asctime)s name=%(name)s loglevel=%(levelname)s message=%(message)s'\\n logging.basicConfig(format=log_format,\\n level=loglevel)\\n \\tmultiprocessing.log_to_stderr(loglevel)\",\n \"def _createlog(self):\\n\\t\\tif self.toemail and self.fromemail and self.smtphost:\\n\\t\\t\\t# Use the email logger as the first logger, so that when sending the email (in :meth:`EmailLogger.close`) fails, it will still be logged to the log file/stdout/stderr\\n\\t\\t\\tself._loggers.append(EmailLogger(self))\\n\\t\\tif self.log2stderr:\\n\\t\\t\\tself._loggers.append(StreamLogger(self, sys.stderr, self._formatlogline))\\n\\t\\tif self.log2stdout:\\n\\t\\t\\tself._loggers.append(StreamLogger(self, sys.stdout, self._formatlogline))\\n\\t\\tif self.log2file:\\n\\t\\t\\t# Create the log file\\n\\t\\t\\tlogfilename = ul4c.Template(self.logfilename, \\\"logfilename\\\").renders(job=self)\\n\\t\\t\\tlogfilename = url.File(logfilename).abs()\\n\\t\\t\\tself.logfileurl = str(url.Ssh(misc.sysinfo.user_name, misc.sysinfo.host_fqdn or misc.sysinfo.host_name, logfilename.local()))\\n\\t\\t\\tskipurls = [logfilename]\\n\\t\\t\\tlogfile = logfilename.open(mode=\\\"w\\\", encoding=self.encoding, errors=self.errors)\\n\\t\\t\\tif self.loglinkname is not None:\\n\\t\\t\\t\\t# Create the log link\\n\\t\\t\\t\\tloglinkname = ul4c.Template(self.loglinkname, \\\"loglinkname\\\").renders(job=self)\\n\\t\\t\\t\\tloglinkname = url.File(loglinkname).abs()\\n\\t\\t\\t\\tskipurls.append(loglinkname)\\n\\t\\t\\t\\tlogfilename = logfilename.relative(loglinkname)\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tlogfilename.symlink(loglinkname)\\n\\t\\t\\t\\texcept OSError as exc:\\n\\t\\t\\t\\t\\tif exc.errno == errno.EEXIST:\\n\\t\\t\\t\\t\\t\\tloglinkname.remove()\\n\\t\\t\\t\\t\\t\\tlogfilename.symlink(loglinkname)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\traise\\n\\t\\t\\tself._loggers.append(URLResourceLogger(self, logfile, skipurls, self._formatlogline))\",\n \"def initLogger(filename, dir_name='data/logs/'):\\n \\n filename = os.path.join(dir_name, filename)\\n \\n if not os.path.exists(dir_name):\\n os.makedirs(dir_name)\\n \\n if os.path.exists(filename):\\n os.remove(filename)\\n \\n formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(message)s', \\n '%m-%d-%Y %H:%M:%S')\\n fh = logging.FileHandler(filename)\\n fh.setFormatter(formatter)\\n \\n logger.addHandler(fh)\",\n \"def __init__(self):\\n\\n self.__logger = logging.getLogger()\\n\\n formatter = logging.Formatter('%(asctime)s :: %(levelname)s :: ' +\\n '%(message)s')\\n\\n file_handler = RotatingFileHandler('.log', 'a', 1000000, 1)\\n\\n file_handler.setLevel(logging.DEBUG)\\n file_handler.setFormatter(formatter)\\n self.__logger.addHandler(file_handler)\\n\\n stream_handler = logging.StreamHandler()\\n stream_handler.setLevel(logging.INFO)\\n self.__logger.addHandler(stream_handler)\",\n \"def setup_log(self, log_file):\\n directory = os.path.dirname(log_file)\\n if directory:\\n os.makedirs(directory, exist_ok=True)\\n\\n logger = logging.getLogger(log_file)\\n formatter = logging.Formatter(config.LOG_FORMAT)\\n\\n file_handler = logging.FileHandler(log_file, mode='a')\\n file_handler.setFormatter(formatter)\\n\\n logger.setLevel(logging.INFO)\\n logger.addHandler(file_handler)\\n\\n return logger\",\n \"def init_logging(file_name, log_path=None, level=logging.INFO):\\n global logger\\n\\n try:\\n if not log_path:\\n log_path = '../logs'\\n except KeyError as e:\\n raise LoggerException(message=str(e))\\n\\n log_path = os.path.join(log_path, file_name + \\\"-\\\" + datetime.utcnow().strftime(\\\"%Y%m%d-%H%M%S\\\") + '.log')\\n\\n logging.basicConfig(\\n filename=log_path,\\n format='%(asctime)s - %(levelname)s: %(message)s',\\n level=level,\\n )\",\n \"def __init_logging(self):\\n\\n logger = logging.getLogger('__name__')\\n if os.path.exists(constants.LOG_FILE):\\n logger.setLevel(logging.DEBUG)\\n logger_file_handler = logging.FileHandler(constants.LOG_FILE)\\n logger_formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')\\n logger_file_handler.setFormatter(logger_formatter)\\n logger.addHandler(logger_file_handler)\\n else:\\n logger.disabled = True\",\n \"def __init__(self):\\r\\n try:\\r\\n self.file = open(REPORT_FILE, 'w')\\r\\n except OSError:\\r\\n print('Problem opening log file')\\r\\n exit(1)\",\n \"def log(length, file):\\n\\n if user_init.check_pre_init() and user_utility.check_drive_init() == 'True':\\n \\n data = user_utility.read_log(length, file)\\n\\n for log in data:\\n print(log)\\n\\n\\n else:\\n user_utility.print_error(\\\"Sink folder not Found! Initialise folder first or reset your configuration!\\\")\",\n \"def get_logging_dir(self):\\n return self.logging_dir\",\n \"def get_logdir(self):\\n return self.event_writer.get_logdir()\",\n \"def __init__(self):\\n #path = \\\"/\\\".join( ( os.path.abspath( __file__ ).replace( \\\"\\\\\\\\\\\", \\\"/\\\" ) ).split( \\\"/\\\" )[:-1])\\n #logging.config.fileConfig( os.path.join ( path, \\\"..\\\\\\\\resources\\\\\\\\config-log.ini\\\" ), disable_existing_loggers=False ) # working in local\\n #logging.config.fileConfig ( \\\"config-log.ini\\\", disable_existing_loggers=False) # working in local\\n self.logger = logging.getLogger(__name__)\\n self.logger.setLevel(logging.INFO)\\n ch = logging.StreamHandler()\\n ch.setLevel(logging.INFO)\\n # create formatter and add it to the handlers\\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\\n #fh.setFormatter(formatter)\\n ch.setFormatter(formatter)\\n # add the handlers to the logger\\n self.logger.addHandler(ch)\\n # TODO decorator it doesnt create file handler which logs even debug messages\\n # fh = logging.FileHandler('spam.log')\\n # fh.setLevel(logging.DEBUG)\\n # create console handler with a higher log level\\n # fh.setFormatter(formatter)\\n # logger.addHandler(fh)\",\n \"def init_logging(input_dir, file_name):\\n create_dir(input_dir)\\n config(file_name, log_level=logging.DEBUG)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7888636","0.78552186","0.75470525","0.75144166","0.74878","0.74750996","0.7430851","0.7277765","0.7158862","0.7153729","0.71011764","0.7067894","0.7024228","0.70093405","0.6994423","0.69160753","0.68876475","0.6852987","0.6842736","0.68300515","0.68281233","0.67878157","0.6760104","0.67453843","0.67244655","0.66858757","0.66510284","0.6644118","0.6638932","0.66376734","0.6605749","0.660009","0.65971404","0.6595335","0.6580577","0.65797174","0.6571852","0.6565737","0.65303695","0.6507568","0.65030074","0.65022254","0.6482626","0.6477777","0.64736706","0.6463684","0.64589024","0.64522856","0.6436705","0.64305633","0.64261657","0.64204144","0.6400615","0.63892394","0.6380715","0.63784224","0.6377967","0.63778436","0.63642246","0.636095","0.63609165","0.63457036","0.63448465","0.6343561","0.6338974","0.6338134","0.6335312","0.63264215","0.6304975","0.629435","0.6292189","0.6287362","0.6257139","0.6256378","0.62518346","0.6248724","0.62450135","0.62422633","0.62364644","0.6234615","0.62327296","0.6224554","0.62204325","0.62183106","0.6213274","0.6206433","0.620467","0.6204194","0.6201217","0.61971396","0.61911297","0.61890167","0.61882985","0.6188151","0.6178861","0.61765516","0.6176053","0.6175566","0.6172225","0.61635536"],"string":"[\n \"0.7888636\",\n \"0.78552186\",\n \"0.75470525\",\n \"0.75144166\",\n \"0.74878\",\n \"0.74750996\",\n \"0.7430851\",\n \"0.7277765\",\n \"0.7158862\",\n \"0.7153729\",\n \"0.71011764\",\n \"0.7067894\",\n \"0.7024228\",\n \"0.70093405\",\n \"0.6994423\",\n \"0.69160753\",\n \"0.68876475\",\n \"0.6852987\",\n \"0.6842736\",\n \"0.68300515\",\n \"0.68281233\",\n \"0.67878157\",\n \"0.6760104\",\n \"0.67453843\",\n \"0.67244655\",\n \"0.66858757\",\n \"0.66510284\",\n \"0.6644118\",\n \"0.6638932\",\n \"0.66376734\",\n \"0.6605749\",\n \"0.660009\",\n \"0.65971404\",\n \"0.6595335\",\n \"0.6580577\",\n \"0.65797174\",\n \"0.6571852\",\n \"0.6565737\",\n \"0.65303695\",\n \"0.6507568\",\n \"0.65030074\",\n \"0.65022254\",\n \"0.6482626\",\n \"0.6477777\",\n \"0.64736706\",\n \"0.6463684\",\n \"0.64589024\",\n \"0.64522856\",\n \"0.6436705\",\n \"0.64305633\",\n \"0.64261657\",\n \"0.64204144\",\n \"0.6400615\",\n \"0.63892394\",\n \"0.6380715\",\n \"0.63784224\",\n \"0.6377967\",\n \"0.63778436\",\n \"0.63642246\",\n \"0.636095\",\n \"0.63609165\",\n \"0.63457036\",\n \"0.63448465\",\n \"0.6343561\",\n \"0.6338974\",\n \"0.6338134\",\n \"0.6335312\",\n \"0.63264215\",\n \"0.6304975\",\n \"0.629435\",\n \"0.6292189\",\n \"0.6287362\",\n \"0.6257139\",\n \"0.6256378\",\n \"0.62518346\",\n \"0.6248724\",\n \"0.62450135\",\n \"0.62422633\",\n \"0.62364644\",\n \"0.6234615\",\n \"0.62327296\",\n \"0.6224554\",\n \"0.62204325\",\n \"0.62183106\",\n \"0.6213274\",\n \"0.6206433\",\n \"0.620467\",\n \"0.6204194\",\n \"0.6201217\",\n \"0.61971396\",\n \"0.61911297\",\n \"0.61890167\",\n \"0.61882985\",\n \"0.6188151\",\n \"0.6178861\",\n \"0.61765516\",\n \"0.6176053\",\n \"0.6175566\",\n \"0.6172225\",\n \"0.61635536\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6496009"},"document_rank":{"kind":"string","value":"42"}}},{"rowIdx":17495,"cells":{"query":{"kind":"string","value":"Helper method to permute the state axis (from MinAtar)"},"document":{"kind":"string","value":"def get_state(self, s):\n return (torch.tensor(s, device=self.device).permute(2, 0, 1)).unsqueeze(0).float()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def permute(ts: Tensor, axes) -> Tensor:\n permute_op = PermuteOp(axes)\n return permute_op(ts, None)","def _apply_swap(self, state, axes, **kwargs):\n all_axes = list(range(len(state.shape)))\n all_axes[axes[0]] = axes[1]\n all_axes[axes[1]] = axes[0]\n return self._transpose(state, all_axes)","def permute(self):\n raise NotImplementedError()","def permute(self, ordering: np.ndarray, *, axis: int) -> None:\n\t\tif axis == 0:\n\t\t\tself.values = self.values[ordering, :]\n\t\telif axis == 1:\n\t\t\tself.values = self.values[:, ordering]\n\t\telse:\n\t\t\traise ValueError(\"axis must be 0 or 1\")","def _evolve_phaseshift_element(self, state, element):\n new_state = PhotonicState()\n for in_modes, in_amp in state.items():\n phases = element.global_U(N).diagonal()\n phase_shift = np.prod([phases[i] for i in in_modes])\n new_state[in_modes] = phase_shift * in_amp \n return new_state","def scatter_state(self,x):\n self.m[0:self.n] = x[0,0:self.n] \n self.r[0:self.n,0] = x[1,0:self.n]\n self.r[0:self.n,1] = x[2,0:self.n]\n self.r[0:self.n,2] = x[3,0:self.n]\n self.v[0:self.n:,0] = x[4,0:self.n]\n self.v[0:self.n:,1] = x[5,0:self.n]\n self.v[0:self.n:,2] = x[6,0:self.n]","def permute(self, arr):\n\n return arr[self.permutation_idxs]","def scatter_state(self,x):\n self.m[0:self.n] = x[0,0:self.n] \n self.r[0:self.n,0] = x[1,0:self.n]\n self.r[0:self.n,1] = x[2,0:self.n]\n self.r[0:self.n,2] = x[3,0:self.n]\n self.v[0:self.n:,0] = x[4,0:self.n]\n self.v[0:self.n:,1] = x[5,0:self.n]\n self.v[0:self.n:,2] = x[6,0:self.n]\n self.rho[0:self.n] = x[7,0:self.n]\n self.p[0:self.n] = x[8,0:self.n]\n self.pco[0:self.n] = x[9,0:self.n]\n self.u[0:self.n] = x[10,0:self.n]","def inv_shift_rows(state):\n state[1][0], state[1][1], state[1][2], state[1][3] = state[1][3], state[1][0], state[1][1], state[1][2]\n state[2][0], state[2][1], state[2][2], state[2][3] = state[2][2], state[2][3], state[2][0], state[2][1]\n state[3][0], state[3][1], state[3][2], state[3][3] = state[3][1], state[3][2], state[3][3], state[3][0]","def permute_inputs(*,\n states: npt.NDArray[np.int32],\n heads: npt.NDArray[np.bool8],\n predictions: npt.NDArray[np.float32]) -> Tuple[npt.NDArray[np.int32],\n npt.NDArray[np.bool8],\n npt.NDArray[np.float32]]:\n flip_axis = len(states.shape)-1\n\n # rotate 90\n state_r90 = np.rot90(states, 1, (1, 2))\n moves_r90 = rotate_predictions(heads, 1)\n heads_r90 = rotate_predictions(predictions, 1)\n\n # rotate 180\n state_r180 = np.rot90(states, 2, (1, 2))\n moves_r180 = rotate_predictions(heads, 2)\n heads_r180 = rotate_predictions(predictions, 2)\n\n # rotate 270\n state_r270 = np.rot90(states, 3, (1, 2))\n moves_r270 = rotate_predictions(heads, 3)\n heads_r270 = rotate_predictions(predictions, 3)\n\n # flip left - right\n state_lr = np.flip(states, axis=flip_axis-1)\n moves_lr = flip_predictions_left_right(heads)\n heads_lr = flip_predictions_left_right(predictions)\n\n # rotate lr 90\n state_lr_r90 = np.rot90(state_lr, 1, (1, 2))\n moves_lr_r90 = rotate_predictions(moves_lr, 1)\n heads_lr_r90 = rotate_predictions(heads_lr, 1)\n\n # rotate lr 180\n state_lr_r180 = np.rot90(state_lr, 2, (1, 2))\n moves_lr_r180 = rotate_predictions(moves_lr, 2)\n heads_lr_r180 = rotate_predictions(heads_lr, 2)\n\n # rotate lr 270\n state_lr_r270 = np.rot90(state_lr, 3, (1, 2))\n moves_lr_r270 = rotate_predictions(moves_lr, 3)\n heads_lr_r270 = rotate_predictions(heads_lr, 3)\n\n state_out = np.vstack([states, state_r90, state_r180, state_r270,\n state_lr, state_lr_r90, state_lr_r180, state_lr_r270])\n moves_out = np.vstack([heads, moves_r90, moves_r180, moves_r270,\n moves_lr, moves_lr_r90, moves_lr_r180, moves_lr_r270])\n heads_out = np.vstack([predictions, heads_r90, heads_r180, heads_r270,\n heads_lr, heads_lr_r90, heads_lr_r180, heads_lr_r270])\n\n return state_out, moves_out, heads_out","def permutation(random_state, size=None, n=1, ndim=None, dtype='int64'):\r\n ndim, size, bcast = _infer_ndim_bcast(ndim, size)\r\n #print \"NDIM\", ndim, size\r\n op = RandomFunction(permutation_helper,\r\n tensor.TensorType(dtype=dtype, broadcastable=bcast + (False,)),\r\n ndim_added=1)\r\n return op(random_state, size, n)","def gather_state(self):\n self.x[0,0:self.n] = self.m[0:self.n]\n self.x[1,0:self.n] = self.r[0:self.n,0]\n self.x[2,0:self.n] = self.r[0:self.n,1]\n self.x[3,0:self.n] = self.r[0:self.n,2]\n self.x[4,0:self.n] = self.v[0:self.n,0]\n self.x[5,0:self.n] = self.v[0:self.n,1]\n self.x[6,0:self.n] = self.v[0:self.n,2]\n return(self.x)","def mix_columns(state, m=ax):\n for c in range(4):\n rows = [0, 0, 0, 0]\n\n # For each value in the column, XOR with the result of multiplying\n # the value from the a(x) matrix with the value from the state\n for i in range(4):\n rows[0] ^= ff_multiply(m[0][i], state[i][c])\n rows[1] ^= ff_multiply(m[1][i], state[i][c])\n rows[2] ^= ff_multiply(m[2][i], state[i][c])\n rows[3] ^= ff_multiply(m[3][i], state[i][c])\n\n # Update the state only after calculating new values\n for r in range(4):\n state[r][c] = rows[r]","def call(self, state):\n x = tf.cast(state, tf.float32)\n x = self.flatten(x)\n x -= self.min_vals\n x /= self.max_vals - self.min_vals\n x = 2.0 * x - 1.0 # Rescale in range [-1, 1].\n x = self.dense1(x)\n x = self.dense2(x)\n x = self.last_layer(x)\n return x","def make_state_appliable_4ch(state):\n size = len(state)\n st_appl = np.zeros((size,)*4, dtype=complex)\n for p1 in range(size):\n for p2 in range(size):\n for p3 in range(size):\n for p4 in range(size):\n st_appl[p1, p2, p3, p4] = state[p1, p2, p3, p4] * sqrt(factorial(p1) * factorial(p2) * factorial(p3) * factorial(p4))\n return st_appl","def op_mirror():\n mir = np.array([[1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, -1, 0],\n [0, 0, 0, -1]])\n return mir","def _apply_cnot(self, state, axes, **kwargs):\n ndim = self._ndim(state)\n sl_0 = _get_slice(0, axes[0], ndim)\n sl_1 = _get_slice(1, axes[0], ndim)\n\n # We will be slicing into the state according to state[sl_1], giving us all of the\n # amplitudes with a |1> for the control qubit. The resulting array has lost an axis\n # relative to state and we need to be careful about the axis we apply the PauliX rotation\n # to. If axes[1] is larger than axes[0], then we need to shift the target axis down by\n # one, otherwise we can leave as-is. For example: a state has [0, 1, 2, 3], control=1,\n # target=3. Then, state[sl_1] has 3 axes and target=3 now corresponds to the second axis.\n if axes[1] > axes[0]:\n target_axes = [axes[1] - 1]\n else:\n target_axes = [axes[1]]\n\n state_x = self._apply_x(state[sl_1], axes=target_axes)\n return self._stack([state[sl_0], state_x], axis=axes[0])","def make_state_appliable(state):\n size = len(state)\n st_appl = np.zeros((size, size), dtype=complex)\n for p1 in range(size):\n for p2 in range(size):\n st_appl[p1, p2] = state[p1, p2] * sqrt(factorial(p1) * factorial(p2))\n return st_appl","def permute_fixed(s_input):\r\n return permute_row_elements(s_input, p_val)","def permute_fixed(s_input):\r\n return permute_row_elements(s_input, p_val)","def permute_fixed(s_input):\r\n return permute_row_elements(s_input, p_val)","def permute_fixed(s_input):\r\n return permute_row_elements(s_input, p_val)","def permute_fixed(s_input):\r\n return permute_row_elements(s_input, p_val)","def shift_observable(self,M):\n u = np.array([[1]])\n for i in range(0,minsite):\n M[i] = np.tensordot(u, M[i],axes=(-1,1)).transpose(1,0,2)\n l,u = self.left_cannonical(M[i])\n M[i] = l","def permute(p, dims, perm):\n if issparse(p):\n return _permute_sparse(p, dims, perm)\n return _permute_dense(p, dims, perm)","def test_direct_shape():\n\n n = 21\n x = np.ones((n, n))\n\n recon = abel.direct.direct_transform(x, direction='forward')\n assert recon.shape == (n, n) \n\n recon = abel.direct.direct_transform(x, direction='inverse')\n assert recon.shape == (n, n)","def onequbit_modes(statemat):\n nqubit = int(np.log2(statemat.shape[0]))\n rep = np.array(list(itertools.product((0, 1), repeat=nqubit)))\n inds = [i for i, x in enumerate(np.sum(rep, 1)) if x==1]\n \n instates = np.around(statemat[:, inds], 3)\n\n outstates = np.zeros((len(inds), len(inds)), dtype=complex)\n #print(inds)\n for ii in range(len(inds)):\n shortstate = np.around(instates[sum(instates[:,ii].nonzero()), ii], 3).todense()\n outstates[:, ii] = np.squeeze( np.array( shortstate ) )\n \n return outstates","def _mutate(self,arr,p_mut):\n mut = np.random.random_sample(arr.shape)<p_mut\n no_mut = ~mut\n mut_val = np.random.uniform(low=self.minval,high=self.maxval,size=arr.shape)\n return (no_mut*arr) + (mut*mut_val)","def inv_mix_columns(state):\n mix_columns(state, m=inv_ax)","def invert_permutation(p):\n s = np.empty_like(p)\n s[p] = np.arange(p.size)\n return s","def process_state(state):\n grid = state.grid\n pos = state.pos\n reshaped_grid = np.reshape(grid,(1, grid_size*grid_size)) # Only use squared for square matrices\n reshaped_grid = reshaped_grid[0]\n processed_state = np.concatenate((pos, reshaped_grid))\n processed_state = np.array([processed_state])\n # processed_state.reshape(1, 1, grid_size*grid_size+2, 1)\n #print(processed_state.shape)\n\n return processed_state","def __init__(self, state):\n super().__init__(\"set_matrix_product_state\", len(state[0]), 0, [state])","def normalize_state(self):\n self.state = 2 * (self.state - 0.5)","def reset(self):\n self.sim.reset() \n state = np.tile(np.concatenate((self.sim.pose,self.sim.v,self.sim.angular_v),axis=0)/self.state_scale,self.action_repeat)\n\n return state","def _permute_sparse(a, dims, perm):\n perm, dims = np.asarray(perm), np.asarray(dims)\n\n # New dimensions & stride (i.e. product of preceding dimensions)\n new_dims = dims[perm]\n odim_stride = np.multiply.accumulate(dims[::-1])[::-1] // dims\n ndim_stride = np.multiply.accumulate(new_dims[::-1])[::-1] // new_dims\n\n # Range of possible coordinates for each subsys\n coos = (tuple(range(dim)) for dim in dims)\n\n # Complete basis using coordinates for current and new dimensions\n basis = np.asarray(tuple(itertools.product(*coos, repeat=1)))\n oinds = np.sum(odim_stride * basis, axis=1)\n ninds = np.sum(ndim_stride * basis[:, perm], axis=1)\n\n # Construct permutation matrix and apply it to state\n perm_mat = sp.coo_matrix((np.ones(a.shape[0]), (ninds, oinds))).tocsr()\n if isop(a):\n return dot(dot(perm_mat, a), dag(perm_mat))\n return dot(perm_mat, a)","def __update_state(self, x0, u):\n N = int(len(u) / 2)\n lower_triangular_ones_matrix = np.tril(np.ones((N, N)))\n kron = np.kron(lower_triangular_ones_matrix, np.eye(2))\n\n new_state = np.vstack([np.eye(2)] * int(N)) @ x0 + kron @ u * self.nmpc_timestep\n\n return new_state","def shortcut(self, states):\n l = len(states)\n t_ = tf.matmul(tf.concat(0, states), self._U_a)\n return tf.split(0, l, t_), l","def _normalizeState(self, Z : vector) -> vector:\n return Z * self.D","def shift_rows(state):\n state[1][0], state[1][1], state[1][2], state[1][3] = state[1][1], state[1][2], state[1][3], state[1][0]\n state[2][0], state[2][1], state[2][2], state[2][3] = state[2][2], state[2][3], state[2][0], state[2][1]\n state[3][0], state[3][1], state[3][2], state[3][3] = state[3][3], state[3][0], state[3][1], state[3][2]","def featurize_state(self,state):\r\n try:\r\n state=(state[0].item(),state[1].item())\r\n except:\r\n pass\r\n \r\n scaled_x = np.divide(np.add(state[0],-self.x_mean),np.add(self.x_max,-self.x_min))\r\n scaled_vx = np.divide(np.add(state[1],-self.vx_mean),np.add(self.vx_max,-self.vx_min))\r\n return [scaled_x,scaled_vx]","def _permute_dense(p, dims, perm):\n p, perm = np.asarray(p), np.asarray(perm)\n d = prod(dims)\n\n if isop(p):\n return (\n p.reshape([*dims, *dims])\n .transpose([*perm, *(perm + len(dims))])\n .reshape([d, d])\n )\n\n return p.reshape(dims).transpose(perm).reshape([d, 1])","def convert_state(self, x, v):\n \n\n #print(self.offset[0] * self.tiling_displacement[len(self.tiling_displacement)-1][0] / self.tile_width[0])\n\n #state = 0\n n_features = self.total_tiles[0] * self.total_tiles[1] * self.n_tilings\n state = np.zeros(n_features, dtype=int)\n print(np.shape(state))\n\n for i in range(self.n_tilings):\n # Finds the index of the tile in both dimensions\n x_tile = (x - self.offset[0] * self.tiling_displacement[i][0] - self.x_range[0] + self.extra_tiles[0] * self.tile_width[0]) // self.tile_width[0]\n v_tile = (v - self.offset[1] * self.tiling_displacement[i][1] - self.v_range[0] + self.extra_tiles[1] * self.tile_width[1]) // self.tile_width[1]\n \n #x_tile = (x - self.offset[0] * self.tiling_displacement[i][0] - self.x_range[0] + self.extra_tiles[0] * self.tile_width[0]) // self.tile_width[0]\n #v_tile = (v - self.offset[1] * self.tiling_displacement[i][1] - self.v_range[0] + self.extra_tiles[1] * self.tile_width[1]) // self.tile_width[1]\n\n index = int(i * (self.total_tiles[0]*self.total_tiles[1]) + x_tile * self.total_tiles[0] + v_tile)\n print(\"INDEX\" , index)\n state[index] = 1\n\n\n\n\n \"\"\"\n # adds the correct bit (corresponding to the state of the tiling) to the state integer\n state += 2 ** (i * self.n_tiles**2 + x_tile * self.n_tiles + v_tile)\n \"\"\"\n print (\"Tiling %s: (%s,%s)\" % (i, x_tile, v_tile))\n\n return state","def Mat_CorrectPerm(X0,X):\n\n Xout = dp(X)\n\n nX = np.shape(X)\n\n for rx in range(nX[2]):\n for ry in range(nX[3]):\n Xt = X[:,:,rx,ry]\n xx,p=CorrectPerm(X0,Xt)\n Xout[:,:,rx,ry]=xx\n\n return Xout","def tanh_inplace(a):","def permutation(self, x):\r\n x = array(x)\r\n x = roll(x, self.num_calls)\r\n self.num_calls += 1\r\n return x","def _evolve_swap_element(self, state, element):\n new_state = PhotonicState()\n for in_modes, amp in state.items():\n out_modes = []\n for in_mode in in_modes:\n offset_in_mode = in_mode - element.offset \n if offset_in_mode in element.in_modes:\n index = element.in_modes.index(offset_in_mode)\n out_mode = element.out_modes[index] + element.offset\n else:\n out_mode = in_mode \n out_modes.append(out_mode)\n new_state[tuple(sorted(out_modes))] = amp \n return new_state","def permutation(self):\n perm = np.random.permutation(self.n_samples)\n self.data = self.data.iloc[perm]\n self.labels = self.labels.iloc[perm]\n self.labels_onehot = self.labels_onehot.iloc[perm]\n self.df_perm = self.df_perm.iloc[perm]","def state_norm(state):\n size = len(state)\n norm_ = 0\n for p1 in range(size):\n for p2 in range(size):\n for p3 in range(size):\n for p4 in range(size):\n norm_ = norm_ + abs(state[p1, p2, p3, p4])**2 * factorial(p1)*factorial(p2)*factorial(p3)*factorial(p4)\n return sqrt(norm_)","def _shuffle_roidb_inds(self):\n self._perm = np.random.permutation(np.arange(len(self._roidb)))\n self._cur = 0","def permute_2d(m, p):\r\n return m[p][:, p]\r\n # unused below\r\n m_t = transpose(m)\r\n r_t = take(m_t, p, axis=0)\r\n return take(transpose(r_t), p, axis=0)","def test_FrameSetPermutationUnequal(self):\n # Initial mapping: 3 inputs, 2 outputs: 1-1, 2-2, 3=z\n # Test using arbitrary values for x,y,z\n x = 75.1\n y = -53.2\n z = 0.123\n frame1 = ast.Frame(3)\n permMap = ast.PermMap([1, 2, -1], [1, 2], [z])\n frame2 = ast.Frame(2)\n frameSet = ast.FrameSet(frame1, permMap, frame2)\n self.assertAlmostEqual(frameSet.applyForward([x, y, z]), [x, y])\n self.assertAlmostEqual(frameSet.applyInverse([x, y]), [x, y, z])\n\n # permuting the axes of the current frame also permutes the mapping\n frameSet.permAxes([2, 1])\n self.assertAlmostEqual(frameSet.applyForward([x, y, z]), [y, x])\n self.assertAlmostEqual(frameSet.applyInverse([x, y]), [y, x, z])\n\n # permuting again puts things back\n frameSet.permAxes([2, 1])\n self.assertAlmostEqual(frameSet.applyForward([x, y, z]), [x, y])\n self.assertAlmostEqual(frameSet.applyInverse([x, y]), [x, y, z])","def random_channel_swap(self, img, p = 0.5):\n if self.decision(p):\n img[:, :, 0], img[:, :, 1] = img[:, :, 1], img[:, :, 0]\n if self.decision(p):\n img[:, :, 1], img[:, :, 2] = img[:, :, 2], img[:, :, 1]\n if self.decision(p):\n img[:, :, 2], img[:, :, 0] = img[:, :, 0], img[:, :, 2]\n return img","def gather_state(self):\n self.x[0,0:self.n] = self.m[0:self.n]\n self.x[1,0:self.n] = self.r[0:self.n,0]\n self.x[2,0:self.n] = self.r[0:self.n,1]\n self.x[3,0:self.n] = self.r[0:self.n,2]\n self.x[4,0:self.n] = self.v[0:self.n,0]\n self.x[5,0:self.n] = self.v[0:self.n,1]\n self.x[6,0:self.n] = self.v[0:self.n,2]\n self.x[7,0:self.n] = self.rho[0:self.n]\n self.x[8,0:self.n] = self.p[0:self.n]\n # added second component of pressure\n self.x[9,0:self.n] = self.pco[0:self.n]\n self.x[10,0:self.n] = self.u[0:self.n]\n return(self.x)","def flip_state(box):\n\n # Pick a random number within the indices\n i = np.random.randint(0, n-1)\n j = np.random.randint(0, n-1)\n\n box[i, j] *= -1 # flip from 1 to -1 or -1 to 1\n\n return box","def reset(self):\r\n self.state = copy.copy(self.mu)","def test_random_permute_inverse_changes_group(self):\n # reproducible arbitrariness\n np.random.seed(232)\n\n nchan = 3\n nsteps = 20\n rho = 1.0/4\n target = np.random.randn(nchan, nsteps)\n\n controller = LinearController(self.G, target, tau=None)\n\n controller.set_random_permute_inverse(rho)\n self.assertIsNotNone(controller.permute_inverse)\n\n n_per_group = self.N/nchan\n groups0 = np.arange(self.N)/n_per_group\n groups1 = controller.permute_inverse/n_per_group\n\n # check that the right fraction of assignments are kept intact\n self.assertEqual(np.sum(groups0 != groups1), rho*self.N)","def permutation_helper(random_state, n, shape):\r\n # n should be a 0-dimension array\r\n assert n.shape == ()\r\n # Note that it is important to convert `n` into an integer, because if it\r\n # is a long, the numpy permutation function will crash on Windows.\r\n n = int(n.item())\r\n\r\n if shape is None:\r\n # Draw only one permutation, equivalent to shape = ()\r\n shape = ()\r\n out_shape = list(shape)\r\n out_shape.append(n)\r\n out = numpy.empty(out_shape, int)\r\n for i in numpy.ndindex(*shape):\r\n out[i] = random_state.permutation(n)\r\n\r\n #print 'RETURNING', out.shape\r\n return out","def permute(n, r):\n\n product = 1\n for i in range(n - r + 1, n + 1):\n product *= i\n return product","def lift_perm(p: Dict[int, int]) -> np.ndarray:\n n = len(p)\n pm = np.zeros((1 << n, 1 << n), dtype=complex)\n for i in range(1 << n):\n j = 0\n mask = 1 << n\n for q in range(n):\n mask >>= 1\n if (i & mask) != 0:\n j |= 1 << (n - 1 - p[q])\n pm[j][i] = 1\n return pm","def permute_As(As, leg=\"id\", cube=\"id\", inverse=False, Rps=None):\n global cubeperms, indexperms, tensorperms\n cubeperm = cubeperms[cube]\n indexperm = indexperms[leg]\n tensorperm = tensorperms[leg]\n if inverse:\n cubeperm = invert_permutation(cubeperm)\n indexperm = invert_permutation(indexperm)\n tensorperm = invert_permutation(tensorperm)\n if cubeperm != tuple(range(8)):\n As = [As[i] for i in cubeperm]\n if indexperm != tuple(range(6)):\n As = [A.transpose(indexperm) for A in As]\n if tensorperm != tuple(range(8)):\n As = [As[i] for i in tensorperm]\n\n # All modifications to Rp are done in place.\n # There was some reason why this was necessary, but now that I'm\n # reading it, I can't remember it.\n if Rps is not None:\n Rpspermed = Rps\n if cubeperm != tuple(range(8)):\n Rpspermed = [Rpspermed[i] for i in cubeperm]\n if tensorperm != tuple(range(8)):\n Rpspermed = [Rpspermed[i] for i in tensorperm]\n if indexperm != tuple(range(6)):\n for i in range(8):\n ARps = Rpspermed[i]\n ARpspermed = [ARps[i] for i in indexperm]\n Rpspermed[i] = ARpspermed\n for i in range(8):\n Rps[i] = Rpspermed[i]\n return As","def reset(self):\n newPerm = randomUtils.randomPermutation(self.xArray.tolist(),self)\n self.pot = np.asarray(newPerm)","def _apply_state_vector(self, state, device_wires):\n\n # translate to wire labels used by device\n device_wires = self.map_wires(device_wires)\n dim = 2 ** len(device_wires)\n\n state = self._asarray(state, dtype=self.C_DTYPE)\n batch_size = self._get_batch_size(state, (dim,), dim)\n output_shape = [2] * self.num_wires\n if batch_size is not None:\n output_shape.insert(0, batch_size)\n\n if len(device_wires) == self.num_wires and sorted(device_wires) == device_wires:\n # Initialize the entire device state with the input state\n self._state = self._reshape(state, output_shape)\n return\n\n # generate basis states on subset of qubits via the cartesian product\n basis_states = np.array(list(itertools.product([0, 1], repeat=len(device_wires))))\n\n # get basis states to alter on full set of qubits\n unravelled_indices = np.zeros((2 ** len(device_wires), self.num_wires), dtype=int)\n unravelled_indices[:, device_wires] = basis_states\n\n # get indices for which the state is changed to input state vector elements\n ravelled_indices = np.ravel_multi_index(unravelled_indices.T, [2] * self.num_wires)\n\n if batch_size is not None:\n state = self._scatter(\n (slice(None), ravelled_indices), state, [batch_size, 2**self.num_wires]\n )\n else:\n state = self._scatter(ravelled_indices, state, [2**self.num_wires])\n state = self._reshape(state, output_shape)\n self._state = self._asarray(state, dtype=self.C_DTYPE)","def permute_qubits(self, axes, inverse=False):\n\n self._t = self._permuted_tensor(axes, inverse=inverse)\n return self","def _create_transpose(cls, onnx_node, inputs, opset_version):\n shape = inputs[0].shape\n perm = onnx_node.getattr(\"perm\", list(range(len(shape) - 1, -1, -1)))\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\n opset_version)\n return _, forward(perm)","def permute(xs):\n return list(permutations(xs))","def transpose_inplace(x, **kwargs):\r\n dims = range(x.ndim-1, -1, -1)\r\n return elemwise.DimShuffle(x.broadcastable, dims, inplace=True)(x)","def change(self):\n return _n.reshape(self.next_x - self.x, self.original_shape)","def magic_sample(self, ys):\n\n #for each non-zero element in y\n #we want to multiply the initial state by HGate(i) SGate(i) HGate(i)\n #this turns out to be equivalent to multiplying the whole final state by\n #U H_k S_k H_k U^\\dagger\n #but H_k S_k H_k = e^{i\\pi/4} \\frac{1}{\\sqrt{2}} (I -i X_k)\n #so now we evolve identity forward by U (trivial)\n #and evolve X_k forward by U (using the AGState)\n #then we have to send the resulting Pauli through UC and UH\n #giving a third Pauli\n #then the state is of the form (we^{i\\pi/4}) UC UH (I + i^d P)/sqrt(2) |s>\n #then we apply Bravyi et al's prop. 4 to turn this into a new ch form\n \n\n chCopy = deepcopy(self.chState) #we update this copy as we go\n\n for i, y in enumerate(ys):\n if y:\n #we want to know what U_c^\\dagger U X_i U^\\dagger U_c is\n #firstly we use the A-G info\n # U X_i U^\\dagger is the i'th destabiliser\n x = self.agState.x[self.n+i]\n z = self.agState.z[self.n+i]\n r = self.agState.r[self.n+i]\n\n #print(x,z,r)\n x_col = np.array([x]).T\n z_col = np.array([z]).T\n \n #now we apply U_c to this using the CH-form info\n x_mat = chCopy.F * x_col\n z_mat = (chCopy.M * x_col + chCopy.G*z_col) % np.uint8(2)\n r = (r + util.sort_pauli_string(x_mat, z_mat)) % np.uint8(2)\n\n u = (x @ chCopy.F) % np.uint8(2)\n h = (x @ chCopy.M + z @ chCopy.G) % np.uint8(2)\n\n g = (x @ (z + chCopy.g)) % np.uint8(4)\n\n #now U_c^dag U X_i U^dag U_C = (-1)^r i^g prod_j Z_j^{h_j} X_j^{u_j}\n #we want to conjugate this by U_H\n #everywhere chCopy.v == 1 we flip a z to an x and an x to a z\n #everywhere chCopy.v == 1 and u == 1 and h == 1 we need to swap the order of our x and z so we get a minus sign\n\n u2 = u*(np.uint8(1) ^ chCopy.v) ^ (h*chCopy.v)\n h2 = (u*chCopy.v) ^ (h*(np.uint8(1) ^ chCopy.v))\n\n r = (r + (u*h*chCopy.v).sum()) % np.uint8(2)\n \n \n #now U_H^dag U_c^dag U X_i U^dag U_C U_H = (-1)^r i^g prod_j Z_j^{h2_j} X_j^{u2_j}\n\n t = u2 ^ chCopy.s\n r = (r + h2 @ t) % np.uint8(2)\n\n #now we have w UC UH |s> = w (-1)^r (i)^g UC UH |t>\n\n if all(t == chCopy.s):\n chCopy.w *= np.exp(1j*np.pi/4) * (1 + (1j)**(g+2*r -1) )/ np.sqrt(2)\n else:\n phase, VCList, v, s = util.desuperpositionise(chCopy.s, t, (g+2*r -1)%np.uint8(4), chCopy.v)\n\n chCopy.w *= phase*np.exp(1j*np.pi/4)/np.sqrt(2)\n chCopy.v = v\n chCopy.s = s\n\n for gate in VCList:\n gate.rightMultiplyC(chCopy)\n \n return chCopy","def _apply_sx(self, state, axes, inverse=False):\n if inverse:\n return 0.5 * ((1 - 1j) * state + (1 + 1j) * self._apply_x(state, axes))\n\n return 0.5 * ((1 + 1j) * state + (1 - 1j) * self._apply_x(state, axes))","def forward(self, state):\n\n x = state # Careful: deepcopy bug?\n # Intermediate Layers\n for layer in self.layers[:-1]:\n\n x = nn.ReLU()(layer(x))\n\n x = nn.Tanh()(self.layers[-1](x))\n return x","def transpose(ts: Tensor) -> Tensor:\n assert len(ts.shape) == 2\n return permute(ts, (1, 0))","def permute(\n array,\n input_order=(\"time\", \"station\", \"profile\", \"z\", \"zb\"),\n output_order=(\"station\", \"time\", \"profile\", \"z\", \"zb\"),\n):\n\n input_dimensions = input_order\n\n # filter out irrelevant dimensions\n dimensions = [x for x in output_order if x in input_dimensions]\n\n # create the mapping\n mapping = [dimensions.index(x) for x in input_dimensions]\n\n return np.transpose(array, mapping)","def permute(x, in_shape='BCD', out_shape='BCD', **kw):\n if (in_shape == out_shape) or (out_shape is None):\n return x\n if isinstance(out_shape, (list, tuple, torch.Size)):\n return x.permute(*out_shape)\n if isinstance(in_shape, str) and isinstance(out_shape, str) :\n assert set(in_shape) == set(out_shape) <= {'B', 'C', 'D'}, 'In and out shapes must have save set of chars among B, C, and D.'\n in_shape = in_shape.lower().replace('d', '...')\n out_shape = out_shape.lower().replace('d', '...')\n return torch.einsum(f'{in_shape}->{out_shape}', x)\n return x","def dens_matrix(state):\n size = len(state)\n state_conj = np.conj(state)\n dm = np.zeros((size,) * 4, dtype=complex)\n\n for p1 in range(size):\n for p2 in range(size):\n for p1_ in range(size):\n for p2_ in range(size):\n dm[p1, p2, p1_, p2_] = state[p1, p2] * state_conj[p1_, p2_]\n\n return dm","def flipud(n):\n times = lambda x: jnp.flipud(x)\n trans = lambda x: jnp.flipud(x)\n return Operator(times=times, trans=trans, shape=(n,n))","def downsample(state):\n return state[::2, ::2, :]","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def generate_pure_initial_state(state0, dimensions, states):\n\n cluster_state = 1\n\n for i, s in enumerate(states):\n d = dimensions[i]\n n = int(round((d - 1) / 2 - s))\n\n state = np.zeros(d)\n state[n] = 1\n cluster_state = np.kron(cluster_state, state)\n\n with_central_spin = np.kron(cluster_state, state0)\n\n return with_central_spin","def dens_matrix_4ch(state):\n size = len(state)\n state_conj = np.conj(state)\n dens_matrix = np.zeros((size,) * 8, dtype=complex)\n\n for p1 in range(size):\n for p2 in range(size):\n for p3 in range(size):\n for p4 in range(size):\n for p1_ in range(size):\n for p2_ in range(size):\n for p3_ in range(size):\n for p4_ in range(size):\n dens_matrix[p1, p2, p3, p4, p1_, p2_, p3_, p4_] = state[p1, p2, p3, p4] * state_conj[p1_, p2_, p3_, p4_]\n\n return dens_matrix","def normalize_transition(self):\n self._t /= self._t.sum(1)[:, np.newaxis]","def _shuffle_roidb_idx(self):\n self.perm = np.random.permutation(np.arange(self.num_images))\n self.cur = 0","def inc_rstate(self):\r\n #self.rstate = ff_2p134(self.rstate)\r\n self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)\r\n assert self.rstate.dtype == numpy.int32","def mirrored( self ):\n return self._modifier(\n self,\n lambda x: mirror_bits( x, self.nr_of_pins )\n )","def evolve_state(self):\n x = self.state\n dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.action_dim)\n self.state = x + dx\n return self.state","def permute_for_sliced_rotary(weight, num_heads, rotary_dim=None):\n if rotary_dim is not None:\n weight = weight.reshape(num_heads, weight.shape[0] // num_heads, -1)\n\n rotary_weight = weight[:, :rotary_dim]\n rotary_weight = permute_for_sliced_rotary(\n rotary_weight.reshape(num_heads * rotary_dim, -1), num_heads\n ).reshape(num_heads, rotary_dim, -1)\n\n weight[:, :rotary_dim] = rotary_weight\n\n return weight.reshape(-1, weight.shape[-1])\n\n return (\n weight.reshape(num_heads, weight.shape[0] // num_heads // 2, 2, weight.shape[1])\n .swapaxes(1, 2)\n .reshape(weight.shape[0], weight.shape[1])\n )","def flip(self, *bits):\n newargs = list(self.qubit_values)\n for i in bits:\n bit = int(self.dimension - i - 1)\n if newargs[bit] == 1:\n newargs[bit] = 0\n else:\n newargs[bit] = 1\n return self.__class__(*tuple(newargs))","def _rel_shift_legacy(self, xs):\n bs, qlen, klen, n_heads = xs.size()\n xs = xs.permute(1, 2, 0, 3).contiguous().view(qlen, klen, bs * n_heads)\n zero_pad = xs.new_zeros((qlen, 1, bs * n_heads))\n xs_shifted = torch.cat([zero_pad, xs], dim=1).view(klen + 1, qlen, bs * n_heads)[1:].view_as(xs)\n return xs_shifted.view(qlen, klen, bs, n_heads).permute(2, 0, 1, 3)","def _vshifted_pool(x):\n x = ZeroPadding2D([[1,0],[0,0]])(x)\n x = Cropping2D([[0,1],[0,0]])(x)\n\n x = MaxPooling2D(pool_size=2,strides=2,padding='same')(x)\n\n return x","def p1_pits(self):\n return self.state[:self.M]","def _updateTransitionMatrix(self):\n N = self.N\n K = self.K\n T= self.T\n\n for i in range(1,self.K+1):\n den = 0\n for t in range(1,self.T):\n for n in range(1,N+1):\n den = den + self.posterior_state_trellis[n][(t,i)]\n \n for j in range(1,self.K+1): \n # For some state i,j\n s = 0\n for n in range(1,N+1): \n for t in range(1,self.T): \n cur_prob = self.posterior_transition_trellis[n][(t,t+1,j,i)]\n s = s+cur_prob\n\n # Compute total \n self.state_transition_mat[(j,i)] = (s/den)","def permute2st(v, ndim_en=1):\n nd = v.ndim\n return v.transpose([*range(-ndim_en, 0)] + [*range(nd - ndim_en)])"],"string":"[\n \"def permute(ts: Tensor, axes) -> Tensor:\\n permute_op = PermuteOp(axes)\\n return permute_op(ts, None)\",\n \"def _apply_swap(self, state, axes, **kwargs):\\n all_axes = list(range(len(state.shape)))\\n all_axes[axes[0]] = axes[1]\\n all_axes[axes[1]] = axes[0]\\n return self._transpose(state, all_axes)\",\n \"def permute(self):\\n raise NotImplementedError()\",\n \"def permute(self, ordering: np.ndarray, *, axis: int) -> None:\\n\\t\\tif axis == 0:\\n\\t\\t\\tself.values = self.values[ordering, :]\\n\\t\\telif axis == 1:\\n\\t\\t\\tself.values = self.values[:, ordering]\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"axis must be 0 or 1\\\")\",\n \"def _evolve_phaseshift_element(self, state, element):\\n new_state = PhotonicState()\\n for in_modes, in_amp in state.items():\\n phases = element.global_U(N).diagonal()\\n phase_shift = np.prod([phases[i] for i in in_modes])\\n new_state[in_modes] = phase_shift * in_amp \\n return new_state\",\n \"def scatter_state(self,x):\\n self.m[0:self.n] = x[0,0:self.n] \\n self.r[0:self.n,0] = x[1,0:self.n]\\n self.r[0:self.n,1] = x[2,0:self.n]\\n self.r[0:self.n,2] = x[3,0:self.n]\\n self.v[0:self.n:,0] = x[4,0:self.n]\\n self.v[0:self.n:,1] = x[5,0:self.n]\\n self.v[0:self.n:,2] = x[6,0:self.n]\",\n \"def permute(self, arr):\\n\\n return arr[self.permutation_idxs]\",\n \"def scatter_state(self,x):\\n self.m[0:self.n] = x[0,0:self.n] \\n self.r[0:self.n,0] = x[1,0:self.n]\\n self.r[0:self.n,1] = x[2,0:self.n]\\n self.r[0:self.n,2] = x[3,0:self.n]\\n self.v[0:self.n:,0] = x[4,0:self.n]\\n self.v[0:self.n:,1] = x[5,0:self.n]\\n self.v[0:self.n:,2] = x[6,0:self.n]\\n self.rho[0:self.n] = x[7,0:self.n]\\n self.p[0:self.n] = x[8,0:self.n]\\n self.pco[0:self.n] = x[9,0:self.n]\\n self.u[0:self.n] = x[10,0:self.n]\",\n \"def inv_shift_rows(state):\\n state[1][0], state[1][1], state[1][2], state[1][3] = state[1][3], state[1][0], state[1][1], state[1][2]\\n state[2][0], state[2][1], state[2][2], state[2][3] = state[2][2], state[2][3], state[2][0], state[2][1]\\n state[3][0], state[3][1], state[3][2], state[3][3] = state[3][1], state[3][2], state[3][3], state[3][0]\",\n \"def permute_inputs(*,\\n states: npt.NDArray[np.int32],\\n heads: npt.NDArray[np.bool8],\\n predictions: npt.NDArray[np.float32]) -> Tuple[npt.NDArray[np.int32],\\n npt.NDArray[np.bool8],\\n npt.NDArray[np.float32]]:\\n flip_axis = len(states.shape)-1\\n\\n # rotate 90\\n state_r90 = np.rot90(states, 1, (1, 2))\\n moves_r90 = rotate_predictions(heads, 1)\\n heads_r90 = rotate_predictions(predictions, 1)\\n\\n # rotate 180\\n state_r180 = np.rot90(states, 2, (1, 2))\\n moves_r180 = rotate_predictions(heads, 2)\\n heads_r180 = rotate_predictions(predictions, 2)\\n\\n # rotate 270\\n state_r270 = np.rot90(states, 3, (1, 2))\\n moves_r270 = rotate_predictions(heads, 3)\\n heads_r270 = rotate_predictions(predictions, 3)\\n\\n # flip left - right\\n state_lr = np.flip(states, axis=flip_axis-1)\\n moves_lr = flip_predictions_left_right(heads)\\n heads_lr = flip_predictions_left_right(predictions)\\n\\n # rotate lr 90\\n state_lr_r90 = np.rot90(state_lr, 1, (1, 2))\\n moves_lr_r90 = rotate_predictions(moves_lr, 1)\\n heads_lr_r90 = rotate_predictions(heads_lr, 1)\\n\\n # rotate lr 180\\n state_lr_r180 = np.rot90(state_lr, 2, (1, 2))\\n moves_lr_r180 = rotate_predictions(moves_lr, 2)\\n heads_lr_r180 = rotate_predictions(heads_lr, 2)\\n\\n # rotate lr 270\\n state_lr_r270 = np.rot90(state_lr, 3, (1, 2))\\n moves_lr_r270 = rotate_predictions(moves_lr, 3)\\n heads_lr_r270 = rotate_predictions(heads_lr, 3)\\n\\n state_out = np.vstack([states, state_r90, state_r180, state_r270,\\n state_lr, state_lr_r90, state_lr_r180, state_lr_r270])\\n moves_out = np.vstack([heads, moves_r90, moves_r180, moves_r270,\\n moves_lr, moves_lr_r90, moves_lr_r180, moves_lr_r270])\\n heads_out = np.vstack([predictions, heads_r90, heads_r180, heads_r270,\\n heads_lr, heads_lr_r90, heads_lr_r180, heads_lr_r270])\\n\\n return state_out, moves_out, heads_out\",\n \"def permutation(random_state, size=None, n=1, ndim=None, dtype='int64'):\\r\\n ndim, size, bcast = _infer_ndim_bcast(ndim, size)\\r\\n #print \\\"NDIM\\\", ndim, size\\r\\n op = RandomFunction(permutation_helper,\\r\\n tensor.TensorType(dtype=dtype, broadcastable=bcast + (False,)),\\r\\n ndim_added=1)\\r\\n return op(random_state, size, n)\",\n \"def gather_state(self):\\n self.x[0,0:self.n] = self.m[0:self.n]\\n self.x[1,0:self.n] = self.r[0:self.n,0]\\n self.x[2,0:self.n] = self.r[0:self.n,1]\\n self.x[3,0:self.n] = self.r[0:self.n,2]\\n self.x[4,0:self.n] = self.v[0:self.n,0]\\n self.x[5,0:self.n] = self.v[0:self.n,1]\\n self.x[6,0:self.n] = self.v[0:self.n,2]\\n return(self.x)\",\n \"def mix_columns(state, m=ax):\\n for c in range(4):\\n rows = [0, 0, 0, 0]\\n\\n # For each value in the column, XOR with the result of multiplying\\n # the value from the a(x) matrix with the value from the state\\n for i in range(4):\\n rows[0] ^= ff_multiply(m[0][i], state[i][c])\\n rows[1] ^= ff_multiply(m[1][i], state[i][c])\\n rows[2] ^= ff_multiply(m[2][i], state[i][c])\\n rows[3] ^= ff_multiply(m[3][i], state[i][c])\\n\\n # Update the state only after calculating new values\\n for r in range(4):\\n state[r][c] = rows[r]\",\n \"def call(self, state):\\n x = tf.cast(state, tf.float32)\\n x = self.flatten(x)\\n x -= self.min_vals\\n x /= self.max_vals - self.min_vals\\n x = 2.0 * x - 1.0 # Rescale in range [-1, 1].\\n x = self.dense1(x)\\n x = self.dense2(x)\\n x = self.last_layer(x)\\n return x\",\n \"def make_state_appliable_4ch(state):\\n size = len(state)\\n st_appl = np.zeros((size,)*4, dtype=complex)\\n for p1 in range(size):\\n for p2 in range(size):\\n for p3 in range(size):\\n for p4 in range(size):\\n st_appl[p1, p2, p3, p4] = state[p1, p2, p3, p4] * sqrt(factorial(p1) * factorial(p2) * factorial(p3) * factorial(p4))\\n return st_appl\",\n \"def op_mirror():\\n mir = np.array([[1, 0, 0, 0],\\n [0, 1, 0, 0],\\n [0, 0, -1, 0],\\n [0, 0, 0, -1]])\\n return mir\",\n \"def _apply_cnot(self, state, axes, **kwargs):\\n ndim = self._ndim(state)\\n sl_0 = _get_slice(0, axes[0], ndim)\\n sl_1 = _get_slice(1, axes[0], ndim)\\n\\n # We will be slicing into the state according to state[sl_1], giving us all of the\\n # amplitudes with a |1> for the control qubit. The resulting array has lost an axis\\n # relative to state and we need to be careful about the axis we apply the PauliX rotation\\n # to. If axes[1] is larger than axes[0], then we need to shift the target axis down by\\n # one, otherwise we can leave as-is. For example: a state has [0, 1, 2, 3], control=1,\\n # target=3. Then, state[sl_1] has 3 axes and target=3 now corresponds to the second axis.\\n if axes[1] > axes[0]:\\n target_axes = [axes[1] - 1]\\n else:\\n target_axes = [axes[1]]\\n\\n state_x = self._apply_x(state[sl_1], axes=target_axes)\\n return self._stack([state[sl_0], state_x], axis=axes[0])\",\n \"def make_state_appliable(state):\\n size = len(state)\\n st_appl = np.zeros((size, size), dtype=complex)\\n for p1 in range(size):\\n for p2 in range(size):\\n st_appl[p1, p2] = state[p1, p2] * sqrt(factorial(p1) * factorial(p2))\\n return st_appl\",\n \"def permute_fixed(s_input):\\r\\n return permute_row_elements(s_input, p_val)\",\n \"def permute_fixed(s_input):\\r\\n return permute_row_elements(s_input, p_val)\",\n \"def permute_fixed(s_input):\\r\\n return permute_row_elements(s_input, p_val)\",\n \"def permute_fixed(s_input):\\r\\n return permute_row_elements(s_input, p_val)\",\n \"def permute_fixed(s_input):\\r\\n return permute_row_elements(s_input, p_val)\",\n \"def shift_observable(self,M):\\n u = np.array([[1]])\\n for i in range(0,minsite):\\n M[i] = np.tensordot(u, M[i],axes=(-1,1)).transpose(1,0,2)\\n l,u = self.left_cannonical(M[i])\\n M[i] = l\",\n \"def permute(p, dims, perm):\\n if issparse(p):\\n return _permute_sparse(p, dims, perm)\\n return _permute_dense(p, dims, perm)\",\n \"def test_direct_shape():\\n\\n n = 21\\n x = np.ones((n, n))\\n\\n recon = abel.direct.direct_transform(x, direction='forward')\\n assert recon.shape == (n, n) \\n\\n recon = abel.direct.direct_transform(x, direction='inverse')\\n assert recon.shape == (n, n)\",\n \"def onequbit_modes(statemat):\\n nqubit = int(np.log2(statemat.shape[0]))\\n rep = np.array(list(itertools.product((0, 1), repeat=nqubit)))\\n inds = [i for i, x in enumerate(np.sum(rep, 1)) if x==1]\\n \\n instates = np.around(statemat[:, inds], 3)\\n\\n outstates = np.zeros((len(inds), len(inds)), dtype=complex)\\n #print(inds)\\n for ii in range(len(inds)):\\n shortstate = np.around(instates[sum(instates[:,ii].nonzero()), ii], 3).todense()\\n outstates[:, ii] = np.squeeze( np.array( shortstate ) )\\n \\n return outstates\",\n \"def _mutate(self,arr,p_mut):\\n mut = np.random.random_sample(arr.shape)<p_mut\\n no_mut = ~mut\\n mut_val = np.random.uniform(low=self.minval,high=self.maxval,size=arr.shape)\\n return (no_mut*arr) + (mut*mut_val)\",\n \"def inv_mix_columns(state):\\n mix_columns(state, m=inv_ax)\",\n \"def invert_permutation(p):\\n s = np.empty_like(p)\\n s[p] = np.arange(p.size)\\n return s\",\n \"def process_state(state):\\n grid = state.grid\\n pos = state.pos\\n reshaped_grid = np.reshape(grid,(1, grid_size*grid_size)) # Only use squared for square matrices\\n reshaped_grid = reshaped_grid[0]\\n processed_state = np.concatenate((pos, reshaped_grid))\\n processed_state = np.array([processed_state])\\n # processed_state.reshape(1, 1, grid_size*grid_size+2, 1)\\n #print(processed_state.shape)\\n\\n return processed_state\",\n \"def __init__(self, state):\\n super().__init__(\\\"set_matrix_product_state\\\", len(state[0]), 0, [state])\",\n \"def normalize_state(self):\\n self.state = 2 * (self.state - 0.5)\",\n \"def reset(self):\\n self.sim.reset() \\n state = np.tile(np.concatenate((self.sim.pose,self.sim.v,self.sim.angular_v),axis=0)/self.state_scale,self.action_repeat)\\n\\n return state\",\n \"def _permute_sparse(a, dims, perm):\\n perm, dims = np.asarray(perm), np.asarray(dims)\\n\\n # New dimensions & stride (i.e. product of preceding dimensions)\\n new_dims = dims[perm]\\n odim_stride = np.multiply.accumulate(dims[::-1])[::-1] // dims\\n ndim_stride = np.multiply.accumulate(new_dims[::-1])[::-1] // new_dims\\n\\n # Range of possible coordinates for each subsys\\n coos = (tuple(range(dim)) for dim in dims)\\n\\n # Complete basis using coordinates for current and new dimensions\\n basis = np.asarray(tuple(itertools.product(*coos, repeat=1)))\\n oinds = np.sum(odim_stride * basis, axis=1)\\n ninds = np.sum(ndim_stride * basis[:, perm], axis=1)\\n\\n # Construct permutation matrix and apply it to state\\n perm_mat = sp.coo_matrix((np.ones(a.shape[0]), (ninds, oinds))).tocsr()\\n if isop(a):\\n return dot(dot(perm_mat, a), dag(perm_mat))\\n return dot(perm_mat, a)\",\n \"def __update_state(self, x0, u):\\n N = int(len(u) / 2)\\n lower_triangular_ones_matrix = np.tril(np.ones((N, N)))\\n kron = np.kron(lower_triangular_ones_matrix, np.eye(2))\\n\\n new_state = np.vstack([np.eye(2)] * int(N)) @ x0 + kron @ u * self.nmpc_timestep\\n\\n return new_state\",\n \"def shortcut(self, states):\\n l = len(states)\\n t_ = tf.matmul(tf.concat(0, states), self._U_a)\\n return tf.split(0, l, t_), l\",\n \"def _normalizeState(self, Z : vector) -> vector:\\n return Z * self.D\",\n \"def shift_rows(state):\\n state[1][0], state[1][1], state[1][2], state[1][3] = state[1][1], state[1][2], state[1][3], state[1][0]\\n state[2][0], state[2][1], state[2][2], state[2][3] = state[2][2], state[2][3], state[2][0], state[2][1]\\n state[3][0], state[3][1], state[3][2], state[3][3] = state[3][3], state[3][0], state[3][1], state[3][2]\",\n \"def featurize_state(self,state):\\r\\n try:\\r\\n state=(state[0].item(),state[1].item())\\r\\n except:\\r\\n pass\\r\\n \\r\\n scaled_x = np.divide(np.add(state[0],-self.x_mean),np.add(self.x_max,-self.x_min))\\r\\n scaled_vx = np.divide(np.add(state[1],-self.vx_mean),np.add(self.vx_max,-self.vx_min))\\r\\n return [scaled_x,scaled_vx]\",\n \"def _permute_dense(p, dims, perm):\\n p, perm = np.asarray(p), np.asarray(perm)\\n d = prod(dims)\\n\\n if isop(p):\\n return (\\n p.reshape([*dims, *dims])\\n .transpose([*perm, *(perm + len(dims))])\\n .reshape([d, d])\\n )\\n\\n return p.reshape(dims).transpose(perm).reshape([d, 1])\",\n \"def convert_state(self, x, v):\\n \\n\\n #print(self.offset[0] * self.tiling_displacement[len(self.tiling_displacement)-1][0] / self.tile_width[0])\\n\\n #state = 0\\n n_features = self.total_tiles[0] * self.total_tiles[1] * self.n_tilings\\n state = np.zeros(n_features, dtype=int)\\n print(np.shape(state))\\n\\n for i in range(self.n_tilings):\\n # Finds the index of the tile in both dimensions\\n x_tile = (x - self.offset[0] * self.tiling_displacement[i][0] - self.x_range[0] + self.extra_tiles[0] * self.tile_width[0]) // self.tile_width[0]\\n v_tile = (v - self.offset[1] * self.tiling_displacement[i][1] - self.v_range[0] + self.extra_tiles[1] * self.tile_width[1]) // self.tile_width[1]\\n \\n #x_tile = (x - self.offset[0] * self.tiling_displacement[i][0] - self.x_range[0] + self.extra_tiles[0] * self.tile_width[0]) // self.tile_width[0]\\n #v_tile = (v - self.offset[1] * self.tiling_displacement[i][1] - self.v_range[0] + self.extra_tiles[1] * self.tile_width[1]) // self.tile_width[1]\\n\\n index = int(i * (self.total_tiles[0]*self.total_tiles[1]) + x_tile * self.total_tiles[0] + v_tile)\\n print(\\\"INDEX\\\" , index)\\n state[index] = 1\\n\\n\\n\\n\\n \\\"\\\"\\\"\\n # adds the correct bit (corresponding to the state of the tiling) to the state integer\\n state += 2 ** (i * self.n_tiles**2 + x_tile * self.n_tiles + v_tile)\\n \\\"\\\"\\\"\\n print (\\\"Tiling %s: (%s,%s)\\\" % (i, x_tile, v_tile))\\n\\n return state\",\n \"def Mat_CorrectPerm(X0,X):\\n\\n Xout = dp(X)\\n\\n nX = np.shape(X)\\n\\n for rx in range(nX[2]):\\n for ry in range(nX[3]):\\n Xt = X[:,:,rx,ry]\\n xx,p=CorrectPerm(X0,Xt)\\n Xout[:,:,rx,ry]=xx\\n\\n return Xout\",\n \"def tanh_inplace(a):\",\n \"def permutation(self, x):\\r\\n x = array(x)\\r\\n x = roll(x, self.num_calls)\\r\\n self.num_calls += 1\\r\\n return x\",\n \"def _evolve_swap_element(self, state, element):\\n new_state = PhotonicState()\\n for in_modes, amp in state.items():\\n out_modes = []\\n for in_mode in in_modes:\\n offset_in_mode = in_mode - element.offset \\n if offset_in_mode in element.in_modes:\\n index = element.in_modes.index(offset_in_mode)\\n out_mode = element.out_modes[index] + element.offset\\n else:\\n out_mode = in_mode \\n out_modes.append(out_mode)\\n new_state[tuple(sorted(out_modes))] = amp \\n return new_state\",\n \"def permutation(self):\\n perm = np.random.permutation(self.n_samples)\\n self.data = self.data.iloc[perm]\\n self.labels = self.labels.iloc[perm]\\n self.labels_onehot = self.labels_onehot.iloc[perm]\\n self.df_perm = self.df_perm.iloc[perm]\",\n \"def state_norm(state):\\n size = len(state)\\n norm_ = 0\\n for p1 in range(size):\\n for p2 in range(size):\\n for p3 in range(size):\\n for p4 in range(size):\\n norm_ = norm_ + abs(state[p1, p2, p3, p4])**2 * factorial(p1)*factorial(p2)*factorial(p3)*factorial(p4)\\n return sqrt(norm_)\",\n \"def _shuffle_roidb_inds(self):\\n self._perm = np.random.permutation(np.arange(len(self._roidb)))\\n self._cur = 0\",\n \"def permute_2d(m, p):\\r\\n return m[p][:, p]\\r\\n # unused below\\r\\n m_t = transpose(m)\\r\\n r_t = take(m_t, p, axis=0)\\r\\n return take(transpose(r_t), p, axis=0)\",\n \"def test_FrameSetPermutationUnequal(self):\\n # Initial mapping: 3 inputs, 2 outputs: 1-1, 2-2, 3=z\\n # Test using arbitrary values for x,y,z\\n x = 75.1\\n y = -53.2\\n z = 0.123\\n frame1 = ast.Frame(3)\\n permMap = ast.PermMap([1, 2, -1], [1, 2], [z])\\n frame2 = ast.Frame(2)\\n frameSet = ast.FrameSet(frame1, permMap, frame2)\\n self.assertAlmostEqual(frameSet.applyForward([x, y, z]), [x, y])\\n self.assertAlmostEqual(frameSet.applyInverse([x, y]), [x, y, z])\\n\\n # permuting the axes of the current frame also permutes the mapping\\n frameSet.permAxes([2, 1])\\n self.assertAlmostEqual(frameSet.applyForward([x, y, z]), [y, x])\\n self.assertAlmostEqual(frameSet.applyInverse([x, y]), [y, x, z])\\n\\n # permuting again puts things back\\n frameSet.permAxes([2, 1])\\n self.assertAlmostEqual(frameSet.applyForward([x, y, z]), [x, y])\\n self.assertAlmostEqual(frameSet.applyInverse([x, y]), [x, y, z])\",\n \"def random_channel_swap(self, img, p = 0.5):\\n if self.decision(p):\\n img[:, :, 0], img[:, :, 1] = img[:, :, 1], img[:, :, 0]\\n if self.decision(p):\\n img[:, :, 1], img[:, :, 2] = img[:, :, 2], img[:, :, 1]\\n if self.decision(p):\\n img[:, :, 2], img[:, :, 0] = img[:, :, 0], img[:, :, 2]\\n return img\",\n \"def gather_state(self):\\n self.x[0,0:self.n] = self.m[0:self.n]\\n self.x[1,0:self.n] = self.r[0:self.n,0]\\n self.x[2,0:self.n] = self.r[0:self.n,1]\\n self.x[3,0:self.n] = self.r[0:self.n,2]\\n self.x[4,0:self.n] = self.v[0:self.n,0]\\n self.x[5,0:self.n] = self.v[0:self.n,1]\\n self.x[6,0:self.n] = self.v[0:self.n,2]\\n self.x[7,0:self.n] = self.rho[0:self.n]\\n self.x[8,0:self.n] = self.p[0:self.n]\\n # added second component of pressure\\n self.x[9,0:self.n] = self.pco[0:self.n]\\n self.x[10,0:self.n] = self.u[0:self.n]\\n return(self.x)\",\n \"def flip_state(box):\\n\\n # Pick a random number within the indices\\n i = np.random.randint(0, n-1)\\n j = np.random.randint(0, n-1)\\n\\n box[i, j] *= -1 # flip from 1 to -1 or -1 to 1\\n\\n return box\",\n \"def reset(self):\\r\\n self.state = copy.copy(self.mu)\",\n \"def test_random_permute_inverse_changes_group(self):\\n # reproducible arbitrariness\\n np.random.seed(232)\\n\\n nchan = 3\\n nsteps = 20\\n rho = 1.0/4\\n target = np.random.randn(nchan, nsteps)\\n\\n controller = LinearController(self.G, target, tau=None)\\n\\n controller.set_random_permute_inverse(rho)\\n self.assertIsNotNone(controller.permute_inverse)\\n\\n n_per_group = self.N/nchan\\n groups0 = np.arange(self.N)/n_per_group\\n groups1 = controller.permute_inverse/n_per_group\\n\\n # check that the right fraction of assignments are kept intact\\n self.assertEqual(np.sum(groups0 != groups1), rho*self.N)\",\n \"def permutation_helper(random_state, n, shape):\\r\\n # n should be a 0-dimension array\\r\\n assert n.shape == ()\\r\\n # Note that it is important to convert `n` into an integer, because if it\\r\\n # is a long, the numpy permutation function will crash on Windows.\\r\\n n = int(n.item())\\r\\n\\r\\n if shape is None:\\r\\n # Draw only one permutation, equivalent to shape = ()\\r\\n shape = ()\\r\\n out_shape = list(shape)\\r\\n out_shape.append(n)\\r\\n out = numpy.empty(out_shape, int)\\r\\n for i in numpy.ndindex(*shape):\\r\\n out[i] = random_state.permutation(n)\\r\\n\\r\\n #print 'RETURNING', out.shape\\r\\n return out\",\n \"def permute(n, r):\\n\\n product = 1\\n for i in range(n - r + 1, n + 1):\\n product *= i\\n return product\",\n \"def lift_perm(p: Dict[int, int]) -> np.ndarray:\\n n = len(p)\\n pm = np.zeros((1 << n, 1 << n), dtype=complex)\\n for i in range(1 << n):\\n j = 0\\n mask = 1 << n\\n for q in range(n):\\n mask >>= 1\\n if (i & mask) != 0:\\n j |= 1 << (n - 1 - p[q])\\n pm[j][i] = 1\\n return pm\",\n \"def permute_As(As, leg=\\\"id\\\", cube=\\\"id\\\", inverse=False, Rps=None):\\n global cubeperms, indexperms, tensorperms\\n cubeperm = cubeperms[cube]\\n indexperm = indexperms[leg]\\n tensorperm = tensorperms[leg]\\n if inverse:\\n cubeperm = invert_permutation(cubeperm)\\n indexperm = invert_permutation(indexperm)\\n tensorperm = invert_permutation(tensorperm)\\n if cubeperm != tuple(range(8)):\\n As = [As[i] for i in cubeperm]\\n if indexperm != tuple(range(6)):\\n As = [A.transpose(indexperm) for A in As]\\n if tensorperm != tuple(range(8)):\\n As = [As[i] for i in tensorperm]\\n\\n # All modifications to Rp are done in place.\\n # There was some reason why this was necessary, but now that I'm\\n # reading it, I can't remember it.\\n if Rps is not None:\\n Rpspermed = Rps\\n if cubeperm != tuple(range(8)):\\n Rpspermed = [Rpspermed[i] for i in cubeperm]\\n if tensorperm != tuple(range(8)):\\n Rpspermed = [Rpspermed[i] for i in tensorperm]\\n if indexperm != tuple(range(6)):\\n for i in range(8):\\n ARps = Rpspermed[i]\\n ARpspermed = [ARps[i] for i in indexperm]\\n Rpspermed[i] = ARpspermed\\n for i in range(8):\\n Rps[i] = Rpspermed[i]\\n return As\",\n \"def reset(self):\\n newPerm = randomUtils.randomPermutation(self.xArray.tolist(),self)\\n self.pot = np.asarray(newPerm)\",\n \"def _apply_state_vector(self, state, device_wires):\\n\\n # translate to wire labels used by device\\n device_wires = self.map_wires(device_wires)\\n dim = 2 ** len(device_wires)\\n\\n state = self._asarray(state, dtype=self.C_DTYPE)\\n batch_size = self._get_batch_size(state, (dim,), dim)\\n output_shape = [2] * self.num_wires\\n if batch_size is not None:\\n output_shape.insert(0, batch_size)\\n\\n if len(device_wires) == self.num_wires and sorted(device_wires) == device_wires:\\n # Initialize the entire device state with the input state\\n self._state = self._reshape(state, output_shape)\\n return\\n\\n # generate basis states on subset of qubits via the cartesian product\\n basis_states = np.array(list(itertools.product([0, 1], repeat=len(device_wires))))\\n\\n # get basis states to alter on full set of qubits\\n unravelled_indices = np.zeros((2 ** len(device_wires), self.num_wires), dtype=int)\\n unravelled_indices[:, device_wires] = basis_states\\n\\n # get indices for which the state is changed to input state vector elements\\n ravelled_indices = np.ravel_multi_index(unravelled_indices.T, [2] * self.num_wires)\\n\\n if batch_size is not None:\\n state = self._scatter(\\n (slice(None), ravelled_indices), state, [batch_size, 2**self.num_wires]\\n )\\n else:\\n state = self._scatter(ravelled_indices, state, [2**self.num_wires])\\n state = self._reshape(state, output_shape)\\n self._state = self._asarray(state, dtype=self.C_DTYPE)\",\n \"def permute_qubits(self, axes, inverse=False):\\n\\n self._t = self._permuted_tensor(axes, inverse=inverse)\\n return self\",\n \"def _create_transpose(cls, onnx_node, inputs, opset_version):\\n shape = inputs[0].shape\\n perm = onnx_node.getattr(\\\"perm\\\", list(range(len(shape) - 1, -1, -1)))\\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\\n opset_version)\\n return _, forward(perm)\",\n \"def permute(xs):\\n return list(permutations(xs))\",\n \"def transpose_inplace(x, **kwargs):\\r\\n dims = range(x.ndim-1, -1, -1)\\r\\n return elemwise.DimShuffle(x.broadcastable, dims, inplace=True)(x)\",\n \"def change(self):\\n return _n.reshape(self.next_x - self.x, self.original_shape)\",\n \"def magic_sample(self, ys):\\n\\n #for each non-zero element in y\\n #we want to multiply the initial state by HGate(i) SGate(i) HGate(i)\\n #this turns out to be equivalent to multiplying the whole final state by\\n #U H_k S_k H_k U^\\\\dagger\\n #but H_k S_k H_k = e^{i\\\\pi/4} \\\\frac{1}{\\\\sqrt{2}} (I -i X_k)\\n #so now we evolve identity forward by U (trivial)\\n #and evolve X_k forward by U (using the AGState)\\n #then we have to send the resulting Pauli through UC and UH\\n #giving a third Pauli\\n #then the state is of the form (we^{i\\\\pi/4}) UC UH (I + i^d P)/sqrt(2) |s>\\n #then we apply Bravyi et al's prop. 4 to turn this into a new ch form\\n \\n\\n chCopy = deepcopy(self.chState) #we update this copy as we go\\n\\n for i, y in enumerate(ys):\\n if y:\\n #we want to know what U_c^\\\\dagger U X_i U^\\\\dagger U_c is\\n #firstly we use the A-G info\\n # U X_i U^\\\\dagger is the i'th destabiliser\\n x = self.agState.x[self.n+i]\\n z = self.agState.z[self.n+i]\\n r = self.agState.r[self.n+i]\\n\\n #print(x,z,r)\\n x_col = np.array([x]).T\\n z_col = np.array([z]).T\\n \\n #now we apply U_c to this using the CH-form info\\n x_mat = chCopy.F * x_col\\n z_mat = (chCopy.M * x_col + chCopy.G*z_col) % np.uint8(2)\\n r = (r + util.sort_pauli_string(x_mat, z_mat)) % np.uint8(2)\\n\\n u = (x @ chCopy.F) % np.uint8(2)\\n h = (x @ chCopy.M + z @ chCopy.G) % np.uint8(2)\\n\\n g = (x @ (z + chCopy.g)) % np.uint8(4)\\n\\n #now U_c^dag U X_i U^dag U_C = (-1)^r i^g prod_j Z_j^{h_j} X_j^{u_j}\\n #we want to conjugate this by U_H\\n #everywhere chCopy.v == 1 we flip a z to an x and an x to a z\\n #everywhere chCopy.v == 1 and u == 1 and h == 1 we need to swap the order of our x and z so we get a minus sign\\n\\n u2 = u*(np.uint8(1) ^ chCopy.v) ^ (h*chCopy.v)\\n h2 = (u*chCopy.v) ^ (h*(np.uint8(1) ^ chCopy.v))\\n\\n r = (r + (u*h*chCopy.v).sum()) % np.uint8(2)\\n \\n \\n #now U_H^dag U_c^dag U X_i U^dag U_C U_H = (-1)^r i^g prod_j Z_j^{h2_j} X_j^{u2_j}\\n\\n t = u2 ^ chCopy.s\\n r = (r + h2 @ t) % np.uint8(2)\\n\\n #now we have w UC UH |s> = w (-1)^r (i)^g UC UH |t>\\n\\n if all(t == chCopy.s):\\n chCopy.w *= np.exp(1j*np.pi/4) * (1 + (1j)**(g+2*r -1) )/ np.sqrt(2)\\n else:\\n phase, VCList, v, s = util.desuperpositionise(chCopy.s, t, (g+2*r -1)%np.uint8(4), chCopy.v)\\n\\n chCopy.w *= phase*np.exp(1j*np.pi/4)/np.sqrt(2)\\n chCopy.v = v\\n chCopy.s = s\\n\\n for gate in VCList:\\n gate.rightMultiplyC(chCopy)\\n \\n return chCopy\",\n \"def _apply_sx(self, state, axes, inverse=False):\\n if inverse:\\n return 0.5 * ((1 - 1j) * state + (1 + 1j) * self._apply_x(state, axes))\\n\\n return 0.5 * ((1 + 1j) * state + (1 - 1j) * self._apply_x(state, axes))\",\n \"def forward(self, state):\\n\\n x = state # Careful: deepcopy bug?\\n # Intermediate Layers\\n for layer in self.layers[:-1]:\\n\\n x = nn.ReLU()(layer(x))\\n\\n x = nn.Tanh()(self.layers[-1](x))\\n return x\",\n \"def transpose(ts: Tensor) -> Tensor:\\n assert len(ts.shape) == 2\\n return permute(ts, (1, 0))\",\n \"def permute(\\n array,\\n input_order=(\\\"time\\\", \\\"station\\\", \\\"profile\\\", \\\"z\\\", \\\"zb\\\"),\\n output_order=(\\\"station\\\", \\\"time\\\", \\\"profile\\\", \\\"z\\\", \\\"zb\\\"),\\n):\\n\\n input_dimensions = input_order\\n\\n # filter out irrelevant dimensions\\n dimensions = [x for x in output_order if x in input_dimensions]\\n\\n # create the mapping\\n mapping = [dimensions.index(x) for x in input_dimensions]\\n\\n return np.transpose(array, mapping)\",\n \"def permute(x, in_shape='BCD', out_shape='BCD', **kw):\\n if (in_shape == out_shape) or (out_shape is None):\\n return x\\n if isinstance(out_shape, (list, tuple, torch.Size)):\\n return x.permute(*out_shape)\\n if isinstance(in_shape, str) and isinstance(out_shape, str) :\\n assert set(in_shape) == set(out_shape) <= {'B', 'C', 'D'}, 'In and out shapes must have save set of chars among B, C, and D.'\\n in_shape = in_shape.lower().replace('d', '...')\\n out_shape = out_shape.lower().replace('d', '...')\\n return torch.einsum(f'{in_shape}->{out_shape}', x)\\n return x\",\n \"def dens_matrix(state):\\n size = len(state)\\n state_conj = np.conj(state)\\n dm = np.zeros((size,) * 4, dtype=complex)\\n\\n for p1 in range(size):\\n for p2 in range(size):\\n for p1_ in range(size):\\n for p2_ in range(size):\\n dm[p1, p2, p1_, p2_] = state[p1, p2] * state_conj[p1_, p2_]\\n\\n return dm\",\n \"def flipud(n):\\n times = lambda x: jnp.flipud(x)\\n trans = lambda x: jnp.flipud(x)\\n return Operator(times=times, trans=trans, shape=(n,n))\",\n \"def downsample(state):\\n return state[::2, ::2, :]\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def generate_pure_initial_state(state0, dimensions, states):\\n\\n cluster_state = 1\\n\\n for i, s in enumerate(states):\\n d = dimensions[i]\\n n = int(round((d - 1) / 2 - s))\\n\\n state = np.zeros(d)\\n state[n] = 1\\n cluster_state = np.kron(cluster_state, state)\\n\\n with_central_spin = np.kron(cluster_state, state0)\\n\\n return with_central_spin\",\n \"def dens_matrix_4ch(state):\\n size = len(state)\\n state_conj = np.conj(state)\\n dens_matrix = np.zeros((size,) * 8, dtype=complex)\\n\\n for p1 in range(size):\\n for p2 in range(size):\\n for p3 in range(size):\\n for p4 in range(size):\\n for p1_ in range(size):\\n for p2_ in range(size):\\n for p3_ in range(size):\\n for p4_ in range(size):\\n dens_matrix[p1, p2, p3, p4, p1_, p2_, p3_, p4_] = state[p1, p2, p3, p4] * state_conj[p1_, p2_, p3_, p4_]\\n\\n return dens_matrix\",\n \"def normalize_transition(self):\\n self._t /= self._t.sum(1)[:, np.newaxis]\",\n \"def _shuffle_roidb_idx(self):\\n self.perm = np.random.permutation(np.arange(self.num_images))\\n self.cur = 0\",\n \"def inc_rstate(self):\\r\\n #self.rstate = ff_2p134(self.rstate)\\r\\n self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)\\r\\n assert self.rstate.dtype == numpy.int32\",\n \"def mirrored( self ):\\n return self._modifier(\\n self,\\n lambda x: mirror_bits( x, self.nr_of_pins )\\n )\",\n \"def evolve_state(self):\\n x = self.state\\n dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(self.action_dim)\\n self.state = x + dx\\n return self.state\",\n \"def permute_for_sliced_rotary(weight, num_heads, rotary_dim=None):\\n if rotary_dim is not None:\\n weight = weight.reshape(num_heads, weight.shape[0] // num_heads, -1)\\n\\n rotary_weight = weight[:, :rotary_dim]\\n rotary_weight = permute_for_sliced_rotary(\\n rotary_weight.reshape(num_heads * rotary_dim, -1), num_heads\\n ).reshape(num_heads, rotary_dim, -1)\\n\\n weight[:, :rotary_dim] = rotary_weight\\n\\n return weight.reshape(-1, weight.shape[-1])\\n\\n return (\\n weight.reshape(num_heads, weight.shape[0] // num_heads // 2, 2, weight.shape[1])\\n .swapaxes(1, 2)\\n .reshape(weight.shape[0], weight.shape[1])\\n )\",\n \"def flip(self, *bits):\\n newargs = list(self.qubit_values)\\n for i in bits:\\n bit = int(self.dimension - i - 1)\\n if newargs[bit] == 1:\\n newargs[bit] = 0\\n else:\\n newargs[bit] = 1\\n return self.__class__(*tuple(newargs))\",\n \"def _rel_shift_legacy(self, xs):\\n bs, qlen, klen, n_heads = xs.size()\\n xs = xs.permute(1, 2, 0, 3).contiguous().view(qlen, klen, bs * n_heads)\\n zero_pad = xs.new_zeros((qlen, 1, bs * n_heads))\\n xs_shifted = torch.cat([zero_pad, xs], dim=1).view(klen + 1, qlen, bs * n_heads)[1:].view_as(xs)\\n return xs_shifted.view(qlen, klen, bs, n_heads).permute(2, 0, 1, 3)\",\n \"def _vshifted_pool(x):\\n x = ZeroPadding2D([[1,0],[0,0]])(x)\\n x = Cropping2D([[0,1],[0,0]])(x)\\n\\n x = MaxPooling2D(pool_size=2,strides=2,padding='same')(x)\\n\\n return x\",\n \"def p1_pits(self):\\n return self.state[:self.M]\",\n \"def _updateTransitionMatrix(self):\\n N = self.N\\n K = self.K\\n T= self.T\\n\\n for i in range(1,self.K+1):\\n den = 0\\n for t in range(1,self.T):\\n for n in range(1,N+1):\\n den = den + self.posterior_state_trellis[n][(t,i)]\\n \\n for j in range(1,self.K+1): \\n # For some state i,j\\n s = 0\\n for n in range(1,N+1): \\n for t in range(1,self.T): \\n cur_prob = self.posterior_transition_trellis[n][(t,t+1,j,i)]\\n s = s+cur_prob\\n\\n # Compute total \\n self.state_transition_mat[(j,i)] = (s/den)\",\n \"def permute2st(v, ndim_en=1):\\n nd = v.ndim\\n return v.transpose([*range(-ndim_en, 0)] + [*range(nd - ndim_en)])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64760816","0.6295351","0.6107544","0.5883316","0.58024627","0.57838786","0.5779762","0.5742571","0.56866443","0.56657296","0.5552952","0.55455023","0.5537342","0.5535267","0.5505405","0.54731023","0.54124594","0.5388099","0.5386475","0.5386475","0.5386475","0.5386475","0.5386475","0.5350013","0.5329798","0.53283423","0.5320189","0.5314892","0.5310758","0.53019047","0.52976876","0.52976596","0.5293928","0.527756","0.527728","0.5276755","0.52743757","0.5266123","0.5248087","0.5245612","0.52344453","0.52266836","0.52233285","0.5220827","0.52194756","0.521606","0.52145463","0.5207259","0.5206057","0.5202842","0.5165991","0.5158903","0.5153775","0.5149773","0.51462954","0.5144254","0.5140177","0.51311034","0.5129926","0.5128349","0.51111186","0.51076686","0.5104957","0.51047647","0.510225","0.5101577","0.50938517","0.5090123","0.5079454","0.50714","0.5058695","0.5050551","0.50317866","0.503138","0.50303733","0.5021431","0.50179684","0.50179684","0.50179684","0.50179684","0.50179684","0.50179684","0.50179684","0.50179684","0.50179684","0.50179684","0.50179684","0.50153625","0.5015218","0.5013616","0.5009782","0.5007276","0.5004024","0.4989362","0.4981161","0.49773678","0.49772236","0.4977077","0.49767196","0.49761257","0.49746537"],"string":"[\n \"0.64760816\",\n \"0.6295351\",\n \"0.6107544\",\n \"0.5883316\",\n \"0.58024627\",\n \"0.57838786\",\n \"0.5779762\",\n \"0.5742571\",\n \"0.56866443\",\n \"0.56657296\",\n \"0.5552952\",\n \"0.55455023\",\n \"0.5537342\",\n \"0.5535267\",\n \"0.5505405\",\n \"0.54731023\",\n \"0.54124594\",\n \"0.5388099\",\n \"0.5386475\",\n \"0.5386475\",\n \"0.5386475\",\n \"0.5386475\",\n \"0.5386475\",\n \"0.5350013\",\n \"0.5329798\",\n \"0.53283423\",\n \"0.5320189\",\n \"0.5314892\",\n \"0.5310758\",\n \"0.53019047\",\n \"0.52976876\",\n \"0.52976596\",\n \"0.5293928\",\n \"0.527756\",\n \"0.527728\",\n \"0.5276755\",\n \"0.52743757\",\n \"0.5266123\",\n \"0.5248087\",\n \"0.5245612\",\n \"0.52344453\",\n \"0.52266836\",\n \"0.52233285\",\n \"0.5220827\",\n \"0.52194756\",\n \"0.521606\",\n \"0.52145463\",\n \"0.5207259\",\n \"0.5206057\",\n \"0.5202842\",\n \"0.5165991\",\n \"0.5158903\",\n \"0.5153775\",\n \"0.5149773\",\n \"0.51462954\",\n \"0.5144254\",\n \"0.5140177\",\n \"0.51311034\",\n \"0.5129926\",\n \"0.5128349\",\n \"0.51111186\",\n \"0.51076686\",\n \"0.5104957\",\n \"0.51047647\",\n \"0.510225\",\n \"0.5101577\",\n \"0.50938517\",\n \"0.5090123\",\n \"0.5079454\",\n \"0.50714\",\n \"0.5058695\",\n \"0.5050551\",\n \"0.50317866\",\n \"0.503138\",\n \"0.50303733\",\n \"0.5021431\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50179684\",\n \"0.50153625\",\n \"0.5015218\",\n \"0.5013616\",\n \"0.5009782\",\n \"0.5007276\",\n \"0.5004024\",\n \"0.4989362\",\n \"0.4981161\",\n \"0.49773678\",\n \"0.49772236\",\n \"0.4977077\",\n \"0.49767196\",\n \"0.49761257\",\n \"0.49746537\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17496,"cells":{"query":{"kind":"string","value":"Helper method that takes in a variable number of namedtuples and construct a joint namedtuple for writeout"},"document":{"kind":"string","value":"def construct_out_logfields(*tups):\n fields = []\n\n for tupStruct in tups:\n for k in tupStruct._fields:\n if k.startswith(\"Diagnostic\"):\n k_str = k.replace('_', '/', 1)\n fields.append(k_str)\n else:\n for st in stat_list:\n fields.append(f'{k}/{st}')\n\n return fields"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def tuple_namer(name,tupl):\n tupl_templ = collections.namedtuple(name, 'battery status neighbour')\n named = tupl_templ(battery = tupl[0], status = tupl[1], neighbour = tupl[2])\n return named","def core_named_tuple(user_kwargs: dict, disdat_kwargs: dict) -> NamedTuple('Output', [('user_kwargs', dict),\n ('disdat_kwargs', dict)]):\n import collections\n Output = collections.namedtuple('Output', ['user_kwargs', 'disdat_kwargs'])\n return Output(user_kwargs, disdat_kwargs)","def namedtuple(typename, field_names, verbose=False, rename=False):\r\n\r\n # Parse and validate the field names. Validation serves two purposes,\r\n # generating informative error messages and preventing template injection attacks.\r\n if isinstance(field_names, basestring):\r\n field_names = field_names.replace(',', ' ').split() # names separated by whitespace and/or commas\r\n field_names = tuple(map(str, field_names))\r\n if rename:\r\n names = list(field_names)\r\n seen = set()\r\n for i, name in enumerate(names):\r\n if (not all(c.isalnum() or c=='_' for c in name) or _iskeyword(name)\r\n or not name or name[0].isdigit() or name.startswith('_')\r\n or name in seen):\r\n names[i] = '_%d' % i\r\n seen.add(name)\r\n field_names = tuple(names)\r\n for name in (typename,) + field_names:\r\n if not all(c.isalnum() or c=='_' for c in name):\r\n raise ValueError('Type names and field names can only contain alphanumeric characters and underscores: %r' % name)\r\n if _iskeyword(name):\r\n raise ValueError('Type names and field names cannot be a keyword: %r' % name)\r\n if name[0].isdigit():\r\n raise ValueError('Type names and field names cannot start with a number: %r' % name)\r\n seen_names = set()\r\n for name in field_names:\r\n if name.startswith('_') and not rename:\r\n raise ValueError('Field names cannot start with an underscore: %r' % name)\r\n if name in seen_names:\r\n raise ValueError('Encountered duplicate field name: %r' % name)\r\n seen_names.add(name)\r\n\r\n # Create and fill-in the class template\r\n numfields = len(field_names)\r\n argtxt = repr(field_names).replace(\"'\", \"\")[1:-1] # tuple repr without parens or quotes\r\n reprtxt = ', '.join('%s=%%r' % name for name in field_names)\r\n template = '''class %(typename)s(tuple):\r\n '%(typename)s(%(argtxt)s)' \\n\r\n __slots__ = () \\n\r\n _fields = %(field_names)r \\n\r\n def __new__(_cls, %(argtxt)s):\r\n 'Create new instance of %(typename)s(%(argtxt)s)'\r\n return _tuple.__new__(_cls, (%(argtxt)s)) \\n\r\n @classmethod\r\n def _make(cls, iterable, new=tuple.__new__, len=len):\r\n 'Make a new %(typename)s object from a sequence or iterable'\r\n result = new(cls, iterable)\r\n if len(result) != %(numfields)d:\r\n raise TypeError('Expected %(numfields)d arguments, got %%d' %% len(result))\r\n return result \\n\r\n def __repr__(self):\r\n 'Return a nicely formatted representation string'\r\n return '%(typename)s(%(reprtxt)s)' %% self \\n\r\n def _asdict(self):\r\n 'Return a new OrderedDict which maps field names to their values'\r\n return OrderedDict(zip(self._fields, self)) \\n\r\n def _replace(_self, **kwds):\r\n 'Return a new %(typename)s object replacing specified fields with new values'\r\n result = _self._make(map(kwds.pop, %(field_names)r, _self))\r\n if kwds:\r\n raise ValueError('Got unexpected field names: %%r' %% kwds.keys())\r\n return result \\n\r\n def __getnewargs__(self):\r\n 'Return self as a plain tuple. Used by copy and pickle.'\r\n return tuple(self) \\n\\n''' % locals()\r\n for i, name in enumerate(field_names):\r\n template += \" %s = _property(_itemgetter(%d), doc='Alias for field number %d')\\n\" % (name, i, i)\r\n if verbose:\r\n print template\r\n\r\n # Execute the template string in a temporary namespace and\r\n # support tracing utilities by setting a value for frame.f_globals['__name__']\r\n namespace = dict(_itemgetter=_itemgetter, __name__='namedtuple_%s' % typename,\r\n OrderedDict=OrderedDict, _property=property, _tuple=tuple)\r\n try:\r\n exec template in namespace\r\n except SyntaxError, e:\r\n raise SyntaxError(e.message + ':\\n' + template)\r\n result = namespace[typename]\r\n\r\n # For pickling to work, the __module__ variable needs to be set to the frame\r\n # where the named tuple is created. Bypass this step in enviroments where\r\n # sys._getframe is not defined (Jython for example) or sys._getframe is not\r\n # defined for arguments greater than 0 (IronPython).\r\n try:\r\n result.__module__ = _sys._getframe(1).f_globals.get('__name__', '__main__')\r\n except (AttributeError, ValueError):\r\n pass\r\n\r\n return result","def make_tuple(in_dict,tupname='values'):\n the_tup = namedtuple(tupname, in_dict.keys())\n the_tup = the_tup(**in_dict)\n return the_tup","def make_tuple(name, **kwargs):\n tuple_class = collections.namedtuple(typename=name, field_names=kwargs.keys())\n return tuple_class(**kwargs)","def writenames(sql, names, indent=0, id_offset=0, lang_offset=0):\n i = 0\n for row in names:\n j = 0\n if i > 0:\n sql.write(\",\\n\")\n for name in row:\n if j > 0:\n sql.write(\",\\n\")\n sql.write(commajoin([i+id_offset, j+lang_offset, name], [2], indent))\n j += 1\n i += 1","def MakeTuple(name, **kwargs):\n tuple_class = collections.namedtuple(\n typename=name,\n field_names=kwargs.keys(),\n )\n return tuple_class(**kwargs)","def create_namedtuple():\n Thing = namedtuple(\"MyCoolTuple\", \"field1 field2\")\n obj = Thing(1, 2)\n # Throws AttributeError\n #obj.random = \"Whoa\"\n print(Thing)\n print(obj)","def get_namedtuple(name, field_names, values=None):\n if values is None:\n values = range(len(field_names))\n field_names = [f.replace(' ', '_') for f in field_names]\n return _namedtuple(name, field_names)(*values)","def namedtuple(*args, **kwargs):\n cls = _namedtuple(*args, **kwargs)\n _NamedTupleABC.register(cls)\n return cls","def create_record_tuple(ilist):\n list_length = len(ilist)\n if list_length:\n rec_tuple = '('\n for row in list[0:list_length-1]:\n rec_tuple += str(row)\n rec_tuple += ','\n rec_tuple += str(list[list_length-1])\n rec_tuple += ')'\n else: rec_tuple = '()'\n return rec_tuple","def tuple(self, arg: SeField[Any]) -> str:\n if is_bare_tuple(arg.type):\n return arg.varname\n elif is_variable_tuple(arg.type):\n earg = arg[0]\n earg.name = \"v\"\n return f\"tuple({self.render(earg)} for v in {arg.varname})\"\n else:\n rvalues = []\n for i, _ in enumerate(type_args(arg.type)):\n r = arg[i]\n r.name = f\"{arg.varname}[{i}]\"\n rvalues.append(self.render(r))\n return f\"({', '.join(rvalues)},)\" # trailing , is required for single element tuples","def _entuple(r):\n return tuple(getattr(r, n) for n in r.__names__)","def add_make_tuple(self, input_names, name=None):\n return self._build_op('make_tuple', input_names, name=name)","def make_out_struct(self):\n args ={\n \"name\": self.python_madz_types + (\"OUTSTRUCT\" if self.namespace == \"\" else self._namespace_mangle(self.namespace)),\n \"fields\":\"\"\n }\n\n res = \\\n\"\"\"class {name}(Structure):\n _fields_ = [{fields}]\n\"\"\"\n for node in self.description.definitions():\n args['fields'] += self.gen_type_tuple_string(node.name, node.type) + \", \"\n\n return res.format(**args)","def _named_attrs(self, parts:dict) -> \\\n (QA4SMNamedAttributes, list, QA4SMNamedAttributes):\n\n if not self.ismetr():\n raise IOError(self.varname, '{} is not in form of a QA4SM metric variable.')\n\n if self.g == 0:\n a = QA4SMAttributes(self.attrs)\n ref_ds = QA4SMNamedAttributes(a.ref_dc - a._offset_id_dc,\n a.get_ref_names()['short_name'], self.attrs)\n return ref_ds, None, None\n else:\n dss = []\n ref_ds = QA4SMNamedAttributes(parts['ref_id'], parts['ref_ds'], self.attrs)\n ds = QA4SMNamedAttributes(parts['sat_id0'], parts['sat_ds0'], self.attrs)\n dss.append(ds)\n if self.g == 3:\n ds = QA4SMNamedAttributes(parts['sat_id1'], parts['sat_ds1'], self.attrs)\n dss.append(ds)\n mds = QA4SMNamedAttributes(parts['mds_id'], parts['mds'], self.attrs)\n else:\n mds = None\n return ref_ds, dss, mds","def __generate_delimited_objects__(self, output, **kwargs):\n triple_map = kwargs.get(\"triple_map\")\n subject = kwargs.get('subject')\n # Subject is blank-node, try to retrieve subject IRI\n predicate = kwargs.get('predicate')\n element = kwargs.get('element')\n datatype = kwargs.get('datatype', None)\n delimiters = kwargs.get('delimiters')\n subjects = []\n for delimiter in delimiters:\n values = element.text.split(delimiter)\n for row in values:\n if datatype is not None:\n obj_ = rdflib.Literal(row.strip(), datatype=datatype)\n else:\n obj_ = rdflib.Literal(row.strip())\n if isinstance(subject, rdflib.BNode):\n new_subject = rdflib.BNode()\n class_ = triple_map.subjectMap.class_\n output.add((new_subject, NS_MGR.rdf.type.rdflib, class_))\n for parent_subject, parent_predicate in \\\n output.subject_predicates(object=subject):\n output.add((parent_subject,\n parent_predicate,\n new_subject))\n else:\n new_subject = subject\n subjects.append(new_subject)\n output.add((new_subject, predicate, obj_))\n return subjects","def namedtuple(*args, **kwargs):\n nt_class = _invisiblenamedtuple(*args, **kwargs)\n # put it on the global scale so it can be tupled correctly\n globals()[nt_class.__name__] = nt_class\n return nt_class","def build_multi_index_tuples(header: list, sub_header: list) -> list:\r\n\r\n tuples = []\r\n\r\n for head in header:\r\n for sub_head in sub_header:\r\n tuples.append((head, sub_head))\r\n\r\n return tuples","def _tuple_to_cpppo_tag_multiple(cls, what, values=None, serializer=':'):\n tag_string = ''\n\n if values == None:\n for i in range(len(what)):\n tag_string += what[i][0] + EnipProtocol._SERIALIZER + str(what[i][1]) + \" \"\n else:\n for i in range(len(what)):\n tag_string += what[i][0] + EnipProtocol._SERIALIZER + str(what[i][1]) + \"=\" + str(values[i]) + \" \"\n\n return tag_string","def gen_type_tuple_string(self, name, node):\n return \"('{}', {})\".format(name, self.gen_type_string(node))","def namedtuple_with_two_defaults(typename, field_names, default_values=('', '')):\n T = collections.namedtuple(typename, field_names)\n T.__new__.__defaults__ = default_values\n return T","def name_fields(record):\n record[\"names_sim\"] = record.get(\"author_tesim\")\n if record.get(\"author_tesim\") is not None:\n if record.get(\"names_sim\") is not None:\n record[\"names_sim\"] = record[\"names_sim\"] + record.get(\"author_tesim\")\n else:\n record[\"names_sim\"] = record.get(\"author_tesim\")\n\n if record.get(\"scribe_tesim\") is not None:\n if record.get(\"names_sim\") is not None:\n record[\"names_sim\"] = record[\"names_sim\"] + record.get(\"scribe_tesim\")\n else:\n record[\"names_sim\"] = record.get(\"scribe_tesim\")\n\n if record.get(\"associated_name_tesim\") is not None:\n if record.get(\"names_sim\") is not None:\n record[\"names_sim\"] = record[\"names_sim\"] + record.get(\"associated_name_tesim\")\n else:\n record[\"names_sim\"] = record.get(\"associated_name_tesim\")\n\n if record.get(\"translator_tesim\") is not None:\n if record.get(\"names_sim\") is not None:\n record[\"names_sim\"] = record[\"names_sim\"] + record.get(\"translator_tesim\")\n else:\n record[\"names_sim\"] = record.get(\"translator_tesim\")\n return record[\"names_sim\"]","def namedtuple(clz: Type) -> Type[NamedTuple]:\n if not is_dataclass(clz):\n raise TypeError(\"{} is not a dataclass.\".format(clz.__name__))\n\n flds: Tuple[Field] = fields(clz)\n flds_by_name: Dict[str, Field] = {f.name: f for f in flds}\n nt_name: str = clz.__name__ + \"NamedTuple\"\n\n defaults: Optional[List[Any]] = None\n field_names: List[str] = [\n f.name\n for f in flds\n if f.default is not None or f.default_factory is not None\n ]\n for fld in field_names:\n if defaults is None:\n defaults = []\n field: Field = flds_by_name[fld]\n default_value: Optional[Any] = field.default\n if default_value is None:\n default_value = field.default_factory()\n\n defaults.append(default_value)\n\n field_names.extend(\n [\n f.name\n for f in flds\n if f.default is None and f.default_factory is None\n ]\n )\n\n return real_namedtuple(nt_name, field_names, defaults=defaults)","def _wr_3fmt_wrtbl(goea_results, wr_params, log):\n from goatools.go_enrichment import get_nts\n goea_nts = get_nts(goea_results)\n # List of all fields, printable or not, in namedtuple (nt):\n log.write(\"\\nnamedtuple FIELDS: {F}\\n\".format(F=\" \".join(goea_nts[0]._fields)))\n # Print to Excel Spreadsheet\n title=\"Print subset of namedtuple fields\"\n wr_xlsx(\"nbt3102_subset_nt.xlsx\", goea_nts, title=title, **wr_params)\n # Print to tab-separated file\n wr_tsv(\"nbt3102_subset_nt.tsv\", goea_nts, **wr_params)","def namedtuples_to_xlsx(filename, values):\n try:\n # Use first row to generate header\n peek = next(values)\n header = list(peek.__class__._fields)\n return xlsx_write_rows(filename, itertools.chain([header], [peek], values))\n except StopIteration: # Empty generator\n # Write empty xlsx\n return xlsx_write_rows(filename, [])","def concat(inp):\n if(type(inp) == tuple):\n return\n if(inp.getName() == '&'):\n if(inp.getFirst().getName() == 'tt' and inp.getSec() is not None):\n inp.setName(inp.getSec().getName())\n inp.setFirst(inp.getSec().getFirst())\n inp.setSec(inp.getSec().getSec())\n if(inp.getSec() is None):\n return\n if(inp.getSec().getName() == 'tt' and inp.getFirst() is not None):\n inp.setName(inp.getFirst().getName())\n if(inp.getName() in doubles or inp.getName() in singles):\n inp.setFirst(inp.getFirst().getFirst())\n inp.setSec(inp.getFirst().getSec())\n else:\n inp.setAtom()","def generate_attributes(self):\n for group in self.dict:\n for param in self.dict[group]:\n if group in self.group_douplicate and param in self.name_douplicate:\n setattr(self, group+'_'+param, self(group, param))\n else:\n setattr(self, param, self(group, param))","def export_names(ibs, nid_list, new_dbpath=None):\n logger.info('Exporting name nid_list={!r}'.format(nid_list))\n if new_dbpath is None:\n new_dbpath = make_new_dbpath(ibs, 'nid', nid_list)\n\n aid_list = ut.flatten(ibs.get_name_aids(nid_list))\n gid_list = ut.unique_unordered(ibs.get_annot_gids(aid_list))\n\n return export_data(ibs, gid_list, aid_list, nid_list, new_dbpath=new_dbpath)","def issue_namedtuple_subclass_works_as_expected():\n from collections import namedtuple\n\n class Crash(namedtuple(\"C\", \"foo bar\")):\n \"\"\" Looking for attributes in __str__ will crash,\n because EmptyNodes can't be infered.\n \"\"\"\n def __str__(self):\n return \"{0.foo}: {0.bar}\".format(self)\n return Crash","def skeleton_getNameDicts(self, combined = False, count = None, iterName= None, cgmType = None, **kws):\n _str_func = 'skeleton_getNameDicts'\n log.debug(\"|{0}| >> \".format(_str_func)+ '-'*80)\n log.debug(\"{0}\".format(self)) \n\n l_res = []\n if count is not None:\n _number = count\n else:\n _number = self.numJoints\n mModule = self.moduleTarget\n _baseNameAttrs = ATTR.datList_getAttrs(self.mNode,'nameList')\n _l_baseNames = ATTR.datList_get(self.mNode, 'nameList')\n log.debug(\"|{0}| >> baseNames: {1}\".format(_str_func,_l_baseNames))\n if not _baseNameAttrs and not _l_baseNames:\n _l_baseNames = []\n for i in range(_number):\n _l_baseNames.append(self.cgmName)\n else:\n _l_baseNames = []\n for i,a in enumerate(_baseNameAttrs):\n _l_baseNames.append(self.getMayaAttr(a))\n #Name dict...\n _nameDict ={}\n \n for s in 'cgmDirection','cgmPosition':\n _val = mModule.getMayaAttr(s)\n if _val and _val not in ['none','None','False']:\n _nameDict[s] = _val\n \"\"\"\n if mModule.getMayaAttr('cgmDirection'):\n _nameDict['cgmDirection'] = mModule.cgmDirection\n if mModule.getMayaAttr('cgmPosition'):\n _pos = mModule.cgmPosition\n if _pos and _pos not in ['none','None','False']:\n _nameDict['cgmPosition']=mModule.cgmPosition\"\"\"\n \n if iterName:\n log.debug(\"|{0}| >> iterName: {1}\".format(_str_func,iterName)) \n _nameDict['cgmName'] = iterName\n elif self.getMayaAttr('nameIter'):\n _nameDict['cgmName'] = self.nameIter\n elif self.getMayaAttr('cgmName'):\n _nameDict['cgmName'] = self.cgmName\n else:\n _nameDict['cgmName'] = self.blockType\n \n if cgmType:\n _nameDict['cgmType'] = cgmType\n else:\n _nameDict['cgmType'] = 'skinJoint'\n \n \n for a,v in kws.iteritems():\n _nameDict[a] = v\n \n log.debug(\"|{0}| >> baseDict: {1}\".format(_str_func,_nameDict))\n\n _cnt = 0\n l_range = range(_number)\n l_dicts = []\n for i in l_range:\n _nameDictTemp = copy.copy(_nameDict)\n _specialName = False\n _cnt+=1\n if i == 0:\n if _l_baseNames[0]:\n log.debug(\"|{0}| >> First and name attr...interupting default name\".format(_str_func)) \n _nameDictTemp['cgmName'] = _l_baseNames[0]\n #_nameDict['cgmName'] = _l_baseNames[0]#...interupt the default name...\n _cnt = 0\n _specialName = True\n elif i == len(l_range) -1:\n log.debug(\"|{0}| >> last...\".format(_str_func)) \n if _l_baseNames[-1]:\n log.debug(\"|{0}| >> found: {1}\".format(_str_func,_l_baseNames[-1])) \n _nameDictTemp['cgmName'] = _l_baseNames[-1]\n _specialName = True\n _cnt = 0\n \n if not _specialName:\n _nameDictTemp['cgmIterator'] = _cnt\n \n if _cnt == 1 and l_dicts and not l_dicts[-1].get('cgmIterator'):\n l_dicts[-1]['cgmIterator'] = 0\n \n l_dicts.append(_nameDictTemp)\n log.debug(\"|{0}| >> [{1}]: {2}\".format(_str_func,i,_nameDict))\n \n if combined:\n return [NAMETOOLS.returnCombinedNameFromDict(d) for d in l_dicts]\n return l_dicts","def create_name_tuple(self, parsl_file_obj, in_or_out):\n # Determine new_name\n new_name = parsl_file_obj.filepath\n if parsl_file_obj.filepath not in self.used_names:\n if self.shared_fs is False:\n new_name = self.create_new_name(os.path.basename(parsl_file_obj.filepath))\n self.used_names[parsl_file_obj.filepath] = new_name\n else:\n new_name = self.used_names[parsl_file_obj.filepath]\n # Determine file_is_shared\n file_is_shared = False\n if parsl_file_obj in self.registered_files:\n file_is_shared = True\n if parsl_file_obj not in self.shared_files:\n self.shared_files.add(parsl_file_obj)\n else:\n self.registered_files.add(parsl_file_obj)\n return (parsl_file_obj.filepath, new_name, file_is_shared, in_or_out)","def strtuple(iterable): \n string = ''\n function = type(strtuple)\n for i in iterable:\n if isinstance(i , function):\n string += i.__name__ + ', '\n else:\n string += str(i) + ', '\n string = string.rstrip(', ')\n string = '(' + string + ')'\n return string","def iter_tuples(self, with_index=True, name=\"Row\"):\n columns = {column: to_identifier(column) for column in self._columns}\n\n fields = [\"index\"] if with_index else []\n fields.extend(columns.values())\n\n container = namedtuple(name, fields)\n for row in self.iter_dicts(with_index):\n row = {columns[k]: v for k, v in row.items()}\n yield container(**row)","def generate_from_descriptor(self, tuple_descriptor):\r\n t = Tuple()\r\n d = {}\r\n t.set_data(d)\r\n for alias in tuple_descriptor.aliases:\r\n fields = self.__tuple_descriptor.get_descriptor(alias).underlying_fields\r\n for field in fields:\r\n setattr(t, field, getattr(self, field))\r\n setattr(t, alias, getattr(self, alias))\r\n t.set_tuple_descriptor(tuple_descriptor)\r\n return t","def make_data(rics: list, fields: list, name: str) -> None:\n df = get_data(rics, fields)\n df.index.name = 'ID'\n to_csv(df, name)","def from_tuples(tuples=list()):\n result = []\n for (user_id, creation_date, name, google_id, email , role_id, student_id) in tuples:\n person = Person()\n person.set_id(user_id)\n person.set_name(name)\n person.set_berechtigung(role_id)\n person.set_email(email)\n person.set_google_id(google_id)\n person.set_creation_date(creation_date)\n person.set_student(student_id)\n result.append(person)\n return result","def unwrap_process_input_tuple(tup: tuple):\n names, quantile, intersection = tup\n name_i, name_j = names\n k = str(name_i) + \"|\" + str(name_j)\n return name_i, name_j, k, quantile, intersection","def write(self, output_dir, names, data=None):\n assert len(names) == len(self.object_list), \"Give as many names as objects to save multi-object\"\n\n pos = 0\n for elt, name in zip(self.object_list, names):\n if data is None:\n elt.write(output_dir, name)\n\n else:\n if elt.type.lower() in ['surfacemesh', 'polyline', 'pointcloud', 'landmark']:\n elt.write(output_dir, name, data['landmark_points'][pos:pos + elt.get_number_of_points()])\n pos += elt.get_number_of_points()\n\n elif elt.type.lower() == 'image':\n elt.write(output_dir, name, data['image_intensities'])","def test_gen_expr_named_tuples() -> None:\n Student = NamedTuple(\"Student\", [(\"gpa\", float), (\"name\", str)])\n graduates: List[Student] = [\n Student(gpa=3.5, name=\"Tallys\"),\n Student(gpa=3.8, name=\"Kaniya\"),\n Student(gpa=3.7, name=\"Toibe\"),\n ]\n\n valedictorian = max((student.gpa, student.name) for student in graduates)\n assert valedictorian == (3.8, \"Kaniya\")","def saveInTuple(list,name,type=\"R\",tname = \"T\", keytypes = {}):\r\n if keytypes == {}:\r\n\t for key in list[0].keys(): keytypes[key] = \"F\"\r\n \r\n print \"saving data\"\r\n labels=[]\r\n vvv=vector(0,0,0)\r\n if len(list)==0:\r\n print \"list size = 0\"\r\n return 0\r\n for i in range(4):\r\n if 'has_key' in dir(list[0]):break\r\n list=desagrupate(list)\r\n for key in list[0].keys():\r\n if (dir(list[0][key])==dir(vvv))or(dir(list[0][key])==dir(labels)):\r\n \r\n s=len(list[0][key])\r\n for j in range(s): \r\n labels.append(key+str(j+1)+\"/\" + keytypes[key])\r\n else: labels.append(key+\"/\"+keytypes[key])\r\n if type in [\"X\", \"ASCII\"]:\r\n TUP=XTuple(name,labels)\r\n if type==\"R\":\r\n TUP=RTuple(name,labels,tname)\r\n if type==\"M\":\r\n TUP=MTuple(name,labels)\r\n for thing in list:\r\n for key in thing.keys():\r\n #if key+\"1/\" + keytypes[key] in labels:\r\n # s=len(thing[key])\r\n # for j in range(s):\r\n # TUP.fillItem(key+str(j+1),thing[key][j])\r\n TUP.fillItem(key,thing[key])\r\n TUP.fill()\r\n TUP.close()","def tup_list_maker(tup_list):\n final_list = []\n for item in tup_list:\n index = item[0]\n sentences = item[1]\n for sentence in sentences:\n pair = (index, sentence)\n final_list.append(pair)\n return final_list","def makeTableNamesList(n, ):","def change_names (fixed_structure, moving_structure, index):\n for chain in moving_structure[0]:\n chain.id = utilities.merged_list[index]+\"-\"+chain.id\n index +=1\n return (fixed_structure, moving_structure, index)","def expandable_from_tuple(tuple_, field_descriptions):\n result = types.Expandable(tuple_[0])\n for (name, _), value in zip(field_descriptions, tuple_[1:]):\n result.__dict__[name] = value\n return result","def make_csv(data_dict):\n fieldnames = ['name'] + data_dict.itervalues().next().keys()\n with open('data.csv', 'w') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n for record in data_dict:\n person = data_dict[record]\n person['name'] = record\n assert set(person.keys()) == set(fieldnames)\n writer.writerow(person)","def make_csv(data_dict):\r\n fieldnames = ['name'] + data_dict.itervalues().next().keys()\r\n with open('data.csv', 'w') as csvfile:\r\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\r\n writer.writeheader()\r\n for record in data_dict:\r\n person = data_dict[record]\r\n person['name'] = record\r\n assert set(person.keys()) == set(fieldnames)\r\n writer.writerow(person)","def namedtuplefetchall(query, param_list):\r\n\twith connection.cursor() as cursor:\r\n\t\tcursor.execute(query, param_list)\r\n\t\tdesc = cursor.description\r\n\t\tnt_result = namedtuple('Result', [col[0] for col in desc])\r\n\t\treturn [nt_result(*row) for row in cursor.fetchall()]","def exportPeople ( c ) :\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n xml = \"\"\n p = sqlQuery ( c, \"select * from People;\" )\n for i in p :\n pL = sqlQuery ( c, \"select * from PeopleLocations where personID = '\" + i [ 0 ] + \"';\" )\n pER = sqlQuery ( c, \"select * from PersonExternalResources where personID = '\" + i [ 0 ] + \"';\" )\n pTO = sqlQuery ( c, \"select * from PeopleToOrganizations where personID = '\" + i [ 0 ] + \"';\" )\n cTP = sqlQuery ( c, \"select * from CrisesToPeople where personID = '\" + i [ 0 ] + \"';\" )\n xml += openTagAtt ( \"Person\", \"personIdent\", i [ 0 ] )\n xml += openTag ( \"Name\" )\n xml += openCloseTag ( \"FirstName\", i [ 1 ] )\n xml += openCloseTag ( \"MiddleName\", i [ 2 ] )\n xml += openCloseTag ( \"LastName\", i [ 3 ] )\n xml += openCloseTag ( \"Suffix\", i [ 4 ] )\n xml += closeTag ( \"Name\" )\n xml += closeTagAtt ( \"Kind\", \"personKindIdent\", i [ 5 ] )\n for j in pL :\n xml += openTag ( \"Location\" )\n xml += openCloseTag ( \"Locality\", j [ 1 ] )\n xml += openCloseTag ( \"Region\", j [ 2 ] )\n xml += openCloseTag ( \"Country\", j [ 3 ] )\n xml += closeTag ( \"Location\" )\n xml += openTag ( \"ExternalResources\" )\n for j in pER :\n xml += openCloseTag ( j [ 1 ] , j [ 2 ] )\n xml += closeTag ( \"ExternalResources\" )\n xml += openTag ( \"RelatedCrises\" )\n for j in cTP :\n xml += closeTagAtt ( \"RelatedCrisis\" , \"crisisIdent\", j [ 0 ] )\n xml += closeTag ( \"RelatedCrises\" )\n xml += openTag ( \"RelatedOrganizations\" )\n for j in pTO :\n xml += closeTagAtt ( \"RelatedOrganization\" , \"organizationIdent\", j [ 1 ] )\n xml += closeTag ( \"RelatedOrganizations\" )\n xml += closeTag ( \"Person\" )\n assert str ( type ( xml ) ) == \"<type 'str'>\"\n return xml","def create_tuple4(kmer1, kmer2, kmer1_neg, kmer2_neg):\n return Tuple4(\n ''.join(kmer1),\n ''.join(kmer2),\n ''.join(kmer1_neg),\n ''.join(kmer2_neg))","def str_tuple(item):\n return \"{}:{}\".format(item[0], item[1])","def __to_tuple(self):\n return (self.bucket, self.name)","def dump_to_csv(fileinfos, out, all_el_names, all_att_names):\n uni_el_names = unique_names(all_el_names)\n uni_att_names = unique_names(all_att_names) \n att_names_prefixed = [\"@%s\" % item for item in uni_att_names]\n csv_filename = \"all_elements_used.csv\"\n \n # transform information from dictionary to CSV format\n with open(os.path.join(out,out_dir_name,csv_filename), \"w\") as fout:\n fout.write(\",\" + \",\".join(uni_el_names) + \",\" + \",\".join(att_names_prefixed) + \"\\n\")\n for key in fileinfos:\n el_str = \"\"\n for eln in uni_el_names:\n if (eln in fileinfos[key][\"usage_el\"]):\n el_str += \",\" + str(fileinfos[key][\"usage_el\"][eln])\n else:\n el_str += \",0\"\n att_str = \"\"\n for attn in uni_att_names:\n if (attn in fileinfos[key][\"usage_att\"]):\n att_str += \",\" + str(fileinfos[key][\"usage_att\"][attn])\n else:\n att_str += \",0\"\n fout.write(key + el_str + att_str + \"\\n\")\n stdout.write(\"... \"+csv_filename+\" created\\n\")","def create_header(numValues):\n\n header = []\n for value in range(numValues):\n header.append(\"att{}\".format(value))\n return header","def output_name_table(outfile: TextIO, do_print: bool, is_name: bool, itemlist: list, unique_set: set,\n notecnt: int, comcnt: int, refdict: dict, name_table: dict, point_locations: dict) -> None:\n\n def create_location_sublink(x: str) -> str:\n \"\"\"\n create a link to location pages, preserving [] info when applicable\n \"\"\"\n tmpname = strip_location_subtext(x)\n if tmpname in point_locations:\n loc = point_locations[tmpname]\n tmpstr = create_location_link(loc, tmpname, do_print, path=\"../locations/\")\n if tmpname != x:\n tmpstr += x[len(tmpname):]\n tmpstr = tmpstr.replace(\"<!>\", fetch_fa_glyph(\"bad location\"))\n # tmpstr = tmpstr.replace(\"<?>\", fetch_fa_glyph(\"questionable id\"))\n tmpstr = tmpstr.replace(\"<?>\", \"\").strip()\n else:\n tmpstr = x\n return tmpstr\n\n # ---main part of loop---\n first_name = True\n ncols = 5\n if notecnt > 0:\n ncols += 1\n if comcnt > 0:\n ncols += 1\n\n for n in itemlist:\n outfile.write(\" <tr>\\n\")\n if is_name:\n nref = n.cite_key\n else:\n nref = n.name\n if nref in unique_set:\n if first_name:\n first_name = False\n else:\n outfile.write(\" <td class=\\\"rowspacer\\\" colspan=\\\"\" + str(ncols) + \"\\\"> </td>\\n\")\n outfile.write(\" </tr>\\n\")\n outfile.write(\" <tr>\\n\")\n\n if nref == \".\":\n outfile.write(\" <td> </td>\\n\")\n elif is_name: # output citation\n crossref = refdict[nref]\n outfile.write(\" <td><a href=\\\"\" + rel_link_prefix(do_print, \"../references/\") +\n crossref.cite_key + \".html\\\">\" + crossref.citation + \"</a></td>\\n\")\n else: # output species name\n outfile.write(\" <td><a href=\\\"\" + rel_link_prefix(do_print, \"../names/\") +\n name_to_filename(nref) + \".html\\\">\" + format_name_string(nref) + \"</a></td>\\n\")\n\n # common names\n if comcnt > 0:\n if n.common == \".\":\n outfile.write(\" <td> </td>\\n\")\n else:\n outfile.write(\" <td>\" + n.common + \"</td>\\n\")\n outfile.write(\" <td>\" + n.where + \"</td>\\n\")\n unique_set -= {nref}\n else:\n outfile.write(\" <td> </td>\\n\")\n if comcnt > 0:\n outfile.write(\" <td> </td>\\n\")\n outfile.write(\" <td> </td>\\n\")\n\n # applies to...\n if n.context == \"location\":\n outstr = create_location_sublink(n.application)\n outfile.write(\" <td>\" + fetch_fa_glyph(\"location\") + \"location: \" + outstr + \"</td>\\n\")\n elif n.context == \"citation\":\n if n.application in refdict:\n crossref = refdict[n.application]\n if n.application in name_table:\n nstr = n.cite_n\n if nstr == \"0\":\n outfile.write(\" <td>\" + fetch_fa_glyph(\"citation\") + \"citation: \"\n \"<a href=\\\"\" + rel_link_prefix(do_print, \"../references/\") + crossref.cite_key +\n \".html\\\">\" + crossref.citation + \"</a></td>\\n\")\n else:\n if \".\" in nstr:\n try:\n extraref = \" [\" + name_table[n.application][nstr][1] + \"]\"\n refname = name_table[n.application][nstr][0]\n except LookupError:\n if is_name: # only print errors on one pass\n report_error(\"Error in citation: \" + n.cite_key + \" cites\" + nstr +\n \" in \" + n.application)\n extraref = \"\"\n refname = \"\"\n else:\n extraref = \"\" \n try:\n refname = name_table[n.application][int(nstr)]\n except ValueError:\n report_error(\"Citation \" + n.cite_key + \" tried to cite \" + n.application +\n \" #\" + nstr)\n refname = \"\"\n outfile.write(\" <td>\" + fetch_fa_glyph(\"citation\") + \"citation: \"\n \"<a href=\\\"\" + rel_link_prefix(do_print, \"../references/\") + crossref.cite_key +\n \".html\\\">\" + crossref.citation + \"</a> → \" + format_name_string(refname) +\n extraref + \"</td>\\n\")\n else:\n outfile.write(\" <td>\" + fetch_fa_glyph(\"citation\") + \"citation: \"\n \"<a href=\\\"\" + rel_link_prefix(do_print, \"../references/\") + crossref.cite_key +\n \".html\\\">\" + crossref.citation + \"</a></td>\\n\")\n else:\n outfile.write(\" <td>\" + fetch_fa_glyph(\"citation\") + \"citation: \" + n.application +\n \"</td>\\n\")\n if is_name and not do_print: # only print on one pass\n report_error(\"Citation not in DB: \" + n.cite_key + \" cites \" + n.application)\n elif n.context == \"specimen\":\n if n.application == \"?\":\n outfile.write(\" <td>\" + fetch_fa_glyph(\"specimen\") + \"specimen: unknown locality</td>\\n\")\n else:\n outstr = create_location_sublink(n.application)\n outfile.write(\" <td>\" + fetch_fa_glyph(\"specimen\") + \"specimen: \" + outstr + \"</td>\\n\")\n elif n.context == \"sequence\":\n if n.application == \"?\":\n outfile.write(\" <td>\" + fetch_fa_glyph(\"sequence\") + \"sequence: unknown locality</td>\\n\")\n else:\n outstr = create_location_sublink(n.application)\n outfile.write(\" <td>\" + fetch_fa_glyph(\"sequence\") + \"sequence: \" + outstr + \"</td>\\n\")\n\n elif n.context == \"unpublished\":\n outfile.write(\" <td>unpublished name <em class=\\\"species\\\">\" +\n n.application + \"</em></td>\\n\")\n else: # \"n/a\"\n outfile.write(\" <td> </td>\\n\")\n\n # accepted species name\n if n.actual == \"?\":\n outfile.write(\" <td>?</td>\\n\")\n elif n.actual in {\".\", \"=\"}:\n outfile.write(\" <td>TBD</td>\\n\")\n elif n.actual == \"n/a\":\n outfile.write(\" <td> </td>\\n\") \n elif n.actual.startswith(\">\"):\n outfile.write(\" <td><em class=\\\"species\\\">\" + n.actual[1:] + \"</em></td>\\n\")\n elif n.actual.startswith(\"#\"):\n t_rank, t_name = n.actual[1:].split(\"#\")\n outfile.write(\" <td>\" + create_taxon_link(t_rank, t_name, do_print, path=\"../\") + \"</td>\\n\")\n else:\n s = find_species_by_name(n.actual)\n if \"<?>\" in n.application:\n glyphstr = \" \" + fetch_fa_glyph(\"questionable id\")\n else:\n glyphstr = \"\"\n outfile.write(\" <td><a href=\\\"\" + rel_link_prefix(do_print, \"../\") + \"u_\" + n.actual +\n \".html\\\"><em class=\\\"species\\\">\" + s.binomial() + \"</em></a>\" + glyphstr + \"</td>\\n\")\n\n # source of accepted species\n if n.source == \".\": # currently not listed\n outfile.write(\" <td> </td>\\n\")\n elif n.source == \"<\": # original name retained\n outfile.write(\" <td>\" + fetch_fa_glyph(\"original\") + \"Original</td>\\n\")\n elif n.source == \"=\": # automatically computer\n outfile.write(\" <td>\" + fetch_fa_glyph(\"computed\") + \"Computed</td>\\n\")\n else:\n if \";\" in n.source:\n source_list = n.source.split(\";\")\n else:\n source_list = [n.source]\n source_strs = []\n for source in source_list:\n if source in refdict: # another reference\n crossref = refdict[source]\n source_strs.append(\"<a href=\\\"\" + rel_link_prefix(do_print, \"../references/\") +\n crossref.cite_key + \".html\\\">\" + crossref.citation + \"</a>\")\n else: # should start with a >\n tmpsource = source[1:]\n tmpsource = tmpsource.replace(\"MSR:\", \"\")\n tmpsource = tmpsource.replace(\"/\", \"/<br />\")\n tmpsource = tmpsource.replace(\"geography\", fetch_fa_glyph(\"geography\") + \"Geography\")\n tmpsource = tmpsource.replace(\"synonymy\", fetch_fa_glyph(\"synonymy\") + \"Synonymy\")\n source_strs.append(tmpsource)\n outfile.write(\" <td>\" + \", \".join(source_strs) + \"</td>\\n\")\n\n # notes\n if notecnt > 0:\n if n.name_note == \".\":\n outfile.write(\" <td> </td>\\n\")\n else:\n outfile.write(\" <td>\" + replace_species_in_string(n.name_note) + \"</td>\\n\")\n outfile.write(\" </tr>\\n\")\n outfile.write(\" </table>\\n\")","def construct_name(p, prefix):\n name = prefix\n for key in p.keys():\n if (type(p[key]) != tuple) and (type(p[key]) != list):\n name = name + '_' + str(key) + '-' + str(p[key])\n else:\n name = name + '_' + str(key) + '-' + str(p[key][0])\n return name","def tupleStrFormat(tupl):\n string = \"this is a tuple (\"\n for element in tupl:\n string += str(element) + \", \"\n string += \")\"\n return string","def tuple_to_string(transcript_info):\n\n return \"\\t\".join(transcript_info.data_attributes())","def serialize(self):\r\n return (self.name, self.values)","def insertJoint(*args, **kwargs)->AnyStr:\n pass","def to_namedtuple(d, classname='struct'):\n if not isinstance(d, dict):\n raise ValueError(\"Can only convert dicts into namedtuple\")\n for k,v in d.iteritems():\n if isinstance(v, dict):\n d[k] = to_namedtuple(v)\n return namedtuple(classname, d.keys())(**d)","def __tuple(self):\n return (self.__app, self.__namespace, self.__pairs)","def make_tag_pairs(self, input, start, end, elements):\n tps = TagPairs()\n for e in elements:\n k = [k for k in e.keys()][0]\n tps[k] = e[k]\n return tps","def serialize_tuple(self, obj):\n return '(' + ''.join([self.serialize(i) for i in obj]) + 't'","def fname_tsk3(tup):\n t_size = str(len(tup))\n fname3 = \"The \" + t_size + \" numbers are: \"\n for t in tup:\n fname3 = fname3 + \"{:d}, \"\n return fname3[:-2].format(*tup)","def namedtuple_with_defaults(typename,\n field_names,\n default_values=(),\n units=None):\n T = collections.namedtuple(typename, field_names)\n T.__new__.__defaults__ = (None, ) * len(T._fields)\n if isinstance(default_values, collections.abc.Mapping):\n prototype = T(**default_values)\n else:\n prototype = T(*default_values)\n T.__new__.__defaults__ = tuple(prototype)\n if units is None:\n T.units = ('-') * len(T._fields)\n else:\n T.units = units\n return T","def nameify(items, sep=',', junction=None, oxford=False, quote=None):\n if not items:\n return ''\n sorted_items = sorted(items)\n quote = quote or ''\n if len(sorted_items) == 2:\n return ''.join([\n quote_item(sorted_items[0], quote),\n sep if oxford or not junction else '',\n ' {} '.format(junction) if junction else ' ',\n quote_item(sorted_items[1], quote)\n ])\n if len(items) == 1:\n return quote_item(sorted_items[0], quote)\n return '{}{} {}'.format(\n quote_item(sorted_items[0], quote),\n sep,\n nameify(\n sorted_items[1:],\n sep=sep,\n junction=junction,\n oxford=oxford,\n quote=quote))","def create_username_name_pairs(usernames, names):\n logger.info('create username name pairs...')\n pairs = []\n for username, name in zip(usernames, names):\n \n pair = username if name == '' else username + ' (' + name + ')' \n pairs.append(pair)\n return pairs","def name_mapper(tup):\n tup = tuple(ix2name[i] for i in tup)\n\n def s(t):\n return sorted(t, key=self.clade_order)\n left, right = sorted((s(tup[:2]), s(tup[2:])))\n return (*left, *right)","def create_type_tuple(*elems):\n tuple = ast.Tuple()\n\n tuple.elts = []\n for elem in elems:\n tuple.elts.append(elem)\n\n return tuple","def get_named_tuple_object(instance: Any) -> Tuple[object, Type[object]]:\n clz: Type = namedtuple(instance.__class__)\n instance_data = asdict(instance)\n for key, value in instance_data.items():\n if is_dataclass(value):\n obj, _ = get_named_tuple_object(value)\n instance_data[key] = obj\n\n # generics currently unsupported, maybe in later versions\n\n new_instance: clz = clz(**instance_data)\n return (new_instance, clz)","def typenames(self):\n return tuple((item[0] for item in self))","def write_Fasta_from_name_seq_pairs(name_seqs, fh):\r\n if fh is None:\r\n raise ValueError(\"Need open file handle to write to.\")\r\n\r\n for (name, seq) in name_seqs:\r\n fh.write(\"%s\\n\" % BiologicalSequence(seq, id=name).to_fasta())","def export(tako_list, filename):\n for tak in tako_list:\n tak = tak[0]\n l1 = [tak.ident, \"a\"]\n for gen in tak.genome.weightchr_a:\n l1.append(gen.ident)\n l1.append(gen.weight)\n l1.append(gen.mut_rate)\n l1.append(gen.dom)\n f = os.path.join(\"Data\", (filename[:-4] + \" gene data.csv\"))\n with open(f, 'a', newline=\"\") as csvfile:\n writ = csv.writer(csvfile)\n writ.writerow(l1)\n if len(tak.genome.weightchr_b) != 0:\n l2 = [tak.ident, \"b\"]\n for gen in tak.genome.weightchr_b:\n l2.append(gen.ident)\n l2.append(gen.weight)\n l2.append(gen.mut_rate)\n l2.append(gen.dom) \n writ.writerow(l2)","def makeRepeatedDataType(tagname, keyword, G):\n value = G(keyword)\n if not value:\n return ''\n\n unit = G(keyword + 'Unit')\n method = G(keyword + 'Method')\n comment = G(keyword + 'Comment')\n acc = G(keyword + 'Accuracy')\n refs = G(keyword + 'Ref')\n name = G(keyword + 'Name')\n\n# make everything iterable\n value, unit, method, comment, acc, refs, name = [[x] if not isiterable(x) else x for x in [value, unit, method, comment, acc, refs, name]]\n\n# if some are shorter than the value list, replicate them\n l = len(value)\n value, unit, method, comment, acc, refs, name = [ x*l if len(x)<l else x for x in [value, unit, method, comment, acc, refs, name]]\n\n string = ''\n for i, val in enumerate(value):\n string += '\\n<%s' % tagname\n if name[i]:\n string += ' name=\"%s\"' % name[i]\n if method[i]:\n string += ' methodRef=\"M%s-%s\"' % (NODEID, method[i])\n string += '>'\n if comment[i]:\n string += '<Comments>%s</Comments>' % escape('%s' % comment[i])\n string += makeSourceRefs(refs[i])\n string += '<Value units=\"%s\">%s</Value>' % (unit[i] or 'unitless', value[i])\n if acc[i]:\n string += '<Accuracy>%s</Accuracy>' % acc[i]\n string += '</%s>' % tagname\n\n return string","def GenericRepr(obj, names):\n\n def ReprIndent(name, as_ref):\n return ' %s=%s' % (name, Repr(getattr(obj, name), as_ref).replace(\n '\\n', '\\n '))\n\n return '%s(\\n%s\\n)' % (obj.__class__.__name__, ',\\n'.join(\n ReprIndent(name, as_ref) for (name, as_ref) in names.items()))","def namedtuples_to_dicts(nts):\n return [dict(nt._asdict()) for nt in nts]","def triplify_object(binding):\n triples = []\n if binding.uri:\n triples.append((binding.subject, RDF.type, binding.uri))\n\n if binding.parent is not None:\n parent = binding.parent.subject\n if binding.parent.is_array:\n parent = binding.parent.parent.subject\n triples.append((parent, binding.predicate, binding.subject))\n if binding.reverse is not None:\n triples.append((binding.subject, binding.reverse, parent))\n\n for prop in binding.properties:\n _, prop_triples = triplify(prop)\n triples.extend(prop_triples)\n\n return binding.subject, triples","def make_echo_csv (list_of_region_tuples):\n\n #initialize the Echo formatted output dataframe\n out = pd.DataFrame(columns= ['Source Plate Name', 'Source Plate Type', 'Source Well', 'Sample ID', 'Sample Name', \\\n 'Sample Group', 'Sample Comment', 'Destination Plate Name', 'Destination Well', 'Transfer Volume'])\n\n idx = 0 #have to use a counter because we go through multiple lists and can't return to idx=0 each time\n #there may be a list of region tuples with source wells, volumes, dest wells\n for region in list_of_region_tuples:\n #for each well location to be shot from the current region\n for well in region[2]:\n #add the dest well\n out.loc[idx, 'Destination Well'] = well\n #Add the source well and transfer volume for that region\n out.loc[idx, ['Source Well', 'Transfer Volume']] = [region[0], region[1]]\n idx += 1\n\n #Set the unchanging names for the dataframe\n out[['Source Plate Name', 'Source Plate Type', 'Destination Plate Name']] = ['Source[1]', '384PP_AQ_BP', 'Destination[1]']\n\n return out","def is_namedtuple(obj):\n return isinstance(obj, tuple) and hasattr(obj, '_asdict')","def create_csv(d, f):\n csv_data = list()\n csv_head = [unicode(x) for x in f]\n\n for row in d:\n row_data = list()\n for field in f:\n fields = field.split('.')\n row_data.append(extract_dict(row, fields))\n csv_data.append(row_data)\n\n csv = (csv_head, csv_data)\n return csv","def write(*name):\n foo = Foo(' '.join(name))\n with open(DBNAME, 'w') as f:\n f.write(pickle.dumps(foo))","def get_tuples_helper(element) -> list:\n try:\n return [element[\"ExportName\"], element[\"OutputValue\"]]\n except:\n return [element[\"OutputKey\"], element[\"OutputValue\"]]","def join_unique(\n headers: List[str], seq: str, OH: IO, **kwargs\n ) -> Optional[Tuple[str, str]]:\n if len(headers) != 1:\n return None\n batch = (headers[0], seq)\n OH.write(\">%s\\n%s\\n\" % batch)\n return batch","def convert_arg((arg, attrs, mode, typ, name)):\n iorname = name\n return iorname, (arg, attrs, mode, typ, name)","def make_tuple(*fields):\n fields2 = []\n for (idx, f) in zip(range(len(fields)), fields):\n if isinstance(f, (_StructField, pb.StructField)):\n fields2.append(f)\n if isinstance(f, pb.SQLType):\n fields2.append(pb.StructField(\n field_name=\"_%s\" % str(idx),\n field_type=f))\n raise ValueError(\"Could not understand type %s for %s\" % (type(f), f))\n return StructType(fields2)","def ntuple_from_dict(d):\n return namedtuple('TupleFromDict', d.keys())(**d)","def test_file_write_attributes_for_each(self):\n\n with OrthoMultiTs(self.testfilename, n_loc=3, mode=\"w\") as dataset:\n n_data = 5\n locations = np.array([1, 2, 3])\n data = {\n \"test\": np.arange(n_data * 3).reshape(3, n_data),\n \"test2\": np.arange(n_data * 3).reshape(3, n_data)\n }\n base = datetime(2007, 1, n_data)\n dates = np.array(\n [base + timedelta(hours=i) for i in range(n_data)])\n descriptions = np.repeat([str(\"station\")], 3).tolist()\n\n dataset.write_all(locations,\n data,\n dates,\n loc_descrs=descriptions,\n lons=np.arange(3),\n lats=np.arange(3),\n alts=np.arange(3),\n attributes={\n \"test\": {\n \"testattribute\": \"teststring\"\n },\n \"test2\": {\n \"testattribute2\": \"teststring2\"\n }\n })\n\n with OrthoMultiTs(self.testfilename) as dataset:\n data = dataset.read_all(2)\n nptest.assert_array_equal(data[\"test\"], np.arange(5) + 5)\n assert dataset.dataset.variables[\n \"test\"].testattribute == \"teststring\"\n assert dataset.dataset.variables[\n \"test2\"].testattribute2 == \"teststring2\"\n test_dates = []\n for n_data in [5]:\n base = datetime(2007, 1, n_data)\n test_dates.append(\n np.array(\n [base + timedelta(hours=i) for i in range(n_data)]))\n dates = np.concatenate(test_dates)\n nptest.assert_array_equal(data[\"time\"], dates)","def genStruct(self, typeinfo, typeName, alias):\n OutputGenerator.genStruct(self, typeinfo, typeName, alias)\n\n if alias:\n # Add name -> alias mapping\n self.addName(self.alias, typeName, alias)\n else:\n # May want to only emit definition on this branch\n True\n\n members = [member.text for member in typeinfo.elem.findall('.//member/name')]\n self.structs[typeName] = members\n memberTypes = [member.text for member in typeinfo.elem.findall('.//member/type')]\n for member_type in memberTypes:\n self.addMapping(typeName, member_type)","def _namedzip_generator(zipped, named_tuple, defaults=None):\n\n for vals in zipped:\n if defaults:\n vals = (x if x is not sentinel else defaults[i] for i, x in enumerate(vals))\n yield named_tuple(*vals)","def write_concat_GO_dicts(self, *GO_dict):\n\n for i, j in zip(self.IDs[0:3], GO_dict):\n with open(i, 'w') as f:\n f.write('transcript_id\\tgene_ontology\\n')\n for k, v in j.iteritems():\n f.write(k + '\\t' + '\\t'.join(v) + '\\n')","def ttuple(name, *types):\n\n sort = z3.Datatype(name)\n sort.declare(name, *[(fname, typ._z3_sort) for fname, typ in types])\n sort = sort.create()\n fields = {\"_z3_sort\" : sort}\n for fname, typ in types:\n code = \"\"\"\\\n@property\ndef %s(self):\n return wrap(self._z3_sort.%s(self._v))\"\"\" % (fname, fname)\n locals_dict = {}\n exec code in globals(), locals_dict\n fields[fname] = locals_dict[fname]\n return type(name, (STupleBase, SymbolicVal), fields)","def test_output_named_tuple_vs_dictionary_2():\n assert oldest_person_age == oldest_person_age_d, \"Max age cannot be different for Named Tuple and Dictionary list\"","def format_out_relations(relations, out_):\n\n if out_ == 'sparse':\n relations_o = relations\n elif out_ == 'network':\n relations_o = nx.from_scipy_sparse_matrix(relations)\n elif out_ == 'sp_relations':\n relations_o = RegionDistances(relations)\n elif out_ == 'list':\n relations_o = []\n for i in range(relations.shape[0]):\n relations_o.append(list(relations.getrow(i).nonzero()[0]))\n return relations_o","def make_fastq_multi(in_fasta, quals, out_fp,\r\n label_transform=split_lib_transform):\r\n mkdir(out_fp)\r\n seen_libs = defaultdict(list)\r\n for rec, label in iter_fastq(in_fasta, quals, label_transform):\r\n lib_id, seq_id = label.rsplit('_', 1)\r\n seen_libs[lib_id].append(rec)\r\n for lib, recs in seen_libs.items():\r\n if lib is None: # skip the seqs we couldn't assign to a library\r\n continue\r\n outfile = open(out_fp + '/' + lib + '.fastq', 'w')\r\n outfile.write('\\n'.join(recs))\r\n outfile.close()","def _make_token_sequence(names) -> tuple:\n if isinstance(names, str):\n names = names.replace(',', ' ').split()\n return tuple(map(str, names))","def build_output_table(cls, name='inputTableName', output_name='output'):\n obj = cls(name)\n obj.exporter = 'get_output_table_name'\n obj.output_name = output_name\n return obj","def build_data_frame(sql_dict, names):\n\td = defaultdict(list)\n\ti = 0\n\tfor category_data in sql_dict:\n\t\tfor attribute_list in category_data:\n\t\t\tfor attribute in category_data[attribute_list]:\n\t\t\t\td[attribute_list].append(attribute)\n\t\td[\"category\"].extend([names[i]]* len(category_data[attribute_list]) )\n\t\td[\"fct\"].extend([i] * len(category_data[attribute_list]) )\n\t\ti += 1\n\t#Translation into R objects\n\tfor attribute_list in d:\n\t\tif attribute_list == \"category\":\n\t\t\td[attribute_list] = robjects.StrVector(d[attribute_list])\n\t\telif attribute_list in WATCH_FOR_MISSING_VALUES:\n\t\t\ttmp = robjects.FloatVector(range(len(d[attribute_list])))\n\t\t\tfor i in range(len(d[attribute_list])):\n\t\t\t\tif d[attribute_list][i] == \"NA\":\n\t\t\t\t\ttmp.rx[i] = robjects.NA_Real\n\t\t\t\telse:\n\t\t\t\t\ttmp.rx[i] = d[attribute_list][i]\n\t\t\td[attribute_list] = tmp\n\t\telse:\n\t\t\tif attribute_list == \"num_sections\":\n\t\t\t\ttmp = []\n\t\t\t\tfor element in d[attribute_list]:\n\t\t\t\t\tif element == None:\n\t\t\t\t\t\ttmp.append(1)\n\t\t\t\t\telse:\n\t\t\t\t\t\ttmp.append(float(element))\n\t\t\t\td[attribute_list] = robjects.FloatVector(tmp)\n\t\t\t\tcontinue\n\t\t\telif attribute_list == \"speechiness\":\n\t\t\t\ttmp = []\n\t\t\t\tfor element in d[attribute_list]:\n\t\t\t\t\tif element == None:\n\t\t\t\t\t\ttmp.append(1)\n\t\t\t\t\telse:\n\t\t\t\t\t\ttmp.append(float(element))\n\t\t\t\td[attribute_list] = robjects.FloatVector(tmp)\n\t\t\t\tcontinue\n\t\t\t\t \n\t\t\telse:\n\t\t\t\td[attribute_list] = robjects.FloatVector(d[attribute_list])\n\tdata_frame = robjects.DataFrame(d)\n\treturn data_frame","def as_tuple(self):\n return self.value, self.name","def _create_sql_columns(self, a_list):\n result = \"\"\n \n cpt = 0\n for elem in a_list:\n if cpt == 0:\n result += \"%s\" % (elem)\n else:\n result += \", %s\" % (elem)\n cpt += 1\n\n return result"],"string":"[\n \"def tuple_namer(name,tupl):\\n tupl_templ = collections.namedtuple(name, 'battery status neighbour')\\n named = tupl_templ(battery = tupl[0], status = tupl[1], neighbour = tupl[2])\\n return named\",\n \"def core_named_tuple(user_kwargs: dict, disdat_kwargs: dict) -> NamedTuple('Output', [('user_kwargs', dict),\\n ('disdat_kwargs', dict)]):\\n import collections\\n Output = collections.namedtuple('Output', ['user_kwargs', 'disdat_kwargs'])\\n return Output(user_kwargs, disdat_kwargs)\",\n \"def namedtuple(typename, field_names, verbose=False, rename=False):\\r\\n\\r\\n # Parse and validate the field names. Validation serves two purposes,\\r\\n # generating informative error messages and preventing template injection attacks.\\r\\n if isinstance(field_names, basestring):\\r\\n field_names = field_names.replace(',', ' ').split() # names separated by whitespace and/or commas\\r\\n field_names = tuple(map(str, field_names))\\r\\n if rename:\\r\\n names = list(field_names)\\r\\n seen = set()\\r\\n for i, name in enumerate(names):\\r\\n if (not all(c.isalnum() or c=='_' for c in name) or _iskeyword(name)\\r\\n or not name or name[0].isdigit() or name.startswith('_')\\r\\n or name in seen):\\r\\n names[i] = '_%d' % i\\r\\n seen.add(name)\\r\\n field_names = tuple(names)\\r\\n for name in (typename,) + field_names:\\r\\n if not all(c.isalnum() or c=='_' for c in name):\\r\\n raise ValueError('Type names and field names can only contain alphanumeric characters and underscores: %r' % name)\\r\\n if _iskeyword(name):\\r\\n raise ValueError('Type names and field names cannot be a keyword: %r' % name)\\r\\n if name[0].isdigit():\\r\\n raise ValueError('Type names and field names cannot start with a number: %r' % name)\\r\\n seen_names = set()\\r\\n for name in field_names:\\r\\n if name.startswith('_') and not rename:\\r\\n raise ValueError('Field names cannot start with an underscore: %r' % name)\\r\\n if name in seen_names:\\r\\n raise ValueError('Encountered duplicate field name: %r' % name)\\r\\n seen_names.add(name)\\r\\n\\r\\n # Create and fill-in the class template\\r\\n numfields = len(field_names)\\r\\n argtxt = repr(field_names).replace(\\\"'\\\", \\\"\\\")[1:-1] # tuple repr without parens or quotes\\r\\n reprtxt = ', '.join('%s=%%r' % name for name in field_names)\\r\\n template = '''class %(typename)s(tuple):\\r\\n '%(typename)s(%(argtxt)s)' \\\\n\\r\\n __slots__ = () \\\\n\\r\\n _fields = %(field_names)r \\\\n\\r\\n def __new__(_cls, %(argtxt)s):\\r\\n 'Create new instance of %(typename)s(%(argtxt)s)'\\r\\n return _tuple.__new__(_cls, (%(argtxt)s)) \\\\n\\r\\n @classmethod\\r\\n def _make(cls, iterable, new=tuple.__new__, len=len):\\r\\n 'Make a new %(typename)s object from a sequence or iterable'\\r\\n result = new(cls, iterable)\\r\\n if len(result) != %(numfields)d:\\r\\n raise TypeError('Expected %(numfields)d arguments, got %%d' %% len(result))\\r\\n return result \\\\n\\r\\n def __repr__(self):\\r\\n 'Return a nicely formatted representation string'\\r\\n return '%(typename)s(%(reprtxt)s)' %% self \\\\n\\r\\n def _asdict(self):\\r\\n 'Return a new OrderedDict which maps field names to their values'\\r\\n return OrderedDict(zip(self._fields, self)) \\\\n\\r\\n def _replace(_self, **kwds):\\r\\n 'Return a new %(typename)s object replacing specified fields with new values'\\r\\n result = _self._make(map(kwds.pop, %(field_names)r, _self))\\r\\n if kwds:\\r\\n raise ValueError('Got unexpected field names: %%r' %% kwds.keys())\\r\\n return result \\\\n\\r\\n def __getnewargs__(self):\\r\\n 'Return self as a plain tuple. Used by copy and pickle.'\\r\\n return tuple(self) \\\\n\\\\n''' % locals()\\r\\n for i, name in enumerate(field_names):\\r\\n template += \\\" %s = _property(_itemgetter(%d), doc='Alias for field number %d')\\\\n\\\" % (name, i, i)\\r\\n if verbose:\\r\\n print template\\r\\n\\r\\n # Execute the template string in a temporary namespace and\\r\\n # support tracing utilities by setting a value for frame.f_globals['__name__']\\r\\n namespace = dict(_itemgetter=_itemgetter, __name__='namedtuple_%s' % typename,\\r\\n OrderedDict=OrderedDict, _property=property, _tuple=tuple)\\r\\n try:\\r\\n exec template in namespace\\r\\n except SyntaxError, e:\\r\\n raise SyntaxError(e.message + ':\\\\n' + template)\\r\\n result = namespace[typename]\\r\\n\\r\\n # For pickling to work, the __module__ variable needs to be set to the frame\\r\\n # where the named tuple is created. Bypass this step in enviroments where\\r\\n # sys._getframe is not defined (Jython for example) or sys._getframe is not\\r\\n # defined for arguments greater than 0 (IronPython).\\r\\n try:\\r\\n result.__module__ = _sys._getframe(1).f_globals.get('__name__', '__main__')\\r\\n except (AttributeError, ValueError):\\r\\n pass\\r\\n\\r\\n return result\",\n \"def make_tuple(in_dict,tupname='values'):\\n the_tup = namedtuple(tupname, in_dict.keys())\\n the_tup = the_tup(**in_dict)\\n return the_tup\",\n \"def make_tuple(name, **kwargs):\\n tuple_class = collections.namedtuple(typename=name, field_names=kwargs.keys())\\n return tuple_class(**kwargs)\",\n \"def writenames(sql, names, indent=0, id_offset=0, lang_offset=0):\\n i = 0\\n for row in names:\\n j = 0\\n if i > 0:\\n sql.write(\\\",\\\\n\\\")\\n for name in row:\\n if j > 0:\\n sql.write(\\\",\\\\n\\\")\\n sql.write(commajoin([i+id_offset, j+lang_offset, name], [2], indent))\\n j += 1\\n i += 1\",\n \"def MakeTuple(name, **kwargs):\\n tuple_class = collections.namedtuple(\\n typename=name,\\n field_names=kwargs.keys(),\\n )\\n return tuple_class(**kwargs)\",\n \"def create_namedtuple():\\n Thing = namedtuple(\\\"MyCoolTuple\\\", \\\"field1 field2\\\")\\n obj = Thing(1, 2)\\n # Throws AttributeError\\n #obj.random = \\\"Whoa\\\"\\n print(Thing)\\n print(obj)\",\n \"def get_namedtuple(name, field_names, values=None):\\n if values is None:\\n values = range(len(field_names))\\n field_names = [f.replace(' ', '_') for f in field_names]\\n return _namedtuple(name, field_names)(*values)\",\n \"def namedtuple(*args, **kwargs):\\n cls = _namedtuple(*args, **kwargs)\\n _NamedTupleABC.register(cls)\\n return cls\",\n \"def create_record_tuple(ilist):\\n list_length = len(ilist)\\n if list_length:\\n rec_tuple = '('\\n for row in list[0:list_length-1]:\\n rec_tuple += str(row)\\n rec_tuple += ','\\n rec_tuple += str(list[list_length-1])\\n rec_tuple += ')'\\n else: rec_tuple = '()'\\n return rec_tuple\",\n \"def tuple(self, arg: SeField[Any]) -> str:\\n if is_bare_tuple(arg.type):\\n return arg.varname\\n elif is_variable_tuple(arg.type):\\n earg = arg[0]\\n earg.name = \\\"v\\\"\\n return f\\\"tuple({self.render(earg)} for v in {arg.varname})\\\"\\n else:\\n rvalues = []\\n for i, _ in enumerate(type_args(arg.type)):\\n r = arg[i]\\n r.name = f\\\"{arg.varname}[{i}]\\\"\\n rvalues.append(self.render(r))\\n return f\\\"({', '.join(rvalues)},)\\\" # trailing , is required for single element tuples\",\n \"def _entuple(r):\\n return tuple(getattr(r, n) for n in r.__names__)\",\n \"def add_make_tuple(self, input_names, name=None):\\n return self._build_op('make_tuple', input_names, name=name)\",\n \"def make_out_struct(self):\\n args ={\\n \\\"name\\\": self.python_madz_types + (\\\"OUTSTRUCT\\\" if self.namespace == \\\"\\\" else self._namespace_mangle(self.namespace)),\\n \\\"fields\\\":\\\"\\\"\\n }\\n\\n res = \\\\\\n\\\"\\\"\\\"class {name}(Structure):\\n _fields_ = [{fields}]\\n\\\"\\\"\\\"\\n for node in self.description.definitions():\\n args['fields'] += self.gen_type_tuple_string(node.name, node.type) + \\\", \\\"\\n\\n return res.format(**args)\",\n \"def _named_attrs(self, parts:dict) -> \\\\\\n (QA4SMNamedAttributes, list, QA4SMNamedAttributes):\\n\\n if not self.ismetr():\\n raise IOError(self.varname, '{} is not in form of a QA4SM metric variable.')\\n\\n if self.g == 0:\\n a = QA4SMAttributes(self.attrs)\\n ref_ds = QA4SMNamedAttributes(a.ref_dc - a._offset_id_dc,\\n a.get_ref_names()['short_name'], self.attrs)\\n return ref_ds, None, None\\n else:\\n dss = []\\n ref_ds = QA4SMNamedAttributes(parts['ref_id'], parts['ref_ds'], self.attrs)\\n ds = QA4SMNamedAttributes(parts['sat_id0'], parts['sat_ds0'], self.attrs)\\n dss.append(ds)\\n if self.g == 3:\\n ds = QA4SMNamedAttributes(parts['sat_id1'], parts['sat_ds1'], self.attrs)\\n dss.append(ds)\\n mds = QA4SMNamedAttributes(parts['mds_id'], parts['mds'], self.attrs)\\n else:\\n mds = None\\n return ref_ds, dss, mds\",\n \"def __generate_delimited_objects__(self, output, **kwargs):\\n triple_map = kwargs.get(\\\"triple_map\\\")\\n subject = kwargs.get('subject')\\n # Subject is blank-node, try to retrieve subject IRI\\n predicate = kwargs.get('predicate')\\n element = kwargs.get('element')\\n datatype = kwargs.get('datatype', None)\\n delimiters = kwargs.get('delimiters')\\n subjects = []\\n for delimiter in delimiters:\\n values = element.text.split(delimiter)\\n for row in values:\\n if datatype is not None:\\n obj_ = rdflib.Literal(row.strip(), datatype=datatype)\\n else:\\n obj_ = rdflib.Literal(row.strip())\\n if isinstance(subject, rdflib.BNode):\\n new_subject = rdflib.BNode()\\n class_ = triple_map.subjectMap.class_\\n output.add((new_subject, NS_MGR.rdf.type.rdflib, class_))\\n for parent_subject, parent_predicate in \\\\\\n output.subject_predicates(object=subject):\\n output.add((parent_subject,\\n parent_predicate,\\n new_subject))\\n else:\\n new_subject = subject\\n subjects.append(new_subject)\\n output.add((new_subject, predicate, obj_))\\n return subjects\",\n \"def namedtuple(*args, **kwargs):\\n nt_class = _invisiblenamedtuple(*args, **kwargs)\\n # put it on the global scale so it can be tupled correctly\\n globals()[nt_class.__name__] = nt_class\\n return nt_class\",\n \"def build_multi_index_tuples(header: list, sub_header: list) -> list:\\r\\n\\r\\n tuples = []\\r\\n\\r\\n for head in header:\\r\\n for sub_head in sub_header:\\r\\n tuples.append((head, sub_head))\\r\\n\\r\\n return tuples\",\n \"def _tuple_to_cpppo_tag_multiple(cls, what, values=None, serializer=':'):\\n tag_string = ''\\n\\n if values == None:\\n for i in range(len(what)):\\n tag_string += what[i][0] + EnipProtocol._SERIALIZER + str(what[i][1]) + \\\" \\\"\\n else:\\n for i in range(len(what)):\\n tag_string += what[i][0] + EnipProtocol._SERIALIZER + str(what[i][1]) + \\\"=\\\" + str(values[i]) + \\\" \\\"\\n\\n return tag_string\",\n \"def gen_type_tuple_string(self, name, node):\\n return \\\"('{}', {})\\\".format(name, self.gen_type_string(node))\",\n \"def namedtuple_with_two_defaults(typename, field_names, default_values=('', '')):\\n T = collections.namedtuple(typename, field_names)\\n T.__new__.__defaults__ = default_values\\n return T\",\n \"def name_fields(record):\\n record[\\\"names_sim\\\"] = record.get(\\\"author_tesim\\\")\\n if record.get(\\\"author_tesim\\\") is not None:\\n if record.get(\\\"names_sim\\\") is not None:\\n record[\\\"names_sim\\\"] = record[\\\"names_sim\\\"] + record.get(\\\"author_tesim\\\")\\n else:\\n record[\\\"names_sim\\\"] = record.get(\\\"author_tesim\\\")\\n\\n if record.get(\\\"scribe_tesim\\\") is not None:\\n if record.get(\\\"names_sim\\\") is not None:\\n record[\\\"names_sim\\\"] = record[\\\"names_sim\\\"] + record.get(\\\"scribe_tesim\\\")\\n else:\\n record[\\\"names_sim\\\"] = record.get(\\\"scribe_tesim\\\")\\n\\n if record.get(\\\"associated_name_tesim\\\") is not None:\\n if record.get(\\\"names_sim\\\") is not None:\\n record[\\\"names_sim\\\"] = record[\\\"names_sim\\\"] + record.get(\\\"associated_name_tesim\\\")\\n else:\\n record[\\\"names_sim\\\"] = record.get(\\\"associated_name_tesim\\\")\\n\\n if record.get(\\\"translator_tesim\\\") is not None:\\n if record.get(\\\"names_sim\\\") is not None:\\n record[\\\"names_sim\\\"] = record[\\\"names_sim\\\"] + record.get(\\\"translator_tesim\\\")\\n else:\\n record[\\\"names_sim\\\"] = record.get(\\\"translator_tesim\\\")\\n return record[\\\"names_sim\\\"]\",\n \"def namedtuple(clz: Type) -> Type[NamedTuple]:\\n if not is_dataclass(clz):\\n raise TypeError(\\\"{} is not a dataclass.\\\".format(clz.__name__))\\n\\n flds: Tuple[Field] = fields(clz)\\n flds_by_name: Dict[str, Field] = {f.name: f for f in flds}\\n nt_name: str = clz.__name__ + \\\"NamedTuple\\\"\\n\\n defaults: Optional[List[Any]] = None\\n field_names: List[str] = [\\n f.name\\n for f in flds\\n if f.default is not None or f.default_factory is not None\\n ]\\n for fld in field_names:\\n if defaults is None:\\n defaults = []\\n field: Field = flds_by_name[fld]\\n default_value: Optional[Any] = field.default\\n if default_value is None:\\n default_value = field.default_factory()\\n\\n defaults.append(default_value)\\n\\n field_names.extend(\\n [\\n f.name\\n for f in flds\\n if f.default is None and f.default_factory is None\\n ]\\n )\\n\\n return real_namedtuple(nt_name, field_names, defaults=defaults)\",\n \"def _wr_3fmt_wrtbl(goea_results, wr_params, log):\\n from goatools.go_enrichment import get_nts\\n goea_nts = get_nts(goea_results)\\n # List of all fields, printable or not, in namedtuple (nt):\\n log.write(\\\"\\\\nnamedtuple FIELDS: {F}\\\\n\\\".format(F=\\\" \\\".join(goea_nts[0]._fields)))\\n # Print to Excel Spreadsheet\\n title=\\\"Print subset of namedtuple fields\\\"\\n wr_xlsx(\\\"nbt3102_subset_nt.xlsx\\\", goea_nts, title=title, **wr_params)\\n # Print to tab-separated file\\n wr_tsv(\\\"nbt3102_subset_nt.tsv\\\", goea_nts, **wr_params)\",\n \"def namedtuples_to_xlsx(filename, values):\\n try:\\n # Use first row to generate header\\n peek = next(values)\\n header = list(peek.__class__._fields)\\n return xlsx_write_rows(filename, itertools.chain([header], [peek], values))\\n except StopIteration: # Empty generator\\n # Write empty xlsx\\n return xlsx_write_rows(filename, [])\",\n \"def concat(inp):\\n if(type(inp) == tuple):\\n return\\n if(inp.getName() == '&'):\\n if(inp.getFirst().getName() == 'tt' and inp.getSec() is not None):\\n inp.setName(inp.getSec().getName())\\n inp.setFirst(inp.getSec().getFirst())\\n inp.setSec(inp.getSec().getSec())\\n if(inp.getSec() is None):\\n return\\n if(inp.getSec().getName() == 'tt' and inp.getFirst() is not None):\\n inp.setName(inp.getFirst().getName())\\n if(inp.getName() in doubles or inp.getName() in singles):\\n inp.setFirst(inp.getFirst().getFirst())\\n inp.setSec(inp.getFirst().getSec())\\n else:\\n inp.setAtom()\",\n \"def generate_attributes(self):\\n for group in self.dict:\\n for param in self.dict[group]:\\n if group in self.group_douplicate and param in self.name_douplicate:\\n setattr(self, group+'_'+param, self(group, param))\\n else:\\n setattr(self, param, self(group, param))\",\n \"def export_names(ibs, nid_list, new_dbpath=None):\\n logger.info('Exporting name nid_list={!r}'.format(nid_list))\\n if new_dbpath is None:\\n new_dbpath = make_new_dbpath(ibs, 'nid', nid_list)\\n\\n aid_list = ut.flatten(ibs.get_name_aids(nid_list))\\n gid_list = ut.unique_unordered(ibs.get_annot_gids(aid_list))\\n\\n return export_data(ibs, gid_list, aid_list, nid_list, new_dbpath=new_dbpath)\",\n \"def issue_namedtuple_subclass_works_as_expected():\\n from collections import namedtuple\\n\\n class Crash(namedtuple(\\\"C\\\", \\\"foo bar\\\")):\\n \\\"\\\"\\\" Looking for attributes in __str__ will crash,\\n because EmptyNodes can't be infered.\\n \\\"\\\"\\\"\\n def __str__(self):\\n return \\\"{0.foo}: {0.bar}\\\".format(self)\\n return Crash\",\n \"def skeleton_getNameDicts(self, combined = False, count = None, iterName= None, cgmType = None, **kws):\\n _str_func = 'skeleton_getNameDicts'\\n log.debug(\\\"|{0}| >> \\\".format(_str_func)+ '-'*80)\\n log.debug(\\\"{0}\\\".format(self)) \\n\\n l_res = []\\n if count is not None:\\n _number = count\\n else:\\n _number = self.numJoints\\n mModule = self.moduleTarget\\n _baseNameAttrs = ATTR.datList_getAttrs(self.mNode,'nameList')\\n _l_baseNames = ATTR.datList_get(self.mNode, 'nameList')\\n log.debug(\\\"|{0}| >> baseNames: {1}\\\".format(_str_func,_l_baseNames))\\n if not _baseNameAttrs and not _l_baseNames:\\n _l_baseNames = []\\n for i in range(_number):\\n _l_baseNames.append(self.cgmName)\\n else:\\n _l_baseNames = []\\n for i,a in enumerate(_baseNameAttrs):\\n _l_baseNames.append(self.getMayaAttr(a))\\n #Name dict...\\n _nameDict ={}\\n \\n for s in 'cgmDirection','cgmPosition':\\n _val = mModule.getMayaAttr(s)\\n if _val and _val not in ['none','None','False']:\\n _nameDict[s] = _val\\n \\\"\\\"\\\"\\n if mModule.getMayaAttr('cgmDirection'):\\n _nameDict['cgmDirection'] = mModule.cgmDirection\\n if mModule.getMayaAttr('cgmPosition'):\\n _pos = mModule.cgmPosition\\n if _pos and _pos not in ['none','None','False']:\\n _nameDict['cgmPosition']=mModule.cgmPosition\\\"\\\"\\\"\\n \\n if iterName:\\n log.debug(\\\"|{0}| >> iterName: {1}\\\".format(_str_func,iterName)) \\n _nameDict['cgmName'] = iterName\\n elif self.getMayaAttr('nameIter'):\\n _nameDict['cgmName'] = self.nameIter\\n elif self.getMayaAttr('cgmName'):\\n _nameDict['cgmName'] = self.cgmName\\n else:\\n _nameDict['cgmName'] = self.blockType\\n \\n if cgmType:\\n _nameDict['cgmType'] = cgmType\\n else:\\n _nameDict['cgmType'] = 'skinJoint'\\n \\n \\n for a,v in kws.iteritems():\\n _nameDict[a] = v\\n \\n log.debug(\\\"|{0}| >> baseDict: {1}\\\".format(_str_func,_nameDict))\\n\\n _cnt = 0\\n l_range = range(_number)\\n l_dicts = []\\n for i in l_range:\\n _nameDictTemp = copy.copy(_nameDict)\\n _specialName = False\\n _cnt+=1\\n if i == 0:\\n if _l_baseNames[0]:\\n log.debug(\\\"|{0}| >> First and name attr...interupting default name\\\".format(_str_func)) \\n _nameDictTemp['cgmName'] = _l_baseNames[0]\\n #_nameDict['cgmName'] = _l_baseNames[0]#...interupt the default name...\\n _cnt = 0\\n _specialName = True\\n elif i == len(l_range) -1:\\n log.debug(\\\"|{0}| >> last...\\\".format(_str_func)) \\n if _l_baseNames[-1]:\\n log.debug(\\\"|{0}| >> found: {1}\\\".format(_str_func,_l_baseNames[-1])) \\n _nameDictTemp['cgmName'] = _l_baseNames[-1]\\n _specialName = True\\n _cnt = 0\\n \\n if not _specialName:\\n _nameDictTemp['cgmIterator'] = _cnt\\n \\n if _cnt == 1 and l_dicts and not l_dicts[-1].get('cgmIterator'):\\n l_dicts[-1]['cgmIterator'] = 0\\n \\n l_dicts.append(_nameDictTemp)\\n log.debug(\\\"|{0}| >> [{1}]: {2}\\\".format(_str_func,i,_nameDict))\\n \\n if combined:\\n return [NAMETOOLS.returnCombinedNameFromDict(d) for d in l_dicts]\\n return l_dicts\",\n \"def create_name_tuple(self, parsl_file_obj, in_or_out):\\n # Determine new_name\\n new_name = parsl_file_obj.filepath\\n if parsl_file_obj.filepath not in self.used_names:\\n if self.shared_fs is False:\\n new_name = self.create_new_name(os.path.basename(parsl_file_obj.filepath))\\n self.used_names[parsl_file_obj.filepath] = new_name\\n else:\\n new_name = self.used_names[parsl_file_obj.filepath]\\n # Determine file_is_shared\\n file_is_shared = False\\n if parsl_file_obj in self.registered_files:\\n file_is_shared = True\\n if parsl_file_obj not in self.shared_files:\\n self.shared_files.add(parsl_file_obj)\\n else:\\n self.registered_files.add(parsl_file_obj)\\n return (parsl_file_obj.filepath, new_name, file_is_shared, in_or_out)\",\n \"def strtuple(iterable): \\n string = ''\\n function = type(strtuple)\\n for i in iterable:\\n if isinstance(i , function):\\n string += i.__name__ + ', '\\n else:\\n string += str(i) + ', '\\n string = string.rstrip(', ')\\n string = '(' + string + ')'\\n return string\",\n \"def iter_tuples(self, with_index=True, name=\\\"Row\\\"):\\n columns = {column: to_identifier(column) for column in self._columns}\\n\\n fields = [\\\"index\\\"] if with_index else []\\n fields.extend(columns.values())\\n\\n container = namedtuple(name, fields)\\n for row in self.iter_dicts(with_index):\\n row = {columns[k]: v for k, v in row.items()}\\n yield container(**row)\",\n \"def generate_from_descriptor(self, tuple_descriptor):\\r\\n t = Tuple()\\r\\n d = {}\\r\\n t.set_data(d)\\r\\n for alias in tuple_descriptor.aliases:\\r\\n fields = self.__tuple_descriptor.get_descriptor(alias).underlying_fields\\r\\n for field in fields:\\r\\n setattr(t, field, getattr(self, field))\\r\\n setattr(t, alias, getattr(self, alias))\\r\\n t.set_tuple_descriptor(tuple_descriptor)\\r\\n return t\",\n \"def make_data(rics: list, fields: list, name: str) -> None:\\n df = get_data(rics, fields)\\n df.index.name = 'ID'\\n to_csv(df, name)\",\n \"def from_tuples(tuples=list()):\\n result = []\\n for (user_id, creation_date, name, google_id, email , role_id, student_id) in tuples:\\n person = Person()\\n person.set_id(user_id)\\n person.set_name(name)\\n person.set_berechtigung(role_id)\\n person.set_email(email)\\n person.set_google_id(google_id)\\n person.set_creation_date(creation_date)\\n person.set_student(student_id)\\n result.append(person)\\n return result\",\n \"def unwrap_process_input_tuple(tup: tuple):\\n names, quantile, intersection = tup\\n name_i, name_j = names\\n k = str(name_i) + \\\"|\\\" + str(name_j)\\n return name_i, name_j, k, quantile, intersection\",\n \"def write(self, output_dir, names, data=None):\\n assert len(names) == len(self.object_list), \\\"Give as many names as objects to save multi-object\\\"\\n\\n pos = 0\\n for elt, name in zip(self.object_list, names):\\n if data is None:\\n elt.write(output_dir, name)\\n\\n else:\\n if elt.type.lower() in ['surfacemesh', 'polyline', 'pointcloud', 'landmark']:\\n elt.write(output_dir, name, data['landmark_points'][pos:pos + elt.get_number_of_points()])\\n pos += elt.get_number_of_points()\\n\\n elif elt.type.lower() == 'image':\\n elt.write(output_dir, name, data['image_intensities'])\",\n \"def test_gen_expr_named_tuples() -> None:\\n Student = NamedTuple(\\\"Student\\\", [(\\\"gpa\\\", float), (\\\"name\\\", str)])\\n graduates: List[Student] = [\\n Student(gpa=3.5, name=\\\"Tallys\\\"),\\n Student(gpa=3.8, name=\\\"Kaniya\\\"),\\n Student(gpa=3.7, name=\\\"Toibe\\\"),\\n ]\\n\\n valedictorian = max((student.gpa, student.name) for student in graduates)\\n assert valedictorian == (3.8, \\\"Kaniya\\\")\",\n \"def saveInTuple(list,name,type=\\\"R\\\",tname = \\\"T\\\", keytypes = {}):\\r\\n if keytypes == {}:\\r\\n\\t for key in list[0].keys(): keytypes[key] = \\\"F\\\"\\r\\n \\r\\n print \\\"saving data\\\"\\r\\n labels=[]\\r\\n vvv=vector(0,0,0)\\r\\n if len(list)==0:\\r\\n print \\\"list size = 0\\\"\\r\\n return 0\\r\\n for i in range(4):\\r\\n if 'has_key' in dir(list[0]):break\\r\\n list=desagrupate(list)\\r\\n for key in list[0].keys():\\r\\n if (dir(list[0][key])==dir(vvv))or(dir(list[0][key])==dir(labels)):\\r\\n \\r\\n s=len(list[0][key])\\r\\n for j in range(s): \\r\\n labels.append(key+str(j+1)+\\\"/\\\" + keytypes[key])\\r\\n else: labels.append(key+\\\"/\\\"+keytypes[key])\\r\\n if type in [\\\"X\\\", \\\"ASCII\\\"]:\\r\\n TUP=XTuple(name,labels)\\r\\n if type==\\\"R\\\":\\r\\n TUP=RTuple(name,labels,tname)\\r\\n if type==\\\"M\\\":\\r\\n TUP=MTuple(name,labels)\\r\\n for thing in list:\\r\\n for key in thing.keys():\\r\\n #if key+\\\"1/\\\" + keytypes[key] in labels:\\r\\n # s=len(thing[key])\\r\\n # for j in range(s):\\r\\n # TUP.fillItem(key+str(j+1),thing[key][j])\\r\\n TUP.fillItem(key,thing[key])\\r\\n TUP.fill()\\r\\n TUP.close()\",\n \"def tup_list_maker(tup_list):\\n final_list = []\\n for item in tup_list:\\n index = item[0]\\n sentences = item[1]\\n for sentence in sentences:\\n pair = (index, sentence)\\n final_list.append(pair)\\n return final_list\",\n \"def makeTableNamesList(n, ):\",\n \"def change_names (fixed_structure, moving_structure, index):\\n for chain in moving_structure[0]:\\n chain.id = utilities.merged_list[index]+\\\"-\\\"+chain.id\\n index +=1\\n return (fixed_structure, moving_structure, index)\",\n \"def expandable_from_tuple(tuple_, field_descriptions):\\n result = types.Expandable(tuple_[0])\\n for (name, _), value in zip(field_descriptions, tuple_[1:]):\\n result.__dict__[name] = value\\n return result\",\n \"def make_csv(data_dict):\\n fieldnames = ['name'] + data_dict.itervalues().next().keys()\\n with open('data.csv', 'w') as csvfile:\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n writer.writeheader()\\n for record in data_dict:\\n person = data_dict[record]\\n person['name'] = record\\n assert set(person.keys()) == set(fieldnames)\\n writer.writerow(person)\",\n \"def make_csv(data_dict):\\r\\n fieldnames = ['name'] + data_dict.itervalues().next().keys()\\r\\n with open('data.csv', 'w') as csvfile:\\r\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\r\\n writer.writeheader()\\r\\n for record in data_dict:\\r\\n person = data_dict[record]\\r\\n person['name'] = record\\r\\n assert set(person.keys()) == set(fieldnames)\\r\\n writer.writerow(person)\",\n \"def namedtuplefetchall(query, param_list):\\r\\n\\twith connection.cursor() as cursor:\\r\\n\\t\\tcursor.execute(query, param_list)\\r\\n\\t\\tdesc = cursor.description\\r\\n\\t\\tnt_result = namedtuple('Result', [col[0] for col in desc])\\r\\n\\t\\treturn [nt_result(*row) for row in cursor.fetchall()]\",\n \"def exportPeople ( c ) :\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n xml = \\\"\\\"\\n p = sqlQuery ( c, \\\"select * from People;\\\" )\\n for i in p :\\n pL = sqlQuery ( c, \\\"select * from PeopleLocations where personID = '\\\" + i [ 0 ] + \\\"';\\\" )\\n pER = sqlQuery ( c, \\\"select * from PersonExternalResources where personID = '\\\" + i [ 0 ] + \\\"';\\\" )\\n pTO = sqlQuery ( c, \\\"select * from PeopleToOrganizations where personID = '\\\" + i [ 0 ] + \\\"';\\\" )\\n cTP = sqlQuery ( c, \\\"select * from CrisesToPeople where personID = '\\\" + i [ 0 ] + \\\"';\\\" )\\n xml += openTagAtt ( \\\"Person\\\", \\\"personIdent\\\", i [ 0 ] )\\n xml += openTag ( \\\"Name\\\" )\\n xml += openCloseTag ( \\\"FirstName\\\", i [ 1 ] )\\n xml += openCloseTag ( \\\"MiddleName\\\", i [ 2 ] )\\n xml += openCloseTag ( \\\"LastName\\\", i [ 3 ] )\\n xml += openCloseTag ( \\\"Suffix\\\", i [ 4 ] )\\n xml += closeTag ( \\\"Name\\\" )\\n xml += closeTagAtt ( \\\"Kind\\\", \\\"personKindIdent\\\", i [ 5 ] )\\n for j in pL :\\n xml += openTag ( \\\"Location\\\" )\\n xml += openCloseTag ( \\\"Locality\\\", j [ 1 ] )\\n xml += openCloseTag ( \\\"Region\\\", j [ 2 ] )\\n xml += openCloseTag ( \\\"Country\\\", j [ 3 ] )\\n xml += closeTag ( \\\"Location\\\" )\\n xml += openTag ( \\\"ExternalResources\\\" )\\n for j in pER :\\n xml += openCloseTag ( j [ 1 ] , j [ 2 ] )\\n xml += closeTag ( \\\"ExternalResources\\\" )\\n xml += openTag ( \\\"RelatedCrises\\\" )\\n for j in cTP :\\n xml += closeTagAtt ( \\\"RelatedCrisis\\\" , \\\"crisisIdent\\\", j [ 0 ] )\\n xml += closeTag ( \\\"RelatedCrises\\\" )\\n xml += openTag ( \\\"RelatedOrganizations\\\" )\\n for j in pTO :\\n xml += closeTagAtt ( \\\"RelatedOrganization\\\" , \\\"organizationIdent\\\", j [ 1 ] )\\n xml += closeTag ( \\\"RelatedOrganizations\\\" )\\n xml += closeTag ( \\\"Person\\\" )\\n assert str ( type ( xml ) ) == \\\"<type 'str'>\\\"\\n return xml\",\n \"def create_tuple4(kmer1, kmer2, kmer1_neg, kmer2_neg):\\n return Tuple4(\\n ''.join(kmer1),\\n ''.join(kmer2),\\n ''.join(kmer1_neg),\\n ''.join(kmer2_neg))\",\n \"def str_tuple(item):\\n return \\\"{}:{}\\\".format(item[0], item[1])\",\n \"def __to_tuple(self):\\n return (self.bucket, self.name)\",\n \"def dump_to_csv(fileinfos, out, all_el_names, all_att_names):\\n uni_el_names = unique_names(all_el_names)\\n uni_att_names = unique_names(all_att_names) \\n att_names_prefixed = [\\\"@%s\\\" % item for item in uni_att_names]\\n csv_filename = \\\"all_elements_used.csv\\\"\\n \\n # transform information from dictionary to CSV format\\n with open(os.path.join(out,out_dir_name,csv_filename), \\\"w\\\") as fout:\\n fout.write(\\\",\\\" + \\\",\\\".join(uni_el_names) + \\\",\\\" + \\\",\\\".join(att_names_prefixed) + \\\"\\\\n\\\")\\n for key in fileinfos:\\n el_str = \\\"\\\"\\n for eln in uni_el_names:\\n if (eln in fileinfos[key][\\\"usage_el\\\"]):\\n el_str += \\\",\\\" + str(fileinfos[key][\\\"usage_el\\\"][eln])\\n else:\\n el_str += \\\",0\\\"\\n att_str = \\\"\\\"\\n for attn in uni_att_names:\\n if (attn in fileinfos[key][\\\"usage_att\\\"]):\\n att_str += \\\",\\\" + str(fileinfos[key][\\\"usage_att\\\"][attn])\\n else:\\n att_str += \\\",0\\\"\\n fout.write(key + el_str + att_str + \\\"\\\\n\\\")\\n stdout.write(\\\"... \\\"+csv_filename+\\\" created\\\\n\\\")\",\n \"def create_header(numValues):\\n\\n header = []\\n for value in range(numValues):\\n header.append(\\\"att{}\\\".format(value))\\n return header\",\n \"def output_name_table(outfile: TextIO, do_print: bool, is_name: bool, itemlist: list, unique_set: set,\\n notecnt: int, comcnt: int, refdict: dict, name_table: dict, point_locations: dict) -> None:\\n\\n def create_location_sublink(x: str) -> str:\\n \\\"\\\"\\\"\\n create a link to location pages, preserving [] info when applicable\\n \\\"\\\"\\\"\\n tmpname = strip_location_subtext(x)\\n if tmpname in point_locations:\\n loc = point_locations[tmpname]\\n tmpstr = create_location_link(loc, tmpname, do_print, path=\\\"../locations/\\\")\\n if tmpname != x:\\n tmpstr += x[len(tmpname):]\\n tmpstr = tmpstr.replace(\\\"<!>\\\", fetch_fa_glyph(\\\"bad location\\\"))\\n # tmpstr = tmpstr.replace(\\\"<?>\\\", fetch_fa_glyph(\\\"questionable id\\\"))\\n tmpstr = tmpstr.replace(\\\"<?>\\\", \\\"\\\").strip()\\n else:\\n tmpstr = x\\n return tmpstr\\n\\n # ---main part of loop---\\n first_name = True\\n ncols = 5\\n if notecnt > 0:\\n ncols += 1\\n if comcnt > 0:\\n ncols += 1\\n\\n for n in itemlist:\\n outfile.write(\\\" <tr>\\\\n\\\")\\n if is_name:\\n nref = n.cite_key\\n else:\\n nref = n.name\\n if nref in unique_set:\\n if first_name:\\n first_name = False\\n else:\\n outfile.write(\\\" <td class=\\\\\\\"rowspacer\\\\\\\" colspan=\\\\\\\"\\\" + str(ncols) + \\\"\\\\\\\"> </td>\\\\n\\\")\\n outfile.write(\\\" </tr>\\\\n\\\")\\n outfile.write(\\\" <tr>\\\\n\\\")\\n\\n if nref == \\\".\\\":\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n elif is_name: # output citation\\n crossref = refdict[nref]\\n outfile.write(\\\" <td><a href=\\\\\\\"\\\" + rel_link_prefix(do_print, \\\"../references/\\\") +\\n crossref.cite_key + \\\".html\\\\\\\">\\\" + crossref.citation + \\\"</a></td>\\\\n\\\")\\n else: # output species name\\n outfile.write(\\\" <td><a href=\\\\\\\"\\\" + rel_link_prefix(do_print, \\\"../names/\\\") +\\n name_to_filename(nref) + \\\".html\\\\\\\">\\\" + format_name_string(nref) + \\\"</a></td>\\\\n\\\")\\n\\n # common names\\n if comcnt > 0:\\n if n.common == \\\".\\\":\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n else:\\n outfile.write(\\\" <td>\\\" + n.common + \\\"</td>\\\\n\\\")\\n outfile.write(\\\" <td>\\\" + n.where + \\\"</td>\\\\n\\\")\\n unique_set -= {nref}\\n else:\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n if comcnt > 0:\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n\\n # applies to...\\n if n.context == \\\"location\\\":\\n outstr = create_location_sublink(n.application)\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"location\\\") + \\\"location: \\\" + outstr + \\\"</td>\\\\n\\\")\\n elif n.context == \\\"citation\\\":\\n if n.application in refdict:\\n crossref = refdict[n.application]\\n if n.application in name_table:\\n nstr = n.cite_n\\n if nstr == \\\"0\\\":\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"citation\\\") + \\\"citation: \\\"\\n \\\"<a href=\\\\\\\"\\\" + rel_link_prefix(do_print, \\\"../references/\\\") + crossref.cite_key +\\n \\\".html\\\\\\\">\\\" + crossref.citation + \\\"</a></td>\\\\n\\\")\\n else:\\n if \\\".\\\" in nstr:\\n try:\\n extraref = \\\" [\\\" + name_table[n.application][nstr][1] + \\\"]\\\"\\n refname = name_table[n.application][nstr][0]\\n except LookupError:\\n if is_name: # only print errors on one pass\\n report_error(\\\"Error in citation: \\\" + n.cite_key + \\\" cites\\\" + nstr +\\n \\\" in \\\" + n.application)\\n extraref = \\\"\\\"\\n refname = \\\"\\\"\\n else:\\n extraref = \\\"\\\" \\n try:\\n refname = name_table[n.application][int(nstr)]\\n except ValueError:\\n report_error(\\\"Citation \\\" + n.cite_key + \\\" tried to cite \\\" + n.application +\\n \\\" #\\\" + nstr)\\n refname = \\\"\\\"\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"citation\\\") + \\\"citation: \\\"\\n \\\"<a href=\\\\\\\"\\\" + rel_link_prefix(do_print, \\\"../references/\\\") + crossref.cite_key +\\n \\\".html\\\\\\\">\\\" + crossref.citation + \\\"</a> → \\\" + format_name_string(refname) +\\n extraref + \\\"</td>\\\\n\\\")\\n else:\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"citation\\\") + \\\"citation: \\\"\\n \\\"<a href=\\\\\\\"\\\" + rel_link_prefix(do_print, \\\"../references/\\\") + crossref.cite_key +\\n \\\".html\\\\\\\">\\\" + crossref.citation + \\\"</a></td>\\\\n\\\")\\n else:\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"citation\\\") + \\\"citation: \\\" + n.application +\\n \\\"</td>\\\\n\\\")\\n if is_name and not do_print: # only print on one pass\\n report_error(\\\"Citation not in DB: \\\" + n.cite_key + \\\" cites \\\" + n.application)\\n elif n.context == \\\"specimen\\\":\\n if n.application == \\\"?\\\":\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"specimen\\\") + \\\"specimen: unknown locality</td>\\\\n\\\")\\n else:\\n outstr = create_location_sublink(n.application)\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"specimen\\\") + \\\"specimen: \\\" + outstr + \\\"</td>\\\\n\\\")\\n elif n.context == \\\"sequence\\\":\\n if n.application == \\\"?\\\":\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"sequence\\\") + \\\"sequence: unknown locality</td>\\\\n\\\")\\n else:\\n outstr = create_location_sublink(n.application)\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"sequence\\\") + \\\"sequence: \\\" + outstr + \\\"</td>\\\\n\\\")\\n\\n elif n.context == \\\"unpublished\\\":\\n outfile.write(\\\" <td>unpublished name <em class=\\\\\\\"species\\\\\\\">\\\" +\\n n.application + \\\"</em></td>\\\\n\\\")\\n else: # \\\"n/a\\\"\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n\\n # accepted species name\\n if n.actual == \\\"?\\\":\\n outfile.write(\\\" <td>?</td>\\\\n\\\")\\n elif n.actual in {\\\".\\\", \\\"=\\\"}:\\n outfile.write(\\\" <td>TBD</td>\\\\n\\\")\\n elif n.actual == \\\"n/a\\\":\\n outfile.write(\\\" <td> </td>\\\\n\\\") \\n elif n.actual.startswith(\\\">\\\"):\\n outfile.write(\\\" <td><em class=\\\\\\\"species\\\\\\\">\\\" + n.actual[1:] + \\\"</em></td>\\\\n\\\")\\n elif n.actual.startswith(\\\"#\\\"):\\n t_rank, t_name = n.actual[1:].split(\\\"#\\\")\\n outfile.write(\\\" <td>\\\" + create_taxon_link(t_rank, t_name, do_print, path=\\\"../\\\") + \\\"</td>\\\\n\\\")\\n else:\\n s = find_species_by_name(n.actual)\\n if \\\"<?>\\\" in n.application:\\n glyphstr = \\\" \\\" + fetch_fa_glyph(\\\"questionable id\\\")\\n else:\\n glyphstr = \\\"\\\"\\n outfile.write(\\\" <td><a href=\\\\\\\"\\\" + rel_link_prefix(do_print, \\\"../\\\") + \\\"u_\\\" + n.actual +\\n \\\".html\\\\\\\"><em class=\\\\\\\"species\\\\\\\">\\\" + s.binomial() + \\\"</em></a>\\\" + glyphstr + \\\"</td>\\\\n\\\")\\n\\n # source of accepted species\\n if n.source == \\\".\\\": # currently not listed\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n elif n.source == \\\"<\\\": # original name retained\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"original\\\") + \\\"Original</td>\\\\n\\\")\\n elif n.source == \\\"=\\\": # automatically computer\\n outfile.write(\\\" <td>\\\" + fetch_fa_glyph(\\\"computed\\\") + \\\"Computed</td>\\\\n\\\")\\n else:\\n if \\\";\\\" in n.source:\\n source_list = n.source.split(\\\";\\\")\\n else:\\n source_list = [n.source]\\n source_strs = []\\n for source in source_list:\\n if source in refdict: # another reference\\n crossref = refdict[source]\\n source_strs.append(\\\"<a href=\\\\\\\"\\\" + rel_link_prefix(do_print, \\\"../references/\\\") +\\n crossref.cite_key + \\\".html\\\\\\\">\\\" + crossref.citation + \\\"</a>\\\")\\n else: # should start with a >\\n tmpsource = source[1:]\\n tmpsource = tmpsource.replace(\\\"MSR:\\\", \\\"\\\")\\n tmpsource = tmpsource.replace(\\\"/\\\", \\\"/<br />\\\")\\n tmpsource = tmpsource.replace(\\\"geography\\\", fetch_fa_glyph(\\\"geography\\\") + \\\"Geography\\\")\\n tmpsource = tmpsource.replace(\\\"synonymy\\\", fetch_fa_glyph(\\\"synonymy\\\") + \\\"Synonymy\\\")\\n source_strs.append(tmpsource)\\n outfile.write(\\\" <td>\\\" + \\\", \\\".join(source_strs) + \\\"</td>\\\\n\\\")\\n\\n # notes\\n if notecnt > 0:\\n if n.name_note == \\\".\\\":\\n outfile.write(\\\" <td> </td>\\\\n\\\")\\n else:\\n outfile.write(\\\" <td>\\\" + replace_species_in_string(n.name_note) + \\\"</td>\\\\n\\\")\\n outfile.write(\\\" </tr>\\\\n\\\")\\n outfile.write(\\\" </table>\\\\n\\\")\",\n \"def construct_name(p, prefix):\\n name = prefix\\n for key in p.keys():\\n if (type(p[key]) != tuple) and (type(p[key]) != list):\\n name = name + '_' + str(key) + '-' + str(p[key])\\n else:\\n name = name + '_' + str(key) + '-' + str(p[key][0])\\n return name\",\n \"def tupleStrFormat(tupl):\\n string = \\\"this is a tuple (\\\"\\n for element in tupl:\\n string += str(element) + \\\", \\\"\\n string += \\\")\\\"\\n return string\",\n \"def tuple_to_string(transcript_info):\\n\\n return \\\"\\\\t\\\".join(transcript_info.data_attributes())\",\n \"def serialize(self):\\r\\n return (self.name, self.values)\",\n \"def insertJoint(*args, **kwargs)->AnyStr:\\n pass\",\n \"def to_namedtuple(d, classname='struct'):\\n if not isinstance(d, dict):\\n raise ValueError(\\\"Can only convert dicts into namedtuple\\\")\\n for k,v in d.iteritems():\\n if isinstance(v, dict):\\n d[k] = to_namedtuple(v)\\n return namedtuple(classname, d.keys())(**d)\",\n \"def __tuple(self):\\n return (self.__app, self.__namespace, self.__pairs)\",\n \"def make_tag_pairs(self, input, start, end, elements):\\n tps = TagPairs()\\n for e in elements:\\n k = [k for k in e.keys()][0]\\n tps[k] = e[k]\\n return tps\",\n \"def serialize_tuple(self, obj):\\n return '(' + ''.join([self.serialize(i) for i in obj]) + 't'\",\n \"def fname_tsk3(tup):\\n t_size = str(len(tup))\\n fname3 = \\\"The \\\" + t_size + \\\" numbers are: \\\"\\n for t in tup:\\n fname3 = fname3 + \\\"{:d}, \\\"\\n return fname3[:-2].format(*tup)\",\n \"def namedtuple_with_defaults(typename,\\n field_names,\\n default_values=(),\\n units=None):\\n T = collections.namedtuple(typename, field_names)\\n T.__new__.__defaults__ = (None, ) * len(T._fields)\\n if isinstance(default_values, collections.abc.Mapping):\\n prototype = T(**default_values)\\n else:\\n prototype = T(*default_values)\\n T.__new__.__defaults__ = tuple(prototype)\\n if units is None:\\n T.units = ('-') * len(T._fields)\\n else:\\n T.units = units\\n return T\",\n \"def nameify(items, sep=',', junction=None, oxford=False, quote=None):\\n if not items:\\n return ''\\n sorted_items = sorted(items)\\n quote = quote or ''\\n if len(sorted_items) == 2:\\n return ''.join([\\n quote_item(sorted_items[0], quote),\\n sep if oxford or not junction else '',\\n ' {} '.format(junction) if junction else ' ',\\n quote_item(sorted_items[1], quote)\\n ])\\n if len(items) == 1:\\n return quote_item(sorted_items[0], quote)\\n return '{}{} {}'.format(\\n quote_item(sorted_items[0], quote),\\n sep,\\n nameify(\\n sorted_items[1:],\\n sep=sep,\\n junction=junction,\\n oxford=oxford,\\n quote=quote))\",\n \"def create_username_name_pairs(usernames, names):\\n logger.info('create username name pairs...')\\n pairs = []\\n for username, name in zip(usernames, names):\\n \\n pair = username if name == '' else username + ' (' + name + ')' \\n pairs.append(pair)\\n return pairs\",\n \"def name_mapper(tup):\\n tup = tuple(ix2name[i] for i in tup)\\n\\n def s(t):\\n return sorted(t, key=self.clade_order)\\n left, right = sorted((s(tup[:2]), s(tup[2:])))\\n return (*left, *right)\",\n \"def create_type_tuple(*elems):\\n tuple = ast.Tuple()\\n\\n tuple.elts = []\\n for elem in elems:\\n tuple.elts.append(elem)\\n\\n return tuple\",\n \"def get_named_tuple_object(instance: Any) -> Tuple[object, Type[object]]:\\n clz: Type = namedtuple(instance.__class__)\\n instance_data = asdict(instance)\\n for key, value in instance_data.items():\\n if is_dataclass(value):\\n obj, _ = get_named_tuple_object(value)\\n instance_data[key] = obj\\n\\n # generics currently unsupported, maybe in later versions\\n\\n new_instance: clz = clz(**instance_data)\\n return (new_instance, clz)\",\n \"def typenames(self):\\n return tuple((item[0] for item in self))\",\n \"def write_Fasta_from_name_seq_pairs(name_seqs, fh):\\r\\n if fh is None:\\r\\n raise ValueError(\\\"Need open file handle to write to.\\\")\\r\\n\\r\\n for (name, seq) in name_seqs:\\r\\n fh.write(\\\"%s\\\\n\\\" % BiologicalSequence(seq, id=name).to_fasta())\",\n \"def export(tako_list, filename):\\n for tak in tako_list:\\n tak = tak[0]\\n l1 = [tak.ident, \\\"a\\\"]\\n for gen in tak.genome.weightchr_a:\\n l1.append(gen.ident)\\n l1.append(gen.weight)\\n l1.append(gen.mut_rate)\\n l1.append(gen.dom)\\n f = os.path.join(\\\"Data\\\", (filename[:-4] + \\\" gene data.csv\\\"))\\n with open(f, 'a', newline=\\\"\\\") as csvfile:\\n writ = csv.writer(csvfile)\\n writ.writerow(l1)\\n if len(tak.genome.weightchr_b) != 0:\\n l2 = [tak.ident, \\\"b\\\"]\\n for gen in tak.genome.weightchr_b:\\n l2.append(gen.ident)\\n l2.append(gen.weight)\\n l2.append(gen.mut_rate)\\n l2.append(gen.dom) \\n writ.writerow(l2)\",\n \"def makeRepeatedDataType(tagname, keyword, G):\\n value = G(keyword)\\n if not value:\\n return ''\\n\\n unit = G(keyword + 'Unit')\\n method = G(keyword + 'Method')\\n comment = G(keyword + 'Comment')\\n acc = G(keyword + 'Accuracy')\\n refs = G(keyword + 'Ref')\\n name = G(keyword + 'Name')\\n\\n# make everything iterable\\n value, unit, method, comment, acc, refs, name = [[x] if not isiterable(x) else x for x in [value, unit, method, comment, acc, refs, name]]\\n\\n# if some are shorter than the value list, replicate them\\n l = len(value)\\n value, unit, method, comment, acc, refs, name = [ x*l if len(x)<l else x for x in [value, unit, method, comment, acc, refs, name]]\\n\\n string = ''\\n for i, val in enumerate(value):\\n string += '\\\\n<%s' % tagname\\n if name[i]:\\n string += ' name=\\\"%s\\\"' % name[i]\\n if method[i]:\\n string += ' methodRef=\\\"M%s-%s\\\"' % (NODEID, method[i])\\n string += '>'\\n if comment[i]:\\n string += '<Comments>%s</Comments>' % escape('%s' % comment[i])\\n string += makeSourceRefs(refs[i])\\n string += '<Value units=\\\"%s\\\">%s</Value>' % (unit[i] or 'unitless', value[i])\\n if acc[i]:\\n string += '<Accuracy>%s</Accuracy>' % acc[i]\\n string += '</%s>' % tagname\\n\\n return string\",\n \"def GenericRepr(obj, names):\\n\\n def ReprIndent(name, as_ref):\\n return ' %s=%s' % (name, Repr(getattr(obj, name), as_ref).replace(\\n '\\\\n', '\\\\n '))\\n\\n return '%s(\\\\n%s\\\\n)' % (obj.__class__.__name__, ',\\\\n'.join(\\n ReprIndent(name, as_ref) for (name, as_ref) in names.items()))\",\n \"def namedtuples_to_dicts(nts):\\n return [dict(nt._asdict()) for nt in nts]\",\n \"def triplify_object(binding):\\n triples = []\\n if binding.uri:\\n triples.append((binding.subject, RDF.type, binding.uri))\\n\\n if binding.parent is not None:\\n parent = binding.parent.subject\\n if binding.parent.is_array:\\n parent = binding.parent.parent.subject\\n triples.append((parent, binding.predicate, binding.subject))\\n if binding.reverse is not None:\\n triples.append((binding.subject, binding.reverse, parent))\\n\\n for prop in binding.properties:\\n _, prop_triples = triplify(prop)\\n triples.extend(prop_triples)\\n\\n return binding.subject, triples\",\n \"def make_echo_csv (list_of_region_tuples):\\n\\n #initialize the Echo formatted output dataframe\\n out = pd.DataFrame(columns= ['Source Plate Name', 'Source Plate Type', 'Source Well', 'Sample ID', 'Sample Name', \\\\\\n 'Sample Group', 'Sample Comment', 'Destination Plate Name', 'Destination Well', 'Transfer Volume'])\\n\\n idx = 0 #have to use a counter because we go through multiple lists and can't return to idx=0 each time\\n #there may be a list of region tuples with source wells, volumes, dest wells\\n for region in list_of_region_tuples:\\n #for each well location to be shot from the current region\\n for well in region[2]:\\n #add the dest well\\n out.loc[idx, 'Destination Well'] = well\\n #Add the source well and transfer volume for that region\\n out.loc[idx, ['Source Well', 'Transfer Volume']] = [region[0], region[1]]\\n idx += 1\\n\\n #Set the unchanging names for the dataframe\\n out[['Source Plate Name', 'Source Plate Type', 'Destination Plate Name']] = ['Source[1]', '384PP_AQ_BP', 'Destination[1]']\\n\\n return out\",\n \"def is_namedtuple(obj):\\n return isinstance(obj, tuple) and hasattr(obj, '_asdict')\",\n \"def create_csv(d, f):\\n csv_data = list()\\n csv_head = [unicode(x) for x in f]\\n\\n for row in d:\\n row_data = list()\\n for field in f:\\n fields = field.split('.')\\n row_data.append(extract_dict(row, fields))\\n csv_data.append(row_data)\\n\\n csv = (csv_head, csv_data)\\n return csv\",\n \"def write(*name):\\n foo = Foo(' '.join(name))\\n with open(DBNAME, 'w') as f:\\n f.write(pickle.dumps(foo))\",\n \"def get_tuples_helper(element) -> list:\\n try:\\n return [element[\\\"ExportName\\\"], element[\\\"OutputValue\\\"]]\\n except:\\n return [element[\\\"OutputKey\\\"], element[\\\"OutputValue\\\"]]\",\n \"def join_unique(\\n headers: List[str], seq: str, OH: IO, **kwargs\\n ) -> Optional[Tuple[str, str]]:\\n if len(headers) != 1:\\n return None\\n batch = (headers[0], seq)\\n OH.write(\\\">%s\\\\n%s\\\\n\\\" % batch)\\n return batch\",\n \"def convert_arg((arg, attrs, mode, typ, name)):\\n iorname = name\\n return iorname, (arg, attrs, mode, typ, name)\",\n \"def make_tuple(*fields):\\n fields2 = []\\n for (idx, f) in zip(range(len(fields)), fields):\\n if isinstance(f, (_StructField, pb.StructField)):\\n fields2.append(f)\\n if isinstance(f, pb.SQLType):\\n fields2.append(pb.StructField(\\n field_name=\\\"_%s\\\" % str(idx),\\n field_type=f))\\n raise ValueError(\\\"Could not understand type %s for %s\\\" % (type(f), f))\\n return StructType(fields2)\",\n \"def ntuple_from_dict(d):\\n return namedtuple('TupleFromDict', d.keys())(**d)\",\n \"def test_file_write_attributes_for_each(self):\\n\\n with OrthoMultiTs(self.testfilename, n_loc=3, mode=\\\"w\\\") as dataset:\\n n_data = 5\\n locations = np.array([1, 2, 3])\\n data = {\\n \\\"test\\\": np.arange(n_data * 3).reshape(3, n_data),\\n \\\"test2\\\": np.arange(n_data * 3).reshape(3, n_data)\\n }\\n base = datetime(2007, 1, n_data)\\n dates = np.array(\\n [base + timedelta(hours=i) for i in range(n_data)])\\n descriptions = np.repeat([str(\\\"station\\\")], 3).tolist()\\n\\n dataset.write_all(locations,\\n data,\\n dates,\\n loc_descrs=descriptions,\\n lons=np.arange(3),\\n lats=np.arange(3),\\n alts=np.arange(3),\\n attributes={\\n \\\"test\\\": {\\n \\\"testattribute\\\": \\\"teststring\\\"\\n },\\n \\\"test2\\\": {\\n \\\"testattribute2\\\": \\\"teststring2\\\"\\n }\\n })\\n\\n with OrthoMultiTs(self.testfilename) as dataset:\\n data = dataset.read_all(2)\\n nptest.assert_array_equal(data[\\\"test\\\"], np.arange(5) + 5)\\n assert dataset.dataset.variables[\\n \\\"test\\\"].testattribute == \\\"teststring\\\"\\n assert dataset.dataset.variables[\\n \\\"test2\\\"].testattribute2 == \\\"teststring2\\\"\\n test_dates = []\\n for n_data in [5]:\\n base = datetime(2007, 1, n_data)\\n test_dates.append(\\n np.array(\\n [base + timedelta(hours=i) for i in range(n_data)]))\\n dates = np.concatenate(test_dates)\\n nptest.assert_array_equal(data[\\\"time\\\"], dates)\",\n \"def genStruct(self, typeinfo, typeName, alias):\\n OutputGenerator.genStruct(self, typeinfo, typeName, alias)\\n\\n if alias:\\n # Add name -> alias mapping\\n self.addName(self.alias, typeName, alias)\\n else:\\n # May want to only emit definition on this branch\\n True\\n\\n members = [member.text for member in typeinfo.elem.findall('.//member/name')]\\n self.structs[typeName] = members\\n memberTypes = [member.text for member in typeinfo.elem.findall('.//member/type')]\\n for member_type in memberTypes:\\n self.addMapping(typeName, member_type)\",\n \"def _namedzip_generator(zipped, named_tuple, defaults=None):\\n\\n for vals in zipped:\\n if defaults:\\n vals = (x if x is not sentinel else defaults[i] for i, x in enumerate(vals))\\n yield named_tuple(*vals)\",\n \"def write_concat_GO_dicts(self, *GO_dict):\\n\\n for i, j in zip(self.IDs[0:3], GO_dict):\\n with open(i, 'w') as f:\\n f.write('transcript_id\\\\tgene_ontology\\\\n')\\n for k, v in j.iteritems():\\n f.write(k + '\\\\t' + '\\\\t'.join(v) + '\\\\n')\",\n \"def ttuple(name, *types):\\n\\n sort = z3.Datatype(name)\\n sort.declare(name, *[(fname, typ._z3_sort) for fname, typ in types])\\n sort = sort.create()\\n fields = {\\\"_z3_sort\\\" : sort}\\n for fname, typ in types:\\n code = \\\"\\\"\\\"\\\\\\n@property\\ndef %s(self):\\n return wrap(self._z3_sort.%s(self._v))\\\"\\\"\\\" % (fname, fname)\\n locals_dict = {}\\n exec code in globals(), locals_dict\\n fields[fname] = locals_dict[fname]\\n return type(name, (STupleBase, SymbolicVal), fields)\",\n \"def test_output_named_tuple_vs_dictionary_2():\\n assert oldest_person_age == oldest_person_age_d, \\\"Max age cannot be different for Named Tuple and Dictionary list\\\"\",\n \"def format_out_relations(relations, out_):\\n\\n if out_ == 'sparse':\\n relations_o = relations\\n elif out_ == 'network':\\n relations_o = nx.from_scipy_sparse_matrix(relations)\\n elif out_ == 'sp_relations':\\n relations_o = RegionDistances(relations)\\n elif out_ == 'list':\\n relations_o = []\\n for i in range(relations.shape[0]):\\n relations_o.append(list(relations.getrow(i).nonzero()[0]))\\n return relations_o\",\n \"def make_fastq_multi(in_fasta, quals, out_fp,\\r\\n label_transform=split_lib_transform):\\r\\n mkdir(out_fp)\\r\\n seen_libs = defaultdict(list)\\r\\n for rec, label in iter_fastq(in_fasta, quals, label_transform):\\r\\n lib_id, seq_id = label.rsplit('_', 1)\\r\\n seen_libs[lib_id].append(rec)\\r\\n for lib, recs in seen_libs.items():\\r\\n if lib is None: # skip the seqs we couldn't assign to a library\\r\\n continue\\r\\n outfile = open(out_fp + '/' + lib + '.fastq', 'w')\\r\\n outfile.write('\\\\n'.join(recs))\\r\\n outfile.close()\",\n \"def _make_token_sequence(names) -> tuple:\\n if isinstance(names, str):\\n names = names.replace(',', ' ').split()\\n return tuple(map(str, names))\",\n \"def build_output_table(cls, name='inputTableName', output_name='output'):\\n obj = cls(name)\\n obj.exporter = 'get_output_table_name'\\n obj.output_name = output_name\\n return obj\",\n \"def build_data_frame(sql_dict, names):\\n\\td = defaultdict(list)\\n\\ti = 0\\n\\tfor category_data in sql_dict:\\n\\t\\tfor attribute_list in category_data:\\n\\t\\t\\tfor attribute in category_data[attribute_list]:\\n\\t\\t\\t\\td[attribute_list].append(attribute)\\n\\t\\td[\\\"category\\\"].extend([names[i]]* len(category_data[attribute_list]) )\\n\\t\\td[\\\"fct\\\"].extend([i] * len(category_data[attribute_list]) )\\n\\t\\ti += 1\\n\\t#Translation into R objects\\n\\tfor attribute_list in d:\\n\\t\\tif attribute_list == \\\"category\\\":\\n\\t\\t\\td[attribute_list] = robjects.StrVector(d[attribute_list])\\n\\t\\telif attribute_list in WATCH_FOR_MISSING_VALUES:\\n\\t\\t\\ttmp = robjects.FloatVector(range(len(d[attribute_list])))\\n\\t\\t\\tfor i in range(len(d[attribute_list])):\\n\\t\\t\\t\\tif d[attribute_list][i] == \\\"NA\\\":\\n\\t\\t\\t\\t\\ttmp.rx[i] = robjects.NA_Real\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttmp.rx[i] = d[attribute_list][i]\\n\\t\\t\\td[attribute_list] = tmp\\n\\t\\telse:\\n\\t\\t\\tif attribute_list == \\\"num_sections\\\":\\n\\t\\t\\t\\ttmp = []\\n\\t\\t\\t\\tfor element in d[attribute_list]:\\n\\t\\t\\t\\t\\tif element == None:\\n\\t\\t\\t\\t\\t\\ttmp.append(1)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\ttmp.append(float(element))\\n\\t\\t\\t\\td[attribute_list] = robjects.FloatVector(tmp)\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telif attribute_list == \\\"speechiness\\\":\\n\\t\\t\\t\\ttmp = []\\n\\t\\t\\t\\tfor element in d[attribute_list]:\\n\\t\\t\\t\\t\\tif element == None:\\n\\t\\t\\t\\t\\t\\ttmp.append(1)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\ttmp.append(float(element))\\n\\t\\t\\t\\td[attribute_list] = robjects.FloatVector(tmp)\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t \\n\\t\\t\\telse:\\n\\t\\t\\t\\td[attribute_list] = robjects.FloatVector(d[attribute_list])\\n\\tdata_frame = robjects.DataFrame(d)\\n\\treturn data_frame\",\n \"def as_tuple(self):\\n return self.value, self.name\",\n \"def _create_sql_columns(self, a_list):\\n result = \\\"\\\"\\n \\n cpt = 0\\n for elem in a_list:\\n if cpt == 0:\\n result += \\\"%s\\\" % (elem)\\n else:\\n result += \\\", %s\\\" % (elem)\\n cpt += 1\\n\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63004136","0.5937142","0.58391273","0.58231133","0.57692385","0.5645236","0.55839163","0.54995245","0.54713005","0.532123","0.52468985","0.523908","0.51835376","0.5111085","0.5099475","0.50992185","0.5072765","0.50404245","0.5036017","0.50100225","0.5000778","0.49867257","0.49850065","0.49689537","0.496554","0.4941311","0.49355245","0.49179378","0.48940077","0.4877206","0.48615634","0.48552677","0.48527786","0.48517752","0.48497465","0.48401603","0.48371857","0.4791781","0.4782347","0.47765434","0.47662455","0.47271642","0.47251216","0.47249082","0.471988","0.4712474","0.47042736","0.47039053","0.46917763","0.46842697","0.46827212","0.46766979","0.46747154","0.46576923","0.46570176","0.46453598","0.46341792","0.46293294","0.46279547","0.46266827","0.4624502","0.46239504","0.4615591","0.46075696","0.46075007","0.46065387","0.46064752","0.4601549","0.45955202","0.45936692","0.45927092","0.4584043","0.4581841","0.4573096","0.4567064","0.45598972","0.4552645","0.4551394","0.45508265","0.45410383","0.45363632","0.4527116","0.45254356","0.45211738","0.45164672","0.4506408","0.45046806","0.44864997","0.44854984","0.4482372","0.44628823","0.4462324","0.44602013","0.44572908","0.4455735","0.44550353","0.44519332","0.44494492","0.44430324","0.44387028"],"string":"[\n \"0.63004136\",\n \"0.5937142\",\n \"0.58391273\",\n \"0.58231133\",\n \"0.57692385\",\n \"0.5645236\",\n \"0.55839163\",\n \"0.54995245\",\n \"0.54713005\",\n \"0.532123\",\n \"0.52468985\",\n \"0.523908\",\n \"0.51835376\",\n \"0.5111085\",\n \"0.5099475\",\n \"0.50992185\",\n \"0.5072765\",\n \"0.50404245\",\n \"0.5036017\",\n \"0.50100225\",\n \"0.5000778\",\n \"0.49867257\",\n \"0.49850065\",\n \"0.49689537\",\n \"0.496554\",\n \"0.4941311\",\n \"0.49355245\",\n \"0.49179378\",\n \"0.48940077\",\n \"0.4877206\",\n \"0.48615634\",\n \"0.48552677\",\n \"0.48527786\",\n \"0.48517752\",\n \"0.48497465\",\n \"0.48401603\",\n \"0.48371857\",\n \"0.4791781\",\n \"0.4782347\",\n \"0.47765434\",\n \"0.47662455\",\n \"0.47271642\",\n \"0.47251216\",\n \"0.47249082\",\n \"0.471988\",\n \"0.4712474\",\n \"0.47042736\",\n \"0.47039053\",\n \"0.46917763\",\n \"0.46842697\",\n \"0.46827212\",\n \"0.46766979\",\n \"0.46747154\",\n \"0.46576923\",\n \"0.46570176\",\n \"0.46453598\",\n \"0.46341792\",\n \"0.46293294\",\n \"0.46279547\",\n \"0.46266827\",\n \"0.4624502\",\n \"0.46239504\",\n \"0.4615591\",\n \"0.46075696\",\n \"0.46075007\",\n \"0.46065387\",\n \"0.46064752\",\n \"0.4601549\",\n \"0.45955202\",\n \"0.45936692\",\n \"0.45927092\",\n \"0.4584043\",\n \"0.4581841\",\n \"0.4573096\",\n \"0.4567064\",\n \"0.45598972\",\n \"0.4552645\",\n \"0.4551394\",\n \"0.45508265\",\n \"0.45410383\",\n \"0.45363632\",\n \"0.4527116\",\n \"0.45254356\",\n \"0.45211738\",\n \"0.45164672\",\n \"0.4506408\",\n \"0.45046806\",\n \"0.44864997\",\n \"0.44854984\",\n \"0.4482372\",\n \"0.44628823\",\n \"0.4462324\",\n \"0.44602013\",\n \"0.44572908\",\n \"0.4455735\",\n \"0.44550353\",\n \"0.44519332\",\n \"0.44494492\",\n \"0.44430324\",\n \"0.44387028\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.50794727"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":17497,"cells":{"query":{"kind":"string","value":"backup database before some critical operations"},"document":{"kind":"string","value":"def backup_database(db_host=None, db_name=None, cfg='project'):\n data = __salt__['mc_project.get_configuration'](cfg)\n db = data['data']['django_settings']['DATABASES']['default']\n if not db_host:\n db_host = db['HOST']\n if not db_name:\n db_name = db['NAME']\n dump_filename = '/tmp/{0}-{1}.dump'.format(\n db_name,\n datetime.now().strftime('%Y-%m-%d-%H-%M'))\n script = BACKUP.format(**locals())\n script += \"exit $?\\n\"\n ret = run(host=db_host, script=script)\n if ret['retcode']:\n pprint(ret)\n raise Exception('dump failed')\n return dump_filename"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def backup_database():\n logger.info(\"start database_backup\")\n management.call_command('dbbackup', compress=True)\n logger.info(\"end database_backup\")","def backup_database():\n db_path = os.path.join(config.cum_dir, 'cum.db')\n backup_path = os.path.join(config.cum_dir, 'cum.db.bak')\n copyfile(db_path, backup_path)","def backup_database(self):\n backup_file = \"{}-{}.sql\".format(\n config.DATABASE_NAME, datetime.today().strftime(\"%Y-%m-%d--%H%M\")\n )\n backup_uri = \"{}/{}\".format(config.DATABASE_BACKUP_BUCKET, backup_file)\n step = \"Backing Up Database:\\nbackup={}\".format(backup_uri)\n try:\n self.slacker.send_thread_reply(step)\n backup_command = [\n \"gcloud\",\n \"sql\",\n \"export\",\n \"sql\",\n config.DATABASE_INSTANCE_NAME,\n backup_uri,\n \"--database={}\".format(config.DATABASE_NAME),\n \"--verbosity=debug\",\n ]\n subprocess.run(backup_command, check=True)\n except Exception as e:\n self.raise_step_error(step=step, error=e)","def backup(self):\n self.rollback_steps.insert(0, self.mongos.start_balancer)\n self.run_step(self.mongos.stop_balancer, 2)\n\n self.run_step(self.wait_for_locks)\n\n self.rollback_steps.insert(0, self.finish_shards_maintenance)\n self.run_step(self.prepare_shards_maintenance)\n\n self.run_step(self.backup_dump)\n\n self.rollback_steps.remove(self.finish_shards_maintenance)\n self.run_step(self.finish_shards_maintenance, 2)\n\n self.rollback_steps.remove(self.mongos.start_balancer)\n self.run_step(self.mongos.start_balancer, 4) # it usually starts on\n # the second try\n\n if self.backup_bucket is not None:\n run(\"rmdir %s\" % self.backup_path)\n\n logging.info(\"Finished successfully\")","def backup():\n backup_shift(os, config.utils.tasks.backup_depth)\n if config.utils.tasks.secret_key is None:\n shutil.copyfile(config.core.database_name, config.core.database_name+'.1')\n else:\n data = get_encrypted_database()\n with open(config.core.database_name+'.1', 'wb') as f:\n f.write(data)","def __makeBackup(self):\n pass #FIXME!!!","def run_backup():\n\n from common.models import InvenTreeSetting\n\n if not InvenTreeSetting.get_setting('INVENTREE_BACKUP_ENABLE', False, cache=False):\n # Backups are not enabled - exit early\n return\n\n interval = int(InvenTreeSetting.get_setting('INVENTREE_BACKUP_DAYS', 1, cache=False))\n\n # Check if should run this task *today*\n if not check_daily_holdoff('run_backup', interval):\n return\n\n logger.info(\"Performing automated database backup task\")\n\n call_command(\"dbbackup\", noinput=True, clean=True, compress=True, interactive=False)\n call_command(\"mediabackup\", noinput=True, clean=True, compress=True, interactive=False)\n\n # Record that this task was successful\n record_task_success('run_backup')","def automatic_backup(self):\n\n if self.observationId:\n logging.info(\"automatic backup\")\n self.save_project_activated()","def test_backup_compact(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n self.backup_cluster_validate()\n self.backup_compact_validate()","def run_backup():\n host = re.search(\"([\\w.-]+)[:]?\", env.host).group()\n date = time.strftime('%Y%m%d%H%M%S')\n fname = '%(host)s-backup-%(date)s.gz' % {'date': date, 'host': host}\n green(\"Ingrese la contraseña de la clave privada local.\")\n sudo(\"pg_dump kine | gzip > /tmp/%s\" % fname, user=\"postgres\")\n get(\"/tmp/%s\" % fname, os.path.join(backup_dir, fname))\n sudo(\"rm /tmp/%s\" % fname, user=\"postgres\")","def __restoreBackup(self):\n pass #FIXME!!!","def web_backup():\n conf = config.utils\n if conf.tasks.secret_key is None:\n upload_path = config.core.database_name\n file = None\n else:\n file = tempfile.NamedTemporaryFile(delete=False)\n file.write(get_encrypted_database())\n file.close()\n upload_path = file.name\n\n factory = ftplib.FTP_TLS if conf.tls else ftplib.FTP\n # noinspection PyDeprecation\n with ftputil.FTPHost(conf.ftp.host, conf.ftp.username, conf.ftp.password,\n session_factory=factory, use_list_a_option=False) as host:\n backup_shift(host, conf.tasks.web_backup_depth)\n host.upload(upload_path, config.core.database_name+'.1')\n if file is not None:\n os.unlink(file.name)","def __removeBackup(self):\n pass #FIXME!!","def sync_db():\n pass","def task_backup_sqlite_database():\n sqlite_dir = os.path.abspath(settings.BASE_DIR)\n os.chdir(sqlite_dir)\n existing_files = glob.glob(\"*.sqlite3*\")\n backup_helper = BackupHelper()\n\n # Check how many existing backups there are.\n # If none, then throw warning\n if len(existing_files) == 0:\n logger.warning(\"Cannot perform backup as no database file currently exists\")\n return\n\n # If one, then do the backup.\n elif len(existing_files) == 1:\n backup_helper.make_backup()\n\n # If two (or more, for whatever reason), then backup and remove oldest\n elif len(existing_files) >= 2:\n backup_helper.make_backup()\n backup_helper.remove_older_backups()\n\n logger.info(\"Database backed up\")","def __on_backup_created(self, logger, *args):","def post_backup(self, backup, manifest_file):\n pass","def doBackup(self):\n self.logger.log(\"Begin to backup instance status...\")\n \n try:\n self.readConfigInfo()\n self.getUserInfo()\n \n # dump status to file\n cmd = ClusterCommand.getQueryStatusCmd(self.user, self.dbNodeInfo.id, self.__bakStatusFile)\n (status, output) = commands.getstatusoutput(cmd)\n if (status != 0):\n self.logger.logExit(\"Query local instance status failed!Error: %s\" % output)\n except Exception, e:\n self.logger.logExit(str(e))\n \n self.logger.log(\"Backup instance status successfully.\")\n self.logger.closeLog()","async def prepare_databases(self):","def test_backup_with_compress_flag(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size,\n end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n self.backupset.backup_compressed = False\n self.backup_cluster()\n no_compression = self.get_database_file_info()\n self.log.info(\"\\nDelete old backup and do backup again with compress flag\")\n self.backup_create()\n self.backupset.backup_compressed = self.input.param(\"backup-compressed\", False)\n self.backup_cluster()\n with_compression = self.get_database_file_info()\n self.validate_backup_compressed_file(no_compression, with_compression)","def destroy(self):\n self.close()\n if self.wantAnyDbm:\n lt = time.asctime(time.localtime())\n trans = maketrans(': ','__')\n t = lt.translate(trans)\n head, tail = os.path.split(self.filepath)\n newFileName = 'UDStoreBak'+t\n if os.path.exists(self.filepath):\n try:\n os.rename(tail, newFileName)\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\n , 'Creating backup of file: %s saving as: %s' %(tail, newFileName))\n except:\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\n , 'Unable to create backup of file: %s ' %tail)\n else:\n # Remove the filename with all sufix's\n # .bak, .dir, .dat\n files = os.listdir(head)\n for file in files:\n if file.find(tail)>-1:\n filename, ext = os.path.splitext(file)\n try:\n os.rename(file, newFileName+ext)\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\n , 'Creating backup of file: %s saving as: %s' %(file,newFileName+ext))\n except:\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\n , 'Unable to create backup of file: %s ' %newFileName+ext)\n else:\n if os.path.exists(self.filepath + '.bu'):\n os.remove(self.filepath + '.bu')\n if os.path.exists(self.filepath):\n os.remove(self.filepath)","def rebuild_db():\n delete_db()\n create_db()\n insert_db()","async def module_command_backup(self, ctx, parsed):\n if parsed.invoker != ctx.owner:\n return\n file = parsed.args[\"name\"]\n file = file.with_suffix(f\"{file.suffix}.sqlite\")\n await self.database_create_backup(file)\n await ctx.core_command_backup(parsed, file)","def test_backup_restore_with_flush(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n self.backup_cluster_validate()\n rest = RestConnection(self.backupset.cluster_host)\n rest.flush_bucket()\n self.log.info(\"Flushed default bucket - restoring data now..\")\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\">=\")","def mysqldump():\n run(\"mysqldump -u database_user database_name -p > ~/tmp/exported_db.sql\")","def copy_db():\n with cd(\"/tmp\"), lcd(\"/tmp\"):\n sudo(\"pg_dump gsi > /tmp/latest.sql\", user=\"postgres\")\n run(\"tar zcvf latest.sql.tgz latest.sql\")\n get(\"/tmp/latest.sql.tgz\", \"latest.sql.tgz\")\n sudo(\"rm /tmp/latest.sql.tgz /tmp/latest.sql\")","def test_backup_purge(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n old_backup_name = \"\"\n new_backup_name = \"\"\n backup_result = self.cluster.async_backup_cluster(backupset=self.backupset,\n objstore_provider=self.objstore_provider,\n resume=self.backupset.resume, purge=self.backupset.purge,\n no_progress_bar=self.no_progress_bar,\n cli_command_location=self.cli_command_location,\n cb_version=self.cb_version)\n self.sleep(10)\n conn = RemoteMachineShellConnection(self.backupset.cluster_host)\n conn.kill_erlang()\n output = backup_result.result(timeout=200)\n self.log.info(str(output))\n status, output, message = self.backup_list()\n if not status:\n self.fail(message)\n if output and output[0]:\n bk_info = json.loads(output[0])\n bk_info = bk_info[\"repos\"][0]\n else:\n return False, \"No output content\"\n if bk_info[\"backups\"]:\n for i in range(0, len(bk_info[\"backups\"])):\n old_backup_name = bk_info[\"backups\"][i][\"date\"]\n self.log.info(\"Backup name before purge: \" + old_backup_name)\n conn.start_couchbase()\n conn.disconnect()\n self.sleep(30)\n output, error = self.backup_cluster()\n if error or not self._check_output(\"Backup completed successfully\", output):\n self.fail(output)\n status, output, message = self.backup_list()\n if not status:\n self.fail(message)\n if output and output[0]:\n bk_info = json.loads(output[0])\n bk_info = bk_info[\"repos\"][0]\n else:\n return False, \"No output content\"\n if bk_info[\"backups\"]:\n for i in range(0, len(bk_info[\"backups\"])):\n new_backup_name = bk_info[\"backups\"][i][\"date\"]\n self.log.info(\"Backup name after purge: \" + new_backup_name)\n\n # Once the purge (and backup) have completed we shouldn't see any orphaned multipart uploads\n if self.objstore_provider:\n self.assertEqual(\n self.objstore_provider.num_multipart_uploads(), 0,\n \"Expected all multipart uploads to have been purged (all newly created ones should have also been completed)\"\n )\n\n self.assertNotEqual(old_backup_name, new_backup_name,\n \"Old backup name and new backup name are same when purge is used\")\n self.log.info(\"Old backup name and new backup name are not same when purge is used\")","def test_restore_backup():","def database_maintenance():\r\n\r\n logging.debug('database_maintenance()')\r\n\r\n # Check datgabase\r\n all_imagepaths = get_all_images_from_database()\r\n for imagepath in all_imagepaths:\r\n if not os.path.isfile(imagepath):\r\n delete_image_from_database(imagepath)\r\n logging.debug('database_maintenance() - image not in folder, deleted')\r\n\r\n # Check temporary folder\r\n all_imagepaths = get_all_images_from_filesystem()\r\n for imagepath in all_imagepaths:\r\n if not exists_image_in_database(imagepath):\r\n delete_image_from_database(imagepath)\r\n logging.debug('database_maintenance() - image not in database, deleted')","def _upgradeDB():\n\n # Set current DB name\n currentDbName = basedefs.DB_NAME\n\n # Before db upgrade we want to make a backup of existing db in case we fail\n # The backup is performed on local system, even for remote DB.\n dbBackupFile = tempfile.mkstemp(suffix=\".sql\", dir=basedefs.DIR_DB_BACKUPS)[1]\n logging.debug(\"backing up %s db to file %s\"%(basedefs.DB_NAME, dbBackupFile))\n\n # Run db backup\n utils.backupDB(basedefs.DB_NAME, getDbUser(), dbBackupFile, getDbHostName(), getDbPort())\n\n # Rename DB first. If it fails - stop with \"active connections\" error.\n # if upgrade passes fine, rename the DB back.\n DB_NAME_TEMP = \"%s_%s\" % (basedefs.DB_NAME, utils.getCurrentDateTime())\n utils.renameDB(basedefs.DB_NAME, DB_NAME_TEMP)\n currentDbName = DB_NAME_TEMP\n\n # if we're here, DB was renamed.\n # upgrade script must run from dbscripts dir\n currentDir = os.getcwd()\n os.chdir(basedefs.DIR_DB_SCRIPTS)\n\n try:\n\n logging.debug(\"upgrading db schema\")\n cmd = [\n os.path.join(basedefs.DIR_DB_SCRIPTS, basedefs.FILE_DB_UPGRADE_SCRIPT),\n \"-u\", getDbUser(),\n \"-d\", DB_NAME_TEMP,\n \"-s\", getDbHostName(),\n \"-p\", getDbPort(),\n ]\n\n # Run upgrade.sh script to update existing db\n output, rc = utils.execCmd(cmdList=cmd, failOnError=True, msg=output_messages.ERR_DB_UPGRADE_FAILED)\n\n # Log the successful upgrade\n logging.debug('Successfully upgraded %s DB'%(basedefs.DB_NAME))\n controller.MESSAGES.append(\"DB was upgraded to latest version. previous DB backup can be found at %s\"%(dbBackupFile))\n\n # Go back to previous dir\n os.chdir(currentDir)\n\n # Upgrade was successful, so rename the DB back.\n utils.renameDB(DB_NAME_TEMP, basedefs.DB_NAME)\n currentDbName = basedefs.DB_NAME\n\n # Update rpm version in vdc options\n utils.updateVDCOption(\"ProductRPMVersion\", utils.getRpmVersion(basedefs.ENGINE_RPM_NAME))\n except:\n # Upgrade failed! we need to restore the old db\n logging.debug(\"DB upgrade failed, restoring it to a previous state. DB was backed up to %s\", dbBackupFile)\n\n # Delete the original DB.\n # TODO: handle the case of failure - it should not stop the flow, but should write to the log\n sqlQuery=\"DROP DATABASE %s\" % currentDbName\n utils.execRemoteSqlCommand(getDbUser(), \\\n getDbHostName(), \\\n getDbPort(), \\\n basedefs.DB_POSTGRES, \\\n sqlQuery, False, \\\n output_messages.ERR_DB_DROP)\n\n # Restore the DB\n utils.restoreDB(getDbUser(), getDbHostName(), getDbPort(), dbBackupFile)\n\n raise Exception(output_messages.ERR_DB_UPGRADE_FAILED)","def test_backup_restore_with_warmup(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n NodeHelper.do_a_warm_up(self.backupset.cluster_host)\n self.sleep(30)\n self.backup_cluster_validate()\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\">=\")\n \"\"\" only membase bucket has warmup state \"\"\"\n if self.bucket_type == \"membase\":\n NodeHelper.wait_warmup_completed([self.backupset.cluster_host])","def backup_database(cls):\n with open('db_backup.csv', 'w', newline='') as csvfile:\n fieldnames = ['product_name',\n 'product_price',\n 'product_quantity',\n 'date_updated']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\n writer.writeheader()\n for product in Product.select().dicts():\n writer.writerow(\n {'product_name': product['product_name'],\n 'product_price': product['product_price'],\n 'product_quantity': product['product_quantity'],\n 'date_updated': product['date_updated']})\n print('Database backup complete! File was saved as \"db_backup.csv\".\\n')","def backup_all_db():\n filename = BACKUP_DIR + \"/\" + str(datetime.datetime.now().isoformat()) + \".yaml\"\n with open(filename, 'w+') as base_fp:\n for model in [Framework, Project, Document, Component, Arch, # Meta models\n WorkItem, AutoCase, Linkage, Bug, AutoCaseFailure]:\n base_fp.write(serializers.serialize('yaml', model.objects.all(), fields=model._min_dump))","def migrate_database():\n log('Migrating the keystone database.', level=INFO)\n service_stop(keystone_service())\n # NOTE(jamespage) > icehouse creates a log file as root so use\n # sudo to execute as keystone otherwise keystone won't start\n # afterwards.\n cmd = ['sudo', '-u', 'keystone', 'keystone-manage', 'db_sync']\n subprocess.check_output(cmd)\n service_start(keystone_service())\n time.sleep(10)\n peer_store('db-initialised', 'True')","def _after_execute(self, db):\n pass","def mysql_import():\n # first make another copy of the db\n run(\"mysqldump -u database_user database_name -p > ~/tmp/exported_db_temp.sql\")\n # then import from the backup\n run(\"mysql -u database_user -p -D database_name < ~/tmp/exported_db.sql\")","def save_backup(\n self):\n self.backup = self.data","def time_step(self):\n\n self.reinitialize_backup_containers()\n\n super().time_step()\n\n self.make_a_backup_for_t()","async def _upgrade_db(self) -> None:\n cur_version = await self._get_db_version()\n for n in range(cur_version + 1, sql_data.CUR_VERSION + 1):\n log.msg('Upgrading database to version %d' % n)\n if n in sql_data.SQL_UPGRADES:\n for command in sql_data.SQL_UPGRADES[n]:\n await self.operation(command)\n if cur_version != sql_data.CUR_VERSION:\n await self._set_db_version(sql_data.CUR_VERSION)","def upgrade_environment(self, db):\n if db is not None:\n db.commit()\n self.create_db()","def save_db(self) -> None:","def save_db(self) -> None:\n self.connection.commit()","def backupQbaseCode(self):\n q.transaction.start(\"Backup /opt/qbase pylabs code on %s\" % self.ipaddr)\n self.execute(\"mkdir -p /opt/backups\", False)\n path=\"/opt/backups/pylabscore_%s.tgz\" % q.base.time.getLocalTimeHRForFilesystem()\n pathstobackup='/opt/qbase3/lib/pylabs/extensions /opt/code/pylabs-core/code/utils /opt/code/pylabs-core/code/packages/pylabs/core'\n # compress them\n self.execute(\"tar zcvfh %s %s \" % (path, pathstobackup) , False)\n # remember last compressed\n self.execute(\"echo '%s' > /opt/backups/path_to_last_pylabscode_backup.txt\" % path, True)\n q.transaction.stop()","def tearDown(self):\r\n empty_db()","def tearDown(self):\r\n empty_db()","def tearDown(self):\r\n empty_db()","def tearDown(self):\r\n empty_db()","def tearDown(self):\r\n empty_db()","def tearDown(self):\r\n empty_db()","def tearDown(self):\r\n\r\n db.session.rollback()","def test_backup_restore_with_deletes(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n self.backup_cluster_validate()\n self._load_all_buckets(self.master, gen, \"delete\", 0)\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\"<=\")","def reset_databases(self):\n\t\tfor db in self.test_dbs:\n\t\t\tif self.db_exists(db):\n\t\t\t\tself.drop_database(db)\n\t # leave some time for synchonization between nodes\n\t\t\ttime.sleep(3)\n\t\t\tself.create_database(db)\n\t\t\tself.create_index(db)","def backup(self, outdir=None):\n import os\n if outdir is None:\n import time\n outdir = os.path.join('backup',time.strftime('%Y%m%d-%H%M'))\n cmd = 'time mongodump -c \"%s\" -h %s:%s -d mfdb -o \"%s\"'%(\n self.collection.name, self.db.host, self.db.port, outdir)\n print cmd\n os.system(cmd)","def tearDown(self): \n db.session.rollback()","def tearDown(self): \n db.session.rollback()","def tearDown(self):\r\n db.session.rollback()","def _commit_now(self):\n self._database.commit()","def exportDB(self):\n sourcesession=svc.connect(self.__source,accessMode=coral.access_Update)\n destsession=svc.connect(self.__dest,accessMode = coral.access_Update)\n try:\n dbcp=DBCopy(sourcesession,destsession,1024)\n if self.__all:\n dbcp.copyDB()\n elif self.__inv:\n dbcp.copyInventory()\n elif len(self.__tree) != 0:\n dbcp.copyTrees([self.__tree])\n del sourcesession\n del destsession\n except Exception, e:\n print str(e)\n del sourcesession\n del destsession","def db():\n\n db_obj = dump_db.DumpDB()\n db_obj.load_from_csv(CONF.BACKUP_DB_PATH)\n return db_obj","def _sync_databases(self):\n host, port = self._src.client().address\n log.info('sync databases from %s:%d' % (host, port))\n for dbname in self._src.client().database_names():\n if dbname in self._ignore_dbs:\n log.info(\"skip database '%s'\" % dbname)\n continue\n if not self._conf.data_filter.valid_db(dbname):\n log.info(\"skip database '%s'\" % dbname)\n continue\n self._sync_database(dbname)\n log.info('all databases done')","def migrate_database(self):\n\n self.db.migrate_database()","def backup(self):\r\n print('Backing up old files...')\r\n\r\n # Connect with SSH-PubKey and execute backup script\r\n subprocess.run(\r\n ['ssh',\r\n '-i', self.ssh_key,\r\n '-o', 'StrictHostKeyChecking=no',\r\n 'robot@{}'.format(self.settings['ip']),\r\n 'robolab-backup'\r\n ])\r\n\r\n print('Done.')","def tearDown(self):\n model.DBSession.rollback()","def tearDown(self):\n db.session.rollback()","def tearDown(self):\n db.session.rollback()","def tearDown(self):\n db.session.rollback()","def tearDown(self):\n db.session.rollback()","def tearDown(self):\n db.session.rollback()","def tearDown(self):\n db.session.rollback()","def stamp(self):\n self.connection.commit()\n self.cursor.close()\n self.connection.close()","def backup(self):\n self.logger.info(\"Backing up current version of model...\")\n self.save_checkpoint(filename='backup.pth.tar')","def save_and_exit():\n con.commit()\n con.close()\n quit()","def backup(self, backup):\n self._backup = backup","def tearDown(self):\n\n db.session.rollback()","def tearDown(self):\n\n db.session.rollback()","def tearDown(self):\n\n db.session.rollback()","def tearDown(self):\n\n db.session.rollback()","def tearDown(self):\n\n db.session.rollback()","def tearDown(self):\n\n db.session.rollback()","def tearDown(self):\n\n db.session.rollback()","def tearDown(self):\n\n db.session.rollback()","def setup():\r\n thread = threading.Thread(target = backup)\r\n thread.start()","def recreate_db():\n drop_db()\n create_db()","def test_backup_restore_after_rebalance(self):\n serv_in = self.servers[self.nodes_init:self.nodes_init + self.nodes_in]\n serv_out = self.servers[self.nodes_init - self.nodes_out:self.nodes_init]\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create_validate()\n self.backupset.number_of_backups = 1\n rebalance = self.cluster.async_rebalance(self.cluster_to_backup, serv_in, serv_out)\n rebalance.result()\n self.backup_cluster_validate()\n if not self.same_cluster:\n self._initialize_nodes(Cluster(), self.input.clusters[0][:self.nodes_init])\n serv_in = self.input.clusters[0][self.nodes_init: self.nodes_init + self.nodes_in]\n serv_out = self.input.clusters[0][self.nodes_init - self.nodes_out: self.nodes_init]\n rebalance = self.cluster.async_rebalance(self.cluster_to_restore, serv_in, serv_out)\n else:\n rebalance = self.cluster.async_rebalance(self.cluster_to_restore, serv_out, serv_in)\n rebalance.result()\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\"<=\")","def commit():\n get_db().commit()","def test_backup_list(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n self.backup_cluster_validate()\n self.backup_list_validate()","def test_restore_from_compacted_backup(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n self.backup_cluster_validate()\n self.backup_compact()\n self.backup_restore_validate()","def restore_database(app):\n app.database().restore()\n app.refresh()","def save_database(app):\n app.database().save()\n app.status.message('Finished saving..')","def backup(context, user=get_local_user(), remote=False, instance=None, stack=None):\n command = \"run --rm postgres backup\"\n run_command(context, user, remote, instance, stack, command)","def test_backup_system_database_correctly_done(self, mock_datetime,\n mysql_provider):\n mock_datetime.return_value = \"20190101_000000\"\n with raises(Exception) as e:\n # By default, it will raise an exception because of access rights.\n # But at least, we'll know it tried to backup it.\n mysql_provider.execute_backup(database=\"information_schema\")\n assert \"Could not backup database information_schema: retcode 2 - stderr None.\" in str(\n e.value)","def recreate_db():\n drop_db()\n create_db()\n populate_db()","def begin_transaction(self):\r\n self.db.commit(False)","def dump_DB(self):\n\t\tprint 'Dumping Data Base...'\n\t\tp=cPickle.Pickler(open(self.DB_file, 'wb'))\n\t\tp.fast=True\n\t\tp.dump(self.DB)\n\t\tprint 'Dumping completed'\n\t\t#stream.close()\n\t\t#return ","def test_backup_restore_after_offline_upgrade(self):\n upgrade_version = self.input.param(\"upgrade_version\", \"5.0.0-3330\")\n if upgrade_version == \"5.0.0-3330\":\n self.fail(\"\\n *** Need param 'upgrade_version=' to run\")\n\n backup_service_test = self.input.param(\"backup_service_test\", False)\n\n if backup_service_test:\n backup_service_hook = BackupServiceHook(self.servers[1], self.servers, self.backupset, self.objstore_provider)\n self.cli_command_location = \"/opt/couchbase/bin\"\n\n self._install(self.servers)\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size,\n end=self.num_items)\n rebalance = self.cluster.async_rebalance(self.servers[:2], [self.servers[1]],\n [])\n rebalance.result()\n self.add_built_in_server_user()\n RestConnection(self.master).create_bucket(bucket='default', ramQuotaMB=512)\n self.buckets = RestConnection(self.master).get_buckets()\n self.total_buckets = len(self.buckets)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n self.backup_cluster_validate()\n self.sleep(5)\n BucketOperationHelper.delete_bucket_or_assert(self.master, \"default\", self)\n\n \"\"\" Start to upgrade \"\"\"\n if self.force_version_upgrade:\n upgrade_version = self.force_version_upgrade\n upgrade_threads = self._async_update(upgrade_version=upgrade_version,\n servers=self.servers[:2])\n for th in upgrade_threads:\n th.join()\n self.log.info(\"Upgraded to: {ver}\".format(ver=upgrade_version))\n self.sleep(30)\n\n \"\"\" Re-create default bucket on upgrade cluster \"\"\"\n RestConnection(self.master).create_bucket(bucket='default', ramQuotaMB=512)\n self.sleep(5)\n\n # Create a backup node and perform a backup service import repository and restore\n if backup_service_test:\n backup_service_hook.backup_service.replace_services(self.servers[1], ['kv,backup'])\n backup_service_hook.backup_service.import_repository(self.backupset.directory, self.backupset.name, \"my_repo\")\n backup_service_hook.backup_service.take_one_off_restore(\"imported\", \"my_repo\", 20, 20)\n backup_service_hook.cleanup()\n return\n\n \"\"\" Only server from Spock needs build in user\n to access bucket and other tasks\n \"\"\"\n if \"5\" <= RestConnection(self.master).get_nodes_version()[:1]:\n self.add_built_in_server_user()\n for user in self.users_check_restore:\n user_name = user.replace('[', '_').replace(']', '_')\n testuser = [{'id': user_name, 'name': user_name,\n 'password': 'password'}]\n rolelist = [{'id': user_name, 'name': user_name,\n 'roles': user}]\n\n self.log.info(\"**** add built-in '%s' user to node %s ****\" % (testuser[0][\"name\"],\n self.master.ip))\n RbacBase().create_user_source(testuser, 'builtin', self.master)\n\n self.log.info(\"**** add '%s' role to '%s' user ****\" % (rolelist[0][\"roles\"],\n testuser[0][\"name\"]))\n RbacBase().add_user_role(rolelist, RestConnection(self.master), 'builtin')\n\n backupsets = [self.backupset]\n if \"5\" <= RestConnection(self.master).get_nodes_version()[:1]:\n for user in self.users_check_restore:\n new_backupset = copy.deepcopy(self.backupset)\n new_backupset.restore_cluster_host_username = user.replace('[', '_').replace(']', '_')\n backupsets.append(new_backupset)\n for backupset in backupsets:\n self.backupset = backupset\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\">=\")\n BucketOperationHelper().delete_bucket_or_assert(self.backupset.cluster_host,\n \"default\", self)","def rollback(self):\r\n self.db.rollback()","def backup(ctx):\n config_path = ctx.obj['config_path']\n logger = ctx.obj['logger']\n\n config = Config(config_path)\n scheduler = BlockingScheduler(\n executors={'default': ThreadPoolExecutor(max_workers=1)}\n )\n\n for job in config.jobs.values():\n logger.info(f'filesystem={job.filesystem} '\n f'cron=\"{job.cron}\" '\n 'msg=\"Adding job.\"')\n scheduler.add_job(job.start, 'cron', **job.cron, coalesce=True)\n\n try:\n scheduler.start()\n except (KeyboardInterrupt, SystemExit):\n pass","def tearDown(self):\n insta485db = sh.Command(\"./bin/insta485db\")\n insta485db(\"reset\")","def makeBackup(self):\n #--File Path\n original = self.path\n #--Backup\n backup = self.path+'.bak'\n shutil.copy(original,backup)\n #--First backup\n firstBackup = self.path+'.baf'\n if not os.path.exists(firstBackup):\n shutil.copy(original,firstBackup)","def backup():\n # Backup the WordPress database.\n db('backup')\n\n # Copy teh wp-config.php file from the server.\n get(os.path.join(env.wordpress_path, 'wp-config.php'),\n './backups/wp-config.php')\n\n now = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n\n theme_list = wp_cli('theme list --format=csv')\n plugin_list = wp_cli('plugin list --format=csv')\n\n # Backup the installed themes\n #with open('./backups/themes.csv', 'w') as f:\n # f.write(theme_list)\n\n # Backup the installed plugins\n #with open('./backups/plugins.csv', 'w') as f:\n # f.write(plugin_list)"],"string":"[\n \"def backup_database():\\n logger.info(\\\"start database_backup\\\")\\n management.call_command('dbbackup', compress=True)\\n logger.info(\\\"end database_backup\\\")\",\n \"def backup_database():\\n db_path = os.path.join(config.cum_dir, 'cum.db')\\n backup_path = os.path.join(config.cum_dir, 'cum.db.bak')\\n copyfile(db_path, backup_path)\",\n \"def backup_database(self):\\n backup_file = \\\"{}-{}.sql\\\".format(\\n config.DATABASE_NAME, datetime.today().strftime(\\\"%Y-%m-%d--%H%M\\\")\\n )\\n backup_uri = \\\"{}/{}\\\".format(config.DATABASE_BACKUP_BUCKET, backup_file)\\n step = \\\"Backing Up Database:\\\\nbackup={}\\\".format(backup_uri)\\n try:\\n self.slacker.send_thread_reply(step)\\n backup_command = [\\n \\\"gcloud\\\",\\n \\\"sql\\\",\\n \\\"export\\\",\\n \\\"sql\\\",\\n config.DATABASE_INSTANCE_NAME,\\n backup_uri,\\n \\\"--database={}\\\".format(config.DATABASE_NAME),\\n \\\"--verbosity=debug\\\",\\n ]\\n subprocess.run(backup_command, check=True)\\n except Exception as e:\\n self.raise_step_error(step=step, error=e)\",\n \"def backup(self):\\n self.rollback_steps.insert(0, self.mongos.start_balancer)\\n self.run_step(self.mongos.stop_balancer, 2)\\n\\n self.run_step(self.wait_for_locks)\\n\\n self.rollback_steps.insert(0, self.finish_shards_maintenance)\\n self.run_step(self.prepare_shards_maintenance)\\n\\n self.run_step(self.backup_dump)\\n\\n self.rollback_steps.remove(self.finish_shards_maintenance)\\n self.run_step(self.finish_shards_maintenance, 2)\\n\\n self.rollback_steps.remove(self.mongos.start_balancer)\\n self.run_step(self.mongos.start_balancer, 4) # it usually starts on\\n # the second try\\n\\n if self.backup_bucket is not None:\\n run(\\\"rmdir %s\\\" % self.backup_path)\\n\\n logging.info(\\\"Finished successfully\\\")\",\n \"def backup():\\n backup_shift(os, config.utils.tasks.backup_depth)\\n if config.utils.tasks.secret_key is None:\\n shutil.copyfile(config.core.database_name, config.core.database_name+'.1')\\n else:\\n data = get_encrypted_database()\\n with open(config.core.database_name+'.1', 'wb') as f:\\n f.write(data)\",\n \"def __makeBackup(self):\\n pass #FIXME!!!\",\n \"def run_backup():\\n\\n from common.models import InvenTreeSetting\\n\\n if not InvenTreeSetting.get_setting('INVENTREE_BACKUP_ENABLE', False, cache=False):\\n # Backups are not enabled - exit early\\n return\\n\\n interval = int(InvenTreeSetting.get_setting('INVENTREE_BACKUP_DAYS', 1, cache=False))\\n\\n # Check if should run this task *today*\\n if not check_daily_holdoff('run_backup', interval):\\n return\\n\\n logger.info(\\\"Performing automated database backup task\\\")\\n\\n call_command(\\\"dbbackup\\\", noinput=True, clean=True, compress=True, interactive=False)\\n call_command(\\\"mediabackup\\\", noinput=True, clean=True, compress=True, interactive=False)\\n\\n # Record that this task was successful\\n record_task_success('run_backup')\",\n \"def automatic_backup(self):\\n\\n if self.observationId:\\n logging.info(\\\"automatic backup\\\")\\n self.save_project_activated()\",\n \"def test_backup_compact(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n self.backup_cluster_validate()\\n self.backup_compact_validate()\",\n \"def run_backup():\\n host = re.search(\\\"([\\\\w.-]+)[:]?\\\", env.host).group()\\n date = time.strftime('%Y%m%d%H%M%S')\\n fname = '%(host)s-backup-%(date)s.gz' % {'date': date, 'host': host}\\n green(\\\"Ingrese la contraseña de la clave privada local.\\\")\\n sudo(\\\"pg_dump kine | gzip > /tmp/%s\\\" % fname, user=\\\"postgres\\\")\\n get(\\\"/tmp/%s\\\" % fname, os.path.join(backup_dir, fname))\\n sudo(\\\"rm /tmp/%s\\\" % fname, user=\\\"postgres\\\")\",\n \"def __restoreBackup(self):\\n pass #FIXME!!!\",\n \"def web_backup():\\n conf = config.utils\\n if conf.tasks.secret_key is None:\\n upload_path = config.core.database_name\\n file = None\\n else:\\n file = tempfile.NamedTemporaryFile(delete=False)\\n file.write(get_encrypted_database())\\n file.close()\\n upload_path = file.name\\n\\n factory = ftplib.FTP_TLS if conf.tls else ftplib.FTP\\n # noinspection PyDeprecation\\n with ftputil.FTPHost(conf.ftp.host, conf.ftp.username, conf.ftp.password,\\n session_factory=factory, use_list_a_option=False) as host:\\n backup_shift(host, conf.tasks.web_backup_depth)\\n host.upload(upload_path, config.core.database_name+'.1')\\n if file is not None:\\n os.unlink(file.name)\",\n \"def __removeBackup(self):\\n pass #FIXME!!\",\n \"def sync_db():\\n pass\",\n \"def task_backup_sqlite_database():\\n sqlite_dir = os.path.abspath(settings.BASE_DIR)\\n os.chdir(sqlite_dir)\\n existing_files = glob.glob(\\\"*.sqlite3*\\\")\\n backup_helper = BackupHelper()\\n\\n # Check how many existing backups there are.\\n # If none, then throw warning\\n if len(existing_files) == 0:\\n logger.warning(\\\"Cannot perform backup as no database file currently exists\\\")\\n return\\n\\n # If one, then do the backup.\\n elif len(existing_files) == 1:\\n backup_helper.make_backup()\\n\\n # If two (or more, for whatever reason), then backup and remove oldest\\n elif len(existing_files) >= 2:\\n backup_helper.make_backup()\\n backup_helper.remove_older_backups()\\n\\n logger.info(\\\"Database backed up\\\")\",\n \"def __on_backup_created(self, logger, *args):\",\n \"def post_backup(self, backup, manifest_file):\\n pass\",\n \"def doBackup(self):\\n self.logger.log(\\\"Begin to backup instance status...\\\")\\n \\n try:\\n self.readConfigInfo()\\n self.getUserInfo()\\n \\n # dump status to file\\n cmd = ClusterCommand.getQueryStatusCmd(self.user, self.dbNodeInfo.id, self.__bakStatusFile)\\n (status, output) = commands.getstatusoutput(cmd)\\n if (status != 0):\\n self.logger.logExit(\\\"Query local instance status failed!Error: %s\\\" % output)\\n except Exception, e:\\n self.logger.logExit(str(e))\\n \\n self.logger.log(\\\"Backup instance status successfully.\\\")\\n self.logger.closeLog()\",\n \"async def prepare_databases(self):\",\n \"def test_backup_with_compress_flag(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size,\\n end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n self.backupset.backup_compressed = False\\n self.backup_cluster()\\n no_compression = self.get_database_file_info()\\n self.log.info(\\\"\\\\nDelete old backup and do backup again with compress flag\\\")\\n self.backup_create()\\n self.backupset.backup_compressed = self.input.param(\\\"backup-compressed\\\", False)\\n self.backup_cluster()\\n with_compression = self.get_database_file_info()\\n self.validate_backup_compressed_file(no_compression, with_compression)\",\n \"def destroy(self):\\n self.close()\\n if self.wantAnyDbm:\\n lt = time.asctime(time.localtime())\\n trans = maketrans(': ','__')\\n t = lt.translate(trans)\\n head, tail = os.path.split(self.filepath)\\n newFileName = 'UDStoreBak'+t\\n if os.path.exists(self.filepath):\\n try:\\n os.rename(tail, newFileName)\\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\\\\n , 'Creating backup of file: %s saving as: %s' %(tail, newFileName))\\n except:\\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\\\\n , 'Unable to create backup of file: %s ' %tail)\\n else:\\n # Remove the filename with all sufix's\\n # .bak, .dir, .dat\\n files = os.listdir(head)\\n for file in files:\\n if file.find(tail)>-1:\\n filename, ext = os.path.splitext(file)\\n try:\\n os.rename(file, newFileName+ext)\\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\\\\n , 'Creating backup of file: %s saving as: %s' %(file,newFileName+ext))\\n except:\\n uber.air.writeServerEvent('Uberdog data store Info', 0 \\\\\\n , 'Unable to create backup of file: %s ' %newFileName+ext)\\n else:\\n if os.path.exists(self.filepath + '.bu'):\\n os.remove(self.filepath + '.bu')\\n if os.path.exists(self.filepath):\\n os.remove(self.filepath)\",\n \"def rebuild_db():\\n delete_db()\\n create_db()\\n insert_db()\",\n \"async def module_command_backup(self, ctx, parsed):\\n if parsed.invoker != ctx.owner:\\n return\\n file = parsed.args[\\\"name\\\"]\\n file = file.with_suffix(f\\\"{file.suffix}.sqlite\\\")\\n await self.database_create_backup(file)\\n await ctx.core_command_backup(parsed, file)\",\n \"def test_backup_restore_with_flush(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n self.backup_cluster_validate()\\n rest = RestConnection(self.backupset.cluster_host)\\n rest.flush_bucket()\\n self.log.info(\\\"Flushed default bucket - restoring data now..\\\")\\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\\\">=\\\")\",\n \"def mysqldump():\\n run(\\\"mysqldump -u database_user database_name -p > ~/tmp/exported_db.sql\\\")\",\n \"def copy_db():\\n with cd(\\\"/tmp\\\"), lcd(\\\"/tmp\\\"):\\n sudo(\\\"pg_dump gsi > /tmp/latest.sql\\\", user=\\\"postgres\\\")\\n run(\\\"tar zcvf latest.sql.tgz latest.sql\\\")\\n get(\\\"/tmp/latest.sql.tgz\\\", \\\"latest.sql.tgz\\\")\\n sudo(\\\"rm /tmp/latest.sql.tgz /tmp/latest.sql\\\")\",\n \"def test_backup_purge(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n old_backup_name = \\\"\\\"\\n new_backup_name = \\\"\\\"\\n backup_result = self.cluster.async_backup_cluster(backupset=self.backupset,\\n objstore_provider=self.objstore_provider,\\n resume=self.backupset.resume, purge=self.backupset.purge,\\n no_progress_bar=self.no_progress_bar,\\n cli_command_location=self.cli_command_location,\\n cb_version=self.cb_version)\\n self.sleep(10)\\n conn = RemoteMachineShellConnection(self.backupset.cluster_host)\\n conn.kill_erlang()\\n output = backup_result.result(timeout=200)\\n self.log.info(str(output))\\n status, output, message = self.backup_list()\\n if not status:\\n self.fail(message)\\n if output and output[0]:\\n bk_info = json.loads(output[0])\\n bk_info = bk_info[\\\"repos\\\"][0]\\n else:\\n return False, \\\"No output content\\\"\\n if bk_info[\\\"backups\\\"]:\\n for i in range(0, len(bk_info[\\\"backups\\\"])):\\n old_backup_name = bk_info[\\\"backups\\\"][i][\\\"date\\\"]\\n self.log.info(\\\"Backup name before purge: \\\" + old_backup_name)\\n conn.start_couchbase()\\n conn.disconnect()\\n self.sleep(30)\\n output, error = self.backup_cluster()\\n if error or not self._check_output(\\\"Backup completed successfully\\\", output):\\n self.fail(output)\\n status, output, message = self.backup_list()\\n if not status:\\n self.fail(message)\\n if output and output[0]:\\n bk_info = json.loads(output[0])\\n bk_info = bk_info[\\\"repos\\\"][0]\\n else:\\n return False, \\\"No output content\\\"\\n if bk_info[\\\"backups\\\"]:\\n for i in range(0, len(bk_info[\\\"backups\\\"])):\\n new_backup_name = bk_info[\\\"backups\\\"][i][\\\"date\\\"]\\n self.log.info(\\\"Backup name after purge: \\\" + new_backup_name)\\n\\n # Once the purge (and backup) have completed we shouldn't see any orphaned multipart uploads\\n if self.objstore_provider:\\n self.assertEqual(\\n self.objstore_provider.num_multipart_uploads(), 0,\\n \\\"Expected all multipart uploads to have been purged (all newly created ones should have also been completed)\\\"\\n )\\n\\n self.assertNotEqual(old_backup_name, new_backup_name,\\n \\\"Old backup name and new backup name are same when purge is used\\\")\\n self.log.info(\\\"Old backup name and new backup name are not same when purge is used\\\")\",\n \"def test_restore_backup():\",\n \"def database_maintenance():\\r\\n\\r\\n logging.debug('database_maintenance()')\\r\\n\\r\\n # Check datgabase\\r\\n all_imagepaths = get_all_images_from_database()\\r\\n for imagepath in all_imagepaths:\\r\\n if not os.path.isfile(imagepath):\\r\\n delete_image_from_database(imagepath)\\r\\n logging.debug('database_maintenance() - image not in folder, deleted')\\r\\n\\r\\n # Check temporary folder\\r\\n all_imagepaths = get_all_images_from_filesystem()\\r\\n for imagepath in all_imagepaths:\\r\\n if not exists_image_in_database(imagepath):\\r\\n delete_image_from_database(imagepath)\\r\\n logging.debug('database_maintenance() - image not in database, deleted')\",\n \"def _upgradeDB():\\n\\n # Set current DB name\\n currentDbName = basedefs.DB_NAME\\n\\n # Before db upgrade we want to make a backup of existing db in case we fail\\n # The backup is performed on local system, even for remote DB.\\n dbBackupFile = tempfile.mkstemp(suffix=\\\".sql\\\", dir=basedefs.DIR_DB_BACKUPS)[1]\\n logging.debug(\\\"backing up %s db to file %s\\\"%(basedefs.DB_NAME, dbBackupFile))\\n\\n # Run db backup\\n utils.backupDB(basedefs.DB_NAME, getDbUser(), dbBackupFile, getDbHostName(), getDbPort())\\n\\n # Rename DB first. If it fails - stop with \\\"active connections\\\" error.\\n # if upgrade passes fine, rename the DB back.\\n DB_NAME_TEMP = \\\"%s_%s\\\" % (basedefs.DB_NAME, utils.getCurrentDateTime())\\n utils.renameDB(basedefs.DB_NAME, DB_NAME_TEMP)\\n currentDbName = DB_NAME_TEMP\\n\\n # if we're here, DB was renamed.\\n # upgrade script must run from dbscripts dir\\n currentDir = os.getcwd()\\n os.chdir(basedefs.DIR_DB_SCRIPTS)\\n\\n try:\\n\\n logging.debug(\\\"upgrading db schema\\\")\\n cmd = [\\n os.path.join(basedefs.DIR_DB_SCRIPTS, basedefs.FILE_DB_UPGRADE_SCRIPT),\\n \\\"-u\\\", getDbUser(),\\n \\\"-d\\\", DB_NAME_TEMP,\\n \\\"-s\\\", getDbHostName(),\\n \\\"-p\\\", getDbPort(),\\n ]\\n\\n # Run upgrade.sh script to update existing db\\n output, rc = utils.execCmd(cmdList=cmd, failOnError=True, msg=output_messages.ERR_DB_UPGRADE_FAILED)\\n\\n # Log the successful upgrade\\n logging.debug('Successfully upgraded %s DB'%(basedefs.DB_NAME))\\n controller.MESSAGES.append(\\\"DB was upgraded to latest version. previous DB backup can be found at %s\\\"%(dbBackupFile))\\n\\n # Go back to previous dir\\n os.chdir(currentDir)\\n\\n # Upgrade was successful, so rename the DB back.\\n utils.renameDB(DB_NAME_TEMP, basedefs.DB_NAME)\\n currentDbName = basedefs.DB_NAME\\n\\n # Update rpm version in vdc options\\n utils.updateVDCOption(\\\"ProductRPMVersion\\\", utils.getRpmVersion(basedefs.ENGINE_RPM_NAME))\\n except:\\n # Upgrade failed! we need to restore the old db\\n logging.debug(\\\"DB upgrade failed, restoring it to a previous state. DB was backed up to %s\\\", dbBackupFile)\\n\\n # Delete the original DB.\\n # TODO: handle the case of failure - it should not stop the flow, but should write to the log\\n sqlQuery=\\\"DROP DATABASE %s\\\" % currentDbName\\n utils.execRemoteSqlCommand(getDbUser(), \\\\\\n getDbHostName(), \\\\\\n getDbPort(), \\\\\\n basedefs.DB_POSTGRES, \\\\\\n sqlQuery, False, \\\\\\n output_messages.ERR_DB_DROP)\\n\\n # Restore the DB\\n utils.restoreDB(getDbUser(), getDbHostName(), getDbPort(), dbBackupFile)\\n\\n raise Exception(output_messages.ERR_DB_UPGRADE_FAILED)\",\n \"def test_backup_restore_with_warmup(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n NodeHelper.do_a_warm_up(self.backupset.cluster_host)\\n self.sleep(30)\\n self.backup_cluster_validate()\\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\\\">=\\\")\\n \\\"\\\"\\\" only membase bucket has warmup state \\\"\\\"\\\"\\n if self.bucket_type == \\\"membase\\\":\\n NodeHelper.wait_warmup_completed([self.backupset.cluster_host])\",\n \"def backup_database(cls):\\n with open('db_backup.csv', 'w', newline='') as csvfile:\\n fieldnames = ['product_name',\\n 'product_price',\\n 'product_quantity',\\n 'date_updated']\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n\\n writer.writeheader()\\n for product in Product.select().dicts():\\n writer.writerow(\\n {'product_name': product['product_name'],\\n 'product_price': product['product_price'],\\n 'product_quantity': product['product_quantity'],\\n 'date_updated': product['date_updated']})\\n print('Database backup complete! File was saved as \\\"db_backup.csv\\\".\\\\n')\",\n \"def backup_all_db():\\n filename = BACKUP_DIR + \\\"/\\\" + str(datetime.datetime.now().isoformat()) + \\\".yaml\\\"\\n with open(filename, 'w+') as base_fp:\\n for model in [Framework, Project, Document, Component, Arch, # Meta models\\n WorkItem, AutoCase, Linkage, Bug, AutoCaseFailure]:\\n base_fp.write(serializers.serialize('yaml', model.objects.all(), fields=model._min_dump))\",\n \"def migrate_database():\\n log('Migrating the keystone database.', level=INFO)\\n service_stop(keystone_service())\\n # NOTE(jamespage) > icehouse creates a log file as root so use\\n # sudo to execute as keystone otherwise keystone won't start\\n # afterwards.\\n cmd = ['sudo', '-u', 'keystone', 'keystone-manage', 'db_sync']\\n subprocess.check_output(cmd)\\n service_start(keystone_service())\\n time.sleep(10)\\n peer_store('db-initialised', 'True')\",\n \"def _after_execute(self, db):\\n pass\",\n \"def mysql_import():\\n # first make another copy of the db\\n run(\\\"mysqldump -u database_user database_name -p > ~/tmp/exported_db_temp.sql\\\")\\n # then import from the backup\\n run(\\\"mysql -u database_user -p -D database_name < ~/tmp/exported_db.sql\\\")\",\n \"def save_backup(\\n self):\\n self.backup = self.data\",\n \"def time_step(self):\\n\\n self.reinitialize_backup_containers()\\n\\n super().time_step()\\n\\n self.make_a_backup_for_t()\",\n \"async def _upgrade_db(self) -> None:\\n cur_version = await self._get_db_version()\\n for n in range(cur_version + 1, sql_data.CUR_VERSION + 1):\\n log.msg('Upgrading database to version %d' % n)\\n if n in sql_data.SQL_UPGRADES:\\n for command in sql_data.SQL_UPGRADES[n]:\\n await self.operation(command)\\n if cur_version != sql_data.CUR_VERSION:\\n await self._set_db_version(sql_data.CUR_VERSION)\",\n \"def upgrade_environment(self, db):\\n if db is not None:\\n db.commit()\\n self.create_db()\",\n \"def save_db(self) -> None:\",\n \"def save_db(self) -> None:\\n self.connection.commit()\",\n \"def backupQbaseCode(self):\\n q.transaction.start(\\\"Backup /opt/qbase pylabs code on %s\\\" % self.ipaddr)\\n self.execute(\\\"mkdir -p /opt/backups\\\", False)\\n path=\\\"/opt/backups/pylabscore_%s.tgz\\\" % q.base.time.getLocalTimeHRForFilesystem()\\n pathstobackup='/opt/qbase3/lib/pylabs/extensions /opt/code/pylabs-core/code/utils /opt/code/pylabs-core/code/packages/pylabs/core'\\n # compress them\\n self.execute(\\\"tar zcvfh %s %s \\\" % (path, pathstobackup) , False)\\n # remember last compressed\\n self.execute(\\\"echo '%s' > /opt/backups/path_to_last_pylabscode_backup.txt\\\" % path, True)\\n q.transaction.stop()\",\n \"def tearDown(self):\\r\\n empty_db()\",\n \"def tearDown(self):\\r\\n empty_db()\",\n \"def tearDown(self):\\r\\n empty_db()\",\n \"def tearDown(self):\\r\\n empty_db()\",\n \"def tearDown(self):\\r\\n empty_db()\",\n \"def tearDown(self):\\r\\n empty_db()\",\n \"def tearDown(self):\\r\\n\\r\\n db.session.rollback()\",\n \"def test_backup_restore_with_deletes(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n self.backup_cluster_validate()\\n self._load_all_buckets(self.master, gen, \\\"delete\\\", 0)\\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\\\"<=\\\")\",\n \"def reset_databases(self):\\n\\t\\tfor db in self.test_dbs:\\n\\t\\t\\tif self.db_exists(db):\\n\\t\\t\\t\\tself.drop_database(db)\\n\\t # leave some time for synchonization between nodes\\n\\t\\t\\ttime.sleep(3)\\n\\t\\t\\tself.create_database(db)\\n\\t\\t\\tself.create_index(db)\",\n \"def backup(self, outdir=None):\\n import os\\n if outdir is None:\\n import time\\n outdir = os.path.join('backup',time.strftime('%Y%m%d-%H%M'))\\n cmd = 'time mongodump -c \\\"%s\\\" -h %s:%s -d mfdb -o \\\"%s\\\"'%(\\n self.collection.name, self.db.host, self.db.port, outdir)\\n print cmd\\n os.system(cmd)\",\n \"def tearDown(self): \\n db.session.rollback()\",\n \"def tearDown(self): \\n db.session.rollback()\",\n \"def tearDown(self):\\r\\n db.session.rollback()\",\n \"def _commit_now(self):\\n self._database.commit()\",\n \"def exportDB(self):\\n sourcesession=svc.connect(self.__source,accessMode=coral.access_Update)\\n destsession=svc.connect(self.__dest,accessMode = coral.access_Update)\\n try:\\n dbcp=DBCopy(sourcesession,destsession,1024)\\n if self.__all:\\n dbcp.copyDB()\\n elif self.__inv:\\n dbcp.copyInventory()\\n elif len(self.__tree) != 0:\\n dbcp.copyTrees([self.__tree])\\n del sourcesession\\n del destsession\\n except Exception, e:\\n print str(e)\\n del sourcesession\\n del destsession\",\n \"def db():\\n\\n db_obj = dump_db.DumpDB()\\n db_obj.load_from_csv(CONF.BACKUP_DB_PATH)\\n return db_obj\",\n \"def _sync_databases(self):\\n host, port = self._src.client().address\\n log.info('sync databases from %s:%d' % (host, port))\\n for dbname in self._src.client().database_names():\\n if dbname in self._ignore_dbs:\\n log.info(\\\"skip database '%s'\\\" % dbname)\\n continue\\n if not self._conf.data_filter.valid_db(dbname):\\n log.info(\\\"skip database '%s'\\\" % dbname)\\n continue\\n self._sync_database(dbname)\\n log.info('all databases done')\",\n \"def migrate_database(self):\\n\\n self.db.migrate_database()\",\n \"def backup(self):\\r\\n print('Backing up old files...')\\r\\n\\r\\n # Connect with SSH-PubKey and execute backup script\\r\\n subprocess.run(\\r\\n ['ssh',\\r\\n '-i', self.ssh_key,\\r\\n '-o', 'StrictHostKeyChecking=no',\\r\\n 'robot@{}'.format(self.settings['ip']),\\r\\n 'robolab-backup'\\r\\n ])\\r\\n\\r\\n print('Done.')\",\n \"def tearDown(self):\\n model.DBSession.rollback()\",\n \"def tearDown(self):\\n db.session.rollback()\",\n \"def tearDown(self):\\n db.session.rollback()\",\n \"def tearDown(self):\\n db.session.rollback()\",\n \"def tearDown(self):\\n db.session.rollback()\",\n \"def tearDown(self):\\n db.session.rollback()\",\n \"def tearDown(self):\\n db.session.rollback()\",\n \"def stamp(self):\\n self.connection.commit()\\n self.cursor.close()\\n self.connection.close()\",\n \"def backup(self):\\n self.logger.info(\\\"Backing up current version of model...\\\")\\n self.save_checkpoint(filename='backup.pth.tar')\",\n \"def save_and_exit():\\n con.commit()\\n con.close()\\n quit()\",\n \"def backup(self, backup):\\n self._backup = backup\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def tearDown(self):\\n\\n db.session.rollback()\",\n \"def setup():\\r\\n thread = threading.Thread(target = backup)\\r\\n thread.start()\",\n \"def recreate_db():\\n drop_db()\\n create_db()\",\n \"def test_backup_restore_after_rebalance(self):\\n serv_in = self.servers[self.nodes_init:self.nodes_init + self.nodes_in]\\n serv_out = self.servers[self.nodes_init - self.nodes_out:self.nodes_init]\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create_validate()\\n self.backupset.number_of_backups = 1\\n rebalance = self.cluster.async_rebalance(self.cluster_to_backup, serv_in, serv_out)\\n rebalance.result()\\n self.backup_cluster_validate()\\n if not self.same_cluster:\\n self._initialize_nodes(Cluster(), self.input.clusters[0][:self.nodes_init])\\n serv_in = self.input.clusters[0][self.nodes_init: self.nodes_init + self.nodes_in]\\n serv_out = self.input.clusters[0][self.nodes_init - self.nodes_out: self.nodes_init]\\n rebalance = self.cluster.async_rebalance(self.cluster_to_restore, serv_in, serv_out)\\n else:\\n rebalance = self.cluster.async_rebalance(self.cluster_to_restore, serv_out, serv_in)\\n rebalance.result()\\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\\\"<=\\\")\",\n \"def commit():\\n get_db().commit()\",\n \"def test_backup_list(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n self.backup_cluster_validate()\\n self.backup_list_validate()\",\n \"def test_restore_from_compacted_backup(self):\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size, end=self.num_items)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n self.backup_cluster_validate()\\n self.backup_compact()\\n self.backup_restore_validate()\",\n \"def restore_database(app):\\n app.database().restore()\\n app.refresh()\",\n \"def save_database(app):\\n app.database().save()\\n app.status.message('Finished saving..')\",\n \"def backup(context, user=get_local_user(), remote=False, instance=None, stack=None):\\n command = \\\"run --rm postgres backup\\\"\\n run_command(context, user, remote, instance, stack, command)\",\n \"def test_backup_system_database_correctly_done(self, mock_datetime,\\n mysql_provider):\\n mock_datetime.return_value = \\\"20190101_000000\\\"\\n with raises(Exception) as e:\\n # By default, it will raise an exception because of access rights.\\n # But at least, we'll know it tried to backup it.\\n mysql_provider.execute_backup(database=\\\"information_schema\\\")\\n assert \\\"Could not backup database information_schema: retcode 2 - stderr None.\\\" in str(\\n e.value)\",\n \"def recreate_db():\\n drop_db()\\n create_db()\\n populate_db()\",\n \"def begin_transaction(self):\\r\\n self.db.commit(False)\",\n \"def dump_DB(self):\\n\\t\\tprint 'Dumping Data Base...'\\n\\t\\tp=cPickle.Pickler(open(self.DB_file, 'wb'))\\n\\t\\tp.fast=True\\n\\t\\tp.dump(self.DB)\\n\\t\\tprint 'Dumping completed'\\n\\t\\t#stream.close()\\n\\t\\t#return \",\n \"def test_backup_restore_after_offline_upgrade(self):\\n upgrade_version = self.input.param(\\\"upgrade_version\\\", \\\"5.0.0-3330\\\")\\n if upgrade_version == \\\"5.0.0-3330\\\":\\n self.fail(\\\"\\\\n *** Need param 'upgrade_version=' to run\\\")\\n\\n backup_service_test = self.input.param(\\\"backup_service_test\\\", False)\\n\\n if backup_service_test:\\n backup_service_hook = BackupServiceHook(self.servers[1], self.servers, self.backupset, self.objstore_provider)\\n self.cli_command_location = \\\"/opt/couchbase/bin\\\"\\n\\n self._install(self.servers)\\n gen = BlobGenerator(\\\"ent-backup\\\", \\\"ent-backup-\\\", self.value_size,\\n end=self.num_items)\\n rebalance = self.cluster.async_rebalance(self.servers[:2], [self.servers[1]],\\n [])\\n rebalance.result()\\n self.add_built_in_server_user()\\n RestConnection(self.master).create_bucket(bucket='default', ramQuotaMB=512)\\n self.buckets = RestConnection(self.master).get_buckets()\\n self.total_buckets = len(self.buckets)\\n self._load_all_buckets(self.master, gen, \\\"create\\\", 0)\\n self.backup_create()\\n self.backup_cluster_validate()\\n self.sleep(5)\\n BucketOperationHelper.delete_bucket_or_assert(self.master, \\\"default\\\", self)\\n\\n \\\"\\\"\\\" Start to upgrade \\\"\\\"\\\"\\n if self.force_version_upgrade:\\n upgrade_version = self.force_version_upgrade\\n upgrade_threads = self._async_update(upgrade_version=upgrade_version,\\n servers=self.servers[:2])\\n for th in upgrade_threads:\\n th.join()\\n self.log.info(\\\"Upgraded to: {ver}\\\".format(ver=upgrade_version))\\n self.sleep(30)\\n\\n \\\"\\\"\\\" Re-create default bucket on upgrade cluster \\\"\\\"\\\"\\n RestConnection(self.master).create_bucket(bucket='default', ramQuotaMB=512)\\n self.sleep(5)\\n\\n # Create a backup node and perform a backup service import repository and restore\\n if backup_service_test:\\n backup_service_hook.backup_service.replace_services(self.servers[1], ['kv,backup'])\\n backup_service_hook.backup_service.import_repository(self.backupset.directory, self.backupset.name, \\\"my_repo\\\")\\n backup_service_hook.backup_service.take_one_off_restore(\\\"imported\\\", \\\"my_repo\\\", 20, 20)\\n backup_service_hook.cleanup()\\n return\\n\\n \\\"\\\"\\\" Only server from Spock needs build in user\\n to access bucket and other tasks\\n \\\"\\\"\\\"\\n if \\\"5\\\" <= RestConnection(self.master).get_nodes_version()[:1]:\\n self.add_built_in_server_user()\\n for user in self.users_check_restore:\\n user_name = user.replace('[', '_').replace(']', '_')\\n testuser = [{'id': user_name, 'name': user_name,\\n 'password': 'password'}]\\n rolelist = [{'id': user_name, 'name': user_name,\\n 'roles': user}]\\n\\n self.log.info(\\\"**** add built-in '%s' user to node %s ****\\\" % (testuser[0][\\\"name\\\"],\\n self.master.ip))\\n RbacBase().create_user_source(testuser, 'builtin', self.master)\\n\\n self.log.info(\\\"**** add '%s' role to '%s' user ****\\\" % (rolelist[0][\\\"roles\\\"],\\n testuser[0][\\\"name\\\"]))\\n RbacBase().add_user_role(rolelist, RestConnection(self.master), 'builtin')\\n\\n backupsets = [self.backupset]\\n if \\\"5\\\" <= RestConnection(self.master).get_nodes_version()[:1]:\\n for user in self.users_check_restore:\\n new_backupset = copy.deepcopy(self.backupset)\\n new_backupset.restore_cluster_host_username = user.replace('[', '_').replace(']', '_')\\n backupsets.append(new_backupset)\\n for backupset in backupsets:\\n self.backupset = backupset\\n self.backup_restore_validate(compare_uuid=False, seqno_compare_function=\\\">=\\\")\\n BucketOperationHelper().delete_bucket_or_assert(self.backupset.cluster_host,\\n \\\"default\\\", self)\",\n \"def rollback(self):\\r\\n self.db.rollback()\",\n \"def backup(ctx):\\n config_path = ctx.obj['config_path']\\n logger = ctx.obj['logger']\\n\\n config = Config(config_path)\\n scheduler = BlockingScheduler(\\n executors={'default': ThreadPoolExecutor(max_workers=1)}\\n )\\n\\n for job in config.jobs.values():\\n logger.info(f'filesystem={job.filesystem} '\\n f'cron=\\\"{job.cron}\\\" '\\n 'msg=\\\"Adding job.\\\"')\\n scheduler.add_job(job.start, 'cron', **job.cron, coalesce=True)\\n\\n try:\\n scheduler.start()\\n except (KeyboardInterrupt, SystemExit):\\n pass\",\n \"def tearDown(self):\\n insta485db = sh.Command(\\\"./bin/insta485db\\\")\\n insta485db(\\\"reset\\\")\",\n \"def makeBackup(self):\\n #--File Path\\n original = self.path\\n #--Backup\\n backup = self.path+'.bak'\\n shutil.copy(original,backup)\\n #--First backup\\n firstBackup = self.path+'.baf'\\n if not os.path.exists(firstBackup):\\n shutil.copy(original,firstBackup)\",\n \"def backup():\\n # Backup the WordPress database.\\n db('backup')\\n\\n # Copy teh wp-config.php file from the server.\\n get(os.path.join(env.wordpress_path, 'wp-config.php'),\\n './backups/wp-config.php')\\n\\n now = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\\n\\n theme_list = wp_cli('theme list --format=csv')\\n plugin_list = wp_cli('plugin list --format=csv')\\n\\n # Backup the installed themes\\n #with open('./backups/themes.csv', 'w') as f:\\n # f.write(theme_list)\\n\\n # Backup the installed plugins\\n #with open('./backups/plugins.csv', 'w') as f:\\n # f.write(plugin_list)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8267241","0.74766415","0.7415895","0.7311228","0.71407634","0.67931277","0.6724161","0.6582637","0.6562484","0.65504694","0.65399987","0.6503169","0.64341927","0.64200234","0.6383028","0.6301118","0.6289786","0.6247211","0.62126917","0.62072396","0.6204987","0.6163196","0.6106347","0.61032057","0.6094978","0.6069389","0.60691214","0.6064923","0.605802","0.60300374","0.6016339","0.60110354","0.59810394","0.59535456","0.59521437","0.5943392","0.59360105","0.59248096","0.59110785","0.5896538","0.5889607","0.58868074","0.5883918","0.58817226","0.58817226","0.58817226","0.58817226","0.58817226","0.58817226","0.58773667","0.58726025","0.5871784","0.58694625","0.5863603","0.5863603","0.58630574","0.58626986","0.5855957","0.58548516","0.5847691","0.58455646","0.5832897","0.5829136","0.5822299","0.5822299","0.5822299","0.5822299","0.5822299","0.5822299","0.5806214","0.57986206","0.5788766","0.57813215","0.57743317","0.57743317","0.57743317","0.57743317","0.57743317","0.57743317","0.57743317","0.57743317","0.57670397","0.5761035","0.57603836","0.5752154","0.5744792","0.5742655","0.5739495","0.5738454","0.5737621","0.5734785","0.5732898","0.5732848","0.57309026","0.5728362","0.5719278","0.5709598","0.5705612","0.5702269","0.5701564"],"string":"[\n \"0.8267241\",\n \"0.74766415\",\n \"0.7415895\",\n \"0.7311228\",\n \"0.71407634\",\n \"0.67931277\",\n \"0.6724161\",\n \"0.6582637\",\n \"0.6562484\",\n \"0.65504694\",\n \"0.65399987\",\n \"0.6503169\",\n \"0.64341927\",\n \"0.64200234\",\n \"0.6383028\",\n \"0.6301118\",\n \"0.6289786\",\n \"0.6247211\",\n \"0.62126917\",\n \"0.62072396\",\n \"0.6204987\",\n \"0.6163196\",\n \"0.6106347\",\n \"0.61032057\",\n \"0.6094978\",\n \"0.6069389\",\n \"0.60691214\",\n \"0.6064923\",\n \"0.605802\",\n \"0.60300374\",\n \"0.6016339\",\n \"0.60110354\",\n \"0.59810394\",\n \"0.59535456\",\n \"0.59521437\",\n \"0.5943392\",\n \"0.59360105\",\n \"0.59248096\",\n \"0.59110785\",\n \"0.5896538\",\n \"0.5889607\",\n \"0.58868074\",\n \"0.5883918\",\n \"0.58817226\",\n \"0.58817226\",\n \"0.58817226\",\n \"0.58817226\",\n \"0.58817226\",\n \"0.58817226\",\n \"0.58773667\",\n \"0.58726025\",\n \"0.5871784\",\n \"0.58694625\",\n \"0.5863603\",\n \"0.5863603\",\n \"0.58630574\",\n \"0.58626986\",\n \"0.5855957\",\n \"0.58548516\",\n \"0.5847691\",\n \"0.58455646\",\n \"0.5832897\",\n \"0.5829136\",\n \"0.5822299\",\n \"0.5822299\",\n \"0.5822299\",\n \"0.5822299\",\n \"0.5822299\",\n \"0.5822299\",\n \"0.5806214\",\n \"0.57986206\",\n \"0.5788766\",\n \"0.57813215\",\n \"0.57743317\",\n \"0.57743317\",\n \"0.57743317\",\n \"0.57743317\",\n \"0.57743317\",\n \"0.57743317\",\n \"0.57743317\",\n \"0.57743317\",\n \"0.57670397\",\n \"0.5761035\",\n \"0.57603836\",\n \"0.5752154\",\n \"0.5744792\",\n \"0.5742655\",\n \"0.5739495\",\n \"0.5738454\",\n \"0.5737621\",\n \"0.5734785\",\n \"0.5732898\",\n \"0.5732848\",\n \"0.57309026\",\n \"0.5728362\",\n \"0.5719278\",\n \"0.5709598\",\n \"0.5705612\",\n \"0.5702269\",\n \"0.5701564\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5757434"},"document_rank":{"kind":"string","value":"84"}}},{"rowIdx":17498,"cells":{"query":{"kind":"string","value":"Execute Ping RPC over NETCONF."},"document":{"kind":"string","value":"def ping(host, destination, repeat_count, vrf_name):\n\n # create NETCONF provider\n provider = NetconfServiceProvider(address=host,\n port=830,\n username='admin',\n password='admin',\n protocol='ssh')\n executor = ExecutorService() # create executor service\n\n ping = xr_ping_act.Ping() # create ping RPC object\n\n ping.input.destination = ping.input.Destination()\n ping.input.destination.destination = destination\n ping.input.destination.repeat_count = repeat_count\n ping.input.destination.vrf_name = vrf_name\n\n ping.output = executor.execute_rpc(provider, ping, ping.output)\n\n return dict(success_rate=int(str(ping.output.ping_response.ipv4[0].success_rate)),\n rtt_min=int(str(ping.output.ping_response.ipv4[0].rtt_min)),\n rtt_avg=int(str(ping.output.ping_response.ipv4[0].rtt_avg)),\n rtt_max=int(str(ping.output.ping_response.ipv4[0].rtt_max)))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ping():\n return ping_response()","def ping():\n return ping_response()","def ping():\n return ping_response()","def ping():\n return ping_response()","def rpc_ping(self):\n\t\treturn True","async def ping(self):\n uri = \"/fapi/v1/ping\"\n success, error = await self.request(\"GET\", uri)\n return success, error","async def ping(self) -> APIReturn:\n return await self._request(\"GET\", \"/ping\")","def ping(self):\n return 'ping'","def ping(self, *args):\n return self._command(b'PING', *args, handler=\"PONG\")","async def ping(self, *args, **kwargs):\n\n return await self._makeApiCall(self.funcinfo[\"ping\"], *args, **kwargs)","def Ping(self): # real signature unknown; restored from __doc__\n pass","def ping(self) -> None:\n ...","def ping(self):\n return rpc.ping(self.service, self.session_handle)","def ping(self):\n request = Empty()\n response = self.stub.Ping(request, timeout=5)","def lnet_ping(self):\n try:\n nodes = Conf.get(self._index, 'cluster>server_nodes')\n except:\n raise MotrError(errno.EINVAL, \"Server nodes not found\")\n\n check_type(nodes, dict, \"server_nodes\")\n\n nids = get_nids(self, nodes)\n\n sys.stdout.write(\"lnet pinging on all nodes in cluster\\n\")\n sys.stdout.write(\"motr_setup init MUST be performed on all nodes before \"\n \"executing this\\n\")\n for nid in nids:\n cmd = f\"lctl ping {nid}\"\n sys.stdout.write(f\"lctl ping on: {nid}\\n\")\n execute_command(self, cmd)","def ping(self):\n pass","def ping(self):\n\t\treturn self._send_command_to_entity_server(us.SERVER_COMMAND_PING)","def ping(self):\n raise AssertionError(\"Ping function is not implemented\")","def _ping(self):\n\n result = self._staf_handle.submit(self._sut.network_address, 'ping', 'ping')\n\n #16 is ping timeout for STAF\n if result.rc == result.NoPathToMachine:\n raise CoreError('Could not ping \"{0}\" '\n 'network address!'.format(self._sut.network_address))\n elif result.rc != result.Ok:\n raise FatalError(result.result)","async def ping(self, ctx):\n await ctx.send('pong')","async def do_ping(self):\n return self._result(\"pong\")","def ping(self):\n self.connect()\n self._write('PING\\r\\n')\n return self._get_simple_response()","def ping(cmd, *args, **argv):\n import os\n context = argv[\"context\"]\n \n def count(num):\n if str(num) == str(int(num)):\n if num < 0:\n context.write(\"%s: bad number of packets to transmit.\" % cmd)\n else:\n return num\n else:\n context.write(\"%s: can't set unicast time-to-live: Unknown host\" % cmd)\n return\n\n ping_p = {\"-c\":count}\n\n def isipaddress(ip):\n import socket\n try:\n ipa = socket.gethostbyname(ip)\n return ipa\n except:\n context.write(\"ping: unknown host %s\" % ip)\n return None\n\n def doping(cmd, sign, sign_param, ipaddress):\n if sign in ping_p.keys():\n # #\n pass\n else:\n context.write(\"connect: Unknown host\")\n return \n \n\tif ping_p[sign](sign_param):\n # #\n pass\n else:\n return \n if isipaddress(ipaddress):\n ip = isipaddress(ipaddress)\n else:\n return\n try:\n os.system(str(cmd) + \" \" + str(sign) + \" \" + str(sign_param) + \" \" + str(ip))\n except:\n context.write(\"has some errors in ping command\")\n return \n\n length = len(args)\n \n if length == 0:\n helpinfo = context.resolver.get_func_doc(getattr(context.resolver.get_module(cmd), cmd))\n helpinfo_format = helpinfo[\"format\"].rstrip()\n helpinfo_format = helpinfo_format.lstrip()\n if helpinfo_format == \"\":\n return\n context.write(helpinfo_format)\n\n elif length < 3:\n cmd_real = context.resolver.has_command(args[0], cmd)\n if cmd_real != None:\n cmds = cmd.split()\n cmd_n = \"_\".join(cmds)\n modulename = cmd_n + \"_\" + cmd_real\n module = context.resolver.get_module(modulename)\n func = getattr(module, modulename)\n func(cmd + \" \" + cmd_real, context = context)\n else:\n doping(cmd, \"-c\", 4, args[0])\n\n else:\n\tif args[1] == \"0\":\n\t os.system(str(cmd) + \" \" + str(args[2]))\n\telse:\n doping(cmd, args[0], args[1], args[2])","def ping():\n\treturn HTTPResponse(status=200)","async def ping(self, ctx):\n pong_msg = await ctx.send(\":ping_pong:\")\n sr_lat = (pong_msg.created_at - ctx.message.created_at).total_seconds() * 1000\n await pong_msg.edit(content=f\"Command latency = `{sr_lat}ms`\\n\"\n f\"API heartbeat = `{self.client.latency * 1000:.1f}ms`\")\n self.logger.info(misolog.format_log(ctx, f\"\"))","def ping():\n return '{}'","def ping(c):\n host, port, f = ClusterCFG.parse_uri(c)\n\n # Create our socket and attempt to connect.\n sock = Network.open_client(host, port)\n if ErrorHandle.is_error(sock):\n return False\n\n # Send a dummy message. Response must not be an error.\n response = Network.write(sock, ['YY'])\n if ErrorHandle.is_error(response):\n return response\n\n sock.close()\n return True","async def _ping_(self)->str:\n return PING_RESPONSE","def ping(event, context):\n logger.info(\"Ping requested.\")\n return _get_response(200, \"PONG!\")","def Ping(self, request, _):\n\n return notifier_pb2.PingReply(data=request.data)","def ping(host):\r\n \r\n # Ping command count option as function of OS\r\n param = '-n' if system_name().lower()=='windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n\r\n process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) \r\n streamdata = process.communicate()[0]\r\n #print_util.print_std(streamdata)\r\n streamdata = string_util.decode_chs_str(streamdata)\r\n print_util.print_std(streamdata)\r\n if system_name().lower()=='windows':\r\n #On windows use this to check ping pass or fail.\r\n if 'TTL=' in str(streamdata):\r\n return True\r\n else:\r\n return False\r\n return process.returncode==0","def ping():\n logging.info(\"Ping received...\")\n\n health = Predictor.load() is not None\n\n status = 200 if health else 404\n return Response(response=\"\\n\", status=status, mimetype=\"application/json\")","def ping(self):\n return","def Ping(self, request, _):\n\n return server_pb2.PingReply(data=request.data)","def test_ping(self):\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\n assert status == 0","def ping(self):\n self._write(f'PING :{self.server.name}')\n self.awaiting_pong_since = datetime.datetime.now()","async def ping(self, ctx):\n self.log_command_call(\"ping\", ctx.message)\n embed_output = create_embed(description=\"pong\")\n await ctx.send(embed=embed_output)","def ping (self,host):\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def ping(self):\n try:\n api_ping = '{}://{}:{}/api/1.0.0/ping/{}/None'.format(\n self._env.api_protocol,\n self._env.api_host,\n self._env.api_port,\n self._key\n )\n self._env.logger.info('PING> {}'.format(api_ping))\n\n def check(response):\n if response.code == 204:\n self._env.logger.info('204 - Registering new routes')\n self.register_routes()\n elif response.code == 404:\n self._env.logger.info('404 - api gateway is not available')\n elif response.code != 200:\n self._env.logger.error('{} - UNKNOWN ERROR'.format(response.code))\n return response\n\n def log(failure):\n \"\"\"\n Just log the error, a return code of 'False' will be returned elsewhere\n :param failure: A treq failure object\n \"\"\"\n return self._env.logger.warning('[ping] {}'.format(failure.getErrorMessage()))\n\n treq.get(api_ping).addCallback(check).addErrback(log)\n\n except requests.exceptions.ConnectionError as e:\n self._env.logger.warning('ping failed for \"{}\"'.format(api_ping))\n self._env.logger.warning('ping return = \"{}\"'.format(e.args[0].reason))\n self._state = False","def ping(host):\n\n # Ping command count option as function of OS\n param = '-n' if system_name().lower()=='windows' else '-c'\n\n # Building the command. Ex: \"ping -c 5 google.com\"\n command = ['ping', param, '5', host]\n\n # Pinging\n return system_call(command) == 0","async def ping(self, ctx):\n await ctx.send(\"Pong\")","def ping(host):\n\n # Ping parameters as function of OS\n parameters = \"-n 1\" if system_name().lower() == \"windows\" else \"-c 1\"\n\n # Pinging\n return system_call(\"ping \" + parameters + \" \" + host) == 0","def test_ping():\n mock = MagicMock(return_value=True)\n with patch.dict(win_network.__salt__, {\"cmd.run\": mock}):\n assert win_network.ping(\"127.0.0.1\")","def ping():\n return 'pong'","async def ping(ctx):\n await ctx.send(\"pong\")","def ping(self) -> Response:\n raise NotImplementedError","async def ping(self, ctx, ip):\n\n # Check for valid IP else do DNS lookup\n valid_ip = re.compile(\"[0-9]{,3}\\.[0-9]{,3}\\.[0-9]{,3}\")\n valid_hostname = re.compile(\".*\\.[a-zA-Z]{2,}\")\n valid = False\n\n if valid_ip.match(ip):\n valid = True\n elif valid_hostname.match(ip):\n valid = True\n try:\n await self.bot.say('Doing DNS lookup...')\n ip = socket.gethostbyname(ip)\n\n if valid == True:\n start = time.time()\n response = os.system(\"sudo ping -c 1 -w3 \" + ip)\n duration = time.time() - start\n duration = round(duration * 1000, 0)\n if response == 0:\n await self.bot.say(ip + ' is up and responding in ' +\n str(duration) + 'ms.')\n else:\n await self.bot.say(ip + ' is not reachable.')\n else:\n await self.bot.say(ip + ' is not a valid IP or Domain.')\n\n except socket.gaierror:\n await self.bot.say('Whoops! That Address cant be resolved!')","def sendPing(self, payload=None):","def Ping(self):\n\n self.helpers(self.config).Info(\"Ping Weaviate...\")\n # get the meta endpoint\n _, _ = self.Get(\"/meta\")\n # would fail is not available.\n self.helpers(self.config).Info(\"Pong from Weaviate...\")","def ping(msg):\n return msg","def ping(self):\n packet = Packet()\n packet.message = MessageType.CLIENT_PING\n packet.data = \"PING\"\n try:\n self.send(packet.encode())\n self.last_ping_time = time.time()\n except socket.error, e:\n self.console.error(repr(e))","async def ping(self, ctx:utils.Context):\r\n\r\n await ctx.send(\"Pong!\")","def ping():\n requestor = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\n logger.info(f\"Health check requested by ip='{requestor}'\")\n return make_response(\n jsonify(status=\"Serving\",\n body=\"pong\"), 200)","def ping():\n return \"pong\"","def ping(self, irc, msg, args):\n irc.reply('pong', prefixNick=False)","def test_ping(client):\n resp = client.get(url_for('cd2h_repo_project.ping'))\n\n assert resp.status_code == 200\n assert resp.get_data(as_text=True) == 'OK'","def handle_ping(self, host):\n self.send(\"PONG :{}\".format(host))","def ping():\n # TODO: this ought to live in ISlaveControl, maybe with disconnect()\n # or something. However the event that is emitted is most useful in\n # the Builder column, so it kinda fits here too.","def ping(self):\n\n url = self.api_url('ping')\n\n return requests.get(url, headers=self.auth_header).json()","def ping(host):\n import os, platform\n\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def ping(host):\n import os, platform\n\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def ping(host):\n import os, platform\n\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def ping(bot, sender, sendmsg, label, args):\n\n sendmsg(\"Pong!\")","def onPing(self, payload):","def ping():\n return json_response({\n 'ping': 'pong',\n 'version': __version__,\n 'imgapi': False,\n })","def Ping(ctx,\n attempts = None,\n hosts = None,\n total_timeout_sec = None,\n packet_size = None,\n ping_timeout_msec = None):\n \"\"\"Note: This method is available only through the per-node API endpoint 5.0 or later.\"\"\"\n if ctx.element is None:\n ctx.logger.error(\"You must establish at least one connection and specify which you intend to use.\")\n exit()\n\n\n\n ctx.logger.info(\"\"\"attempts = \"\"\"+str(attempts)+\"\"\";\"\"\"+\"\"\"hosts = \"\"\"+str(hosts)+\"\"\";\"\"\"+\"\"\"total_timeout_sec = \"\"\"+str(total_timeout_sec)+\"\"\";\"\"\"+\"\"\"packet_size = \"\"\"+str(packet_size)+\"\"\";\"\"\"+\"\"\"ping_timeout_msec = \"\"\"+str(ping_timeout_msec)+\"\"\";\"\"\"+\"\")\n try:\n TestPingResult = ctx.element.test_ping(attempts=attempts, hosts=hosts, total_timeout_sec=total_timeout_sec, packet_size=packet_size, ping_timeout_msec=ping_timeout_msec)\n except common.ApiServerError as e:\n ctx.logger.error(e.message)\n exit()\n except BaseException as e:\n ctx.logger.error(e.__str__())\n exit()\n\n cli_utils.print_result(TestPingResult, ctx.logger, as_json=ctx.json, depth=ctx.depth, filter_tree=ctx.filter_tree)","def testPing(self, minion_id):\n self.console_manager.printRed(''.join([\"Ping minion id: \", minion_id]))\n command = ['sudo', 'salt', minion_id, 'test.ping']\n self.console_manager.runCommandFromShell(command)\n return","def ping() -> str:\n return \"Server is here\"","def ping(host,hidden=True):\r\n\r\n # Ping command count option as function of OS\r\n param = '-n' if platform.system()=='Windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n \r\n startupinfo = None\r\n if hidden:\r\n if os.name == 'nt':\r\n startupinfo = subprocess.STARTUPINFO()\r\n startupinfo.dwFlags |= subprocess._subprocess.STARTF_USESHOWWINDOW#subprocess.STARTF_USESHOWWINDOW\r\n #proc = subprocess.Popen(command, startupinfo=startupinfo) \r\n return subprocess.call(command, startupinfo=startupinfo) == 0","def ping(self):\n return True","def ping(self):\n return True","def ping(self):\n\n rlog(1, self.name, 'sending ping')\n try:\n self.putonqueue(1, 'PING :%s' % self.server)\n return 1\n except Exception, ex:\n rlog(10, self.name, \"can't send ping: %s\" % str(ex))\n return 0","def test_ctcpQuery_PING(self):\n self.client.ctcpQuery_PING(self.user, self.channel, \"data\")\n self.assertEqual(\n self.client.methods, [(\"ctcpMakeReply\", (\"Wolf\", [(\"PING\", \"data\")]))]\n )","def test_ping():\n\n api = Client()\n response = api.ping()\n response.should.equal({})","def ping():\n # type: () -> str\n return 'pong'","def ping(host):\n # Option for the number of packets as a function of\n param = '-n' if platform.system().lower() == 'windows' else '-c'\n # Building the command. Ex: \"ping -c 1 google.com\"\n command = ['ping', param, '1', host]\n return subprocess.call(command, stdout=subprocess.PIPE) == 0","def ping(context):\n\n ssh_config = aws_infrastructure.tasks.ssh.SSHConfig.load(SSH_CONFIG_PATH)\n documentdb_config = aws_infrastructure.tasks.library.documentdb.DocumentDBConfig.load(DOCUMENTDB_CONFIG_PATH)\n\n with aws_infrastructure.tasks.ssh.SSHClientContextManager(\n ssh_config=ssh_config,\n ) as ssh_client:\n with aws_infrastructure.tasks.ssh.SSHPortForwardContextManager(\n ssh_client=ssh_client,\n remote_host=documentdb_config.endpoint,\n remote_port=documentdb_config.port,\n ) as ssh_port_forward:\n client = MongoClient(\n host=[\n 'localhost'\n ],\n port=ssh_port_forward.local_port,\n connect=True,\n username=documentdb_config.admin_user,\n password=documentdb_config.admin_password,\n tls=True,\n tlsInsecure=True,\n )\n\n print(client.admin.command('ping'))","async def ping(self, ctx):\r\n embed = discord.Embed(\r\n title = \"Ping\",\r\n description = \"Pinging...\",\r\n color = Config.MAINCOLOR\r\n )\r\n t1 = time.perf_counter()\r\n msg = await ctx.send(embed = embed)\r\n t2 = time.perf_counter()\r\n embed = discord.Embed(\r\n title = \"🏓 Pong!\",\r\n description = f\"API latency is {round((t2 - t1) * 1000)}ms\\nHost latency is {round(self.bot.latency * 1000, 2)}ms\",\r\n color = Config.MAINCOLOR\r\n )\r\n await msg.edit(embed = embed)","async def test_app_ping(cli):\n resp = await cli.get('/monitor/ping')\n assert resp.status == 200","def ping(self, datacenter=None, network=None):\r\n resp = requests.get('%s/v1/ping' %\r\n self.get_endpoint(datacenter, network))\r\n resp.raise_for_status()\r\n return True","def ping(self,\n *opts, # type: PingOptions\n **kwargs # type: Any\n ) -> PingResult:\n return super().ping(*opts, **kwargs)","def ping(host):\n\n # Option for the number of packets as a function of\n param = '-n' if platform.system().lower()=='windows' else '-c'\n\n # Building the command. Ex: \"ping -c 1 google.com\"\n command = ['ping', param, '1', host]\n\n return subprocess.call(command) == 0","def ping(host):\n\n # Option for the number of packets as a function of\n param = '-n' if platform.system().lower()=='windows' else '-c'\n\n # Building the command. Ex: \"ping -c 1 google.com\"\n command = ['ping', param, '1', host]\n\n return subprocess.call(command) == 0","def ping(host):\r\n\r\n # Option for the number of packets as a function of\r\n param = '-n' if platform.system().lower()=='windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n\r\n return subprocess.call(command) == 0","def ping(self, cmd):\n cmd.finish(\"text='Present and (probably) well'\")","def icmp_ping(host, timeout=1):\n result = Subprocess.execute(\n \"ping -c 1 -W '{timeout:d}' '{host:s}'\".format(\n host=host, timeout=timeout))\n return result.exit_code == 0","def ping(self, cmd):\n\n cmd.finish(\"text='Present and (probably) well'\")","def ping():\n return jsonify({'response': 'pong'}), 200","def ping(self):\n response = self._request(\"GET\", [ROUTE_PING])\n\n if response.status_code == 200:\n logging.info(\"OK\")\n return True\n logging.error(\"FAILED\")\n return False","def ping(host):\r\n\r\n # Option for the number of packets as a function of\r\n param = '-n' if platform.system().lower() == 'windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n\r\n return subprocess.call(command) == 0","def ping(metaServer):\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.connect((metaServer.node, metaServer.port))\n req = \"PING\\r\\nVersion: KFS/1.0\\r\\nCseq: 1\\r\\n\\r\\n\"\n sock.send(req)\n sockIn = sock.makefile('r')\n for line in sockIn:\n if line.find('Down Servers:') == 0:\n if (len(line.split(':')) < 2):\n break\n pos = line.find(':')\n downNodes = line[pos+1:].strip()\n break\n \n if line.find('Servers:') != 0:\n continue\n nodes = line.split(':')[1].strip()\n processUpNodes(nodes)\n\n gUpServers.sort()\n sock.close()","def ping(self):\n return self.hub.ping()","def ping(self,dest):\n\t\tself.tn.write('ping -c 4 %s\\n'%(dest))\n\t\tself.tn.write('exit\\n')\n\t\tresp = self.tn.read_all()\n\t\treturn resp","def ping():\r\n health1 = ScoringService.get_model1() is not None # You can insert a health check here\r\n ping_response = \"Docker for Discover non prime users\"\r\n status = 200 if (health1) else 404\r\n return flask.Response(response=ping_response, status=status, mimetype='application/json')","def ping(self, cookie = 0xDEC0DE, async = False):\n\n\t\tdata = pack(\"!bL\", 0, cookie)\n\t\tself._send_message(\"PING\", data)\n\n\t\tif not async:\n\t\t\treturn EndpointSync(self, \"PING\").get_data()","def ping():\r\n return make_response(\"pong!\", 200)","def test_ping(self):\n responses.add(responses.GET, \"http://localhost:3030/{0}/ping\".format(\"$\"), \"2017-09-18T11:41:19.915+00:00\", status=200)\n fuseki = GraphStore()\n result = fuseki._graph_health()\n self.assertTrue(result)","def f_ping(jenni, input):\n jenni.reply('pong!')","def ping(self):\n return self.pingval","async def ping(ctx):\n latencies = {\n \"websocket\": bot.latency,\n }\n\n def comp_message():\n msgs = []\n for title in latencies:\n msgs.append(f\"{title.title()}: {(latencies[title] * 1000):.0f}ms\")\n return '\\n'.join(msgs)\n\n start = time.perf_counter()\n await ctx.respond(comp_message())\n end = time.perf_counter()\n\n latencies[\"round trip\"] = end - start\n\n await ctx.edit(content=comp_message())","async def ping_command(self, ctx):\n ping = int(self.client.latency * 1000)\n embed = Embed(\n title=\"Pong!\", description=f\"My ping is {ping}ms.\", color=Color.green()\n )\n await ctx.send(embed=embed)"],"string":"[\n \"def ping():\\n return ping_response()\",\n \"def ping():\\n return ping_response()\",\n \"def ping():\\n return ping_response()\",\n \"def ping():\\n return ping_response()\",\n \"def rpc_ping(self):\\n\\t\\treturn True\",\n \"async def ping(self):\\n uri = \\\"/fapi/v1/ping\\\"\\n success, error = await self.request(\\\"GET\\\", uri)\\n return success, error\",\n \"async def ping(self) -> APIReturn:\\n return await self._request(\\\"GET\\\", \\\"/ping\\\")\",\n \"def ping(self):\\n return 'ping'\",\n \"def ping(self, *args):\\n return self._command(b'PING', *args, handler=\\\"PONG\\\")\",\n \"async def ping(self, *args, **kwargs):\\n\\n return await self._makeApiCall(self.funcinfo[\\\"ping\\\"], *args, **kwargs)\",\n \"def Ping(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def ping(self) -> None:\\n ...\",\n \"def ping(self):\\n return rpc.ping(self.service, self.session_handle)\",\n \"def ping(self):\\n request = Empty()\\n response = self.stub.Ping(request, timeout=5)\",\n \"def lnet_ping(self):\\n try:\\n nodes = Conf.get(self._index, 'cluster>server_nodes')\\n except:\\n raise MotrError(errno.EINVAL, \\\"Server nodes not found\\\")\\n\\n check_type(nodes, dict, \\\"server_nodes\\\")\\n\\n nids = get_nids(self, nodes)\\n\\n sys.stdout.write(\\\"lnet pinging on all nodes in cluster\\\\n\\\")\\n sys.stdout.write(\\\"motr_setup init MUST be performed on all nodes before \\\"\\n \\\"executing this\\\\n\\\")\\n for nid in nids:\\n cmd = f\\\"lctl ping {nid}\\\"\\n sys.stdout.write(f\\\"lctl ping on: {nid}\\\\n\\\")\\n execute_command(self, cmd)\",\n \"def ping(self):\\n pass\",\n \"def ping(self):\\n\\t\\treturn self._send_command_to_entity_server(us.SERVER_COMMAND_PING)\",\n \"def ping(self):\\n raise AssertionError(\\\"Ping function is not implemented\\\")\",\n \"def _ping(self):\\n\\n result = self._staf_handle.submit(self._sut.network_address, 'ping', 'ping')\\n\\n #16 is ping timeout for STAF\\n if result.rc == result.NoPathToMachine:\\n raise CoreError('Could not ping \\\"{0}\\\" '\\n 'network address!'.format(self._sut.network_address))\\n elif result.rc != result.Ok:\\n raise FatalError(result.result)\",\n \"async def ping(self, ctx):\\n await ctx.send('pong')\",\n \"async def do_ping(self):\\n return self._result(\\\"pong\\\")\",\n \"def ping(self):\\n self.connect()\\n self._write('PING\\\\r\\\\n')\\n return self._get_simple_response()\",\n \"def ping(cmd, *args, **argv):\\n import os\\n context = argv[\\\"context\\\"]\\n \\n def count(num):\\n if str(num) == str(int(num)):\\n if num < 0:\\n context.write(\\\"%s: bad number of packets to transmit.\\\" % cmd)\\n else:\\n return num\\n else:\\n context.write(\\\"%s: can't set unicast time-to-live: Unknown host\\\" % cmd)\\n return\\n\\n ping_p = {\\\"-c\\\":count}\\n\\n def isipaddress(ip):\\n import socket\\n try:\\n ipa = socket.gethostbyname(ip)\\n return ipa\\n except:\\n context.write(\\\"ping: unknown host %s\\\" % ip)\\n return None\\n\\n def doping(cmd, sign, sign_param, ipaddress):\\n if sign in ping_p.keys():\\n # #\\n pass\\n else:\\n context.write(\\\"connect: Unknown host\\\")\\n return \\n \\n\\tif ping_p[sign](sign_param):\\n # #\\n pass\\n else:\\n return \\n if isipaddress(ipaddress):\\n ip = isipaddress(ipaddress)\\n else:\\n return\\n try:\\n os.system(str(cmd) + \\\" \\\" + str(sign) + \\\" \\\" + str(sign_param) + \\\" \\\" + str(ip))\\n except:\\n context.write(\\\"has some errors in ping command\\\")\\n return \\n\\n length = len(args)\\n \\n if length == 0:\\n helpinfo = context.resolver.get_func_doc(getattr(context.resolver.get_module(cmd), cmd))\\n helpinfo_format = helpinfo[\\\"format\\\"].rstrip()\\n helpinfo_format = helpinfo_format.lstrip()\\n if helpinfo_format == \\\"\\\":\\n return\\n context.write(helpinfo_format)\\n\\n elif length < 3:\\n cmd_real = context.resolver.has_command(args[0], cmd)\\n if cmd_real != None:\\n cmds = cmd.split()\\n cmd_n = \\\"_\\\".join(cmds)\\n modulename = cmd_n + \\\"_\\\" + cmd_real\\n module = context.resolver.get_module(modulename)\\n func = getattr(module, modulename)\\n func(cmd + \\\" \\\" + cmd_real, context = context)\\n else:\\n doping(cmd, \\\"-c\\\", 4, args[0])\\n\\n else:\\n\\tif args[1] == \\\"0\\\":\\n\\t os.system(str(cmd) + \\\" \\\" + str(args[2]))\\n\\telse:\\n doping(cmd, args[0], args[1], args[2])\",\n \"def ping():\\n\\treturn HTTPResponse(status=200)\",\n \"async def ping(self, ctx):\\n pong_msg = await ctx.send(\\\":ping_pong:\\\")\\n sr_lat = (pong_msg.created_at - ctx.message.created_at).total_seconds() * 1000\\n await pong_msg.edit(content=f\\\"Command latency = `{sr_lat}ms`\\\\n\\\"\\n f\\\"API heartbeat = `{self.client.latency * 1000:.1f}ms`\\\")\\n self.logger.info(misolog.format_log(ctx, f\\\"\\\"))\",\n \"def ping():\\n return '{}'\",\n \"def ping(c):\\n host, port, f = ClusterCFG.parse_uri(c)\\n\\n # Create our socket and attempt to connect.\\n sock = Network.open_client(host, port)\\n if ErrorHandle.is_error(sock):\\n return False\\n\\n # Send a dummy message. Response must not be an error.\\n response = Network.write(sock, ['YY'])\\n if ErrorHandle.is_error(response):\\n return response\\n\\n sock.close()\\n return True\",\n \"async def _ping_(self)->str:\\n return PING_RESPONSE\",\n \"def ping(event, context):\\n logger.info(\\\"Ping requested.\\\")\\n return _get_response(200, \\\"PONG!\\\")\",\n \"def Ping(self, request, _):\\n\\n return notifier_pb2.PingReply(data=request.data)\",\n \"def ping(host):\\r\\n \\r\\n # Ping command count option as function of OS\\r\\n param = '-n' if system_name().lower()=='windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n\\r\\n process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) \\r\\n streamdata = process.communicate()[0]\\r\\n #print_util.print_std(streamdata)\\r\\n streamdata = string_util.decode_chs_str(streamdata)\\r\\n print_util.print_std(streamdata)\\r\\n if system_name().lower()=='windows':\\r\\n #On windows use this to check ping pass or fail.\\r\\n if 'TTL=' in str(streamdata):\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n return process.returncode==0\",\n \"def ping():\\n logging.info(\\\"Ping received...\\\")\\n\\n health = Predictor.load() is not None\\n\\n status = 200 if health else 404\\n return Response(response=\\\"\\\\n\\\", status=status, mimetype=\\\"application/json\\\")\",\n \"def ping(self):\\n return\",\n \"def Ping(self, request, _):\\n\\n return server_pb2.PingReply(data=request.data)\",\n \"def test_ping(self):\\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\\n assert status == 0\",\n \"def ping(self):\\n self._write(f'PING :{self.server.name}')\\n self.awaiting_pong_since = datetime.datetime.now()\",\n \"async def ping(self, ctx):\\n self.log_command_call(\\\"ping\\\", ctx.message)\\n embed_output = create_embed(description=\\\"pong\\\")\\n await ctx.send(embed=embed_output)\",\n \"def ping (self,host):\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def ping(self):\\n try:\\n api_ping = '{}://{}:{}/api/1.0.0/ping/{}/None'.format(\\n self._env.api_protocol,\\n self._env.api_host,\\n self._env.api_port,\\n self._key\\n )\\n self._env.logger.info('PING> {}'.format(api_ping))\\n\\n def check(response):\\n if response.code == 204:\\n self._env.logger.info('204 - Registering new routes')\\n self.register_routes()\\n elif response.code == 404:\\n self._env.logger.info('404 - api gateway is not available')\\n elif response.code != 200:\\n self._env.logger.error('{} - UNKNOWN ERROR'.format(response.code))\\n return response\\n\\n def log(failure):\\n \\\"\\\"\\\"\\n Just log the error, a return code of 'False' will be returned elsewhere\\n :param failure: A treq failure object\\n \\\"\\\"\\\"\\n return self._env.logger.warning('[ping] {}'.format(failure.getErrorMessage()))\\n\\n treq.get(api_ping).addCallback(check).addErrback(log)\\n\\n except requests.exceptions.ConnectionError as e:\\n self._env.logger.warning('ping failed for \\\"{}\\\"'.format(api_ping))\\n self._env.logger.warning('ping return = \\\"{}\\\"'.format(e.args[0].reason))\\n self._state = False\",\n \"def ping(host):\\n\\n # Ping command count option as function of OS\\n param = '-n' if system_name().lower()=='windows' else '-c'\\n\\n # Building the command. Ex: \\\"ping -c 5 google.com\\\"\\n command = ['ping', param, '5', host]\\n\\n # Pinging\\n return system_call(command) == 0\",\n \"async def ping(self, ctx):\\n await ctx.send(\\\"Pong\\\")\",\n \"def ping(host):\\n\\n # Ping parameters as function of OS\\n parameters = \\\"-n 1\\\" if system_name().lower() == \\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Pinging\\n return system_call(\\\"ping \\\" + parameters + \\\" \\\" + host) == 0\",\n \"def test_ping():\\n mock = MagicMock(return_value=True)\\n with patch.dict(win_network.__salt__, {\\\"cmd.run\\\": mock}):\\n assert win_network.ping(\\\"127.0.0.1\\\")\",\n \"def ping():\\n return 'pong'\",\n \"async def ping(ctx):\\n await ctx.send(\\\"pong\\\")\",\n \"def ping(self) -> Response:\\n raise NotImplementedError\",\n \"async def ping(self, ctx, ip):\\n\\n # Check for valid IP else do DNS lookup\\n valid_ip = re.compile(\\\"[0-9]{,3}\\\\.[0-9]{,3}\\\\.[0-9]{,3}\\\")\\n valid_hostname = re.compile(\\\".*\\\\.[a-zA-Z]{2,}\\\")\\n valid = False\\n\\n if valid_ip.match(ip):\\n valid = True\\n elif valid_hostname.match(ip):\\n valid = True\\n try:\\n await self.bot.say('Doing DNS lookup...')\\n ip = socket.gethostbyname(ip)\\n\\n if valid == True:\\n start = time.time()\\n response = os.system(\\\"sudo ping -c 1 -w3 \\\" + ip)\\n duration = time.time() - start\\n duration = round(duration * 1000, 0)\\n if response == 0:\\n await self.bot.say(ip + ' is up and responding in ' +\\n str(duration) + 'ms.')\\n else:\\n await self.bot.say(ip + ' is not reachable.')\\n else:\\n await self.bot.say(ip + ' is not a valid IP or Domain.')\\n\\n except socket.gaierror:\\n await self.bot.say('Whoops! That Address cant be resolved!')\",\n \"def sendPing(self, payload=None):\",\n \"def Ping(self):\\n\\n self.helpers(self.config).Info(\\\"Ping Weaviate...\\\")\\n # get the meta endpoint\\n _, _ = self.Get(\\\"/meta\\\")\\n # would fail is not available.\\n self.helpers(self.config).Info(\\\"Pong from Weaviate...\\\")\",\n \"def ping(msg):\\n return msg\",\n \"def ping(self):\\n packet = Packet()\\n packet.message = MessageType.CLIENT_PING\\n packet.data = \\\"PING\\\"\\n try:\\n self.send(packet.encode())\\n self.last_ping_time = time.time()\\n except socket.error, e:\\n self.console.error(repr(e))\",\n \"async def ping(self, ctx:utils.Context):\\r\\n\\r\\n await ctx.send(\\\"Pong!\\\")\",\n \"def ping():\\n requestor = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\\n logger.info(f\\\"Health check requested by ip='{requestor}'\\\")\\n return make_response(\\n jsonify(status=\\\"Serving\\\",\\n body=\\\"pong\\\"), 200)\",\n \"def ping():\\n return \\\"pong\\\"\",\n \"def ping(self, irc, msg, args):\\n irc.reply('pong', prefixNick=False)\",\n \"def test_ping(client):\\n resp = client.get(url_for('cd2h_repo_project.ping'))\\n\\n assert resp.status_code == 200\\n assert resp.get_data(as_text=True) == 'OK'\",\n \"def handle_ping(self, host):\\n self.send(\\\"PONG :{}\\\".format(host))\",\n \"def ping():\\n # TODO: this ought to live in ISlaveControl, maybe with disconnect()\\n # or something. However the event that is emitted is most useful in\\n # the Builder column, so it kinda fits here too.\",\n \"def ping(self):\\n\\n url = self.api_url('ping')\\n\\n return requests.get(url, headers=self.auth_header).json()\",\n \"def ping(host):\\n import os, platform\\n\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def ping(host):\\n import os, platform\\n\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def ping(host):\\n import os, platform\\n\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def ping(bot, sender, sendmsg, label, args):\\n\\n sendmsg(\\\"Pong!\\\")\",\n \"def onPing(self, payload):\",\n \"def ping():\\n return json_response({\\n 'ping': 'pong',\\n 'version': __version__,\\n 'imgapi': False,\\n })\",\n \"def Ping(ctx,\\n attempts = None,\\n hosts = None,\\n total_timeout_sec = None,\\n packet_size = None,\\n ping_timeout_msec = None):\\n \\\"\\\"\\\"Note: This method is available only through the per-node API endpoint 5.0 or later.\\\"\\\"\\\"\\n if ctx.element is None:\\n ctx.logger.error(\\\"You must establish at least one connection and specify which you intend to use.\\\")\\n exit()\\n\\n\\n\\n ctx.logger.info(\\\"\\\"\\\"attempts = \\\"\\\"\\\"+str(attempts)+\\\"\\\"\\\";\\\"\\\"\\\"+\\\"\\\"\\\"hosts = \\\"\\\"\\\"+str(hosts)+\\\"\\\"\\\";\\\"\\\"\\\"+\\\"\\\"\\\"total_timeout_sec = \\\"\\\"\\\"+str(total_timeout_sec)+\\\"\\\"\\\";\\\"\\\"\\\"+\\\"\\\"\\\"packet_size = \\\"\\\"\\\"+str(packet_size)+\\\"\\\"\\\";\\\"\\\"\\\"+\\\"\\\"\\\"ping_timeout_msec = \\\"\\\"\\\"+str(ping_timeout_msec)+\\\"\\\"\\\";\\\"\\\"\\\"+\\\"\\\")\\n try:\\n TestPingResult = ctx.element.test_ping(attempts=attempts, hosts=hosts, total_timeout_sec=total_timeout_sec, packet_size=packet_size, ping_timeout_msec=ping_timeout_msec)\\n except common.ApiServerError as e:\\n ctx.logger.error(e.message)\\n exit()\\n except BaseException as e:\\n ctx.logger.error(e.__str__())\\n exit()\\n\\n cli_utils.print_result(TestPingResult, ctx.logger, as_json=ctx.json, depth=ctx.depth, filter_tree=ctx.filter_tree)\",\n \"def testPing(self, minion_id):\\n self.console_manager.printRed(''.join([\\\"Ping minion id: \\\", minion_id]))\\n command = ['sudo', 'salt', minion_id, 'test.ping']\\n self.console_manager.runCommandFromShell(command)\\n return\",\n \"def ping() -> str:\\n return \\\"Server is here\\\"\",\n \"def ping(host,hidden=True):\\r\\n\\r\\n # Ping command count option as function of OS\\r\\n param = '-n' if platform.system()=='Windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n \\r\\n startupinfo = None\\r\\n if hidden:\\r\\n if os.name == 'nt':\\r\\n startupinfo = subprocess.STARTUPINFO()\\r\\n startupinfo.dwFlags |= subprocess._subprocess.STARTF_USESHOWWINDOW#subprocess.STARTF_USESHOWWINDOW\\r\\n #proc = subprocess.Popen(command, startupinfo=startupinfo) \\r\\n return subprocess.call(command, startupinfo=startupinfo) == 0\",\n \"def ping(self):\\n return True\",\n \"def ping(self):\\n return True\",\n \"def ping(self):\\n\\n rlog(1, self.name, 'sending ping')\\n try:\\n self.putonqueue(1, 'PING :%s' % self.server)\\n return 1\\n except Exception, ex:\\n rlog(10, self.name, \\\"can't send ping: %s\\\" % str(ex))\\n return 0\",\n \"def test_ctcpQuery_PING(self):\\n self.client.ctcpQuery_PING(self.user, self.channel, \\\"data\\\")\\n self.assertEqual(\\n self.client.methods, [(\\\"ctcpMakeReply\\\", (\\\"Wolf\\\", [(\\\"PING\\\", \\\"data\\\")]))]\\n )\",\n \"def test_ping():\\n\\n api = Client()\\n response = api.ping()\\n response.should.equal({})\",\n \"def ping():\\n # type: () -> str\\n return 'pong'\",\n \"def ping(host):\\n # Option for the number of packets as a function of\\n param = '-n' if platform.system().lower() == 'windows' else '-c'\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\n command = ['ping', param, '1', host]\\n return subprocess.call(command, stdout=subprocess.PIPE) == 0\",\n \"def ping(context):\\n\\n ssh_config = aws_infrastructure.tasks.ssh.SSHConfig.load(SSH_CONFIG_PATH)\\n documentdb_config = aws_infrastructure.tasks.library.documentdb.DocumentDBConfig.load(DOCUMENTDB_CONFIG_PATH)\\n\\n with aws_infrastructure.tasks.ssh.SSHClientContextManager(\\n ssh_config=ssh_config,\\n ) as ssh_client:\\n with aws_infrastructure.tasks.ssh.SSHPortForwardContextManager(\\n ssh_client=ssh_client,\\n remote_host=documentdb_config.endpoint,\\n remote_port=documentdb_config.port,\\n ) as ssh_port_forward:\\n client = MongoClient(\\n host=[\\n 'localhost'\\n ],\\n port=ssh_port_forward.local_port,\\n connect=True,\\n username=documentdb_config.admin_user,\\n password=documentdb_config.admin_password,\\n tls=True,\\n tlsInsecure=True,\\n )\\n\\n print(client.admin.command('ping'))\",\n \"async def ping(self, ctx):\\r\\n embed = discord.Embed(\\r\\n title = \\\"Ping\\\",\\r\\n description = \\\"Pinging...\\\",\\r\\n color = Config.MAINCOLOR\\r\\n )\\r\\n t1 = time.perf_counter()\\r\\n msg = await ctx.send(embed = embed)\\r\\n t2 = time.perf_counter()\\r\\n embed = discord.Embed(\\r\\n title = \\\"🏓 Pong!\\\",\\r\\n description = f\\\"API latency is {round((t2 - t1) * 1000)}ms\\\\nHost latency is {round(self.bot.latency * 1000, 2)}ms\\\",\\r\\n color = Config.MAINCOLOR\\r\\n )\\r\\n await msg.edit(embed = embed)\",\n \"async def test_app_ping(cli):\\n resp = await cli.get('/monitor/ping')\\n assert resp.status == 200\",\n \"def ping(self, datacenter=None, network=None):\\r\\n resp = requests.get('%s/v1/ping' %\\r\\n self.get_endpoint(datacenter, network))\\r\\n resp.raise_for_status()\\r\\n return True\",\n \"def ping(self,\\n *opts, # type: PingOptions\\n **kwargs # type: Any\\n ) -> PingResult:\\n return super().ping(*opts, **kwargs)\",\n \"def ping(host):\\n\\n # Option for the number of packets as a function of\\n param = '-n' if platform.system().lower()=='windows' else '-c'\\n\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\n command = ['ping', param, '1', host]\\n\\n return subprocess.call(command) == 0\",\n \"def ping(host):\\n\\n # Option for the number of packets as a function of\\n param = '-n' if platform.system().lower()=='windows' else '-c'\\n\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\n command = ['ping', param, '1', host]\\n\\n return subprocess.call(command) == 0\",\n \"def ping(host):\\r\\n\\r\\n # Option for the number of packets as a function of\\r\\n param = '-n' if platform.system().lower()=='windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n\\r\\n return subprocess.call(command) == 0\",\n \"def ping(self, cmd):\\n cmd.finish(\\\"text='Present and (probably) well'\\\")\",\n \"def icmp_ping(host, timeout=1):\\n result = Subprocess.execute(\\n \\\"ping -c 1 -W '{timeout:d}' '{host:s}'\\\".format(\\n host=host, timeout=timeout))\\n return result.exit_code == 0\",\n \"def ping(self, cmd):\\n\\n cmd.finish(\\\"text='Present and (probably) well'\\\")\",\n \"def ping():\\n return jsonify({'response': 'pong'}), 200\",\n \"def ping(self):\\n response = self._request(\\\"GET\\\", [ROUTE_PING])\\n\\n if response.status_code == 200:\\n logging.info(\\\"OK\\\")\\n return True\\n logging.error(\\\"FAILED\\\")\\n return False\",\n \"def ping(host):\\r\\n\\r\\n # Option for the number of packets as a function of\\r\\n param = '-n' if platform.system().lower() == 'windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n\\r\\n return subprocess.call(command) == 0\",\n \"def ping(metaServer):\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n sock.connect((metaServer.node, metaServer.port))\\n req = \\\"PING\\\\r\\\\nVersion: KFS/1.0\\\\r\\\\nCseq: 1\\\\r\\\\n\\\\r\\\\n\\\"\\n sock.send(req)\\n sockIn = sock.makefile('r')\\n for line in sockIn:\\n if line.find('Down Servers:') == 0:\\n if (len(line.split(':')) < 2):\\n break\\n pos = line.find(':')\\n downNodes = line[pos+1:].strip()\\n break\\n \\n if line.find('Servers:') != 0:\\n continue\\n nodes = line.split(':')[1].strip()\\n processUpNodes(nodes)\\n\\n gUpServers.sort()\\n sock.close()\",\n \"def ping(self):\\n return self.hub.ping()\",\n \"def ping(self,dest):\\n\\t\\tself.tn.write('ping -c 4 %s\\\\n'%(dest))\\n\\t\\tself.tn.write('exit\\\\n')\\n\\t\\tresp = self.tn.read_all()\\n\\t\\treturn resp\",\n \"def ping():\\r\\n health1 = ScoringService.get_model1() is not None # You can insert a health check here\\r\\n ping_response = \\\"Docker for Discover non prime users\\\"\\r\\n status = 200 if (health1) else 404\\r\\n return flask.Response(response=ping_response, status=status, mimetype='application/json')\",\n \"def ping(self, cookie = 0xDEC0DE, async = False):\\n\\n\\t\\tdata = pack(\\\"!bL\\\", 0, cookie)\\n\\t\\tself._send_message(\\\"PING\\\", data)\\n\\n\\t\\tif not async:\\n\\t\\t\\treturn EndpointSync(self, \\\"PING\\\").get_data()\",\n \"def ping():\\r\\n return make_response(\\\"pong!\\\", 200)\",\n \"def test_ping(self):\\n responses.add(responses.GET, \\\"http://localhost:3030/{0}/ping\\\".format(\\\"$\\\"), \\\"2017-09-18T11:41:19.915+00:00\\\", status=200)\\n fuseki = GraphStore()\\n result = fuseki._graph_health()\\n self.assertTrue(result)\",\n \"def f_ping(jenni, input):\\n jenni.reply('pong!')\",\n \"def ping(self):\\n return self.pingval\",\n \"async def ping(ctx):\\n latencies = {\\n \\\"websocket\\\": bot.latency,\\n }\\n\\n def comp_message():\\n msgs = []\\n for title in latencies:\\n msgs.append(f\\\"{title.title()}: {(latencies[title] * 1000):.0f}ms\\\")\\n return '\\\\n'.join(msgs)\\n\\n start = time.perf_counter()\\n await ctx.respond(comp_message())\\n end = time.perf_counter()\\n\\n latencies[\\\"round trip\\\"] = end - start\\n\\n await ctx.edit(content=comp_message())\",\n \"async def ping_command(self, ctx):\\n ping = int(self.client.latency * 1000)\\n embed = Embed(\\n title=\\\"Pong!\\\", description=f\\\"My ping is {ping}ms.\\\", color=Color.green()\\n )\\n await ctx.send(embed=embed)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68399215","0.68399215","0.68399215","0.68399215","0.6729562","0.67104983","0.6690351","0.6629603","0.66143537","0.6611325","0.6556811","0.64874136","0.64824206","0.64485735","0.64286697","0.63662034","0.63428634","0.6277983","0.6233143","0.6226974","0.6199807","0.6182159","0.61737955","0.61230963","0.6111268","0.6098913","0.6094957","0.60764873","0.6074028","0.60520816","0.60434866","0.6012394","0.5996523","0.5985038","0.5974258","0.59500706","0.5946921","0.5935215","0.5933529","0.59304225","0.593013","0.5923652","0.5913062","0.59077334","0.59037787","0.59004676","0.5894454","0.5893075","0.58720195","0.5860042","0.5840911","0.5839321","0.5816206","0.5809304","0.5808614","0.5808353","0.57995933","0.57813257","0.5769751","0.57691234","0.57691234","0.57691234","0.5757175","0.57424617","0.5732012","0.572555","0.57251","0.57220304","0.5687184","0.56600374","0.56600374","0.5658202","0.5658092","0.5657226","0.56513166","0.56496835","0.5648955","0.56433475","0.5630163","0.56282336","0.562474","0.56036496","0.56036496","0.55881613","0.55836236","0.55734026","0.5569175","0.5566667","0.5566647","0.55567926","0.55418557","0.55409205","0.553844","0.5517828","0.5517123","0.5514739","0.5508139","0.55068094","0.5500453","0.5488647","0.54835707"],"string":"[\n \"0.68399215\",\n \"0.68399215\",\n \"0.68399215\",\n \"0.68399215\",\n \"0.6729562\",\n \"0.67104983\",\n \"0.6690351\",\n \"0.6629603\",\n \"0.66143537\",\n \"0.6611325\",\n \"0.6556811\",\n \"0.64874136\",\n \"0.64824206\",\n \"0.64485735\",\n \"0.64286697\",\n \"0.63662034\",\n \"0.63428634\",\n \"0.6277983\",\n \"0.6233143\",\n \"0.6226974\",\n \"0.6199807\",\n \"0.6182159\",\n \"0.61737955\",\n \"0.61230963\",\n \"0.6111268\",\n \"0.6098913\",\n \"0.6094957\",\n \"0.60764873\",\n \"0.6074028\",\n \"0.60520816\",\n \"0.60434866\",\n \"0.6012394\",\n \"0.5996523\",\n \"0.5985038\",\n \"0.5974258\",\n \"0.59500706\",\n \"0.5946921\",\n \"0.5935215\",\n \"0.5933529\",\n \"0.59304225\",\n \"0.593013\",\n \"0.5923652\",\n \"0.5913062\",\n \"0.59077334\",\n \"0.59037787\",\n \"0.59004676\",\n \"0.5894454\",\n \"0.5893075\",\n \"0.58720195\",\n \"0.5860042\",\n \"0.5840911\",\n \"0.5839321\",\n \"0.5816206\",\n \"0.5809304\",\n \"0.5808614\",\n \"0.5808353\",\n \"0.57995933\",\n \"0.57813257\",\n \"0.5769751\",\n \"0.57691234\",\n \"0.57691234\",\n \"0.57691234\",\n \"0.5757175\",\n \"0.57424617\",\n \"0.5732012\",\n \"0.572555\",\n \"0.57251\",\n \"0.57220304\",\n \"0.5687184\",\n \"0.56600374\",\n \"0.56600374\",\n \"0.5658202\",\n \"0.5658092\",\n \"0.5657226\",\n \"0.56513166\",\n \"0.56496835\",\n \"0.5648955\",\n \"0.56433475\",\n \"0.5630163\",\n \"0.56282336\",\n \"0.562474\",\n \"0.56036496\",\n \"0.56036496\",\n \"0.55881613\",\n \"0.55836236\",\n \"0.55734026\",\n \"0.5569175\",\n \"0.5566667\",\n \"0.5566647\",\n \"0.55567926\",\n \"0.55418557\",\n \"0.55409205\",\n \"0.553844\",\n \"0.5517828\",\n \"0.5517123\",\n \"0.5514739\",\n \"0.5508139\",\n \"0.55068094\",\n \"0.5500453\",\n \"0.5488647\",\n \"0.54835707\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17499,"cells":{"query":{"kind":"string","value":"Ansible module to verify IP reachability using Ping RPC over NETCONF."},"document":{"kind":"string","value":"def main():\n module = AnsibleModule(\n argument_spec=dict(\n host=dict(type='str', required=True),\n destination=dict(type='str', required=True),\n repeat_count=dict(type='int', default=5),\n vrf_name=dict(type='str'),\n min_success_rate=dict(type='int', default=100)\n ),\n supports_check_mode=True\n )\n\n if module.check_mode:\n module.exit_json(changed=False)\n\n try:\n retvals = ping(module.params['host'],\n module.params['destination'],\n module.params['repeat_count'],\n module.params['vrf_name'])\n except Exception as exc:\n module.fail_json(msg='Reachability validation failed ({})'.format(exc))\n\n retvals['changed'] = False\n\n if retvals['success_rate'] >= module.params['min_success_rate']:\n module.exit_json(**retvals)\n else:\n module.fail_json(msg=('Success rate lower than expected ({}<{})').\n format(retvals['success_rate'],\n module.params['min_success_rate']))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ping():\n return ping_response()","def ping():\n return ping_response()","def ping():\n return ping_response()","def ping():\n return ping_response()","def ip_check():\n hosts = []\n valid_hosts = []\n for item in sys.argv:\n if '@' in item:\n hosts.append(item)\n for i in hosts:\n host = i.split('@')[1].split(':')[0]\n command = os.system('ping -c 1 '+host+' > /dev/null')\n if command == 0:\n valid_hosts.append(i)\n if valid_hosts:\n path_check(valid_hosts)","def test_ping(self):\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\n assert status == 0","def icmp_probe(self, ip):\n\n\t\tcmd = 'ping %s -n 10' % ip\n\t\tp = Popen(cmd, shell=True, stdin=PIPE, stderr=PIPE, stdout=PIPE)\n\t\tres = p.stdout.read()\n\n\t\tres = res.decode()\n\t\tif len(p.stderr.read()) == 0:\n\t\t\tif 'Destination host unreachable' in res:\n\t\t\t\treturn False\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","def ping():\n requestor = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\n logger.info(f\"Health check requested by ip='{requestor}'\")\n return make_response(\n jsonify(status=\"Serving\",\n body=\"pong\"), 200)","async def ping(self, ctx, ip):\n\n # Check for valid IP else do DNS lookup\n valid_ip = re.compile(\"[0-9]{,3}\\.[0-9]{,3}\\.[0-9]{,3}\")\n valid_hostname = re.compile(\".*\\.[a-zA-Z]{2,}\")\n valid = False\n\n if valid_ip.match(ip):\n valid = True\n elif valid_hostname.match(ip):\n valid = True\n try:\n await self.bot.say('Doing DNS lookup...')\n ip = socket.gethostbyname(ip)\n\n if valid == True:\n start = time.time()\n response = os.system(\"sudo ping -c 1 -w3 \" + ip)\n duration = time.time() - start\n duration = round(duration * 1000, 0)\n if response == 0:\n await self.bot.say(ip + ' is up and responding in ' +\n str(duration) + 'ms.')\n else:\n await self.bot.say(ip + ' is not reachable.')\n else:\n await self.bot.say(ip + ' is not a valid IP or Domain.')\n\n except socket.gaierror:\n await self.bot.say('Whoops! That Address cant be resolved!')","def verify_dot1x(task):\n # run \"show dot1x all\" on each host\n sh_dot1x = task.run(task=netmiko_send_command, command_string=\"show dot1x all\")\n # TTP template for dot1x status\n dot1x_ttp_template = \"Sysauthcontrol {{ status }}\"\n # magic TTP parsing\n parser = ttp(data=sh_dot1x.result, template=dot1x_ttp_template)\n parser.parse()\n dot1x_status = json.loads(parser.result(format=\"json\")[0])\n\n # write dot1x verification report for each host\n with open(f\"output/{task.host}_dot1x_verified.txt\", \"w+\") as file:\n file.write(sh_dot1x.result)\n\n # print dot1x status\n c_print(f\"*** {task.host} dot1x status: {dot1x_status[0]['status']} ***\")","def check_vm_connectivity(env, os_conn, vm_keypair=None, timeout=4 * 60):\n servers = os_conn.get_servers()\n for server1 in servers:\n ips_to_ping = [settings.PUBLIC_TEST_IP]\n for server2 in servers:\n if server1 == server2:\n continue\n ips_to_ping += os_conn.get_nova_instance_ips(\n server2).values()\n check_ping_from_vm(env, os_conn, server1, vm_keypair, ips_to_ping,\n timeout=timeout)","def ping(self):\n raise AssertionError(\"Ping function is not implemented\")","def ping_ip(ip_address, count):\n reply = subprocess.run(\n f\"ping -c {count} -n {ip_address}\",\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n encoding=\"utf-8\",\n )\n if reply.returncode == 0:\n return True\n else:\n return False","def check_vpn_interface():\n return validate_vpn_interface(call_command('netstat -i')[0].split('\\n'))","def checkPingVMStatus(self, conn, vmHost, hostUser, hostPass, vmNames):\n #temp - do a simple ping test to each vm\n hypervisor = libclient.SfClient()\n\n try:\n hypervisor.Connect(vmHost, hostUser, hostPass)\n mylog.info(\"The connection to the hypervisor has been established\")\n except libclient.ClientError as e:\n mylog.error(\"There was an error connecting to the hypervisor. Message: \" + str(e))\n #return False\n\n #get the list of VM MAC and IP addresses\n retcode, stdout, stderr = hypervisor.ExecuteCommand(\"cat /var/lib/libvirt/dnsmasq/default.leases\")\n\n ip_list = []\n mac_list = []\n mac_ip_list = []\n full_info = []\n\n #split the info\n stdout = stdout.split()\n for element in stdout:\n if libsf.IsValidIpv4Address(element):\n ip_list.append(element)\n if self.isValidMACAddress(element):\n mac_list.append(element)\n\n #make sure there are the same number of MAC and IP addresses\n if len(mac_list) == len(ip_list):\n for x in xrange(0, len(mac_list)):\n temp_info = mac_list[x], ip_list[x]\n mac_ip_list.append(temp_info)\n\n\n #match VM name with mac address\n for name in vmNames:\n try:\n vm = conn.lookupByName(name)\n except libvirt.libvirtError as e:\n mylog.error(str(e))\n xml = ElementTree.fromstring(vm.XMLDesc(0))\n\n for elem in xml.iterfind('devices/interface/mac'):\n for addr in mac_ip_list:\n if elem.attrib.get('address') == addr[0]:\n temp = addr[0], addr[1], name\n full_info.append(temp)\n\n #temp - try to ping all the VMs \n for ip in full_info:\n recode, stdout, stderr = hypervisor.ExecuteCommand(\"ping -n -i 0.2 -c 3 -W 1 -q \" + ip[1])\n if recode == 0:\n mylog.info(\"Was able to ping \" + ip[2])\n else:\n mylog.error(\"Was not able to ping \" + ip[2])\n return False\n return True","def test_ip(response):\n \n # from comeon_core import update\n ip = getIP()\n print(ip)\n #init_db(engine)\n #update()\n assert True","def _checknet():\n exit_code = os.system('ping -c 1 www.baidu.com 1>/dev/null 2>&1')\n return exit_code","def _get_ipaddress(node):\n if \"ipaddress\" not in node:\n with settings(hide('stdout'), warn_only=True):\n output = sudo('ohai ipaddress')\n if output.succeeded:\n node['ipaddress'] = json.loads(output)[0]\n return True\n return False","def ping(self) -> None:\n ...","def ping(host):\r\n \r\n # Ping command count option as function of OS\r\n param = '-n' if system_name().lower()=='windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n\r\n process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) \r\n streamdata = process.communicate()[0]\r\n #print_util.print_std(streamdata)\r\n streamdata = string_util.decode_chs_str(streamdata)\r\n print_util.print_std(streamdata)\r\n if system_name().lower()=='windows':\r\n #On windows use this to check ping pass or fail.\r\n if 'TTL=' in str(streamdata):\r\n return True\r\n else:\r\n return False\r\n return process.returncode==0","async def ping(self):\n uri = \"/fapi/v1/ping\"\n success, error = await self.request(\"GET\", uri)\n return success, error","def ip_status(ip, rnge):\n\tactive = []\n\tdown = []\n\ttimed_out = []\n\t\n\t\"\"\" Get the first 3 fields of IP \"\"\"\n\taddres = ip.split(\".\")[:3]\n\tne_ip = \".\".join(addres)\n\t\n\t\"\"\" Get the last field of IP \"\"\"\n\tlst_field = ip.split(\".\")[3]\n\t\n\tif rnge == \"0\" or rnge == \"\":\n\t\trnge = 1\n\t\n\tif 0 <= int(rnge) <= 255 and ip_pattern.match(ip):\n\t\t\"\"\" Calculate the actual range \"\"\"\n\t\tactual_rnge = int(lst_field) + int(rnge)\n\n\t\t\"\"\" Range of IP address to be pinged \"\"\"\n\t\t\"\"\" Right now it starts from 0 through 10 but can be changed in the below line \"\"\"\n\t\n\t\tfor i in range(int(lst_field), actual_rnge):\t\t\n\t\t\n\t\t\tip_fin = ne_ip + \".\" + str(i)\n\t\t\tstatus = sp.check_output([\"ping \", ip_fin])\n\t\t\n\t\t\tif pattern1.search(status) :\n\t\t\t\tdown.append(ip_fin)\n\t\t\telif pattern2.search(status):\n\t\t\t\ttimed_out.append(ip_fin)\n\t\t\telse:\n\t\t\t\tactive.append(ip_fin)\n\t\n\t\tresult = \"\\nActive IP addresses are: \" + str(active) + \"\\n\\n\" + \"Unreachable IP: \" + str(down) + \"\\n\\n\" + \"Timed out IP's: \" + str(timed_out)\n\t\treturn result\n\t\n\telse:\n\t\treturn \"1) Range should be in between 0 and 255 inclusive. \\n2) Check the IP format.\"","def rpc_ping(self):\n\t\treturn True","def test_check_nip(client):\n is_assigned, request_id = client.check_nip(\n \"8655104670\", \"41146786026458860703735932\"\n )\n\n assert is_assigned","def check_ip_fwd(duthosts, all_cfg_facts, nbrhosts, tbinfo):\n for porttype in [\"ethernet\", \"portchannel\"]:\n for version in [4, 6]:\n\n ports = pick_ports(duthosts, all_cfg_facts, nbrhosts, tbinfo, port_type_a=porttype, version=version)\n\n for ttl, size in [(2, 64), (1, 1450)]:\n # local interfaces\n check_packet(sonic_ping, ports, 'portB', 'portA', size=size, ttl=ttl, ttl_change=0)\n\n # local neighbors\n check_packet(sonic_ping, ports, 'portA', 'portA',\n dst_ip_fld='nbr_ip', size=size, ttl=ttl, ttl_change=0)\n\n vm_host_to_A = nbrhosts[ports['portA']['nbr_vm']]['host']\n\n check_packet(eos_ping, ports, 'portD', 'portA', dst_ip_fld='my_lb4096_ip', src_ip_fld='nbr_lb',\n dev=vm_host_to_A, size=size, ttl=ttl)\n\n # loopbacks\n check_packet(sonic_ping, ports, 'portA', 'portA', dst_ip_fld='nbr_lb', size=size, ttl=ttl, ttl_change=0)\n\n # inband\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='inband', size=size, ttl=ttl, ttl_change=0)\n\n # DUT loopback\n # these don't decrement ttl\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='my_lb_ip', dst_ip_fld='my_ip', size=size,\n ttl=ttl, ttl_change=0)\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='my_lb_ip', dst_ip_fld='nbr_ip', size=size,\n ttl=ttl, ttl_change=0)\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='my_lb_ip', dst_ip_fld='nbr_lb', size=size,\n ttl=ttl, ttl_change=0)\n\n vm_host_to_A = nbrhosts[ports['portA']['nbr_vm']]['host']\n check_packet(eos_ping, ports, 'portA', 'portA', dst_ip_fld='my_lb4096_ip', src_ip_fld='nbr_lb',\n dev=vm_host_to_A, size=size, ttl=ttl, ttl_change=0)\n\n # end to end\n vm_host_to_A = nbrhosts[ports['portA']['nbr_vm']]['host']\n check_packet(eos_ping, ports, 'portB', 'portA', dst_ip_fld='nbr_lb', src_ip_fld='nbr_lb',\n dev=vm_host_to_A, size=size, ttl=ttl)\n check_packet(eos_ping, ports, 'portC', 'portA', dst_ip_fld='nbr_lb', src_ip_fld='nbr_lb',\n dev=vm_host_to_A, size=size, ttl=ttl)\n check_packet(eos_ping, ports, 'portD', 'portA', dst_ip_fld='nbr_lb', src_ip_fld='nbr_lb',\n dev=vm_host_to_A, size=size, ttl=ttl)","def test_ip_addresses_exists():\n load_ips()\n validate_names()","def checklan(ipaddr, network):\n return True","def Ping(self): # real signature unknown; restored from __doc__\n pass","def ping():\n api_online = bool(check_url(\"https://rest.ensembl.org/info/ping?\"))\n vertebrate_url_online = bool(check_url(\"http://ftp.ensembl.org\"))\n other_url_online = bool(check_url(\"http://ftp.ensemblgenomes.org\"))\n return api_online and vertebrate_url_online and other_url_online","def test_ip(self):\n ##Todo: Improve this check\n ip = socket.gethostbyname(socket.gethostname())\n ip = [int(i) for i in ip.split('.')]\n assert len(ip) == 4\n assert ip[0] == 10\n assert ip[1] == 137\n assert ip[2] == 1\n assert ip[3] >= 1 and ip[3] <= 255","def tcp_ping_nodes(self, timeout=20.0):\n for node in self.all_instances:\n if node.instance_type in [\n InstanceType.RESILIENT_SINGLE,\n InstanceType.SINGLE,\n InstanceType.DBSERVER,\n ]:\n node.check_version_request(timeout)","async def test_app_ping(cli):\n resp = await cli.get('/monitor/ping')\n assert resp.status == 200","def ping(cmd, *args, **argv):\n import os\n context = argv[\"context\"]\n \n def count(num):\n if str(num) == str(int(num)):\n if num < 0:\n context.write(\"%s: bad number of packets to transmit.\" % cmd)\n else:\n return num\n else:\n context.write(\"%s: can't set unicast time-to-live: Unknown host\" % cmd)\n return\n\n ping_p = {\"-c\":count}\n\n def isipaddress(ip):\n import socket\n try:\n ipa = socket.gethostbyname(ip)\n return ipa\n except:\n context.write(\"ping: unknown host %s\" % ip)\n return None\n\n def doping(cmd, sign, sign_param, ipaddress):\n if sign in ping_p.keys():\n # #\n pass\n else:\n context.write(\"connect: Unknown host\")\n return \n \n\tif ping_p[sign](sign_param):\n # #\n pass\n else:\n return \n if isipaddress(ipaddress):\n ip = isipaddress(ipaddress)\n else:\n return\n try:\n os.system(str(cmd) + \" \" + str(sign) + \" \" + str(sign_param) + \" \" + str(ip))\n except:\n context.write(\"has some errors in ping command\")\n return \n\n length = len(args)\n \n if length == 0:\n helpinfo = context.resolver.get_func_doc(getattr(context.resolver.get_module(cmd), cmd))\n helpinfo_format = helpinfo[\"format\"].rstrip()\n helpinfo_format = helpinfo_format.lstrip()\n if helpinfo_format == \"\":\n return\n context.write(helpinfo_format)\n\n elif length < 3:\n cmd_real = context.resolver.has_command(args[0], cmd)\n if cmd_real != None:\n cmds = cmd.split()\n cmd_n = \"_\".join(cmds)\n modulename = cmd_n + \"_\" + cmd_real\n module = context.resolver.get_module(modulename)\n func = getattr(module, modulename)\n func(cmd + \" \" + cmd_real, context = context)\n else:\n doping(cmd, \"-c\", 4, args[0])\n\n else:\n\tif args[1] == \"0\":\n\t os.system(str(cmd) + \" \" + str(args[2]))\n\telse:\n doping(cmd, args[0], args[1], args[2])","def ping(self):\n pass","def check(self, target, port):\n pass","def icmp_ping(ip_addr, timeout = 6, count = 1024):\n for i in range(count):\n print('Ping wait:')\n try:\n delay = ping_wait(ip_addr, timeout)\n except socket.gaierror as e:\n print('Failed. (socket error: %s)' % e[1])\n break\n\n if delay == None:\n print('Failed. (timeout within %s second.)' % timeout)\n else:\n print('get ICMP in %0.4f ms' % (delay * 1000))","async def ping(self) -> APIReturn:\n return await self._request(\"GET\", \"/ping\")","def testIP(IP):\n \"\"\" will feed this an array with mp.Pool.map() \"\"\"\n IPup = []\n IPdown = []\n with open(os.devnull, \"w\") as fnull:\n if subprocess.call(['ping','-c 1 -W 1',IP,],stdout=fnull, stderr=fnull)==0:\n print 'host %s is UP' % IP\n return IP\n else:\n print 'host %s is DOWN' % IP\n return None","def ping():\r\n health1 = ScoringService.get_model1() is not None # You can insert a health check here\r\n ping_response = \"Docker for Discover non prime users\"\r\n status = 200 if (health1) else 404\r\n return flask.Response(response=ping_response, status=status, mimetype='application/json')","def ping():\n\treturn HTTPResponse(status=200)","def ping(self):\n return True","def ping(self):\n return True","def test_check_ip_on_whitelist_true(self):\n\n ip_name = 'mail-ed1-f51.google.com'\n\n result = check_ip_on_whitelist(ip_name, self.pattern_ip)\n\n self.assertTrue(result)","def ping():\n logging.info(\"Ping received...\")\n\n health = Predictor.load() is not None\n\n status = 200 if health else 404\n return Response(response=\"\\n\", status=status, mimetype=\"application/json\")","def test_ping():\n\n api = Client()\n response = api.ping()\n response.should.equal({})","def icmp_ping(ip_addr, timeout=0.5, count=4):\n is_connect = 1\n\n for i in range(count):\n try:\n delay = ping_once(ip_addr, timeout)\n except socket.gaierror, e:\n print \"failed. (socket error: '%s')\" % e[1]\n if delay == None:\n print 'failed. (timeout within %s second.)' % timeout\n is_connect = 0\n else:\n pass\n result = [ip_addr, round(delay, 4), is_connect]\n return result","def test_ping(self):\n response = self.client.get(reverse(\"api_hello:ping\"))\n self.assertTrue(response.json()[\"status\"])","def ping_to_ip(self, ip, return_output=False, other_opt='', size='56', count='5', timewait='1'):\n host = self.inputs.host_data[self.vm_node_ip]\n output = ''\n fab_connections.clear()\n af = get_af_type(ip)\n try:\n vm_host_string = '%s@%s' % (self.vm_username, self.local_ip)\n if af is None:\n cmd = \"\"\"python -c 'import socket;socket.getaddrinfo(\"%s\", None, socket.AF_INET6)'\"\"\" % ip\n output = remote_cmd(\n vm_host_string, cmd, gateway_password=host['password'],\n gateway='%s@%s' % (host['username'], self.vm_node_ip),\n with_sudo=True, password=self.vm_password,\n logger=self.logger\n )\n util = 'ping' if output else 'ping6'\n else:\n util = 'ping6' if af == 'v6' else 'ping'\n\n cmd = '%s -s %s -c %s -W %s %s %s' % (\n util, str(size), str(count), str(timewait), other_opt, ip\n )\n\n output = remote_cmd(\n vm_host_string, cmd, gateway_password=host['password'],\n gateway='%s@%s' % (host['username'], self.vm_node_ip),\n with_sudo=True, password=self.vm_password,\n logger=self.logger\n )\n self.logger.debug(output)\n if return_output is True:\n return output\n except Exception, e:\n self.logger.exception(\n 'Exception occured while trying ping from VM')\n return False\n\n expected_result = ' 0% packet loss'\n try:\n if expected_result not in output:\n self.logger.warn(\"Ping to IP %s from VM %s failed\" %\n (ip, self.vm_name))\n return False\n else:\n self.logger.info('Ping to IP %s from VM %s passed' %\n (ip, self.vm_name))\n return True\n except Exception as e:\n self.logger.warn(\"Got exception in ping_to_ip:%s\" % (e))\n return False","def ping():\n return '{}'","def checkIP(self):\n\t\tself.get(\"https://ifconfig.me/\")\n\t\treturn self.findId(\"ip_address\").text","def validateIP():\n try:\n s = socket.inet_aton(args.target)\n except socket.error:\n print(\"\")\n print(f\"{bad_cmd} Bad IP address\")\n print(\"\")\n sys.exit()","def ping(self):\n return 'ping'","def ping(self):\n response = self._request(\"GET\", [ROUTE_PING])\n\n if response.status_code == 200:\n logging.info(\"OK\")\n return True\n logging.error(\"FAILED\")\n return False","def icmp_ping(host, timeout=1):\n result = Subprocess.execute(\n \"ping -c 1 -W '{timeout:d}' '{host:s}'\".format(\n host=host, timeout=timeout))\n return result.exit_code == 0","def vt_ip_check(ip, vt_api):\n if not is_IPv4Address(ip):\n return None\n\n url = 'https://www.virustotal.com/vtapi/v2/ip-address/report'\n parameters = {'ip': ip, 'apikey': vt_api}\n response = requests.get(url, params=parameters)\n try:\n return response.json()\n except ValueError:\n return None","def verify_ipn(data):\n data = dict(data)\n data['cmd'] = '_notify-validate'\n resp = requests.post(app.config['PAYPAL']['endpoint'], data=data)\n if resp.text == 'VERIFIED':\n return True\n return False","def ip_command():\n # 1. Get input host from Demisto\n ip = demisto.args().get('ip')\n if not is_ip_valid(ip):\n return_error('Invalid IP address, Please retry with a valid IP address')\n # 2. Get the host reputation from SlashNext API\n response = ip_lookup(ip=ip)\n if response.get('errorNo') != 0:\n return\n # 3. Parse and format the response\n dbot_score_cont, ip_cont = get_dbot_std_context(\n ip, 'IP', response.get('threatData').get('verdict'), response.get('threatData').get('threatType'))\n\n snx_ioc_cont = get_snx_host_ioc_context(ip, 'IP', response.get('threatData'))\n\n ec = {\n 'SlashNext.IP(val.Value === obj.Value)': snx_ioc_cont,\n 'DBotScore': dbot_score_cont,\n 'IP': ip_cont\n }\n\n title = 'SlashNext Phishing Incident Response - IP Lookup\\n' \\\n '##### ip = {}'.format(ip)\n\n md = tableToMarkdown(\n title,\n snx_ioc_cont,\n ['Value',\n 'Type',\n 'Verdict',\n 'ThreatStatus',\n 'ThreatName',\n 'ThreatType',\n 'FirstSeen',\n 'LastSeen']\n )\n\n return_outputs(md, ec, snx_ioc_cont)","def online_check():\n try_first_ips = [\n \"216.58.213.238\", # google\n \"8.8.8.8\", # google\n \"8.8.4.4\", # google\n \"46.228.47.115\", # yahoo\n ]\n last_resort_ips = [ # dns root servers\n \"198.41.0.4\",\n \"192.228.79.201\",\n \"192.33.4.12\",\n \"128.8.10.90\",\n \"192.203.230.10\",\n \"192.5.5.241\",\n \"192.112.36.4\",\n \"128.63.2.53\",\n \"192.36.148.17\",\n \"192.58.128.30\",\n \"193.0.14.129\",\n \"198.32.64.12\",\n \"202.12.27.33\"\n ]\n\n iplists = []\n iplists.append(try_first_ips)\n iplists.append(rand_ips(max_num=50))\n iplists.append(last_resort_ips)\n\n return any(can_ping_host(ip) for ip in chain(*iplists))","def test_ping():\n # Build topology\n platform = DockerPlatform(None, None)\n platform.pre_build()\n\n h1 = Node(identifier='hs1', type='host')\n h2 = Node(identifier='hs2', type='host')\n s1 = Node(identifier='sw1', type='host')\n s2 = Node(identifier='sw2', type='host')\n\n hs1 = platform.add_node(h1)\n hs2 = platform.add_node(h2)\n sw1 = platform.add_node(s1)\n sw2 = platform.add_node(s2)\n\n s1p1 = BidirectionalPort(identifier='3')\n s1p2 = BidirectionalPort(identifier='4')\n platform.add_biport(s1, s1p1)\n platform.add_biport(s1, s1p2)\n\n s2p1 = BidirectionalPort(identifier='3')\n s2p2 = BidirectionalPort(identifier='4')\n platform.add_biport(s2, s2p1)\n platform.add_biport(s2, s2p2)\n\n h1p1 = BidirectionalPort(identifier='hs1-1')\n h2p1 = BidirectionalPort(identifier='hs2-1')\n platform.add_biport(h1, h1p1)\n platform.add_biport(h2, h2p1)\n\n link1 = BidirectionalLink(identifier='link1')\n platform.add_bilink((s1, s1p1), (h1, h1p1), link1)\n link2 = BidirectionalLink(identifier='link2')\n platform.add_bilink((s1, s1p2), (s2, s2p1), link2)\n link3 = BidirectionalLink(identifier='link3')\n platform.add_bilink((s2, s2p2), (h2, h2p1), link3)\n\n platform.post_build()\n\n # Ping test\n ###########\n\n # Configure IP and bring UP host 1 interfaces\n hs1('ip link set dev hs1-1 up')\n hs1('ip addr add 10.0.10.1/24 dev hs1-1')\n\n # Configure IP and bring UP host 2 interfaces\n hs2('ip link set dev hs2-1 up')\n hs2('ip addr add 10.0.30.1/24 dev hs2-1')\n\n # Configure IP and bring UP switch 1 interfaces\n sw1('ip link set dev 3 up')\n sw1('ip link set dev 4 up')\n\n sw1('ip addr add 10.0.10.2/24 dev 3')\n sw1('ip addr add 10.0.20.1/24 dev 4')\n\n # Configure IP and bring UP switch 2 interfaces\n sw2('ip link set dev 3 up')\n sw2('ip addr add 10.0.20.2/24 dev 3')\n\n sw2('ip link set dev 4 up')\n sw2('ip addr add 10.0.30.2/24 dev 4')\n\n # Set static routes in switches\n sw1('ip route add 10.0.30.0/24 via 10.0.20.2')\n sw2('ip route add 10.0.10.0/24 via 10.0.20.1')\n\n # Set gateway in hosts\n hs1('ip route add default via 10.0.10.2')\n hs2('ip route add default via 10.0.30.2')\n\n ping_result = hs1('ping -c 1 10.0.30.1')\n platform.destroy()\n assert '1 packets transmitted, 1 received' in ping_result","def _check_connection() -> bool:\n return bool(subprocess.check_output([\"hostname\", \"-I\"]))","def main():\n module = AnsibleModule(\n argument_spec=dict(\n pn_cliusername=dict(required=False, type='str'),\n pn_clipassword=dict(required=False, type='str', no_log=True),\n pn_switch_list=dict(required=False, type='list', default=[]),\n pn_vrrp_id=dict(required=False, type='str', default='18'),\n pn_vrrp_data=dict(required=False, type='str', default=''),\n )\n )\n\n global CHANGED_FLAG\n results = []\n message = ''\n switch_list = module.params['pn_switch_list']\n\n # Configure vrrp\n vrrp_data = module.params['pn_vrrp_data']\n if vrrp_data:\n vrrp_data = vrrp_data.replace(' ', '')\n vrrp_data_list = vrrp_data.split('\\n')\n for row in vrrp_data_list:\n if row.startswith('#'):\n continue\n else:\n elements = row.split(',')\n vlan_id = elements.pop(0).strip()\n gateway_ip = elements.pop(0).strip()\n primary_ip = elements.pop(0).strip()\n secondary_ip = elements.pop(0).strip()\n active_switch = elements.pop(0).strip()\n\n message += create_vlan(module, switch_list[0], vlan_id)\n\n for switch in switch_list:\n if switch == active_switch:\n vrrp_priority = '110'\n vrrp_ip = primary_ip\n else:\n vrrp_priority = '109'\n vrrp_ip = secondary_ip\n\n message += create_vrouter_interface(module, switch, vrrp_ip,\n gateway_ip, vlan_id,\n vrrp_priority)\n\n for switch in switch_list:\n replace_string = switch + ': '\n for line in message.splitlines():\n if replace_string in line:\n results.append({\n 'switch': switch,\n 'output': (line.replace(replace_string, '')).strip()\n })\n\n # Exit the module and return the required JSON.\n module.exit_json(\n unreachable=False,\n msg='Vrrp configuration succeeded',\n summary=results,\n exception='',\n failed=False,\n changed=True if True in CHANGED_FLAG else False,\n task='Configure vrrp'\n )","def check_agw_cloud_connectivity(c, timeout=10):\n with vagrant_connection(c, \"magma_deb\") as c_gw:\n with c_gw.cd(\"/usr/local/bin/\"):\n dev_utils.run_remote_command_with_repetition(c_gw, \"./checkin_cli.py\", timeout)","def checkIP(self, ip = None):\n\t\treturn os.system(\"ping -c 1 -w2 \" + ip + \" > /dev/null 2>&1\") == 0","def ping(self):\n return","def ping(host):\n\n # Option for the number of packets as a function of\n param = '-n' if platform.system().lower()=='windows' else '-c'\n\n # Building the command. Ex: \"ping -c 1 google.com\"\n command = ['ping', param, '1', host]\n\n return subprocess.call(command) == 0","def ping(host):\n\n # Option for the number of packets as a function of\n param = '-n' if platform.system().lower()=='windows' else '-c'\n\n # Building the command. Ex: \"ping -c 1 google.com\"\n command = ['ping', param, '1', host]\n\n return subprocess.call(command) == 0","def ping(host):\n # Option for the number of packets as a function of\n param = '-n' if platform.system().lower() == 'windows' else '-c'\n # Building the command. Ex: \"ping -c 1 google.com\"\n command = ['ping', param, '1', host]\n return subprocess.call(command, stdout=subprocess.PIPE) == 0","def test_ping(client):\n resp = client.get(url_for('cd2h_repo_project.ping'))\n\n assert resp.status_code == 200\n assert resp.get_data(as_text=True) == 'OK'","def ping(self):\n request = Empty()\n response = self.stub.Ping(request, timeout=5)","def cmd_net_contest():\n\n print(\"DNS: %s\" % contest.check_dns())\n print(\"FTP: %s\" % contest.check_ftp())\n print(\"SSH: %s\" % contest.check_ssh())\n print(\"HTTP: %s\" % contest.check_http())\n print(\"HTTPS: %s\" % contest.check_https())","def ping(host):\n\n # Ping parameters as function of OS\n parameters = \"-n 1\" if system_name().lower() == \"windows\" else \"-c 1\"\n\n # Pinging\n return system_call(\"ping \" + parameters + \" \" + host) == 0","def onPing(self, payload):","def ping(host):\r\n\r\n # Option for the number of packets as a function of\r\n param = '-n' if platform.system().lower()=='windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n\r\n return subprocess.call(command) == 0","def ping(host):\n\n # Ping command count option as function of OS\n param = '-n' if system_name().lower()=='windows' else '-c'\n\n # Building the command. Ex: \"ping -c 5 google.com\"\n command = ['ping', param, '5', host]\n\n # Pinging\n return system_call(command) == 0","def ping(event, context):\n logger.info(\"Ping requested.\")\n return _get_response(200, \"PONG!\")","def test_static_nat_on_ip_from_non_src_nat_ip_range(self):\n\n # Validate the following:\n # 1. Create a new public IP range and dedicate to a account\n # 2. Acquire a IP from new public range\n # 3. Enable static NAT on acquired IP from new range\n # 4. Create a firewall rule to open up the port\n # 5. Test SSH works to the VM\n\n self.services[\"extrapubliciprange\"][\"zoneid\"] = self.services[\"zoneid\"]\n self.public_ip_range = PublicIpRange.create(\n self.apiclient,\n self.services[\"extrapubliciprange\"]\n )\n self.cleanup.append(self.public_ip_range)\n logger.debug(\"Dedicating Public IP range to the account\");\n dedicate_public_ip_range_response = PublicIpRange.dedicate(\n self.apiclient,\n self.public_ip_range.vlan.id,\n account=self.account.name,\n domainid=self.account.domainid\n )\n ip_address = PublicIPAddress.create(\n self.apiclient,\n self.account.name,\n self.zone.id,\n self.account.domainid,\n self.services[\"virtual_machine\"]\n )\n self.cleanup.append(ip_address)\n # Check if VM is in Running state before creating NAT and firewall rules\n vm_response = VirtualMachine.list(\n self.apiclient,\n id=self.virtual_machine.id\n )\n\n self.assertEqual(\n isinstance(vm_response, list),\n True,\n \"Check list VM returns a valid list\"\n )\n\n self.assertNotEqual(\n len(vm_response),\n 0,\n \"Check Port Forwarding Rule is created\"\n )\n self.assertEqual(\n vm_response[0].state,\n 'Running',\n \"VM state should be Running before creating a NAT rule.\"\n )\n\n # Open up firewall port for SSH\n fwr = FireWallRule.create(\n self.apiclient,\n ipaddressid=ip_address.ipaddress.id,\n protocol=self.services[\"natrule\"][\"protocol\"],\n cidrlist=['0.0.0.0/0'],\n startport=self.services[\"natrule\"][\"publicport\"],\n endport=self.services[\"natrule\"][\"publicport\"]\n )\n self.cleanup.append(fwr)\n\n # Create Static NAT rule\n StaticNATRule.enable(\n self.apiclient,\n ip_address.ipaddress.id,\n self.virtual_machine.id,\n self.defaultNetworkId\n )\n\n try:\n logger.debug(\"SSHing into VM with IP address %s with NAT IP %s\" %\n (\n self.virtual_machine.ipaddress,\n ip_address.ipaddress.ipaddress\n ))\n self.virtual_machine.get_ssh_client(ip_address.ipaddress.ipaddress)\n except Exception as e:\n self.fail(\n \"SSH Access failed for %s: %s\" %\n (self.virtual_machine.ipaddress, e)\n )\n\n StaticNATRule.disable(\n self.apiclient,\n ip_address.ipaddress.id,\n self.virtual_machine.id\n )","def arping(iprange=\"10.0.1.0/24\"):\n\n conf.verb=0\n ans,unans=srp(Ether(dst=\"ff:ff:ff:ff:ff:ff\")/ARP(pdst=iprange),\n timeout=2)\n\n collection = []\n for snd, rcv in ans:\n result = rcv.sprintf(r\"%ARP.psrc% %Ether.src%\").split()\n collection.append(result)\n return collection","def status(NetPort):\n\t\n\tcommand = [_IPCONFIG, 'getifaddr', NetPort]\n\ttask = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t(stdout, stderr) = task.communicate()\n\treturn task.returncode","def check_ping(self):\n # Print ping status of all of your hosts, minimum padding of 8 spaces\n padding_size = max(len(max(self.hosts, key=len)) + 4, 8)\n print('{:{padding_size}}{}'.format('Host', 'Status', padding_size=padding_size))\n for host in self.hosts:\n # Get output of ping command\n output = str(Popen('ping -n 1 {}'.format(host), stdout=PIPE).communicate()[0])\n\n result = '{:{padding_size}}'.format(host, padding_size=padding_size)\n if 'unreachable' in output:\n result = result + 'Offline - unreachable'\n self.offline_hosts.append(host)\n elif 'could not find' in output:\n result = result + 'Offline - could not find'\n self.offline_hosts.append(host)\n elif 'transmit failed' in output:\n result = result + 'Offline - transmit failed'\n self.offline_hosts.append(host)\n elif 'timed out' in output:\n result = result + 'Offline - timed out'\n self.offline_hosts.append(host)\n else:\n result = result + 'Online'\n print(result)\n print()","def ping(host):\r\n\r\n # Option for the number of packets as a function of\r\n param = '-n' if platform.system().lower() == 'windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n\r\n return subprocess.call(command) == 0","def test_connectivity(self):\n \n for ip in self.IPs:\n try:\n s=pxssh.pxssh(options={\"PasswordAuthentication\" : \"no\"})\n s.login(ip, \"as\", ssh_key=\"~/.ssh/id_as_ed25519\")\n # send inocuous command\n s.sendline('true')\n self.assertTrue(s.prompt())\n s.logout()\n except pxssh.ExceptionPxssh as e:\n print 'Login to {} failed!, error: {}'.format(ip, e)\n self.assertTrue(False)\n self.assertTrue(True)","def ipsec_status(strongswan_obj, **kwargs):\r\n return strongswan_obj.ipsec_status_check(**kwargs)","def test_ping_from_neighbor(duthosts, enum_rand_one_per_hwsku_frontend_hostname, nbrhosts):\n duthost = duthosts[enum_rand_one_per_hwsku_frontend_hostname]\n cfg_facts = duthost.config_facts(host=duthost.hostname, source=\"running\")[\"ansible_facts\"]\n dut_ports = cfg_facts[\"PORT\"]\n portchannel_itfs = cfg_facts[\"PORTCHANNEL_INTERFACE\"]\n for portchannel, ip_list in list(portchannel_itfs.items()):\n portchannel_members = list(cfg_facts[\"PORTCHANNEL_MEMBER\"][portchannel].keys())\n hostname = dut_ports[portchannel_members[0]]['description'].split(':')[0]\n for nbr_hostname, nbrhost in list(nbrhosts.items()):\n if nbr_hostname != hostname:\n continue\n for ip in ip_list:\n ip = ip.split('/')[0]\n pytest_assert(nbrhost['host'].ping_dest(ip), \"{} ping port channel {} failed\".format(nbr_hostname, ip))","def main():\n\n # the AnsibleModule object will be our abstraction for working with Ansible.\n # This includes instantiation, a couple of common attr that will be the\n # args/params passed to the execution, as well as if the module\n # supports check mode\n module = AnsibleModule(\n argument_spec=dict(\n hostvars=dict(type='raw', required=True),\n report_timestamp=dict(type=str, required=False, default=''),\n registered_dict_name=dict(type=str, required=False, default=\"get_sas_host_details_results\"),\n include_hotfix_report=dict(type=bool, required=False, default=True),\n hotfix_url = dict(type=str, required=True),\n hotfix_master_file = dict(type=str, required=True)\n ),\n supports_check_mode=True\n )\n\n # get module parameters\n hostvars = module.params['hostvars']\n report_timestamp = module.params['report_timestamp']\n registered_dict_name = module.params['registered_dict_name']\n include_hotfix_report = module.params['include_hotfix_report']\n hotfix_url = module.params['hotfix_url']\n hotfix_master_file = module.params['hotfix_master_file']\n\n # Starting in Ansible 2.8.1, there is the potential for hostvars\n # to be passed as a byte string, if the dict is too large\n # This will convert the str back to a dict before proceeding\n if isinstance(hostvars, str):\n hostvars = ast.literal_eval(hostvars.decode())\n\n results = dict()\n results['sas_hosts'] = dict()\n results['created'] = report_timestamp\n\n for inventory_hostname, host_vars in hostvars.items():\n\n # set up returnable values\n unreachable = True\n failed = True\n failure_details = dict(\n msg=\"\",\n rc=0,\n stderr=\"\",\n stdout=\"\",\n )\n\n # get the host details dict\n host_details = host_vars.get(registered_dict_name)\n\n # check if the host has the registered dict\n if host_details is not None:\n\n # host details exist, so host was reachable\n unreachable = False\n\n # check if the host failed\n failed = host_details['failed']\n\n # if the module reported a failure, collect details\n if failed:\n failure_details['msg'] = host_details['msg']\n failure_details['rc'] = host_details['rc']\n failure_details['stderr'] = host_details['module_stderr']\n failure_details['stdout'] = host_details['module_stdout']\n else:\n # get module results\n host_results = host_details.get('sas_host_details')\n\n if host_results is not None:\n results['sas_hosts'].update(host_results)\n else:\n failed = True\n\n # if the results dict could not be found, mark the host as unreachable\n if failed or unreachable:\n host_groups = host_vars.get('group_names')\n\n if host_groups is not None and 'sas_all' in host_groups:\n hostname = host_vars.get('ansible_fqdn')\n if hostname is None or hostname == \"\":\n hostname = host_vars.get('ansible_hostname')\n if hostname is None or hostname == \"\":\n hostname = host_vars.get('ansible_host')\n if hostname is None or hostname == \"\":\n hostname = host_vars.get('inventory_hostname')\n if hostname is None or hostname == \"\":\n hostname = inventory_hostname\n\n try:\n host_groups.remove('sas_all')\n host_groups.remove('sas-all')\n except ValueError:\n pass # do nothing\n\n results['sas_hosts'][hostname] = dict(\n _id=hostname.replace('.', '-'),\n _unreachable=unreachable,\n _failed=failed,\n _failure_details=failure_details,\n ansible_host_groups=host_groups\n )\n else:\n pass # this host isn't in sas_all so there's no need to try and report on it\n\n ##################################################################################\n # This section will find all of the hotfixes available and add them to the report.\n ##################################################################################\n\n # There are a few data structures that are complicated enough to warrant a description:\n # fullReport\n # This will hold all of the data in a format condusive to printing it out in the final report. This is how\n # It is structured:\n # fullReport (dict):\n # key=Hot Fix Name, point to another dict:\n # key=\"released\", points to a string containing the release date of the hotfix.\n # key= \"installed\", points to a boolean that will reflect whether any of the packages used by this hotfix are installed on any of the machines in the deployment.\n # key=\"upToDate\", point to a boolean that will reflest whether ALL of the packages used by this hotfix are up to date on ALL of the machines in the deployment.\n # key=\"sasnote\", points to another dict:\n # key=SASNote number, points to the description of the SASNote.\n # key=\"package\", points to another dict:\n # key=\"platform\" , points to another dict:\n # key=OS, points to another dict:\n # key=\"version\", points to the string of the version of the package.\n # key=\"installed\", points to a boolean which reflects whether this package is installed on any machine in the deployment.\n # key=\"upToDate\", points to a boolean which reflects whether this package is up to data on ALL of the machines in the deployment.\n # key=\"os\", points to the fully qualified name of the operating system.\n # key=\"arch\", points to the architecture of the OS (NOTE: This does not exist on Windows systems.)\n # key=\"alreadyUpdated\", points to a boolean, which is used to keep track of whether the upToDate has already been set.\n # key=\"installedVersions\", points to another dict:\n # key=machineName, points to a 2 element list:\n # [0]=string containing package version that is currently installed.\n # [1]=boolean reflecting whether this version is at or above the package delevered in this hotfix.\n #\n ###########################################################################\n #\n # packageToHotFix\n # This will hold a dict of lists:\n # key: package name, pointing to a 2 element list:\n # [0] OS\n # [1] The Hotfix that this package is associated with.\n #\n ###########################################################################\n #\n # environmentReportDict\n # This is inherited from the environment report, but it's probably worth documenting what it looks like.\n # There is a lot of data inerherited, and I'm only describing what is used in this script.\n # environmentReportDict\n # key=hostname (for each machine in the deployment), pointing to another dict:\n # key=\"OS\", pointing to string for the OS family.\n # key=\"arch\", pointing to the string for the architecture of the host.\n # key=\"sas_packages\", pointing to another dict:\n # key=package number, pointing to another dict:\n # key=\"attributes\", pointing to another dict:\n # key=\"version\", pointing to a string of the package versions currently installed on the host.\n ############################################################################\n\n results[\"include_hotfix_report\"] = include_hotfix_report\n if include_hotfix_report:\n # This is the URL from which to pull the hotfix files.\n if hotfix_url[-1:] == '/':\n baseURL = hotfix_url\n else:\n baseURL = hotfix_url + '/'\n # This is the master file that lists which other files should be examined for the actual hotfixes themselves.\n masterFile = hotfix_master_file\n # This is the top level object to store the hotfix report information (see above).\n fullReport = {}\n # This is a dict of package to hotfixes (see above).\n packageToHotfix = {}\n # This boolean will help with debugging.\n debug = False\n\n try:\n # Parse the master file to obtain where the hotfix files are.\n masterFileXML = urllib2.urlopen(baseURL + masterFile)\n\n # Parse the master file and build a list of all files.\n allFilesRoot = ET.fromstring(masterFileXML.read())\n results[\"contact_hotfix_website\"] = True\n except urllib2.URLError :\n results[\"contact_hotfix_website\"] = False\n results[\"master_website\"] = baseURL + masterFile\n if debug:\n print(\"***** Error parsing \" + baseURL + masterFile)\n print(traceback.format_exc())\n print(\"***** No hot fix information obtained. Skipping hot fix report.\\n\\n\")\n\n if results[\"contact_hotfix_website\"]:\n # Loop through the files discoverd in the master file\n if debug:\n print(\"Building hot fix report, based on master file input.\")\n for file_tag in allFilesRoot.findall('File'):\n currentFile = file_tag.get('fileName')\n fileToParse = baseURL + currentFile\n # Retrieve each file.\n # Inside of each file, the lines are keyed by the hot fix id. There are three types of lines, in order:\n # 1) id and release date\n # 2) id, sasnote, sasnotetitle\n # 3) id, OS, package.\n # This script loops through to build a dictionary of dictonaries with the basic structure:\n # ID\n # Release Date\n # SASNotes\n # SASNote and Title\n # ...\n # Packages\n # Package Name, Version, and OS\n try:\n currentFileXML = urllib2.urlopen(fileToParse)\n currentFileRoot = ET.fromstring(currentFileXML.read())\n updateID = \"\"\n for update_tag in currentFileRoot.findall('update'):\n currentUpdate = update_tag.get('id')\n releaseDate = update_tag.get('released')\n # To get the top level Dictionary seeded with the hot fix Name and release date.\n if releaseDate is not None:\n if currentUpdate in fullReport:\n if debug:\n print(\"WARNING! Hot Fix \" + currentUpdate + \" already discovered. Skipping\")\n updateID = \"DUPLICATE-SKIP\"\n else:\n # The SCXXXX hot fixes are special. The package files are only included in\n # Viya_<version>_<platform>_home.xml files. So, the entries in the\n # scheduled_update_<platform>_<shipevent>.xml files can be skipped.\n if currentUpdate.startswith(\"SC\") and currentFile.find(\"scheduled_update_\") < 0:\n continue\n updateID = currentUpdate\n fullReport[updateID] = {}\n fullReport[updateID][\"release_date\"] = releaseDate\n fullReport[updateID][\"installed\"] = False\n fullReport[updateID][\"upToDate\"] = False\n # To get the SASNote information under the hot fix\n else:\n if updateID == \"DUPLICATE-SKIP\":\n continue\n sasNote = update_tag.get('sasnote')\n sasNoteTitle = update_tag.get('sasnoteTitle')\n if sasNote is not None:\n if \"sasnote\" not in fullReport[updateID]:\n fullReport[updateID][\"sasnote\"] = {}\n # This string needs to be encoded because some non-ASCII characters are\n # in some of the titles.\n fullReport[updateID][\"sasnote\"][sasNote] = sasNoteTitle.encode('utf-8')\n # To get the Package information under the hot fix.\n else:\n os = update_tag.get(\"os\")\n fullPackage = update_tag.get(\"package\")\n if fullPackage is not None:\n if \"package\" not in fullReport[updateID]:\n fullReport[updateID][\"package\"] = {}\n\n lastPeriodIndex = fullPackage.rfind(\".\")\n # Format the package information.\n # Windows does not have a dash in the version; Linux does. So, we need to break differently,\n # depending on the OS.\n if os.lower().find(\"windows\") > -1:\n versionStartIndex = fullPackage.rfind(\"-\")\n achitectureStartIndex = -1\n versionEndIndex = lastPeriodIndex\n osFamily = \"Windows\"\n else:\n versionStartIndex = fullPackage.rfind(\"-\", 0, fullPackage.rfind(\"-\"))\n # Linux has architecture in the package. This will be stored in its own key.\n achitectureStartIndex = fullPackage.rfind(\".\", 0, lastPeriodIndex)\n # SLES has the string 'suse' in its package. This will strip it out (as well as an extra .).\n if os.lower().find(\"suse\") > -1:\n versionEndIndex = achitectureStartIndex - 5\n osFamily = \"Suse\"\n else:\n if os.lower().find(\"yocto\") > -1:\n versionEndIndex = achitectureStartIndex - 6\n osFamily = \"Yocto\"\n else:\n if os.lower().find(\"ubuntu\") > -1:\n versionStartIndex = fullPackage.rfind(\"_\", 0, fullPackage.rfind(\"_\"))\n versionEndIndex = fullPackage.rfind(\"_\")\n achitectureStartIndex = versionEndIndex\n osFamily = \"Ubuntu\"\n else:\n if os.lower().find(\"red hat enterprise linux 7\") > -1:\n versionStartIndex = versionStartIndex = fullPackage.rfind(\":\")\n versionEndIndex = len(fullPackage)\n achitectureStartIndex = -1\n osFamily = \"RedHat\"\n else:\n versionEndIndex = achitectureStartIndex\n osFamily = \"RedHat\"\n package = fullPackage[:versionStartIndex]\n packageVersion = fullPackage[versionStartIndex + 1:versionEndIndex]\n architecture = fullPackage[achitectureStartIndex + 1:lastPeriodIndex]\n\n if package not in fullReport[updateID][\"package\"]:\n fullReport[updateID][\"package\"][package] = {}\n if \"platform\" not in fullReport[updateID][\"package\"][package]:\n fullReport[updateID][\"package\"][package][\"platform\"] = {}\n if osFamily not in fullReport[updateID][\"package\"][package][\"platform\"]:\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily] = {}\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily][\"version\"] = packageVersion\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily][\"installed\"] = False\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily][\"upToDate\"] = False\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily][\"os\"] = os\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily][\"installedVersions\"] = {}\n if achitectureStartIndex != -1:\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily][\"arch\"] = architecture\n # This property is used to make sure that when evaluating the installed packages,\n # the upToDate=false does not get overridden by a True at the end.\n fullReport[updateID][\"package\"][package][\"platform\"][osFamily][\"alreadyUpdated\"] = False\n\n # Add to the package to hot fix dict.\n if package not in packageToHotfix:\n packageToHotfix[package] = []\n packageToHotfix[package].append([osFamily, updateID])\n\n except ET.ParseError:\n if debug:\n print(\"***** Error parsing \" + fileToParse)\n print(traceback.format_exc())\n print(\"***** Skipping file.\\n\\n\")\n except urllib2.HTTPError:\n if debug:\n print(\"***** Cannot access \" + fileToParse)\n print(traceback.format_exc())\n print(\"***** Skipping the file.\\n\\n\")\n except:\n if debug:\n print(\"***** Error encountered with \" + fileToParse)\n print(traceback.format_exc())\n print(\"***** Skipping the file.\\n\\n\")\n\n if debug:\n print(\"**** Build complete. Here are the hot fixes:\")\n print_Full_Report(fullReport)\n print(\"***********************************************************************************\")\n print(\"**** Here is the package to hot fix dict:\")\n print(\"***********************************************************************************\")\n for current_package in packageToHotfix:\n print(\" \" + current_package)\n for machine_list in packageToHotfix[current_package]:\n print(\" \" + machine_list[0] + \" @ \" + machine_list[1] + \".\")\n print(\"***********************************************************************************\")\n print(\"Report built.\")\n print(\"Accessing environment Data.\")\n\n for currentMachine in results['sas_hosts']:\n if not results['sas_hosts'][currentMachine][\"_unreachable\"] and not results['sas_hosts'][currentMachine][\"_failed\"]:\n currentOS = results['sas_hosts'][currentMachine]['os']['family']\n for currentPackage in results['sas_hosts'][currentMachine]['sas_packages']:\n if currentPackage in packageToHotfix:\n for osHotfix in packageToHotfix[currentPackage]:\n if osHotfix[0] == currentOS:\n currentHotfix = osHotfix[1]\n installedVersion = \\\n results['sas_hosts'][currentMachine]['sas_packages'][currentPackage]['attributes']['version']\n if installedVersion.endswith('.suse'):\n installedVersion = installedVersion[:-5]\n else:\n if installedVersion.endswith('.yocto'):\n installedVersion = installedVersion[:-6]\n else:\n if '_' in installedVersion:\n installedVersion = installedVersion[0:installedVersion.rfind(\"_\")]\n hotfixVersion = fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"version\"]\n upToDate = compare_versions(installedVersion, hotfixVersion) >= 0\n fullReport[currentHotfix][\"installed\"] = True\n fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"installed\"] = True\n # If a previous pacakage marked updateToDate=True, it can still be pulled back to false if another package isn't\n # up to date. If the previous package was marked upToDate=false, the hotfix cannot be marked true.\n if not fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"alreadyUpdated\"] or \\\n (fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"alreadyUpdated\"] and\n fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"upToDate\"]):\n fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"upToDate\"] = upToDate\n fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"alreadyUpdated\"] = True\n fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"installedVersions\"][currentMachine] = [installedVersion, upToDate]\n\n if debug:\n print(\"Comparing evironment data to hotfix data.\")\n for currentHotFix in fullReport:\n cumulativeOverallUpToDate = True\n # This will only allow the top level \"upToDate\" property to be set, if there is a package installed on this OS.\n allowTopLevelUpdate = False\n for currentPackage in fullReport[currentHotFix][\"package\"]:\n cumulativeOSUpToDate = True\n for currentOS in fullReport[currentHotFix][\"package\"][currentPackage][\"platform\"]:\n if len(fullReport[currentHotFix][\"package\"][currentPackage][\"platform\"][currentOS][\"installedVersions\"]) > 0:\n cumulativeOSUpToDate = cumulativeOSUpToDate and \\\n fullReport[currentHotFix][\"package\"][currentPackage][\"platform\"][currentOS][\n \"upToDate\"]\n allowTopLevelUpdate = True\n\n cumulativeOverallUpToDate = cumulativeOverallUpToDate and cumulativeOSUpToDate\n if allowTopLevelUpdate:\n fullReport[currentHotFix][\"upToDate\"] = cumulativeOverallUpToDate\n\n # Now that the fullReport has been updated, go back and add to results, for the final report.\n results[\"available_hotfixes\"] = {}\n results[\"installed_hotfixes\"] = {}\n\n for currentHotfix in fullReport:\n if not fullReport[currentHotfix][\"installed\"]:\n continue\n if fullReport[currentHotfix][\"upToDate\"]:\n hotfix_dict_to_use = \"installed_hotfixes\"\n else:\n hotfix_dict_to_use = \"available_hotfixes\"\n results[hotfix_dict_to_use][currentHotfix] = {}\n results[hotfix_dict_to_use][currentHotfix][\"release_date\"] = fullReport[currentHotfix][\"release_date\"]\n results[hotfix_dict_to_use][currentHotfix][\"packages\"] = []\n for currentPackage in fullReport[currentHotfix][\"package\"]:\n for currentOS in fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"]:\n if not fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"installed\"]:\n continue\n for currentHost in fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"installedVersions\"]:\n temp_dict = {}\n temp_dict[\"hostname\"] = currentHost\n temp_dict[\"package\"] = currentPackage\n temp_dict[\"installed_version\"] = fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"installedVersions\"][currentHost][0]\n temp_dict[\"hotfix_version\"] = fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"version\"]\n temp_dict[\"up_to_date\"] = fullReport[currentHotfix][\"package\"][currentPackage][\"platform\"][currentOS][\"installedVersions\"][currentHost][1]\n results[hotfix_dict_to_use][currentHotfix][\"packages\"].append(temp_dict)\n # Format the SAS Note description so that we can respect any HTML tags that are included in the text.\n results[hotfix_dict_to_use][currentHotfix][\"sas_notes\"] = {}\n for current_number in fullReport[currentHotfix][\"sasnote\"]:\n # Honor any html that is coming through.\n temp_sasnote_description = fullReport[currentHotfix][\"sasnote\"][current_number]\n temp_sasnote_description = temp_sasnote_description.replace(\"<\", \"<\")\n temp_sasnote_description = temp_sasnote_description.replace(\">\", \">\")\n # Build a link to the URL for the SAS Note.\n hot_fix_prefix = current_number[:2]\n hot_fix_postfix = current_number[2:]\n sas_note_url = \"http://support.sas.com/kb/\" + hot_fix_prefix + \"/\" + hot_fix_postfix + \".html\"\n sas_note_html_link = \"<a href=\\\"\" + sas_note_url + \"\\\"\\>\" + current_number + \"</a>\"\n results[hotfix_dict_to_use][currentHotfix][\"sas_notes\"][current_number] = {\"sas_note_link\":sas_note_html_link, \"description\":temp_sasnote_description}\n\n # in the event of a successful module execution, you will want to\n # simple AnsibleModule.exit_json(), passing the key/value results\n #\n # changed will always be 'False' since we'll never alter state on a host\n module.exit_json(changed=False, processed_host_details=results)","def ping():\n return 'pong'","def test_ping_host1(self, chirouter_runner):\n chirouter_runner.start_mininet(\"3router.json\")\n mn = chirouter_runner.mininet\n\n ping = chirouter_runner.ping(\"host4\", \"10.1.0.42\", count=4)\n\n ping.validate_output_success(num_expected=4, expected_source=\"10.1.0.42\")","def arping(ipaddress=\"10.0.1.1\"):\n\n #Assuming use of arping on Red Hat Linux\n p = subprocess.Popen(\"/usr/sbin/arping -c 2 %s\" % ipaddress, shell=True,\n stdout=subprocess.PIPE)\n out = p.stdout.read()\n result = out.split()\n #pattern = re.compile(\":\")\n for item in result:\n if ':' in item:\n print(item)","def ping(host):\n import os, platform\n\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def ping(host):\n import os, platform\n\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def ping(host):\n import os, platform\n\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def ping():\n return jsonify({'response': 'pong'}), 200","def ping(host, network_timeout=3):\n \n args = [\n 'ping'\n ]\n\n platform_os = platform.system()\n\n if platform_os == 'Windows':\n args.extend(['-n', '1'])\n args.extend(['-w', str(network_timeout * 1000)])\n elif platform_os in ('Linux', 'Darwin'):\n args.extend(['-c', '1'])\n args.extend(['-W', str(network_timeout)])\n else:\n raise NotImplementedError('Unsupported OS: {}'.format(platform_os))\n\n args.append(host)\n\n try:\n if platform_os == 'Windows':\n output = subprocess.run(args, check=True, universal_newlines=True).stdout\n\n if output and 'TTL' not in output:\n return False\n else:\n subprocess.run(args, check=True)\n\n return True\n \n except (subprocess.CalledProcessError, subprocess.TimeoutExpired):\n return False","async def ping(self, *args, **kwargs):\n\n return await self._makeApiCall(self.funcinfo[\"ping\"], *args, **kwargs)","def test_ipam_ip_addresses_list(self):\n pass","def ping_test(hostaddr, count=2, timeout=1):\n xping = subprocess.Popen(['/bin/ping', '-W', str(timeout), '-c', str(count), hostaddr],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n xping.communicate()\n xping.wait()\n return not bool(xping.returncode)","def ping (self,host):\n # Ping parameters as function of OS\n ping_str = \"-n 1\" if platform.system().lower()==\"windows\" else \"-c 1\"\n\n # Ping\n return os.system(\"ping \" + ping_str + \" \" + host) == 0","def HandlePingRequest(self, request, response):\n self._publish_helper.HandlePingRequest(request, response)","def remote_status():","def validate_ping(result):\n if '0 packets received' in str(result) or 'no answer from' in str(result) or '0 received' in str(result):\n print 'Conectividade - DOWN'\n return False\n print 'Conectividade - OK'\n return True","def ping(self):\n try:\n api_ping = '{}://{}:{}/api/1.0.0/ping/{}/None'.format(\n self._env.api_protocol,\n self._env.api_host,\n self._env.api_port,\n self._key\n )\n self._env.logger.info('PING> {}'.format(api_ping))\n\n def check(response):\n if response.code == 204:\n self._env.logger.info('204 - Registering new routes')\n self.register_routes()\n elif response.code == 404:\n self._env.logger.info('404 - api gateway is not available')\n elif response.code != 200:\n self._env.logger.error('{} - UNKNOWN ERROR'.format(response.code))\n return response\n\n def log(failure):\n \"\"\"\n Just log the error, a return code of 'False' will be returned elsewhere\n :param failure: A treq failure object\n \"\"\"\n return self._env.logger.warning('[ping] {}'.format(failure.getErrorMessage()))\n\n treq.get(api_ping).addCallback(check).addErrback(log)\n\n except requests.exceptions.ConnectionError as e:\n self._env.logger.warning('ping failed for \"{}\"'.format(api_ping))\n self._env.logger.warning('ping return = \"{}\"'.format(e.args[0].reason))\n self._state = False"],"string":"[\n \"def ping():\\n return ping_response()\",\n \"def ping():\\n return ping_response()\",\n \"def ping():\\n return ping_response()\",\n \"def ping():\\n return ping_response()\",\n \"def ip_check():\\n hosts = []\\n valid_hosts = []\\n for item in sys.argv:\\n if '@' in item:\\n hosts.append(item)\\n for i in hosts:\\n host = i.split('@')[1].split(':')[0]\\n command = os.system('ping -c 1 '+host+' > /dev/null')\\n if command == 0:\\n valid_hosts.append(i)\\n if valid_hosts:\\n path_check(valid_hosts)\",\n \"def test_ping(self):\\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\\n assert status == 0\",\n \"def icmp_probe(self, ip):\\n\\n\\t\\tcmd = 'ping %s -n 10' % ip\\n\\t\\tp = Popen(cmd, shell=True, stdin=PIPE, stderr=PIPE, stdout=PIPE)\\n\\t\\tres = p.stdout.read()\\n\\n\\t\\tres = res.decode()\\n\\t\\tif len(p.stderr.read()) == 0:\\n\\t\\t\\tif 'Destination host unreachable' in res:\\n\\t\\t\\t\\treturn False\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def ping():\\n requestor = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\\n logger.info(f\\\"Health check requested by ip='{requestor}'\\\")\\n return make_response(\\n jsonify(status=\\\"Serving\\\",\\n body=\\\"pong\\\"), 200)\",\n \"async def ping(self, ctx, ip):\\n\\n # Check for valid IP else do DNS lookup\\n valid_ip = re.compile(\\\"[0-9]{,3}\\\\.[0-9]{,3}\\\\.[0-9]{,3}\\\")\\n valid_hostname = re.compile(\\\".*\\\\.[a-zA-Z]{2,}\\\")\\n valid = False\\n\\n if valid_ip.match(ip):\\n valid = True\\n elif valid_hostname.match(ip):\\n valid = True\\n try:\\n await self.bot.say('Doing DNS lookup...')\\n ip = socket.gethostbyname(ip)\\n\\n if valid == True:\\n start = time.time()\\n response = os.system(\\\"sudo ping -c 1 -w3 \\\" + ip)\\n duration = time.time() - start\\n duration = round(duration * 1000, 0)\\n if response == 0:\\n await self.bot.say(ip + ' is up and responding in ' +\\n str(duration) + 'ms.')\\n else:\\n await self.bot.say(ip + ' is not reachable.')\\n else:\\n await self.bot.say(ip + ' is not a valid IP or Domain.')\\n\\n except socket.gaierror:\\n await self.bot.say('Whoops! That Address cant be resolved!')\",\n \"def verify_dot1x(task):\\n # run \\\"show dot1x all\\\" on each host\\n sh_dot1x = task.run(task=netmiko_send_command, command_string=\\\"show dot1x all\\\")\\n # TTP template for dot1x status\\n dot1x_ttp_template = \\\"Sysauthcontrol {{ status }}\\\"\\n # magic TTP parsing\\n parser = ttp(data=sh_dot1x.result, template=dot1x_ttp_template)\\n parser.parse()\\n dot1x_status = json.loads(parser.result(format=\\\"json\\\")[0])\\n\\n # write dot1x verification report for each host\\n with open(f\\\"output/{task.host}_dot1x_verified.txt\\\", \\\"w+\\\") as file:\\n file.write(sh_dot1x.result)\\n\\n # print dot1x status\\n c_print(f\\\"*** {task.host} dot1x status: {dot1x_status[0]['status']} ***\\\")\",\n \"def check_vm_connectivity(env, os_conn, vm_keypair=None, timeout=4 * 60):\\n servers = os_conn.get_servers()\\n for server1 in servers:\\n ips_to_ping = [settings.PUBLIC_TEST_IP]\\n for server2 in servers:\\n if server1 == server2:\\n continue\\n ips_to_ping += os_conn.get_nova_instance_ips(\\n server2).values()\\n check_ping_from_vm(env, os_conn, server1, vm_keypair, ips_to_ping,\\n timeout=timeout)\",\n \"def ping(self):\\n raise AssertionError(\\\"Ping function is not implemented\\\")\",\n \"def ping_ip(ip_address, count):\\n reply = subprocess.run(\\n f\\\"ping -c {count} -n {ip_address}\\\",\\n shell=True,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n encoding=\\\"utf-8\\\",\\n )\\n if reply.returncode == 0:\\n return True\\n else:\\n return False\",\n \"def check_vpn_interface():\\n return validate_vpn_interface(call_command('netstat -i')[0].split('\\\\n'))\",\n \"def checkPingVMStatus(self, conn, vmHost, hostUser, hostPass, vmNames):\\n #temp - do a simple ping test to each vm\\n hypervisor = libclient.SfClient()\\n\\n try:\\n hypervisor.Connect(vmHost, hostUser, hostPass)\\n mylog.info(\\\"The connection to the hypervisor has been established\\\")\\n except libclient.ClientError as e:\\n mylog.error(\\\"There was an error connecting to the hypervisor. Message: \\\" + str(e))\\n #return False\\n\\n #get the list of VM MAC and IP addresses\\n retcode, stdout, stderr = hypervisor.ExecuteCommand(\\\"cat /var/lib/libvirt/dnsmasq/default.leases\\\")\\n\\n ip_list = []\\n mac_list = []\\n mac_ip_list = []\\n full_info = []\\n\\n #split the info\\n stdout = stdout.split()\\n for element in stdout:\\n if libsf.IsValidIpv4Address(element):\\n ip_list.append(element)\\n if self.isValidMACAddress(element):\\n mac_list.append(element)\\n\\n #make sure there are the same number of MAC and IP addresses\\n if len(mac_list) == len(ip_list):\\n for x in xrange(0, len(mac_list)):\\n temp_info = mac_list[x], ip_list[x]\\n mac_ip_list.append(temp_info)\\n\\n\\n #match VM name with mac address\\n for name in vmNames:\\n try:\\n vm = conn.lookupByName(name)\\n except libvirt.libvirtError as e:\\n mylog.error(str(e))\\n xml = ElementTree.fromstring(vm.XMLDesc(0))\\n\\n for elem in xml.iterfind('devices/interface/mac'):\\n for addr in mac_ip_list:\\n if elem.attrib.get('address') == addr[0]:\\n temp = addr[0], addr[1], name\\n full_info.append(temp)\\n\\n #temp - try to ping all the VMs \\n for ip in full_info:\\n recode, stdout, stderr = hypervisor.ExecuteCommand(\\\"ping -n -i 0.2 -c 3 -W 1 -q \\\" + ip[1])\\n if recode == 0:\\n mylog.info(\\\"Was able to ping \\\" + ip[2])\\n else:\\n mylog.error(\\\"Was not able to ping \\\" + ip[2])\\n return False\\n return True\",\n \"def test_ip(response):\\n \\n # from comeon_core import update\\n ip = getIP()\\n print(ip)\\n #init_db(engine)\\n #update()\\n assert True\",\n \"def _checknet():\\n exit_code = os.system('ping -c 1 www.baidu.com 1>/dev/null 2>&1')\\n return exit_code\",\n \"def _get_ipaddress(node):\\n if \\\"ipaddress\\\" not in node:\\n with settings(hide('stdout'), warn_only=True):\\n output = sudo('ohai ipaddress')\\n if output.succeeded:\\n node['ipaddress'] = json.loads(output)[0]\\n return True\\n return False\",\n \"def ping(self) -> None:\\n ...\",\n \"def ping(host):\\r\\n \\r\\n # Ping command count option as function of OS\\r\\n param = '-n' if system_name().lower()=='windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n\\r\\n process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) \\r\\n streamdata = process.communicate()[0]\\r\\n #print_util.print_std(streamdata)\\r\\n streamdata = string_util.decode_chs_str(streamdata)\\r\\n print_util.print_std(streamdata)\\r\\n if system_name().lower()=='windows':\\r\\n #On windows use this to check ping pass or fail.\\r\\n if 'TTL=' in str(streamdata):\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n return process.returncode==0\",\n \"async def ping(self):\\n uri = \\\"/fapi/v1/ping\\\"\\n success, error = await self.request(\\\"GET\\\", uri)\\n return success, error\",\n \"def ip_status(ip, rnge):\\n\\tactive = []\\n\\tdown = []\\n\\ttimed_out = []\\n\\t\\n\\t\\\"\\\"\\\" Get the first 3 fields of IP \\\"\\\"\\\"\\n\\taddres = ip.split(\\\".\\\")[:3]\\n\\tne_ip = \\\".\\\".join(addres)\\n\\t\\n\\t\\\"\\\"\\\" Get the last field of IP \\\"\\\"\\\"\\n\\tlst_field = ip.split(\\\".\\\")[3]\\n\\t\\n\\tif rnge == \\\"0\\\" or rnge == \\\"\\\":\\n\\t\\trnge = 1\\n\\t\\n\\tif 0 <= int(rnge) <= 255 and ip_pattern.match(ip):\\n\\t\\t\\\"\\\"\\\" Calculate the actual range \\\"\\\"\\\"\\n\\t\\tactual_rnge = int(lst_field) + int(rnge)\\n\\n\\t\\t\\\"\\\"\\\" Range of IP address to be pinged \\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\" Right now it starts from 0 through 10 but can be changed in the below line \\\"\\\"\\\"\\n\\t\\n\\t\\tfor i in range(int(lst_field), actual_rnge):\\t\\t\\n\\t\\t\\n\\t\\t\\tip_fin = ne_ip + \\\".\\\" + str(i)\\n\\t\\t\\tstatus = sp.check_output([\\\"ping \\\", ip_fin])\\n\\t\\t\\n\\t\\t\\tif pattern1.search(status) :\\n\\t\\t\\t\\tdown.append(ip_fin)\\n\\t\\t\\telif pattern2.search(status):\\n\\t\\t\\t\\ttimed_out.append(ip_fin)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tactive.append(ip_fin)\\n\\t\\n\\t\\tresult = \\\"\\\\nActive IP addresses are: \\\" + str(active) + \\\"\\\\n\\\\n\\\" + \\\"Unreachable IP: \\\" + str(down) + \\\"\\\\n\\\\n\\\" + \\\"Timed out IP's: \\\" + str(timed_out)\\n\\t\\treturn result\\n\\t\\n\\telse:\\n\\t\\treturn \\\"1) Range should be in between 0 and 255 inclusive. \\\\n2) Check the IP format.\\\"\",\n \"def rpc_ping(self):\\n\\t\\treturn True\",\n \"def test_check_nip(client):\\n is_assigned, request_id = client.check_nip(\\n \\\"8655104670\\\", \\\"41146786026458860703735932\\\"\\n )\\n\\n assert is_assigned\",\n \"def check_ip_fwd(duthosts, all_cfg_facts, nbrhosts, tbinfo):\\n for porttype in [\\\"ethernet\\\", \\\"portchannel\\\"]:\\n for version in [4, 6]:\\n\\n ports = pick_ports(duthosts, all_cfg_facts, nbrhosts, tbinfo, port_type_a=porttype, version=version)\\n\\n for ttl, size in [(2, 64), (1, 1450)]:\\n # local interfaces\\n check_packet(sonic_ping, ports, 'portB', 'portA', size=size, ttl=ttl, ttl_change=0)\\n\\n # local neighbors\\n check_packet(sonic_ping, ports, 'portA', 'portA',\\n dst_ip_fld='nbr_ip', size=size, ttl=ttl, ttl_change=0)\\n\\n vm_host_to_A = nbrhosts[ports['portA']['nbr_vm']]['host']\\n\\n check_packet(eos_ping, ports, 'portD', 'portA', dst_ip_fld='my_lb4096_ip', src_ip_fld='nbr_lb',\\n dev=vm_host_to_A, size=size, ttl=ttl)\\n\\n # loopbacks\\n check_packet(sonic_ping, ports, 'portA', 'portA', dst_ip_fld='nbr_lb', size=size, ttl=ttl, ttl_change=0)\\n\\n # inband\\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='inband', size=size, ttl=ttl, ttl_change=0)\\n\\n # DUT loopback\\n # these don't decrement ttl\\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='my_lb_ip', dst_ip_fld='my_ip', size=size,\\n ttl=ttl, ttl_change=0)\\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='my_lb_ip', dst_ip_fld='nbr_ip', size=size,\\n ttl=ttl, ttl_change=0)\\n check_packet(sonic_ping, ports, 'portA', 'portA', src_ip_fld='my_lb_ip', dst_ip_fld='nbr_lb', size=size,\\n ttl=ttl, ttl_change=0)\\n\\n vm_host_to_A = nbrhosts[ports['portA']['nbr_vm']]['host']\\n check_packet(eos_ping, ports, 'portA', 'portA', dst_ip_fld='my_lb4096_ip', src_ip_fld='nbr_lb',\\n dev=vm_host_to_A, size=size, ttl=ttl, ttl_change=0)\\n\\n # end to end\\n vm_host_to_A = nbrhosts[ports['portA']['nbr_vm']]['host']\\n check_packet(eos_ping, ports, 'portB', 'portA', dst_ip_fld='nbr_lb', src_ip_fld='nbr_lb',\\n dev=vm_host_to_A, size=size, ttl=ttl)\\n check_packet(eos_ping, ports, 'portC', 'portA', dst_ip_fld='nbr_lb', src_ip_fld='nbr_lb',\\n dev=vm_host_to_A, size=size, ttl=ttl)\\n check_packet(eos_ping, ports, 'portD', 'portA', dst_ip_fld='nbr_lb', src_ip_fld='nbr_lb',\\n dev=vm_host_to_A, size=size, ttl=ttl)\",\n \"def test_ip_addresses_exists():\\n load_ips()\\n validate_names()\",\n \"def checklan(ipaddr, network):\\n return True\",\n \"def Ping(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def ping():\\n api_online = bool(check_url(\\\"https://rest.ensembl.org/info/ping?\\\"))\\n vertebrate_url_online = bool(check_url(\\\"http://ftp.ensembl.org\\\"))\\n other_url_online = bool(check_url(\\\"http://ftp.ensemblgenomes.org\\\"))\\n return api_online and vertebrate_url_online and other_url_online\",\n \"def test_ip(self):\\n ##Todo: Improve this check\\n ip = socket.gethostbyname(socket.gethostname())\\n ip = [int(i) for i in ip.split('.')]\\n assert len(ip) == 4\\n assert ip[0] == 10\\n assert ip[1] == 137\\n assert ip[2] == 1\\n assert ip[3] >= 1 and ip[3] <= 255\",\n \"def tcp_ping_nodes(self, timeout=20.0):\\n for node in self.all_instances:\\n if node.instance_type in [\\n InstanceType.RESILIENT_SINGLE,\\n InstanceType.SINGLE,\\n InstanceType.DBSERVER,\\n ]:\\n node.check_version_request(timeout)\",\n \"async def test_app_ping(cli):\\n resp = await cli.get('/monitor/ping')\\n assert resp.status == 200\",\n \"def ping(cmd, *args, **argv):\\n import os\\n context = argv[\\\"context\\\"]\\n \\n def count(num):\\n if str(num) == str(int(num)):\\n if num < 0:\\n context.write(\\\"%s: bad number of packets to transmit.\\\" % cmd)\\n else:\\n return num\\n else:\\n context.write(\\\"%s: can't set unicast time-to-live: Unknown host\\\" % cmd)\\n return\\n\\n ping_p = {\\\"-c\\\":count}\\n\\n def isipaddress(ip):\\n import socket\\n try:\\n ipa = socket.gethostbyname(ip)\\n return ipa\\n except:\\n context.write(\\\"ping: unknown host %s\\\" % ip)\\n return None\\n\\n def doping(cmd, sign, sign_param, ipaddress):\\n if sign in ping_p.keys():\\n # #\\n pass\\n else:\\n context.write(\\\"connect: Unknown host\\\")\\n return \\n \\n\\tif ping_p[sign](sign_param):\\n # #\\n pass\\n else:\\n return \\n if isipaddress(ipaddress):\\n ip = isipaddress(ipaddress)\\n else:\\n return\\n try:\\n os.system(str(cmd) + \\\" \\\" + str(sign) + \\\" \\\" + str(sign_param) + \\\" \\\" + str(ip))\\n except:\\n context.write(\\\"has some errors in ping command\\\")\\n return \\n\\n length = len(args)\\n \\n if length == 0:\\n helpinfo = context.resolver.get_func_doc(getattr(context.resolver.get_module(cmd), cmd))\\n helpinfo_format = helpinfo[\\\"format\\\"].rstrip()\\n helpinfo_format = helpinfo_format.lstrip()\\n if helpinfo_format == \\\"\\\":\\n return\\n context.write(helpinfo_format)\\n\\n elif length < 3:\\n cmd_real = context.resolver.has_command(args[0], cmd)\\n if cmd_real != None:\\n cmds = cmd.split()\\n cmd_n = \\\"_\\\".join(cmds)\\n modulename = cmd_n + \\\"_\\\" + cmd_real\\n module = context.resolver.get_module(modulename)\\n func = getattr(module, modulename)\\n func(cmd + \\\" \\\" + cmd_real, context = context)\\n else:\\n doping(cmd, \\\"-c\\\", 4, args[0])\\n\\n else:\\n\\tif args[1] == \\\"0\\\":\\n\\t os.system(str(cmd) + \\\" \\\" + str(args[2]))\\n\\telse:\\n doping(cmd, args[0], args[1], args[2])\",\n \"def ping(self):\\n pass\",\n \"def check(self, target, port):\\n pass\",\n \"def icmp_ping(ip_addr, timeout = 6, count = 1024):\\n for i in range(count):\\n print('Ping wait:')\\n try:\\n delay = ping_wait(ip_addr, timeout)\\n except socket.gaierror as e:\\n print('Failed. (socket error: %s)' % e[1])\\n break\\n\\n if delay == None:\\n print('Failed. (timeout within %s second.)' % timeout)\\n else:\\n print('get ICMP in %0.4f ms' % (delay * 1000))\",\n \"async def ping(self) -> APIReturn:\\n return await self._request(\\\"GET\\\", \\\"/ping\\\")\",\n \"def testIP(IP):\\n \\\"\\\"\\\" will feed this an array with mp.Pool.map() \\\"\\\"\\\"\\n IPup = []\\n IPdown = []\\n with open(os.devnull, \\\"w\\\") as fnull:\\n if subprocess.call(['ping','-c 1 -W 1',IP,],stdout=fnull, stderr=fnull)==0:\\n print 'host %s is UP' % IP\\n return IP\\n else:\\n print 'host %s is DOWN' % IP\\n return None\",\n \"def ping():\\r\\n health1 = ScoringService.get_model1() is not None # You can insert a health check here\\r\\n ping_response = \\\"Docker for Discover non prime users\\\"\\r\\n status = 200 if (health1) else 404\\r\\n return flask.Response(response=ping_response, status=status, mimetype='application/json')\",\n \"def ping():\\n\\treturn HTTPResponse(status=200)\",\n \"def ping(self):\\n return True\",\n \"def ping(self):\\n return True\",\n \"def test_check_ip_on_whitelist_true(self):\\n\\n ip_name = 'mail-ed1-f51.google.com'\\n\\n result = check_ip_on_whitelist(ip_name, self.pattern_ip)\\n\\n self.assertTrue(result)\",\n \"def ping():\\n logging.info(\\\"Ping received...\\\")\\n\\n health = Predictor.load() is not None\\n\\n status = 200 if health else 404\\n return Response(response=\\\"\\\\n\\\", status=status, mimetype=\\\"application/json\\\")\",\n \"def test_ping():\\n\\n api = Client()\\n response = api.ping()\\n response.should.equal({})\",\n \"def icmp_ping(ip_addr, timeout=0.5, count=4):\\n is_connect = 1\\n\\n for i in range(count):\\n try:\\n delay = ping_once(ip_addr, timeout)\\n except socket.gaierror, e:\\n print \\\"failed. (socket error: '%s')\\\" % e[1]\\n if delay == None:\\n print 'failed. (timeout within %s second.)' % timeout\\n is_connect = 0\\n else:\\n pass\\n result = [ip_addr, round(delay, 4), is_connect]\\n return result\",\n \"def test_ping(self):\\n response = self.client.get(reverse(\\\"api_hello:ping\\\"))\\n self.assertTrue(response.json()[\\\"status\\\"])\",\n \"def ping_to_ip(self, ip, return_output=False, other_opt='', size='56', count='5', timewait='1'):\\n host = self.inputs.host_data[self.vm_node_ip]\\n output = ''\\n fab_connections.clear()\\n af = get_af_type(ip)\\n try:\\n vm_host_string = '%s@%s' % (self.vm_username, self.local_ip)\\n if af is None:\\n cmd = \\\"\\\"\\\"python -c 'import socket;socket.getaddrinfo(\\\"%s\\\", None, socket.AF_INET6)'\\\"\\\"\\\" % ip\\n output = remote_cmd(\\n vm_host_string, cmd, gateway_password=host['password'],\\n gateway='%s@%s' % (host['username'], self.vm_node_ip),\\n with_sudo=True, password=self.vm_password,\\n logger=self.logger\\n )\\n util = 'ping' if output else 'ping6'\\n else:\\n util = 'ping6' if af == 'v6' else 'ping'\\n\\n cmd = '%s -s %s -c %s -W %s %s %s' % (\\n util, str(size), str(count), str(timewait), other_opt, ip\\n )\\n\\n output = remote_cmd(\\n vm_host_string, cmd, gateway_password=host['password'],\\n gateway='%s@%s' % (host['username'], self.vm_node_ip),\\n with_sudo=True, password=self.vm_password,\\n logger=self.logger\\n )\\n self.logger.debug(output)\\n if return_output is True:\\n return output\\n except Exception, e:\\n self.logger.exception(\\n 'Exception occured while trying ping from VM')\\n return False\\n\\n expected_result = ' 0% packet loss'\\n try:\\n if expected_result not in output:\\n self.logger.warn(\\\"Ping to IP %s from VM %s failed\\\" %\\n (ip, self.vm_name))\\n return False\\n else:\\n self.logger.info('Ping to IP %s from VM %s passed' %\\n (ip, self.vm_name))\\n return True\\n except Exception as e:\\n self.logger.warn(\\\"Got exception in ping_to_ip:%s\\\" % (e))\\n return False\",\n \"def ping():\\n return '{}'\",\n \"def checkIP(self):\\n\\t\\tself.get(\\\"https://ifconfig.me/\\\")\\n\\t\\treturn self.findId(\\\"ip_address\\\").text\",\n \"def validateIP():\\n try:\\n s = socket.inet_aton(args.target)\\n except socket.error:\\n print(\\\"\\\")\\n print(f\\\"{bad_cmd} Bad IP address\\\")\\n print(\\\"\\\")\\n sys.exit()\",\n \"def ping(self):\\n return 'ping'\",\n \"def ping(self):\\n response = self._request(\\\"GET\\\", [ROUTE_PING])\\n\\n if response.status_code == 200:\\n logging.info(\\\"OK\\\")\\n return True\\n logging.error(\\\"FAILED\\\")\\n return False\",\n \"def icmp_ping(host, timeout=1):\\n result = Subprocess.execute(\\n \\\"ping -c 1 -W '{timeout:d}' '{host:s}'\\\".format(\\n host=host, timeout=timeout))\\n return result.exit_code == 0\",\n \"def vt_ip_check(ip, vt_api):\\n if not is_IPv4Address(ip):\\n return None\\n\\n url = 'https://www.virustotal.com/vtapi/v2/ip-address/report'\\n parameters = {'ip': ip, 'apikey': vt_api}\\n response = requests.get(url, params=parameters)\\n try:\\n return response.json()\\n except ValueError:\\n return None\",\n \"def verify_ipn(data):\\n data = dict(data)\\n data['cmd'] = '_notify-validate'\\n resp = requests.post(app.config['PAYPAL']['endpoint'], data=data)\\n if resp.text == 'VERIFIED':\\n return True\\n return False\",\n \"def ip_command():\\n # 1. Get input host from Demisto\\n ip = demisto.args().get('ip')\\n if not is_ip_valid(ip):\\n return_error('Invalid IP address, Please retry with a valid IP address')\\n # 2. Get the host reputation from SlashNext API\\n response = ip_lookup(ip=ip)\\n if response.get('errorNo') != 0:\\n return\\n # 3. Parse and format the response\\n dbot_score_cont, ip_cont = get_dbot_std_context(\\n ip, 'IP', response.get('threatData').get('verdict'), response.get('threatData').get('threatType'))\\n\\n snx_ioc_cont = get_snx_host_ioc_context(ip, 'IP', response.get('threatData'))\\n\\n ec = {\\n 'SlashNext.IP(val.Value === obj.Value)': snx_ioc_cont,\\n 'DBotScore': dbot_score_cont,\\n 'IP': ip_cont\\n }\\n\\n title = 'SlashNext Phishing Incident Response - IP Lookup\\\\n' \\\\\\n '##### ip = {}'.format(ip)\\n\\n md = tableToMarkdown(\\n title,\\n snx_ioc_cont,\\n ['Value',\\n 'Type',\\n 'Verdict',\\n 'ThreatStatus',\\n 'ThreatName',\\n 'ThreatType',\\n 'FirstSeen',\\n 'LastSeen']\\n )\\n\\n return_outputs(md, ec, snx_ioc_cont)\",\n \"def online_check():\\n try_first_ips = [\\n \\\"216.58.213.238\\\", # google\\n \\\"8.8.8.8\\\", # google\\n \\\"8.8.4.4\\\", # google\\n \\\"46.228.47.115\\\", # yahoo\\n ]\\n last_resort_ips = [ # dns root servers\\n \\\"198.41.0.4\\\",\\n \\\"192.228.79.201\\\",\\n \\\"192.33.4.12\\\",\\n \\\"128.8.10.90\\\",\\n \\\"192.203.230.10\\\",\\n \\\"192.5.5.241\\\",\\n \\\"192.112.36.4\\\",\\n \\\"128.63.2.53\\\",\\n \\\"192.36.148.17\\\",\\n \\\"192.58.128.30\\\",\\n \\\"193.0.14.129\\\",\\n \\\"198.32.64.12\\\",\\n \\\"202.12.27.33\\\"\\n ]\\n\\n iplists = []\\n iplists.append(try_first_ips)\\n iplists.append(rand_ips(max_num=50))\\n iplists.append(last_resort_ips)\\n\\n return any(can_ping_host(ip) for ip in chain(*iplists))\",\n \"def test_ping():\\n # Build topology\\n platform = DockerPlatform(None, None)\\n platform.pre_build()\\n\\n h1 = Node(identifier='hs1', type='host')\\n h2 = Node(identifier='hs2', type='host')\\n s1 = Node(identifier='sw1', type='host')\\n s2 = Node(identifier='sw2', type='host')\\n\\n hs1 = platform.add_node(h1)\\n hs2 = platform.add_node(h2)\\n sw1 = platform.add_node(s1)\\n sw2 = platform.add_node(s2)\\n\\n s1p1 = BidirectionalPort(identifier='3')\\n s1p2 = BidirectionalPort(identifier='4')\\n platform.add_biport(s1, s1p1)\\n platform.add_biport(s1, s1p2)\\n\\n s2p1 = BidirectionalPort(identifier='3')\\n s2p2 = BidirectionalPort(identifier='4')\\n platform.add_biport(s2, s2p1)\\n platform.add_biport(s2, s2p2)\\n\\n h1p1 = BidirectionalPort(identifier='hs1-1')\\n h2p1 = BidirectionalPort(identifier='hs2-1')\\n platform.add_biport(h1, h1p1)\\n platform.add_biport(h2, h2p1)\\n\\n link1 = BidirectionalLink(identifier='link1')\\n platform.add_bilink((s1, s1p1), (h1, h1p1), link1)\\n link2 = BidirectionalLink(identifier='link2')\\n platform.add_bilink((s1, s1p2), (s2, s2p1), link2)\\n link3 = BidirectionalLink(identifier='link3')\\n platform.add_bilink((s2, s2p2), (h2, h2p1), link3)\\n\\n platform.post_build()\\n\\n # Ping test\\n ###########\\n\\n # Configure IP and bring UP host 1 interfaces\\n hs1('ip link set dev hs1-1 up')\\n hs1('ip addr add 10.0.10.1/24 dev hs1-1')\\n\\n # Configure IP and bring UP host 2 interfaces\\n hs2('ip link set dev hs2-1 up')\\n hs2('ip addr add 10.0.30.1/24 dev hs2-1')\\n\\n # Configure IP and bring UP switch 1 interfaces\\n sw1('ip link set dev 3 up')\\n sw1('ip link set dev 4 up')\\n\\n sw1('ip addr add 10.0.10.2/24 dev 3')\\n sw1('ip addr add 10.0.20.1/24 dev 4')\\n\\n # Configure IP and bring UP switch 2 interfaces\\n sw2('ip link set dev 3 up')\\n sw2('ip addr add 10.0.20.2/24 dev 3')\\n\\n sw2('ip link set dev 4 up')\\n sw2('ip addr add 10.0.30.2/24 dev 4')\\n\\n # Set static routes in switches\\n sw1('ip route add 10.0.30.0/24 via 10.0.20.2')\\n sw2('ip route add 10.0.10.0/24 via 10.0.20.1')\\n\\n # Set gateway in hosts\\n hs1('ip route add default via 10.0.10.2')\\n hs2('ip route add default via 10.0.30.2')\\n\\n ping_result = hs1('ping -c 1 10.0.30.1')\\n platform.destroy()\\n assert '1 packets transmitted, 1 received' in ping_result\",\n \"def _check_connection() -> bool:\\n return bool(subprocess.check_output([\\\"hostname\\\", \\\"-I\\\"]))\",\n \"def main():\\n module = AnsibleModule(\\n argument_spec=dict(\\n pn_cliusername=dict(required=False, type='str'),\\n pn_clipassword=dict(required=False, type='str', no_log=True),\\n pn_switch_list=dict(required=False, type='list', default=[]),\\n pn_vrrp_id=dict(required=False, type='str', default='18'),\\n pn_vrrp_data=dict(required=False, type='str', default=''),\\n )\\n )\\n\\n global CHANGED_FLAG\\n results = []\\n message = ''\\n switch_list = module.params['pn_switch_list']\\n\\n # Configure vrrp\\n vrrp_data = module.params['pn_vrrp_data']\\n if vrrp_data:\\n vrrp_data = vrrp_data.replace(' ', '')\\n vrrp_data_list = vrrp_data.split('\\\\n')\\n for row in vrrp_data_list:\\n if row.startswith('#'):\\n continue\\n else:\\n elements = row.split(',')\\n vlan_id = elements.pop(0).strip()\\n gateway_ip = elements.pop(0).strip()\\n primary_ip = elements.pop(0).strip()\\n secondary_ip = elements.pop(0).strip()\\n active_switch = elements.pop(0).strip()\\n\\n message += create_vlan(module, switch_list[0], vlan_id)\\n\\n for switch in switch_list:\\n if switch == active_switch:\\n vrrp_priority = '110'\\n vrrp_ip = primary_ip\\n else:\\n vrrp_priority = '109'\\n vrrp_ip = secondary_ip\\n\\n message += create_vrouter_interface(module, switch, vrrp_ip,\\n gateway_ip, vlan_id,\\n vrrp_priority)\\n\\n for switch in switch_list:\\n replace_string = switch + ': '\\n for line in message.splitlines():\\n if replace_string in line:\\n results.append({\\n 'switch': switch,\\n 'output': (line.replace(replace_string, '')).strip()\\n })\\n\\n # Exit the module and return the required JSON.\\n module.exit_json(\\n unreachable=False,\\n msg='Vrrp configuration succeeded',\\n summary=results,\\n exception='',\\n failed=False,\\n changed=True if True in CHANGED_FLAG else False,\\n task='Configure vrrp'\\n )\",\n \"def check_agw_cloud_connectivity(c, timeout=10):\\n with vagrant_connection(c, \\\"magma_deb\\\") as c_gw:\\n with c_gw.cd(\\\"/usr/local/bin/\\\"):\\n dev_utils.run_remote_command_with_repetition(c_gw, \\\"./checkin_cli.py\\\", timeout)\",\n \"def checkIP(self, ip = None):\\n\\t\\treturn os.system(\\\"ping -c 1 -w2 \\\" + ip + \\\" > /dev/null 2>&1\\\") == 0\",\n \"def ping(self):\\n return\",\n \"def ping(host):\\n\\n # Option for the number of packets as a function of\\n param = '-n' if platform.system().lower()=='windows' else '-c'\\n\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\n command = ['ping', param, '1', host]\\n\\n return subprocess.call(command) == 0\",\n \"def ping(host):\\n\\n # Option for the number of packets as a function of\\n param = '-n' if platform.system().lower()=='windows' else '-c'\\n\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\n command = ['ping', param, '1', host]\\n\\n return subprocess.call(command) == 0\",\n \"def ping(host):\\n # Option for the number of packets as a function of\\n param = '-n' if platform.system().lower() == 'windows' else '-c'\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\n command = ['ping', param, '1', host]\\n return subprocess.call(command, stdout=subprocess.PIPE) == 0\",\n \"def test_ping(client):\\n resp = client.get(url_for('cd2h_repo_project.ping'))\\n\\n assert resp.status_code == 200\\n assert resp.get_data(as_text=True) == 'OK'\",\n \"def ping(self):\\n request = Empty()\\n response = self.stub.Ping(request, timeout=5)\",\n \"def cmd_net_contest():\\n\\n print(\\\"DNS: %s\\\" % contest.check_dns())\\n print(\\\"FTP: %s\\\" % contest.check_ftp())\\n print(\\\"SSH: %s\\\" % contest.check_ssh())\\n print(\\\"HTTP: %s\\\" % contest.check_http())\\n print(\\\"HTTPS: %s\\\" % contest.check_https())\",\n \"def ping(host):\\n\\n # Ping parameters as function of OS\\n parameters = \\\"-n 1\\\" if system_name().lower() == \\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Pinging\\n return system_call(\\\"ping \\\" + parameters + \\\" \\\" + host) == 0\",\n \"def onPing(self, payload):\",\n \"def ping(host):\\r\\n\\r\\n # Option for the number of packets as a function of\\r\\n param = '-n' if platform.system().lower()=='windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n\\r\\n return subprocess.call(command) == 0\",\n \"def ping(host):\\n\\n # Ping command count option as function of OS\\n param = '-n' if system_name().lower()=='windows' else '-c'\\n\\n # Building the command. Ex: \\\"ping -c 5 google.com\\\"\\n command = ['ping', param, '5', host]\\n\\n # Pinging\\n return system_call(command) == 0\",\n \"def ping(event, context):\\n logger.info(\\\"Ping requested.\\\")\\n return _get_response(200, \\\"PONG!\\\")\",\n \"def test_static_nat_on_ip_from_non_src_nat_ip_range(self):\\n\\n # Validate the following:\\n # 1. Create a new public IP range and dedicate to a account\\n # 2. Acquire a IP from new public range\\n # 3. Enable static NAT on acquired IP from new range\\n # 4. Create a firewall rule to open up the port\\n # 5. Test SSH works to the VM\\n\\n self.services[\\\"extrapubliciprange\\\"][\\\"zoneid\\\"] = self.services[\\\"zoneid\\\"]\\n self.public_ip_range = PublicIpRange.create(\\n self.apiclient,\\n self.services[\\\"extrapubliciprange\\\"]\\n )\\n self.cleanup.append(self.public_ip_range)\\n logger.debug(\\\"Dedicating Public IP range to the account\\\");\\n dedicate_public_ip_range_response = PublicIpRange.dedicate(\\n self.apiclient,\\n self.public_ip_range.vlan.id,\\n account=self.account.name,\\n domainid=self.account.domainid\\n )\\n ip_address = PublicIPAddress.create(\\n self.apiclient,\\n self.account.name,\\n self.zone.id,\\n self.account.domainid,\\n self.services[\\\"virtual_machine\\\"]\\n )\\n self.cleanup.append(ip_address)\\n # Check if VM is in Running state before creating NAT and firewall rules\\n vm_response = VirtualMachine.list(\\n self.apiclient,\\n id=self.virtual_machine.id\\n )\\n\\n self.assertEqual(\\n isinstance(vm_response, list),\\n True,\\n \\\"Check list VM returns a valid list\\\"\\n )\\n\\n self.assertNotEqual(\\n len(vm_response),\\n 0,\\n \\\"Check Port Forwarding Rule is created\\\"\\n )\\n self.assertEqual(\\n vm_response[0].state,\\n 'Running',\\n \\\"VM state should be Running before creating a NAT rule.\\\"\\n )\\n\\n # Open up firewall port for SSH\\n fwr = FireWallRule.create(\\n self.apiclient,\\n ipaddressid=ip_address.ipaddress.id,\\n protocol=self.services[\\\"natrule\\\"][\\\"protocol\\\"],\\n cidrlist=['0.0.0.0/0'],\\n startport=self.services[\\\"natrule\\\"][\\\"publicport\\\"],\\n endport=self.services[\\\"natrule\\\"][\\\"publicport\\\"]\\n )\\n self.cleanup.append(fwr)\\n\\n # Create Static NAT rule\\n StaticNATRule.enable(\\n self.apiclient,\\n ip_address.ipaddress.id,\\n self.virtual_machine.id,\\n self.defaultNetworkId\\n )\\n\\n try:\\n logger.debug(\\\"SSHing into VM with IP address %s with NAT IP %s\\\" %\\n (\\n self.virtual_machine.ipaddress,\\n ip_address.ipaddress.ipaddress\\n ))\\n self.virtual_machine.get_ssh_client(ip_address.ipaddress.ipaddress)\\n except Exception as e:\\n self.fail(\\n \\\"SSH Access failed for %s: %s\\\" %\\n (self.virtual_machine.ipaddress, e)\\n )\\n\\n StaticNATRule.disable(\\n self.apiclient,\\n ip_address.ipaddress.id,\\n self.virtual_machine.id\\n )\",\n \"def arping(iprange=\\\"10.0.1.0/24\\\"):\\n\\n conf.verb=0\\n ans,unans=srp(Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\")/ARP(pdst=iprange),\\n timeout=2)\\n\\n collection = []\\n for snd, rcv in ans:\\n result = rcv.sprintf(r\\\"%ARP.psrc% %Ether.src%\\\").split()\\n collection.append(result)\\n return collection\",\n \"def status(NetPort):\\n\\t\\n\\tcommand = [_IPCONFIG, 'getifaddr', NetPort]\\n\\ttask = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\t(stdout, stderr) = task.communicate()\\n\\treturn task.returncode\",\n \"def check_ping(self):\\n # Print ping status of all of your hosts, minimum padding of 8 spaces\\n padding_size = max(len(max(self.hosts, key=len)) + 4, 8)\\n print('{:{padding_size}}{}'.format('Host', 'Status', padding_size=padding_size))\\n for host in self.hosts:\\n # Get output of ping command\\n output = str(Popen('ping -n 1 {}'.format(host), stdout=PIPE).communicate()[0])\\n\\n result = '{:{padding_size}}'.format(host, padding_size=padding_size)\\n if 'unreachable' in output:\\n result = result + 'Offline - unreachable'\\n self.offline_hosts.append(host)\\n elif 'could not find' in output:\\n result = result + 'Offline - could not find'\\n self.offline_hosts.append(host)\\n elif 'transmit failed' in output:\\n result = result + 'Offline - transmit failed'\\n self.offline_hosts.append(host)\\n elif 'timed out' in output:\\n result = result + 'Offline - timed out'\\n self.offline_hosts.append(host)\\n else:\\n result = result + 'Online'\\n print(result)\\n print()\",\n \"def ping(host):\\r\\n\\r\\n # Option for the number of packets as a function of\\r\\n param = '-n' if platform.system().lower() == 'windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n\\r\\n return subprocess.call(command) == 0\",\n \"def test_connectivity(self):\\n \\n for ip in self.IPs:\\n try:\\n s=pxssh.pxssh(options={\\\"PasswordAuthentication\\\" : \\\"no\\\"})\\n s.login(ip, \\\"as\\\", ssh_key=\\\"~/.ssh/id_as_ed25519\\\")\\n # send inocuous command\\n s.sendline('true')\\n self.assertTrue(s.prompt())\\n s.logout()\\n except pxssh.ExceptionPxssh as e:\\n print 'Login to {} failed!, error: {}'.format(ip, e)\\n self.assertTrue(False)\\n self.assertTrue(True)\",\n \"def ipsec_status(strongswan_obj, **kwargs):\\r\\n return strongswan_obj.ipsec_status_check(**kwargs)\",\n \"def test_ping_from_neighbor(duthosts, enum_rand_one_per_hwsku_frontend_hostname, nbrhosts):\\n duthost = duthosts[enum_rand_one_per_hwsku_frontend_hostname]\\n cfg_facts = duthost.config_facts(host=duthost.hostname, source=\\\"running\\\")[\\\"ansible_facts\\\"]\\n dut_ports = cfg_facts[\\\"PORT\\\"]\\n portchannel_itfs = cfg_facts[\\\"PORTCHANNEL_INTERFACE\\\"]\\n for portchannel, ip_list in list(portchannel_itfs.items()):\\n portchannel_members = list(cfg_facts[\\\"PORTCHANNEL_MEMBER\\\"][portchannel].keys())\\n hostname = dut_ports[portchannel_members[0]]['description'].split(':')[0]\\n for nbr_hostname, nbrhost in list(nbrhosts.items()):\\n if nbr_hostname != hostname:\\n continue\\n for ip in ip_list:\\n ip = ip.split('/')[0]\\n pytest_assert(nbrhost['host'].ping_dest(ip), \\\"{} ping port channel {} failed\\\".format(nbr_hostname, ip))\",\n \"def main():\\n\\n # the AnsibleModule object will be our abstraction for working with Ansible.\\n # This includes instantiation, a couple of common attr that will be the\\n # args/params passed to the execution, as well as if the module\\n # supports check mode\\n module = AnsibleModule(\\n argument_spec=dict(\\n hostvars=dict(type='raw', required=True),\\n report_timestamp=dict(type=str, required=False, default=''),\\n registered_dict_name=dict(type=str, required=False, default=\\\"get_sas_host_details_results\\\"),\\n include_hotfix_report=dict(type=bool, required=False, default=True),\\n hotfix_url = dict(type=str, required=True),\\n hotfix_master_file = dict(type=str, required=True)\\n ),\\n supports_check_mode=True\\n )\\n\\n # get module parameters\\n hostvars = module.params['hostvars']\\n report_timestamp = module.params['report_timestamp']\\n registered_dict_name = module.params['registered_dict_name']\\n include_hotfix_report = module.params['include_hotfix_report']\\n hotfix_url = module.params['hotfix_url']\\n hotfix_master_file = module.params['hotfix_master_file']\\n\\n # Starting in Ansible 2.8.1, there is the potential for hostvars\\n # to be passed as a byte string, if the dict is too large\\n # This will convert the str back to a dict before proceeding\\n if isinstance(hostvars, str):\\n hostvars = ast.literal_eval(hostvars.decode())\\n\\n results = dict()\\n results['sas_hosts'] = dict()\\n results['created'] = report_timestamp\\n\\n for inventory_hostname, host_vars in hostvars.items():\\n\\n # set up returnable values\\n unreachable = True\\n failed = True\\n failure_details = dict(\\n msg=\\\"\\\",\\n rc=0,\\n stderr=\\\"\\\",\\n stdout=\\\"\\\",\\n )\\n\\n # get the host details dict\\n host_details = host_vars.get(registered_dict_name)\\n\\n # check if the host has the registered dict\\n if host_details is not None:\\n\\n # host details exist, so host was reachable\\n unreachable = False\\n\\n # check if the host failed\\n failed = host_details['failed']\\n\\n # if the module reported a failure, collect details\\n if failed:\\n failure_details['msg'] = host_details['msg']\\n failure_details['rc'] = host_details['rc']\\n failure_details['stderr'] = host_details['module_stderr']\\n failure_details['stdout'] = host_details['module_stdout']\\n else:\\n # get module results\\n host_results = host_details.get('sas_host_details')\\n\\n if host_results is not None:\\n results['sas_hosts'].update(host_results)\\n else:\\n failed = True\\n\\n # if the results dict could not be found, mark the host as unreachable\\n if failed or unreachable:\\n host_groups = host_vars.get('group_names')\\n\\n if host_groups is not None and 'sas_all' in host_groups:\\n hostname = host_vars.get('ansible_fqdn')\\n if hostname is None or hostname == \\\"\\\":\\n hostname = host_vars.get('ansible_hostname')\\n if hostname is None or hostname == \\\"\\\":\\n hostname = host_vars.get('ansible_host')\\n if hostname is None or hostname == \\\"\\\":\\n hostname = host_vars.get('inventory_hostname')\\n if hostname is None or hostname == \\\"\\\":\\n hostname = inventory_hostname\\n\\n try:\\n host_groups.remove('sas_all')\\n host_groups.remove('sas-all')\\n except ValueError:\\n pass # do nothing\\n\\n results['sas_hosts'][hostname] = dict(\\n _id=hostname.replace('.', '-'),\\n _unreachable=unreachable,\\n _failed=failed,\\n _failure_details=failure_details,\\n ansible_host_groups=host_groups\\n )\\n else:\\n pass # this host isn't in sas_all so there's no need to try and report on it\\n\\n ##################################################################################\\n # This section will find all of the hotfixes available and add them to the report.\\n ##################################################################################\\n\\n # There are a few data structures that are complicated enough to warrant a description:\\n # fullReport\\n # This will hold all of the data in a format condusive to printing it out in the final report. This is how\\n # It is structured:\\n # fullReport (dict):\\n # key=Hot Fix Name, point to another dict:\\n # key=\\\"released\\\", points to a string containing the release date of the hotfix.\\n # key= \\\"installed\\\", points to a boolean that will reflect whether any of the packages used by this hotfix are installed on any of the machines in the deployment.\\n # key=\\\"upToDate\\\", point to a boolean that will reflest whether ALL of the packages used by this hotfix are up to date on ALL of the machines in the deployment.\\n # key=\\\"sasnote\\\", points to another dict:\\n # key=SASNote number, points to the description of the SASNote.\\n # key=\\\"package\\\", points to another dict:\\n # key=\\\"platform\\\" , points to another dict:\\n # key=OS, points to another dict:\\n # key=\\\"version\\\", points to the string of the version of the package.\\n # key=\\\"installed\\\", points to a boolean which reflects whether this package is installed on any machine in the deployment.\\n # key=\\\"upToDate\\\", points to a boolean which reflects whether this package is up to data on ALL of the machines in the deployment.\\n # key=\\\"os\\\", points to the fully qualified name of the operating system.\\n # key=\\\"arch\\\", points to the architecture of the OS (NOTE: This does not exist on Windows systems.)\\n # key=\\\"alreadyUpdated\\\", points to a boolean, which is used to keep track of whether the upToDate has already been set.\\n # key=\\\"installedVersions\\\", points to another dict:\\n # key=machineName, points to a 2 element list:\\n # [0]=string containing package version that is currently installed.\\n # [1]=boolean reflecting whether this version is at or above the package delevered in this hotfix.\\n #\\n ###########################################################################\\n #\\n # packageToHotFix\\n # This will hold a dict of lists:\\n # key: package name, pointing to a 2 element list:\\n # [0] OS\\n # [1] The Hotfix that this package is associated with.\\n #\\n ###########################################################################\\n #\\n # environmentReportDict\\n # This is inherited from the environment report, but it's probably worth documenting what it looks like.\\n # There is a lot of data inerherited, and I'm only describing what is used in this script.\\n # environmentReportDict\\n # key=hostname (for each machine in the deployment), pointing to another dict:\\n # key=\\\"OS\\\", pointing to string for the OS family.\\n # key=\\\"arch\\\", pointing to the string for the architecture of the host.\\n # key=\\\"sas_packages\\\", pointing to another dict:\\n # key=package number, pointing to another dict:\\n # key=\\\"attributes\\\", pointing to another dict:\\n # key=\\\"version\\\", pointing to a string of the package versions currently installed on the host.\\n ############################################################################\\n\\n results[\\\"include_hotfix_report\\\"] = include_hotfix_report\\n if include_hotfix_report:\\n # This is the URL from which to pull the hotfix files.\\n if hotfix_url[-1:] == '/':\\n baseURL = hotfix_url\\n else:\\n baseURL = hotfix_url + '/'\\n # This is the master file that lists which other files should be examined for the actual hotfixes themselves.\\n masterFile = hotfix_master_file\\n # This is the top level object to store the hotfix report information (see above).\\n fullReport = {}\\n # This is a dict of package to hotfixes (see above).\\n packageToHotfix = {}\\n # This boolean will help with debugging.\\n debug = False\\n\\n try:\\n # Parse the master file to obtain where the hotfix files are.\\n masterFileXML = urllib2.urlopen(baseURL + masterFile)\\n\\n # Parse the master file and build a list of all files.\\n allFilesRoot = ET.fromstring(masterFileXML.read())\\n results[\\\"contact_hotfix_website\\\"] = True\\n except urllib2.URLError :\\n results[\\\"contact_hotfix_website\\\"] = False\\n results[\\\"master_website\\\"] = baseURL + masterFile\\n if debug:\\n print(\\\"***** Error parsing \\\" + baseURL + masterFile)\\n print(traceback.format_exc())\\n print(\\\"***** No hot fix information obtained. Skipping hot fix report.\\\\n\\\\n\\\")\\n\\n if results[\\\"contact_hotfix_website\\\"]:\\n # Loop through the files discoverd in the master file\\n if debug:\\n print(\\\"Building hot fix report, based on master file input.\\\")\\n for file_tag in allFilesRoot.findall('File'):\\n currentFile = file_tag.get('fileName')\\n fileToParse = baseURL + currentFile\\n # Retrieve each file.\\n # Inside of each file, the lines are keyed by the hot fix id. There are three types of lines, in order:\\n # 1) id and release date\\n # 2) id, sasnote, sasnotetitle\\n # 3) id, OS, package.\\n # This script loops through to build a dictionary of dictonaries with the basic structure:\\n # ID\\n # Release Date\\n # SASNotes\\n # SASNote and Title\\n # ...\\n # Packages\\n # Package Name, Version, and OS\\n try:\\n currentFileXML = urllib2.urlopen(fileToParse)\\n currentFileRoot = ET.fromstring(currentFileXML.read())\\n updateID = \\\"\\\"\\n for update_tag in currentFileRoot.findall('update'):\\n currentUpdate = update_tag.get('id')\\n releaseDate = update_tag.get('released')\\n # To get the top level Dictionary seeded with the hot fix Name and release date.\\n if releaseDate is not None:\\n if currentUpdate in fullReport:\\n if debug:\\n print(\\\"WARNING! Hot Fix \\\" + currentUpdate + \\\" already discovered. Skipping\\\")\\n updateID = \\\"DUPLICATE-SKIP\\\"\\n else:\\n # The SCXXXX hot fixes are special. The package files are only included in\\n # Viya_<version>_<platform>_home.xml files. So, the entries in the\\n # scheduled_update_<platform>_<shipevent>.xml files can be skipped.\\n if currentUpdate.startswith(\\\"SC\\\") and currentFile.find(\\\"scheduled_update_\\\") < 0:\\n continue\\n updateID = currentUpdate\\n fullReport[updateID] = {}\\n fullReport[updateID][\\\"release_date\\\"] = releaseDate\\n fullReport[updateID][\\\"installed\\\"] = False\\n fullReport[updateID][\\\"upToDate\\\"] = False\\n # To get the SASNote information under the hot fix\\n else:\\n if updateID == \\\"DUPLICATE-SKIP\\\":\\n continue\\n sasNote = update_tag.get('sasnote')\\n sasNoteTitle = update_tag.get('sasnoteTitle')\\n if sasNote is not None:\\n if \\\"sasnote\\\" not in fullReport[updateID]:\\n fullReport[updateID][\\\"sasnote\\\"] = {}\\n # This string needs to be encoded because some non-ASCII characters are\\n # in some of the titles.\\n fullReport[updateID][\\\"sasnote\\\"][sasNote] = sasNoteTitle.encode('utf-8')\\n # To get the Package information under the hot fix.\\n else:\\n os = update_tag.get(\\\"os\\\")\\n fullPackage = update_tag.get(\\\"package\\\")\\n if fullPackage is not None:\\n if \\\"package\\\" not in fullReport[updateID]:\\n fullReport[updateID][\\\"package\\\"] = {}\\n\\n lastPeriodIndex = fullPackage.rfind(\\\".\\\")\\n # Format the package information.\\n # Windows does not have a dash in the version; Linux does. So, we need to break differently,\\n # depending on the OS.\\n if os.lower().find(\\\"windows\\\") > -1:\\n versionStartIndex = fullPackage.rfind(\\\"-\\\")\\n achitectureStartIndex = -1\\n versionEndIndex = lastPeriodIndex\\n osFamily = \\\"Windows\\\"\\n else:\\n versionStartIndex = fullPackage.rfind(\\\"-\\\", 0, fullPackage.rfind(\\\"-\\\"))\\n # Linux has architecture in the package. This will be stored in its own key.\\n achitectureStartIndex = fullPackage.rfind(\\\".\\\", 0, lastPeriodIndex)\\n # SLES has the string 'suse' in its package. This will strip it out (as well as an extra .).\\n if os.lower().find(\\\"suse\\\") > -1:\\n versionEndIndex = achitectureStartIndex - 5\\n osFamily = \\\"Suse\\\"\\n else:\\n if os.lower().find(\\\"yocto\\\") > -1:\\n versionEndIndex = achitectureStartIndex - 6\\n osFamily = \\\"Yocto\\\"\\n else:\\n if os.lower().find(\\\"ubuntu\\\") > -1:\\n versionStartIndex = fullPackage.rfind(\\\"_\\\", 0, fullPackage.rfind(\\\"_\\\"))\\n versionEndIndex = fullPackage.rfind(\\\"_\\\")\\n achitectureStartIndex = versionEndIndex\\n osFamily = \\\"Ubuntu\\\"\\n else:\\n if os.lower().find(\\\"red hat enterprise linux 7\\\") > -1:\\n versionStartIndex = versionStartIndex = fullPackage.rfind(\\\":\\\")\\n versionEndIndex = len(fullPackage)\\n achitectureStartIndex = -1\\n osFamily = \\\"RedHat\\\"\\n else:\\n versionEndIndex = achitectureStartIndex\\n osFamily = \\\"RedHat\\\"\\n package = fullPackage[:versionStartIndex]\\n packageVersion = fullPackage[versionStartIndex + 1:versionEndIndex]\\n architecture = fullPackage[achitectureStartIndex + 1:lastPeriodIndex]\\n\\n if package not in fullReport[updateID][\\\"package\\\"]:\\n fullReport[updateID][\\\"package\\\"][package] = {}\\n if \\\"platform\\\" not in fullReport[updateID][\\\"package\\\"][package]:\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"] = {}\\n if osFamily not in fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"]:\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily] = {}\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily][\\\"version\\\"] = packageVersion\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily][\\\"installed\\\"] = False\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily][\\\"upToDate\\\"] = False\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily][\\\"os\\\"] = os\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily][\\\"installedVersions\\\"] = {}\\n if achitectureStartIndex != -1:\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily][\\\"arch\\\"] = architecture\\n # This property is used to make sure that when evaluating the installed packages,\\n # the upToDate=false does not get overridden by a True at the end.\\n fullReport[updateID][\\\"package\\\"][package][\\\"platform\\\"][osFamily][\\\"alreadyUpdated\\\"] = False\\n\\n # Add to the package to hot fix dict.\\n if package not in packageToHotfix:\\n packageToHotfix[package] = []\\n packageToHotfix[package].append([osFamily, updateID])\\n\\n except ET.ParseError:\\n if debug:\\n print(\\\"***** Error parsing \\\" + fileToParse)\\n print(traceback.format_exc())\\n print(\\\"***** Skipping file.\\\\n\\\\n\\\")\\n except urllib2.HTTPError:\\n if debug:\\n print(\\\"***** Cannot access \\\" + fileToParse)\\n print(traceback.format_exc())\\n print(\\\"***** Skipping the file.\\\\n\\\\n\\\")\\n except:\\n if debug:\\n print(\\\"***** Error encountered with \\\" + fileToParse)\\n print(traceback.format_exc())\\n print(\\\"***** Skipping the file.\\\\n\\\\n\\\")\\n\\n if debug:\\n print(\\\"**** Build complete. Here are the hot fixes:\\\")\\n print_Full_Report(fullReport)\\n print(\\\"***********************************************************************************\\\")\\n print(\\\"**** Here is the package to hot fix dict:\\\")\\n print(\\\"***********************************************************************************\\\")\\n for current_package in packageToHotfix:\\n print(\\\" \\\" + current_package)\\n for machine_list in packageToHotfix[current_package]:\\n print(\\\" \\\" + machine_list[0] + \\\" @ \\\" + machine_list[1] + \\\".\\\")\\n print(\\\"***********************************************************************************\\\")\\n print(\\\"Report built.\\\")\\n print(\\\"Accessing environment Data.\\\")\\n\\n for currentMachine in results['sas_hosts']:\\n if not results['sas_hosts'][currentMachine][\\\"_unreachable\\\"] and not results['sas_hosts'][currentMachine][\\\"_failed\\\"]:\\n currentOS = results['sas_hosts'][currentMachine]['os']['family']\\n for currentPackage in results['sas_hosts'][currentMachine]['sas_packages']:\\n if currentPackage in packageToHotfix:\\n for osHotfix in packageToHotfix[currentPackage]:\\n if osHotfix[0] == currentOS:\\n currentHotfix = osHotfix[1]\\n installedVersion = \\\\\\n results['sas_hosts'][currentMachine]['sas_packages'][currentPackage]['attributes']['version']\\n if installedVersion.endswith('.suse'):\\n installedVersion = installedVersion[:-5]\\n else:\\n if installedVersion.endswith('.yocto'):\\n installedVersion = installedVersion[:-6]\\n else:\\n if '_' in installedVersion:\\n installedVersion = installedVersion[0:installedVersion.rfind(\\\"_\\\")]\\n hotfixVersion = fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"version\\\"]\\n upToDate = compare_versions(installedVersion, hotfixVersion) >= 0\\n fullReport[currentHotfix][\\\"installed\\\"] = True\\n fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"installed\\\"] = True\\n # If a previous pacakage marked updateToDate=True, it can still be pulled back to false if another package isn't\\n # up to date. If the previous package was marked upToDate=false, the hotfix cannot be marked true.\\n if not fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"alreadyUpdated\\\"] or \\\\\\n (fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"alreadyUpdated\\\"] and\\n fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"upToDate\\\"]):\\n fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"upToDate\\\"] = upToDate\\n fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"alreadyUpdated\\\"] = True\\n fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"installedVersions\\\"][currentMachine] = [installedVersion, upToDate]\\n\\n if debug:\\n print(\\\"Comparing evironment data to hotfix data.\\\")\\n for currentHotFix in fullReport:\\n cumulativeOverallUpToDate = True\\n # This will only allow the top level \\\"upToDate\\\" property to be set, if there is a package installed on this OS.\\n allowTopLevelUpdate = False\\n for currentPackage in fullReport[currentHotFix][\\\"package\\\"]:\\n cumulativeOSUpToDate = True\\n for currentOS in fullReport[currentHotFix][\\\"package\\\"][currentPackage][\\\"platform\\\"]:\\n if len(fullReport[currentHotFix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"installedVersions\\\"]) > 0:\\n cumulativeOSUpToDate = cumulativeOSUpToDate and \\\\\\n fullReport[currentHotFix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\n \\\"upToDate\\\"]\\n allowTopLevelUpdate = True\\n\\n cumulativeOverallUpToDate = cumulativeOverallUpToDate and cumulativeOSUpToDate\\n if allowTopLevelUpdate:\\n fullReport[currentHotFix][\\\"upToDate\\\"] = cumulativeOverallUpToDate\\n\\n # Now that the fullReport has been updated, go back and add to results, for the final report.\\n results[\\\"available_hotfixes\\\"] = {}\\n results[\\\"installed_hotfixes\\\"] = {}\\n\\n for currentHotfix in fullReport:\\n if not fullReport[currentHotfix][\\\"installed\\\"]:\\n continue\\n if fullReport[currentHotfix][\\\"upToDate\\\"]:\\n hotfix_dict_to_use = \\\"installed_hotfixes\\\"\\n else:\\n hotfix_dict_to_use = \\\"available_hotfixes\\\"\\n results[hotfix_dict_to_use][currentHotfix] = {}\\n results[hotfix_dict_to_use][currentHotfix][\\\"release_date\\\"] = fullReport[currentHotfix][\\\"release_date\\\"]\\n results[hotfix_dict_to_use][currentHotfix][\\\"packages\\\"] = []\\n for currentPackage in fullReport[currentHotfix][\\\"package\\\"]:\\n for currentOS in fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"]:\\n if not fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"installed\\\"]:\\n continue\\n for currentHost in fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"installedVersions\\\"]:\\n temp_dict = {}\\n temp_dict[\\\"hostname\\\"] = currentHost\\n temp_dict[\\\"package\\\"] = currentPackage\\n temp_dict[\\\"installed_version\\\"] = fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"installedVersions\\\"][currentHost][0]\\n temp_dict[\\\"hotfix_version\\\"] = fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"version\\\"]\\n temp_dict[\\\"up_to_date\\\"] = fullReport[currentHotfix][\\\"package\\\"][currentPackage][\\\"platform\\\"][currentOS][\\\"installedVersions\\\"][currentHost][1]\\n results[hotfix_dict_to_use][currentHotfix][\\\"packages\\\"].append(temp_dict)\\n # Format the SAS Note description so that we can respect any HTML tags that are included in the text.\\n results[hotfix_dict_to_use][currentHotfix][\\\"sas_notes\\\"] = {}\\n for current_number in fullReport[currentHotfix][\\\"sasnote\\\"]:\\n # Honor any html that is coming through.\\n temp_sasnote_description = fullReport[currentHotfix][\\\"sasnote\\\"][current_number]\\n temp_sasnote_description = temp_sasnote_description.replace(\\\"<\\\", \\\"<\\\")\\n temp_sasnote_description = temp_sasnote_description.replace(\\\">\\\", \\\">\\\")\\n # Build a link to the URL for the SAS Note.\\n hot_fix_prefix = current_number[:2]\\n hot_fix_postfix = current_number[2:]\\n sas_note_url = \\\"http://support.sas.com/kb/\\\" + hot_fix_prefix + \\\"/\\\" + hot_fix_postfix + \\\".html\\\"\\n sas_note_html_link = \\\"<a href=\\\\\\\"\\\" + sas_note_url + \\\"\\\\\\\"\\\\>\\\" + current_number + \\\"</a>\\\"\\n results[hotfix_dict_to_use][currentHotfix][\\\"sas_notes\\\"][current_number] = {\\\"sas_note_link\\\":sas_note_html_link, \\\"description\\\":temp_sasnote_description}\\n\\n # in the event of a successful module execution, you will want to\\n # simple AnsibleModule.exit_json(), passing the key/value results\\n #\\n # changed will always be 'False' since we'll never alter state on a host\\n module.exit_json(changed=False, processed_host_details=results)\",\n \"def ping():\\n return 'pong'\",\n \"def test_ping_host1(self, chirouter_runner):\\n chirouter_runner.start_mininet(\\\"3router.json\\\")\\n mn = chirouter_runner.mininet\\n\\n ping = chirouter_runner.ping(\\\"host4\\\", \\\"10.1.0.42\\\", count=4)\\n\\n ping.validate_output_success(num_expected=4, expected_source=\\\"10.1.0.42\\\")\",\n \"def arping(ipaddress=\\\"10.0.1.1\\\"):\\n\\n #Assuming use of arping on Red Hat Linux\\n p = subprocess.Popen(\\\"/usr/sbin/arping -c 2 %s\\\" % ipaddress, shell=True,\\n stdout=subprocess.PIPE)\\n out = p.stdout.read()\\n result = out.split()\\n #pattern = re.compile(\\\":\\\")\\n for item in result:\\n if ':' in item:\\n print(item)\",\n \"def ping(host):\\n import os, platform\\n\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def ping(host):\\n import os, platform\\n\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def ping(host):\\n import os, platform\\n\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def ping():\\n return jsonify({'response': 'pong'}), 200\",\n \"def ping(host, network_timeout=3):\\n \\n args = [\\n 'ping'\\n ]\\n\\n platform_os = platform.system()\\n\\n if platform_os == 'Windows':\\n args.extend(['-n', '1'])\\n args.extend(['-w', str(network_timeout * 1000)])\\n elif platform_os in ('Linux', 'Darwin'):\\n args.extend(['-c', '1'])\\n args.extend(['-W', str(network_timeout)])\\n else:\\n raise NotImplementedError('Unsupported OS: {}'.format(platform_os))\\n\\n args.append(host)\\n\\n try:\\n if platform_os == 'Windows':\\n output = subprocess.run(args, check=True, universal_newlines=True).stdout\\n\\n if output and 'TTL' not in output:\\n return False\\n else:\\n subprocess.run(args, check=True)\\n\\n return True\\n \\n except (subprocess.CalledProcessError, subprocess.TimeoutExpired):\\n return False\",\n \"async def ping(self, *args, **kwargs):\\n\\n return await self._makeApiCall(self.funcinfo[\\\"ping\\\"], *args, **kwargs)\",\n \"def test_ipam_ip_addresses_list(self):\\n pass\",\n \"def ping_test(hostaddr, count=2, timeout=1):\\n xping = subprocess.Popen(['/bin/ping', '-W', str(timeout), '-c', str(count), hostaddr],\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n xping.communicate()\\n xping.wait()\\n return not bool(xping.returncode)\",\n \"def ping (self,host):\\n # Ping parameters as function of OS\\n ping_str = \\\"-n 1\\\" if platform.system().lower()==\\\"windows\\\" else \\\"-c 1\\\"\\n\\n # Ping\\n return os.system(\\\"ping \\\" + ping_str + \\\" \\\" + host) == 0\",\n \"def HandlePingRequest(self, request, response):\\n self._publish_helper.HandlePingRequest(request, response)\",\n \"def remote_status():\",\n \"def validate_ping(result):\\n if '0 packets received' in str(result) or 'no answer from' in str(result) or '0 received' in str(result):\\n print 'Conectividade - DOWN'\\n return False\\n print 'Conectividade - OK'\\n return True\",\n \"def ping(self):\\n try:\\n api_ping = '{}://{}:{}/api/1.0.0/ping/{}/None'.format(\\n self._env.api_protocol,\\n self._env.api_host,\\n self._env.api_port,\\n self._key\\n )\\n self._env.logger.info('PING> {}'.format(api_ping))\\n\\n def check(response):\\n if response.code == 204:\\n self._env.logger.info('204 - Registering new routes')\\n self.register_routes()\\n elif response.code == 404:\\n self._env.logger.info('404 - api gateway is not available')\\n elif response.code != 200:\\n self._env.logger.error('{} - UNKNOWN ERROR'.format(response.code))\\n return response\\n\\n def log(failure):\\n \\\"\\\"\\\"\\n Just log the error, a return code of 'False' will be returned elsewhere\\n :param failure: A treq failure object\\n \\\"\\\"\\\"\\n return self._env.logger.warning('[ping] {}'.format(failure.getErrorMessage()))\\n\\n treq.get(api_ping).addCallback(check).addErrback(log)\\n\\n except requests.exceptions.ConnectionError as e:\\n self._env.logger.warning('ping failed for \\\"{}\\\"'.format(api_ping))\\n self._env.logger.warning('ping return = \\\"{}\\\"'.format(e.args[0].reason))\\n self._state = False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6234672","0.6234672","0.6234672","0.6234672","0.6228038","0.6227247","0.616574","0.60879576","0.607467","0.5944119","0.5944096","0.58443975","0.57498163","0.5747767","0.57241446","0.57212025","0.57056487","0.57054055","0.5702251","0.56238407","0.561111","0.56032324","0.5597133","0.5589898","0.5584241","0.5577437","0.557345","0.5567787","0.55620044","0.5549474","0.5544909","0.5541308","0.55261475","0.55201674","0.551795","0.5510635","0.5507625","0.549685","0.5490442","0.5489458","0.5486113","0.5486113","0.54782945","0.54650277","0.54596573","0.54525673","0.5426319","0.54231423","0.5414821","0.5413409","0.54013133","0.54011923","0.5399502","0.53920907","0.5388747","0.53859603","0.53757024","0.53597766","0.5359746","0.5350582","0.53496754","0.534489","0.534418","0.53350115","0.5315745","0.5315745","0.5307793","0.53028226","0.529996","0.5289476","0.52788335","0.5274973","0.5267537","0.5265466","0.5260874","0.52561843","0.52530974","0.52491707","0.5245504","0.52442414","0.52409","0.5231784","0.5230063","0.52290946","0.522506","0.52164894","0.52009606","0.51924294","0.51924294","0.51924294","0.51887083","0.5179203","0.5167832","0.5161675","0.5159773","0.5153702","0.5148496","0.51414055","0.51401657","0.51350105"],"string":"[\n \"0.6234672\",\n \"0.6234672\",\n \"0.6234672\",\n \"0.6234672\",\n \"0.6228038\",\n \"0.6227247\",\n \"0.616574\",\n \"0.60879576\",\n \"0.607467\",\n \"0.5944119\",\n \"0.5944096\",\n \"0.58443975\",\n \"0.57498163\",\n \"0.5747767\",\n \"0.57241446\",\n \"0.57212025\",\n \"0.57056487\",\n \"0.57054055\",\n \"0.5702251\",\n \"0.56238407\",\n \"0.561111\",\n \"0.56032324\",\n \"0.5597133\",\n \"0.5589898\",\n \"0.5584241\",\n \"0.5577437\",\n \"0.557345\",\n \"0.5567787\",\n \"0.55620044\",\n \"0.5549474\",\n \"0.5544909\",\n \"0.5541308\",\n \"0.55261475\",\n \"0.55201674\",\n \"0.551795\",\n \"0.5510635\",\n \"0.5507625\",\n \"0.549685\",\n \"0.5490442\",\n \"0.5489458\",\n \"0.5486113\",\n \"0.5486113\",\n \"0.54782945\",\n \"0.54650277\",\n \"0.54596573\",\n \"0.54525673\",\n \"0.5426319\",\n \"0.54231423\",\n \"0.5414821\",\n \"0.5413409\",\n \"0.54013133\",\n \"0.54011923\",\n \"0.5399502\",\n \"0.53920907\",\n \"0.5388747\",\n \"0.53859603\",\n \"0.53757024\",\n \"0.53597766\",\n \"0.5359746\",\n \"0.5350582\",\n \"0.53496754\",\n \"0.534489\",\n \"0.534418\",\n \"0.53350115\",\n \"0.5315745\",\n \"0.5315745\",\n \"0.5307793\",\n \"0.53028226\",\n \"0.529996\",\n \"0.5289476\",\n \"0.52788335\",\n \"0.5274973\",\n \"0.5267537\",\n \"0.5265466\",\n \"0.5260874\",\n \"0.52561843\",\n \"0.52530974\",\n \"0.52491707\",\n \"0.5245504\",\n \"0.52442414\",\n \"0.52409\",\n \"0.5231784\",\n \"0.5230063\",\n \"0.52290946\",\n \"0.522506\",\n \"0.52164894\",\n \"0.52009606\",\n \"0.51924294\",\n \"0.51924294\",\n \"0.51924294\",\n \"0.51887083\",\n \"0.5179203\",\n \"0.5167832\",\n \"0.5161675\",\n \"0.5159773\",\n \"0.5153702\",\n \"0.5148496\",\n \"0.51414055\",\n \"0.51401657\",\n \"0.51350105\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65288395"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":174,"numItemsPerPage":100,"numTotalItems":95772,"offset":17400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjIxOTAyNiwianRpIjoiZDZjYTAxYmQtZDMxYS00OWRhLWI4MzYtZjkyYzkyOWE2NzBlIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyMjI2MjYsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.6NxhTFssqLMuDXeslSER3zsJZ3FWsKuCSLjC0r-LwcERHqukC1tlyVkWuFVQup1fX3NS6oRFo3aj8XfnU3Q3DA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Perform a osd "action." | def _action(self, action, osd, info=None, **kwargs):
body = {action: info}
self.run_hooks('modify_body_for_action', body, **kwargs)
url = '/osds/%s/action' % base.getid(osd)
return self.api.client.post(url, body=body) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def device_action(self, client, action):\r\n client.deviceAction(action)",
"def perform_actual_action(self, action):\n self.game.perform_action(action)",
"def execute_action(self, agent, action):\n agent.bump = False\n agent.performance_measure -= 1\n \n if action == '... | [
"0.6288845",
"0.59816945",
"0.5836515",
"0.5802067",
"0.5802067",
"0.57797366",
"0.574658",
"0.56953174",
"0.56953174",
"0.5662237",
"0.5661676",
"0.5661676",
"0.5661676",
"0.5661676",
"0.5660734",
"0.56254244",
"0.5604492",
"0.5561712",
"0.5526677",
"0.5524998",
"0.55161124"... | 0.6980433 | 0 |
A clade in NCBI taxonomy, containing all leaf taxon's KEGG organisms. | def __init__(self, ncbiNames: 'e.g. Enterobacter or Proteobacteria/Gammaproteobacteria. Allows list of names, e.g. ["Gammaproteobacteria", "/Archaea"]', excludeUnclassified = defaultExcludeUnclassified, oneOrganismPerSpecies = defaultOneOrganismPerSpecies):
taxonomy = NCBI.getTaxonomy()
if isin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def HaarGOB(treeG):\n # number of level of the chain (or tree)\n Ntr = len(treeG)\n # reorder chain (optional) \n # reordering each level so that in each level the nodes are in the\n # descent order of degrees\n # compute u_l^c for level J_0 (top level)\n clusterJ0 = treeG[Ntr-1]['clusters']\n... | [
"0.5632232",
"0.55822456",
"0.54981554",
"0.5494588",
"0.5481911",
"0.54404086",
"0.54022205",
"0.5397675",
"0.53802574",
"0.5376222",
"0.5372132",
"0.5346099",
"0.5334962",
"0.5320827",
"0.5275802",
"0.5263081",
"0.52418846",
"0.5239825",
"0.52368",
"0.52352065",
"0.521025",... | 0.5424304 | 6 |
The SubstanceEC graph representing the collective metabolic network, occuring in any organism of the clade. This includes each and every EC number which occurs in any organism of this clade. | def collectiveMetabolism(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes, addEcDescriptions = False) -> SubstanceEcGraph:
graph = self.group.collectiveEcGraph(noMultifunctional = excludeMultifunctionalEnzymes, addCount = True, keepOnHeap = True, addEcDescriptions = addEcDescriptions)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def c_edges(self):\n self.compute_c_edges(self)\n return self._c_edges",
"def graph(self):\n assert self._modeled, \"Need to do calc_covariance\"\n return self._graph",
"def scc(self):\n return self.to_ddm().scc()",
"def edges(self):\r\n return self.capacity.keys()",... | [
"0.6119846",
"0.59690535",
"0.5778467",
"0.5737808",
"0.5701327",
"0.56940114",
"0.56930697",
"0.563835",
"0.5614581",
"0.5609551",
"0.55770725",
"0.5549687",
"0.55336034",
"0.55317324",
"0.54990387",
"0.5468927",
"0.5466693",
"0.54418373",
"0.54250294",
"0.5423668",
"0.54227... | 0.5447247 | 17 |
The SubstanceEnzyme graph representing the collective metabolic network, occuring in any organism of the clade. This includes each and every enzyme of every organism of this clade. | def collectiveMetabolismEnzymes(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:
graph = self.group.collectiveEnzymeGraph(noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)
graph.name = 'Collective metabolism enzymes ' + ' '.join(sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_graph_karateclub():\n all_members = set(range(34))\n club1 = {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 16, 17, 19, 21}\n # club2 = all_members - club1\n\n G = eg.Graph(name=\"Zachary's Karate Club\")\n for node in all_members:\n G.add_node(node+1)\n\n zacharydat = \"\"\"\\\n0 1 1... | [
"0.6028162",
"0.59044474",
"0.57775235",
"0.5567599",
"0.554882",
"0.5535332",
"0.5522798",
"0.5515686",
"0.55137914",
"0.55136275",
"0.54814845",
"0.5472295",
"0.5457305",
"0.5456937",
"0.54244757",
"0.54244757",
"0.54244757",
"0.5421997",
"0.5420029",
"0.5370936",
"0.536334... | 0.626008 | 0 |
The SubstanceEC graph representing the common metabolic network, shared among all organisms of the clade. This includes only EC numbers which occur in at least `majorityPercentageCoreMetabolism` % of all organisms of this clade. | def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:
graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excludeMul... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conservedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabo... | [
"0.6851328",
"0.6829522",
"0.67308336",
"0.67119503",
"0.6634819",
"0.66100425",
"0.6589145",
"0.6467408",
"0.624838",
"0.611285",
"0.60297996",
"0.60168624",
"0.60092956",
"0.5978053",
"0.5830879",
"0.58009404",
"0.575013",
"0.5714629",
"0.5646468",
"0.5639464",
"0.56288904"... | 0.7358444 | 0 |
The SubstanceEnzyme graph representing the common metabolic network, shared among all organisms of the clade. | def coreMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:
graph = self.group.collectiveEnzymeGraphByEcMajority(majorityPercentage = majorityPercentageCoreMetabolism, m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def network(self):\n G = nx.MultiDiGraph()\n reaction_hash = []\n product_count = 0\n mapping = {}\n reaction_count = 0\n\n for r in self.reactions:\n reaction_count += 1\n\n reaction_dict = r.__dict__\n G.add_edge(reaction_dict.get('left')... | [
"0.6422785",
"0.6301998",
"0.6166783",
"0.61175174",
"0.60750496",
"0.6073714",
"0.60270894",
"0.5952155",
"0.5944933",
"0.59022546",
"0.5872501",
"0.58624905",
"0.58611155",
"0.585283",
"0.5848682",
"0.58481604",
"0.58378226",
"0.5821275",
"0.5820361",
"0.58025944",
"0.57834... | 0.53822803 | 73 |
The number of organisms (leaf taxons) in this clade. Returns int The number of organisms (leaf taxons) in this clade. | def organismsCount(self) -> int:
return self.group.organismsCount | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def leaf_count(self) -> int:\n if self.children == []:\n return 1\n else:\n return sum([x.leaf_count() for x in self.children])",
"def n_trees(self):\n return len(self.data_kd)",
"def count_atoms(self):\n n = 0\n for chain in self.iter_chains():\n ... | [
"0.67566955",
"0.6556457",
"0.6517457",
"0.6517457",
"0.6510434",
"0.64950323",
"0.6437851",
"0.6435725",
"0.64108914",
"0.6374372",
"0.6369585",
"0.6362572",
"0.63609064",
"0.63561183",
"0.63489836",
"0.63383377",
"0.6318547",
"0.63147897",
"0.63098747",
"0.63017595",
"0.627... | 0.77582914 | 0 |
The substanceEnzyme graph of all gene duplicated enzymes of the core metabolism. | def geneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
geneDuplicationModel = SimpleGeneDup... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorit... | [
"0.6935128",
"0.6869094",
"0.63864636",
"0.63718253",
"0.62681776",
"0.6187902",
"0.6169614",
"0.61466265",
"0.6134928",
"0.61197543",
"0.61049575",
"0.60786825",
"0.58720994",
"0.5807282",
"0.5740409",
"0.5649418",
"0.56281716",
"0.5621698",
"0.5607962",
"0.5592912",
"0.5568... | 0.6968888 | 0 |
All gene duplicated enzymes of the core metabolism, pointing to all their duplicates. | def geneDuplicatedEnzymesDict(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Dict[Enzyme, Set[GeneID]]:
enzymeGraph = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
geneDuplicationModel = SimpleGeneDuplication
geneIDs... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:\n \n \n enzymes = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()\n geneDuplicationModel = SimpleGeneDuplication\n ... | [
"0.7099837",
"0.7025168",
"0.69302565",
"0.6652433",
"0.65650713",
"0.65152824",
"0.6477518",
"0.6289955",
"0.6230133",
"0.62202966",
"0.60999846",
"0.59928745",
"0.5987962",
"0.58550674",
"0.5808454",
"0.5769425",
"0.564335",
"0.5591993",
"0.5578701",
"0.5500059",
"0.5488227... | 0.71266633 | 0 |
All gene duplicated enzymes of the core metabolism, paired with each of their duplicates. If enzyme A is a duplicate of enzyme B and vice versa, this does not return duplicates, but returns only one pair, with the "smaller" enzyme as the first value. An enzyme is "smaller" if its gene ID string is "smaller". | def geneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
enzymes = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
geneDuplicationModel = SimpleGeneDuplication
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def divergedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:\n # get diverged metabolism\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()\n ... | [
"0.74381024",
"0.7337452",
"0.7129101",
"0.690591",
"0.6749732",
"0.66438884",
"0.6258788",
"0.6047571",
"0.59916437",
"0.583954",
"0.5826309",
"0.5580656",
"0.5540774",
"0.54498655",
"0.52426744",
"0.5234347",
"0.5220493",
"0.5200757",
"0.5197881",
"0.51871806",
"0.51799005"... | 0.7607282 | 0 |
The substanceEnzyme graph of all neofunctionalised enzymes of the core metabolism. | def neofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEnzymeGraph:
# get neofunctionalisations
neofunctionalisedEnzymes = self._neofunctionalisedEnzymes(majorityPe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collectiveMetabolismEnzymes(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:\n graph = self.group.collectiveEnzymeGraph(noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\n graph.name = 'Collective metabolism enzymes ' + ' '.j... | [
"0.65755415",
"0.65146273",
"0.6336113",
"0.6197786",
"0.6159029",
"0.60107636",
"0.5905536",
"0.58942384",
"0.5848229",
"0.58273816",
"0.57506675",
"0.57506675",
"0.57506675",
"0.57395303",
"0.5701725",
"0.56882715",
"0.5686614",
"0.5670043",
"0.56549966",
"0.56356984",
"0.5... | 0.590379 | 7 |
The substanceEC graph of EC numbers belonging to function changes of neofunctionalised enzymes of the core metabolism. Only EC numbers which could have actually taken part in a function change are reported. This is because enzymes can have multiple EC numbers, while only some might be eligible for a function change. Fo... | def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:
# get neofunctionalisations ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def redundantECsForContributingNeofunctionalisation(self, \n majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, \n majorityPercentageNeofunctionalisation = defaultMajorityPercentag... | [
"0.6066636",
"0.5614624",
"0.5553752",
"0.5487054",
"0.54392904",
"0.54148823",
"0.5396551",
"0.53906614",
"0.52966624",
"0.52825737",
"0.5221849",
"0.5204589",
"0.51931787",
"0.5125437",
"0.5115151",
"0.51081955",
"0.5107046",
"0.5076023",
"0.50700116",
"0.50495934",
"0.5038... | 0.5634353 | 1 |
Get neofunctionalisation events of all enzymes in the core metabolism. | def neofunctionalisations(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None) -> Set[Neofunctionalisation]:
# get neofunctionalisations
return self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eValue, co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:\n ... | [
"0.59909326",
"0.57868326",
"0.57269204",
"0.5654965",
"0.5592295",
"0.5470599",
"0.54684854",
"0.54546684",
"0.54503614",
"0.54164416",
"0.54042697",
"0.5348139",
"0.5230972",
"0.52255017",
"0.52217627",
"0.52217627",
"0.5192401",
"0.51829016",
"0.5179526",
"0.5167479",
"0.5... | 0.6120901 | 0 |
Get neofunctionalisation events of all enzymes in the core metabolism, grouped by each possible function change event. | def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def neofunctionalisations(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, eValue = defaultEValue, considerOnlyECs = None) -> Set[Neofunctionalisation]:\n # get neofunctionalisations \n return self._neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, eVa... | [
"0.5865203",
"0.55186284",
"0.54669976",
"0.54044694",
"0.5370907",
"0.53479284",
"0.532519",
"0.52754444",
"0.5167715",
"0.5159104",
"0.5134438",
"0.5128673",
"0.5123458",
"0.5117896",
"0.5097917",
"0.5074114",
"0.49859592",
"0.49851054",
"0.49779522",
"0.49597242",
"0.49597... | 0.68809205 | 0 |
Get neofunctionalisation events of all enzymes in the core metabolism, which contribute to redundancy, pointing to the EC numbers their function changes' EC numbers provides redundancy for. | def redundantECsForContributingNeofunctionalisation(self,
majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism,
majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofun... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def neofunctionalisationsForFunctionChange(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, eValue = defaultEValue, considerOnlyECs = None) -> Dict[FunctionChange, Set[Neofunctionalisation]]:\n ... | [
"0.6847851",
"0.6301637",
"0.6094617",
"0.60497826",
"0.5845832",
"0.58000714",
"0.5776784",
"0.5648149",
"0.54955536",
"0.547115",
"0.5464117",
"0.5397465",
"0.5397401",
"0.5384487",
"0.5378686",
"0.5366517",
"0.5358685",
"0.5353077",
"0.53527224",
"0.5348591",
"0.53361887",... | 0.6713973 | 1 |
Two clades in NCBI taxonomy, 'child' is assumed younger than 'parent'. Does not check if the child taxon is actually a child of the parent taxon. | def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified, oneOrganismPerSpecies = defaultOneOrganismPerSpecies):
# read NCBI names from Clade object, if necessary
if isinstance(parent, Clade):
self.parentClade = parent
else:
self.parentClade = Cl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified):\n # read first NCBI name from Clade object, if necessary\n if isinstance(parent, Clade):\n parentNCBIname = parent.ncbiNames[0]\n elif not isinstance(parent, str):\n # must be iterable, e... | [
"0.6561486",
"0.60595864",
"0.5947192",
"0.5942709",
"0.5786277",
"0.57073885",
"0.5304779",
"0.52430177",
"0.52060413",
"0.51680994",
"0.51654136",
"0.51636094",
"0.5141324",
"0.5114871",
"0.5113854",
"0.51016045",
"0.5086166",
"0.5085786",
"0.50788975",
"0.50663656",
"0.505... | 0.5611119 | 6 |
All names/paths in NCBI taxonomy used to create the parent clade. | def parentNCBInames(self):
return self.parentClade.ncbiNames | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def childNCBInames(self):\n return self.childClade.ncbiNames",
"def display_all_paths(taxonomy):\n for i,entry in enumerate(taxonomy):\n print \"For nodeId : {} :: NodeName : {} \" .format(entry['nodeId'], entry['nodeName'])\n parentId = entry['parentId']\n parentName = entry['pare... | [
"0.66094685",
"0.5948672",
"0.59455013",
"0.5883354",
"0.58186686",
"0.57299733",
"0.5665493",
"0.5624762",
"0.55911696",
"0.55900514",
"0.55431145",
"0.554038",
"0.55155194",
"0.5509813",
"0.55068725",
"0.55050105",
"0.5497012",
"0.5494448",
"0.54918534",
"0.546851",
"0.5436... | 0.6904 | 0 |
All names/paths in NCBI taxonomy used to create the child clade. | def childNCBInames(self):
return self.childClade.ncbiNames | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def children(self, taxon, taxonomy):\n\n c = set()\n for taxon_id, taxa in taxonomy.items():\n if taxon in taxa:\n\n if taxon.startswith('s__'):\n c.add(taxon_id)\n else:\n taxon_index = taxa.index(taxon)\n ... | [
"0.626776",
"0.6162786",
"0.60941124",
"0.60593927",
"0.6052144",
"0.5884141",
"0.5847267",
"0.56961423",
"0.56444347",
"0.5616592",
"0.56161517",
"0.55935484",
"0.55766165",
"0.5564394",
"0.55252403",
"0.54684275",
"0.5455569",
"0.54430205",
"0.5435376",
"0.54307735",
"0.539... | 0.6842796 | 0 |
SubstanceEC graph of the conserved core metabolism. | def conservedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEcGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excl... | [
"0.6580189",
"0.6125802",
"0.5889315",
"0.5867898",
"0.5865846",
"0.5857007",
"0.5807236",
"0.5736168",
"0.57251334",
"0.5632387",
"0.5575763",
"0.5573248",
"0.5559867",
"0.5544407",
"0.5537933",
"0.55150014",
"0.5493232",
"0.5482677",
"0.5473975",
"0.5453563",
"0.5412237",
... | 0.6567516 | 1 |
SubstanceEC graph of the added core metabolism. | def addedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEcGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excl... | [
"0.6702209",
"0.6497114",
"0.61611027",
"0.61203563",
"0.6050524",
"0.59818614",
"0.5975307",
"0.5904523",
"0.58224356",
"0.58078855",
"0.57479113",
"0.574062",
"0.56759965",
"0.560662",
"0.5584677",
"0.5557808",
"0.5547269",
"0.54960835",
"0.54925925",
"0.54916406",
"0.54622... | 0.6712265 | 0 |
SubstanceEC graph of the lost core metabolism. | def lostMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEcGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCoreMetabolism) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excl... | [
"0.6420541",
"0.6397231",
"0.62007076",
"0.602886",
"0.5997335",
"0.5955496",
"0.59275013",
"0.5926646",
"0.5912952",
"0.5824851",
"0.58129376",
"0.57867503",
"0.57554734",
"0.57294613",
"0.5672753",
"0.5659814",
"0.55902183",
"0.5589726",
"0.554685",
"0.55202645",
"0.5518092... | 0.62924904 | 2 |
SubstanceEC graph of the diverged core metabolism. | def divergedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEcGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def coreMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEcGraph:\n graph = self.group.majorityEcGraph(majorityPercentage = majorityPercentageCoreMetabolism, noMultifunctional = excl... | [
"0.6751704",
"0.60177904",
"0.60164267",
"0.590895",
"0.59067005",
"0.58751273",
"0.5843088",
"0.58061826",
"0.56646645",
"0.56605947",
"0.5620607",
"0.55881226",
"0.55458206",
"0.5473036",
"0.5414878",
"0.537738",
"0.5358601",
"0.5356833",
"0.5347023",
"0.534355",
"0.5315393... | 0.5925859 | 3 |
SubstanceEC graph of the unified core metabolisms. The lost metabolism of the parent is coloured in blue, the conserved metabolism of both in red, and the added metabolism of the child in pink. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured. | def unifiedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEcGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercentageCor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def divergedMetabolism(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorityPerce... | [
"0.66438884",
"0.653458",
"0.6519239",
"0.64919496",
"0.63315225",
"0.63292813",
"0.6266335",
"0.6138613",
"0.60851824",
"0.60435796",
"0.59619445",
"0.5899194",
"0.5817667",
"0.5779564",
"0.57427",
"0.57339853",
"0.5727987",
"0.5700911",
"0.5666281",
"0.563707",
"0.5624694",... | 0.6774996 | 0 |
SubstanceEnzyme graph derived from the unified core metabolisms. The lost metabolism of the parent is coloured in blue, the conserved metabolism of both in red, and the added metabolism of the child in pink. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not coloured. | def unifiedMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
parentGraph = self.parentClade.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)
childGraph = self.childClade.coreMetabolismEnzymes(majorityPercen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unifiedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph: \n parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)\n ch... | [
"0.6252327",
"0.60120577",
"0.5928985",
"0.5926421",
"0.58765775",
"0.58404446",
"0.5823357",
"0.58075684",
"0.57982713",
"0.5772567",
"0.57336694",
"0.5562557",
"0.5510959",
"0.54624",
"0.5434476",
"0.5418058",
"0.5414687",
"0.54030573",
"0.54009295",
"0.5381006",
"0.5378098... | 0.64225346 | 0 |
Two SubstanceEnzyme graphs of geneduplicated enzymes, derived from the conserved core metabolism. First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually. Then, for parent and child, the... | def conservedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentGeneDuplicated = self.parentClade... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorit... | [
"0.66521823",
"0.64501685",
"0.6347639",
"0.62944245",
"0.62218815",
"0.6211035",
"0.5993993",
"0.5903329",
"0.5869201",
"0.5856222",
"0.58459413",
"0.5815877",
"0.5802752",
"0.57704216",
"0.5740368",
"0.567628",
"0.5618934",
"0.55103505",
"0.5490561",
"0.5435735",
"0.5388179... | 0.6191911 | 6 |
SubstanceEnzyme graph of geneduplicated enzymes, derived from the added core metabolism. First, the added core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the child's enzyme metabolism. | def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majo... | [
"0.67369306",
"0.6330515",
"0.6303329",
"0.62048954",
"0.6128838",
"0.6119623",
"0.6048695",
"0.5975053",
"0.5937219",
"0.5809199",
"0.57253855",
"0.5707537",
"0.56158155",
"0.5594471",
"0.55635613",
"0.5519335",
"0.5513304",
"0.5461732",
"0.53989816",
"0.5391257",
"0.5377590... | 0.69637746 | 0 |
SubstanceEnzyme graph of geneduplicated enzymes, derived from the lost core metabolism. First, the lost core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the parent's enzyme metabolism. | def lostMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPercent... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorit... | [
"0.6755201",
"0.6535308",
"0.62986535",
"0.62453306",
"0.6057391",
"0.60101473",
"0.5982564",
"0.5913863",
"0.5899153",
"0.5793252",
"0.57070243",
"0.56880486",
"0.5584127",
"0.5576912",
"0.55608404",
"0.5558294",
"0.5490599",
"0.5468845",
"0.54171175",
"0.54139686",
"0.53863... | 0.66248214 | 1 |
Two SubstanceEnzyme graphs of geneduplicated enzymes, derived from the diverged core metabolism. First, the diverged core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the collective parent's and child's metabolism individually. | def divergedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentGeneDuplicated = self.parentClade.ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorit... | [
"0.6781583",
"0.6486627",
"0.63396204",
"0.6203486",
"0.6108755",
"0.6007883",
"0.59520006",
"0.59370637",
"0.59090215",
"0.58419424",
"0.5797574",
"0.5780279",
"0.5770737",
"0.5715894",
"0.5696853",
"0.555999",
"0.5557011",
"0.55346835",
"0.5528872",
"0.5479832",
"0.5477482"... | 0.6241954 | 3 |
SubstanceEnzyme graph of geneduplicated enzymes, derived from the unified core metabolisms. The lost metabolism of the parent is coloured in blue, the conserved metabolism of both in red, and the added metabolism of the child in pink. The colouring is realised by adding a 'colour' attribute to each edge. Nodes are not ... | def unifiedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childGeneDuplicate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unifiedMetabolismEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:\n parentGraph = self.parentClade.coreMetabolismEnzymes(majorityPercentageCoreMetabolism)\n childGraph = self.childClade.coreMetabolismEnzymes(majorit... | [
"0.6744551",
"0.6610802",
"0.6297652",
"0.6144678",
"0.6123074",
"0.59895116",
"0.59827703",
"0.59156704",
"0.5911058",
"0.58746463",
"0.58439463",
"0.5841829",
"0.5766261",
"0.57605356",
"0.56579214",
"0.5651781",
"0.5634423",
"0.5581046",
"0.5564763",
"0.5537896",
"0.549665... | 0.65293896 | 2 |
Pairs of geneduplicated enzymes, derived from the conserved core metabolism. First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually. Then, for parent and child, the geneduplicated enzym... | def conservedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Tuple[Set[Tuple[Enzyme, Enzyme]]]:
# get conserved metabolism
conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def divergedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:\n # get diverged metabolism\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()\n ... | [
"0.70549107",
"0.69402546",
"0.6706098",
"0.6682713",
"0.6562122",
"0.64990956",
"0.63584626",
"0.6323621",
"0.62530905",
"0.61966795",
"0.61394745",
"0.6038204",
"0.60298336",
"0.5788429",
"0.5784712",
"0.56987387",
"0.56817615",
"0.56580746",
"0.564051",
"0.555223",
"0.5536... | 0.703319 | 1 |
Pairs of geneduplicated enzymes, derived from the added core metabolism. First, the added core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the child's enzyme metabolism. Then the geneduplicated enzymes are calculated. Finally, the geneduplicated enzyme pairs of th... | def addedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
# get added metabolism
addedMetabolismEnzymes = self.addedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorit... | [
"0.6991242",
"0.6888838",
"0.6858794",
"0.66786027",
"0.64065367",
"0.6397439",
"0.63523155",
"0.6340977",
"0.6323248",
"0.6269163",
"0.61335814",
"0.61182314",
"0.590612",
"0.58313143",
"0.58045435",
"0.5669029",
"0.5563966",
"0.54859126",
"0.54399043",
"0.5420107",
"0.54190... | 0.6781111 | 3 |
Pairs of geneduplicated enzymes, derived from the lost core metabolism. First, the lost core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the parent's enzyme metabolism. Then the geneduplicated enzymes are calculated. Finally, the geneduplicated enzyme pairs of the... | def lostMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
# get added metabolism
lostMetabolismEnzymes = self.lostMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
# get gen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def divergedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:\n # get diverged metabolism\n divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()\n ... | [
"0.67933184",
"0.6792436",
"0.6789464",
"0.6684693",
"0.6612867",
"0.6583527",
"0.6384082",
"0.63719314",
"0.6275588",
"0.61921793",
"0.61527735",
"0.61174685",
"0.59615004",
"0.5912156",
"0.58016247",
"0.57320845",
"0.5670212",
"0.5606411",
"0.55643255",
"0.5555347",
"0.5532... | 0.6283631 | 8 |
Pairs of geneduplicated enzymes, derived from the diverged core metabolism. First, the diverged core metabolism is calculated. Then, the enzymes associated with the added EC numbers are extracted from the collective parent's and child's metabolism individually. Then, for parent and child, the geneduplicated enzyme pair... | def divergedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
# get diverged metabolism
divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unifiedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]: \n parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)\n childGeneDuplicated = self.ch... | [
"0.68335646",
"0.6791156",
"0.6528488",
"0.647306",
"0.6471037",
"0.6426987",
"0.6238266",
"0.6163832",
"0.61556786",
"0.61163086",
"0.60860956",
"0.59248537",
"0.5918993",
"0.5840949",
"0.58134377",
"0.5805267",
"0.5802707",
"0.5529467",
"0.5491607",
"0.54788053",
"0.5477662... | 0.7014142 | 0 |
Pairs of geneduplicated enzymes, derived from the unified core metabolisms. | def unifiedMetabolismGeneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:
parentGeneDuplicated = self.parentClade.geneDuplicatedEnzymePairs(majorityPercentageCoreMetabolism)
childGeneDuplicated = self.childClad... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geneDuplicatedEnzymePairs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> Set[Tuple[Enzyme, Enzyme]]:\n \n \n enzymes = self.coreMetabolismEnzymes(majorityPercentageCoreMetabolism).getEnzymes()\n geneDuplicationModel = SimpleGeneDuplication\n ... | [
"0.66328585",
"0.6220458",
"0.61605203",
"0.59667987",
"0.59406346",
"0.59398246",
"0.592409",
"0.58372545",
"0.58349013",
"0.5752567",
"0.5708292",
"0.5680166",
"0.5635978",
"0.55386114",
"0.5525389",
"0.548303",
"0.5480818",
"0.5471715",
"0.54622984",
"0.54544353",
"0.54533... | 0.6404736 | 1 |
Two SubstanceEnzyme graphs of neofunctionalised enzymes, derived from the conserved core metabolism. First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually. Then, for parent and child, ... | def conservedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
conservedMetabolismEnzymes = self.conservedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentNeofunctionalised= self.parent... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismGeneDuplicatedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majorit... | [
"0.64256305",
"0.63676214",
"0.622601",
"0.61938006",
"0.6062135",
"0.6010304",
"0.6010293",
"0.60097474",
"0.59877676",
"0.5869369",
"0.5815081",
"0.5781504",
"0.5677861",
"0.5672876",
"0.5646275",
"0.5618267",
"0.557111",
"0.5519256",
"0.5493546",
"0.5470436",
"0.54425895",... | 0.5749789 | 12 |
SubstanceEnzyme graph of neofunctionalised enzymes, derived from the added core metabolism. | def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collectiveMetabolismEnzymes(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:\n graph = self.group.collectiveEnzymeGraph(noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\n graph.name = 'Collective metabolism enzymes ' + ' '.j... | [
"0.62832594",
"0.6032561",
"0.59919894",
"0.5936504",
"0.58536375",
"0.5780906",
"0.5709709",
"0.56339985",
"0.56321144",
"0.561988",
"0.56182474",
"0.5617661",
"0.5614264",
"0.5597388",
"0.5581094",
"0.5547819",
"0.5524394",
"0.54902864",
"0.5435478",
"0.54295534",
"0.534995... | 0.6251779 | 1 |
SubstanceEnzyme graph of neofunctionalised enzymes, derived from the lost core metabolism. | def lostMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)
childCoreMetabolism = self.childClade.coreMetabolism(majorityPerc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majo... | [
"0.619215",
"0.61078405",
"0.59768206",
"0.58706874",
"0.5782916",
"0.5741197",
"0.57312053",
"0.5701871",
"0.56801665",
"0.56514645",
"0.56266373",
"0.56028616",
"0.56004995",
"0.55960053",
"0.5593098",
"0.55900544",
"0.5576522",
"0.55513394",
"0.5510709",
"0.54889655",
"0.5... | 0.61164635 | 1 |
Two SubstanceEnzyme graphs of neofunctionalised enzymes, derived from the diverged core metabolism. | def divergedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False):
divergedMetabolismEnzymes = self.divergedMetabolismEnzymes(majorityPercentageCoreMetabolism, colour = colour)
parentNeofunctionalised = self.parentCl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism) -> SubstanceEnzymeGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetabolism)\n childCoreMetabolism = self.childClade.coreMetabolism(majo... | [
"0.59838927",
"0.589952",
"0.5832633",
"0.56326586",
"0.56310344",
"0.5626422",
"0.56225055",
"0.55621135",
"0.54962337",
"0.54747903",
"0.54667443",
"0.5462511",
"0.54569364",
"0.53659475",
"0.53473836",
"0.5333074",
"0.5322089",
"0.53175056",
"0.5287885",
"0.5286352",
"0.52... | 0.5563202 | 7 |
SubstanceEnzyme graph of neofunctionalised enzymes, derived from the unified core metabolisms. | def unifiedMetabolismNeofunctionalisedEnzymes(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, colour = False) -> SubstanceEnzymeGraph:
parentNeofunctionalised = self.parentClade.neofunctionalisedEnzymes(majorityPercentageCoreMetabolism, colour = False)
childNeof... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collectiveMetabolismEnzymes(self, excludeMultifunctionalEnzymes = defaultExcludeMultifunctionalEnzymes) -> SubstanceEnzymeGraph:\n graph = self.group.collectiveEnzymeGraph(noMultifunctional = excludeMultifunctionalEnzymes, keepOnHeap = True)\n graph.name = 'Collective metabolism enzymes ' + ' '.j... | [
"0.617909",
"0.61629224",
"0.5923444",
"0.5919812",
"0.59115475",
"0.58200246",
"0.56218606",
"0.56167334",
"0.55720574",
"0.5556307",
"0.5541195",
"0.5539453",
"0.5454966",
"0.54378295",
"0.54142547",
"0.541205",
"0.5399196",
"0.5363297",
"0.5334433",
"0.52926433",
"0.528798... | 0.6051317 | 2 |
Two SubstanceEC graphs of "neofunctionalised" EC numbers, derived from the conserved core metabolism. First, the conserved core metabolism is calculated. Then, the enzymes associated with the conserved EC numbers are extracted from the collective parent's and child's metabolism individually. Then, for parent and child,... | def conservedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):
conservedMetabolism = self.conservedMetabolism(majorityPercentageCoreMetabolism)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:\n parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCo... | [
"0.64958113",
"0.6374259",
"0.62749887",
"0.6169494",
"0.6111205",
"0.5938053",
"0.5924793",
"0.58652455",
"0.57341826",
"0.56797636",
"0.5670327",
"0.56631213",
"0.56203586",
"0.5609623",
"0.55797595",
"0.5485678",
"0.5472537",
"0.54486245",
"0.5443317",
"0.5437867",
"0.5432... | 0.63496363 | 2 |
SubstanceEC graph of "neofunctionalised" EC numbers, derived from the added core metabolism. | def addedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMeta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \n parentNeofunctionalised = self.parentClade.neofun... | [
"0.6423988",
"0.6371142",
"0.63001156",
"0.62050164",
"0.6046099",
"0.5791749",
"0.5715767",
"0.55602425",
"0.5521478",
"0.5479102",
"0.54652435",
"0.5458608",
"0.54561746",
"0.54515",
"0.5428723",
"0.5406992",
"0.53999406",
"0.53977454",
"0.52835554",
"0.5282024",
"0.526819"... | 0.65178627 | 0 |
SubstanceEC graph of "neofunctionalised" EC numbers, derived from the lost core metabolism. | def lostMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation) -> SubstanceEcGraph:
parentCoreMetabolism = self.parentClade.coreMetabolism(majorityPercentageCoreMetab... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \n parentNeofunctionalised = self.parentClade.neofun... | [
"0.62724626",
"0.6260132",
"0.6220738",
"0.62084836",
"0.61106306",
"0.5663413",
"0.5543432",
"0.5502784",
"0.5501765",
"0.5500046",
"0.54863024",
"0.54842806",
"0.54061544",
"0.5403373",
"0.5400027",
"0.53941774",
"0.5352519",
"0.5325761",
"0.53250736",
"0.53149056",
"0.5311... | 0.61469126 | 4 |
Two SubstanceEC graphs of "neofunctionalised" EC numbers, derived from the diverged core metabolism. | def divergedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False):
divergedMetabolism = self.divergedMetabolism(majorityPercentageCoreMetabolism, col... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph: \n parentNeofunctionalised = self.parentClade.neofun... | [
"0.6214158",
"0.60957927",
"0.59478533",
"0.58485806",
"0.57059896",
"0.5507641",
"0.5501157",
"0.54937583",
"0.54590195",
"0.5301212",
"0.52908236",
"0.52827936",
"0.5239076",
"0.5235464",
"0.5215645",
"0.52147555",
"0.52099645",
"0.5206502",
"0.5192779",
"0.5181326",
"0.517... | 0.61443126 | 1 |
SubstanceEC graph of "neofunctionalised" EC numbers, derived from the unified core metabolisms. | def unifiedMetabolismNeofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False) -> SubstanceEcGraph:
parentNeofunctionalised = self.parentClade.neofunctiona... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def neofunctionalisedECs(self, majorityPercentageCoreMetabolism = defaultMajorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation = defaultMajorityPercentageNeofunctionalisation, colour = False, eValue = defaultEValue, considerOnlyECs = None) -> SubstanceEcGraph:\n # get neofunctionalisation... | [
"0.6325263",
"0.6321526",
"0.62352514",
"0.6154268",
"0.6030487",
"0.5650482",
"0.5579346",
"0.55359584",
"0.55139494",
"0.5471832",
"0.5470082",
"0.54345083",
"0.54121524",
"0.53877515",
"0.53301024",
"0.53161114",
"0.5314621",
"0.5297274",
"0.5284816",
"0.5276731",
"0.52697... | 0.6479725 | 0 |
Two clades in NCBI taxonomy, 'child' is assumed younger and must be nested somewhere inside 'parent'. | def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified):
# read first NCBI name from Clade object, if necessary
if isinstance(parent, Clade):
parentNCBIname = parent.ncbiNames[0]
elif not isinstance(parent, str):
# must be iterable, else fail
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, parent, child, excludeUnclassified = defaultExcludeUnclassified, oneOrganismPerSpecies = defaultOneOrganismPerSpecies):\n # read NCBI names from Clade object, if necessary\n if isinstance(parent, Clade):\n self.parentClade = parent\n else:\n self.parent... | [
"0.5975638",
"0.58271253",
"0.5484291",
"0.5396265",
"0.5390652",
"0.53713834",
"0.53584284",
"0.53490835",
"0.5338156",
"0.53251886",
"0.5263821",
"0.5227132",
"0.51992923",
"0.5184101",
"0.51213396",
"0.5080763",
"0.50803787",
"0.5078497",
"0.50769305",
"0.5075885",
"0.5074... | 0.68073165 | 0 |
Placeholders for input NB images are expressed in terms of vector and not matrices. | def main(restore_save=False):
x = tf.placeholder(tf.float32, shape=[None, 784])
# Placeholder for targets
targets = tf.placeholder(tf.float32, shape=[None, 10])
# Placeholder for discerning train/eval mode
is_training = tf.placeholder(dtype=tf.bool)
# Define global step to indicize checkpoint... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logits_placeholder(self):",
"def neural_net_image_input(image_shape):\n return tf.placeholder(tf.float32, shape=[None]+list(image_shape), name=\"x\")",
"def neural_net_image_input(image_shape):\n # TODO: Implement Function\n n_input_1 = image_shape[0]\n n_input_2 = image_shape[1]\n n_input_3... | [
"0.6088899",
"0.59365016",
"0.5740839",
"0.573186",
"0.5731295",
"0.5677845",
"0.56754416",
"0.56236225",
"0.55761564",
"0.5560068",
"0.5500983",
"0.5489259",
"0.5475015",
"0.5471705",
"0.54692245",
"0.54186827",
"0.5388585",
"0.53639835",
"0.5345162",
"0.5341476",
"0.5339761... | 0.0 | -1 |
Update the current file metadata with component versions on the database | def checkVersions():
item = Item(fromScene=True)
for ns, componentMData in item.components.iteritems():
if ns == 'cam':
# todo tratar versoes da camera
continue
if componentMData['assembleMode'] == 'reference':
refComponent = ReferenceComponent(ns, component... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_version(self, version):\n self._metadata['version'] = version\n\n if self._type == '.json':\n with open(self._filename, 'w') as f:\n f.write(json.dumps(self._metadata, indent=2))\n\n dof_filename = os.path.join(self.path, self.name + '.dof')\n if os.... | [
"0.6678135",
"0.6618736",
"0.6559671",
"0.65394354",
"0.6385025",
"0.6356499",
"0.627543",
"0.61180854",
"0.6045036",
"0.60443014",
"0.6028333",
"0.601643",
"0.59997",
"0.59620434",
"0.5956937",
"0.59510905",
"0.5949729",
"0.59445655",
"0.59294254",
"0.5921162",
"0.591343",
... | 0.6078741 | 8 |
Compare the current scene references versions with metadata and update as needed | def sceneRefCheck(silent=False):
uptodate = True
logger.debug('init sceneChecking...')
currentProject = database.getCurrentProject()
projName = pm.fileInfo.get('projectName')
if currentProject != projName:
logger.error('This file is from a project different from the current project')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkVersions():\n item = Item(fromScene=True)\n\n for ns, componentMData in item.components.iteritems():\n if ns == 'cam':\n # todo tratar versoes da camera\n continue\n\n if componentMData['assembleMode'] == 'reference':\n refComponent = ReferenceComponent... | [
"0.6834863",
"0.6241142",
"0.6191124",
"0.6186932",
"0.6126859",
"0.5808705",
"0.57901436",
"0.5686815",
"0.5674683",
"0.56705105",
"0.5636066",
"0.5609275",
"0.55931807",
"0.553265",
"0.54316425",
"0.5389392",
"0.53655267",
"0.52925307",
"0.52915853",
"0.52761626",
"0.525988... | 0.6645423 | 1 |
Renvoie la liste des noeuds du graph | def getNoeuds(self) -> list:
return self._noeuds | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def graph(self):\n ...",
"def _get_full_graph(self):",
"def populate_graph(self):",
"def print_list_of_nodes(self):\n\n for node in self.list_empty_nodes:\n print(\"--------------------------\")\n print(\"Node num : \"+str(node.num))\n print(\"Node distance from... | [
"0.61656684",
"0.6132704",
"0.5961668",
"0.5949655",
"0.5797799",
"0.5743925",
"0.57238185",
"0.57080424",
"0.5702499",
"0.56886774",
"0.5665844",
"0.5662841",
"0.55272645",
"0.5525712",
"0.55255455",
"0.5511591",
"0.5497783",
"0.5492539",
"0.54852766",
"0.54834795",
"0.54732... | 0.59119767 | 4 |
Renvoie la liste des Arrettes du graph | def getArrettes(self) -> list:
return self._arrettes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def graph(self):\n ...",
"def populate_graph(self):",
"def _get_full_graph(self):",
"def gen_graph(self):",
"def plot_graph(self) -> None:",
"def list(self):",
"def allGraphs(date):\n g = getGraph()\n for uri, label, filename in subgraphs(date):\n if not label:\n label = ... | [
"0.6653496",
"0.6502852",
"0.61809665",
"0.5993094",
"0.59424686",
"0.57306266",
"0.5700172",
"0.5697255",
"0.5596236",
"0.5589515",
"0.55849475",
"0.55651873",
"0.5546893",
"0.5532899",
"0.55003935",
"0.5471013",
"0.54704064",
"0.5469078",
"0.5429392",
"0.54259014",
"0.54221... | 0.574315 | 5 |
Normalizes string, converts to lowercase, removes nonalpha characters, and converts spaces to hyphens. From Django's "django/template/defaultfilters.py". | def _slugify(value):
import unicodedata
if not isinstance(value, unicode):
value = unicode(value)
value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')
value = unicode(_slugify_strip_re.sub('', value).strip().lower())
return _slugify_hyphenate_re.sub('-', value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sanitize(str):\n res = str.lower()\n return res.replace(' ', '-')",
"def normalize(s):\n s = replace_whitespace(s)\n s = remove_dashes(s)\n s = s.lower()\n return s",
"def sanitize(string):\n retval = string.lower()\n retval = re.sub(r\"[^\\w\\s]\", '', retval)\n retval = re.sub(... | [
"0.8050005",
"0.7715769",
"0.7337892",
"0.7264596",
"0.7262135",
"0.7178926",
"0.7171534",
"0.714335",
"0.71391594",
"0.70830053",
"0.7070548",
"0.70457333",
"0.70426863",
"0.70101064",
"0.70101064",
"0.6989748",
"0.69828415",
"0.69823",
"0.69683266",
"0.69340587",
"0.6910592... | 0.6851728 | 28 |
Read a .plist file from filepath. Return the unpacked root object (which usually is a dictionary). | def readPlist(filepath):
plistData = NSData.dataWithContentsOfFile_(filepath)
dataObject, plistFormat, error = NSPropertyListSerialization.propertyListFromData_mutabilityOption_format_errorDescription_(plistData, NSPropertyListMutableContainers, None, None)
if error:
errmsg = "%s in file %s" % (error, filepath)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_plist(path: str, format: plistlib.PlistFormat) -> dict:\n if not os.path.isfile(path):\n raise ValueError(f'File {path} does not exist')\n\n with open(path, 'rb') as file:\n return plistlib.load(file, fmt=format, dict_type=dict)",
"def _read_plist(self, filename):\n file_path... | [
"0.7377023",
"0.7077552",
"0.7020647",
"0.70009124",
"0.69380563",
"0.6635612",
"0.66037655",
"0.6400661",
"0.6191319",
"0.60980314",
"0.59968185",
"0.5973855",
"0.5956443",
"0.5919474",
"0.5916405",
"0.59139293",
"0.58797735",
"0.58763933",
"0.58301824",
"0.5828497",
"0.5816... | 0.76269853 | 0 |
Print out the end status of the game when the server closes the connection. | def server_closed_connection(self):
print("Game Over!")
if self._winner:
print("Player {} wins!".format(self._winner))
else:
print("Draw!") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_connection_end() -> None:\r\n print(\"Connection lost with G-Earth\")\r\n print()",
"def endSession(self):\n if(self.verb >= DLS_VERB_HIGH):\n print \"--Ending session with %s (no action)\" % (self.server)",
"def end(self):\n winners = mafia.str_player_list(self.game.winners())\... | [
"0.7442786",
"0.7357788",
"0.70812464",
"0.7068161",
"0.6758912",
"0.67495376",
"0.6671296",
"0.6662621",
"0.66588444",
"0.6629597",
"0.6612989",
"0.6597301",
"0.658959",
"0.6576882",
"0.657107",
"0.65640134",
"0.65556294",
"0.64894456",
"0.6479372",
"0.64617205",
"0.64595443... | 0.78706425 | 0 |
Find the next empty square in the board. | def _find_empty_cell(self):
for r, row in enumerate(self._board):
for c, cell in enumerate(row):
if cell is None:
return r, c | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_next_empty_cell(self):\n for row in range(len(self.grid)):\n for col in range(len(self.grid[0])):\n if self.grid[row][col] == 0:\n return (row, col)\n return None",
"def find_empty_squares(board):\n for i in range(len(board)):\n for j i... | [
"0.771726",
"0.76045936",
"0.759847",
"0.74498457",
"0.72153336",
"0.70994276",
"0.6846962",
"0.67881215",
"0.67627925",
"0.67542005",
"0.6752573",
"0.6666043",
"0.6641799",
"0.66194654",
"0.6548566",
"0.6522966",
"0.6478559",
"0.64705473",
"0.6379805",
"0.63471836",
"0.63375... | 0.7049689 | 6 |
Handle a received message from the server. | def message_received_from_server(self, message):
if message["type"] == "state":
self._board = message["board"]
self._winner = message["winner"]
elif message["type"] == "turn":
row, col = self._find_empty_cell()
self.send_message_to_server({
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle(self, message):\n print(\"You received a message:\")\n print(message)\n # Overwrite this function to do something with the message!",
"def received_message(self, m):\n self.receiver.handle_message(m)",
"def handle(self):\n try:\n # Wait for data\n ... | [
"0.81080824",
"0.7629044",
"0.7614227",
"0.759747",
"0.7582353",
"0.7567339",
"0.738713",
"0.7327663",
"0.72030395",
"0.7197576",
"0.71963334",
"0.7182009",
"0.7154054",
"0.7151546",
"0.7145494",
"0.7141526",
"0.71328634",
"0.7116805",
"0.7099164",
"0.70977116",
"0.7085751",
... | 0.6640116 | 55 |
Clean AFOS and friends | def main():
pgconn = get_dbconn("afos")
acursor = pgconn.cursor()
# reflect changes to docs/datasets/afos.md
# RRM removed due to request
acursor.execute(
"delete from products WHERE "
"entered < ('YESTERDAY'::date - '7 days'::interval) and "
"entered > ('YESTERDAY'::date - ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleaning (data):",
"def clean(_context):",
"def clean(c):",
"def cleanup(self):\n for residue in self.debumper.biomolecule.residues:\n if not isinstance(residue, aa.Amino):\n continue\n if residue.name == \"GLH\" or \"GLH\" in residue.patches:\n ... | [
"0.6322474",
"0.63047546",
"0.6143992",
"0.6081225",
"0.6070681",
"0.5969005",
"0.5901452",
"0.5889662",
"0.584422",
"0.58323973",
"0.5830357",
"0.5830357",
"0.58122694",
"0.5788065",
"0.57749754",
"0.57663184",
"0.5693208",
"0.5692589",
"0.56887585",
"0.56775504",
"0.5664054... | 0.0 | -1 |
Track a download in Piwik | def track_download_request(download_url, download_title):
from indico_piwik.plugin import PiwikPlugin
if not download_url:
raise ValueError("download_url can't be empty")
if not download_title:
raise ValueError("download_title can't be empty")
request = PiwikRequest(server_url=PiwikPlu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download(self):\n pass",
"def download(self):\n pass",
"def download(self, download_request):\n raise NotImplementedError",
"def download_progress(self, cloud_file, size, downloaded):",
"def dowload_vt():\n print get_date_time_now() + \" ==> Download VT Samples started!\"\n p... | [
"0.62857395",
"0.62857395",
"0.6190119",
"0.61862576",
"0.60990036",
"0.60830104",
"0.59435177",
"0.5895922",
"0.58797216",
"0.58775985",
"0.58351547",
"0.5832015",
"0.5803117",
"0.579149",
"0.5750479",
"0.5704647",
"0.5701332",
"0.5676492",
"0.5647217",
"0.5612867",
"0.56082... | 0.7677783 | 0 |
T.__new__(S, ...) > a new object with type S, a subtype of T | def __new__(S, *more): # real signature unknown; restored from __doc__
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __new__(cls):\n return object.__new__(cls)",
"def __new__(cls):\n return object.__new__(cls)",
"def __newobj__(cls, *args):\n return cls.__new__(cls, *args)",
"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass",
"def __new__(S, *more): # real signatur... | [
"0.72864395",
"0.72864395",
"0.7262999",
"0.7261351",
"0.7261351",
"0.7261351",
"0.7261351",
"0.7228152",
"0.72100246",
"0.72100246",
"0.66210115",
"0.66210115",
"0.66210115",
"0.66210115",
"0.66210115",
"0.66210115",
"0.6530774",
"0.6427577",
"0.6424623",
"0.64146465",
"0.64... | 0.73813504 | 0 |
Create the test server application instance. | def create_app(self):
coverage.process_startup()
app = create_test_app()
selenium_server_url = "http://{}:{}/wd/hub".format(
os.environ.get("{{ cookiecutter.project_slug|upper }}_SELENIUM_HOST", "chrome"),
os.environ.get("{{ cookiecutter.project_slug|upper }}_SELENIUM_POR... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_app(test_config=None):\n app = Flask(__name__, instance_relative_config=True)\n app.config.from_mapping(\n SECRET_KEY=os.environ.get('FLASK_SECRET_KEY', 'dev'),\n # SQLALCHEMY_DATABASE_URI='sqlite:////' + os.path.join(app.instance_path, 'app.sqlite'),\n SQLALCHEMY_DATABASE_URI... | [
"0.7417531",
"0.73058623",
"0.7296637",
"0.7289872",
"0.72620034",
"0.72145504",
"0.71641845",
"0.7141066",
"0.7107825",
"0.71020865",
"0.7096506",
"0.70654595",
"0.70639825",
"0.704083",
"0.70407116",
"0.7004533",
"0.6990313",
"0.6979766",
"0.69694114",
"0.6966402",
"0.69436... | 0.7206358 | 6 |
Close the browser, and clean up the database. | def tearDown(self):
self.browser.quit()
clean_db() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def close(self):\n\t\tself.db.close()",
"def close(self):\n self.db.close()",
"def close_db():\n if ( not g.get( 'db' ) is None ):\n g.db.dispose()\n\n g.db = None",
"def disconnect(self):\n self.db.close()",
"def close(self):\n self.db.remove()\n self.engine.dispos... | [
"0.77067506",
"0.7608747",
"0.7564536",
"0.748835",
"0.7487941",
"0.74589837",
"0.7395597",
"0.73916876",
"0.73889124",
"0.73546255",
"0.7339338",
"0.73319304",
"0.7327354",
"0.7327354",
"0.7327354",
"0.73114276",
"0.7295338",
"0.72949415",
"0.7287185",
"0.72680247",
"0.72311... | 0.7449147 | 6 |
Returns the names of all qualities with exitsting expert knowledge. | def get_qualities_with_expert_knowledge(self) -> List[str]:
return sorted(list(set([q for _, q in self.expert_knowledge]))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:\n expert_knowledge = reduce(\n lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),\n qualities,\n set()\n )\n return sorted(list(expert_knowledge))",
"def ... | [
"0.6796296",
"0.6399958",
"0.5745922",
"0.570581",
"0.56431264",
"0.5579781",
"0.5524196",
"0.5486619",
"0.5414175",
"0.5371931",
"0.5336818",
"0.5334099",
"0.5322436",
"0.53192633",
"0.5316867",
"0.5283304",
"0.5277264",
"0.527555",
"0.52340245",
"0.5215532",
"0.5200703",
... | 0.68818367 | 0 |
Returns the names of all parameters with exitsting expert knowledge. | def get_parameters_with_expert_knowledge(self) -> List[str]:
return sorted(list(set([p for p, _ in self.expert_knowledge]))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameter_names(self) -> List[str]:",
"def _get_fitted_param_names(self):\n return self._fitted_param_names",
"def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params",
"def get_parameter_names(self):\n parNames = []\n # for par in self.variabl... | [
"0.6681219",
"0.65302336",
"0.6523821",
"0.6485351",
"0.64587325",
"0.64377654",
"0.63435304",
"0.63268703",
"0.6247378",
"0.62124157",
"0.6184946",
"0.61841184",
"0.6173412",
"0.6156799",
"0.61424565",
"0.6138973",
"0.6092514",
"0.6074142",
"0.6068428",
"0.60556334",
"0.6051... | 0.7096718 | 0 |
Returns the names of all qualities that are affected by the given parameter. | def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:
return [q for p, q in self.correlating_pq_tuples if p == parameter] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))",
"def get_parameters_affec... | [
"0.7138901",
"0.65434414",
"0.60828495",
"0.5970741",
"0.5706705",
"0.56861335",
"0.5615085",
"0.5553577",
"0.5414541",
"0.53965795",
"0.5352581",
"0.5300973",
"0.52954173",
"0.52879333",
"0.5286698",
"0.5282407",
"0.5249492",
"0.5248632",
"0.52475625",
"0.52351964",
"0.52118... | 0.7769609 | 0 |
Returns the names of all parameters are affecting the given quality. | def get_parameters_affecting_quality(self, quality: str) -> List[str]:
return [p for p, q in self.correlating_pq_tuples if q == quality] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parameters(self, quality):\n if (quality.upper() == 'NAV') or (quality.upper() == 'NAVIGATION'):\n return self._sensor_param_dict['navigation'].copy()\n \n elif (quality.upper() == 'TAC') or (quality.upper() == 'TACTICAL'):\n return self._sensor_param_dict['tactic... | [
"0.6934809",
"0.67903876",
"0.6167672",
"0.61589646",
"0.6091067",
"0.6062602",
"0.59788996",
"0.59663045",
"0.5930198",
"0.5925212",
"0.5914016",
"0.587946",
"0.57363737",
"0.5702479",
"0.57009995",
"0.57009995",
"0.5699666",
"0.5673059",
"0.5664768",
"0.5636903",
"0.5629475... | 0.8470386 | 0 |
Returns the names of all parameters are affecting the given qualities. | def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:
parameters = reduce(
lambda res, q: res | set(self.get_parameters_affecting_quality(q)),
qualities,
set()
)
return sorted(list(parameters)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parameters_affecting_quality(self, quality: str) -> List[str]:\n return [p for p, q in self.correlating_pq_tuples if q == quality]",
"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]",
"def par... | [
"0.684624",
"0.6697432",
"0.64668196",
"0.6275496",
"0.62012315",
"0.60170823",
"0.5992412",
"0.597497",
"0.5971461",
"0.5962293",
"0.5953351",
"0.5907317",
"0.58813965",
"0.5875759",
"0.58324844",
"0.57914823",
"0.5765543",
"0.57585037",
"0.5748246",
"0.5748246",
"0.5661398"... | 0.82784075 | 0 |
Returns the names of all parameters that are expert knowledge of the given qualities. | def get_expert_knowledge_for_qualities(self, qualities: List[str]) -> List[str]:
expert_knowledge = reduce(
lambda res, q: res | set(p for p, _q in self.expert_knowledge if _q == q),
qualities,
set()
)
return sorted(list(expert_knowledge)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parameters_affecting_qualites(self, qualities: List[str]) -> List[str]:\n parameters = reduce(\n lambda res, q: res | set(self.get_parameters_affecting_quality(q)),\n qualities,\n set()\n )\n return sorted(list(parameters))",
"def get_parameters_with_... | [
"0.7242944",
"0.6727106",
"0.6571987",
"0.63019365",
"0.6018897",
"0.60036373",
"0.57837",
"0.57691544",
"0.5760132",
"0.5750516",
"0.5735969",
"0.5735225",
"0.56694657",
"0.56694657",
"0.56474066",
"0.56144816",
"0.5601561",
"0.55747986",
"0.55496305",
"0.5520627",
"0.551223... | 0.6919965 | 1 |
Returns the names of all qualties that are expert knowledge of the given parameter. | def get_expert_knowledge_for_parameter(self, parameter: str) -> List[str]:
expert_knowledge = [q for (p, q) in self.expert_knowledge if p == parameter]
return sorted(list(set(expert_knowledge))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_qualities_affected_by_paramter(self, parameter: str) -> List[str]:\n return [q for p, q in self.correlating_pq_tuples if p == parameter]",
"def get_qualities_with_expert_knowledge(self) -> List[str]:\n return sorted(list(set([q for _, q in self.expert_knowledge])))",
"def get_expert_knowl... | [
"0.69750583",
"0.6972947",
"0.6572471",
"0.6532671",
"0.6255699",
"0.62491757",
"0.5842264",
"0.5311929",
"0.52768415",
"0.5177957",
"0.5048739",
"0.50431305",
"0.502833",
"0.49590343",
"0.49437335",
"0.4934118",
"0.49310294",
"0.49303398",
"0.49248543",
"0.4907331",
"0.48799... | 0.7195483 | 0 |
method tests timetable records in STATE_EMBRYO state | def test_state_embryo(self):
self.pipeline_real.insert_uow = then_return_uow
pipeline = spy(self.pipeline_real)
job_record = get_job_record(job.STATE_EMBRYO,
TEST_PRESET_TIMEPERIOD,
PROCESS_SITE_HOURLY)
pipeline.ma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.... | [
"0.6052409",
"0.60238993",
"0.60199136",
"0.5990552",
"0.59654564",
"0.59051305",
"0.58771414",
"0.587141",
"0.5824163",
"0.57776195",
"0.575167",
"0.57320946",
"0.57115203",
"0.57089746",
"0.57080287",
"0.56987756",
"0.5684115",
"0.5668643",
"0.564905",
"0.56462383",
"0.5624... | 0.68013006 | 0 |
method tests timetable records in STATE_EMBRYO state | def test_duplicatekeyerror_state_embryo(self):
self.pipeline_real.insert_uow = then_raise
pipeline = spy(self.pipeline_real)
job_record = get_job_record(job.STATE_EMBRYO,
TEST_PRESET_TIMEPERIOD,
PROCESS_SITE_HOURLY)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_state_embryo(self):\n self.pipeline_real.insert_uow = then_return_uow\n pipeline = spy(self.pipeline_real)\n\n job_record = get_job_record(job.STATE_EMBRYO,\n TEST_PRESET_TIMEPERIOD,\n PROCESS_SITE_HOURLY)\n\n ... | [
"0.68013006",
"0.6052409",
"0.60238993",
"0.60199136",
"0.5990552",
"0.59654564",
"0.59051305",
"0.58771414",
"0.587141",
"0.5824163",
"0.57776195",
"0.575167",
"0.57320946",
"0.57115203",
"0.57089746",
"0.57080287",
"0.5684115",
"0.5668643",
"0.564905",
"0.56462383",
"0.5624... | 0.56987756 | 16 |
method tests timetable records in STATE_IN_PROGRESS state | def test_future_timeperiod_state_in_progress(self):
when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)
uow_dao_mock = mock(UnitOfWorkDao)
when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))
self.pipeline_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.... | [
"0.71193224",
"0.6860334",
"0.66941416",
"0.628547",
"0.6147146",
"0.6099557",
"0.60727316",
"0.59782106",
"0.59703326",
"0.59561163",
"0.58597606",
"0.5858871",
"0.5750034",
"0.57215893",
"0.57205486",
"0.5681524",
"0.5675636",
"0.56590813",
"0.5616347",
"0.56143284",
"0.558... | 0.7257185 | 0 |
method tests timetable records in STATE_IN_PROGRESS state | def test_preset_timeperiod_state_in_progress(self):
when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)
uow_dao_mock = mock(UnitOfWorkDao)
when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))
self.pipeline_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.... | [
"0.7257716",
"0.6861403",
"0.6694048",
"0.6286829",
"0.61476827",
"0.6099142",
"0.6072867",
"0.59780765",
"0.5971016",
"0.595613",
"0.5860175",
"0.58586013",
"0.5750365",
"0.5722227",
"0.57212484",
"0.5681915",
"0.56768733",
"0.56603575",
"0.5617645",
"0.56150514",
"0.5581628... | 0.71204054 | 1 |
method tests timetable records in STATE_IN_PROGRESS state | def test_transfer_to_final_timeperiod_state_in_progress(self):
when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)
uow_dao_mock = mock(UnitOfWorkDao)
when(uow_dao_mock).get_one(any()).thenReturn(
create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.... | [
"0.7257185",
"0.71193224",
"0.6860334",
"0.628547",
"0.6147146",
"0.6099557",
"0.60727316",
"0.59782106",
"0.59703326",
"0.59561163",
"0.58597606",
"0.5858871",
"0.5750034",
"0.57215893",
"0.57205486",
"0.5681524",
"0.5675636",
"0.56590813",
"0.5616347",
"0.56143284",
"0.5581... | 0.66941416 | 3 |
method tests timetable records in STATE_IN_PROGRESS state | def test_retry_state_in_progress(self):
when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)
uow_dao_mock = mock(UnitOfWorkDao)
when(uow_dao_mock).get_one(any()).thenReturn(
create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PRO... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_future_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.... | [
"0.7257185",
"0.71193224",
"0.66941416",
"0.628547",
"0.6147146",
"0.6099557",
"0.60727316",
"0.59782106",
"0.59703326",
"0.59561163",
"0.58597606",
"0.5858871",
"0.5750034",
"0.57215893",
"0.57205486",
"0.5681524",
"0.5675636",
"0.56590813",
"0.5616347",
"0.56143284",
"0.558... | 0.6860334 | 2 |
method tests timetable records in STATE_FINAL_RUN state | def test_processed_state_final_run(self):
uow_dao_mock = mock(UnitOfWorkDao)
when(uow_dao_mock).get_one(any()).thenReturn(
create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_PROCESSED))
self.pipeline_real.uow_dao = uow_dao_mock
pipeline = spy(self.pipeline... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1,... | [
"0.66075",
"0.6143854",
"0.61161435",
"0.61069274",
"0.6051925",
"0.59604484",
"0.58838767",
"0.58052",
"0.5779052",
"0.5769028",
"0.57552785",
"0.5731349",
"0.5673748",
"0.56650174",
"0.56536967",
"0.5641838",
"0.56204814",
"0.5584373",
"0.5582211",
"0.55599564",
"0.55164605... | 0.65768546 | 1 |
method tests timetable records in STATE_FINAL_RUN state | def test_cancelled_state_final_run(self):
uow_dao_mock = mock(UnitOfWorkDao)
when(uow_dao_mock).get_one(any()).thenReturn(
create_unit_of_work(PROCESS_UNIT_TEST, 1, 1, None, unit_of_work.STATE_CANCELED))
self.pipeline_real.uow_dao = uow_dao_mock
pipeline = spy(self.pipeline_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_transfer_to_final_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(\n create_unit_of_work(PROCESS_UNIT_TEST, 1,... | [
"0.66093767",
"0.6576982",
"0.6142489",
"0.6118232",
"0.6109181",
"0.6053264",
"0.5884454",
"0.5807409",
"0.57819825",
"0.5768662",
"0.5756515",
"0.57349944",
"0.56748885",
"0.56665564",
"0.5654893",
"0.5644546",
"0.5621295",
"0.55847925",
"0.55834424",
"0.5561242",
"0.551816... | 0.5960294 | 6 |
method tests timetable records in STATE_SKIPPED state | def test_state_skipped(self):
pipeline = spy(self.pipeline_real)
job_record = get_job_record(job.STATE_SKIPPED,
TEST_PRESET_TIMEPERIOD,
PROCESS_SITE_HOURLY)
pipeline.manage_pipeline_for_process(job_record.process_name, job_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_skip(self, d_stmt, table): \n pass",
"def skip_dti_tests():\n return True",
"def IsSkipped(self):\n state = self.GetState()\n return state.status == TestState.SKIPPED",
"def test_table_false_positives(self):\n pass",
"def test_csv_skipped_status_report(self):\n skip... | [
"0.64526933",
"0.62094676",
"0.61737317",
"0.6080123",
"0.6063041",
"0.59828675",
"0.59114105",
"0.5883286",
"0.5864295",
"0.5789228",
"0.578475",
"0.5716462",
"0.56791854",
"0.5611211",
"0.5603914",
"0.5570936",
"0.5559213",
"0.55535394",
"0.5551212",
"0.5548782",
"0.5545937... | 0.6912011 | 0 |
method tests timetable records in STATE_PROCESSED state | def test_state_processed(self):
pipeline = spy(self.pipeline_real)
job_record = get_job_record(job.STATE_PROCESSED,
TEST_PRESET_TIMEPERIOD,
PROCESS_SITE_HOURLY)
pipeline.manage_pipeline_for_process(job_record.process_name, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_preset_timeperiod_state_in_progress(self):\n when(self.time_table_mocked).can_finalize_job_record(any(str), any(Job)).thenReturn(True)\n uow_dao_mock = mock(UnitOfWorkDao)\n when(uow_dao_mock).get_one(any()).thenReturn(create_unit_of_work(PROCESS_UNIT_TEST, 0, 1, None))\n self.... | [
"0.67496896",
"0.6644145",
"0.6472953",
"0.62294924",
"0.6101738",
"0.5977244",
"0.59124064",
"0.5691305",
"0.55319977",
"0.5511322",
"0.55038923",
"0.5429801",
"0.54225284",
"0.5324777",
"0.5302315",
"0.52370924",
"0.5232825",
"0.5190848",
"0.5188738",
"0.5178702",
"0.514694... | 0.72442085 | 0 |
This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. | def plot_confusion_matrix(cm, classes=[],
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
plt.figure()
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion ma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n... | [
"0.8194366",
"0.8095442",
"0.8029135",
"0.8019134",
"0.7993755",
"0.7991176",
"0.79808146",
"0.79755133",
"0.79609716",
"0.7956584",
"0.79375756",
"0.79346216",
"0.79343396",
"0.7931212",
"0.7926087",
"0.7924503",
"0.7923946",
"0.79228765",
"0.79227114",
"0.7921956",
"0.79178... | 0.7835582 | 61 |
function to create employee manager. | def create_manager(self, name, pos, dept):
self.manager[dept.upper()].append(
{
'name': name,
'pos': pos,
'dept': dept,
'senior': [],
'junior': [],
'trainee': []
}
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n els... | [
"0.70986205",
"0.70202345",
"0.6854372",
"0.6521865",
"0.63904655",
"0.62510014",
"0.6242744",
"0.6056396",
"0.60380864",
"0.6034902",
"0.6033909",
"0.6022222",
"0.60213274",
"0.5988317",
"0.59657365",
"0.591008",
"0.5900008",
"0.58407825",
"0.58102566",
"0.57738036",
"0.5749... | 0.74205023 | 0 |
function to create employee senior. | def create_senior(self, name, pos, dept):
self.senior[dept.upper()].append(
{
'name': name,
'pos': pos,
'dept': dept,
'manager': self.manager[dept.upper()][0]['name'],
'junior': [],
'trainee': []
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n els... | [
"0.7395388",
"0.6873678",
"0.6652067",
"0.6417113",
"0.63125676",
"0.6297487",
"0.6267351",
"0.62289935",
"0.6222155",
"0.6122551",
"0.6114099",
"0.6063726",
"0.6037256",
"0.60092777",
"0.60071415",
"0.59452486",
"0.5924796",
"0.5911677",
"0.58706707",
"0.58596087",
"0.585595... | 0.72263277 | 1 |
functions to create employee junior. | def create_junior(self, name, pos, dept):
self.junior[dept.upper()].append(
{
'name': name,
'pos': pos,
'dept': dept,
'manager': self.manager[dept.upper()][0]['name'],
'senior': self.senior[dept.upper()][0]['name'],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n els... | [
"0.6703623",
"0.6629079",
"0.58912194",
"0.5825029",
"0.57799065",
"0.557808",
"0.5561385",
"0.55040497",
"0.5481859",
"0.54713845",
"0.5368414",
"0.5327975",
"0.52699995",
"0.52693903",
"0.52427757",
"0.52367204",
"0.52185136",
"0.52170134",
"0.5204411",
"0.5148115",
"0.5135... | 0.6939935 | 0 |
function to create employee trainee. | def create_trainee(self, name, pos, dept):
self.trainee[dept.upper()].append(
{
'name': name,
'pos': pos,
'dept': dept,
'manager': self.manager[dept.upper()][0]['name'],
'senior': self.senior[dept.upper()][0]['name'],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_emp(self, name, pos, dept):\n if pos.upper() == 'MANAGER':\n self.create_manager(name, pos, dept)\n elif pos.upper() == 'SENIOR':\n self.create_senior(name, pos, dept)\n elif pos.upper() == 'JUNIOR':\n self.create_junior(name, pos, dept)\n els... | [
"0.71946114",
"0.64039135",
"0.6397927",
"0.60852325",
"0.60668916",
"0.5989232",
"0.59618133",
"0.5926233",
"0.59155416",
"0.584691",
"0.5840692",
"0.58354217",
"0.57622826",
"0.5757782",
"0.57444525",
"0.57208085",
"0.5719296",
"0.56969446",
"0.56930053",
"0.56875426",
"0.5... | 0.696836 | 1 |
function to create employee based on postion. | def create_emp(self, name, pos, dept):
if pos.upper() == 'MANAGER':
self.create_manager(name, pos, dept)
elif pos.upper() == 'SENIOR':
self.create_senior(name, pos, dept)
elif pos.upper() == 'JUNIOR':
self.create_junior(name, pos, dept)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_employee(self,personal_identity):\r\n new_emp = Employee(*personal_identity)\r\n registration_str = new_emp.get_registration_str()\r\n\r\n return_value = self.save_object_to_DB(\"employee\",registration_str)\r\n return return_value",
"def create_employee(self):\n try... | [
"0.7340392",
"0.7060474",
"0.6856134",
"0.66949964",
"0.66828686",
"0.6662708",
"0.6607132",
"0.66030127",
"0.6332911",
"0.6295497",
"0.6276587",
"0.62654424",
"0.626206",
"0.62513125",
"0.62459964",
"0.6240261",
"0.6239734",
"0.62260616",
"0.6169861",
"0.61182666",
"0.607446... | 0.7889773 | 0 |
test attrs of City when created | def test_attrs(self):
city = City()
self.assertEqual(city.name, "")
self.assertEqual(City.name, "")
self.assertEqual(city.state_id, "")
self.assertEqual(City.state_id, "")
self.assertIn("id", city.__dict__)
self.assertIn("created_at", city.to_dict())
self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_attributes(self):\n self.assertTrue(hasattr(self.city, 'name'))\n self.assertTrue(hasattr(self.city, 'state_id'))",
"def test_set_attrs(self):\n city2 = City()\n city2.name = \"Hawaii\"\n self.assertEqual(city2.name, \"Hawaii\")\n city2.state_id = \"<3\"\n ... | [
"0.79669297",
"0.7755154",
"0.76454324",
"0.75021863",
"0.7164923",
"0.7156203",
"0.70737207",
"0.70595056",
"0.70520234",
"0.6963021",
"0.69118476",
"0.69103384",
"0.67882353",
"0.67593557",
"0.67574525",
"0.6731778",
"0.6674009",
"0.66470456",
"0.6544833",
"0.6540936",
"0.6... | 0.76267827 | 3 |
test the attrs of City when set | def test_set_attrs(self):
city2 = City()
city2.name = "Hawaii"
self.assertEqual(city2.name, "Hawaii")
city2.state_id = "<3"
self.assertEqual(city2.state_id, "<3")
self.assertEqual(City.name, "")
self.assertEqual(City.state_id, "") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_attributes(self):\n self.assertTrue(hasattr(self.city, 'name'))\n self.assertTrue(hasattr(self.city, 'state_id'))",
"def test_city(self):\n c = City()\n self.assertEqual(c.name, \"\")\n self.assertEqual(c.state_id, \"\")\n c.name = \"San Francisco\"\n c.s... | [
"0.77947956",
"0.74044555",
"0.7307759",
"0.7248982",
"0.71909946",
"0.71890724",
"0.69839954",
"0.68954945",
"0.6838572",
"0.6797316",
"0.6596423",
"0.6592114",
"0.6484486",
"0.64782625",
"0.6418063",
"0.6380564",
"0.6375229",
"0.62248665",
"0.62208533",
"0.61856556",
"0.618... | 0.79602754 | 0 |
test the inheritance of City from BaseModel | def test_inheritance(self):
city3 = City()
self.assertIsInstance(city3, BaseModel)
self.assertIsInstance(city3, City) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_inheritence(self):\n self.assertTrue(issubclass(City, BaseModel))",
"def test_subclass(self):\n self.assertIsInstance(self.city, BaseModel)\n self.assertTrue(hasattr(self.city, \"id\"))\n self.assertTrue(hasattr(self.city, \"created_at\"))\n self.assertTrue(hasattr(sel... | [
"0.8789952",
"0.8498339",
"0.8354064",
"0.7497503",
"0.7128531",
"0.70219684",
"0.700617",
"0.698174",
"0.6981573",
"0.6797616",
"0.6792511",
"0.6769275",
"0.6727983",
"0.66833794",
"0.6660511",
"0.6656261",
"0.65980506",
"0.6585381",
"0.6571345",
"0.6492039",
"0.64787066",
... | 0.86559975 | 1 |
check that fifo matches expected types and perms, catch security hold were it could be replace with another file | def __checkFifo(path):
pass # FIXME implement | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vsys_fifo_exists(path):\n if not os.path.exists(path):\n collectd.error('File does not exist: %s' % path)\n return False\n if not stat.S_ISFIFO(os.stat(path).st_mode):\n collectd.error('File is not a fifo: %s' % path)\n return False\n return True",
"def _check_fifo(self):... | [
"0.6131964",
"0.60713536",
"0.57345945",
"0.56298536",
"0.5624965",
"0.56156325",
"0.5565259",
"0.5526661",
"0.5506482",
"0.5501304",
"0.5494585",
"0.5419358",
"0.5417479",
"0.5339467",
"0.5335473",
"0.5235633",
"0.5192825",
"0.51720446",
"0.51720446",
"0.51584595",
"0.514473... | 0.6538033 | 0 |
Returns all the grid positions that are currently available for scoring. YB is only available after YZ has been scored other than 0 or NB, NT and GT are never available for scoring If 13 positions have been scored in the grid, no further positions can be filled | def available_positions(self):
if len([x for x in self.grid.values() if x[0] != None]) < 13:
return [x for x in assignable_positions if self.grid[x][1] == "---"]
else:
return [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def free_positions(self):\n positions = []\n for i in range(self.grid_size):\n for j in range(self.grid_size):\n if self.grid[i][j] == 0:\n positions.append((i, j))\n if positions == []:\n raise GameException('Game Over. No free position ... | [
"0.6839561",
"0.6820858",
"0.6375092",
"0.6179692",
"0.6175604",
"0.61393595",
"0.6134617",
"0.6129079",
"0.61183804",
"0.6112216",
"0.6101577",
"0.6083837",
"0.5964636",
"0.59512347",
"0.58837503",
"0.5817103",
"0.5804341",
"0.5783051",
"0.5754948",
"0.57523113",
"0.5745287"... | 0.72613794 | 0 |
Returns all the grid positions that have been filled in. | def filled_positions(self):
return [x for x in assignable_positions if self.grid[x][0]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def free_positions(self):\n positions = []\n for i in range(self.grid_size):\n for j in range(self.grid_size):\n if self.grid[i][j] == 0:\n positions.append((i, j))\n if positions == []:\n raise GameException('Game Over. No free position ... | [
"0.77568364",
"0.723596",
"0.7119489",
"0.70678014",
"0.7052116",
"0.69976157",
"0.68781596",
"0.68656486",
"0.68310523",
"0.6827777",
"0.67618763",
"0.66904217",
"0.6631231",
"0.65709645",
"0.65524876",
"0.65520054",
"0.65291786",
"0.65274036",
"0.6515479",
"0.6496653",
"0.6... | 0.80966496 | 0 |
Assigns a tuple to a position in the grid, of the form (hand, score of this hand for this position) | def assign(self, hand, position):
assert isinstance(hand, h.Hand)
# print "POSITION:", position
# print self
try:
assert self.grid[position][1] == "---"
except AssertionError:
raise FilledInError
self.grid[position] = (hand, self.score(hand, positi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def score_tuple( hand ):\n m = matches(hand)\n #print( m )\n #royal_flush -- a special case of straight flush.\n if flush(hand) and straight(hand) and hand[4].rank == 14:\n return (8, hand[4].rank, 0)\n #straight_flush\n elif flush(hand) and straight(hand):\n return (8, hand[4].rank... | [
"0.6446688",
"0.60046214",
"0.5860571",
"0.5825233",
"0.57931954",
"0.5780548",
"0.57786965",
"0.5750296",
"0.57402545",
"0.57364994",
"0.5714486",
"0.5714486",
"0.57086986",
"0.5672831",
"0.5660482",
"0.5629688",
"0.5591937",
"0.5587683",
"0.55868065",
"0.55848455",
"0.55837... | 0.7689742 | 0 |
This function checks how many points you would get if the hand would be filled in at the given position. | def score(self, hand, position):
try:
assert self.grid[position][1] == "---"
except AssertionError:
print self
print position
raise FilledInError
except KeyError:
print "\nCheck your code. This is not a valid position:", position, "\n"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_masked (self, pos : list,) :\n count = 0\n total = 0\n for x in range(pos[0],min(pos[0] + AUTO_width1, self.m_x)) :\n for y in range(pos[1], min(pos[1] + AUTO_width1, self.m_y)) :\n total += 1\n if self.current_grid[x][y] :\n ... | [
"0.6565153",
"0.63837904",
"0.63379383",
"0.6248904",
"0.62295264",
"0.6200083",
"0.6106756",
"0.6105779",
"0.60929984",
"0.6092066",
"0.60837704",
"0.60635835",
"0.60598457",
"0.60598457",
"0.6055476",
"0.60407025",
"0.60384566",
"0.60374635",
"0.6029937",
"0.60184675",
"0.5... | 0.5970365 | 22 |
Sums all scores of the currently filled number positions | def number_total(self):
return sum(self.grid[pos][1] for pos in ["n1", "n2", "n3", "n4", "n5", "n6"] if self.grid[pos][0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_scores(self):\n self.score[0] = (-1)*sum(self.board[self.board == -1])\n self.score[1] = sum(self.board[self.board == 1])\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)",
"def grades_sum(grades_input):\n total = 0\n for score in sco... | [
"0.7418236",
"0.6753837",
"0.6666424",
"0.64958215",
"0.6413631",
"0.6385383",
"0.63700944",
"0.6284259",
"0.62794924",
"0.62260115",
"0.62230796",
"0.620351",
"0.62015486",
"0.6172633",
"0.6159928",
"0.61399513",
"0.6136497",
"0.6111552",
"0.6099969",
"0.6067096",
"0.6052499... | 0.5861139 | 39 |
Sums all scores of the currently filled assignable positions plus the number bonus | def grand_total(self):
return sum(self.grid[pos][1] for pos in assignable_positions if self.grid[pos][0]) + self.grid["nb"][1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_scores(self):\n self.score[0] = (-1)*sum(self.board[self.board == -1])\n self.score[1] = sum(self.board[self.board == 1])\n #self.score[i] = sum(1 for j in range(len(stones_on_board)) if stones_on_board[j] == i)",
"def update_scores(self):\r\n totals = [0, 0, 0, 0]\r\n ... | [
"0.69169897",
"0.6778899",
"0.66893876",
"0.6492624",
"0.6446686",
"0.64220786",
"0.6389673",
"0.63117045",
"0.62862825",
"0.627944",
"0.62590975",
"0.62550724",
"0.6167822",
"0.6160577",
"0.6156003",
"0.6153973",
"0.6143272",
"0.6137463",
"0.6123372",
"0.6122803",
"0.6114237... | 0.6580822 | 3 |
Aggregate the statistics of a log dict | def aggregate_log_dict(agg_dict, new_dict) -> dict:
for k in new_dict:
# init new if not present
if k not in agg_dict:
agg_dict[k] = {
'n': 0,
'sum': 0.0,
'max': new_dict[k],
'min': new_dict[k],
}
# aggre... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def aggregate_logging_outputs(logging_outputs):\n cider_sum = sum(log.get('cider', 0) for log in logging_outputs)\n loss_sum = sum(log.get('loss', 0) for log in logging_outputs)\n sc_loss_sum = sum(log.get('sc_loss', 0) for log in logging_outputs)\n n_pos_sum = sum(log.get('n_positive',... | [
"0.72006446",
"0.7089349",
"0.7069627",
"0.6966819",
"0.6952808",
"0.6841634",
"0.683194",
"0.6784331",
"0.65914315",
"0.64268196",
"0.606027",
"0.60507745",
"0.6043965",
"0.6035893",
"0.60186166",
"0.5961135",
"0.59582734",
"0.59497863",
"0.5914297",
"0.5878846",
"0.5870959"... | 0.7331248 | 0 |
Initializes the path to write the log to | def init_logger(self, logger_path,
logger_name='Experiment') -> logging.Logger:
self.logger = logging.getLogger(logger_name)
self.logger.setLevel(logging.INFO)
file_handler = logging.FileHandler(logger_path) # TOD bug here
formatter = logging.Formatter('%(asctime)s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_log(path):\n file = open(path, 'w+')\n file.close()",
"def _init_log(self):\n if not os_path_exists(self.log_file):\n self._write('', 'w')",
"def __init__(self, log_path, on=True):\n self.log_path = log_path\n self.on = on\n\n if self.on:\n while... | [
"0.7888636",
"0.78552186",
"0.75470525",
"0.75144166",
"0.74878",
"0.74750996",
"0.7430851",
"0.7277765",
"0.7158862",
"0.7153729",
"0.71011764",
"0.7067894",
"0.7024228",
"0.70093405",
"0.6994423",
"0.69160753",
"0.68876475",
"0.6852987",
"0.6842736",
"0.68300515",
"0.682812... | 0.6496009 | 42 |
Helper method to permute the state axis (from MinAtar) | def get_state(self, s):
return (torch.tensor(s, device=self.device).permute(2, 0, 1)).unsqueeze(0).float() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permute(ts: Tensor, axes) -> Tensor:\n permute_op = PermuteOp(axes)\n return permute_op(ts, None)",
"def _apply_swap(self, state, axes, **kwargs):\n all_axes = list(range(len(state.shape)))\n all_axes[axes[0]] = axes[1]\n all_axes[axes[1]] = axes[0]\n return self._transpose(... | [
"0.64760816",
"0.6295351",
"0.6107544",
"0.5883316",
"0.58024627",
"0.57838786",
"0.5779762",
"0.5742571",
"0.56866443",
"0.56657296",
"0.5552952",
"0.55455023",
"0.5537342",
"0.5535267",
"0.5505405",
"0.54731023",
"0.54124594",
"0.5388099",
"0.5386475",
"0.5386475",
"0.53864... | 0.0 | -1 |
Helper method that takes in a variable number of namedtuples and construct a joint namedtuple for writeout | def construct_out_logfields(*tups):
fields = []
for tupStruct in tups:
for k in tupStruct._fields:
if k.startswith("Diagnostic"):
k_str = k.replace('_', '/', 1)
fields.append(k_str)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tuple_namer(name,tupl):\n tupl_templ = collections.namedtuple(name, 'battery status neighbour')\n named = tupl_templ(battery = tupl[0], status = tupl[1], neighbour = tupl[2])\n return named",
"def core_named_tuple(user_kwargs: dict, disdat_kwargs: dict) -> NamedTuple('Output', [('user_kwargs', dict)... | [
"0.63004136",
"0.5937142",
"0.58391273",
"0.58231133",
"0.57692385",
"0.5645236",
"0.55839163",
"0.54995245",
"0.54713005",
"0.532123",
"0.52468985",
"0.523908",
"0.51835376",
"0.5111085",
"0.5099475",
"0.50992185",
"0.5072765",
"0.50404245",
"0.5036017",
"0.50100225",
"0.500... | 0.50794727 | 16 |
backup database before some critical operations | def backup_database(db_host=None, db_name=None, cfg='project'):
data = __salt__['mc_project.get_configuration'](cfg)
db = data['data']['django_settings']['DATABASES']['default']
if not db_host:
db_host = db['HOST']
if not db_name:
db_name = db['NAME']
dump_filename = '/tmp/{0}-{1}.du... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def backup_database():\n logger.info(\"start database_backup\")\n management.call_command('dbbackup', compress=True)\n logger.info(\"end database_backup\")",
"def backup_database():\n db_path = os.path.join(config.cum_dir, 'cum.db')\n backup_path = os.path.join(config.cum_dir, 'cum.db.bak')\n c... | [
"0.8267241",
"0.74766415",
"0.7415895",
"0.7311228",
"0.71407634",
"0.67931277",
"0.6724161",
"0.6582637",
"0.6562484",
"0.65504694",
"0.65399987",
"0.6503169",
"0.64341927",
"0.64200234",
"0.6383028",
"0.6301118",
"0.6289786",
"0.6247211",
"0.62126917",
"0.62072396",
"0.6204... | 0.5757434 | 84 |
Execute Ping RPC over NETCONF. | def ping(host, destination, repeat_count, vrf_name):
# create NETCONF provider
provider = NetconfServiceProvider(address=host,
port=830,
username='admin',
password='admin',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ping():\n return ping_response()",
"def ping():\n return ping_response()",
"def ping():\n return ping_response()",
"def ping():\n return ping_response()",
"def rpc_ping(self):\n\t\treturn True",
"async def ping(self):\n uri = \"/fapi/v1/ping\"\n success, error = await self.r... | [
"0.68399215",
"0.68399215",
"0.68399215",
"0.68399215",
"0.6729562",
"0.67104983",
"0.6690351",
"0.6629603",
"0.66143537",
"0.6611325",
"0.6556811",
"0.64874136",
"0.64824206",
"0.64485735",
"0.64286697",
"0.63662034",
"0.63428634",
"0.6277983",
"0.6233143",
"0.6226974",
"0.6... | 0.0 | -1 |
Ansible module to verify IP reachability using Ping RPC over NETCONF. | def main():
module = AnsibleModule(
argument_spec=dict(
host=dict(type='str', required=True),
destination=dict(type='str', required=True),
repeat_count=dict(type='int', default=5),
vrf_name=dict(type='str'),
min_success_rate=dict(type='int', defaul... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ping():\n return ping_response()",
"def ping():\n return ping_response()",
"def ping():\n return ping_response()",
"def ping():\n return ping_response()",
"def ip_check():\n hosts = []\n valid_hosts = []\n for item in sys.argv:\n if '@' in item:\n hosts.append(ite... | [
"0.6234672",
"0.6234672",
"0.6234672",
"0.6234672",
"0.6228038",
"0.6227247",
"0.616574",
"0.60879576",
"0.607467",
"0.5944119",
"0.5944096",
"0.58443975",
"0.57498163",
"0.5747767",
"0.57241446",
"0.57212025",
"0.57056487",
"0.57054055",
"0.5702251",
"0.56238407",
"0.561111"... | 0.65288395 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.